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How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study, taking He...
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... study region, Heilongjiang Province, is located in the northeastern part of China between 121´13´´-135´06´´E and 43´26´´-53´34´´N (Fig. 1) (Sun et al. 2015). Its topography is characterized by mountains to the north and east which surrounds the central great plains. Heilongjiang Province has a long and severe winter and a short summer, with an average annual temperature of -4 to 4°C. Annual precipitation averages 380 to 600 mm, which mostly concentrated in June through ...
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... efficient crop mapping. Sample data In this study, we collected a total of 1 920 sampling points, including 1 135 crop (rice, corn, soybean and wheat) samples and 785 natural vegetations (mainly forest and grasslands) samples through visual interpretation of high spatial resolution images (i.e., SPOT 4, SPOT5 and Landsat TM+) and field surveys ( Fig. 1). Only the sampling plots of more than 25 ha (500 m×500 m) in size were selected to guarantee relatively homogeneous samples for subsequent SI-based separability analysis and classifier learning. In this study, 100 samples of rice, corn, soybean, wheat, forest and grassland were randomly selected and used to conduct separability ...
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Accurate data about the spatial distribution and planting area of maize is important for policy making, economic development, environmental protection and food security under climate change. This paper proposes a new identification method for spring maize based on spectral and phenological features derived from the moderate resolution imaging spect...
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... The increasing role of agriculture in the management of sustainable natural Communicated (Matton et al. 2015). Traditional methods of obtaining and updating the crop type and planting area information are mainly based on sampling surveys and statistical reports (Qiong et al. 2017) which have problems such as strong subjectivity, time consuming, labor-intensive, delayed updating, and the lack of spatial distribution information . Agricultural applications are one of the most widely used areas of remote sensing technology and agricultural crop type mapping with this technology has delivers wide coverage. ...
In this study, the performances of Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms in agricultural crop classification were compared using multi-temporal Sentinel-2 images. The study area is located within an area of approximately 45* 45 km2 within the borders of Çukurova Plain. Within the scope of the study, agricultural crop pattern classification including corn, cotton, wheat, sunflower, sunflower, watermelon, peanut, and citrus trees, as well as double crop corn, soya, and cotton crops planted after wheat using RF, ANN, SVM, XGBoost algorithms with multi-time Sentinel-2 images of 2021. The study used parcels registered in the Farmer Registration System (FRS) as reference data. Before defining the parcels that are called Farmer Declaration Parcels (FDP) as ground truth data, pre-editing and rule-based deletion operations were performed, afterwards false and incorrect declarations were eliminated.10 bands (B2, B3, B4, B5, B6, B7, B8, B8A, B11, B12) of Sentinel-2 imagery from 11 different dates and Enhanced Vegetation Index (EVI) were used. All classification results showed a high overall accuracy (OA) ranging from 85% to 92%. As a result of the classification using a total of 121 different features, the highest overall accuracy OA (92.14%) belongs to the XGBoost algorithm. XGBoost algorithm was followed by RF (89.15%), SVM (86.14%) and ANN (85.48%). In all algorithms, although cotton, maize and wheat gave the highest accuracy in the classification results, double crops (wheat-maize, wheat-soybean, wheat-cotton) especially those with very close phenological periods, could not reach very high accuracy values. As a result of the study, it was observed that crops at different phenological stages achieved high classification accuracy using all algorithms, while crops at very similar phenological stages lower classification results. With the methodology applied in this study, a large number of ground truth data were generated from FDP plots and used for classification.
... Typically, input data for these ML models are seasonal time series of various indices (especially optical) that are obtained after processing images. This is because time series usually provide higher accuracy in mapping than an index calculated from a single image [4][5][6]. ...
