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

Deadline for manuscript submissions: 20 April 2025 | Viewed by 29836

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Dr. Hui Lu

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Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Interests: microwave remote sensing; soil moisture; land surface data assimilation; hydrological model; climate change
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Dr. Hongquan Wang

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Guest Editor

1. Department of Environement and Resource Sciences, Zhejiang University, HangZhou, China
2. Center for Research and Application of Remote Sensing (CARTEL), University of Sherbrooke, Sherbrooke, QC, Canada
Interests: remote sensing
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Dr. Lun Gao

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Guest Editor

Saint Anthony Falls Laboratory, Department of Civil Environmental and Geo-Engineering, University of Minnesota, Minneapolis, MN 55414, USA
Interests: microwave remote sensing; ecohydrology

Dr. Xiaojun Li

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Guest Editor

INRAE, UMR1391 ISPA, F-33140 Villenave d'Ornon, France
Interests: microwave soil moisture modeling; validation; carbon cycle estimation
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Special Issue Information

Dear Colleagues,

As an essential hydrologic state variable in the Earth system, soil moisture plays an important role in modulating water and energy exchange within the soil–vegetation–atmosphere continuum, from a watershed to a global scale, largely through controlling the partitioning of precipitation into evapotranspiration, surface runoff, and infiltration. The global monitoring of soil moisture from space is important for improved land and weather forecasts, and the understanding of water, energy, and carbon cycles, as well as the improved management of water and food resources. Today, multiple space-borne platforms, such as the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite and NASA's Soil Moisture Active Passive (SMAP) satellite, provide an unprecedented opportunity to estimate soil moisture. However, the retrieval of soil moisture remains challenging due to limited satellite observations, the high correlation between different polarizations, angles, and channels, as well as uncertainties in radiative transfer models and ancillary datasets.

Therefore, this Special Issue aims to collect articles concerning, but not limited to, the following:

Advancing remote sensing techniques in retrieving soil moisture and/or relevant parameters, such as vegetation optical depth, scattering albedo, and surface roughness
Validation/comparison of soil moisture products
Airborne calibration/validation experiments
Assimilating soil moisture into hydrological/atmospheric/vegetation models
Integration of remote sensing and in situ observations
Downscaling soil moisture products

Dr. Hui Lu
Dr. Hongquan Wang
Dr. Lun Gao
Dr. Xiaojun Li
Guest Editors

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Keywords

Microwave Remote Sensing
Optical Remote Sensing
Soil Moisture
Vegetation Optical Depth
SMOS
SMAP

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Published Papers (10 papers)

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Research

16 pages, 4039 KiB

Open AccessArticle

A Soil Moisture and Vegetation-Based Susceptibility Mapping Approach to Wildfire Events in Greece

by Kyriakos Chaleplis, Avery Walters, Bin Fang, Venkataraman Lakshmi and Alexandra Gemitzi

Remote Sens. 2024, 16(10), 1816; https://doi.org/10.3390/rs16101816 - 20 May 2024

Cited by 2 | Viewed by 1158

Abstract

Wildfires in Mediterranean areas are becoming more frequent, and the fire season is extending toward the spring and autumn months. These alarming findings indicate an urgent need to develop fire susceptibility methods capable of identifying areas vulnerable to wildfires. The present work aims to uncover possible soil moisture and vegetation condition precursory signals of the largest and most devastating wildfires in Greece that occurred in 2021, 2022, and 2023. Therefore, the time series of two remotely sensed datasets–MAP L4 Soil Moisture (SM) and Landsat 8 NDVI, which represent vegetation and soil moisture conditions—were examined before five destructive wildfires in Greece during the study period. The results of the analysis highlighted specific properties indicative of fire-susceptible areas. NDVI in all fire-affected areas ranged from 0.13 to 0.35, while mean monthly soil moisture showed negative anomalies in the spring periods preceding fires. Accordingly, fire susceptibility maps were developed, verifying the usefulness of remotely sensed information related to soil moisture and NDVI. This information should be used to enhance fire models and identify areas at risk of wildfires in the near future. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications)

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24 pages, 13401 KiB

Open AccessArticle

A Spatial Downscaling Framework for SMAP Soil Moisture Based on Stacking Strategy

by Jiaxin Xu, Qiaomei Su, Xiaotao Li, Jianwei Ma, Wenlong Song, Lei Zhang and Xiaoye Su

