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Spectral soil analysis and inference systems: A powerful combination for solving the soil data crisis
Abstract
Diffuse reflectance spectroscopy (DRS) is attracting much interest in the soil science community because it has a number of advantages over conventional methods of soil analyses. The techniques are more rapid, timely, cheaper and hence more efficient at obtaining the data when a large number of samples and analysis are required. Moreover, a single spectrum may be used to assess various physical, chemical and biological soil properties. Until now, research in soil spectroscopy has focused on spectral calibration and prediction of soil properties using multivariate statistics. In this paper we show how these predictions may be used in an inference system to predict other important and functional soil properties using pedotransfer functions (PTFs). Thus we propose the use of soil spectral calibration and its predictions as input and as a complement to a soil inference system (SPEC-SINFERS). We demonstrate the implementation of SPEC-SINFERS with two examples. As a first step, soil mid-infrared (MIR) spectra and partial least squares (PLS) regression are used to estimate soil pH, clay, silt, sand, organic carbon content and cation exchange capacity. A bootstrap method is used to determine the uncertainties of these predictions. These predictions and their uncertainties are then used as input into the inference system, where established PTFs are used to infer (i) soil water content and (ii) soil pH buffering capacity together with their uncertainties. An important feature of SPEC-SINFERS is the propagation of both input and model uncertainties. (c) 2006 Elsevier B.V. All rights reserved.
... However, the challenge faced by soil scientists today is that current methods of SOC and TN measurements are expensive and time-consuming. Additionally, the demand for SOC and TN data is not met by the existing information from legacy soil data [3]. This highlights the importance of a more direct, rapid, and cost-effective method of quantifying SOC and TN. ...
... Vis-NIR spectroscopy provides valuable information by analysing the interaction between electromagnetic energy and matter, specifically in the case of soil. The broad and overlapping bands observed in vis-NIR spectra offer insights into the presence of organic and inorganic materials in soils, including chromophores; iron minerals; overtones of OH, CO 3 , and SO 4 groups; as well as combinations of CO 2 and H 2 O [4,5]. ...
... Nonetheless, useful information about organic and inorganic materials in soils can still be inferred from chromophores [31] and iron minerals [32] in the visible spectral region (400-700 nm). In the NIR spectral region (400-700 nm), absorption features can be associated with overtones of hydroxyl (OH − ), carbonate (CO 3 2− ), and sulphate (SO 4 2− ) groups, as well as combinations of fundamental features of carbon dioxide (CO 2 ) and water (H 2 O) [31,32]. Furthermore, diagnostic bands in the vis-NIR spectra can facilitate the detection of clay minerals (e.g., kaolinite:~2080-~2270 nm and smectite:~2120-~2290 nm), iron oxides (e.g., goethite:~415 and~445 nm and hematite:~535 and~580 nm), organic carbon (~600,~1910, and~2100 nm), and carbonates (~2300-~2350 nm) [33]. ...
Visible and near-infrared (vis-NIR) spectroscopy has proven to be a straightforward method for sample preparation and scaling soil testing, while the increasing availability of high-resolution remote sensing (RS) data has further facilitated the understanding of spatial variability in soil organic carbon (SOC) and total nitrogen (TN) across landscapes. However, the impact of combining vis-NIR spectroscopy with high-resolution RS data for SOC and TN prediction remains an open question. This study evaluated the effects of incorporating a high-resolution LiDAR-derived digital elevation model (DEM) and a medium-resolution SRTM-derived DEM with vis-NIR spectroscopy for predicting SOC and TN in peatlands. A total of 57 soil cores, comprising 262 samples from various horizons (<2 m), were collected and analysed for SOC and TN content using traditional methods and ASD Fieldspec ® 4. The 262 observations, along with elevation data from LiDAR and SRTM, were divided into 80% training and 20% testing datasets. By employing the Cubist modelling approach, the results demonstrated that incorporating high-resolution LiDAR data with vis-NIR spectra improved predictions of SOC (RMSE: 4.60%, RPIQ: 9.00) and TN (RMSE: 3.06 g kg −1 , RPIQ: 7.05). In conclusion, the integration of LiDAR and soil spectroscopy holds significant potential for enhancing soil mapping and promoting sustainable soil management.
... Chemical analysis is a useful tool for understanding soil characteristics, but these tests are often expensive and time-consuming, making them unsuitable for quick soil quality monitoring in precision agriculture ( McCarty and Reeves, 2006 ). Soil mid-infrared reflectance spectroscopy has shown to be a rapid, costeffective, environmentally friendly, non-destructive, reproducible, and repeatable analytical approach during the last 30 years ( Rossel et al., 2006 a;Soriano-Disla et al., 2014 ;Nocita et al., 2015 ). The combination of mid-infrared (MIR) spectroscopy with chemometrics provides a viable quantitative analytical tool in soils ( Sanderman et al., 2020 ). ...
... The efficiency of MIR bands in predicting TOC in soil is satisfactory. However, the function of distinct bands and the existence of alternative interference can vary substantially from one soil type to the next, affecting the ability to make reliable predictions ( Matamala et al., 2017 ;Janik et al., 1998 ;Rossel et al., 2006 ). The comparison between short time mineralized C both measured and predicted with elevated total organic carbon (TOC) and very low TOC models are differentiated in Fig. 4 A. The low TOC version depicted peaks at wave number 3694-3620, 2924, 2852, 2516, 1788, 1634, 1528, 1473, 1380-1270, 874, 811, along with 714 cm − 1 respectively that was expressed by the coefficients ( Fig.4B ), along with same precise peaks have been depicted for the low total nitrogen (TN) models. ...
... Hively et al., (2011) , on the other hand, obtained higher values in a research of soil characteristics mapping utilizing hyperspectral imaging and the PLSR approach (except silt). The bootstrap or bagging approach is used to assess the uncertainty of the PLSR predictions ( McBratney et al., 2006 ;Efron and Tibshirani, 1993 ). By constructing different PLSR models from different data, the bootstrap measures the accuracy of a prediction ( McBratney et al., 2006 ). ...
Evaluating the all-encompassing idea of soil quality requires an enormous set-up of estimations of soil physical, chemical, and biological properties. The fast and appropriate evaluation of soil quality (SQ) is expected to keep up soil wellbeing. Appraisal of soil quality is currently turning into a standard work for crop production and management of soil. Nevertheless, regular research centre investigation dependent on the assurance of soil properties, which is time and cost devouring and is not reasonable for precision agriculture. Mid-infrared spectroscopy is a quick, cheap, ecological agreeable, non-dangerous, and repeatable method. We observed a fantastic presentation of foreseeing soil C and N substance utilizing mid-infrared spectra. In a large portion of cases, the expectations of soil N, P, K, S, and few micronutrients are acceptable. Clay minerals, soil water (volumetric and gravimetric water content), and soil microorganisms can likewise be demonstrated and assessed utilizing mid-infrared spectroscopy. "Here, we review the possibility of mid-infrared spectroscopy to assess soil quality. The significant focuses are the accompanying: 1) predict physical properties of soil 2) predict chemical properties of soil 3) predict biological properties of soil with mid-infrared spectroscopy". Physical properties of soil that depend on void connection, for example, hydraulic conductivity (HC), bulk density (BD), as well as water-holding capacity (WHC) and can't be anticipated well utilizing mid-infrared spectra. Whereas properties dependent on the soil composition, for example, percentage of clay and shrinking- swelling properties, can be expected sensibly well. Even though presently using mid-infrared spectroscopy in the estimation of soil properties is constrained, it seems favourable to gauge the quality of soil reliant on spectral pre-treatment and multivariate alignment because of the powerful intervene in the spectral band. Partial least square (PLS) is a broadly applied statistical tool to enhance various soil properties' prediction operations. A spectral library and a standard protocol can be an option in the future so that proper utilization of the mid-infrared spectral region is possible. Accordingly, mid-infrared spectroscopy coupled with a suitable statistical tool can assess soil quality, which will play a significant role in maintaining agriculture sustainability.
... As a result of this observation, recent SOC research has identified the need to generate large amounts of spatial explicit soil data (Smith et al. 2012). At present, this data simply does not exist yet on a global scale and traditional analysis is too expensive and too time intensive to provide suitable tools to solve this "data crisis" (McBratney et al. 2006). This largely hampers our ability to provide accurate data on the future of SOC dynamics on scales interesting for landscape planners and policy makers. ...
... Because of their costly and time intensive nature, existing sampling strategies and standard analytical methods typically provide data restricted to the upper 30 cm of the soil or at best with a coarse vertical resolution (i.e. between 10 to 30 cm), and SOC analyses are often based on composite samples. The current methodologies cannot provide enough data for correctly monitoring and modeling the soil system and are therefore not able to solve the "soil data crisis" (McBratney et al. 2006). Visible and Near-Infrared (Vis-NIR) diffuse reflectance spectroscopy has been suggested as a cost and time saving alternative to established analysis methods (O'Rourke and Holden 2011). ...
... SOC dynamics in eroding landscapes are fundamentally different from those in stable landscapes, which are the basis for most SOC models. Nevertheless, improvements in model structure (Van Oost et al. 2005b;Billings et al. 2010;Dlugoß 2011) available data (FAO 2012) and assessment techniques (McBratney et al. 2006) might help to close this gap between our good understanding of soil carbon dynamics at the plot scale to coarse assessments on a global scale. ...
The dynamic and stability of the soil organic carbon (C) reservoir are currently receiving attention in the context of global change. The aim of this thesis was to improve our understanding of the effects of soil redistribution on C dynamics. For this, a multi-scale analysis covering different aspects of the C/Soil redistribution continuum has been conducted. The presented work particularly focused on (i) the identification of controls on C dynamics and C distribution patterns along geomorphic gradients in landscapes affected by soil redistribution, (ii) the different C stabilization mechanisms and (iii) the role of the mineral phase in stabilizing C. A global assessment of the importance of agricultural soil erosion for C dynamics revealed the significance of soil redistribution for carbon studies. It was further demonstrated that it is mandatory to consider various environmental processes to predict C accurately, especially in deeper soil layers. A significantly higher mean residence time for buried C at depositional positions was observed, compared to non-eroding and eroding positions. This resulted from the physical protection of C associated with microaggregates and silt-sized particles. The chemical and mineralogical soil components involved in stabilizing C at various depths, slope positions and fractions differed significantly. Current rates of soil erosion and the associated rejuvenation of soils at eroding sites and burial of soil at depositional sites provide a temporally limited local net sink for atmospheric C by stabilizing C with minerals.
... The accuracy of the prediction is increased when the prediction method is unstable, i.e. small changes in the calibration data used in bootstrap can result in large changes in the resulting predictor. It was used by McBratney et al. (2006) for soil prediction and quantifying its uncertainty. The improvement over a single PLSR could be small, but it may be more robust against noise in the spectra, and it is also possible to obtain uncertainty intervals of the prediction (Mevik et al. 2004). ...
... Another way to estimate soil properties is to combine the spectra with pedotransfer functions (PTF, Bouma 1989), in a soil spectral inference system (McBratney et al. 2006). Several soil physical, chemical and biological properties cannot be estimated directly from the spectra, as it done in Sects. ...
... The reasons are that i) some soil properties do not have a clear spectral response in the spectrum and that ii) the development of calibration functions of a soil property from soil spectral libraries is not always possible due, for example, to budget constraints. McBratney et al. (2006) thus proposed a two-step approach to estimate a large range of soil properties by combining spectroscopy and PTFs, as follows: ...
The most common way of estimating soil properties from pre-processed spectra is by calibrating a statistical model. If the response of the spectra at a particular wavelength follows the Beer-Lambert law, the degree of reflectance at a particular wavelength is proportional to the concentration of a soil property. In this case, a linear model can be fitted between this wavelength and the measured values of a soil property.
... O uso e manejo adequado do solo requer o conhecimento de seus atributos químicos, físicos, mineralógicos e biológicos (BELLINASO et al., 2010). O uso de sensores que façam a caracterização espectral dos solos traz avanços nesse sentido, pois muitos trabalhos têm mostrado relações entre classes de solos e diversos atributos com o espectro eletromagnético (Pizarro et al., 2001;McBratney et al., 2006;Nanni & Demattê, 2006;Sousa Júnior et al., 2008;Bellinaso et al., 2010;Genú et al., 2010;Demattê et al., 2016;Viscarra Rossel et al., 2016;Terra et al., 2018;Romero et al., 2018;Dotto et al., 2018, Poppiel et al., 2019Demattê et al., 2019;Mendes et al., 2022). ...
... A caracterização espectral dos solos tem se mostrado promissora e vantajosa em relação a outros métodos de análise devido a vantagens como rapidez, custos operacionais e capacidade de analisar grande número de amostras (McBratney et al., 2006). Com o avanço do mapeamento digital dos solos, a assinatura espectral do solo é uma ferramenta promissora e aplicável a levantamentos pedológicos (McBratney et al., 2003;Dematte et al., 2004;Demattê et al., 2016;Teng et al., 2018;Srivastava, 2018). ...
O litoral do Piauí tem uma extensão de apenas 66km, e sua planície litorânea têm sofrido muita pressão para ocupação, que vem ocorrendo de forma desordenada. O conhecimento do recurso natural solo é fundamental para o ordenamento territorial responsável. São raros os trabalhos sobre os solos do Piauí em escala mais detalhada. O presente trabalho traz maior detalhamento de solos piauienses e tem como objetivo analisar a resposta espectral de solos representativos das diferentes paisagens do litoral piauiense na faixa do visível e do infravermelho (350 a 2500nm) e faz inferência para fins de classificação. Foi possível com a interpretação das curvas espectrais fazer associação a vários atributos dos solos e relacionar com a sua gênese e classificação. Os resultados indicam que essa metodologia pode ser eficiente em levantamentos de solos do litoral piauiense.
