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Laluet et al. (2024) Drainage assessment of irrigation districts: on the precision and accuracy of four parsimonious models
This study assesses the precision (site-calibrated performance) and accuracy (default parameter performance) of four parsimonious drainage models combining two surface (RU, SAMIR) and two subsurface (Reservoir, SIDRA) components in a semi-arid irrigated district. The RU-Reservoir model demonstrated the highest precision (average KGE($Q^{0.5}$) of 0.87) when calibrated, while the SAMIR-Reservoir model provided the most consistent rough estimates of drainage dynamics and amounts using default parameters.
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Dari et al. (2024) The Temporal-Stability-Based Irrigation MAPping (TSIMAP) Method: A Virtuous Trade-Off between Accuracy, Flexibility, and Facility for End-Users
This study updates the Temporal-Stability-derived Irrigation MAPping (TSIMAP) method by replacing satellite soil moisture input with 1 km Normalised Difference Vegetation Index (NDVI) data over the Ebro basin (Spain), achieving high overall accuracy (up to 93% in focus areas) and demonstrating improved performance compared to the original implementation.
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Dari et al. (2024) Quantifying the Hydrological Impacts of Irrigation on a Mediterranean Agricultural Context Through Explicit Satellite‐Derived Irrigation Estimates
This study investigates the hydrological impact of irrigation in the Ebro basin (Spain) using the SURFEX/ISBA Land Surface Model, finding that incorporating satellite-derived irrigation data significantly improves the simulation of soil moisture and evaporative flux, leading to maximum increases of +30% for soil moisture and +220% for evaporation in July.
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Zappa et al. (2024) Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture
This study comprehensively assesses and inter-compares two satellite soil moisture-based irrigation retrieval methods (SM_Delta and SM_Inversion) using Sentinel-1 data over the Ebro basin, demonstrating their reliability for estimating irrigation timing and water volumes at district and seasonal scales, despite limitations at the pixel level.
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Bellvert et al. (2024) Analysis and forecast of crop water demand in irrigation districts across the eastern part of the Ebro river basin (Catalonia, Spain): estimation of evapotranspiration through copernicus-based inputs
This study quantified and analyzed the actual and gross water demands (GWD) across eight irrigation districts (IDs) in the eastern Ebro basin (Catalonia, Spain) over six growing seasons (2017–2022) using a Copernicus-based evapotranspiration model, finding large variability in water use efficiency and projecting significant increases in water demand (up to 28% by 2100 under RCP8.5) due to climate change.
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Wang et al. (2024) Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects
This study systematically reviews the literature on data assimilation (DA) methods in precision agriculture, finding that emerging remote sensing platforms (UAVs, satellite constellations) and sequential assimilation algorithms (like EnKF) significantly enhance yield prediction and monitoring capabilities. The review identifies Leaf Area Index (LAI) as the most preferred assimilation variable and highlights data quality and resolution as key bottlenecks.
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Li et al. (2024) Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies
This paper provides a thorough review of Land Data Assimilation (LDA), detailing its theoretical and methodological evolution, highlighting successful applications in enhancing the understanding and prediction of various land surface processes (e.g., soil moisture, snow), and outlining future grand challenges such as coupled land-atmosphere assimilation and integration with human systems.
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Sahaar et al. (2024) Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning
A machine learning framework integrating remote sensing and soil data was developed to estimate soil moisture across the coterminous US at five depths, finding that the XGBoost model provided the highest accuracy (R up to 0.86) and significantly outperformed the standard SMAP Level 4 product.
