Remote Sensing Research and Application of Agricultural Drought and Water Management

Topic Information

Dear Colleagues,

In the context of global environmental and climate change, the world’s water resources are facing conflicting circumstances when balancing rapidly increasing demand and maintaining a sustainable ecological environment. The intensification of the water cycle leads to either drought and desertification or flooding and soil erosion, causing severe damage to ecosystems and planting systems. Among them, the problem of agricultural water use seriously affects food security and sustainable ecological development, especially in areas with severe water shortages, and agricultural water use problems will lead to significant economic and social challenges. Therefore, it is crucial to monitor agricultural drought and water resource management effectively, which can provide strong support for the formulation of scientific governance measures.

In recent decades, remote sensing technology, with its rapid detection capability, has opened up a new perspective for agricultural hydrological monitoring, water resource protection and planning, and irrigation water utilization. Remote sensing technology has freed the field from a dependence on traditional field measurements, enabling people to observe and estimate agricultural water-related issues on a larger spatial and temporal scale by using multi-sensor remote sensing technology, providing unique advantages for regional and even global agricultural drought and water use research.

This Topic focuses on innovative methods of agricultural drought and water resource planning and management based on remote sensing, including but not limited to：

• Drought research using a combination of sensors and technologies on the space–time scale (such as optical, microwave, hyperspectral, lidar, and constellation);
• Agricultural hydrological modeling;
• Irrigation and water resource management;
• Modeling evapotranspiration at the field and irrigation district scale;
• Eco-hydrology;
• Modeling irrigation district water–salt balance and non-point source pollution;
• Efficiently utilizing agricultural water resources;
• Interactions between water, agriculture, and natural ecosystems;
• Data assimilation for agricultural ecosystem modeling in irrigation systems;
• The use of drones and satellites for agricultural water management.

Prof. Dr. Songhao Shang
Dr. Khalil Ur Rahman
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agronomy agronomy	4.1	7.6	2011	17.7 Days	CHF 2600	Submit
Earth earth	4.0	5.3	2020	19 Days	CHF 1400	Submit
Hydrology hydrology	3.1	6.0	2014	16.5 Days	CHF 1800	Submit
Limnological Review limnolrev	-	2.4	2001	19.1 Days	CHF 1200	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit
Water water	3.5	6.7	2009	17.7 Days	CHF 2600	Submit

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Published Papers (8 papers)

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24 pages, 17503 KB  

Open AccessArticle

Dynamic Refinement of Temporally Static Land-Use Maps Using Satellite-Derived Moisture Signatures

by Nutchanart Sriwongsitanon, Chainarong Ophaphaibun, James Alexander Williams, Raj Mehrotra and Hubert H. G. Savenije

Hydrology 2026, 13(8), 210; https://doi.org/10.3390/hydrology13080210 - 4 Aug 2026

Abstract

Accurate land use/land cover (LULC) classification in monsoon-driven and heterogeneous landscapes is challenged by strong seasonal variability and inconsistencies between dynamic satellite observations and static reference datasets. This study proposes a time-series-based framework integrating MODIS-derived Normalized Difference Vegetation Index (NDVI) and Normalized Difference [...] Read more.

Accurate land use/land cover (LULC) classification in monsoon-driven and heterogeneous landscapes is challenged by strong seasonal variability and inconsistencies between dynamic satellite observations and static reference datasets. This study proposes a time-series-based framework integrating MODIS-derived Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII) with unsupervised K-means clustering and a temporally consistent refinement strategy. Multi-temporal NDVI (23 composites year⁻¹) and NDII (46 composites year⁻¹) data from 2010–2021 were used to derive spectral clusters and aggregate them into five land use classes using percentile-based temporal signatures and RMSE-based similarity with Land Development Department (LDD) data. To reconcile discrepancies between dynamic satellite observations and static reference datasets, a refinement procedure combining spatial agreement and temporal similarity was applied to reassign misclassified pixels. Initial classifications achieved Overall Accuracies (OA) of 57.35% for NDII and 51.27% for NDVI, increasing to 87.28% and 86.24% after refinement, with Kappa coefficients of 0.82 and 0.81, respectively. NDII consistently outperformed NDVI, highlighting the value of moisture-sensitive indices for distinguishing vegetation classes in tropical environments. The modular Python-based version 3.11 implementation ensures reproducibility and transferability, providing a robust and scalable framework for LULC classification in dynamic landscapes. Full article