Remote Sens. 2024, 16(1), 200; https://doi.org/10.3390/rs16010200 - 3 Jan 2024

Cited by 4 | Viewed by 2084

Abstract

Soil moisture (SM) data can provide guidance for decision-makers in fields such as drought monitoring and irrigation management. Soil Moisture Active Passive (SMAP) satellite offers sufficient spatial resolution for global-scale applications, but its utility is limited in regional areas due to its lower spatial resolution. To address this issue, this study proposed a downscaling framework based on the Stacking strategy. The framework integrated extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) to generate 1 km resolution SM data using 15 high-resolution factors derived from multi-source datasets. In particular, to test the influence of terrain partitioning on downscaling results, Anhui Province, which has diverse terrain features, was selected as the study area. The results indicated that the performance of the three base models varied, and the developed Stacking strategy maximized the potential of each model with encouraging downscaling results. Specifically, we found that: (1) The Stacking model achieved the highest accuracy in all regions, and the performance order of the base models was: XGBoost > CatBoost > LightGBM. (2) Compared with the measured SM at 87 sites, the downscaled SM outperformed other 1 km SM products as well as the downscaled SM without partitioning, with an average ubRMSE of 0.040 m³/m³. (3) The downscaled SM responded positively to rainfall events and mitigated the systematic bias of SMAP. It also preserved the spatial trend of the original SMAP, with higher levels in the humid region and relatively lower levels in the semi-humid region. Overall, this study provided a new strategy for soil moisture downscaling and revealed some interesting findings related to the effectiveness of the Stacking model and the impact of terrain partitioning on downscaling accuracy. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications)

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19 pages, 3434 KiB

Open AccessArticle

Retrieval of Surface Soil Moisture over Wheat Fields during Growing Season Using C-Band Polarimetric SAR Data

by Kalifa Goïta, Ramata Magagi, Vincent Beauregard and Hongquan Wang

Remote Sens. 2023, 15(20), 4925; https://doi.org/10.3390/rs15204925 - 12 Oct 2023

Cited by 2 | Viewed by 1597

Abstract

Accurate estimation and regular monitoring of soil moisture is very important for many agricultural, hydrological, or climatological applications. Our objective was to evaluate potential contributions of polarimetry to soil moisture estimation during crop growing cycles using RADARSAT-2 C-band images. The research focused on wheat field data collected during Soil Moisture Active Passive Validation Experiment (SMAPVEX12) conducted in 2012 in Manitoba (Canada). A sensitivity analysis was performed to select the most relevant non-polarimetric and polarimetric variables extracted from RADARSAT-2, and statistical models were developed to estimate soil moisture. In fine, three models were developed and validated: a non-polarimetric model based on cross-polarized backscattering coefficient

σ_{H V}^{0}

; a polarimetric mixed model using six polarimetric and non-polarimetric retained variables after the sensitivity analysis; and a simplified polarimetric mixed model considering only the phase difference (

ϕ_{H H - V V}

) and the co-polarized backscattering coefficient

σ_{H H}^{0}

. The validation reveals significant positive contributions of polarimetry. It shows that the non-polarimetric model has a much larger error (RMSE = 0.098 m³/m³) and explains only 19% of observed soil moisture variation compared to the polarimetric mixed model, which has an error of 0.087 m³/m³, with an explained variance of 44%. The simplified model has the lowest error (0.074 m³/m³) and explains 53.5% of soil moisture variation. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications)

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28 pages, 15640 KiB

Open AccessArticle

Real-Time Retrieval of Daily Soil Moisture Using IMERG and GK2A Satellite Images with NWP and Topographic Data: A Machine Learning Approach for South Korea

by Soo-Jin Lee, Eunha Sohn, Mija Kim, Ki-Hong Park, Kyungwon Park and Yangwon Lee

Remote Sens. 2023, 15(17), 4168; https://doi.org/10.3390/rs15174168 - 24 Aug 2023

Viewed by 1728

Abstract

Soil moisture (SM) is an indicator of the moisture status of the land surface, which is useful for monitoring extreme weather events. Representative global SM datasets include the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP), the Global Land Data Assimilation System (GLDAS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5), but due to their low spatial resolutions, none of these datasets well describe SM changes in local areas, and they tend to have a low accuracy. Machine learning (ML)-based SM predictions have demonstrated high accuracy, but obtaining semi-real-time SM information remains challenging, and the dependence of the validation accuracy on the data sampling method used, such as random or yearly sampling, has led to uncertainties. In this study, we aimed to develop an ML-based model for real-time SM estimation that can capture local-scale variabilities in SM and have reliable accuracy, regardless of the sampling method. This study was conducted in South Korea, and satellite image data, numerical weather prediction (NWP) data, and topographic data provided within one day were used as the input data. For SM modeling, 13 input variables affecting the surface SM status were selected: 10- and 20-day cumulative standardized precipitation indexes (SPI10 and SPI20), a normalized difference vegetation index (NDVI), downward shortwave radiation (DSR), air temperature (Tair), land surface temperature (LST), soil temperature (Tsoil), relative humidity (RH), latent heat flux (LE), slope, elevation, topographic ruggedness index (TRI), and aspect. Then, SM models based on random forest (RF) and automated machine learning (AutoML) were constructed, trained, and validated using random sampling and leave-one-year-out (LOYO) cross-validation. The RF- and AutoML-based SM models had significantly high accuracy rates based on comparisons with in situ SM (mean absolute error (MAE) = 2.212–4.132%; mean bias error (MBE) = −0.110–0.136%; root mean square error (RMSE) = 3.186–5.384%; correlation coefficient (CC) = 0.732–0.913), while the AutoML-based SM model tended to have a higher accuracy than the RF-based SM model, regardless of the data sampling method used. In addition, when compared to in situ SM data, the SM models demonstrated the highest accuracy, outperforming both GLDAS and ERA5 SM data and well representing changes in the dryness/wetness of the land surface according to meteorological events (heatwave, drought, and rainfall). The SM models proposed in this study can, thus, offer semi-real-time SM data, aiding in the monitoring of moisture changes in the land surface, as well as short-term meteorological disasters, like flash droughts or floods. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications)

