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Microwave Remote Sensing of Soil Moisture

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 24647

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Special Issue Editors

Dr. Jiangyuan Zeng

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Guest Editor

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing; soil moisture; hydrology; radar and radiometry; water cycle; climate change
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Prof. Dr. Jian Peng

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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
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Dr. Wei Zhao

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Guest Editor

Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Interests: mountain water and heat exchange; soil moisture estimation and downscaling; mountain climate change
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Dr. Chunfeng Ma

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Guest Editor

Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Heihe Remote Sensing Experimental Research Station, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: remote sensing; soil moisture; ecohydrology; precision agriculture; uncertainty quantification
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Dr. Hongliang Ma

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Guest Editor

INRAE, UMR 1114 EMMAH, UMT CAPTE, F-84000 Avignon, France
Interests: microwave/optical remote sensing; soil moisture; vegetation water/biomass; L-MEB; PROSAIL; hydro-ecological applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil moisture is well recognized as a pivotal parameter to link the water, energy, and carbon cycles. Active and passive microwave remote sensing has been well-recognized as the most promising means to infer soil moisture spatially and temporally. Active microwave remote sensing, particularly the synthetic aperture radar (SAR), has a much finer spatial resolution than passive sensors but suffers more from geometrical features of the scene (e.g., surface roughness, vegetation, and topography). Passive microwave remote sensing has higher sensitivity to soil moisture than active radar but is limited by its coarse spatial resolution. Moreover, active and passive microwave signals respond differently to soil and vegetation parameters and thus can provide complementary information for each other.

Over the past several decades, great progress has been made in microwave remote sensing of soil moisture. Several field or aircraft experiments (e.g., SGP, SMEX, HiWATER, SMAPEx1-5, and SMAPVEX) have been organized to support the assessment and refinement of active and passive microwave soil moisture retrieval algorithms. At the same time, a number of microwave spaceborne satellites/sensors have been successfully launched to provide valuable opportunities to obtain soil moisture with various spatial scales from meters to tens of kilometers. These include the passive microwave instruments, such as the multi-frequency AMSR-E/2 (2002-), FY-3 MWRI (2008-), L-band SMOS (2009-), and SMAP (2015-), as well as the active microwave instruments, such as the scatterometer-based Metop/ASCAT series (2006-), monostatic ALOS-2 (2014-), Sentinel-1 (2014-), and Gaofen-3 (2016-), bistatic CYGNSS (2016-), and the P-band Biomass (planned launch in 2023). All of these open a wide range of possibilities to estimate soil moisture at regional and global scales. In this context, this Special Issue aims to present the most advanced theories, models, algorithms, and products related to microwave remote sensing of soil moisture.

The topics of the Special Issue include, but are not limited to, the following:

Review on microwave remote sensing of soil moisture;
Introduction to field or aircraft experiments and future satellite missions for soil moisture;
Evaluation or comparison of remotely sensed soil moisture products using in situ measurements, model simulations, or other mathematical approaches (e.g., TCA, TCH, IVd, etc.);
Development, calibration, or validation of the theoretical or semi-empirical forward models (e.g., microwave scattering model and radiative transfer model) used for soil moisture retrieval;
Development, improvement, or comparison of remotely sensed soil moisture retrieval algorithms;
Development, improvement, or comparison of spatial downscaling/upscaling methods and spatiotemporal fusion techniques of remotely sensed soil moisture;
Application of remotely sensed soil moisture products in data assimilation, agriculture, ecology, hydrology, and other fields.

Dr. Jiangyuan Zeng
Prof. Dr. Jian Peng
Dr. Wei Zhao
Dr. Chunfeng Ma
Dr. Hongliang Ma
Guest Editors

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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.

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Keywords

soil moisture
active microwave remote sensing
passive microwave remote sensing
product validation and error analysis
retrieval algorithms
downscaling/upscaling methods
spatiotemporal fusion techniques
GNSS-R
data assimilation
eco-hydrological applications

Related Special Issue

Microwave Remote Sensing of Soil Moisture II in Remote Sensing (1 article)

Published Papers (11 papers)

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Editorial

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5 pages, 555 KiB

Open AccessEditorial

Microwave Remote Sensing of Soil Moisture

by Jiangyuan Zeng, Jian Peng, Wei Zhao, Chunfeng Ma and Hongliang Ma

Remote Sens. 2023, 15(17), 4243; https://doi.org/10.3390/rs15174243 - 29 Aug 2023

Viewed by 1696

Abstract

Soil moisture is an important component of the global terrestrial ecosystem and has been recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS) [...] Full article

