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Remote Sensing
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Remote Sensing for Soil Moisture and Vegetation Parameters Retrieval (Second Edition)

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 December 2025 | Viewed by 1969

Special Issue Editors

Dr. Liangliang Tao

E-Mail Website
Guest Editor
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: microwave and optical remote sensing to retrieve soil moisture and vegetation parameters; agricultural remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dongryeol Ryu

E-Mail Website
Guest Editor
Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Austrilia
Interests: microwave remote sensing of soil moisture; hydrological applications of remote sensing; hydrological data assimilation
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Sun

E-Mail Website
Guest Editor
College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: optical and thermal remote sensing; remote sensing of soil moisture, agricultural and ecological drought; remote sensing of ecological environment; remote sensing of mining area
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil moisture and vegetation parameters (leaf area index, biomass, etc.) are fundamental environmental variables in the global energy, carbon and water exchange, and have great relevance for crop yield estimation, drought monitoring, evapotranspiration and agricultural management. Remote sensing can provide non-destructive and cost-efficient measurements and data to understand and estimate soil moisture and vegetation parameters over local to regional spatial scales. Over the years, various remote-sensing-based methods have been developed for soil moisture and vegetation parameters estimation, especially with the development of advanced technology in GNSS-R, SAR, passive microwave, multispectral/hyperspectral and thermal imaging, and some methods with theoretical models. Therefore, the main goal of this Special Issue is to summarize the development achievements of soil moisture and vegetation parameters estimation using remote sensing, provide insight into extensive progress in agricultural regions, and promote the rapid application of relative products in different fields.

We encourage the submission of novel techniques/approaches for retrieving and estimating soil moisture and vegetation parameters at various spatial and temporal scales, using any form of remote sensing data (proximal, airborne, and satellite). Original research contributions, exhaustive reviews, remote-sensing methodologies, and relevant applications in soil moisture and vegetation parameters retrieval are welcome. In addition to the points above, topics may include but are not limited to:

  • Retrieval of soil moisture and vegetation parameters (leaf area index, biomass, etc.);
  • Validation of remote sensing estimates with ground observations;
  • Application of new sensors/algorithms and in practice monitoring systems;
  • Comparison and evaluation of different remote sensing methods (statistical, physical and hybrid models) in agriculture and drought monitoring;
  • Efforts to improve the accuracy of remotely sensed products in different spatial scales.

Dr. Liangliang Tao
Prof. Dr. Dongryeol Ryu
Dr. Hao Sun
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

  • soil moisture
  • agricultural monitoring
  • microwave remote sensing
  • machine learning
  • vegetation dynamics estimates
  • modeling
  • drought assessment

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Related Special Issue

Published Papers (2 papers)

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Research

32 pages, 12440 KiB  
Article
Intercomparison of Leaf Area Index Products Derived from Satellite Data over the Heihe River Basin
by Pan Zhou, Liying Geng, Jun Li and Haibo Wang
Remote Sens. 2025, 17(7), 1233; https://doi.org/10.3390/rs17071233 - 31 Mar 2025
Viewed by 418
Abstract
The leaf area index (LAI) is a crucial parameter for climate change research, agricultural management, and ecosystem monitoring. Despite extensive use of remote sensing data to estimate the LAI, comprehensive evaluations of product consistency and uncertainty remain limited. This study evaluated the uncertainties [...] Read more.
The leaf area index (LAI) is a crucial parameter for climate change research, agricultural management, and ecosystem monitoring. Despite extensive use of remote sensing data to estimate the LAI, comprehensive evaluations of product consistency and uncertainty remain limited. This study evaluated the uncertainties of four LAI products—GLASS, MCD15A2H, VNP15A2H, and CLMS—across diverse land cover types in the Heihe River Basin through two triple collocation approaches, innovatively. Each approach, respectively, focused on achieving more precise temporal characteristics and spatial characteristics of product uncertainties. The results indicate that all products generally met the Global Climate Observing System’s precision requirement (±0.5) for most biomes during the growing season. When comparing monthly uncertainties within grid cells, GLASS demonstrates superior performance, particularly in grasslands and croplands, whereas CLMS exhibits a slightly weaker ability to represent the spatial distribution of the LAI, especially in regions with high LAI values. When time series data are used to analyze the seasonal uncertainties of the products, MCD15A2H and VNP15A2H show more pronounced distortions, indicating their limited capability in capturing the temporal dynamics of the LAI. Correlation analyses revealed strong product agreement in regions with a low LAI, but discrepancies increased during the growing season and in heterogeneous land covers like croplands. These findings provide critical insights into the reliability of LAI products, offering a robust reference for validating their performance and ensuring their alignment with user requirements across diverse applications. The study highlights the importance of addressing spatial and temporal variability in uncertainties to improve the practical utility of LAI datasets in ecological and climate-related research. Full article
(This article belongs to the Special Issue Remote Sensing for Soil Moisture and Vegetation Parameters Retrieval (Second Edition))
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26 pages, 43142 KiB  
Article
Can Measurement and Input Uncertainty Explain Discrepancies Between the Wheat Canopy Scattering Model and SMAPVEX12 Observations?
by Lilangi Wijesinghe, Andrew W. Western, Jagannath Aryal and Dongryeol Ryu
Remote Sens. 2025, 17(1), 164; https://doi.org/10.3390/rs17010164 - 6 Jan 2025
Viewed by 883
Abstract
Realistic representation of microwave backscattering from vegetated surfaces is important for developing accurate soil moisture retrieval algorithms that use synthetic aperture radar (SAR) imagery. Many studies have reported considerable discrepancies between the simulated and observed backscatter. However, there has been limited effort to [...] Read more.
Realistic representation of microwave backscattering from vegetated surfaces is important for developing accurate soil moisture retrieval algorithms that use synthetic aperture radar (SAR) imagery. Many studies have reported considerable discrepancies between the simulated and observed backscatter. However, there has been limited effort to identify the sources of errors and contributions quantitatively using process-based backscatter simulation in comparison with extensive ground observations. This study examined the influence of input uncertainties on simulated backscatter from a first-order radiative transfer model, named the Wheat Canopy Scattering Model (WCSM), using ground-based and airborne data collected during the SMAPVEX12 campaign. Input uncertainties to WCSM were simulated using error statistics for two crop growth stages. The Sobol’ method was adopted to analyze the uncertainty in WCSM-simulated backscatters originating from different inputs before and after the wheat ear emergence. The results show that despite the presence of wheat ears, uncertainty in root mean square (RMS) height of 0.2 cm significantly influences simulated co-polarized backscatter uncertainty. After ear emergence, uncertainty in ears dominates simulated cross-polarized backscatter uncertainty. In contrast, uncertainty in RMS height before ear emergence dominates the accuracy of simulated cross-polarized backscatter. These findings suggest that considering wheat ears in the model structure and precise representation of surface roughness is essential to accurately simulate backscatter from a wheat field. Since the discrepancy between the simulated and observed backscatter coefficients is due to both model and observation uncertainty, the uncertainty of the UAVSAR data was estimated by analyzing the scatter between multiple backscatter coefficients obtained from the same targets near-simultaneously, assuming the scatter represents the observation uncertainty. Observation uncertainty of UAVSAR backscatter for HH, VV, and HV polarizations are 0.8 dB, 0.87 dB, and 0.86 dB, respectively. Discrepancies between WCSM-simulated backscatter and UAVSAR observations are discussed in terms of simulation and observation uncertainty. Full article
(This article belongs to the Special Issue Remote Sensing for Soil Moisture and Vegetation Parameters Retrieval (Second Edition))
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