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Multi-Source Remote Sensing Data for Water Resource Management in Agriculture
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Multi-Source Remote Sensing Data for Water Resource Management in Agriculture

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 4213

Special Issue Editors

Dr. Pasquale Nino

E-Mail Website
Guest Editor
Council for Agricultural Research and Economics (CREA) Research Centre for Agricultural Policies and Bioeconomy, Borgo XX Giugno 74, 06121 Perugia, Italy
Interests: environmental impact assessment; remote sensing applications; water resources management; vegetation mapping agricultural statistics
Dr. Nicolas Baghdadi

E-Mail Website
Guest Editor
French National Institute for Agriculture, Food, and Environment (INRAE), Maison de la Télédétection—UMR TETIS, 500 rue JF Breton, CEDEX 05, 34093 Montpellier, France
Interests: environmental science; irrigation and water management; soil science; microwave remote sensing; lidar
Special Issues, Collections and Topics in MDPI journals
Artur Łopatka

E-Mail Website
Guest Editor Assistant
Institute of Soil Science and Plant Cultivation (IUNG).ul. Czartoryskich 8, 24-100 Pulawy, Poland
Interests: evapotranspiration; water stress; drought; agricultural policy; environmental impact assessment

Special Issue Information

Dear Colleagues,

Water plays a fundamental role for agricultural production and plays an important role in food security. Irrigated agriculture represents 20% of the total cultivated land and contributes of 40% percent of the total food produced worldwide. Irrigated agriculture is, on average, at least twice as productive per unit of land as rainfed agriculture, thereby allowing for more production intensification and crop diversification. Due to population growth, urbanization, and climate change, competition for water resources is expected to increase, with a particular impact on agriculture. Irrigation monitoring is of great importance in agricultural water management to guarantee better water use efficiency, especially under changing climatic conditions and water scarcity. In that context the evolution of remote sensing techniques and availability of data from different platform (Ground-Based, UAV-Based, Satellite-Based) in recent years, has opened new perspectives for supporting sustainable water resources management. A great contribution of Remote Sensing on irrigation monitoring is to provide detailed spatial/temporal information of the dynamics of the irrigated areas and the key elements of wich irrigation depend like crop Evapotranspiration (ET) and Soil Moisture (SM). This information would be helpful for supporting policy makers in the formulation of strategic agricultural plans to increase the efficiency of water use in agriculture.

The purpose of this Special Issue is to identify current research trends and key issues relate to water resource management in agriculture. We welcome novel research, reviews covering all irrigation related topics. We are especially interested in recent integrated water resources research using data fusion techniques, combining different sources (e.g., multi/hyperspectral sensor, optical/microwave, UAV with ground measurements), for retrieve ET constituents' parameters (such as land surface temperature, LAI, crop height, and albedo), and SM component (e.g., vegetation index, soil backscattering model, roughness and vegetation effects of radar signal).

The topic “Multi-Source Remote Sensing Data for Water Resource Management in Agriculture” invites high-quality papers focused on the design and development of methods, algorithm, strategies, and new technologies for water resource management and development impact assessment using multi-source remote sensing technologies under land use and climate changes. Potential topics include, but are not limited to:

  • Mapping irrigated areas;
  • Evapotranspiration mapping;
  • Soil Moisture mapping;
  • Synergy between radar and other sensors for SM and ET retrieval;
  • Role of remote sensing in supporting water policy;
  • Application of remote sensing techniques to estimate water stored volume in artificial reservoir

Dr. Pasquale Nino
Dr. Nicolas Baghdadi
Guest Editors

Artur Łopatka
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • irrigation
  • optical and microwave remote sensing
  • evapotranspiration
  • soil moisture
  • unmanned aerial vehicles (UAV)
  • ground sensor

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Published Papers (3 papers)

