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Advances in Remote Sensing for Regional Soil Moisture Monitoring
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Advances in Remote Sensing for Regional Soil Moisture Monitoring

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: 15 August 2025 | Viewed by 3327

Special Issue Editors

Dr. Qi Gao

E-Mail Website
Guest Editor
Remote Sensing Department, Division of Geomatics, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Av. Gauss, 7 E-08860 Castelldefels, Barcelona, Spain
Interests: SAR and SAR data processing; SAR interferometry; land deformation; hydrology; water management; hazard monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Mehrez Zribi

E-Mail Website
Guest Editor
CESBIO, University of Toulouse, CNRS/UT3/IRD/CNES/INRAE, 31400 Toulouse, France
Interests: remote sensing; synthetic aperture radar; GNSS-R; airborne microwave instrumentation; soil moisture; vegetation; hydrology; water resources
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Massimo Menenti

E-Mail Website
Guest Editor
1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2. Department of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 Delft, The Netherlands
Interests: land surface processes; terrestrial water cycle; water management; optical remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Peng

E-Mail Website
Guest Editor
Helmholtz Centre for Environmental Research—UFZ, Permoserstraße 15, 04318 Leipzig, Germany
Interests: quantitative retrieval of land surface parameters from remote sensing data; radiative transfer in soil–vegetation–atmosphere systems; process-based modeling and data-driven methods; hydroclimatic extremes
Special Issues, Collections and Topics in MDPI journals
Dr. Tianjie Zhao

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: soil moisture; passive microwave; hydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing technologies have revolutionized our ability to monitor soil moisture dynamics at a regional scale, facilitating effective water resource management and sustainable agriculture practices. By leveraging multi-sensor remote sensing observations, in situ measurements, geographical data from multiple thematic scales, and model-data fusion techniques, researchers can capture the spatiotemporal variations of soil moisture and provide valuable insights for decision-making.

This Special Issue focuses on advances in remote sensing for regional soil moisture monitoring. It aims to bring together cutting-edge research in the field, highlighting innovative approaches, case studies, and review discussions that enhance our ability to retrieve and understand soil moisture dynamics and their applications. We welcome submissions that explore the forefront of remote sensing techniques and methodologies specifically tailored to regional soil moisture monitoring.

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

  • Unmanned aerial vehicle remote sensing for the high-resolution and near-real-time monitoring of soil moisture
  • Innovative remote sensing techniques for the retrieval of soil moisture
  • Analysis of recently available and near future satellite data products for regional soil moisture monitoring
  • Airborne calibration and validation experiments to showcase potential innovations for future remote sensing technologies
  • Case studies demonstrating the application of remote sensing in regional-scale soil moisture assessment
  • Approaches for integrating remote sensing and in situ observations to improve soil moisture monitoring accuracy
  • The impacts of soil moisture variability on hydrological processes, agricultural productivity, and climate dynamics
  • Uncertainty quantification and error propagation in remote-sensing-based soil moisture retrieval

Dr. Qi Gao
Dr. Mehrez Zribi
Prof. Dr. Massimo Menenti
Prof. Dr. Jian Peng
Dr. Tianjie Zhao
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 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

  • remote sensing
  • soil moisture
  • water resources management
  • regional scale
  • hydrological processes
  • data integration and interpretation
  • multi-sensor remote sensing observations

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Published Papers (3 papers)

