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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

Published Papers (2 papers)

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Research

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23 pages, 9445 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/rs16030493 - 27 Jan 2024

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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 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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29 pages, 4564 KiB

Open AccessReview

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://0-doi-org.brum.beds.ac.uk/10.3390/rs16081328 - 10 Apr 2024

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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 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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