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Scatterplot between the input ERA5 ensemble meant reanalysis data and in situ measurements for the main atmospheric forcings. Data from all validation sites is shown on the same plot.
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The Sentinel-2 and Sentinel-3 satellite constellation contains most of the spatial, temporal and spectral characteristics required for accurate, field-scale actual evapotranspiration (ET) estimation. The one remaining major challenge is the spatial scale mismatch between the thermal-infrared observations acquired by the Sentinel-3 satellites at aro...
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Accurate quantification of energy budget components and its partitioning between canopy and soil is essential for improving water resource management. During the last few decades, several two-source evapotranspiration (ET) models have been developed by exploiting satellite datasets in conjunction with meteorological observations to estimate fluxes...
Hydrological responses corresponding to the agricultural land use alterations are critical for planning crop management strategies, water resources management, and environmental evaluations. However, accurate estimation and evaluation of these hydrological responses are restricted by the limited availability of detailed crop classification in land...
Robust information on consumptive water use (evapotranspiration, ET) derived from remote sensing can significantly benefit water decision-making in agriculture, informing irrigation schedules and water management plans over extended regions. To be of optimal utility for operational usage, these remote sensing ET data should be generated at the sub-...
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... However, the outputs had a coarser resolution than those using ALEXI/DisALEXI for CONUS (70 m). Sen-ET is a model based on combining TSEB and PT methods and the fusion of S-2 and Sentinel-3 (S-3) LST data, and the model relies on sharpening the coarse 1 km resolution S-3 data using the data mining sharpener to match the 20 m resolution of S-2 data and allow for field-scale analysis [54]. The PT approximation allows for the unknown latent heat flux from the canopy to be initially estimated, while latent heat flux from the soil is estimated using the balance of other soil fluxes [33,55]. ...
Irrigation is an essential component of our food production system and a large user of freshwater. Pressure on irrigated agriculture is likely to increase with growing populations and climate uncertainty. Efforts to ensure sustainable water use in this sector have had mixed results. Some of these efforts have been used in the interest of political or financial gain. The situation is complicated by the vulnerability of irrigating farmers, locally within irrigation schemes and in the global agricultural supply chain. An opportunity exists in the form of increasing the accessibility of open-source remote sensing products and wireless sensor networks. Irrigating farmers can define and assess their irrigation performance at different spatial and temporal scales. A review of irrigation performance assessment approaches and the available products and sensors is presented. Potential implementations for sensing and monitoring, as well as irrigation performance, are presented. The possibilities at different time scales and the influence on performance of different groups within the irrigation scheme are discussed. The particular circumstances of specific irrigation schemes need to be assessed with a cost–benefit analysis. The implementation of irrigation performance analysis tools should be led by irrigating farmers, as it directly impacts this group.
... While direct in situ evapotranspiration measurements of ETa, such as the EC method, are focused on the individual ecosystem scale, remote sensing models allow mapping of ETa to the global scale as a whole while maintaining the possibility of analyzing individual ecosystems and their role in catchment hydrology (Allen et al., 2007a;Guzinski et al., 2020;Aboelsoud et al., 2023). ...
... Similarly, as in other studies (Allen et al., 2007b;Anderson et al., 2011;Zhang et al., 2015;Liebert et al., 2016;Ghisi et al., 2023;Guzinski et al., 2020;Guzinski et al., 2023), this study demonstrated the significant potential of both models for practical quantification of ETa and energy fluxes using satellite sensors. For addressing scientific inquiries regarding the water balance in the landscape of Central Europe, however, it may not be sufficient to evaluate models within floodplain ecosystem alone. ...
... Remote sensing appears to be a suitable tool for assessing landscape water balance because satellite sensors can easily identify relevant land surface state variables and properties needed to model water and energy fluxes (Anderson et al., 2012), and archives can be used to perform time series analyses. While direct in situ evapotranspiration measurements of ET a, such as the EC method, are focused on the individual ecosystem scale, remote sensing models allow mapping of ET a to the global scale as a whole while maintaining the possibility of analyzing individual ecosystems and their role in catchment hydrology (Aboelsoud et al., 2023;Allen et al., 2007a;Guzinski et al., 2020). ...