(This article belongs to the Special Issue Microwave Remote Sensing of Soil Moisture)

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Research

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24 pages, 7594 KiB

Open AccessArticle

The Characterization of the Vertical Distribution of Surface Soil Moisture Using ISMN Multilayer In Situ Data and Their Comparison with SMOS and SMAP Soil Moisture Products

by Na Yang, Feng Xiang and Hengjie Zhang

Remote Sens. 2023, 15(16), 3930; https://doi.org/10.3390/rs15163930 - 08 Aug 2023

Cited by 1 | Viewed by 820

Abstract

In this paper, we investigated the vertical distribution characteristics of surface soil moisture based on ISMN (International Soil Moisture Network) multilayer in situ data (5, 10, and 20 cm; 2, 4, and 8 in) and performed comparisons between the in situ data and four microwave satellite remote sensing products (SMOS L2, SMOS-IC, SMAP L2, and SMAP L4). The results showed that the mean soil moisture difference between layers can be −0.042~−0.024 (for the centimeter group)/−0.067~−0.044 (for the inch group) m³/m³ in negative terms and 0.020~0.028 (for the centimeter group)/0.036~0.040 (for the inch group) m³/m³ in positive terms. The surface soil moisture was found to have very significant stratification characteristics, and the interlayer difference was close to or beyond the SMOS and SMAP 0.04 m³/m³ nominal retrieval accuracy. Comparisons revealed that the satellite retrievals had a higher correlation with the field measurements of 5 cm/2 in, and SMAP L4 had the smallest difference with the in situ data. The mean difference caused by using 10 cm/4 in and 20 cm/8 in in situ data instead of the 5 cm/2 in data could be about −0.019~−0.018/−0.18~−0.015 m³/m³ and −0.026~−0.023/−0.043~−0.039 m³/m³, respectively, meaning that there would be a potential depth mismatch in the data validation. Full article

(This article belongs to the Special Issue Microwave Remote Sensing of Soil Moisture)

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19 pages, 3724 KiB

Open AccessArticle

A Performance Analysis of Soil Dielectric Models over Organic Soils in Alaska for Passive Microwave Remote Sensing of Soil Moisture

by Runze Zhang, Steven Chan, Rajat Bindlish and Venkataraman Lakshmi

Remote Sens. 2023, 15(6), 1658; https://doi.org/10.3390/rs15061658 - 19 Mar 2023

Cited by 1 | Viewed by 1511

Abstract

Passive microwave remote sensing of soil moisture (SM) requires a physically based dielectric model that quantitatively converts the volumetric SM into the soil bulk dielectric constant. Mironov 2009 is the dielectric model used in the operational SM retrieval algorithms of the NASA Soil Moisture Active Passive (SMAP) and the ESA Soil Moisture and Ocean Salinity (SMOS) missions. However, Mironov 2009 suffers a challenge in deriving SM over organic soils, as it does not account for the impact of soil organic matter (SOM) on the soil bulk dielectric constant. To this end, we presented a comparative performance analysis of nine advanced soil dielectric models over organic soil in Alaska, four of which incorporate SOM. In the framework of the SMAP single-channel algorithm at vertical polarization (SCA-V), SM retrievals from different dielectric models were derived using an iterative optimization scheme. The skills of the different dielectric models over organic soils were reflected by the performance of their respective SM retrievals, which was measured by four conventional statistical metrics, calculated by comparing satellite-based SM time series with in-situ benchmarks. Overall, SM retrievals of organic-soil-based dielectric models tended to overestimate, while those from mineral-soil-based models displayed dry biases. All the models showed comparable values of unbiased root-mean-square error (ubRMSE) and Pearson Correlation (R), but Mironov 2019 exhibited a slight but consistent edge over the others. An integrated consideration of the model inputs, the physical basis, and the validated accuracy indicated that the separate use of Mironov 2009 and Mironov 2019 in the SMAP SCA-V for mineral soils (SOM

<

15%) and organic soils (SOM

\geq

15%) would be the preferred option. Full article

(This article belongs to the Special Issue Microwave Remote Sensing of Soil Moisture)

