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Remote Sensing
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Satellite Soil Moisture Estimation, Assessment, and Applications (Second Edition)
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Satellite Soil Moisture Estimation, Assessment, and Applications (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 3129

Special Issue Editors

Dr. Xiaojun Li

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Guest Editor
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: microwave soil moisture modeling; validation; carbon cycle estimation
Special Issues, Collections and Topics in MDPI journals
Dr. Lun Gao

E-Mail Website
Guest Editor
Institute on the Environment, University of Minnesota, St. Paul, MN, USA
Interests: microwave remote sensing; ecohydrology; agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Frédéric Frappart

E-Mail Website
Guest Editor
Interactions Sol Plante Atmosphère, UMR 1391 INRAE/Bordeaux Science Agro 71, Avenue Edouard Bourlaux, 33882 Villenave d'Ornon, France
Interests: remote sensing; water cycle; carbon cycle; wetlands
Special Issues, Collections and Topics in MDPI journals
Dr. Hui Lu

E-Mail Website
Guest Editor
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Interests: microwave remote sensing; soil moisture; land surface data assimilation; hydrological model; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil moisture, identified by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV), plays a pivotal role in regulating water and energy exchanges within the soil–vegetation–atmosphere continuum, from local watersheds to the global scale. It influences the partitioning of precipitation into evapotranspiration, surface runoff, and infiltration, thereby affecting hydrological and climatic processes. Accurate global monitoring of soil moisture from space is important for improving land and weather forecasts; understanding water, energy, and carbon cycles; and enhancing management of water and food resources. Today, multiple space-borne platforms, such as the ESA’s Soil Moisture and Ocean Salinity (SMOS) satellite and NASA’s Soil Moisture Active Passive (SMAP) satellite, provide unprecedented opportunities to analyze soil moisture. Looking ahead, ESA’s Copernicus Imaging Microwave Radiometer (CIMR), expected to launch in 2029, will operate across multiple microwave frequencies, further advancing soil moisture monitoring. However, the measurement of soil moisture remains challenging due to limited satellite observations; the high correlation between different polarizations, angles, and channels; and uncertainties in radiative transfer models and ancillary datasets.

Therefore, topics of interest include, but are not limited to, the following:

  • Advancing remote sensing techniques in retrieving soil moisture and/or relevant parameters, such as vegetation optical depth, vegetation scattering albedo, and surface soil roughness;
  • Exploration of synergistic use of multi-sensor data (e.g., SMOS, SMAP, AMSR2, and CYGNSS) for improving soil moisture estimation;
  • Development and benchmarking of soil moisture retrieval algorithms for upcoming missions, such as CIMR
  • AI-based methods and hybrid approaches for enhancing retrievals under complex surface conditions;
  • Validation/comparison of soil moisture products;
  • Airborne calibration/validation experiments;
  • Assimilating soil moisture into hydrological/atmospheric/vegetation models;
  • Integration of remote sensing and in situ observations;
  • Downscaling soil moisture products.

Dr. Xiaojun Li
Dr. Lun Gao
Dr. Frédéric Frappart
Dr. Hui Lu
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

  • surface and root-zone soil moisture
  • passive and active microwaves
  • SMOS/SMAP/AMSR2/CIMR
  • vegetation optical depth
  • evaluation/intercomparison
  • data assimilation

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Related Special Issue

Published Papers (3 papers)