... As was espoused by McBratney et al. (2006) and Horta et al. (2015aHorta et al. ( , 2015b, the perspective of improvement in prediction accuracy by the combination of sensor data is interpreted as being either more complete, dependable or accurate. In cases where data fusion approaches did not lead to a significant improvement in prediction accuracies over the single sensor techniques for the current study, comparable accuracies to that of the single sensors can be observed. ...
... In cases where data fusion approaches did not lead to a significant improvement in prediction accuracies over the single sensor techniques for the current study, comparable accuracies to that of the single sensors can be observed. Therefore, assessing improvement from the perspective of McBratney et al. (2006) and Horta et al. (2015aHorta et al. ( , 2015b, the data fusion approaches could be said to have led to a gain in the certainty of the predictions for all the three studied indices. One can therefore conclude that the data fusion approaches enhanced the robustness of the predictions. ...
Soil aggregate stability (AS) is a crucial soil physical property for sustainable agricultural and environmental land management practices. Yet, its conventional laboratory methods are fastidious and time consuming thereby restricting high-density sampling and large-scale estimation. Therefore, the potential of fusing visible near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy to enhance the prediction of three AS indices, namely, fast wetting (FW), slow wetting (SW) and mechanical breakdown (MB) on some Belgian Retisol, Cambisol and Luvisol topsoils was evaluated. Partial least squares regression (PLSR) was used to build calibration models for the three AS indices using the vis-NIR spectra, MIR spectra and the fusion of both spectra (SF). Another data fusion approach (i.e. model output averaging, MOA) included the use of four averaging algorithms. Results showed that MIR models outperformed the vis-NIR models for all three indices. The SF approach showed an improved prediction performance over both individual sensor techniques for the FW index only. MOA models outperformed the individual and SF models and yielded the best prediction accuracy for SW and MB indices. Data fusion modelling thus enhanced the accuracy of FW, SW and MB predictions, although the selection of the best data fusion approach is dependent on the nature of the dataset and the stability index to be assessed.
... Visible and near-infrared (VNIR) spectroscopy has demonstrated its ability to predict many soil physiochemical properties, such as soil organic matter (SOM), particle size, and iron content [1][2][3]. In addition to its wide use for soil properties, comparison of spectra from soil samples is used in several soil science-related applications [4], such as forensic soil science, archeology, and soil pollution assessments. ...
... PC analysis was performed in R (v. 3.5.2) using the prcomp function of the stats package. We tested different numbers of PCs (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), and the similarity distance remained unchanged > 15 PCs, so only the original vectors transformed to 15 PC scores were retained for subsequent analysis. ...
Spectral similarity indices were used to select similar soil samples from a spectral library and improve the predictive accuracy of target samples. There are many similarity indices available, and precisely how to select the optimum index has become a critical question. Five similarity indices were evaluated: Spectral angle mapper (SAM), Euclidean distance (ED), Mahalanobis distance (MD), SAM_pca and ED_pca in the space of principal components applied to a global soil spectral library. The accordance between spectral and compositional similarity was used to select the optimum index. Then the optimum index was evaluated if it can maintain the greatest predictive accuracy when selecting similar samples from a spectral library for the prediction of a target sample using a partial least squares regression (PLSR) model. The evaluated physiochemical properties were: soil organic carbon, pH, cation exchange capacity (CEC), clay, silt, and sand content. SAM and SAM_pca selected samples were closer in composition compared to the target samples. Based on similar samples selected using these two indices, PLSR models achieved the highest predictive accuracy for all soil properties, save for CEC. This validates the hypothesis that the accordance information between spectral and compositional similarity can help select the appropriate similarity index when selecting similar samples from a spectral library for prediction.
... In this context, it is necessary to provide critical model inputs using fast, accurate and low-cost methods to quantify soil properties. The diffuse reflectance spectroscopy (DRS) and magnetic susceptibility (MS) techniques can provide promising approaches achieving this goal (Ayoubi and Mirsaidi 2019;Siqueira et al. 2010Siqueira et al. , 2015Karimi et al. 2013;McBratney et al. 2006). Using these techniques, the cost and time of the spatial data collections to be used in soil sensing and environmental assessments will be reduced (Visccara . ...
The accuracy, speed, and feasibility of studying soil properties depend largely on the techniques used and their efficiency to correlate soil attributes. This study aimed to investigate the capability of the diffuse reflectance spectroscopy (DRS) and magnetic susceptibility (MS) methods in predicting and mapping soil physicochemical properties and monitoring their spatial distribution in an arid and semi-arid region, southern Iran. A total of 100 surface soil samples were collected from the depth of 0–20 cm and analyzed for OC, CCE, gypsum, clay, EC, Fed, χlf, and VIS–NIR–SWIR (350–2500 nm) spectroscopy using standard laboratory methods. The DRS and MS techniques were then used to estimate soil properties using the partial least squares regression (PLSR) and the linear regression methods, respectively. Results showed an accurate estimation of gypsum (RPD = 2.3 and R² = 0.81), and reliable estimations for clay, Fed, OC, CCE, and EC by the PLSR method. However, the MS method presented reliable results only for the Fed (RPD = 1.5, R² = 0.55). The DRS- and MS-derived predictive maps showed different spatial distribution patterns from the interpolated maps which were derived based on the observed data. The Fed parameter showed the most similarity with its observed data in both DRS- and MS-derived maps; whereas in the case of other soil properties, the DRS method presented a better prediction performance compared to the MS one. The findings of the present study highlighted the significant capability of the DRS technique as an efficient predictor for soil properties in the studied area.
... At the moment, there is no generally accepted technique for measuring mechanical properties in a way that aids in estimating soil compaction in a field (Alaoui and Diserens, 2018). McBratney et al. (2006) explored the usage of spectral data to develop predictions based on PTFs by integrating spectral data into soil inference systems. A large amount of information can be extracted from spectral data, and currently, researchers are focusing on visible, near-infrared, and middleinfrared spectrum data to obtain soil information. ...
Soil compaction resulting from heavy machinery use in agricultural and forestry operations poses a significant threat to sustainable agriculture. Advancements in remote sensing technology have enabled the acquisition of vegetation, water, and soil spectral indices, which offer valuable insights into soil properties. This study focuses on estimating the pre-compression stress (Pc) by developing pedotransfer functions (PTFs) using Sentinel-2 satellite-derived spectral indices and soil properties (clay, CaCo 3 , and bulk density) as inputs. Two machine learning methods, Random Forest (RF) and Boosted Regression Tree (BRT), are employed for the estimation. A total of 140 surface soil samples were collected randomly from agricultural areas in Qazvin province, Iran. The results indicate that the BRT method outperforms the RF method in terms of accuracy. The estimation of Pc achieved better results when the Redness Index (RI) was used as the soil spectral index and the Surface Water Capacity Index (SWCI) was employed as the water spectral index, along with the soil properties as inputs for PTF 3 and PTF 11. In the training and testing steps, the root mean square error (RMSE) decreased from 0.100 and 0.114 (kPa) in PTF 1 to 0.071 and 0.098 in PTF 3 , and 0.072 and 0.097 in PTF 11 , respectively. These outcomes demonstrate the practical applicability of estimating Pc through the integration of soil properties and spectral indices. The findings highlight the potential of remote sensing technologies, such as spectral indices, as effective and cost-efficient tools for studying soil compaction. The proposed methodology contributes to our understanding of soil compaction processes and provides valuable insights for developing sustainable land management strategies. This study has implications for the agricultural sector and offers practical solutions to mitigate soil compaction and its detrimental effects on agricultural productivity.
... PLSR was run using a random cross-validation calibration set (leave-2 and leave-3 out). More detail about pH predictions using a MIR-PLSR approach can be found in (Janik et al., 2009;McBratney et al., 2006;Viscarra Rossel et al., 2006). Given the expected low concentrations of CO 3 in the soil samples, for the CO 3 prediction model, a carbonate-specific spectral band (2560-2460 cm -1 ) was used for PLSR calibration. ...
... PLSR was run using a random cross-validation calibration set (leave-2 and leave-3 out). More detail about pH predictions using a MIR-PLSR approach can be found in (Janik et al., 2009;McBratney et al., 2006;Viscarra Rossel et al., 2006). Given the expected low concentrations of CO 3 in the soil samples, for the CO 3 prediction model, a carbonate-specific spectral band (2560-2460 cm -1 ) was used for PLSR calibration. ...
Acidification of surface and sub-surface soils limits agricultural production globally. Conventional surface application of lime is the most common amelioration approach for surface acidity. However, amelioration of sub-surface acidity is challenging to achieve using this approach. Testing the efficacy of liming to treat acidity through the soil profile via the measurement of lime movement requires high throughput information about soil properties at a high spatial resolution, which can be time consuming and expensive using traditional laboratory analysis. Here, we investigated the potential of Mid Infrared (MIR) spectroscopy as a tool to monitor lime dissolution and vertical movement through soils at high spatial resolution. Soil samples were collected at 2.5 cm intervals to 20 cm depth at three trial sites in South Australia, with various lime treatments applied either 6 years or 1 year prior to sampling. MIR Partial least squares regression (PLSR) predictions were undertaken to measure lime dissolution and alkalinity movement via soil pH, and undissolved lime presence via soil carbonate concentrations. Lime balance calculations were then performed to determine the fate of applied lime and assess efficacy of various rates of lime products applied at the surface only or via incorporation. MIR-PLSR prediction model performance was strong for both soil pH (R2 = 0.923 and RMSE = 0.202) and carbonate (R2 =0.829 and RMSE=0.042 CO3%). Results indicated the movement of alkalinity at all sites was limited, and revealed increased movement at the longer-term (∼6 years) vs shorter-term (∼1 year) sites where lime was surface applied. Lime balance calculations indicated that residual lime remained in the top 7.5 cm of the soil profile while soils remained acidic below this depth. Findings suggest that incorporation of residual lime and additional lime applications may be necessary to remediate sub-surface acidity. A decision tree was developed to inform management of surface and sub-surface soil acidity. The study validates the potential of MIR spectroscopy to measure and monitor the effectiveness and movement of lime with improved resolution in acidic soils.
... respectively (Rehman et al., 2020). Utilizing both soil spectroscopy and pedotransfer functions together, i.e. soil spectral inference (McBratney et al., 2006), is seen as a more appropriate way to estimate some soil physical properties, particularly those based on soil pore-space relationships, e.g. volumetric water retention, hydraulic conductivity and penetration resistance, and bulk density. ...
Soil Infrared spectroscopy uses electromagnetic energy to characterize the soil. In the past decades, visible, near to mid infrared spectroscopy has gained considerable interest as a viable alternative for in-field and laboratory measurements, especially where a high spatial density of measurements is needed, because of its speed and cost-effectiveness. Once chemometric models have been calibrated, a single spectrum can be used to predict a range of soil properties simultaneously. This chapter provides an overview of spectroscopy in soil research, including predicting physical, chemical, and biological properties, as well as determination of soil contaminants.
... Moreover, soil spectroscopy does not require the use of hazardous chemical extractants; therefore, it is less harmful to the environment (O'Rourke & Holden, 2011;Viscarra Rossel et al., 2006). Lastly, a single spectrum can be used to assess several soil properties, making it a robust analysis method (Comstock et al., 2019;McBratney et al., 2006;Viscarra Rossel et al., 2006, Viscarra Rossel et al., 2008. ...
Resource‐efficient techniques for accurate soil property estimation are necessary to satisfy the increasing demand for soil data to support environmental monitoring, precision agriculture, and spatial modeling. Over the last 30 yr, infrared soil spectroscopy has developed into a rapid, robust, and cost‐effective technique for soil carbon analysis. Ongoing global efforts to make soil spectroscopy operational require the development of soil spectral libraries, which are the main source of data for the construction of calibration models. Understanding calibration optimization is important to ensure the efficient use of soil spectral libraries for the accurate estimation of soil carbon. Moreover, spectral library transfer can benefit new data collection, soil monitoring, and modeling efforts. This review presents techniques for optimization of calibration models and library transfer. Selection of calibration set size and subsetting are presented as current calibration optimization techniques. Moreover, spiking is discussed as an effective technique for spectral library transfer. Overall, studies have suggested that an increase in calibration size improves model performance and this continues until an optimal size is reached. Additionally, subsetting can improve model performance if the resulting subsets reduce the variability of spectrally active components. Studies have also suggested that spiking is effective when used in conjunction with subsetting techniques. These findings denote the current applicability and potential of optimization and library transfer techniques for the accurate estimation of soil carbon with soil spectroscopy. Future efforts should focus on refining optimization techniques to further expand the operability of soil spectroscopy for soil carbon estimation.
... PLSR is a widespread modeling technique used in chemometrics and is commonly used for quantitative spectral analysis. The PLSR algorithm selects successive orthogonal factors that maximize the covariance between predictors and response variables [27]. The CV (cross-validation) method is often preferred to assess PLSR models for datasets with fewer than 50 samples [28][29][30]; however, we validated the calibrated models using an independent dataset. ...