... Similarly, as in other studies (Allen et al., 2007b;Anderson et al., 2011;Zhang et al., 2021;Liebert et al., 2016;Ghisi et al., 2023;Guzinski et al., 2020;Guzinski et al., 2023), this study demonstrated the significant potential of both models for practical quantification of ET a and energy fluxes using satellite sensors. For addressing scientific inquiries regarding the water balance in the landscape of Central Europe, however, it may not be sufficient to evaluate models within floodplain ecosystem alone. ...
Study region: Floodplain ecosystem region at the confluence of the Morava and Thaya Rivers, Czech Republic. Study focus: Accurate determination of actual evapotranspiration (ETa) is essential for understanding surface hydrological conditions. The aim of this study was to evaluate two remote sensing models, METRIC and TSEB, for estimating ETa and energy fluxes in two ecosystems using the eddy covariance (EC) as a reference. New hydrological insights for the region: Both models demonstrate the ability to quantify ETa across the region. Compared with the METRIC, which had a mean bias error (MBE) = 0.12 mm/day, the TSEB better detected ETa in the forest test site (MBETSEB = -0.03 mm/day). In contrast, the METRIC improved detection of ETa (MBEMETRIC = -0.03 mm/day) in grassland test site, where the TSEB overestimate daily ETa (MBETSEB = 0.52 mm/day). The models and EC indicate similar seasonal dynamics of the evaporative fraction and Bowen ratio throughout the growing season. Despite the overall agreement between the models and EC, the selected spatial outputs indicate some disagreement among them in terms of the spatial patterns of ETa. This disagreement is related to the sensitivity of TSEB to canopy height/roughness, as well as the a priori Priestley-Taylor coefficient in forests. Despite these shortcomings, this study highlights the applicability of remote sensing energy balance-based diagnostic models for studying hydrological processes in a spatially distributed manner.
... The feasibility of the Sen-ET approach was evaluated in a study in Denmark, which achieved promising results (Guzinski and Nieto 2019) and led to the implementation of the Sen-ET approach as an open-source plugin for the SNAP toolbox (https://www.esa-sen4et.org) (Guzinski et al., 2020). In this study, the approach was evaluated using EC measurements in Europe, Africa and North America while running with purely global datasets and without any site-specific parameterization and resulting in good accuracies. ...
Thermal infrared (TIR) remote sensing of the land-surface temperature (LST) provides an invaluable diagnostic of surface fluxes and vegetation state, from plant and sub-field scales up to regional and global coverage. However, without proper consideration of the nuances of the remotely sensed LST signal, TIR imaging can give poor results for estimating sensible and latent heating. For example, sensor view angle, atmospheric impacts, and differential coupling of soil and canopy sub-pixel elements with the overlying atmosphere can affect the use of satellite-based LST retrievals in land-surface modeling systems. A concerted effort to address the value and perceived shortcomings of TIR-based modeling culminated in the Workshop on Thermal Remote Sensing of the Energy and Water Balance, held in La Londe les Maures, France in September of 1993. One of the outcomes of this workshop was the Two-Source Energy Balance (TSEB) model, which has fueled research and applications over a range of spatial scales.
In this paper we provide some historical context for the development of TSEB and TSEB-based multi-scale modeling systems (ALEXI/DisALEXI) aimed at providing physically based, diagnostic estimates of latent heating (evapotranspiration, or ET, in mass units) and other surface energy fluxes. Applications for TSEB-based ET retrievals are discussed: in drought monitoring and yield estimation, water and forest management, and data assimilation into – and assessment of – prognostic modeling systems. New research focuses on augmenting temporal sampling afforded in the thermal bands by integrating cloud-tolerant, microwave-based LST information, as well as evaluating the capabilities of TSEB for separating ET estimates into evaporation and transpiration components. While the TSEB has demonstrated promise in supplying water use and water stress information down to sub-field scales, improved operational capabilities may be best realized in conjunction with ensemble modeling systems such as OpenET, which can effectively combine strengths of multiple ET retrieval approaches.