Crop identification is one of the most important tasks in digital farming. The use of remote sensing data makes it possible to clarify the boundaries of fields and identify fallow land. This study considered the possibility of using the seasonal variation in the Dual-polarization Radar Vegetation Index (DpRVI), which was calculated based on data acquired by the Sentinel-1B satellite between May and October 2021, as the main characteristic. Radar images of the Khabarovskiy District of the Khabarovsk Territory, as well as those of the Arkharinskiy, Ivanovskiy, and Oktyabrskiy districts in the Amur Region (Russian Far East), were obtained and processed. The identifiable classes were soybean and oat crops, as well as fallow land. Classification was carried out using the Support Vector Machines, Quadratic Discriminant Analysis (QDA), and Random Forest (RF) algorithms. The training (848 ha) and test (364 ha) samples were located in Khabarovskiy District. The best overall accuracy on the test set (82.0%) was achieved using RF. Classification accuracy at the field level was 79%. When using the QDA classifier on cropland in the Amur Region (2324 ha), the overall classification accuracy was 83.1% (F1 was 0.86 for soybean, 0.84 for fallow, and 0.79 for oat). Application of the Radar Vegetation Index (RVI) and VV/VH ratio enabled an overall classification accuracy in the Amur region of 74.9% and 74.6%, respectively. Thus, using DpRVI allowed us to achieve greater performance compared to other SAR data, and it can be used to identify crops in the south of the Far East and serve as the basis for the automatic classification of cropland.
... Subsequently, paddy rice develops swiftly and the growth phases can span three to four months. Finally, paddy rice matures in late September and is often harvested between late September and October (Hu et al., 2017;You and Dong, 2020). The crop calendar for some major crops is shown in Fig. 2. ...
... The above studies illustrate that the integration of multi-temporal data can achieve complementary information and thus improve vegetation classification accuracy. However, the critical periods of vegetation growth include budding, flowering, and maturation, and not every cycle of images can provide useful information, and images using all-time sequences may not achieve the best classification performance [22]. Piaser et al. [23] used Sentinel-2 images to classify aquatic vegetation in temperate wetlands and assessed the classification accuracy by eight machine learning algorithms, and the results showed that the classification scenario covering the widest temporal range (April-November) achieved better classification results than the scenario covering the narrow temporal range scenario (May-October, June-September, and July-August) to Remote Sens. 2023, 15, 4003 3 of 29 achieve better classification results. ...
Combining machine learning algorithms with multi-temporal remote sensing data for fine classification of wetland vegetation has received wide attention from researchers. However, wetland vegetation has different physiological characteristics and phenological information in different growth periods, so it is worth exploring how to use different growth period characteristics to achieve fine classification of vegetation communities. To resolve these issues, we developed an ensemble learning model by stacking Random Forest (RF), CatBoost, and XGBoost algorithms for karst wetland vegetation community mapping and evaluated its classification performance using three growth periods of UAV images. We constructed six classification scenarios to quantitatively evaluate the effects of combining multi-growth periods UAV images on identifying vegetation communities in the Huixian Karst Wetland of International Importance. Finally, we clarified the influence and contribution of different feature bands on vegetation communities’ classification from local and global perspectives based on the SHAP (Shapley Additive explanations) method. The results indicated that (1) the overall accuracies of the four algorithms ranged from 82.03% to 93.37%, and the classification performance was Stacking > CatBoost > RF > XGBoost in order. (2) The Stacking algorithm significantly improved the classification results of vegetation communities, especially Huakolasa, Reed-Imperate, Linden-Camphora, and Cephalanthus tetrandrus-Paliurus ramosissimus. Stacking had better classification performance and generalization ability than the other three machine learning algorithms. (3) Our study confirmed that the combination of spring, summer, and autumn growth periods of UAV images produced the highest classification accuracy (OA, 93.37%). In three growth periods, summer-based UAVs achieved the highest classification accuracy (OA, 85.94%), followed by spring (OA, 85.32%) and autumn (OA, 84.47%) growth period images. (4) The interpretation of black-box stacking model outputs found that vegetation indexes and texture features provided more significant contributions to classifying karst wetland vegetation communities than the original spectral bands, geometry features, and position features. The vegetation indexes (COM and NGBDI) and texture features (Homogeneity and Standard Deviation) were very sensitive when distinguishing Bermudagrass, Bamboo, and Linden-Camphora. These research findings provide a scientific basis for the protection, restoration, and sustainable development of karst wetlands.
... This research opted to use Enhanced Vegetation Index (EVI) following studies on improved indexes for vegetation and crop classification (Hu et al., 2017;Ji & Peters, 2007;Jiang et al., 2008) considering the biophysics characteristics, the sudden densification of vegetation and the specific seasonal component. ...