(This article belongs to the Topic Remote Sensing Research and Application of Agricultural Drought and Water Management)

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15 pages, 2261 KB  

Open AccessArticle

Evaluation of SMAP Level 4 Versions 7 and 8 Soil Moisture Data in Rain-Fed Argentine Pampas Crops

by María Florencia Degano, Sabrina Beninato, José Pasapera, Mauro Ezequiel Holzman and Raúl Eduardo Rivas

Hydrology 2026, 13(6), 146; https://doi.org/10.3390/hydrology13060146 - 4 Jun 2026

Cited by 1 | Viewed by 606

Abstract

Soil moisture (SM) is a key variable for assessing plant water availability, especially in rain-fed systems where imbalances strongly affect crop development. Satellite missions such as SMAP provide global SM estimates, though representing vertical SM variability remains challenging. This study evaluates the performance [...] Read more.

Soil moisture (SM) is a key variable for assessing plant water availability, especially in rain-fed systems where imbalances strongly affect crop development. Satellite missions such as SMAP provide global SM estimates, though representing vertical SM variability remains challenging. This study evaluates the performance of SMAP Level 4 Global 3-hourly 9 km grid EASE-Grid Surface and Root-Zone Soil Moisture Geophysical Data (SPL4SMGP, version 7 and the new and scarcely evaluated version 8) using field observations from the Argentine Pampas, a region dominated by Typic Argiudolls soils (~16 million ha). The analysis covered normal-wet and dry conditions across several crop seasons. Surface (SSM, ~5 cm) and root zone (RZSM, 0–100 cm) soil moisture were compared against field data using Pearson’s correlation (r), bias, and unbiased root mean square deviation (ubRMSD). Both SSM and RZSM achieved ubRMSD values close to the SMAP accuracy target (≈0.04 m³/m³). SSM correlated moderately with observations (r = 0.57–0.72) and showed a consistent negative bias (−0.08 ± 0.05 m³/m³). In contrast, RZSM exhibited low sensitivity to soil profile variability and a narrow dynamic range. Version 8 showed similar performance to version 7, with a tendency toward overestimation, mainly during dry periods. Overall, SPL4SMGP products effectively capture SSM dynamics but show limited skill in representing root zone variability in Typic Argiudolls. Full article

(This article belongs to the Topic Remote Sensing Research and Application of Agricultural Drought and Water Management)

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23 pages, 7764 KB  

Open AccessArticle

Spatio-Temporal Dynamics of Vegetation and Water Stress in the Trichonida Basin Using Remote Sensing and Climatic Drought Indicators

by Fatima Daide, Eleni Ioanna Koutsovili, Mohammed Mouad Mliyeh, Abderrahim Lahrach, Isavela N. Monioudi and Ourania Tzoraki

Limnol. Rev. 2026, 26(2), 22; https://doi.org/10.3390/limnolrev26020022 - 28 May 2026

Viewed by 510

Abstract

Freshwater lakes in Mediterranean regions are highly sensitive to climatic variability, particularly to droughts intensified by rising temperatures and increasing atmospheric evaporative demand. This study investigates drought variability and ecosystem responses in the Trichonida basin, the largest natural freshwater system in Greece, using [...] Read more.