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21 pages, 25085 KiB

Open AccessArticle

Surface Soil Moisture Retrieval on Qinghai-Tibetan Plateau Using Sentinel-1 Synthetic Aperture Radar Data and Machine Learning Algorithms

by Leilei Dong, Weizhen Wang, Rui Jin, Feinan Xu and Yang Zhang

Remote Sens. 2023, 15(1), 153; https://doi.org/10.3390/rs15010153 - 27 Dec 2022

Cited by 9 | Viewed by 1991

Abstract

Soil moisture is a key factor in the water and heat exchange and energy transformation of the ecological systems and is of critical importance to the accurate obtainment of the soil moisture content for supervising water resources and protecting regional and global eco environments. In this study, we selected the soil moisture monitoring networks of Naqu, Maqu, and Tianjun on the Qinghai–Tibetan Plateau as the research areas, and we established a database of surface microwave scattering with the AIEM (advanced integral equation model) and the mathematical expressions for the backscattering coefficient, soil moisture, and surface roughness of the VV and VH polarizations.We proposed the soil moisture retrieval models of empirical and machine learnings algorithms (backpropagation neural network (BPNN), support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF)) for the ascending and descending orbits using Sentinel-1 and measurement data, and we also validated the accuracies of the retrieval model in the research areas. According to the results, there is a substantial logarithmic correlation among the backscattering coefficient, soil moisture, and combined roughness. Generally, we can use empirical models to estimate the soil moisture content, with an R² of 0.609, RMSE of 0.08, and MAE of 0.064 for the ascending orbit model and an R² of 0.554, RMSE of 0.086, and MAE of 0.071 for the descending orbit model. The soil moisture contents are underestimated when the volumetric water content is high. The soil moisture retrieval accuracy is improved with machine learning algorithms compared to the empirical model, and the performance of the RF algorithm is superior to those of the other machine learning algorithms. The RF algorithm also achieved satisfactory performances for the Maqu and Tianjun networks. The accuracies of the inversion models for the ascending orbit in the three soil moisture monitoring networks were better than those for the descending orbit. Full article

(This article belongs to the Special Issue Microwave Remote Sensing of Soil Moisture)

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22 pages, 8824 KiB

Open AccessArticle

A Novel Freeze-Thaw State Detection Algorithm Based on L-Band Passive Microwave Remote Sensing

by Shaoning Lv, Jun Wen, Clemens Simmer, Yijian Zeng, Yuanyuan Guo and Zhongbo Su

Remote Sens. 2022, 14(19), 4747; https://doi.org/10.3390/rs14194747 - 22 Sep 2022

Cited by 4 | Viewed by 1512

Abstract

Knowing the freeze-thaw (FT) state of the land surface is essential for many aspects of weather forecasting, climate, hydrology, and agriculture. Microwave L-band emission contains rather direct information about the FT-state because of its impact on the soil dielectric constant, which determines microwave emissivity and the optical depth profile. However, current L-band-based FT algorithms need reference values to distinguish between frozen and thawed soil, which are often not well known. We present a new FT-state-detection algorithm based on the daily variation of the H-polarized brightness temperature of the SMAP L3c FT global product for the northern hemisphere, which is available from 2015 to 2021. Exploiting the daily variation signal allows for a more reliable state detection, particularly during the transition periods, when the near-surface soil layer may freeze and thaw on sub-daily time scales. The new algorithm requires no reference values; its results agree with the SMAP FT state product by up to 98% in summer and up to 75% in winter. Compared to the FT state inferred indirectly from the 2-m air temperature and collocated soil temperature at 0–7 cm of the ERA5-land reanalysis, the new FT algorithm has a similar performance to the SMAP FT product. The most significant differences occur over the midlatitudes, including the Tibetan plateau and its downstream area. Here, daytime surface heating may lead to daily FT transitions, which are not considered by the SMAP FT state product but are correctly identified by the new algorithm. The new FT algorithm suggests a 15 days earlier start of the frozen-soil period than the ERA5-land’s estimate. This study is expected to extend the L-band microwave remote sensing data for improved FT detection. Full article

(This article belongs to the Special Issue Microwave Remote Sensing of Soil Moisture)

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23 pages, 8405 KiB

Open AccessArticle

Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping

by Zebin Zhao, Rui Jin, Jian Kang, Chunfeng Ma and Weizhen Wang

Remote Sens. 2022, 14(14), 3373; https://doi.org/10.3390/rs14143373 - 13 Jul 2022

