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Recent Advances in Remote Sensing of Soil Moisture
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Recent Advances in Remote Sensing of Soil Moisture

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 5765

Special Issue Editors

Dr. Indishe Senanayake

E-Mail Website
Guest Editor
School of Engineering, The University of Newcastle, Callaghan, Australia
Interests: remote sensing; GIS; natural resource management; soil moisture
Dr. Natthachet Tangdamrongsub

E-Mail Website
Guest Editor
Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani, Thailand
Interests: satellite gravimetry; remote sensing; data assimilation; land surface and hydrology modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Fang

E-Mail Website
Guest Editor
Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA
Interests: remote sensing; soil moisture; hydrology

Special Issue Information

Dear Colleagues,

Soil moisture is a key variable in a number of environmental processes, at both regional and global scales, due to its contribution to water, carbon and energy cycles. Therefore, soil moisture information is important for a wide range of applications, including hydrology, climatology and agriculture. However, obtaining reliable soil moisture information at the required spatial/temporal resolution and along soil depth with a high accuracy level is still challenging, especially due to highly variable soil moisture behavior on its spatiotemporal domains and its complex relationships with forcing factors such as vegetation, soil texture, topography and meteorology.

Advancements in both active and passive remote sensing technologies, satellite remote sensing, drone technologies and data assimilation methods have been able to provide soil moisture estimations at different spatial scales from meters to tens of kilometers, as well as temporal resolutions from regional to global coverage. Data from passive microwave instruments, such as the multifrequency AMSR-E/2, FY-3 MWRI, L-band SMOS and SMAP, and active microwave instruments, including ASCAT/MetOp, ALOS-2, Sentinel-1 and the P-band GRACE, have in the last two decades been widely used for soil moisture applications at different spatial scales.

This Special Issue aims to encourage the submission of studies covering recent advances in remote sensing in soil moisture. We welcome high-quality studies covering a wide-range of topics, including novel soil moisture missions, datasets and resolution enhancement algorithms, including spatiotemporal fusion techniques of different datasets, validation methods and novel applications.

Topics may address, but are not limited to, the following:

  • Retrieval algorithms;
  • Calibration and validation methods;
  • Data integration and assimilation methods;
  • Downscaling methods;
  • Data fusion methods to build time-dense datasets;
  • Novel applications of remotely sensed soil moisture products.

Dr. Indishe Senanayake
Dr. Natthachet Tangdamrongsub
Dr. Bin Fang
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
  • remote sensing
  • retrieval algorithms
  • downscaling
  • microwave remote sensing
  • SMAP
  • SMOS
  • hydrology

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Published Papers (3 papers)

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Research

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23 pages, 9445 KiB  
Article
Evaluation of an Adaptive Soil Moisture Bias Correction Approach in the ECMWF Land Data Assimilation System
by David Fairbairn, Patricia de Rosnay and Peter Weston
Remote Sens. 2024, 16(3), 493; https://doi.org/10.3390/rs16030493 - 27 Jan 2024
Cited by 1 | Viewed by 1053
Abstract
Satellite-derived soil moisture (SM) observations are widely assimilated in global land data assimilation systems. These systems typically assume zero-mean errors in the land surface model and observations. In practice, systematic differences (biases) exist between the observed and modelled SM. Commonly, the observed SM [...] Read more.
Satellite-derived soil moisture (SM) observations are widely assimilated in global land data assimilation systems. These systems typically assume zero-mean errors in the land surface model and observations. In practice, systematic differences (biases) exist between the observed and modelled SM. Commonly, the observed SM biases are removed by rescaling techniques or via a machine learning approach. However, these methods do not account for non-stationary biases, which can result from issues with the satellite retrieval algorithms or changes in the land surface model. Therefore, we test a novel application of adaptive SM bias correction (BC) in the European Centre for Medium Range Weather Forecasts (ECMWF) land data assimilation system. A two-stage filter is formulated to dynamically correct biases from satellite-derived active ASCAT C-band and passive L-band SMOS surface SM observations. This complements the operational seasonal rescaling of the ASCAT observations and the SMOS neural network retrieval while allowing the assimilation to correct subseasonal-scale errors. Experiments are performed on the ECMWF stand-alone surface analysis, which is a simplified version of the integrated forecasting system. Over a 3 year test period, the adaptive BC reduces the seasonal-scale (observation−forecast) departures by up to 20% (30%) for the ASCAT (SMOS). The adaptive BC leads to (1) slight improvements in the SM analysis performance and (2) moderate but statistically significant reductions in the 1–5 day relative humidity forecast errors in the boundary layer of the Northern Hemisphere midlatitudes. Future work will test the adaptive SM BC in the full integrated forecasting system. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Moisture)
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Review