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Research

21 pages, 5182 KB  
Article
A New Joint Retrieval of Soil Moisture and Vegetation Optical Depth from Spaceborne GNSS-R Observations
by Mina Rahmani, Jamal Asgari and Alireza Amiri-Simkooei
Remote Sens. 2026, 18(2), 353; https://doi.org/10.3390/rs18020353 - 20 Jan 2026
Viewed by 1115
Abstract
Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide reliable global SM and VOD products, they are limited by coarse [...] Read more.
Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide reliable global SM and VOD products, they are limited by coarse spatial resolution and infrequent revisit times. Global Navigation Satellite System Reflectometry (GNSS-R) observations, particularly from the Cyclone GNSS (CYGNSS) mission, offer an improved spatiotemporal sampling rate. This study presents a deep learning framework based on an artificial neural network (ANN) for the simultaneous retrieval of SM and VOD from CYGNSS observations across the contiguous United States (CONUS). Ancillary input features, including specular point latitude and longitude (for spatial context), CYGNSS reflectivity and incidence angle (for surface signal characterization), total precipitation and soil temperature (for hydrological context), and soil clay content and surface roughness (for soil properties), are used to improve the estimates. Results demonstrate strong agreement between the predicted and reference values (SMAP SM and SMOS VOD), achieving correlation coefficients of R = 0.83 and 0.89 and RMSE values of 0.063 m3/m3 and 0.088 for SM and VOD, respectively. Temporal analyses show that the ANN accurately reproduces both seasonal and daily variations in SMAP SM and SMOS VOD (R ≈ 0.89). Moreover, the predicted SM and VOD maps show strong agreement with the reference SM and VOD maps (R ≈ 0.93). Additionally, ANN-derived VOD demonstrates strong consistency with above-ground biomass (R ≈ 0.77), canopy height (R ≈ 0.95), leaf area index (R = 96), and vegetation water content (R ≈ 0.90). These results demonstrate the generalizability of the approach and its applicability to broader environmental sensing tasks. Full article
(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications (Second Edition))
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25 pages, 18928 KB  
Article
Temperature and Moisture Variability Drive Resilience Shifts in Canada’s Undisturbed Forests During 2001–2018
by Chenlin Yang, Tianxiang Cui, Lei Fan, Jian Wang and Jean-Pierre Wigneron
Remote Sens. 2026, 18(2), 190; https://doi.org/10.3390/rs18020190 - 6 Jan 2026
Cited by 1 | Viewed by 821
Abstract
Canada’s forests are a critical component of the global carbon pool and play an essential role in regulating the Earth’s climate. Since 2000, increasing disturbances have reduced ecosystem resilience, raising concerns about the long-term carbon sequestration capacity of Canada’s forests. Yet, the resilience [...] Read more.
Canada’s forests are a critical component of the global carbon pool and play an essential role in regulating the Earth’s climate. Since 2000, increasing disturbances have reduced ecosystem resilience, raising concerns about the long-term carbon sequestration capacity of Canada’s forests. Yet, the resilience of Canada’s undisturbed forests remains poorly understood. In this study, we assessed resilience across undisturbed forests from 2001 to 2018 by applying the lag-1 autocorrelation (AR(1)) metric to leaf area index (LAI) time series. Our analyses revealed a widespread and substantial temporal shift in resilience from declining to increasing despite a persistently greening trend. These resilience transitions were most pronounced in mixed-species and intermediate-aged forests. By quantifying the influence of multiple environmental drivers, we found that variability in temperature and moisture exerted dominant controls on resilience shifts. Cooler conditions and higher moisture availability contributed to increased resilience, with the largest resilience shifts occurring in regions experiencing sustained cooling or wetting trends. These findings imply that conservation strategies favoring mixed-species and intermediate-aged forests under cooler, wetter conditions could promote long-term ecosystem stability. Full article
(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications (Second Edition))
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21 pages, 12673 KB  
Article
Validation of Downscaled SoilMERGE with NDVI and Storm-Event Analysis in Oklahoma and Kansas
by Kenneth Tobin, Aaron Sanchez, Alejandro X. Alaniz, Stephanie Hernandez, Adriana Perez, Deepak Ganta and Marvin Bennett
Remote Sens. 2025, 17(24), 4058; https://doi.org/10.3390/rs17244058 - 18 Dec 2025
Viewed by 567
Abstract
SoilMERGE (SMERGE) is a 0.125-degree root zone soil moisture (RZSM) product (0 to 40 cm depth) covering the contiguous United States. The study area included most of Oklahoma and Kansas, a region where SMERGE exhibited superior performance. The time frame examined was the [...] Read more.
SoilMERGE (SMERGE) is a 0.125-degree root zone soil moisture (RZSM) product (0 to 40 cm depth) covering the contiguous United States. The study area included most of Oklahoma and Kansas, a region where SMERGE exhibited superior performance. The time frame examined was the warm season from 2008 to 2019. In this study, evaluation of a prototype downscaled (500 m) version of SMERGE was made using (1) Ranked correlation (R2) benchmarking against Normalized Difference Vegetation Index (NDVI) datasets and (2) Ranked correlation (R2) analysis of antecedent RZSM with storm-event streamflow across a range of precipitation intensities (5 to >35 mm/day) at a watershed scale. In the NDVI benchmarking, all three downscaled products outperformed (0.52 to 0.59) default SMERGE (0.44). EXtreme Gradient Boosting (XGB) and Gradient Boost recorded a higher ranked correlation (0.59) than Random Forest (0.52). Within the study area, ranked correlation analysis of antecedent RZSM with storm-event United States Geological Survey streamflow was examined in five watersheds. For the most intense storm events (>35 mm), antecedent XGB downscaled SMERGE (0.64) outperformed antecedent streamflow (0.43) and all other versions of SMERGE (0.52 to 0.56) as a predictor of storm event response. The results of this study demonstrated broad-scale benefits of Machine Learning-assisted downscaling, providing proof of concept for the development of state-based SMERGE products across the US Great Plains. Full article
(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications (Second Edition))
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