The contemporary food system pushes agriculture to a globalized value-chain, affecting landscapes , resource access, and institutional arrangements. Institutions operating in Africa adopt development corridors to integrate multisector investments and induce export-driven primary sector, leading to massive land deals, also known as land-grabbing. Organizations struggle to monitor land deals accurately, lacking spatial precision and contextual information for affected communities. This research examines Mozambique's Nacala Corridor, using geospatial data as a tool to detect spatial (re)configurations due to exported-oriented policies and infrastructure. Data from land conflicts databases (Land Matrix and Environmental Justice) were analyzed with remote sensing Landsat and MODIS imagery using multiple indexes, an EVI time series, and the application of the LandTrendr algorithm. The results show that the temporal and spatial analysis of remote-sensing data is in line with the major political and economic dynamics of the region. Hotspots of land cover changes were detected in the same areas where land grabbing were reported; however, reported and detected land areas did not coincide. Temporal analysis showed that institutional changes played a greater role in triggering land use changes than infrastructure implementation. We conclude that land cover modifications, conflicts, and spatial development initiatives follows policies and institutional arrangements targeting international investments.
... In addition to the challenges presented by the high intra-class spectral variance between and within fields, the rapid temporal dynamics create obstacles for accurate cropland extraction. The spectral reflectance of crops varies throughout the growing season because of distinct biological characteristics [32]. Moreover, additional management practices (e.g., field preparation and fallow periods before seeding and after harvesting) can increase the intra-class variance of the spectral-temporal signal [33]. ...
Accurate cropland information is crucial for the assessment of food security and the formulation of effective agricultural policies. Extracting cropland from remote sensing imagery is challenging due to spectral diversity and mixed pixels. Recent advances in remote sensing technology have facilitated the availability of very high-resolution (VHR) remote sensing images that provide detailed ground information. However, VHR cropland extraction in southern China is difficult because of the high heterogeneity and fragmentation of cropland and the insufficient observations of VHR sensors. To address these challenges, we proposed a deep learning-based method for automated high-resolution cropland extraction. The method used an improved HRRS-U-Net model to accurately identify the extent of cropland and explicitly locate field boundaries. The HRRS-U-Net maintained high-resolution details throughout the network to generate precise cropland boundaries. Additionally, the residual learning (RL) and the channel attention mechanism (CAM) were introduced to extract deeper discriminative representations. The proposed method was evaluated over four city-wide study areas (Qingyuan, Yangjiang, Guangzhou, and Shantou) with a diverse range of agricultural systems, using GaoFen-2 (GF-2) images. The cropland extraction results for the study areas had an overall accuracy (OA) ranging from 97.00% to 98.33%, with F1 scores (F1) of 0.830–0.940 and Kappa coefficients (Kappa) of 0.814–0.929. The OA was 97.85%, F1 was 0.915, and Kappa was 0.901 over all study areas. Moreover, our proposed method demonstrated advantages compared to machine learning methods (e.g., RF) and previous semantic segmentation models, such as U-Net, U-Net++, U-Net3+, and MPSPNet. The results demonstrated the generalization ability and reliability of the proposed method for cropland extraction in southern China using VHR remote images.
... With the development of image processing and artificial intelligence, the technologies of crop identification can be summarized into three streams. In the first stream, the traditional remote sensing feature extraction is mainly based on spectral, spatial, and temporal features (Zhang et al., 2016;Qiong et al., 2017;Sun et al., 2019b). Tian et al. (2021a) analyzed spectral characteristics and vegetation indices at each growth stage of crops and used reasonable thresholds to screen these parameters and successfully identified winter wheat and garlic planting areas. ...