The characterization of vineyard soil is a key issue for crop management, which directly affects the quality and yield of grapes. However, traditional laboratory analysis of soil properties is tedious and both time and cost consuming, which is not suitable for precision viticulture. For this reason, a fast and convenient soil characterization technique is needed for soil quality assessment and precision soil management. Here, spectroscopy appears as a suitable alternative to assist laboratory analysis. This work focuses on estimating soil properties by spectroscopy. Our study was carried out using 96 soil samples collected from three vineyards in Rias Baixas Designation of Origen (Galicia, Spain). The soils that were characterized include nitrogen (N), organic matter (OM) and clay content (Clay). The presented work compared two regression techniques (partial least squares (PLSR) and random forest (RF)) and four spectral ranges: visible—VIS (350–700 nm), near infrared—NIR (701–1000 nm), short wave infrared—SWIR (1001–2500 nm) and VIS-NIR-SWIR (350–2500 nm) in order to identify the more suitable prediction models. Moreover, the effect of pre-treatments in reflectance data (smoothing Svitzky–Golay, SG, baseline normalization, BN, first derivative, FD, standard normal variate, SNV, logarithm of 1/reflectance or spectroscopy (SP) and detrending, SNV-D) was evaluated. Finally, continuous maps of the soil properties were created based on estimated values of regression models. Our results identified PLSR as the best regression technique, with less computation time than RF. The data improved after applying transformation in reflectance data, with the best results from spectroscopy pre-treatment (logarithm of 1/Reflectance). PLSR performances have obtained determination coefficients (R2) of 0.69, 0.73 and 0.52 for nitrogen, organic matter, and clay, respectively, with acceptable accuracy (RMSE: 0.03, 1.06 and 2.90 %) in a short time. Furthermore, the mapping of soil vineyards generates information of high interest for the precision viticulture management, as well as a comparison between the methodologies used.
... Therefore, developing fast, efficient and accurate SOM content measurement methods is important for both agricultural production and environmental management. The traditional chemical method is timeconsuming and expensive, making it difficult to meet the demands for the rapid and accurate monitoring of the extensive data in digital agriculture [6][7][8]. ...
Rapid quantification of soil organic matter (SOM) is a great challenge for the health assessment and fertility management of agricultural soil. Laser-induced breakdown spectroscopy (LIBS) with appropriate modeling algorithms is an alternative tool for this measurement. However, the current calibration strategy limits the prediction performance of the LIBS technique. In this study, 563 soil samples from Hetao Irrigation District in China were collected; the LIBS spectra of the soils were recorded in the wavenumber range of 288–950 nm with a resolution of 0.116 nm; a self-adaptive partial least squares regression model (SAM–PLSR) was employed to explore optimal model parameters for SOM prediction; and calibration parameters including sample selection for the calibration database, sample numbers and sample location sites were optimized. The results showed that the sample capacity around 60–80, rather than all of the samples in the soil library database, was selected for calibration from a spectral similarity re-ordered database regarding unknown samples; the model produced excellent predictions, with R2 = 0.92, RPD = 3.53 and RMSEP = 1.03 g kg−1. Both the soil variances of the target property and the spectra similarity of the soil background were the key factors for the calibration model, and the small sample set led to poor predictions due to the low variances of the target property, while negative effects were observed for the large sample set due to strong interferences from the soil background. Therefore, the specific unknown sample depended strategy, i.e., self-adaptive modelling, could be applied for fast SOM sensing using LIBS for soils in varied scales with improved robustness and accuracy.
... These findings demonstrate the potential of using MIR spectra for the assessment of nutrient deficiencies and can be embedded in the implementation of PFS approach for smallholder nutrient management strategies [57,80]. The main advantage of this technique is the reduction of cost and rapidness, especially when a target population has many soil and plant samples, to be analysed for large areas [81]. ...
Adept use of fertilizers is critical if sustainable development goal two of zero hunger and agroecosystem resilience are to be achieved for African smallholder agroecosystems. These heterogeneous systems are characterized by poor soil health mainly attributed to soil nutrient depletion. However, conventional methods do not take into account spatial patterns across geographies within agroecosystems, which poses great challenges for targeted interventions of nutrient management. This study aimed to develop a novel population-based farm survey approach for diagnosing soil nutrient deficiencies. The approach embraces principles of land health surveillance of problem definition and rigorous sampling scheme. The advent of rapid soil testing techniques, like infrared spectroscopy, offers opportune avenues for high-density soil and plant characterization. A farm survey was conducted on 64 maize fields, to collect data on soil and plant tissue nutrient concentration and grain yield (GY) for maize crops, using hierarchical and purposive sampling. Correlations between soil test values with GY and biomass were established. The relationship between GY, soil NPK, and the tissue nutrient concentrations was evaluated to guide the setting up of localized critical soil test values. Diagnosis Recommendation Integrated System (DRIS) indices for total nitrogen (N), total phosphorus (P), and total potassium (K) were used to rank and map the prevalence of nutrient limitations. A positive correlation existed between plant tissue nutrient concentration with GY with R 2 values of 0.089, 0.033, and 0.001 for NPK, respectively. Soil test cutoff values were 0.01%, 12 mg kg-1 , 4.5 cmol c kg-1 for NPK, respectively, which varied slightly from established soil critical values for soil nutrient diag-nostics. N and K were the most limiting nutrients for maize production in 67% of sampled fields. The study demonstrates that a population-based farm survey of crop fields can be a useful tool in nutrient diagnostics and setting priorities for site-specific fertilizer recommendations. A larger-scale application of the approach is warranted.
... For similar reasons, Babaeian, Homaee, also reported better predictions of water contents and SWR with vis-NIR than with PTFs. McBratney et al. (2006) proposed the development of spectral inference systems to infer soil properties that are difficult to measure, such as SWR and AWC. These systems use infrared spectra to derive soil properties for input into PTFs (e.g., Tranter et al., 2008). ...
We need measurements of soil water retention (SWR) and available water capacity (AWC) to assess and model soil functions, but methods are time‐consuming and expensive. Our aim here was to investigate the modelling of AWC and SWR with visible–near‐infrared spectra (vis–NIR) and the machine‐learning method cubist . We used soils from 54 locations across Australian agricultural regions, from three depths: 0–15 cm, 15–30 cm and 30–60 cm. The volumetric water content of the samples and their vis–NIR spectra were measured at seven matric potentials from −1 kPa to −1500 kPa. We modelled the following: (i) AWC directly with the average spectra of the samples measured at different water contents, (ii) water contents at field capacity and permanent wilting point and calculated AWC from those estimates, (iii) AWC with spectra of air‐dried soils, and (iv) parameters of the Kosugi and van Genuchten SWR models, then reconstructed the SWR curves to calculate AWC. We compared the estimates with those from a local pedotransfer function (PTF) and an established Australian PTF. The accuracy of the spectroscopic approaches varied but was generally better than the PTFs. The spectroscopic methods are also more practical because they do not require additional soil properties for the modelling. The root‐mean squared‐error (RMSE) of the spectroscopic methods ranged from 0.033 cm ³ cm ⁻³ to 0.059 cm ³ cm ⁻³ . The RMSEs of the PTFs were 0.050 cm ³ cm ⁻³ for the local and 0.077 cm ³ cm ⁻³ for the general PTF. Spectroscopy with machine learning provides a rapid and versatile method for estimating the AWC and SWR characteristics of diverse agricultural soils.
Highlights
Soil available water capacity can be estimated with vis‐NIR specta.
Parameters of water retention models can be estimated with vis‐NIR spectra.
vis‐NIR spectroscopy performed better than pedotransfer functions.
The results apply to a diverse range of soils.
... Using the spectral response of natural and artificial materials to determine their physical and chemical properties has been the subject of research for many years. Much research has been conducted using this method for quantitative estimation of soil properties, as an alternative to costly and time-consuming laboratory determinations (Kokaly et al., 2017;McBratney et al., 2006). Research using near-infrared (NIR), visible (Vis) is particularly advanced. ...
Soil Vis-NIR spectral response had been widely proposed as an alternative to costly and time-consuming laboratory determination of soil physical and chemical properties. However its use for measuring soil quality index directly has not been well explored. This study compares the effectiveness of different machine learning models on a large spectral library using a database collected by the European Union project “Land Use and Coverage Area frame Survey” (LUCAS). Three approaches to predicting mineral soil features by processing their spectral response for the Vis-NIR range were tested. Prediction models of clay content, pH in CaCl2, organic carbon (SOC), calcium carbonate (CaCO3), nitrogen (N), and cation exchange capacity (CEC) were analyzed. Three types of models were assessed: a Stacked AutoEncoder, a convolutional neural network, and a stack model composed of a set of multilayer perceptron algorithms with two different regression estimation solutions. Modeling with CNN was identified as the optimal solution. Similar, and in some cases, better results can be obtained from ensembles of machine learning algorithms. The estimates of soil characteristics made with the help of the Stacked AutoEncoder showed the greatest errors. The use of soil feature estimates to support soil and land classification was also analyzed. An indicator describing the state of the topsoil is presented, which assists the objective classification of soils. The research showed that the accuracy of the estimation of the proposed Topsoil Quality Index (TQI) estimated directly based on Vis-NIR spectral response and indirectly based on estimated values of selected soil features is practically identical. The research confirms the suitability of Vis-NIR spectroscopy for topsoil assessment.
... Besides, the toxic heavy metals in the soil cause potential health risk not only to human beings but also to other living organisms/plants via ingestion, drinking polluted water, food chains, contact with polluted soil, etc. The soil provides an important environment filtering medium that decomposes most of the toxic metals (the major part of soil/water pollutions) by natural anthropogenic activities [10]. Hence, it is essential to characterize the soil to reduce such exposure. ...
The information about the chemical composition of the soil is essential for agricultural, environmental, geological as well as in forensic science. The chemical constituents of soil could be revealed either by elemental methods or by spectroscopic techniques. The present review highlights the potential application of spectroscopic techniques, i.e., Fourier Transform Infrared spectroscopy (FTIR), Raman, UV–Vis-NIR, and laser-induced breakdown spectroscopy (LIBS) for the examination of spectral properties of soil samples which ultimately reveal its chemical makeup. This review explains various spectroscopic methods that are used for characterization and discrimination of soil based on organic matters; study on soil pollution, carbonates, etc.; and the principal finding of the studies are emphasized. Some lacunae in soil studies are still present in the literature. These lacunae are highlighted in the future challenges section in the hope to escalate the interest of researchers/readers of soil science.
... For example, the authors of [8] simultaneously determined soil organic carbon (SOC), moisture, and total nitrogen (TN) using NIR spectroscopy. The authors of [9] simultaneously determined pH, clay, silt, sand, SOC content and cation exchange capacity using mid-IR. ...
Measuring soil texture and soil organic matter (SOM) is essential given the way they affect the availability of crop nutrients and water during the growing season. Among the different proximal soil sensing (PSS) technologies, diffuse reflectance spectroscopy (DRS) has been deployed to conduct rapid soil measurements in situ. This technique is indirect and, therefore, requires site- and data-specific calibration. The quality of soil spectra is affected by the level of soil preparation and can be accessed through the repeatability (precision) and predictability (accuracy) of unbiased measurements and their combinations. The aim of this research was twofold: First, to develop a novel method to improve data processing, focusing on the reproducibility of individual soil reflectance spectral elements of the visible and near-infrared (vis–NIR) kind, obtained using a commercial portable soil profiling tool, and their direct link with a selected set of soil attributes. Second, to assess both the precision and accuracy of the vis–NIR hyperspectral soil reflectance measurements and their derivatives, while predicting the percentages of sand, clay and SOM content, in situ as well as in laboratory conditions. Nineteen locations in three agricultural fields were identified to represent an extensive range of soils, varying from sand to clay loam. All measurements were repeated three times and a ratio spread over error (RSE) was used as the main indicator of the ability of each spectral parameter to distinguish among field locations with different soil attributes. Both simple linear regression (SLR) and partial least squares regression (PLSR) models were used to define the predictability of % SOM, % sand, and % clay. The results indicated that when using a SLR, the standard error of prediction (SEP) for sand was about 10–12%, with no significant difference between in situ and ex situ measurements. The percentage of clay, on the other hand, had 3–4% SEP and 1–2% measurement precision (MP), indicating both the reproducibility of the spectra and the ability of a SLR to accurately predict clay. The SEP for SOM was only a quarter lower than the standard deviation of laboratory measurements, indicating that SLR is not an appropriate model for this soil property for the given set of soils. In addition, the MPs of around 2–4% indicated relatively strong spectra reproducibility, which indicated the need for more expanded models. This was apparent since the SEP of PLSR was always 2–3 times smaller than that of SLR. However, the relatively small number of test locations limited the ability to develop widely applicable calibration models. The most important finding in this study is that the majority of vis–NIR spectral measurements were sufficiently reproducible to be considered for distinguishing among diverse soil samples, while certain parts of the spectra indicate the capability to achieve this at α = 0.05. Therefore, the innovative methodology of evaluating both the precision and accuracy of DRS measurements will help future developers evaluate the robustness and applicability of any PSS instrument.
... accuracy, experimental design, linear mixed-effects model, replicate measurements, soil chemical data, uncertainty 1 | INTRODUCTION A soil system's physical and chemical properties are commonly determined by the collection and subsequent wet chemistry analysis of soil samples. The results from wet chemistry measurements can be further used to, for instance, develop soil spectroscopy models (McBratney, Minasny, & Rossel, 2006) or estimate soil organic carbon stocks (Smith et al., 2020). Accurate and reliable analytical data of relevant soil properties are key to achieve accurate calibration and validation of such models (Dangal, Sanderman, Wills, & Ramirez-Lopez, 2019). ...