Cited by 1 | Viewed by 1699

Abstract

Soil moisture is one of the core hydrological and climate variables that crucially influences water and energy budgets. The spatial resolution of available soil moisture products is generally coarser than 25 km, which limits their hydro-meteorological and eco-hydrological applications and the management of water resources at watershed and agricultural scales. A feasible solution to overcome these limitations is to downscale coarse soil moisture products with the support of higher-resolution spatial information. Although many auxiliary variables have been used for this purpose, few studies have analyzed their applicability and effectiveness in arid regions. To this end, we comprehensively evaluated four commonly used auxiliary variables, including NDVI (Normalized Difference Vegetation Index), LST (Land Surface Temperature), TVDI (Temperature Vegetation Dryness Index), and SEE (Soil Evaporative Efficiency), against ground-based soil moisture observations during the vegetation growing season in the Heihe River Basin, China. Performance metrics indicated that SEE is most sensitive (R² ≥ 0.67) to soil moisture because it is controlled by soil evaporation limited by the available soil moisture. The similarity of spatial patterns also showed that SEE best captures soil moisture changes, with the STD (standard deviation) of the HD (Hausdorff Distance) less than 0.058 when compared with PLMR (Polarimetric L-band Multi-beam Radiometer) soil moisture products. In addition, soil moisture was mapped by RF (Random Forests) using both single auxiliary variables and 11 types of multiple auxiliary variable combinations. SEE was found to be the best auxiliary variable for scaling and mapping soil moisture with accuracy of 0.035 cm³/cm³. Among the multiple auxiliary variables, the combination of LST, NDVI, and SEE was found to best enhance the scaling and mapping accuracy of soil moisture with 0.034 cm³/cm³. Full article

(This article belongs to the Special Issue Microwave Remote Sensing of Soil Moisture)

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20 pages, 3774 KiB

Open AccessArticle

Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework

by Ricardo M. Llamas, Leobardo Valera, Paula Olaya, Michela Taufer and Rodrigo Vargas

Remote Sens. 2022, 14(13), 3137; https://doi.org/10.3390/rs14133137 - 29 Jun 2022

Cited by 3 | Viewed by 2172

Abstract

Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor <KKNN> and Random Forest <RF>). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of independent validation. We postulate that more heterogeneous landscapes (i.e., high topographic variation) may be more challenging for downscaling and predicting soil moisture; therefore, moisture networks should increase monitoring efforts across these complex landscapes. Future opportunities for development of modular cyberinfrastructure tools for downscaling satellite-derived soil moisture are discussed. Full article

(This article belongs to the Special Issue Microwave Remote Sensing of Soil Moisture)

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21 pages, 4053 KiB

Open AccessArticle

A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau

by Hongtao Jiang, Sanxiong Chen, Xinghua Li, Jingan Wu, Jing Zhang and Longfeng Wu

Remote Sens. 2022, 14(12), 2902; https://doi.org/10.3390/rs14122902 - 17 Jun 2022

Cited by 2 | Viewed by 1642

Abstract

The coarse scale of passive microwave surface soil moisture (SSM) is not suitable for regional agricultural and hydrological applications such as drought monitoring and irrigation management. The optical/thermal infrared (OTI) data-based passive microwave SSM downscaling method can effectively improve its spatial resolution to fine scale for regional applications. However, the estimation capability of SSM with long time series is limited by OTI data, which are heavily polluted by clouds. To reduce the dependence of the method on OTI data, an SSM retrieval and spatio-temporal fusion model (SMRFM) is proposed in the study. Specifically, a model coupling in situ data, MODerate-resolution Imaging Spectro-radiometer (MODIS) OTI data, and topographic information is developed to retrieve MODIS SSM (1 km) using the least squares method. Then the retrieved MODIS SSM and the spatio-temporal fusion model are employed to downscale the passive microwave SSM from coarse scale to 1 km. The proposed SMRFM is implemented in a grassland dominated area over Naqu, central Tibet Plateau, for Advanced Microwave Scanning Radiometer—Earth Observing System sensor (AMSR-E) SSM downscaling in unfrozen period. The in situ SSM and Noah land surface model 0.01° SSM are used to validate the estimated MODIS SSM with long time series. The evaluations show that the estimated MODIS SSM has the same temporal resolution with AMSR-E and obtains significantly improved detailed spatial information. Moreover, the temporal accuracy of estimated MODIS SSM against in situ data (r = 0.673, μbRMSE = 0.070 m³/m³) is better than the AMSR-E (r = 0.661, μbRMSE = 0.111 m³/m³). In addition, the temporal r of estimated MODIS SSM is obviously higher than that of Noah data. Therefore, this suggests that the SMRFM can be used to estimate MODIS SSM with long time series by AMSR-E SSM downscaling in the study. Overall, the study can provide help for the development and application of microwave SSM-related scientific research at the regional scale. Full article

(This article belongs to the Special Issue Microwave Remote Sensing of Soil Moisture)

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16 pages, 6227 KiB

Open AccessArticle

Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation

by Simon Nativel, Emna Ayari, Nemesio Rodriguez-Fernandez, Nicolas Baghdadi, Remi Madelon, Clement Albergel and Mehrez Zribi