Freshwater lakes in Mediterranean regions are highly sensitive to climatic variability, particularly to droughts intensified by rising temperatures and increasing atmospheric evaporative demand. This study investigates drought variability and ecosystem responses in the Trichonida basin, the largest natural freshwater system in Greece, using an integrated approach that combines the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple time scales with satellite-derived Normalized Difference Vegetation Index (NDVI), Crop Water Stress Index (CWSI), and lake surface water temperature. SPEI analysis revealed increasingly recurrent and persistent drought conditions in recent years, especially at medium- and long-term scales. NDVI exhibited pronounced seasonal variability and a moderate long-term increase at the basin scale, largely associated with agricultural activity and irrigation practices, while sharp declines were observed during severe drought episodes. CWSI showed strong seasonal patterns characterized by recurrent summer water stress events, but no significant long-term trend. Correlation analysis indicated positive relationships between NDVI and SPEI at medium- to long-term time scales, and significant negative correlations between CWSI and SPEI at short and medium time scales. A strong relationship between NDVI and CWSI further suggests the sensitivity of vegetation greenness to water stress, particularly during summer and autumn. Lake surface water temperature exhibited seasonal warming trends that coincided with periods of increased vegetation water stress. Drought-related water risks arise for calcareous fens dominated by Cladium mariscus in the Lake Trichonida system, a habitat of high conservation value, whose productivity is strongly seasonally controlled and closely linked to thermal dynamics. Overall, the combined multi-indicator analysis provides valuable insights into drought impacts and seasonal ecosystem vulnerability in Mediterranean lake basin environments, highlighting the importance of integrated monitoring frameworks for sustainable freshwater ecosystem management under increasing climatic variability. Full article

(This article belongs to the Topic Remote Sensing Research and Application of Agricultural Drought and Water Management)

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38 pages, 8554 KB  

Open AccessReview

Space–Air–Ground Synergistic Approaches for Field Water Status Precision Monitoring: A Review

by Tao Li, Jiang Li, Hongzhe Jiang, Lei Jiang, Xiyun Jiao and Yue Luo

Remote Sens. 2026, 18(10), 1542; https://doi.org/10.3390/rs18101542 - 13 May 2026

Viewed by 565

Abstract

Field water status is a critical variable for agricultural water management. In recent years, the development of space–air–ground multi-platform collaborative observation and data fusion technologies has provided new options for precision monitoring. However, challenges in applicability, robustness, and transferability persist. This study employs [...] Read more.

Field water status is a critical variable for agricultural water management. In recent years, the development of space–air–ground multi-platform collaborative observation and data fusion technologies has provided new options for precision monitoring. However, challenges in applicability, robustness, and transferability persist. This study employs bibliometric analysis to systematically synthesize the literature, revealing that research has evolved from single-point observations to multi-platform synergy. Satellite, unmanned aerial vehicle (UAV), and ground-based monitoring are analyzed, as well as challenges in multi-source data fusion, including scale mismatch, error propagation, and uncertainty quantification. Finally, applicability and other barriers are evaluated across three typical agricultural scenarios: large-scale surface soil moisture monitoring, crop root zone soil moisture retrieval, and paddy field water depth estimation. The results indicate that space–air–ground collaborative observation constitutes a mature framework, with satellite and ground-based monitoring as core components and UAV technology as a supplement. However, scale transformation and error propagation mechanisms in multi-source data fusion remain unresolved. Currently available vertical water information is limited, and quantitative retrieval has yet to achieve the reliability required for operational applications. This limitation is particularly evident in paddy field water depth retrieval and root zone soil moisture retrieval. This review provides a theoretical reference for precision field water status monitoring and identifies future research priorities, including the integration of physical mechanisms with machine learning (ML) in multi-source data fusion, as well as error quantification and paddy field water depth retrieval. Full article

(This article belongs to the Topic Remote Sensing Research and Application of Agricultural Drought and Water Management)

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28 pages, 7529 KB  

Open AccessArticle

Integrating GLASS LAI into the SWAT Model for Improved Hydrological Simulation in Semi-Arid Regions

by Xun Zhang, Yanan Jiang, Ting Yan, Kun Xie, Ping Li, Jiping Niu, Kexin Li and Xiaojun Wang

Agronomy 2026, 16(6), 639; https://doi.org/10.3390/agronomy16060639 - 18 Mar 2026

Cited by 1 | Viewed by 709

Abstract

The Soil and Water Assessment Tool (SWAT) model has been widely used to simulate ecohydrological processes in watersheds. However, the SWAT model uses a simplified Environmental Policy Impact Climate (EPIC) model to simulate the leaf area index (LAI), creating a critical gap in [...] Read more.