There is a growing demand for high‐quality soil data. However, soil measurements are subject to many error sources. We aimed to quantify uncertainties in synthetic and real‐world wet chemistry soil data through a linear mixed‐effects model, including batch and laboratory effects. The use of synthetic data allowed us to investigate how accurately the model parameters were estimated for various experimental measurement designs, whereas the real‐world case served to explore if estimates of the random effect variances were still accurate for unbalanced datasets with few replicates. The variance estimates for synthetic data were unbiased, but limited laboratory information led to imprecise estimates. The same was observed for unbalanced synthetic datasets, where 20, 50 and 80% of the data were removed randomly. Removal led to a sharp increase of the interquartile range (IQR) of the variance estimates for batch effect and the residual. The model was also fitted to real‐world and total organic carbon (TOC) data, provided by the Wageningen Evaluating Programmes for Analytical Laboratories (WEPAL). For , the model yielded unbiased estimates with relatively small IQRs. However, the limited number of batches with replicate measurements (5.8%) caused the batch effect to be larger than expected. A strong negative correlation between batch effect and residual variance suggested that the model could not distinguish well between these two random effects. For TOC, batch effect was removed from the model as no replicates were available within batches. Again, unbiased model estimates were obtained. However, the IQRs were relatively large, which could be attributed to the smaller dataset with only a single replicate measurement. Our findings demonstrated the importance of experimental measurement design and replicate measurements in the quantification of uncertainties in wet chemistry soil data.
Highlights
Accurate uncertainty quantification depends on the experimental measurement design.
Linear mixed‐effects models can be used as a tool to quantify uncertainty in wet chemistry soil data.
Lack of replicate measurements leads to poor estimates of error variance components.
Measurement error in wet chemistry soil data should not be ignored.
... This was higher than 2% for 175 samples, with the potential to influence any spectral curve, as discussed by Baumgardner et al. [43] and Bilgili et al. [44]. Systematic Error, (5) Correlation Coefficient, (6) Ratio of Performance to Deviation; (7) Root-Mean-Square Error for prediction; (8) Standard error prediction; n: number of Paraná and target site soil samples. ...
Visible (V), Near Infrared (NIR) and Short Waves Infrared (SWIR) spectroscopy has been indicated as a promising tool in soil studies, especially in the last decade. However, in order to apply this method, it is necessary to develop prediction models with the capacity to capture the intrinsic differences between agricultural areas and incorporate them in the modeling process. High quality estimates are generally obtained when these models are applied to soil samples displaying characteristics similar to the samples used in their construction. However, low quality predictions are noted when applied to samples from new areas presenting different characteristics. One way to solve this problem is by recalibrating the models using selected samples from the area of interest. Based on this premise, the aim of this study was to use the spiking technique and spiking associated with hybridization to expand prediction models and estimate organic matter content in a target area undergoing different uses and management. A total of 425 soil samples were used for the generation of the state model, as well as 200 samples from a target area to select the subsets (10 samples) used for model recalibration. The spectral readings of the samples were obtained in the laboratory using the ASD FieldSpec 3 Jr. Sensor from 350 to 2500 nm. The spectral curves of the samples were then associated to the soil attributes by means of a partial least squares regression (PLSR). The state model obtained better results when recalibrated with samples selected through a cluster analysis. The use of hybrid spectral curves did not generate significant improvements, presenting estimates, in most cases, lower than the state model applied without recalibration. The use of the isolated spiking technique was more effective in comparison with the spiked and hybridized state models, reaching r2, square root of mean prediction error (RMSEP) and ratio of performance to deviation (RPD) values of 0.43, 4.4 g dm−3, and 1.36, respectively.
... The need for large amounts of soil data, when confronted with high costs and time -consuming implementations, justifies the search for alternative testing or survey methods: namely, aerial or satellite level spectrum analyses, and laboratory spectrum analysis [1]. Spectral response, as a source of information on the chemical and physical properties of soils, has been studied since the 1970s [2][3][4][5]. ...
Using the Land Use and Coverage Frame Survey (LUCAS) database of European soil surface layer properties, statistical and machine learning predictive models for several key soil characteristics (clay content, pH in CaCl2, concentration of organic carbon, calcium carbonates and nitrogen and exchange cations capacity) were compared on the basis of processing their spectral responses in the visible (Vis) and near‑infrared (NIR) parts. Standard methods of relationship modeling were used: stepwise regression, partial least squares regression and linear regression with input data obtained from principal components analysis. Using the inputs extracted by statistical algorithms various machine learning algorithms were used in the modeling. The usefulness of the models was analyzed by comparison with the values of the determination coefficients, the root mean square error and the distribution of residual values. The mean square error of estimation in the cross‑validation procedure for the stack model using the multilayer perceptron and the distributed random forest were as follows: for clay content – ca. 4.5%; for pH – ca. 0.35; for SOC – ca. 7.5 g/kg (0.75% by weight); for CaCO3 content – ca. 19 g/kg; for N content – ca. 0.50 g/kg; and for CEC – ca. 3.5 cmol(+)/kg.
... WASP is derived from the spectra of the aqueous/ biosystem using the aquaphotomics multivariate analysis approach (Tsenkova et al. 2018), and it is related directly to the system functionality. Soil vis-NIR libraries already are powerful tool in soil science, allowing analysis and storage of large body of data and information (McBratney, Minasny, and Viscarra Rossel 2006). There are also pioneering aquaphotomics works in soil science, which related the WASPs as holistic descriptors of particular soil types or particular functionalities (Mura et al. 2019). ...
... The development of new modelling tools leads to the need for new, appropriate data for the parametrisation of these modelling tools as well as the need for new rapid analytical methods to handle the increasing amount of samples. For example, Mid Infrared Spectroscopy is a promising fast measurement tool commonly used for the derivation of soil physico-chemical characteristics such as soil water capacity, organic matter as well as being an analytical aid for the derivation of solidsolution partitioning coefficients for cationic metals, such as in soils at European scale in the GEMAS project (McBratney et al., 2006;Calderón et al., 2011;Janik et al., 2014) and soil-transfer factors for heavy metals (Wang et al., 2017). Several studies seem to indicate that soil clay mineralogy is a more appropriate soil parameter than the clay content for estimating the soil-plant transfer of radiocesium (Uematsu et al., 2015). ...
In case of a nuclear accident, adequate protection of the public and the environment requires timely assessment of the short- and long-term radiological exposure. Measurements of the radiation dose and the radioactive contamination in the environment are essential for the optimization of radiation protection and the decision making process. In the early phase, however, such measurements are rarely available or sufficient.To compensate for the lack of monitoring data during nuclear emergencies, especially in the early phase of the emergency, mathematical models are frequently used to assess the temporal and spatial distribution of radioactive contamination. During the transition and recovery phase, models are typically used to optimise remediation strategies by assessing the cost-effectiveness of different countermeasures. A prerequisite of course is that these models are fit for purpose. Different models may be needed during different phases of the accident. In this paper, we discuss the role of radioecological models during a nuclear emergency, and give an outlook on the scientific challenges which need to be addressed to further improve our predictions of human and wildlife exposure.
... To date, many modeling efforts have been made to relate soil texture (expressed as particle size distribution), soil structural properties, bulk density, and/or organic matter content to soil water retention [4][5][6][7] . Soil water retention was estimated using multiple regression, neural network analyses, and other methods [8][9][10][11][12][13][14] . However, the applicability and accuracy of the models are more or less unsatisfactory. ...
Soil water retention determines plant water availability and contaminant transport processes in the subsurface environment. However, it is usually difficult to measure soil water retention characteristics. in this study, an analytical model based on a fractional bulk density (fBD) concept was presented for estimating soil water retention curves. the concept allows partitioning of soil pore space according to the relative contribution of certain size fractions of particles to the change in total pore space. the input parameters of the model are particle size distribution (pSD), bulk density, and residual water content at water pressure head of 15,000 cm. The model was tested on 30 sets of water retention data obtained from various types of soils that cover wide ranges of soil texture from clay to sand and soil bulk density from 0.33 g/cm 3 to 1.65 g/cm 3. Results showed that the FBD model was effective for all soil textures and bulk densities. the estimation was more sensitive to the changes in soil bulk density and residual water content than pSD parameters. the proposed model provides an easy way to evaluate the impacts of soil bulk density on water conservation in soils that are manipulated by mechanical operation.
... These studies have involved several practical approaches that aim to provide a better view of this phenomenon. Since the seventies, spaceborne sensors have attracted the attention of scientists and have been used in understanding the mechanisms linked to the propagation of salt-affected soils (McBratney, Minasny, and Viscarra Rossel 2006). The advantage of these sensors is the large or even unlimited amount of data that can be acquired. ...
Modelling approaches are becoming an efficient tool in the forecasting of the salinization spread impact on the local and global scales. In the Lower-Cheliff plain, updating the information on soil salinity expansion is more than required as it continues to damage the agricultural environment in there. Through this study, we adopted an artificial methodology that consists of a combination of buffer analysis using Sentinel-2 data and machine learning modelling to assess their aptitude in the prediction and mapping of soil
salinity. The adopted random forest (RF) algorithm included the reflectance information from of the bands from Blue (ρBlue), Green (ρGreen), Red (ρRed), Near infrared (NIR), Vegetation red-edge (VRE) and Shortwave Infrared (SWIR), optimized with the geospatial buffering based on the 91 soil random samples collected during the summer of 2019 and measured for the Electrical Conductivity (EC) in the laboratory. The outputs from the geospatial buffering refined the goodness of the correlation between field data and the variables set from bands reflectance and selected salinity indices. The obtained coefficient of determination (R2) with Multiple Linear Regression (MLR) and Partial Least Square regression (PLS) models proved an improvement in the multivariable prediction of soil salinity with the optimized Sentinel-2 data (R2 = 0.61 and 0.68 + RMSE of 3.36 and 2.87 dS m−1 respectively), compared to the results from the 2015 multiple regression model, Given the high values of R2 = 0.77 and 0.95 and the low values of RMSE = 2.29 and
1.18 dS m−1 respectively; the RF regressor was very efficient in predicting EC either with all variables or with selected variables from the variable importance measure based on the mean decrease accuracy (MDA). The random forest classifier performed top of the range classification with an overall Accuracy (OA) about 99%, estimating that over 75% of the study area surface is suffering from the salinization extent.
Soil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduous, and time-consuming process. However, the integration of cutting-edge technology can significantly aid in the prediction of SOM, presenting a promising alternative to traditional methods. In this study, we tested the hypothesis that an accurate estimate of SOM might be obtained by combining the ground-based sensor-captured soil parameters and soil analysis data along with drone images of the farm. The data are gathered using three different methods: ground-based sensors detect soil parameters such as temperature, pH, humidity, nitrogen, phosphorous, and potassium of the soil; aerial photos taken by UAVs display the vegetative index (NDVI); and the Haney test of soil analysis reports measured in a lab from collected samples. Our datasets combined the soil parameters collected using ground-based sensors, soil analysis reports, and NDVI content of farms to perform the data analysis to predict SOM using different machine learning algorithms. We incorporated regression and ANOVA for analyzing the dataset and explored seven different machine learning algorithms, such as linear regression, Ridge regression, Lasso regression, random forest regression, Elastic Net regression, support vector machine, and Stochastic Gradient Descent regression to predict the soil organic matter content using other parameters as predictors.
Drought and the impacts of climate change have led to an escalation in soil salinity and alkalinity across various regions worldwide, including Iran. The Chahardowli Plain in western Iran, in particular, has witnessed a significant intensification of this phenomenon over the past decade. Consequently, modeling of soil attributes that serve as indicators of soil salinity and alkalinity became a priority in this region. To date, only a limited number of studies have been conducted to assess indicators of salinity and alkalinity through spectrometry across diverse spectral ranges. The spectral ranges encompassing mid-infrared (mid-IR), visible, and near-infrared (vis-NIR) spectroscopy were employed to estimate soil properties including sodium adsorption ratio (SAR), exchangeable sodium ratio (ESR), exchangeable sodium percentage (ESP), pH, and electrical conductivity (EC). Five distinct models were employed: Partial Least Squares Regression (PLSR), bootstrapping aggregation PLSR (BgPLSR), Memory-Based Learning (MBL), Random Forest (RF), and Cubist. The calibration and assessment of model performance were carried out using several key metrics including Ratio of Performance to Deviation (RPD) and the coefficient of determination (R2). Analysis of the outcomes indicates that the accuracy and precision of the mid-IR spectra surpassed that of vis-NIR spectra, except for pH, which exhibited a superior RPD compared to other properties. Notably, in the prediction of pH utilizing vis-NIR reflectance spectra, the BgPLSR model exhibited the highest accuracy and precision, boasting an RPD value of 2.56. In the domain of EC prediction, the PLSR model yielded an RPD of 2.64. For SAR, the MBL model achieved an RPD of 2.70, while ESR prediction benefited from the MBL model with an impressive RPD of 4.36. Likewise, the MBL model demonstrated remarkable precision and accuracy in ESP prediction, garnering an RPD of 4.41. The MBL model's efficacy in forecasting with limited datasets was notably pronounced among the models considered. This study underscores the valuable role of spectral predictions in facilitating the work of soil surveyors in gauging salinity and alkalinity indicators. It is recommended that the integration of spectrometry-based salinity and alkalinity predictions be incorporated into forthcoming soil mapping endeavors within semi-arid and arid regions.