... For instance, remote sensing soil moisture products at coarse resolution can be obtained from different satellite platforms and sensors, such as the Soil Moisture and Ocean Salinity (SMOS), the Soil Moisture Active and Passive (SMAP), or the Advanced SCATterometer (ASCAT) platforms. More recently, the Sentinel-1 Copernicus satellites have enabled native high-resolution (<1 km) soil moisture datasets (i.e., not considering products obtained through downscaling) (17)(18)(19)(20)(21). Exploitation of the Sentinel constellation (Sentinel-1, -2, and -3) has also allowed this for variables such as evaporation (22)(23)(24), precipitation [e.g., Karger et al. (25); Filippucci et al. (26); He et al. (27)], snow depth (28), and river discharge (29-31). Moreover, the integration of Sentinel with Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) provides the potential to develop long-term datasets for evaporation [e.g., Jaafar et al. (32)] and river discharge (33) at high spatial resolution. ...
... The quality and usability of satellite-based hydrological products have recently been enhanced, which is in part thanks to the wealth of new data sources from the Sentinel constellation. Indeed, besides the products used in the DTE Hydrology project, additional high-resolution products for soil moisture [e.g., the plot-scale S2MP dataset (59)] and prototype products for evaporation [e.g., Sen-ET, Guzinski et al. (24) and ECOSTRESS, Fisher et al. (60)] are available, and a large-scale, comprehensive comparison of their characteristics and accuracy is a research priority. Here, we present an overview of the challenges in EO-based datasets and the potential role of AI and machine learning in addressing these gaps (Figure 7). ...
Climate change is profoundly affecting the global water cycle, increasing the likelihood and severity of extreme water-related events. Better decision-support systems are vital to accurately predict and monitor water-related environmental disasters and optimally manage water resources. These must integrate advances in remote sensing, in situ, and citizen observations with high-resolution Earth system modeling, artificial intelligence (AI), information and communication technologies, and high-performance computing. Digital Twin Earth (DTE) models are a ground-breaking solution offering digital replicas to monitor and simulate Earth processes with unprecedented spatiotemporal resolution. Advances in Earth observation (EO) satellite technology are pivotal, and here we provide a roadmap for the exploitation of these methods in a DTE for hydrology. The 4-dimensional DTE Hydrology datacube now fuses high-resolution EO data and advanced modeling of soil moisture, precipitation, evaporation, and river discharge, and here we report the latest validation data in the Mediterranean Basin. This system can now be explored to forecast flooding and landslides and to manage irrigation for precision agriculture. Large-scale implementation of such methods will require further advances to assess high-resolution products across different regions and climates; create and integrate compatible multidimensional datacubes, EO data retrieval algorithms, and models that are suitable across multiple scales; manage uncertainty both in EO data and models; enhance computational capacity via an interoperable, cloud-based processing environment embodying open data principles; and harness AI/machine learning. We outline how various planned satellite missions will further facilitate a DTE for hydrology toward global benefit if the scientific and technological challenges we identify are addressed.
... While the need for knowledge of irrigation water use is growing, and the irrigation models are still too uncertain to meet the challenge, more and more remote sensing observations relevant to irrigation retrieval are becoming freely available. Over the last two decades, the number of satellites carrying solar and thermal spectrum sensors (e.g., Moderate resolution Imaging Spectroradiometer (MODIS), Landsat, Sentinel-2 (S2), Sentinel-3) from which evapotranspiration (ET) products can be derived (such as SEN-ET; Guzinski et al., 2020), and passive (e.g., Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP)) and active microwave (e.g., Advanced SCATterometer (ASCAT), Sentinel-1 (S1)) sensors, from which soil moisture products can be derived (e.g., El Hajj et al., 2017;Ojha et al., 2019;Paolini et al., 2022), has steadily increased. This opens up increasingly interesting possibilities in terms of spatial and temporal resolution (Peng et al., 2021). ...
... The two-source energy balance (TSEB) model is one of the most widely applied and has a more reasonable physical mechanism compared to singlesource models [6]. It has been shown that the TSEB model can more accurately simulate energy exchanges between the atmosphere, soil and vegetation and is more adaptable to different vegetation types and climatic regions [17][18][19]. The input parameters of the TSEB model include surface boundary parameters based on remote sensing and meteorological parameters [6]. ...
