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Remote Sensing
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Remote Sensing of Agricultural Water Resources
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Remote Sensing of Agricultural Water Resources

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 3933

Special Issue Editors

Dr. Pei Leng

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: soil moisture; agricultural drought; thermal remote sensing; evapotranspiration; irrigation
Special Issues, Collections and Topics in MDPI journals
Dr. Chunfeng Ma

E-Mail Website
Guest Editor
Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Heihe Remote Sensing Experimental Research Station, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: remote sensing; soil moisture; ecohydrology; precision agriculture; uncertainty quantification
Special Issues, Collections and Topics in MDPI journals
Dr. Jianwei Ma

E-Mail Website
Guest Editor
China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Interests: soil moisture; drought monitoring; microwave remote sensing

Special Issue Information

Dear Colleagues,

As we know, agriculture is the foundation of a sustainable economy, and water is the core for agriculture. With the aggravation of global climate change and population increase, agricultural water resources have been suffering increasing risk, especially in developing countries. Various studies have highlighted the increment of agricultural water consumption in future.

In recent decades, the development of advanced observation instruments and high spatial resolution satellite data allows for the obtaining of essential variables for describing water cycle at farmland scale, which has provided an unprecedented opportunity to monitor agricultural water resources. It is the right time to summarize accomplishments and provide guidance for future research directions in this field, and especially to promote new methods in the monitoring of agricultural water resources.

This Special Issue on “Remote Sensing of Agricultural Water Resources” seeks contributions reflecting the current innovative research progress in this field. The topics can range from the ground-based experiments/satellite retrievals for obtaining essential variables (e.g., soil moisture, evapotranspiration, and precipitation) for agricultural water resources at farmland/regional scale, detection of irrigation events or agricultural droughts based on these aforementioned variables, as well as the assessment of agricultural water resources in future climate scenarios. Specifically, research articles and review papers are warmly welcomed in this special issue.

Dr. Pei Leng
Dr. Chunfeng Ma
Dr. Jianwei Ma
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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

  • agricultural water resources (soil moisture/evapotranspiration/precipitation…)
  • crop water consumption
  • water use efficiency
  • detection of irrigation events
  • agricultural drought
  • crop monitoring and vegetation parameters retrieval
  • remote sensing experiments
  • thermal/SAR remote sensing

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Published Papers (2 papers)

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Research

24 pages, 11675 KB  
Article
A New Model Incorporating Soil–Vegetation Interaction Scattering for Improving SAR-Based Soil Moisture Retrieval in Croplands
by Jiliu Hu, Dong Fan, Bo-Hui Tang and Xin-Ming Zhu
Remote Sens. 2026, 18(5), 673; https://doi.org/10.3390/rs18050673 - 24 Feb 2026
Viewed by 624
Abstract
The Water Cloud Model (WCM) provides a simple and effective framework for quantifying vegetation canopy absorption and scattering components and has been widely applied to active microwave-based soil moisture retrieval on vegetated areas. However, under conditions of dense vegetation, the WCM neglects soil–vegetation [...] Read more.
The Water Cloud Model (WCM) provides a simple and effective framework for quantifying vegetation canopy absorption and scattering components and has been widely applied to active microwave-based soil moisture retrieval on vegetated areas. However, under conditions of dense vegetation, the WCM neglects soil–vegetation interaction scattering, which limits its retrieval accuracy. To mitigate this limitation, this study analyzes the active microwave radiative transfer process under vegetated conditions and proposes an approach to explicitly quantify soil–vegetation interaction scattering by incorporating the first-order soil–vegetation-scattering component into the WCM, thereby enhancing the performance of the WCM at high vegetation coverage. The effectiveness of the proposed model is validated using in situ observations from three study areas with different vegetation characteristics: (a) a pure farmland area, (b) a mixed landscape with small forest and shrubland patches and large cropland areas, and (c) a mixed landscape with large forest and shrubland patches and small cropland areas. Data from 2020–2022 were used for model training and parameter calibration, while independent datasets from 2023 and 2024 were employed to validate the model performance. In both the model training and validation phases, the proposed model improved the soil moisture retrieval accuracy across all study areas while exhibiting slight differences in the backscatter simulation performance. During the model training period, the root-mean-square error (RMSE) between simulated and measured backscatter in study area (a) increased slightly by 1.9%, whereas it decreased by 2.79% and 2.0% in study areas (b) and (c), respectively. In terms of soil moisture retrieval, the RMSEs in study areas (a), (b), and (c) decreased by 6.66%, 1.18%, and 6.03%, respectively. In the validation experiments, for the year 2023, the RMSEs of simulated versus observed backscatter in study areas (a), (b), and (c) were reduced by 9.6%, 1.51%, and 4.35%, respectively, while the corresponding soil moisture retrieval RMSEs decreased by 12.6%, 4.53%, and 7.24%. For the year 2024, the backscatter RMSE in study area (a) increased by 6.07%, whereas it decreased by 2.17% and 6.47% in study areas (b) and (c), respectively; meanwhile, the soil moisture retrieval RMSEs were reduced by 2.81%, 3.69%, and 9.45%, respectively. In summary, this study improves the accuracy of active microwave remote sensing-based soil moisture retrieval in areas with different vegetation cover by explicitly quantifying soil–vegetation interaction scattering. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Water Resources)
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20 pages, 2627 KB  
Article
Automated Detection of Center-Pivot Irrigation Systems from Remote Sensing Imagery Using Deep Learning
by Aliasghar Bazrafkan, James Kim, Rob Proulx and Zhulu Lin
Remote Sens. 2025, 17(13), 2276; https://doi.org/10.3390/rs17132276 - 3 Jul 2025
Cited by 1 | Viewed by 2523
Abstract
Effective detection of center-pivot irrigation systems is crucial in understanding agricultural activity and managing groundwater resources for sustainable uses, especially in semi-arid regions such as North Dakota, where irrigation primarily depends on groundwater resources. In this study, we have adopted YOLOv11 to detect [...] Read more.
Effective detection of center-pivot irrigation systems is crucial in understanding agricultural activity and managing groundwater resources for sustainable uses, especially in semi-arid regions such as North Dakota, where irrigation primarily depends on groundwater resources. In this study, we have adopted YOLOv11 to detect the center-pivot irrigation systems using multiple remote sensing datasets, including Landsat 8, Sentinel-2, and NAIP (National Agriculture Imagery Program). We developed an ArcGIS custom tool to facilitate data preparation and large-scale model execution for YOLOv11, which was not included in the ArcGIS Pro deep learning package. YOLOv11 was compared against other popular deep learning model architectures such as U-Net, Faster R-CNN, and Mask R-CNN. YOLOv11, using Landsat 8 panchromatic data, achieved the highest detection accuracy (precision: 0.98; recall: 0.91; and F1-score: 0.94) among all tested datasets and models. Spatial autocorrelation and hotspot analysis revealed systematic prediction errors, suggesting a need to adjust training data regionally. Our research demonstrates the potential of deep learning in combination with GIS-based workflows for large-scale irrigation system analysis, adopting precision agricultural technologies for sustainable water resource management. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Water Resources)
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