The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500–25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0–100 cm) or the shallow layers (0–40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm). A total of 90 soil samples containing 450 subsamples (0–10 cm, 10–20 cm, 20–40 cm, 40–70 cm, and 70–100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSEp = 1.07–3.82 g/kg, RPD = 1.10–5.80; TN: RMSEp = 0.11–0.15 g/kg, RPD = 1.70–4.39) outperformed those based on vis-NIR (OM: RMSEp = 1.75–8.95 g/kg, RPD = 0.50–3.61; TN: RMSEp = 0.12–0.27 g/kg; RPD = 1.00–3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSEp = 1.09–2.97 g/kg, RPD = 2.13–5.80; TN: RMSEp = 0.08–0.19 g/kg, RPD = 1.86–4.39) outperformed those based on the shallow layers (OM: RMSEp = 1.07–8.95 g/kg, RPD = 0.50–3.93; TN: RMSEp = 0.11–0.27 g/kg, RPD = 1.00–2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths.
Large and publicly available soil spectral libraries, such as the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (NSSC‐KSSL) mid‐infrared (MIR) spectral library, are enormously valuable resources enabling laboratories around the world to make rapid low‐cost estimates of a number of soil properties. A limitation to widespread sharing of soil spectral data is the need to ensure that spectra collected on a secondary spectrometer are compatible with the spectra in the primary or reference library. Various spectral preprocessing and calibration transfer techniques have been proposed to overcome this limitation. We tested the transferability of models developed using the USDA NSSC‐KSSL MIR library to a secondary instrument. For the soil properties, total carbon (TC), pH, and clay content, we found that good performance (ratio of performance to deviation [RPD] = 4.9, 2.0, and 3.6, respectively) could be achieved on an independent test set with Savitzky‐Golay smoothing and first derivative preprocessing of the secondary spectra using a memory‐based learning chemometric approach. We tested three calibration transfer techniques (direct standardization [DS], piecewise direct standardization [PDS], and spectral space transformation [SST]) using different size transfer sets selected to be representative of the entire NSSC‐KSSL library. Among the transfer methods, SST consistently outperformed DS and PDS with 50 transfer samples being an optimal number for transfer model development. For TC and pH, performance was improved using the SST transfer (RPD = 7.7 and 2.2, respectively) primarily through the elimination of bias. Calibration transfer could not improve predictions for clay. These findings suggest that calibration transfer may not always be necessary, but users should test to confirm this assumption using a small set of representative samples scanned at both laboratories.
Precision Agriculture is a leading international journal on advances in precision farming. Established in 1999, it focuses on natural resource variability, engineering technology, profitability, environment, and technology transfer. It serves as an effective platform for disseminating original and fundamental research and understanding in the continuously evolving field of precision farming. With the onset of the technological era, the agriculture sector has witnessed remarkable changes in the use of drones, artificial intelligence and the latest automation and technology-driven developments. To gauge the journal's influence, the authors conducted a comprehensive overview of Precision Agriculture papers from 1999 to 2021. The journal reached its 22 nd year of publishing in 2021. The study undertaken is a first-hand attempt to outline the current state of the art and develop a comprehensive understanding of the theoretical foundations, concepts and recent developments in this field. The findings show the fast-paced growth that this journal has experienced, thereby attracting and encouraging researchers and authors to contribute to developing this field.
Precision Agriculture is a leading international journal on advances in precision farming. Established in 1999, it focuses on natural resource variability, engineering technology, profitability, environment, and technology transfer. It serves as an effective platform for disseminating original and fundamental research and understanding in the continuously evolving field of precision farming. With the onset of the technological era, the agriculture sector has witnessed remarkable changes in the use of drones, artificial intelligence and the latest automation and technology-driven developments. To gauge the journal’s influence, the authors conducted a comprehensive overview of Precision Agriculture papers from 1999 to 2021. The journal reached its 22nd year of publishing in 2021. The study undertaken is a first-hand attempt to outline the current state of the art and develop a comprehensive understanding of the theoretical foundations, concepts and recent developments in this field. The findings show the fast-paced growth that this journal has experienced, thereby attracting and encouraging researchers and authors to contribute to developing this field.
The main objectives for semi-mechanistic models enhancement are justified in the article. The "soil-plant" chain is an essential part of radioisotopes flows from nuclear accident depositions to human beings. Therefore a model which describes this system should be integrated into decision support systems for liquidation consequences of accidents with releasing radioisotopes into the environment, evaluation effectiveness of measures for radiation protection, and designing hazardous radiation facilities. Such a model must show rather exact forecast results, flexibility and wide application area convenience for practical use, and other properties. Presented now models of radionuclides behavior in "soil-plant" system divided on empiric, mechanistic, and semi-mechanistic. The empirical ones do not take into account the basic mechanisms of changes in the biological availability of radionuclides and their absorption by plants, and require constant updating and refinement of the transition parameters. Mechanistic models are of little use in real life. The last ones best meet the requirements noted above. However, substantial efforts are needed for improving their accuracy, usability, and generalization. This requires integration into data models from existing and planned sensor systems; consideration of additional factors influ-encing the transfer of radionuclides to plants; increasing the level of generalization of models with adjustment to local conditions; the use of machine learning methods to integrate information accumulated in related fields into the model; coverage of more radioactive isotopes; adding an uncertainty estimate to the simulation result; integration of models of radionuclide behavior into geoinformation systems; maintaining a sufficient level of interpretability and visibility of modeling results.
Soil organic carbon (SOC), as a key property of soil quality maintenance, varies over space
and time. The assessment and monitoring of SOC is important to ensure sustainable soil
management. SOC can be determined by conventional laboratory analytical techniques, but the preparation and measurement of numerous soil samples can be costly.
Near infrared spectroscopy (NIRS) offers a novel, non-destructive technique allowing for rapid and lowcost soil analyses. The work for this thesis comprised two aspects of NIRS analysis: its application in the laboratory as well as in the field on-line.
Although laboratory NIRS is an established method, there are no standard measurement procedures simplifying the comparability of spectral data from different NIR-devices and spectra collected over time from the same device. Therefore, the laboratory application of NIRS was investigated with the aim to optimise soil sample preparation and measurement in order to give recommendations for a standard measurement protocol.
Furthermore, the on-line field application of NIRS is a relatively new method, and thus there is still a need for an evaluation of the NIR-system, manufactured by the North American company VERIS Technologies Inc., used in this study. The field application of NIRS was examined via a comparison between horizontal measurements with a shank and vertical measurements with a probe. Further investigations were carried out to test the accuracy and reproducibility of the horizontal mapping.
All measurements were used to map and characterise agricultural soils in Northern
Germany, with the main focus on the calibration and prediction of SOC and total nitrogen (N) concentrations and SOC stocks.
There is growing global interest in the potential for soil reflectance spectroscopy to fill an urgent need for more data on soil properties for improved decision-making on soil security at local to global scales. This is driven by the capability of soil spectroscopy to estimate a wide range of soil properties from a rapid, inexpensive, and highly reproducible measurement using only light. However, several obstacles are preventing wider adoption of soil spectroscopy. The biggest obstacles are the large variation in the soil analytical methods and operating procedures used in different laboratories, poor reproducibility of analyses within and among laboratories and a lack of soil physical archives. In addition, adoption is hindered by the expense and complexity of building soil spectral libraries and calibration models. The Global Soil Spectral Calibration Library and Estimation Service is proposed to overcome these obstacles by providing a freely available estimation service based on an open, high quality and diverse spectral calibration library and the extensive soil archives of the Kellogg Soil Survey Laboratory (KSSL) of the Natural Resources Conservation Service of the United States Department of Agriculture (USDA). The initiative is supported by the Global Soil Laboratory Network (GLOSOLAN) of the Global Soil Partnership and the Soil Spectroscopy for Global Good network, which provide additional support through dissemination of standards, capacity development and research. This service is a global public good which stands to benefit soil assessments globally, but especially developing countries where soil data and resources for conventional soil analyses are most limited.
Soil pollution is a big issue caused by anthropogenic activities. The spatial distribution of potentially toxic elements (PTEs) varies in most urban and peri-urban areas. As a result, spatially predicting the PTEs content in such soil is difficult. A total number of 115 samples were obtained from Frydek Mistek in the Czech Republic. Calcium (Ca), magnesium (Mg), potassium (K), and nickel (Ni) concentrations were determined using Inductively Coupled Plasma Optical Emission Spectroscopy. The response variable was Ni, while the predictors were Ca, Mg, and K. The correlation matrix between the response variable and the predictors revealed a satisfactory correlation between the elements. The prediction results indicated that support vector machine regression (SVMR) performed well, although its estimated root mean square error (RMSE) (235.974 mg/kg) and mean absolute error (MAE) (166.946 mg/kg) were higher when compared with the other methods applied. The hybridized model of empirical bayesian kriging-multiple linear regression (EBK-MLR) performed poorly, as evidenced by a coefficient of determination value of less than 0.1. The empirical bayesian kriging-support vector machine regression (EBK-SVMR) model was the optimal model, with low RMSE (95.479 mg/kg) and MAE (77.368 mg/kg) values and a high coefficient of determination (R2 = 0.637). EBK-SVMR modelling technique output was visualized using a self-organizing map. The clustered neurons of the hybridized model CakMg-EBK-SVMR component plane showed a diverse colour pattern predicting the concentration of Ni in the urban and peri-urban soil. The results proved that combining EBK and SVMR is an effective technique for predicting Ni concentrations in urban and peri-urban soil.
We set out in this work to create a suite of 3D-like digital soil attribute maps for a research farming enterprise situated in the South Eastern Agricultural Zone of NSW, Australia. These digital maps come about because of a reconnaissance soil survey to map out the farm's soil baseline conditions and bring to bear the tools and processes of the digital convergence. This entails on-the-go proximal soil sensing for rapid and granular farm landscape characterisation, which helped inform field-based soil survey and sampling, and later the soil mapping too. Soil infrared spectroscopy associated inference (vis-NIR) was then used to facilitate very detailed soil characterisations of key soil attributes that included soil pH, carbon, soil texture, bulk density, and cation exchange capacity amongst others. This work then enabled the ability to build bespoke spatial models to generate 3D-like inherent and dynamic digital soil attribute maps. We highlight some of the digital soil infrastructure features which include flexibility, customisation, and work pipelines that will ease the updating and improvement of maps. We also highlight some immediate goals which include a formal inclusion of measurement uncertainties brought about by soil spectral inference into the spatial modelling process. To date, some of the digital soil mapping has been used for applications such as lime requirement estimations to address subsoil acidity issues and therefore changing/improving the soil's capability at BARS. Overall, the intention of this work is to provide a detailed account of what is entailed in creating a comprehensive digital soils infrastructure for a farm including the likely costs to implement and the associated opportunities it provides.
Intensification of water erosion by agricultural activities is one of the main causes of soil degradation in subtropical regions. In addition to the on-site negative impacts observed in cropland, the excess of surface runoff increases the transfer of sediments and contaminants to water bodies, resulting in environmental, economic and social deleterious effects. The objective of this thesis is to develop and apply original sediment source fingerprinting techniques in two tributaries of the Uruguay River Basin with contrasting conditions in terms of geology, land use, management, and soil types, representative of those found in the drainage area (266,132 km²). The Conceição River catchment (804 km²) is located in upper parts of the Uruguay River Basin. This catchment is representative of the basaltic plateau, where deep Oxisols rich in clay and iron oxides predominate, and are cultivated for soybean, corn and cereals production or covered with pastures for dairy farming. In contrast, the Ibirapuitã River catchment (5,943 km²) is representative of the sandstone plateau. With a predominance of shallow Regosols and sandy Acrisols more sensitive to degradation, it is mainly occupied by native grasslands of the Brazilian Pampa Biome with extensive cattle ranching, although irrigated rice fields are also found in lowlands. Moreover, a rapid increase in the surface area cultivated with soybean in this catchment has been observed in the last twenty years. Soil samples were collected to characterize land use-based sediment sources and suspended sediment was sampled at the outlet of these catchments using different strategies. All samples were analyzed for a panel of properties. Their geochemical composition, diffuse reflectance spectroscopy properties, radionuclide activities and magnetic susceptibility were determined to provide a set of potential tracing parameters. Magnetic and diffuse reflectance spectroscopy analyses were also carried out on sediment samples collected on the main stream of the Uruguay River, downstream of its confluence with both representative tributaries. Results of sediment source contribution are consistent with field observations. However, as catchment area and source number increase, more uncertainty is observed. Surface sources (pasture and cropland) were indicated as the main source of suspended sediment in the Conceição catchment, contributing with approximately 50%. Stream banks provided the second main source of fine bed sediment, contributing with approximately 35%. Cropland was also found to provide the main source in the Ibirapuitã catchment, contributing 32% of the sediment despite occupying only 9.5% of the total catchment area, followed by subsurface sources (stream bank and gully) with 29%. Unpaved roads and native grassland provided lower contributions (24 and 15%, respectively) in Ibirapuitã catchment. These results indicate that the soil conservation practices used in these catchments are not enough to prevent soil erosion in cropland. Moreover, agricultural activities may enhance erosion and sediment delivery to the river systems, causing soil impoverishment and contamination of water resources. Finally, through the comparison of sediment properties of both tributary catchments, this thesis provided – to our knowledge – the first insights into the contribution of both contrasting geomorphologic regions to the main Uruguay River. The main river sediment samples had similar characteristics to those observed in the Ibirapuitã River, indicating the Pampa region as the main likely source. Despite the associated uncertainties, these results further increase the current concerns regarding the ongoing land use changes observed in the Pampa biome. Appropriate soil conservation practices should therefore be urgently applied in agricultural areas of this region to reduce soil erosion and sediment delivery to the river systems.
