Topic Editors

Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Department of Hydraulic Engineering, School of Civil Engineering, Shandong University, Jinan 250061, China

Remote Sensing Research and Application of Agricultural Drought and Water Management

Abstract submission deadline
31 August 2026
Manuscript submission deadline
30 November 2026
Viewed by
5006

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

  • agricultural hydrology
  • eco-hydrology
  • agricultural water use
  • agro-hydrological modeling
  • irrigation district
  • water and salt balance
  • non-point source contamination
  • climate change
  • remote sensing

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Agronomy
agronomy
3.4 6.7 2011 17 Days CHF 2600 Submit
Earth
earth
3.4 5.9 2020 21.3 Days CHF 1400 Submit
Hydrology
hydrology
3.2 5.9 2014 17.9 Days CHF 1800 Submit
Limnological Review
limnolrev
- 1.4 2001 23.3 Days CHF 1200 Submit
Remote Sensing
remotesensing
4.1 8.6 2009 24.3 Days CHF 2700 Submit
Water
water
3.0 6.0 2009 18.9 Days CHF 2600 Submit

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Published Papers (4 papers)

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28 pages, 7529 KB  
Article
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
Viewed by 466
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
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32 pages, 16700 KB  
Article
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
Viewed by 594
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
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26 pages, 4407 KB  
Article
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 1 | Viewed by 2312
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
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20 pages, 3146 KB  
Article
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
Viewed by 735
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
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