Remote Sens. 2022, 14(10), 2434; https://doi.org/10.3390/rs14102434 - 19 May 2022

Cited by 7 | Viewed by 4484

Abstract

Soil moisture is an essential parameter for a better understanding of water processes in the soil–vegetation–atmosphere continuum. Satellite synthetic aperture radar (SAR) is well suited for monitoring water content at fine spatial resolutions on the order of 1 km or higher. Several methodologies are often considered in the inversion of SAR signals: machine learning techniques, such as neural networks, empirical models and change detection methods. In this study, we propose two hybrid methodologies by improving a change detection approach with vegetation consideration or by combining a change detection approach together with a neural network algorithm. The methodology is based on Sentinel-1 and Sentinel-2 data with the use of numerous metrics, including vertical–vertical (VV) and vertical–horizontal (VH) polarization radar signals, the classical change detection surface soil moisture (SSM) index

I_{SSM}

, radar incidence angle, normalized difference vegetation index (NDVI) optical index, and the VH/VV ratio. Those approaches are tested using in situ data from the ISMN (International Soil Moisture Network) with observations covering different climatic contexts. The results show an improvement in soil moisture estimations using the hybrid algorithms, in particular the change detection with the neural network one, for which the correlation increases by 54% and 33% with respect to that of the neural network or change detection alone, respectively. Full article

(This article belongs to the Special Issue Microwave Remote Sensing of Soil Moisture)

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Review

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21 pages, 5375 KiB

Open AccessReview

Recent Progress on Modeling Land Emission and Retrieving Soil Moisture on the Tibetan Plateau Based on L-Band Passive Microwave Remote Sensing

by Xiaojing Wu and Jun Wen

Remote Sens. 2022, 14(17), 4191; https://doi.org/10.3390/rs14174191 - 25 Aug 2022

Cited by 2 | Viewed by 1344

Abstract

L-band passive microwave remote sensing (RS) is an important tool for monitoring global soil moisture (SM) and freeze/thaw state. In recent years, progress has been made in its in-depth application and development in the Tibetan Plateau (TP) which has a complex natural environment. This paper systematically reviews and summarizes the research progress and the main applications of L-band passive microwave RS observations and associated SM retrievals on the TP. The progress of observing and simulating L-band emission based on ground-, aircraft-based and spaceborne platforms, developing regional-scale SM observation networks, as well as validating satellite-based SM products and developing SM retrieval algorithms are reviewed. On this basis, current problems of L-band emission simulation and SM retrieval on the TP are outlined, such as the fact that current evaluations of SM products are limited to a short-term period, and evaluation and improvement of the forward land emission model and SM retrieval algorithm are limited to the site or grid scale. Accordingly, relevant suggestions and prospects for addressing the abovementioned existing problems are finally put forward. For future work, we suggest (i) sorting out the in situ observations and conducting long-term trend evaluation and analysis of current L-band SM products, (ii) extending current progress made at the site/grid scale to improve the L-band emission simulation and SM retrieval algorithms and products for both frozen and thawed ground at the plateau scale, and (iii) enhancing the application of L-band satellite-based SM products on the TP by implementing methods such as data assimilation to improve the understanding of plateau-scale water cycle and energy balance. Full article

(This article belongs to the Special Issue Microwave Remote Sensing of Soil Moisture)

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32 pages, 1124 KiB

Open AccessReview

Advances in the Quality of Global Soil Moisture Products: A Review

by Yangxiaoyue Liu and Yaping Yang

Remote Sens. 2022, 14(15), 3741; https://doi.org/10.3390/rs14153741 - 04 Aug 2022

Cited by 11 | Viewed by 2666

Abstract

Soil moisture is a crucial component of land–atmosphere interaction systems. It has a decisive effect on evapotranspiration and photosynthesis, which then notably impacts the land surface water cycle, energy transfer, and material exchange. Thus, soil moisture is usually treated as an indispensable parameter in studies that focus on drought monitoring, climate change, hydrology, and ecology. After consistent efforts for approximately half a century, great advances in soil moisture retrieval from in situ measurements, remote sensing, and reanalysis approaches have been achieved. The quality of soil moisture estimates, including spatial coverage, temporal span, spatial resolution, time resolution, time latency, and data precision, has been remarkably and steadily improved. This review outlines the recently developed techniques and algorithms used to estimate and improve the quality of soil moisture estimates. Moreover, the characteristics of each estimation approach and the main application fields of soil moisture are summarized. The future prospects of soil moisture estimation trends are highlighted to address research directions in the context of increasingly comprehensive application requirements. Full article

(This article belongs to the Special Issue Microwave Remote Sensing of Soil Moisture)

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