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Remote Sensing for Eco-Environmental Monitoring and Assessment in Agricultural Watersheds
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Remote Sensing for Eco-Environmental Monitoring and Assessment in Agricultural Watersheds

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 740

Editor

Dr. Rituraj Shukla

E-Mail Website
Guest Editor
Department of Civil, Environmental and Water Resource Engineering, University of Guelph, Guelph, ON, Canada
Interests: hydrology; climate change; soil and water engineering; remote sensing and GIS ap-plications; wetlands; land use and land cover modeling; trend analysis; satellite data processing; best management practices; groundwater; water quality and quantity monitoring and modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wetlands and forest ecosystems play a critical role in regulating hydrological processes, maintaining biodiversity, and supporting climate resilience. However, these ecosystems are increasingly threatened by climate change, land-use dynamics, and anthropogenic pressures. Recent advances in remote sensing technologies, cloud computing platforms, and machine learning techniques have enabled unprecedented opportunities for eco-environmental monitoring and assessment at multiple spatial and temporal scales. This Special Issue aims to bring together high-quality research that leverages satellite remote sensing, geospatial analysis, and data-driven approaches to monitor, model, and assess eco-environmental conditions in wetlands and forested regions. We invite original research articles and review papers addressing both methodological advancements and applied studies across diverse geographic regions.

Contributions focusing on hydrological processes, ecosystem health, land-use and land-cover change, climate change impacts, and sustainability assessment are particularly encouraged. This Special Issue seeks to provide a comprehensive platform for researchers and practitioners to share innovative methodologies, case studies, and integrated frameworks that enhance understanding of eco-hydrological interactions and support evidence-based environmental management and policy development.

Keywords: Wetlands, Ecosystems, Water Quality and Quantity Monitoring, Soil and Water, Remote Sensing, GIS, Land-use and Land-cover.

Dr. Rituraj Shukla
Guest Editor

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water 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 2600 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

  • remote sensing
  • wetlands
  • forest ecosystems
  • ecohydrology
  • climate change
  • land use and land cover
  • machine learning
  • hydrological modeling
  • environmental monitoring

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Published Papers (1 paper)

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Review

27 pages, 1809 KB  
Review
Deep Learning for Remote Sensing-Based Surface Soil Moisture Monitoring and Prediction: A Review
by Shengtao Yang, Wenbin Shao, Jing Wang and Dongying Zhang
Water 2026, 18(15), 1920; https://doi.org/10.3390/w18151920 - 6 Aug 2026
Viewed by 330
Abstract
Surface soil moisture (SM) is the keystone variable of terrestrial ecohydrology. Yet, the rapid diversification and development of deep learning architectures for satellite SM estimation have outpaced practitioners’ capacity to select among them. This review synthesizes 37 deep learning studies from the SMAP [...] Read more.
Surface soil moisture (SM) is the keystone variable of terrestrial ecohydrology. Yet, the rapid diversification and development of deep learning architectures for satellite SM estimation have outpaced practitioners’ capacity to select among them. This review synthesizes 37 deep learning studies from the SMAP era (2015–2026) across five architecture families (MLP and physics-informed neural networks [MLP/PINN], long short-term memory [LSTM] and gated recurrent unit [GRU] networks, convolutional neural networks [CNN], convolutional LSTM and graph neural networks [GNN], and Transformer-based models) to establish an architecture–task-matching framework that links each family to its dominant estimation niche. The analysis reveals consistent specializations: MLP/PINN models achieve competitive surface SM retrieval from satellite inputs; recurrent networks extend SMAP temporally (RMSE ≤ 0.035  m3 m−3); CNN disaggregates SMAP to 1 km (reported unbiased root-mean-square error (ubRMSE) approaching 0.04  m3 m−3); ConvLSTM and GNN address spatiotemporal gap-filling (low reported ubRMSE 0.022  m3 m−3); and Transformers enable global multi-source fusion and decadal climate-scenario projection. Across all families, four physics-DL integration modes (hard architectural constraints, soft loss-function penalties, physics-as-input feature engineering, and physics-ML hybrid output fusion) consistently yield RMSE reductions of 8–50% relative to data-driven baselines. These findings provide a practitioner-oriented framework that is applicable to ecohydrological monitoring of plant water stress, agricultural drought, early flood warnings, and land–atmosphere coupling. Full article
(This article belongs to the Special Issue Remote Sensing for Eco-Environmental Monitoring and Assessment in Agricultural Watersheds)
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