The Soil and Water Assessment Tool (SWAT) model has been widely used to simulate ecohydrological processes in watersheds. However, the SWAT model uses a simplified Environmental Policy Impact Climate (EPIC) model to simulate the leaf area index (LAI), creating a critical gap in accurately simulating evapotranspiration (ET) and runoff in semi-arid regions. This work aims to fill this gap by modifying the SWAT source code to integrate high-resolution Global Land Surface Satellite (GLASS) leaf area index (LAI) data. The modified version was applied to the semi-arid Wuding River Basin and calibrated using a Fortran-based dynamic dimension search (DDS) algorithm. The results show a relatively significant improvement in the accuracy of the daily-scale runoff simulation (R² from 0.52 to 0.71 and NSE from 0.52 to 0.7 for the calibration period, and R² from 0.21 to 0.58 and NSE from 0.2 to 0.51 for the validation period). The improved version also corrects the unrealistic default LAI peak (from >5.0 to 1.5–3.0), correcting the multi-year average ET from 251.7 mm to 341.8 mm. The improved vegetation growth module of the SWAT model effectively improved the accuracy of hydrologic simulation in the semi-arid region and enhanced the structural robustness of SWAT for water management. Full article

(This article belongs to the Topic Remote Sensing Research and Application of Agricultural Drought and Water Management)

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32 pages, 16700 KB  

Open AccessArticle

Integration of Spatio-Temporal Satellite Data, Machine Learning, and Water Quality Indices for Depicting Precise Water Quality Levels

by Essam Sharaf El Din and Ahmed Shaker

Earth 2026, 7(2), 48; https://doi.org/10.3390/earth7020048 - 12 Mar 2026

Cited by 1 | Viewed by 1175

Abstract

Monitoring surface water quality over large river systems remains challenging due to sparse in situ sampling and the need for decision-ready indicators. This study aims to address this problem by developing and evaluating an integrated Landsat 8-based backpropagation neural network and Canadian Council [...] Read more.

Monitoring surface water quality over large river systems remains challenging due to sparse in situ sampling and the need for decision-ready indicators. This study aims to address this problem by developing and evaluating an integrated Landsat 8-based backpropagation neural network and Canadian Council of Ministers of the Environment Water Quality Index (L8-BPNN-CCME-WQI) for precise surface water quality assessment over the Saint John River (SJR), New Brunswick, Canada. The proposed approach combines atmospherically corrected Landsat 8 imagery, BPNN for estimating multiple surface water quality parameters (SWQPs), and CCME-WQI to translate SWQP fields into transparent water quality levels. The L8-BPNN-CCME-WQI models were trained using in situ measurements of turbidity, total suspended solids (TSS), total solids (TS), total dissolved solids (TDS), chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, electrical conductivity (EC), and temperature collected during our five field campaigns (from June 2015 to August 2016) and surface reflectance from five Landsat 8 scenes. The developed models achieved high performance during internal calibration and testing (R² ≥ 0.80 for all SWQPs) and demonstrated robust performance (R² ≈ 0.75–0.88) when applied to two independent surface water quality datasets from additional rivers across New Brunswick. Pixel-wise SWQP predictions were then input to the CCME-WQI formulation to derive reach-scale water quality levels, revealing that the lower Saint John River basin (below the Mactaquac Dam) is generally classified as “Fair” (CCME-WQI ≈ 67), whereas the middle basin upstream (above the Mactaquac Dam) is “Marginal” (CCME-WQI ≈ 59), reflecting stronger industrial and agricultural pressures. Overall, the L8-BPNN-CCME-WQI framework provides a scalable methodology for converting multi-parameter satellite-derived water quality information into spatially exhaustive CCME-WQI classes, supporting targeted regulation, prioritization of mitigation in critical reaches, and evaluation of management actions in large river systems. Full article

(This article belongs to the Topic Remote Sensing Research and Application of Agricultural Drought and Water Management)

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26 pages, 4407 KB  

Open AccessArticle

Optimizing Agricultural Drought Monitoring in East Africa: Evaluating Integrated Soil Moisture and Vegetation Health Index (SM-VHI)

by Albert Poponi Maniraho, Jie Bai, Lanhai Li, Habimana Fabien, Patient Mindje Kayumba, Ogbue Chukwuka Prince, Muhirwa Fabien and Lingjie Bu

Remote Sens. 2025, 17(21), 3560; https://doi.org/10.3390/rs17213560 - 28 Oct 2025

Cited by 2 | Viewed by 3861

Abstract

This study presents a comprehensive analysis of the integrated Soil Moisture–Vegetation Health Index (SM-VHI) as a robust tool for drought detection and agricultural monitoring across East Africa using data from 2000 to 2020. A sensitivity analysis within the SM-VHI algorithm identified an optimal [...] Read more.

