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Applications of Multi-Source Remote Sensing Technologies in Soil Moisture Monitoring
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Applications of Multi-Source Remote Sensing Technologies in Soil Moisture Monitoring

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: 25 July 2025 | Viewed by 1130

Special Issue Editors

Dr. Lingli Zhao

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: synthetic aperture radar; polarimetric synthetic aperature radar; soil moisture inversion; crop growth monitoring; disaster monitoring using multi-source remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Hongtao Shi

E-Mail Website
Guest Editor
School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221008, China.
Interests: soil moisture inversion; crop growth monitoring; polarimetric synthetic aperture radar; passive microwave remote sensing; crop parameters monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Weidong Sun

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: soil moisture inversion; polarimetric synthetic aperture radar; target detection using SAR; classification using polarimetric SAR images
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil moisture (SM) is a key parameter in hydrological, meteorological, and climatological modelling as it influences the exchange of water, carbon and heat flux between the atmosphere and the land surface. It also provides essential information for drought forecasting, flood prediction and crop management. Over the past decades, mutli-source remote sensing techniques have been developed to measure SM in a large scale. Multi-spectral remote sensing, applying active and passive microwave remote sensing techniques, can measure SM from the regional to the global scale with a high accuracy.

This Special Issue aims to showcase the latest progress in remote sensing-based soil moisture monitoring, addressing existing challenges and exploring future research directions. Contributions discussing the effectiveness, limitations, and potential of remote sensing technologies and models in soil moisture monitoring are especially encouraged.

Original research articles, review papers, and technical investigations are welcome, focusing on topics including but not limited to the following:

  • Validation and evaluation of remote sensing based soil moisture products with in situ measurements;
  • Application of remote sensing technologies for soil moisture monitoring in various land covers;
  • Advancements in models and algorithms for soil moisture inversion using spacebore or airborne multispectral remote sensing, passive microwave remote sensing or SAR;
  • Investigations the soil moisture changes in a large scale or for a long time;
  • Advancements in models and algorithms for soil moisture inversion by fusing multi-source remote sensing data.

We encourage submissions that highlight interdisciplinary approaches, innovative methodologies, and their implications for practical applications in soil moisture monitoring.

Dr. Lingli Zhao
Dr. Hongtao Shi
Dr. Weidong 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. 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

  • soil moisture inversion
  • mulit-source remote sensing technologies
  • data fusion

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Published Papers (2 papers)

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Research

18 pages, 4854 KiB  
Article
Comparing UAV-Based Hyperspectral and Satellite-Based Multispectral Data for Soil Moisture Estimation Using Machine Learning
by Hadi Shokati, Mahmoud Mashal, Aliakbar Noroozi, Saham Mirzaei, Zahra Mohammadi-Doqozloo, Kamal Nabiollahi, Ruhollah Taghizadeh-Mehrjardi, Pegah Khosravani, Rabindra Adhikari, Ling Hu and Thomas Scholten
Water 2025, 17(11), 1715; https://doi.org/10.3390/w17111715 - 5 Jun 2025
Viewed by 538
Abstract
Accurate estimation of soil moisture content (SMC) is crucial for effective water management, enabling improved monitoring of water stress and a deeper understanding of hydrological processes. While satellite remote sensing provides broad coverage, its spatial resolution often limits its ability to capture small-scale [...] Read more.
Accurate estimation of soil moisture content (SMC) is crucial for effective water management, enabling improved monitoring of water stress and a deeper understanding of hydrological processes. While satellite remote sensing provides broad coverage, its spatial resolution often limits its ability to capture small-scale variations in SMC, especially in landscapes with diverse land-cover types. Unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors offer a promising solution to overcome this limitation. This study compares the effectiveness of Sentinel-2, Landsat-8/9 multispectral data and UAV hyperspectral data (from 339.6 nm to 1028.8 nm with spectral bands) in estimating SMC in a research farm consisting of bare soil, cropland and grassland. A DJI Matrice 100 UAV equipped with a hyperspectral spectrometer collected data on 14 field campaigns, synchronized with satellite overflights. Five machine-learning algorithms including extreme learning machines (ELMs), Gaussian process regression (GPR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) were used to estimate SMC, focusing on the influence of land cover on the accuracy of SMC estimation. The findings indicated that GPR outperformed the other models when using Landsat-8/9 and hyperspectral photography data, demonstrating a tight correlation with the observed SMC (R2 = 0.64 and 0.89, respectively). For Sentinel-2 data, ELM showed the highest correlation, with an R2 value of 0.46. In addition, a comparative analysis showed that the UAV hyperspectral data outperformed both satellite sources due to better spatial and spectral resolution. In addition, the Landsat-8/9 data outperformed the Sentinel-2 data in terms of SMC estimation accuracy. For the different land-cover types, all types of remote-sensing data showed the highest accuracy for bare soil compared to cropland and grassland. This research highlights the potential of integrating UAV-based spectroscopy and machine-learning techniques as complementary tools to satellite platforms for precise SMC monitoring. The findings contribute to the further development of remote-sensing methods and improve the understanding of SMC dynamics in heterogeneous landscapes, with significant implications for precision agriculture. By enhancing the SMC estimation accuracy at high spatial resolution, this approach can optimize irrigation practices, improve cropping strategies and contribute to sustainable agricultural practices, ultimately enabling better decision-making for farmers and land managers. However, its broader applicability depends on factors such as scalability and performance under different conditions. Full article
(This article belongs to the Special Issue Applications of Multi-Source Remote Sensing Technologies in Soil Moisture Monitoring)
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17 pages, 1247 KiB  
Article
Soil Moisture Retrieval in North America with Passive Microwave and Auxiliary Data Based on Variable Spatial Optimization
by Qixin Liu, Huishi Du, Yulin Zhan and Faisal Mumtaz
Water 2025, 17(11), 1604; https://doi.org/10.3390/w17111604 - 26 May 2025
Viewed by 322
Abstract
Soil moisture content (SMC) is critical in hydrological, agricultural, and meteorological research. There is an urgent need for spatiotemporal information on accurate SMC distribution on a large scale. Passive microwave remote sensing data are among the most commonly used sources for soil moisture [...] Read more.
Soil moisture content (SMC) is critical in hydrological, agricultural, and meteorological research. There is an urgent need for spatiotemporal information on accurate SMC distribution on a large scale. Passive microwave remote sensing data are among the most commonly used sources for soil moisture retrieval. However, due to the high spatial heterogeneity of SMC and the low spatial resolution of passive microwave data, the SMC condition in the pixel of passive microwave data is rather complex. We propose a method incorporating spatially optimized auxiliary data related to land cover and normalized difference vegetation index (NDVI) to represent the SMC spatial heterogeneity. New variables, “percentages of typical land cover classes” and “average NDVIs corresponding to typical land cover classes”, were introduced. Random forest was adopted to construct an SMC retrieving model. The results of testing samples showed that after “percentages of typical land cover classes” were added into the input parameters, the maximum rise of correlation coefficient (r) was 0.114, and the ultimate decline of unbiased root mean square error (ubRMSE) was 0.0239 cm3cm−3. Similarly, substituting NDVI with “average NDVIs corresponding to typical land cover classes” increasesd r by 0.023, and ubRMSE declined by 0.0042 cm3cm−3 at most. For the optimal situation, where both groups of new variables were applied, the highest rise of r is 0.127, and the maximum decrease of ubRMSE is 0.0277 cm3cm−3. Full article
(This article belongs to the Special Issue Applications of Multi-Source Remote Sensing Technologies in Soil Moisture Monitoring)
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