Improving our understanding of the functions and processes of soil microbial communities and their interactions with the physicochemical soil environment requires large amounts of timely and cost-efficient soil data, which is difficult to obtain with routine laboratory-analytical methods. Soil spectroscopy with portable visible-to-near infrared (VNIR) and mid-infrared (MIR) instruments can fill this gap by facilitating the rapid acquisition of biotic and abiotic soil information.
In this study, we evaluated the capabilities of VNIR and MIR spectroscopy to analyze physicochemical and microbial soil properties in a long-term grassland biodiversity-ecosystem functioning experiment. Soil samples were collected at the Jena Experiment (Jena, Germany) and measured with portable VNIR and MIR spectrometers in field-moist condition to determine their potential for on-site data collection and analysis. Reference data to calibrate spectroscopic models were acquired with routine analytical methods, including PLFA extractions of microbial biomarkers. We further collected reference VNIR and MIR data on pre-treated soils (dried and finely ground) to assess the anticipated impact of field measurements on spectroscopic calibrations.
On pre-treated samples, MIR spectra allowed more accurate estimates of physicochemical and microbial soil properties than VNIR data. For soils in field condition, MIR calibrations were more accurate for physicochemical properties, but VNIR data gave significantly better estimates of microbial properties. Combined VNIR/MIR estimates achieved the most accurate estimation results for all soil properties in each case.
Physicochemical soil properties could be estimated from VNIR/MIR data with high accuracy (R² = 0.72–0.99) on pre-treated soil samples, whereas results for microbial soil properties were more moderate (R² = 0.66–0.72). On field-moist soils, estimation accuracies decreased notably for organic and inorganic carbon (ΔRMSE = 52–72%), improved slightly for soil texture (ΔRMSE = 4–7%) and decreased slightly for microbial properties (ΔRMSE = 4–9%). The VNIR/MIR estimates derived from soils in field condition were sufficiently accurate to detect experimental plant treatment effects on organic carbon, as well as bacterial and fungal biomass.
We further found that spectroscopic estimates of microbial soil properties were primarily enabled through indirect correlations with spectrally active soil constituents, i.e., associations between microbial soil properties and the physicochemical soil environment.
Our findings highlight the capacity of VNIR and MIR spectroscopy to analyze the physicochemical soil environment, including on-site data collection and analysis on soils in field condition, and indicate potential to estimate microbial soil properties through VNIR/MIR data when physicochemical soil properties shape the distribution of soil microbial communities.
Geliş Tarihi (Received) : 01.02.2012 Kabul Tarihi (Accepted) : 29.11.2012 Özet: Erozif güçlere karşı toprağın direnci fiziksel ve kimyasal toprak özellikleri ile sıkı derecede ilişkilidir. Toprağın erozyona hassasiyetini değerlendirmek için strüktürel dayanımı esas alan pek çok indeks geliştirilmiştir. Islak agregat stabilitesi (WAS) toprak aşınabilirliğini ifade etmede kullanılan en önemli ve yaygın aşınım göstergesidir. Bu indeksin ölçümü çoğunlukla zaman alıcı ve usandırıcıdır. Bu sebeple, adı geçen indeks doğrudan ölçüm yerine farklı araçlarla tahminlenebilmektedir. Pedotransfer modeller zor belirlenen toprak özelliklerinin tahminlenmesi için yarayışlı ve kullanışlı eşitliklerdir. Bu çalışmanın amacı Kahramanmaraş Narlı Ovası topraklarının aşınabilirliğini pedotransfer eşitlikler ile tahminlemektir. Laboratuar analizlerinin sonunda elde edilen veri kümesi dijital ortama aktarılmış ve istatistiksel olarak test edilmiştir. WAS'ı tahminlemek için çoklu lineer regresyon yoluyla pedotransfer eşitlikler geliştirilmiştir. Pedotransfer eşitliklerin tahminleme performansı seçilen bağımsız değişkenlere göre farklılık göstermiştir. Ölçülen ve tahminlenen WAS değerleri arasındaki interaksiyonu gösteren determinasyon katsayısı en yüksek R 2 = 0.8906 olarak elde edilmiştir. Bu bulgu Narlı Ovası'nda toprak aşınabilirliğinin tahminlenmesinde pedotransfer yaklaşımın kullanılabileceği anlamına gelmektedir. Abstract: A resistance of soils against erosive power relates to their physical and chemical properties closely. Many indices based on structural strength were developed for conceive soil erosion sensitivity. Wet aggregate stability (WAS) is an important and common indicator at express of soil erodibility. Measuring of this index is time consuming and tedious usually. For this reason, mentioned index can be predict by different tools instead of direct measurement. Pedotransfer functions are available and practical equations for estimating of difficult determining soil properties. The objective of this study is to predict of soil erodibility in Kahramanmaraş Narlı Plain with pedotransfer functions. End of the laboratory analyses whole data was transferred digital platform and tested statistically. Pedotransfer functions were generated for WAS by using multiple linear regression method. Performances of pedotransfer functions show differences depending on selected independent variables. Maximum determination coefficient reflect to interactions between measured and estimated values were obtained as R 2 = 0.8906. This finding means that pedotransfer approach can be used for estimating of soil erodibility in Narlı Plain. GİRİŞ Toprak korunması ile ilişkili farklı stratejik planların ve yönetim uygulamalarının geliştirilmesinde en önemli safha toprağa ait bilginin toplanmasıdır. Söz konusu bilgi veri tabanı şeklinde olduğunda sistematikleşmekte ve faydalı duruma dönüşmektedir. Değişik ölçeklerdeki toprak veritabanlarına girdi oluşturacak sayıların derlenmesinde en büyük engel doğrudan gözlem ya da ölçüm yapmanın çok zahmetli ve pahalı olması, geleneksel ölçüm yöntemlerinin çoğunun kullanışsız olmasıdır. Birçok bilim dalında olduğu gibi pedolojide de bu sorunun üstesinden gelmek için modellerin oluşturulması ve kullanımı yoluna gidilmektedir. İşlem-simülasyon modelleri, toprak kalitesi üzerine tarımsal yönetim çıktılarını tahminlemede çok kullanışlıdırlar fakat modelleme işlemine gelene kadar en zor ve pahalı aşama modele girdi oluşturacak verinin toplanmasıdır. Çalışma alanının görece çok büyük olmasına bağlı olarak örnek sayısı (n) fazla olduğunda, özellikle toprağın aşınabilirliğini yorumlamada kullanılan indekslerin laboratuar şartlarında ölçümü, araştırmacı için usandırıcı ve zaman öldürücü olmaktadır. Diğer taraftan artan örnek sayısı sarf malzemesi şeklindeki girdi maliyetini artırmakta, analiz çeşidi sayısının artması daha fazla sayıda makine-teçhizata gereksinim duyulmasına neden olmaktadır. Herhangi bir modelin oluşturulmasında da verinin toplanması en büyük iş olarak karşımıza çıkmakta ve özellikle gelişmekte olan ülkeler için istenmeyen sonuçları doğurmaktadır. Doğrudan gözlem yapmak yerine eldeki sonuçlar ve koşullara bağlı olarak değişen kanıtların esas alınmasıyla mantıksal sonuçlar elde etme işlemi "çıkarım yapma" ya da "çıkarsama" olarak tanımlanmaktadır (McBratney ve ark., 2006). McBratney ve ark. (2002) güncel toprak veri tabanının oluşturulması için SINFERS'i (Soil Inference Systems) önermişlerdir. SINFERS verilen kesin ölçüm sonuçlarını alarak mantıksal olarak bağlantılı tahmin eşitlikleriyle veri çıkarımı yapmaktadır. Uzamsal bağlamda olmayan bu çıkarım eşitlikleri pedotransfer eşitlikler (PTFs) olarak tanımlanmaktadır (Bouma __________________________ Sorumlu Yazar: Yakupoğlu, T., yakupoglu@ksu.edu.tr
Soil is essential to agriculture and a resource that cannot be replaced easily. Nevertheless, its importance to food production and the threats to its sustainability are often overlooked. This book, the 35th volume of Issues in Environmental Science and Technology, examines the current status of soils across the globe and their potential for food production to meet the needs of the World's population in the 21st Century. Threats, such as the degradation, pollution and erosion of soil are discussed, along with the possible consequences of climate change for soil and food production. As an ecosystem service, soil also serves to capture nutrients and sequester carbon, and these issues are discussed in the context of adding value to soil protection. The influence of modern agricultural techniques in enhancing soil productivity is also discussed. Throughout the book case studies support the discussion. Together with the books on Ecosystem Services, Sustainable Water, and Environmental Impacts of Modern Agriculture, this addition to the series will be essential reading for anyone concerned with the environment, whether as scientist, policy maker, student or lay reader.
In recent years, several sediment fingerprinting studies have used ultraviolet–visible (UV–Vis), near-infrared (NIR) and middle-infrared (MIR) spectroscopy as a low cost, non-destructive and fast alternative to obtain tracer properties to estimate sediment source contributions. For this purpose, partial last square regression (PLSR) has often been used to build predictive parametric models. However, spectra preprocessing and more robust and non-parametric models such as support vector machines (SVM) has gained little attention in these studies. Accordingly, the objectives of the current research were to evaluate (i) the accuracy of two multivariate methods (PLSR and SVM), (ii) the effect of eight spectra preprocessing techniques, and (iii) the effect of using the in- formation contained in the UV–Vis, NIR and MIR regions considered either separately or in combination on sediment source apportionment. The estimated source contribution was then compared with contributions ob- tained by the conventional fingerprinting approach based on geochemical tracers. This study was carried out in the Arvorezinha catchment (1.23 km2) located in southern Brazil. Forty soil samples were collected in three main potential source (cropland surface, unpaved roads and stream channels) and twenty-nine suspended sediment samples collected at the catchment outlet during nine rainfall-runoff events were used in this study. Both PLSR and SVM models showed a higher accuracy when calibrated and validated with the spectra submitted to spectral processing when compared to the direct use of the raw spectra. The best model results were obtained with PLSR and SVM mathematical models associated with the spectral preprocessing techniques 1st derivative Savitzky- Golay (SGD1), normalization (NOR) and combining NOR + SGD1 in the UV–Vis + NIR + MIR. The lowest er- rors were observed when the UV–Vis + NIR + MIR bands were combined due to the gain in information and, consequently, the increase in discriminant power achieved by the models. Despite the good accuracy of the models calibrated and validated with the artificial mixtures, significant errors remain when results of source contributions are compared to those obtained with the conventional sediment fingerprinting technique based on geochemical tracers. Nevertheless, the magnitude of the contributions calculated by the spectroscopy and geochemical approaches remains very similar for all sources, especially when using the SVM-UV–Vis + NIR + MIR model. Therefore, spectroscopy proved to be a fast, cheap and accurate technique, offering an alternative to the conventional geochemical approach for discriminating sediment source contributions in agricultural catchments located in subtropical regions.
Traditional laboratory methods for determining soil properties require a great deal of time and expense, while reflectance spectroscopy technology is a fast, inexpensive, and convenient way to predict physical and chemical soil properties. This technology in the spectral range of 400–2500 nm (Vis-NIR) as a suitable alternative method to get soil properties are accompanied by problems and challenges to extract considered properties. In this paper, we propose Minimum Variance based-Bayes Combination (MVBC) method to predict the soil properties. In the proposed MVBC method, we design two steps, prediction and combination for the training phase. Firstly, in the prediction step, five regression methods, i.e., partial least squares regression (PLSR), kernel Ridge regression (KRR), linear regression (LR), gradient boosting regression (GBR) and random forest (RF) method used to calculate and estimate nine soil properties, i.e., CaCo3, CEC, Clay, N, OC, PH in CaCl2, PH in H2O, Sand and Silt, separately. Secondly, in the combination step, the estimation errors of all regressions in the prediction step are determined to assign appropriate weight to each of them in the Bayesian framework based on minimum variance. These two steps are repeated until the final estimation error reaches an acceptable minimum value. Finally, these results and the trained system are used for the test phase. Experiments are reported to evaluate the effectiveness of the proposed MVBC method on real soil data, which shows the good performance of the proposed method with better results than other methods.
The main aim of this research was to assess the use of mid-infrared (MIR) spectroscopy and geostatistical model for the evaluation and mapping of the spatial variability of some selected soil properties across a field. It is with the view of aiding site-specific soil management decisions. The performance of the model for the prediction of the components (soil parameters) was reported using the coefficient of determination (R2) and root mean square error (RMSE) values of the validation data set. Results revealed that least square regression model performed better in predicting cation exchange capacity-CEC (R2 = 0.88 and RMSE = 8.98), soil organic carbon-OC (R2 = 0.88, RMSE = 0.55), and total nitrogen-TN (R2 = 0.91 and RMSE = 0.04). The first five principal components (PC) accounted for 78.17% of the total variance (PC1 = 25.75%, PC2 = 18.06%, PC3 = 13.85%, PC4 = 11.12%, and PC5 = 9.39%) and represented most of the variation within the data set. The coefficient of variation ranged from 6.73% for soil pH to 57.02% for available phosphorus (av. P). The soil pH values ranged from 4.21 to 6.57. The mean soil OC density was 2.14 kg m-2 within 50 cm soil depth. Nearly 96-97% of the soils contained av. P and sulfur (
SO
4
2
-
-S) below the critical levels. The overall results revealed that soil properties varied spatially. Hence, we suggest that mapping the spatial variability of soils across a field is a cost-effective solution for soil management.