This study presents a comprehensive analysis of the integrated Soil Moisture–Vegetation Health Index (SM-VHI) as a robust tool for drought detection and agricultural monitoring across East Africa using data from 2000 to 2020. A sensitivity analysis within the SM-VHI algorithm identified an optimal parameter weighting (α = 0.5), which improved detection accuracy, achieving a Critical Success Index (CSI) of 0.78. The SM-VHI exhibited strong correlations with independent drought indicators, including the Standardized Soil Moisture Index (SSMI), Vegetation Health Index (VHI), and one-month Standardized Precipitation-Evapotranspiration Index (SPEI-1), confirming its reliability in capturing agricultural drought dynamics and vegetation stress responses across diverse climatic conditions. Through spatial and temporal trend analyses, we identified patterns of drought severity and recovery, which emphasized the importance of tailored management strategies. Furthermore, the analysis incorporated historical maize yield data to evaluate the effectiveness of SM-VHI in representing agricultural drought conditions. A notable positive correlation (R = 0.45–0.72) was identified between SM-VHI anomalies and detrended maize yield throughout East Africa, suggesting that enhanced vegetation and soil moisture conditions are strongly linked to increased crop productivity. This validation demonstrates the capability of SM-VHI to effectively capture drought-induced yield variability. The findings confirm the effectiveness of SM-VHI as a reliable remote-sensing tool for monitoring drought conditions and have strong potential to inform agricultural practices and policy decisions aimed at enhancing food security in a changing climate. Full article

(This article belongs to the Topic Remote Sensing Research and Application of Agricultural Drought and Water Management)

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20 pages, 3146 KB  

Open AccessArticle

Identification of Driving Factors of Long-Term Terrestrial Water Storage Anomaly Trend Changes in the Yangtze River Basin Based on Multisource Data and Geographical Detector Method

by Qin Li, Song Ye, Ying Wang, Yingjie Qu, Zhengli Yao, Bocheng Liao and Junke Wang

Water 2025, 17(19), 2914; https://doi.org/10.3390/w17192914 - 9 Oct 2025

Cited by 1 | Viewed by 896

Abstract

Terrestrial water storage anomaly (TWSA) plays a vital role in regulating the global water cycle and freshwater availability. Understanding the drivers behind long-term TWSA changes is critical, yet disentangling natural and anthropogenic influences remains challenging. This study employs the Geographical Detector method and [...] Read more.

Terrestrial water storage anomaly (TWSA) plays a vital role in regulating the global water cycle and freshwater availability. Understanding the drivers behind long-term TWSA changes is critical, yet disentangling natural and anthropogenic influences remains challenging. This study employs the Geographical Detector method and multisource data to quantify the individual and interactive effects of multiple drivers on TWSA trends across the upper, middle, and lower reaches of the Yangtze River Basin (YRB). In the upper YRB, temperature, snow water equivalent, vegetation, precipitation, and reservoir storage are the primary contributors. In the middle YRB, precipitation, temperature, and soil moisture dominate. Although nighttime light (a proxy for urbanization) alone explains only 1.94% of the variation in this region, its interaction with precipitation increases explanatory power to 56.3%, highlighting a strong nonlinear effect. In the lower YRB, precipitation and runoff are the leading factors, while nighttime light again exhibits enhanced influence through interactions. These findings reveal the spatial heterogeneity and synergistic nature of TWSA drivers and underscore the need to consider both natural variability and human-induced processes when assessing long-term water storage dynamics. The results offer valuable insights for sustainable water resource management in the context of climate change and rapid urban development. Full article

(This article belongs to the Topic Remote Sensing Research and Application of Agricultural Drought and Water Management)

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