Remote sensing-based models usually have difficulty in generating spatio-temporally continuous terrestrial evapotranspiration (ET) due to cloud cover and model failures. To overcome this problem, machine learning methods have been widely used to reconstruct ET. Therefore, studies comparing and evaluating the accuracy and effectiveness of reconstruction among different machine learning methods at the basin scale are necessary. In this study, four popular machine learning methods, including deep forest (DF), deep neural network (DNN), random forest (RF) and extreme gradient boosting (XGB), were used to reconstruct the ET product, addressing gaps resulting from cloud cover and model failure. The ET reconstructed by the four methods was evaluated and compared for Heihe River Basin. The results showed that the four methods performed well for Heihe River Basin, but the RF method was particularly robust. It not only performed well compared with ground measurements (R = 0.73) but also demonstrated the ability to fully reconstruct gaps generated by the TSEB model across the entire basin. Validation based on ground measurements showed that the DNN and XGB models performed well (R > 0.70). However, some gaps still existed in the desert after reconstruction using the DNN and XGB models, especially for the XGB model. The DF model filled these gaps throughout the basin, but this model had lower consistency compared with ground measurements (R = 0.66) and yielded many low values. The results of this study suggest that machine learning methods have considerable potential in the reconstruction of ET at the basin scale.
... Two source energy balance (TSEB) model is one of the most widely applied, which has a more reasonable physical mechanism compared to single source models [6]. It has been shown that the TSEB model can more accurately simulate the energy exchanges between the atmosphere, soil and vegetation, and is more adaptable under different vegetation types and climatic regions [17][18][19]. However, the TSEB model relies on inputting thermal infrared-based surface temperature as boundary constrain. ...
... Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 9 January 2024 doi:10.20944/preprints202401.0644.v118 ...
Remote sensing-based models usually have difficulty in generating spatio-temporally continuous terrestrial evapotranspiration (ET) due to cloud cover and model failures. To overcome this problem, machine learning methods have been widely used to reconstruct ET. However, studies comparing and evaluating the accuracy and effectiveness of reconstruction among different machine learning methods remain scarce. In this study, four popular machine learning methods (deep forest, deep neural network, random forest, extreme gradient boosting) were used to reconstruct the ET product, addressing gaps resulting from cloud cover and model failure. The ET reconstructed by four methods were evaluated and compared in Heihe River Basin. The results showed that four methods performed well in the Heihe River Basin, but the RF method was particularly robust. It not only performed well compared with ground measurement (R = 0.73), but also reconstructed ET throughout the basin. Validation based on ground measurement showed that DNN and XGB models performed well (R > 0.70). However, few gaps still existed in the desert after reconstruction, especially for the XGB model. The DF model filled these gaps throughout the basin, but the model had lower consistency compared with ground measurement (R = 0.66) and yielded many low values. The results of this study suggested that machine learning methods had considerable potential in reconstruction of ET at regional scale.
... The input parameters for the SEBS model, based on the energy balance formula, include the normalized vegetation index, surface temperature, and surface albedo. Excluding soil moisture information could hinder obtaining these parameters in a timely manner, which may impede the energy balance model's ability to estimate surface ET in real time [37]. ...
Reasonable evaluation of evapotranspiration (ET) is crucial for optimizing agricultural water resource management. In the study, we utilized the Data Mining Sharpener (DMS) model; the Landsat thermal infrared images were sharpened from a spatial resolution of 100 m to 30 m. We then used the Surface Energy Balance System (SEBS) to estimate daily ET during the winter wheat growing season in the People’s Victory Irrigation District in Henan, China. It was concluded that the spatiotemporal patterns of land surface temperature and daily evapotranspiration remained consistent before and after sharpening. Results showed that the R2 value between the ET of 30 m spatial resolution and the value by eddy covariance method reached 0.814, with an RMSE of 0.516 mm and an MAE of 0.245 mm. All of these were higher than those of 100 m spatial resolution (R2 was 0.802, the RMSE was 0.534 mm, and the MAE was 0.253 mm). Furthermore, the daily ET image with a 30 m spatial resolution exhibited clear texture and distinct boundaries, without any noticeable mosaic effects. The changes in surface temperature and ET were more consistent in complex subsurface environments. The daily evapotranspiration of winter wheat was significantly higher in areas with intricate drainage systems compared to other regions. During the early growth stage, daily evapotranspiration decreased steadily until the overwintering stage. After the greening and jointing stages, it began to increase and peaked during the sizing period. The correlation between net solar radiation and temperature with ET was significant, while relative humidity and soil moisture were negatively correlated with ET. Throughout the growth period, net solar radiation had the greatest effect on ET.