The field of computer vision has shown great potential for the identification of crops at large scales based on multispectral images. However, the challenge in designing crop identification networks lies in striking a balance between accuracy and a lightweight framework. Furthermore, there is a lack of accurate recognition methods for non-large-scale crops. In this paper, we propose an improved encoder-decoder framework based on DeepLab v3+ to accurately identify crops with different planting patterns. The network employs ShuffleNet v2 as the backbone to extract features at multiple levels. The decoder module integrates a convolutional block attention mechanism that combines both channel and spatial attention mechanisms to fuse attention features across the channel and spatial dimensions. We establish two datasets, DS1 and DS2, where DS1 is obtained from areas with large-scale crop planting, and DS2 is obtained from areas with scattered crop planting. On DS1, the improved network achieves a mean intersection over union (mIoU) of 0.972, overall accuracy (OA) of 0.981, and recall of 0.980, indicating a significant improvement of 7.0%, 5.0%, and 5.7%, respectively, compared to the original DeepLab v3+. On DS2, the improved network improves the mIoU, OA, and recall by 5.4%, 3.9%, and 4.4%, respectively. Notably, the number of parameters and giga floating-point operations (GFLOPs) required by the proposed Deep-agriNet is significantly smaller than that of DeepLab v3+ and other classic networks. Our findings demonstrate that Deep-agriNet performs better in identifying crops with different planting scales, and can serve as an effective tool for crop identification in various regions and countries.
... The information on spatial distribution pattern and area of different crops is particularly useful for monitoring and managing the sustainability of agricultural resources. It is the basis for estimating crop yields (Hu et al., 2017), water resources management (Vogels et al., 2019) and disaster assessment (Zhang, 2004). Traditional methods to obtain and update crop type and planting area information are based mainly on field sampling surveys and statistical reports, which are time-consuming, labor-intensive, and lack timely update and spatial distribution information (Zhong et al., 2016;Gilbertson et al., 2017). ...
... For example, MODIS has been widely used in the past decade. However, the spatial resolution of MODIS products is too rough and only suitable for plots larger than 32 ha (Wardlow and Egbert, 2008;Hu et al., 2017), which limits its applications in assessing small arable lands. Landsat products alleviate this problem, but its spatial resolution scale still fails to capture the actual spatial distribution pattern for fragmented farmland (e.g., field size less than 0.5 ha). ...
... For example, Yang et al. (2011) used SPOT5 images from May 2006 for identifying different crop types in south Texas, USA, as most fields were already at or near their peak canopy growth. The "Single-date image" method is time-efficient and user-friendly (Hu et al., 2017), however, image acquisition at the proper recognition time is not guaranteed due to potential cloud contamination (Han et al., 2019). Meanwhile, a single image may fail to identify several crop types with similar spectral responses, resulting in inaccurate classification results (Hu et al., 2017). ...
The information on spatial distribution pattern and area of different crops is particularly useful for monitoring and managing the sustainability of agricultural resources. However, there are still challenges in timely mapping of crop types and planting areas to support production during the present growing season. Here, we proposed to use time-series Sentinel-2 imagery and a deep learning method for major crop mapping (wheat, maize, sunflower, and squash) in the agricultural production areas in the Hetao Irrigation District (HID), Inner Mongolia Autonomous Region, China. A feature selection method that combines the global separability index and feature recursive elimination was proposed to optimize the band features of Sentinel-2 imagery. The Random Forest, Extreme Gradient Boosting, U-Net and deeplabv3+ algorithms were then used for image classification with all spectral bands and the selected band features, respectively. The selected bands and best method were used to determine the key time window for the early identification of four major crops. Integrate all image datasets in the early window period, and build mapping models suitable for images at different times in the window period by combining the selected bands and best method. The results showed that the proposed feature selection method effectively removed redundant features and improved the classification efficiency. The U-Net algorithm was more suitable for the classification of major crops in HID with a higher accuracy whether using all band features or the selected features. The key periods for early recognition of wheat, maize, sunflower, and squash in HID were mid-May, late June, early July, and late June, respectively. The constructed models for crop type mapping in the early period were tested in 2020 and 2021 in the HID, and the F1-score, the mean intersection-over-union and overall accuracy were 86.71 %, 84.66 %, and 98.89 %, respectively. The proposed feature selection method, U-Net classification algorithm, and screening of the early mapping window period in this study provide an efficient, accurate and timely crop mapping tool in HID. Future research can further verify the applicability of the early identification time window proposed in this study to support other agricultural management activities in the areas.
