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Microwave Remote Sensing for Soil Moisture and Vegetation Optical Depth Retrievals

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 September 2026 | Viewed by 248

Special Issue Editors


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Guest Editor
INRAE, UMR1391 ISPA, Université de Bordeaux, 33140 Villenave d’Ornon, France
Interests: soil moisture; vegetation optical depth; remote sensing

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Guest Editor
School of Geo-Science and Technology, Zhengzhou University, ‌Zhengzhou 450001‌, China
Interests: microwave soil moisture and vegetation optical depth modeling; carbon cycle, vegetation drought

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Guest Editor
Center for the Pan-Third Pole Environment, Lanzhou University, Lanzhou 730000, China
Interests: microwave soil moisture modeling and evaluation; spatiotemporal data analysis

Special Issue Information

Dear Colleagues,

Microwave remote sensing has become a cornerstone for the observation of terrestrial water and carbon cycles, offering unique capabilities for the retrieval of soil moisture and vegetation optical depth (VOD) across a wide range of spatial and temporal scales. Unlike optical and thermal sensors, microwave instruments—both active and passive—can penetrate cloud cover and are sensitive to surface and near-surface dielectric properties, enabling all-weather, day-and-night monitoring. Soil moisture plays a critical role in land–atmosphere interactions, hydrological processes, and agricultural productivity, while VOD provides complementary information on vegetation biomass, water content, and phenological dynamics.

Recent advances in sensor technology, retrieval algorithms, and data assimilation have significantly improved the accuracy and resolution of microwave-derived soil moisture and VOD products. Missions such as SMAP, SMOS, AMSR, and Sentinel-1 have expanded opportunities for multi-frequency, multi-polarization, and synergistic retrieval approaches. At the same time, challenges remain related to radio-frequency interference, surface heterogeneity, vegetation complexity, and scale mismatch with in situ observations. Addressing these challenges is essential for advancing climate monitoring, drought assessment, ecosystem characterization, and Earth system modeling. This special issue aims to highlight recent methodological developments, validation efforts, and emerging applications in microwave-based soil moisture and VOD retrievals.

The aim of this Special Issue is to advance the understanding and application of microwave remote sensing techniques for the retrieval of soil moisture and vegetation optical depth, with an emphasis on methodological innovation, product evaluation, and Earth system applications. The issue seeks to bring together contributions that address both theoretical and practical challenges, including retrieval algorithm development, multi-sensor data fusion, uncertainty quantification, validation using in situ and airborne observations, and the integration of microwave-derived products into hydrological, agricultural, and ecological models.

This topic aligns closely with the journal’s scope by contributing to the core objectives of remote sensing science: improving observation capabilities of land surface processes, enhancing the interpretation of satellite data, and supporting applications relevant to climate change, water resources, and ecosystem monitoring. By focusing on soil moisture and vegetation dynamics—key variables linking the water, energy, and carbon cycles—this Special Issue supports interdisciplinary research at the interface of geosciences, environmental monitoring, and Earth observation. The collected papers are expected to provide timely insights that strengthen the scientific foundations and practical utility of microwave remote sensing within the journal’s research community.

This Special Issue welcomes original research articles, review papers, and technical notes that address recent advances and emerging challenges in microwave remote sensing for soil moisture and vegetation optical depth retrievals. Contributions may include, but are not limited to, the following themes:

(1) Development and improvement of active and passive microwave retrieval algorithms for soil moisture and VOD
(2) Multi-frequency, multi-polarization, and multi-angle observations and their synergistic use
(3) Data fusion and downscaling approaches combining microwave, optical, thermal, and ancillary data
(4) Validation and intercomparison of satellite-derived soil moisture and VOD products using in situ, airborne, or model-based references
(5) Impacts of vegetation structure, surface roughness, topography, and radio-frequency interference on retrieval accuracy
(6) Assimilation of microwave-derived variables into hydrological, agricultural, and land surface models
(7) Applications in drought monitoring, crop assessment, ecosystem dynamics, and climate studies
(8) Long-term data records and trend analysis of soil moisture and vegetation water status

The Special Issue encourages both methodological and application-oriented studies, as well as comprehensive review articles that synthesize recent progress and identify future research directions. Interdisciplinary contributions that bridge remote sensing science with hydrology, ecology, and climate research are particularly encouraged.

Dr. Xiangzhuo Liu
Dr. Mengjia Wang
Dr. Zanpin Xing 
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

  • microwave remote sensing
  • soil moisture retrieval
  • vegetation optical depth (VOD)
  • passive and active microwave sensors
  • multi-frequency observations
  • retrieval algorithms
  • data fusion and downscaling
  • product validation
  • land–atmosphere interactions

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Published Papers (1 paper)

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Research

22 pages, 3063 KB  
Article
Machine Learning-Based Soil Moisture Retrieval from Sentinel-1A Observations over the International Soil Moisture Networks
by Jingyang Wang, Yuzhu Wang, Xiaojing Bai and Wei Shao
Remote Sens. 2026, 18(12), 1914; https://doi.org/10.3390/rs18121914 (registering DOI) - 10 Jun 2026
Abstract
Soil moisture (SM) is a critical variable in land–atmosphere water and energy exchange, and synthetic aperture radar (SAR) observations offer an effective means for large-scale and fine-resolution SM monitoring. Sentinel-1A, with its all-time and all-weather capability, has become an indispensable data source for [...] Read more.
Soil moisture (SM) is a critical variable in land–atmosphere water and energy exchange, and synthetic aperture radar (SAR) observations offer an effective means for large-scale and fine-resolution SM monitoring. Sentinel-1A, with its all-time and all-weather capability, has become an indispensable data source for SM retrieval, while comprehensive comparisons of machine learning and deep learning methods for regional and global scale SM retrieval remain insufficient. In this study, four widely used machine learning (ML) algorithms, including random forest (RF), eXtreme gradient boosting (XGBoost), convolutional neural network (CNN), and long short-term memory (LSTM), are evaluated for SM retrieval from Sentinel-1A observations across the International Soil Moisture Network (ISMN) at global and regional scales. Multiple-source dynamic parameters, including Sentinel-1A observations, MODIS vegetation parameters, ERA5-Land meteorological and soil variables, are used as inputs, as well as static geospatial parameters. Validation results demonstrate that tree-based ensemble methods (RF and XGBoost) consistently outperform deep learning methods across all scales. Specifically, XGBoost achieves the best performance with satisfactory SM retrieval results. Moreover, XGBoost is insensitive to Sentinel-1A viewing geometry, allowing fusion of multi-orbit observations to improve temporal resolution without accuracy loss. These findings demonstrate the effectiveness of tree-based ML for global/regional SM retrieval from Sentinel-1A. In addition, this study performs a comprehensive evaluation of spatial generalization ability and orbit robustness of different retrieval models under global heterogeneous environments, and proposes a reliable scheme for generating high-spatiotemporal-resolution SM products. Full article
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