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Research

23 pages, 5897 KiB  
Article
Evaluating the Performance of Satellite-Derived Soil Moisture Products Across South America Using Minimal Ground-Truth Assumptions in Spatiotemporal Statistical Analysis
by B. G. Mousa, Alim Samat and Hong Shu
Remote Sens. 2025, 17(5), 753; https://doi.org/10.3390/rs17050753 - 21 Feb 2025
Viewed by 482
Abstract
South America (SA) features diverse land cover types and varied climate conditions, both of which significantly influence the variability of soil moisture (SMO). Obtaining ground-truth measurements for SMO is often costly and labor-intensive, and the limited number of ground SMO stations in SA [...] Read more.
South America (SA) features diverse land cover types and varied climate conditions, both of which significantly influence the variability of soil moisture (SMO). Obtaining ground-truth measurements for SMO is often costly and labor-intensive, and the limited number of ground SMO stations in SA further complicates the evaluation of satellite-derived SMO products. In this work, we proposed an approach that integrates some statistical methods to assess the reliability of Soil Moisture Active Passive (SMAP), the H113 dataset from the Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite-derived SMO products in SA from 14 May 2015 to 31 December 2016. The integrated methods are error metrics (correlation (R), bias, and ubiased root mean square error (ubRMSE)), Triple Collocation Method (TCM), and Hovmöller diagrams. ERA5 and GLDAS-Noah SM products were used as references for validation. The quality of SMO products was assessed by considering environmental variables, including land cover, vegetation density, and precipitation, within the different climate zones of SA. The results presented that SMAP overall outperforms SMOS and ASCAT, with the highest average correlation (0.55 with GLDAS and 0.61 with ERA5), slight average bias (−0.058 with GLDAS and −0.014 with ERA5), and lowest average ubRMSE (0.045 with GLDAS and 0.041 with ERA5). In arid, semi-arid, and moderate vegetation regions, the SMAP satellite outperforms SMOS and ASCAT, achieving better statistics values with GLDAS and ERA5 datasets, and achieving low error variance and high S/N in the TCM analysis. While the ASCAT H113 product showed good performance, which makes it a good alternative to SMAP, it still has limitations in more dense vegetation regions. SMOS showed the lowest performance across SA, especially in the Amazon basin. The Amazon basin emerges as a critical region where all SMO products displayed a significant SMO variability; however, SMAP showed slightly better results than ASCAT and SMOS. In the absence of ground truths, the proposed approach provides a better evaluation of satellite SMO products. Meanwhile, it provides new spatiotemporal statistical insights into satellite SMO retrieval performance evaluation within diverse climate zones of SA. This research provides valuable guidance for improving SMO monitoring and agricultural management in tropical and semi-arid ecosystems. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Regional Soil Moisture Monitoring)
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19 pages, 7218 KiB  
Article
Relationship between Vegetation and Soil Moisture Anomalies Based on Remote Sensing Data: A Semiarid Rangeland Case
by Juan José Martín-Sotoca, Ernesto Sanz, Antonio Saa-Requejo, Rubén Moratiel, Andrés F. Almeida-Ñauñay and Ana M. Tarquis
Remote Sens. 2024, 16(18), 3369; https://doi.org/10.3390/rs16183369 - 11 Sep 2024
Viewed by 1188
Abstract
The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of [...] Read more.
The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being ZVCI and ZWCI, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the ZWCI had a strong probability of informing in advance, where the negative ZVCI will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of ZWCI to aid the prediction of ZVCI. During these months, using ZWCI as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Regional Soil Moisture Monitoring)
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16 pages, 6217 KiB  
Article
Medium-Scale Soil Moisture Retrievals Using an ELBARA L-Band Radiometer Using Time-Dependent Parameters for Wetland-Meadow-Cropland Site
by Kamil Szewczak and Mateusz Łukowski
Remote Sens. 2024, 16(12), 2200; https://doi.org/10.3390/rs16122200 - 17 Jun 2024
Viewed by 730
Abstract
The soil moisture at the medium spatial scale is strongly desired in the context of satellite remote sensing data validation. The use of a ground-installed passive L-band radiometer ELBARA at the Bubnów-Sęków test site in the east of Poland gave a possibility to [...] Read more.
The soil moisture at the medium spatial scale is strongly desired in the context of satellite remote sensing data validation. The use of a ground-installed passive L-band radiometer ELBARA at the Bubnów-Sęków test site in the east of Poland gave a possibility to provide reference soil moisture data from the area with a radius of 100 m. In addition, the test site comprised three different land cover types that could be investigated continuously with one day resolution. The studies were focused on the evaluation of the ω-τ model coefficients for three types of land cover, including meadow, wetland, and cropland, to allow for the assessment of the soil moisture retrievals at a medium scale. Consequently, a set of reference time-dependent coefficients of effective scattering albedo, optical depth, and constant-in-time roughness parameters were estimated. The mean annual values of the effective scattering albedo including two polarisations were 0.45, 0.26, 0.14, and 0.54 for the meadow with lower organic matter, the meadow with higher organic matter, the wetland, and the cropland, respectively. The values of optical depth were in the range from 0.30 to 0.80 for the cropland, from 0.40 to 0.52 for the meadows (including the two investigated meadows), and from 0.60 to 0.70 for the wetland. Time-constant values of roughness parameters at the level of 0.45 were obtained. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Regional Soil Moisture Monitoring)
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