Soil salinization is an important threat for agriculture and environment in the eastern coast of Urmia hyper saline Lake,
a lake in the western part of Iran. Predicting soil salinization requires rapid and low-cost measurement tools of soil
salinity. It is hypothesized that remote sensing and visible near-infrared spectroscopy may offer a feasible method for
that purpose. To test this hypothesis, 96 soil samples were collected using Latin hypercube sampling method. Soil
salinity (ECe) and spectral reflectance at various bands were measured in all samples in the laboratory. The spectral
indices derived from Landsat OLI, Sentinel-2 MSI and visible near-infrared (Vis-NIR) spectroscopy data, as input
variables, along with partial least squares regression (PLSR) method were used for predicting contents soil salt. The
results revealed that the PLSR derived regression models of the spectral indices accounted for 42.7 and 45.3% of the
soil salinity variation by Landsat OLI and Sentinel-2 MSI data, respectively. And PLSR derived model using Vis-NIR
spectroscopy data explained 59.4% of the variation in soil salt contents. Compared to derived models using Vis-NIR
spectroscopy, Landsat OLI and Sentinel-2 MSI data, the derived PLSR models (which were developed as a result of
integrating Vis-NIR spectroscopy and remote sensing data) increased the power of soil salinity prediction. Finally, the
combined of Vis-NIR spectroscopy + Sentinel-2 MSI data was found to provide the best input data for the derived
regression model to estimate soil salt contents (R2 = 0.713 and RMSE = 21.76 dS m-1).
The ability to inventory soil C on landscapes is limited by the ability to rapidly measure soil C. Diffuse reflectance spectroscopic analysis in the near-infrared (NIR, 400-2500 nm) and mid-infrared (MIR, 2500-25000 nm) regions provides means for measurement of soil C. To assess the utility of spectroscopy for soil C analysis, we compared the ability to obtain information from these spectral regions to quantify total, organic, and inorganic C in samples representing 14 soil series collected over a large region in the west central United States. The soils temperature regimes ranged from thermic to frigid and the soil moisture regimes from udic to aridic. The soils ranged considerably in organic (0.23-98 g C kg(-1)) and inorganic C content (0.0-65.4 g CO3-C kg(-1)). These soil samples were analyzed with and without an acid treatment for removal of CO3. Both spectral regions contained substantial information on organic and inorganic C in soils studied and MIR analysis substantially outperformed NIR. The superior performance of the MIR region likely reflects higher quality of information for soil C in this region. The spectral signature of inorganic C was very strong relative to Soil organic C. The presence of CO3 reduced ability to quantify organic C using MIR as indicated by improved ability to measure organic C in acidified soil samples. The ability of MIR spectroscopy to quantify C in diverse soils collected over a large geographic region indicated that regional calibrations are feasible.
A fast and convenient soil analytical technique is needed for soil quality assessment and precision soil management. The main objective of this study was to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to predict diverse soil properties, Near-infrared reflectance spectra, obtained from a Perstrop NIR Systems 6500 scanning monochromator (Foss NIRSystems, Silver Spring, MD), and 33 chemical, physical, and biochemical properties were studied for 802 soil samples collected from four Major Land Resource Areas (MLRAs). Calibrations were based on principal component regression (PCR) using the first derivatives of optical density [log(YR)I for the 1300- to 2500-nm spectral range. Total C, total N, moisture, cation-exchange capacity (CEC), 1.5 MPa water, basal respiration rate, sand, silt, and Mehlich III extractable Ca were successfully predicted by NIRS (r(2) > 0.80). Some Mehlich III extractable metals (Fe, K, Mg, Mn) and exchangeable cations (Ca, Mg, and K), sum of exchangeable bases, exchangeable acidity, clay, potentially mineralizable N, total respiration rate, biomass C, and pH were also estimated by NIRS but with less accuracy (r(2) = 0.80 similar to0.50). The predicted results For aggregation (wt% > 2, 1, 0.5, 0.25 mm, and macroaggregation) were not reliable (r(2) = 0.46 similar to0.60). Mehlich III extractable Cu, P, and Zn, and exchangeable Na could not be predicted using the NIRS-PCR technique (r(2) < 0.50), The results indicate that NIRS can be used as a rapid analytical technique to simultaneously estimate several soil properties with acceptable accuracy in a very short time.
Soil survey activities in many countries have reached a crucial phase. Standard country wide surveys either have been completed or will be completed within the near future. This development implies that more attention can be paid to soil survey interpretations and applications. The opportunity to emphasize use of soil survey data is particularly timely because of a change in the type of questions being asked by users of soil survey data and because of the rapid advances in information technology. Questions have become more specific, and quantitative answers are increasingly required.
Soil mechanical resistance to penetration by roots can potentially contribute to the spatial and temporal variability in root and shoot growth. Functions that accurately relate penetrometer resistance to soil properties are important tools for assessing the contribution of soil mechanical resistance (SMR) when the temporal and spatial variability in SMR cannot be readily measured. Although effective stress can make a significant contribution to SMR, the role of texture and compaction on the contribution of effective stress to SMR has not been explored and functions that are currently used to describe the relation between SMR, water content/potential and other soil properties do not contain terms explicitly linked to effective stress. The objectives of this study were to assess functions that included terms that would be compatible with effective stress and to subsequently develop a pedotransfer function to quantify the dependence of SMR on soil properties. Soil resistance was measured on disturbed and undisturbed soils with a range of textures, organic carbon (OC) contents and bulk density after equilibrating the soils at different water potentials (ψ). The SMR decreased with decreasing ψ at the lowest ψ in coarser-textured soils and the effects persisted into medium-textured soils at the higher level of compaction. These effects were attributed to a decrease in effective stress. The function found to be most successful in describing SMR in both disturbed and undisturbed soils was of the form:
Prediction of the movement and storage of water in soil is central to quantitative land evaluation. However, spatial and temporal predictions have not been provided by most Australian soil surveys. The saturated hydraulic conductivity (K-s) is an essential parameter for description of water movement in soil and its estimation has been considered too difficult for logistic and technical reasons. The K-s cannot be measured everywhere and relationships with readily observed morphological variables have to be established. However, conventional morphology by itself is a poor predictor of K-s. We have developed a more functional set of morphological descriptors better suited to the prediction of K-s. The descriptors can be applied at several levels of detail. Measurements of functional morphology and K-s were made on 99 horizons from 36 sites across south-eastern Australia. Useful predictions of K-s were possible using field texture, grade of structure, areal porosity, bulk density, dispersion index, and horizon type. A simple visual estimate of areal porosity was satisfactory, although a more quantitative system of measurement provided only slightly better predictions. Regression trees gave more plausible predictive models than standard multiple regressions because they provided a realistic portrayal of the non-additive and conditional nature of the relationships between morphology and K-s. The results are encouraging and indicate that coarse-level prediction of K-s is possible in routine soil survey. Direct measurement of K-s does not appear to be generally feasible because of the high cost, dynamic nature of K-s, and substantial short-range variation in the field. Prediction is further constrained by the limited returns from more sophisticated morphological predictors. The degree to which this limits practical land evaluation is yet to be demonstrated.
Mid-infrared diffuse reflectance Fourier transform (DRIFT) spectra of powdered soils present the major mineralogical and organic components within each soil, relative to their concentrations. The theory indicates that experimentally derived soil properties may be correlated withthe infrared spectra of some of these components, and the covariance between soil properties and spectra can then be modelled by PLS loadings and scores. The XRD results confirmed the mineralogy obtained from the infrared spectra and PLS weight loadings. For this study, the depiction of the samples in the score maps was found to be of particular importance for demonstrating similarities in composition of the samples. -from Authors
Rapid prediction of properties to describe soil variability is essential for site-specific crop management. Accurate predictions require the collection and analysis of a large number of soil samples, which is laborious and costly, and sometimes impossible. On the other hand, the diffuse reflectance spectrum of a soil sample provides multivariate data that are often related to various physical and chemical properties. One way to calculate and plot the spectral variation of different soil types is with the principal component biplot. For the soil that we studied, the first two principal components represent more than 90% of the variation among soil spectra. Our objective was to evaluate the hypothesis that the convex hull biplot area of a geographical region is proportional to the soil variation found in that region. An initial experiment that includes two different geographical regions suggested that the region, which was more variable in relation to pH, has a larger biplot area than the less variable one. A further analysis of the biplots of five fields and the associated variances of pH, organic carbon and clay showed that there was no direct relationship between the convex hull biplot area and the variation in the above soil properties. In this case, the convex hull biplot area might be a combined result of many soil variables, which we have not measured individually. The question of whether the spectral biplot area is a good and quick method of measuring multi-property soil variation is still open.
The particle density of soil (ρS) represents one of the soil's basic physical properties and it depends on the composition of both the mineral and the organic soil components. It therefore varies for different soils, e.g. within the group of mineral soils, and ranges from 2.4–2.9 g cm−3. Hence, awareness of this variability is important for properties estimated by a calculation involving particle density. Because ρS depends on both the soil's solid mineral particles and soil organic matter composition, we derived a function based on the mixture ratio of these two soil components. This approach represents a further development of earlier investigations dealing with the influence of organic carbon (Corg) on ρS. To parameterise this function, two data sets were used: (1) data from soils with Corg contents between 0% and 54.88% and corresponding values of ρS between 1.49 and 2.72 g cm−3; and (2) data from soils of 17 German long-term experiments contrasting in soil texture and in soil mineral inventory. Data set 1 was used to quantify the influence of soil organic matter on ρS, and data set 2 was used to calculate the influence of mineral matrix on ρS. The soil organic matter has two major influences on ρS: (1) via a mass effect (expressed as a mixture ratio between organic and mineral soil components); and (2) via a quality effect (expressed as calculated changes in particle density of organic soil components). Here, we calculated that with increasing content of soil organic matter (0–100%), the particle density of organic soil components rose from about 1.10 to 1.50 g cm−3, and present possible reasons for this phenomenon. Additionally, we demonstrate that the mineral matrix of the soil affects ρS especially via variations in the mineral inventory, but conclude that differences in particle size distribution of soils were to a lesser extent suitable for describing the influence of the mineral matrix on ρS. Overall, using our approach should generate more realistic values of ρS, and consequently of all calculated parameters which are sensitive to ρS.
In temperate Australia, accelerated soil acidiflcation through the introduction of legume-based pasture systems is a major limiting factor to their sustainability. In this study, soil samples were collected from paired sites (developed v. undeveloped) to a depth of 90 cm to assess acidiflcation rates under Stylosanthes spp. based pasture production systems in the semi-arid tropics of central and northern Queensland and the Northern Territory, Australia. Net acidiflcation rates varied from 0¢ 2t o 10¢6 kmol H+/ha¢year for the sites sampled. The highest rate of acidiflcation was observed under an irrigated Stylosanthes seed production system. Since acidiflcation was measured by difierence between paired sites, the total acidiflcation rate of the developed sites is likely to have been underestimated. The contribution from the export of meat products was estimated to be 0¢022{0¢035 kmol H+/ha¢year on those sites with suitable records. Acidiflcation occurred to depth on all sites that exhibited accelerated acidiflcation, which makes conventional remediation methods impractical in these extensive grazing systems. Pedotransfer functions to predict the pH bufiering capacity of a soil were developed for surface, subsoil, and the entire proflle based on soil organic carbon, clay, and silt content. Management strategies to minimise the negative impact of Stylosanthes pastures are suggested.
The new Australian classification system is a multi-categoric scheme with classes defined on the basis of diagnostic horizons or materials and their arrangement in vertical sequence as seen in an exposed soil profile. This book brings together soils data from all over Australia accumulated over the past three decades. Serving as a framework for organising knowledge about Australian soils it provides a better means of communication among and between scientists of various disciplines and those who use the land. In the new scheme classes are mutually exclusive, and the allocation and identification of new and unknown soil types is by means of a key.
Pedotransfer functions (PTFs) for predicting saturated hydraulic conductivity (K(s)) were evaluated using published Australian soil data sets. Eight published PTFs were evaluated. Generally, published PTFs provide a satisfactory estimation of Ks depending on the spatial scale and accuracy of prediction. Several PTFs were developed in this study, including the power function of effective porosity, multiple linear regression, fractal model, and artificial neural networks. Different methods for estimating the fractal dimension of particle-size distributions showed no significant differences in predicting K(s). The simplest model for estimating fractal dimension from the log-log plot of particle-size distribution is therefore recommended. The data set was also stratified into 3 broad classes of texture: sandy, loamy, and clayey. Stratification of PTFs based on textural class showed small improvements in estimation. The published PTF of Dane and Puckett (1994) Proc. Int. Workshop (Univ. of California: Riverside, CA) gives the best prediction for sandy soil; the PTF of Cosby et al. (1984) Water Resources Research 20, 682-90 gives the best production for loamy soil; and the PTF of Schaap et al. (1998) Soil Science Society of America Journal 62, 847-55 gives the best prediction for clayey soil. The data set used comprised different field and laboratory measurements over large areas, and limited predictive variables were available. The PTFs developed here may predict adequately in large areas (residuals = 10-20 mm/h), but for site-specific applications, local calibration is needed.
This study sought to establish diagnostic spectral characteristics in the short-wave infrared (SWIR) that could be used to classify soils in terms of their swelling potential. Three widely accepted soil-swelling indices, i.e. Atterberg limits, cation exchange capacity (CEC) and coefficient of linear extensibility (COLE), were used as controlling parameters to identify these spectral parameters. The results show that several spectral absorption feature parameters, namely position, depth and asymmetry, can be used in the classification on the basis of the discrete thresholds of these indices. The results show potential application of soil spectral characteristics in the construction industry and add a physical basis to the identification of clay mineral types dominant in engineering soils from currently used indices.