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30 pages, 4005 KiB  
Review
Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review
by Indishe P. Senanayake, Kalani R. L. Pathira Arachchilage, In-Young Yeo, Mehdi Khaki, Shin-Chan Han and Peter G. Dahlhaus
Remote Sens. 2024, 16(12), 2067; https://doi.org/10.3390/rs16122067 - 7 Jun 2024
Viewed by 1653
Abstract
Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM [...] Read more.
Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuous global-scale SM information. However, the coarse spatial resolution of these L-band microwave SM retrievals poses uncertainties in many regional- and local-scale SM applications which require a high amount of spatial details. Numerous studies have been conducted to develop downscaling algorithms to enhance the spatial resolution of coarse-resolution satellite-derived SM datasets. Machine Learning (ML)-based downscaling models have gained prominence recently due to their ability to capture non-linear, complex relationships between SM and its driving factors, such as vegetation, surface temperature, topography, and climatic conditions. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling. The usage of classical, ensemble, neural nets, and deep learning methods to downscale SM products and the comparison of multiple algorithms are detailed in this paper. Insights into the significance of surface ancillary variables for model accuracy and the improvements made to ML-based SM downscaling approaches are also discussed. Overall, this paper provides useful insights for future studies on developing reliable, high-spatial-resolution SM datasets using ML-based algorithms. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Moisture)
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29 pages, 4564 KiB  
Review
Recent Advances in Dielectric Properties-Based Soil Water Content Measurements
by Mukhtar Iderawumi Abdulraheem, Hongjun Chen, Linze Li, Abiodun Yusuff Moshood, Wei Zhang, Yani Xiong, Yanyan Zhang, Lateef Bamidele Taiwo, Aitazaz A. Farooque and Jiandong Hu
Remote Sens. 2024, 16(8), 1328; https://doi.org/10.3390/rs16081328 - 10 Apr 2024
Cited by 2 | Viewed by 2201
Abstract
Dielectric properties are crucial in understanding the behavior of water within soil, particularly the soil water content (SWC), as they measure a material’s ability to store an electric charge and are influenced by water and other minerals in the soil. However, a comprehensive [...] Read more.
Dielectric properties are crucial in understanding the behavior of water within soil, particularly the soil water content (SWC), as they measure a material’s ability to store an electric charge and are influenced by water and other minerals in the soil. However, a comprehensive review paper is needed that synthesizes the latest developments in this field, identifies the key challenges and limitations, and outlines future research directions. In addition, various factors, such as soil salinity, temperature, texture, probing space, installation gap, density, clay content, sampling volume, and environmental factors, influence the measurement of the dielectric permittivity of the soil. Therefore, this review aims to address the research gap by critically analyzing the current state-of-the-art dielectric properties-based methods for SWC measurements. The motivation for this review is the increasing importance of precise SWC data for various applications such as agriculture, environmental monitoring, and hydrological studies. We examine time domain reflectometry (TDR), frequency domain reflectometry (FDR), ground-penetrating radar (GPR), remote sensing (RS), and capacitance, which are accurate and cost-effective, enabling real-time water resource management and soil health understanding through measuring the travel time of electromagnetic waves in soil and the reflection coefficient of these waves. SWC can be estimated using various approaches, such as TDR, FDR, GPR, and microwave-based techniques. These methods are made possible by increasing the dielectric permittivity and loss factor with SWC. The available dielectric properties are further synthesized on the basis of mathematical models relating apparent permittivity to water content, providing an updated understanding of their development, applications, and monitoring. It also analyzes recent mathematical calibration models, applications, algorithms, challenges, and trends in dielectric permittivity methods for estimating SWC. By consolidating recent advances and highlighting the remaining challenges, this review article aims to guide researchers and practitioners toward more effective strategies for SWC measurements. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Moisture)
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