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Research

19 pages, 6469 KiB  
Article
Long-Term Impact of Extreme Weather Events on Grassland Growing Season Length on the Mongolian Plateau
by Wanyi Zhang, Qun Guo, Genan Wu, Kiril Manevski and Shenggong Li
Remote Sens. 2025, 17(9), 1560; https://doi.org/10.3390/rs17091560 - 28 Apr 2025
Viewed by 227
Abstract
Quantifying extreme weather events (EWEs) and understanding their impacts on vegetation phenology is crucial for assessing ecosystem stability under climate change. This study systematically investigated the ecosystem growing season length (GL) response to four types of EWEs—extreme heat, extreme cold, extreme wetness (surplus [...] Read more.
Quantifying extreme weather events (EWEs) and understanding their impacts on vegetation phenology is crucial for assessing ecosystem stability under climate change. This study systematically investigated the ecosystem growing season length (GL) response to four types of EWEs—extreme heat, extreme cold, extreme wetness (surplus precipitation), and extreme drought (lack of precipitation). The EWE extremity thresholds were found statistically using detrended long time series (2000–2022) ERA5 meteorological data through z-score transformation. The analysis was based on a grassland ecosystem in the Mongolian Plateau (MP) from 2000 to 2022. Using solar-induced chlorophyll fluorescence data and event coincidence analysis, we evaluated the probability of GL anomalies coinciding with EWEs and assessed the vegetation sensitivity to climate variability. The analysis showed that 83.7% of negative and 87.4% of positive GL anomalies were associated with one or more EWEs, with extreme wetness (27.0%) and extreme heat (25.4%) contributing the most. These findings highlight the dominant role of EWEs in shaping phenological shifts. Negative GL anomalies were more strongly linked to EWEs, particularly in arid and cold regions where extreme drought and cold shortened the growing season. Conversely, extreme heat and wetness had a greater influence in warmer and wetter areas, driving both the lengthening and shortening of GL. Furthermore, background hydrothermal conditions modulated the vegetation sensitivity, with warmer regions being more susceptible to heat stress and drier regions more vulnerable to drought. These findings emphasize the importance of regional weather variability and climate characteristics in shaping vegetation phenology and provide new insights into how weather extremes impact ecosystem stability in semi-arid and arid regions. Future research should explore extreme weather events and the role of human activities to enhance predictions of vegetation–climate interactions in grassland ecosystems of the MP. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Data for Water Resource Management in Agriculture)
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27 pages, 10742 KiB  
Article
A Deep Learning Framework for Long-Term Soil Moisture-Based Drought Assessment Across the Major Basins in China
by Ye Duan, Yong Bo, Xin Yao, Guanwen Chen, Kai Liu, Shudong Wang, Banghui Yang and Xueke Li
Remote Sens. 2025, 17(6), 1000; https://doi.org/10.3390/rs17061000 - 12 Mar 2025
Viewed by 450
Abstract
Drought is a critical hydrological challenge with ecological and socio-economic impacts, but its long-term variability and drivers remain insufficiently understood. This study proposes a deep learning-based framework to explore drought dynamics and their underlying drivers across China’s major basins over the past four [...] Read more.
Drought is a critical hydrological challenge with ecological and socio-economic impacts, but its long-term variability and drivers remain insufficiently understood. This study proposes a deep learning-based framework to explore drought dynamics and their underlying drivers across China’s major basins over the past four decades. The Long Short-Term Memory network was employed to reconstruct gaps in satellite-derived soil moisture (SM) datasets, achieving high accuracy (R2 = 0.928 and RMSE = 0.020 m3m−3). An advanced explainable artificial intelligence (XAI) approach was applied to unravel the mechanistic relationships between SM and critical hydrometeorological variables. Our results revealed a slight increasing trend in SM value across China’s major basins over the past four decades, with a more pronounced downward trend in cropland that was more sensitive to water resource management. XAI results demonstrated distinct regional disparities: the northern arid regions displayed pronounced seasonality in drought dynamics, whereas the southern humid regions were less influenced by seasonal fluctuations. Surface solar radiation and air temperature were identified as the primary drivers of droughts in the Haihe, Yellow, Southwest, and Pearl River Basins, whereas precipitation is the dominant factor in the Middle and Lower Yangtze River Basins. Collectively, our study offers valuable insights for sustainable water resource management and land-use planning. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Data for Water Resource Management in Agriculture)
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19 pages, 7992 KiB  
Article
Improving the STARFM Fusion Method for Downscaling the SSEBOP Evapotranspiration Product from 1 km to 30 m in an Arid Area in China
by Jingjing Sun, Wen Wang, Xiaogang Wang and Luca Brocca
Remote Sens. 2023, 15(22), 5411; https://doi.org/10.3390/rs15225411 - 18 Nov 2023
Viewed by 2305
Abstract
Continuous evapotranspiration (ET) data with high spatial resolution are crucial for water resources management in irrigated agricultural areas in arid regions. Many global ET products are available now but with a coarse spatial resolution. Spatial-temporal fusion methods, such as the spatial and temporal [...] Read more.
Continuous evapotranspiration (ET) data with high spatial resolution are crucial for water resources management in irrigated agricultural areas in arid regions. Many global ET products are available now but with a coarse spatial resolution. Spatial-temporal fusion methods, such as the spatial and temporal adaptive reflectance fusion model (STARFM), can help to downscale coarse spatial resolution ET products. In this paper, the STARFM model is improved by incorporating the temperature vegetation dryness index (TVDI) into the data fusion process, and we propose a spatial and temporal adaptive evapotranspiration downscaling method (STAEDM). The modified method STAEDM was applied to the 1 km SSEBOP ET product to derive a downscaled 30 m ET for irrigated agricultural fields of Northwest China. The STAEDM exhibits a significant improvement compared to the original STARFM method for downscaling SSEBOP ET on Landsat-unavailable dates, with an increase in the squared correlation coefficients (r2) from 0.68 to 0.77 and a decrease in the root mean square error (RMSE) from 10.28 mm/10 d to 8.48 mm/10 d. The ET based on the STAEDM additionally preserves more spatial details than STARFM for heterogeneous agricultural fields and can better capture the ET seasonal dynamics. The STAEDM ET can better capture the temporal variation of 10-day ET during the whole crop growing season than SSEBOP. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Data for Water Resource Management in Agriculture)
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