... In addition, the RE1 band of the Sentinel-2 data, which is more sensitive to changes in vegetation chlorophyll, was also chosen to construct the vegetation index [45]. The shortwave infrared band is sensitive to leaf moisture and soil moisture and is often used to estimate the vegetation canopy moisture thickness; therefore, we chose the normalized difference water index (NDWI) to reflect the changes in the moisture content [46]. Remote Sens. 2022, 14, x FOR PEER REVIEW 6 of 23 ...
Timely and accurate crop identification and mapping are of great significance for crop yield estimation, disaster warning, and food security. Early-season crop identification places higher demands on the quality and mining of time-series information than post-season mapping. In recent years, great strides have been made in the development of deep-learning algorithms, and the emergence of Sentinel-2 data with a higher temporal resolution has provided new opportunities for early-season crop identification. In this study, we aimed to fully exploit the potential of deep-learning algorithms and time-series Sentinel-2 data for early-season crop identification and early-season crop mapping. In this study, four classifiers, i.e., two deep-learning algorithms (one-dimensional convolutional networks and long and short-term memory networks) and two shallow machine-learning algorithms (a random forest algorithm and a support vector machine), were trained using early-season Sentinel-2 images and field samples collected in 2019. Then, these algorithms were applied to images and field samples for 2020 in the Shiyang River Basin. Twelve scenarios with different classifiers and time intervals were compared to determine the optimal combination for the earliest crop identification. The results show that: (1) the two deep-learning algorithms outperformed the two shallow machine-learning algorithms in early-season crop identification; (2) the combination of a one-dimensional convolutional network and 5-day interval time-series Sentinel-2 data outperformed the other schemes in obtaining the early-season crop identification time and achieving early mapping; and (3) the early-season crop identification mapping time in the Shiyang River Basin was identified as the end of July, and the overall classification accuracy reached 0.83. In addition, the early identification time for each crop was as follows: the wheat was in the flowering stage (mid-late June); the alfalfa was in the first harvest (mid-late June); the corn was in the early tassel stage (mid-July); the fennel and sunflower were in the flowering stage (late July); and the melons were in the fruiting stage (around late July). This study demonstrates the potential of using Sentinel-2 time-series data and deep-learning algorithms to achieve early-season crop identification, and this method is expected to provide new solutions and ideas for addressing early-season crop identification monitoring.
... Accurate and timely geographical knowledge of crop types on local, regional, and global scales provides a valuable source of information for various agricultural, environmental, social, and economic applications [1,2]. The new generation of satellite Earth observation systems (e.g., PlanetScope, RapidEye, and Sentinel 2) provide massive amounts of data with very high spatial and temporal resolutions. ...
... Two hyperparameters of the guided filter were optimized as follows. A grid search method was used for selecting the neighborhood size (z) considering window sizes (i.e., | |) in the range of [2,35] pixels and evaluating the results on the validation data. Finally, 31 pixels were selected as the best window size, and hence z = √31. ...
Accurate crop mapping is a fundamental requirement in various agricultural applications, such as inventory, yield modeling, and resource management. However, it is challenging due to crop fields’ high spectral, spatial, and temporal variabilities. New technology in space-borne Earth observation systems has provided high spatial and temporal resolution image data as a valuable source of information, which can produce accurate crop maps through efficient analytical approaches. Spatial information has high importance in accurate crop mapping; a Window-based strategy is a common way to extract spatial information by considering neighbourhood information. However, crop field boundaries implicitly exist in image data and can be more helpful in identifying different crop types. This study proposes Guided Filtered Sparse Auto-Encoder (GFSAE) as a deep learning framework guided implicitly with field boundary information to produce accurate crop maps. The proposed GFSAE was evaluated over two time-series datasets of high-resolution PlanetScope (3 m) and RapidEye (5 m) imagery, and the results were compared against the usual Sparse Auto Encoder (SAE). The results show impressive improvements in terms of all performance metrics for both datasets (namely 3.69% in Overal Accuracy, 0.04 in Kappa, and 4.15% in F-score for the PlanetScope dataset, and 3.71% in OA, 0.05 in K, and 1.61% in F-score for RapidEye dataset). Comparing accuracy metrics in field boundary areas has also proved the superiority of GFSAE over the original classifier in classifying these areas. It is also appropriate to be used in field boundary delineation applications.