... When considering the Copernicus Sentinel satellites the second category is generally not applicable since that constellation does not include a high resolution thermal sensor. However, combining data from Sentinel-2 satellites, providing shortwave observations with 10-20 m resolution and 5-day geometric revisit time at the equator, and Sentinel-3 satellites, acquiring daily LST observations with nominal resolution of 1 km, in a LST sharpening approach was previously shown to produce inputs highly suitable for field-scale ET estimations (Guzinski and Nieto, 2019;Guzinski et al., 2020Guzinski et al., , 2021. ...
... One of the major challenges in modeling ET with Copernicus data is overcoming the limitation of low spatial resolution thermal data. In Guzinski and Nieto (2019), Guzinski et al. (2020Guzinski et al. ( , 2021 it was demonstrated that good results can be obtained when modeling highresolution ET using Sentinel-3 LST sharpened with the Data Mining Sharpener (DMS) approach. However, certain limitations were also observed and they are addressed with the methodological modifications presented in the sections below. ...
... The original approach was to select 80% of the most homogeneous Sentinel-3 resolution reflectance pixels (i.e. pixels from Sentinel-2 reflectance image resampled to Sentinel-3 grid) based on coefficient of variation (CV -standard deviation over mean) of the Sentinel-2 pixels falling within each Sentinel-3 pixel (see Gao et al., 2012;Guzinski et al., 2020). The inverse of CV was also used as a weighing factor during the model training, penalizing heterogeneous pixels and advancing homogeneous ones. ...
One of the primary applications of satellite Land Surface Temperature (LST) observations lies in their utilization for modeling of actual evapotranspiration (ET) in agricultural crops, with the primary goals of monitoring and enhancing irrigation practices and improving crop water use productivity, as stipulated by Sustainable Development Goal (SDG) indicator 6.4.1. Evapotranspiration is a complex and dynamic process, both temporally and spatially, necessitating LST observations with high spatio-temporal resolution. Presently, none of the existing spaceborne thermal sensors can provide quasi-daily field-scale LST observations, prompting the development of methods for data fusion (thermal sharpening) of observations from various shortwave and thermal sensors to meet this spatio-temporal requirement. Previous research has demonstrated the effectiveness of combining shortwave-multispectral Sentinel-2 observations with thermal-infrared Sentinel-3 observations to derive daily, field-scale LST and ET estimates. However, these studies also highlighted limitations in capturing the distinct thermal contrast between cooler LST in irrigated agricultural areas and the hotter, adjacent dry regions. In this study, we aim to address this limitation by incorporating information on thermal spatial variability observed by Landsat satellites into the data fusion process, without being constrained by infrequent or cloudy Landsat thermal observations and while retaining the longwave radiance emission captured by the Sentinel-3 thermal sensor at its native resolution. Two approaches are evaluated, both individually and as a complementary combination, and validated against in situ LST measurements. The best performing approach, which leads to reduction in root mean square error of up to 1.5 K when compared to previous research, is subsequently used to estimate parcel-level actual evapotranspiration. The ET modeling process has also undergone various improvements regarding the gap-filling of input and output data, input datasets and code implementation. The resulting ET is validated using lysimeters and eddy covariance towers in Spain, Lebanon, Tunisia, and Senegal resulting in minimal overall bias (systematic underestimation of less than 0.07 mm/day) and a low root mean square error (down to 0.84 mm/day) when using fully global input datasets. The enhanced LST sharpening methodology is sensor agnostic and should remain relevant for the upcoming thermal missions while the accuracy of the modeled ET fluxes is encouraging for further utilization of observations from Sentinel satellites, and other Copernicus data, for monitoring SDG indicator 6.4.1.