Parametric pedotransfer functions (PTFs), which predict parameters of a model from basic soil properties are useful in deriving continuous functions of soil properties, such as water retention curves. The common method for deriving parametric water retention PTFs involves estimating the parameters of a soil hydraulic model by fitting the model to the data, and then forming empirical relationships between basic soil properties and parameters. The latter step usually utilizes multiple linear regression or artificial neural networks. Neural network analysis is a powerful tool and has been shown to perform better than multiple linear regression. However neural-network PTFs are usually trained with an objective function that fits the estimated parameters of a soil hydraulic model. We called this the neuro-p method. The estimated parameters may carry errors and since the aim is to be able to estimate water retention, it is sensible to train the network to fit the measured water content. We propose a new objective function for neural network training, which predicts the parameters of the soil hydraulic model and optimizes the PTF to match the measured and observed water content, we called this neuro-m method. This method was used to predict the parameters of the van Genuchten model. Using Australian soil hydraulic data as a training set, neuro-m predicted the water retention from bulk density and particle-size distribution with a mean accuracy of 0.04 m3 m-3. The relative improvement of neuro-m over neural networks that was optimized to fit the parameters (neuro-p) is 13%. Compared with a published neural network PTF, the new method is 30% more accurate and less biased.
Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.
The rare‐earth orthoferrites are a family of canted antiferromagnets which show an unusual variety of magnetic properties. This article begins with a brief review of the ``allowed'' spin configurations compatible with the crystallographic symmetry of the orthoferrite structure and a summary of the experimentally observed spin configurations, spin reorientation temperatures, compensation temperatures, etc. We then review recent research on these materials, grouping most of the recent work into four major categories; studies of the spin reorientation transition, studies of the rare‐earth spin ordering, spectroscopic studies directed at determining magnetic interaction parameters, and studies of magnetic domains and domain walls. The spin reorientation process has been established to occur over a finite temperature range as a fourth‐order anisotropy, generally small, comes to dominate the orientational behavior in the temperature region where the usually domainant second‐order anisotropy passes through zero and changes sign. The spin reorientation has been monitored in a number of RE orthoferrites by several techniques; magnetic torque, microwave absorption, neutron diffraction, and optical spectroscopy. A few of the rare‐earth orthoferrites show RE‐RE interaction strong enough to cause an ordering of the rare‐earth ions at temperatures on the order of 2°–6°K; this ordering process has been documented by all the above techniques plus Mössbauer‐effect and magnetic‐susceptibility measurements. Spectroscopic measurements, showing the increase in RE ground‐state exchange splittings for the configurations stable at the lower temperatures, give a qualitative understanding of the mechanisms causing the spin reorientation and spin ordering processes, and promise detailed quantiative understanding of the unique magnetic behaviors seen in the orthoferrites. The low moment -
and high anisotropy of the orthoferrites make possible the fabrication of thin plates magnetized normal to the plate. Such plates are semitransparent, and the Faraday rotation through them can be used to study domain structures or can be used as a readout mechanism for memory and logic devices utilizing single small stable domains as the active element. Recent studies of such domains and of domain wall energy and mobility in the orthoferrites are accordingly reviewed. Finally, several investigations are summarized which utilize the orthoferrites as a vehicle for examining general properties of magnetic systems, such as critical‐point phenomena.
Historically, our understanding of the soil and assessment of its quality and function has been gained through routine soil chemical and physical laboratory analysis. There is a global thrust towards the development of more time- and cost-efficient methodologies for soil analysis as there is a great demand for larger amounts of good quality, inexpensive soil data to be used in environmental monitoring, modelling and precision agriculture. Diffuse reflectance spectroscopy provides a good alternative that may be used to enhance or replace conventional methods of soil analysis, as it overcomes some of their limitations. Spectroscopy is rapid, timely, less expensive, non-destructive, straightforward and sometimes more accurate than conventional analysis. Furthermore, a single spectrum allows for simultaneous characterisation of various soil properties and the techniques are adaptable for ‘on-the-go’ field use. The aims of this paper are threefold: (i) determine the value of qualitative analysis in the visible (VIS) (400–700 nm), near infrared (NIR) (700–2500 nm) and mid infrared (MIR) (2500–25,000 nm); (ii) compare the simultaneous predictions of a number of different soil properties in each of these regions and the combined VIS–NIR–MIR to determine whether the combined information produces better predictions of soil properties than each of the individual regions; and (iii) deduce which of these regions may be best suited for simultaneous analysis of various soil properties. In this instance we implemented partial least-squares regression (PLSR) to construct calibration models, which were independently validated for the prediction of various soil properties from the soil spectra. The soil properties examined were soil pHCa, pHw, lime requirement (LR), organic carbon (OC), clay, silt, sand, cation exchange capacity (CEC), exchangeable calcium (Ca), exchangeable aluminium (Al), nitrate–nitrogen (NO3–N), available phosphorus (PCol), exchangeable potassium (K) and electrical conductivity (EC). Our results demonstrated the value of qualitative soil interpretations using the loading weight vectors from the PLSR decomposition. The MIR was more suitable than the VIS or NIR for this type of analysis due to the higher incidence spectral bands in this region as well as the higher intensity and specificity of the signal. Quantitatively, the accuracy of PLSR predictions in each of the VIS, NIR, MIR and VIS–NIR–MIR spectral regions varied considerably amongst properties. However, more accurate predictions were obtained using the MIR for pH, LR, OC, CEC, clay, silt and sand contents, P and EC. The NIR produced more accurate predictions for exchangeable Al and K than any of the ranges. There were only minor improvements in predictions of clay, silt and sand content using the combined VIS–NIR–MIR range. This work demonstrates the potential of diffuse reflectance spectroscopy using the VIS, NIR and MIR for more efficient soil analysis and the acquisition of soil information.
Tillage causes changes to soil structure, and if the soil is too wet when tillage is performed, the change to the soil structure will be detrimental. A methodology that could be used for estimating the maximum gravimetric soil water content for optimum tillage would be helpful to prevent soil structural damage. The objective of this study was to compare five methods for estimating the soil water content for tillage. These methods utilized existing data of water retention, Atterberg limits, and Proctor compaction tests. The database included 80 soils, 51 from Germany and 29 from north central USA. Additionally, on three sites in Germany, soil water content in the field was measured intensively and related to the estimates of soil water content for workability. Maximum soil water content for optimum tillage of cohesive soils was at a consistency index of 1.15 and 90% of the water content at the lower plastic limit. For both cohesive and non-cohesive soils, the maximum soil water content for optimum workability was equal to either the water content at maximum Proctor density or 70% of the water content at a tension of −5 kPa. At the water content of the inflection point of the water retention curve, in many cases the soil would be too wet for tillage. The results provide methods for estimation of the maximum water content for optimum tillage from databases with existing soil physical properties.
A formal analysis was carried out to evaluate the efficiency of the different methods in predicting water retention and hydraulic conductivity. Efficiency can be defined in terms of effort, cost or value of information. The analysis identified the contribution of individual sources of measurement errors to the overall uncertainty. The value of information summarises the quality of the prediction, the cost of information, the application of predicted hydraulic properties, and the effect of spatial variability. For single measurements, the inverse disc-permeameter analysis is economically more efficient than using pedotransfer functions or measuring hydraulic properties in the laboratory. However, given the large amount of spatial variation of soil hydraulic properties it is found that lots of cheap and imprecise measurements, e.g. by hand texturing, are more efficient than a few expensive precise ones.
There has been growing interest in the use of diffuse infrared reflectance as a quick, inexpensive tool for soil characterization. In studies reported to date, calibration and validation samples have been collected at either a local or regional scale. For this study, we selected 3768 samples from all 50 U.S. states and two tropical territories and an additional 416 samples from 36 different countries in Africa (125), Asia (104), the Americas (75) and Europe (112). The samples were selected from the National Soil Survey Center archives in Lincoln, NE, USA, with only one sample per pedon and a weighted random sampling to maximize compositional diversity. Applying visible and near-infrared (VNIR) diffuse reflectance spectroscopy (DRS) to air-dry soil (< 2 mm) with auxiliary predictors including sand content or pH, we obtained validation root mean squared deviation (RMSD) estimates of 54 g kg− 1 for clay, 7.9 g kg− 1 for soil organic C (SOC), 5.6 g kg− 1 for inorganic C (IC), 8.9 g kg− 1 for dithionate–citrate extractable Fe (FEd), and 5.5 cmolc kg− 1 for cation exchange capacity (CEC) with NH4 at pH = 7. For all of these properties, boosted regression trees (BRT) outperformed PLS regression, suggesting that this might be a preferred method for VNIR-DRS soil characterization. Using BRT, we were also able to predict ordinal clay mineralogy levels for montmorillonite and kaolinite, with 88% and 96%, respectively, falling within one ordinal unit of reference X-ray diffraction (XRD) values (0–5 on ordinal scale). Given the amount of information obtained in this study with ∼4 × 103 samples, we anticipate that calibrations sufficient for many applications might be obtained with large but obtainable soil-spectral libraries (perhaps 104–105 samples). The use of auxiliary predictors (potentially from complementary sensors), supplemental local calibration samples and theoretical spectroscopy all have the potential to improve predictions. Our findings suggest that VNIR soil characterization has the potential to replace or augment standard soil characterization techniques where rapid and inexpensive analysis is required.
Pedotransfer functions (PTFs) have become a ‘white-hot’ topic in the area of soil science and environmental research. Most current PTF research focuses only on the development of new functions for predicting soil physical and chemical properties for different geographical areas or soil types while there are also efforts to collate and use the available PTFs. This paper reviews the brief history of the use of pedotransfer functions and discusses types of PTFs that exist. Different approaches to developing PTFs are considered and we suggest some principles for developing and using PTFs.
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
Infrared partial least squares (PLS) analysis is shown to provide a simple, rapid chemometric technique for the simultaneous analysis of soil properties. The method is capable of extracting both qualitative and quantitative information from soil spectra. A number of the mineral and organic components which are responsible for certain soil properties have been identified and the prediction of these properties assessed. Diffuse reflectance infrared Fourier-transform (DRIFT) spectra of whole soils were recorded to form a large training data set. The spectral information from this set was compressed into a small number of subspectra (called weight loadings) which contained positive and negative peaks reflecting correlations between the soil mineral and organic components and corresponding analytical data. -from Authors
Reflectance spectrometry is an emerging and non-destructive detection technique bearing fast, cheap, and accurate results compared with conventional assessments. Most field and laboratory-based spectrometers are restricted to VNIR (visible near-infrared). However, soils fail to show well-defined narrow absorption bands in this region. This obstructs the use of curve feature as a diagnostic criterion for soil nutrient predictions.
In this paper artificial neural network (ANN) is implemented to estimate soil organic matter, phosphorous, and potassium from the VNIR spectrum (400-1100 nm). Macronutrients were modelled from 41 bare soil reflectances of Lop Buri province, Thailand. Neurons were trained from 7 bandwidth categories derived from laboratory-based StellarNet spectroradiometer and in situ photometer.
Satisfactory results were attained and compared across different synthesised bandwidths. Models exhibited slightly better estimates from the laboratory than in situ spectra, and from narrower than broader bandwidths. Widening bandwidth corresponds with attenuated predictive powers, coupled with rising errors. Cross validation of models yielded acceptable correlations. The strength of models confirmed the capability of ANN to estimate macronutrients by solving difficulties incurred from high cross-channel correlations prevailing in conventional statistical techniques.
Global soil characterization with VNIR diffuse reflectance spectroscopy Near-infrared reflectance spectroscopy-principal components re-gression analyses of soil properties
- D J Brown
- K D Shepherd
- M G Walsh
- M D Mays
- T G Reinsch
Brown, D.J., Shepherd, K.D., Walsh, M.G., Mays, M.D., Reinsch, T.G., in press. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma. Chang, C.W., Laird, D.A., Mausbach, M.J., Hurburgh, C.R., 2001. Near-infrared reflectance spectroscopy-principal components re-gression analyses of soil properties. Soil Science Society of America Journal 65, 480–490.
Particle-size analysis Methods of Soil Analysis Part I, Physical and Mineral-ogical Methods
- Chapman
- Hall
- Uk London
- G W Gee
- J W Bauder
Chapman & Hall, London, UK. Gee, G.W., Bauder, J.W., 1986. Particle-size analysis. In: Klute, A. (Ed.), Methods of Soil Analysis Part I, Physical and Mineral-ogical Methods. Soil Science Society of America, Madison, WI, pp. 383–412.
Methods of Soil Analysis Part I, Physical and Mineralogical Methods
- G W Gee
- J W Bauder
Gee, G.W., Bauder, J.W., 1986. Particle-size analysis. In: Klute, A.
(Ed.), Methods of Soil Analysis Part I, Physical and Mineralogical Methods. Soil Science Society of America, Madison, WI,
pp. 383-412.
Australian Laboratory Handbook of Soil and Water Chemical Methods -Australian Soil and Land Survey Handbook
- G E Rayment
- F R Higginson
Rayment, G.E., Higginson, F.R., 1992. Australian Laboratory
Handbook of Soil and Water Chemical Methods -Australian
Soil and Land Survey Handbook. Inkata Press, Melbourne.
Mid-infared and near-infared diffuse reflectance spectroscopy for soil carbon measurement
- McCarty
Particle-size analysis
- Gee