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19 pages, 6982 KiB

Open AccessArticle

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

by Yi Zhu, Lanhui Zhang, Feng Li, Jiaxin Xu and Chansheng He

Remote Sens. 2023, 15(15), 3789; https://doi.org/10.3390/rs15153789 - 30 Jul 2023

Viewed by 1263

Abstract

In arid and semi-arid areas, soil moisture (SM) plays a crucial role in land-atmosphere interactions, hydrological processes, and ecosystem sustainability. SM data at large scales are critical for related climatic, hydrological, and ecohydrological research. Data fusion based on satellite products and model simulations is an important way to obtain SM data at large scales; however, little has been reported on the comparison of the data fusion methods in different categories. Here, we compared the performance of two widely used data fusion methods, the Ensemble Kalman Filter (EnKF) and the Back-Propagation Artificial Neural Network (BPANN), in the degraded grassland site (DGS) and the alpine grassland site (AGS). The SM data from the Community Land Model 5.0 (CLM5.0) and the Soil Moisture Active and Passive (SMAP) were fused and validated against the observations of the Cosmic-Ray Neutron Sensor (CRNS) to avoid the impacts of scale-mismatch. Results show that compared with the original data sets at both sites, the RMSE of the fused data by BPANN (FD-BPANN) and EnKF (FD-EnKF) had improved by more than 50% and 31%, respectively. Overall, the FD-BPANN performs better than the FD-EnKF because the BPANN method assigned higher weights to input data with better performance and the EnKF method is affected by the strong variabilities of both the fused CLM5.0 and SMAP data and the CRNS data. However, in terms of the percentile range, the FD-BPANN showed the worst performance, with overestimations in the low SM range of 25th percentile (<Q25), because the BPANN method tends to be trapped in a local minimum. The BPANN method performed better in humid areas, then followed by semi-humid areas, and finally arid and semi-arid areas. Moreover, compared with the previous studies in arid and semi-arid areas, the BPANN method in this study performed better. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications)

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18 pages, 7023 KiB

Open AccessArticle

Merging Microwave, Optical, and Reanalysis Data for 1 Km Daily Soil Moisture by Triple Collocation

by Luyao Zhu, Wenjie Li, Hongquan Wang, Xiaodong Deng, Cheng Tong, Shan He and Ke Wang

Remote Sens. 2023, 15(1), 159; https://doi.org/10.3390/rs15010159 - 27 Dec 2022

Cited by 3 | Viewed by 2216

Abstract

High-spatiotemporal resolution soil moisture (SM) plays an essential role in optimized irrigation, agricultural droughts, and hydrometeorological model simulations. However, producing high-spatiotemporal seamless soil moisture products is challenging due to the inability of optical bands to penetrate clouds and the coarse spatiotemporal resolution of microwave and reanalysis products. To address these issues, this study proposed a framework for multi-source data merging based on the triple collocation (TC) method with an explicit physical mechanism, which was dedicated to generating seamless 1 km daily soil moisture products. Current merging techniques based on the TC method often lack seamless daily optical data input. To remedy this deficiency, our study performed a spatiotemporal reconstruction on MODIS LST and NDVI, and retrieved seamless daily optical soil moisture products. Then, the optical-derived sm₁, microwave-retrieved sm₂ (ESA CCI combined), and reanalysis sm₃ (CLDAS) were matched by the cumulative distribution function (CDF) method to eliminate bias, and their weights were determined by the TC method. Finally, the least squares algorithm and the significance judgment were adopted to complete the merging. Although the CLDAS soil moisture presented anomalies over several stations, our proposed method can detect and reduce this impact by minimizing its weight, which shows the robustness of the method. This framework was implemented in the Naqu region, and the results showed that the merged products captured the temporal variability of the SM and depicted spatial information in detail; the validation with the in situ measurement obtained an average ubRMSE of 0.046 m³/m³. Additionally, this framework is transferrable to any area with measured sites for better agricultural and hydrological applications. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications)

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22 pages, 4413 KiB

Open AccessArticle

Soil-Moisture Estimation Based on Multiple-Source Remote-Sensing Images

by Tianhao Mu, Guiwei Liu, Xiguang Yang and Ying Yu

Remote Sens. 2023, 15(1), 139; https://doi.org/10.3390/rs15010139 - 26 Dec 2022

Cited by 12 | Viewed by 5182

Abstract

Soil moisture plays a significant role in the global hydrological cycle, which is an important component of soil parameterization. Remote sensing is one of the most important methods used to estimate soil moisture. In this study, we developed a new nonlinear Erf-BP neural network method to establish a soil-moisture-content-estimation model with integrated multiple-resource remote-sensing data from high-resolution, hyperspectral and microwave sensors. Next, we compared the result with the single-resource remote-sensing data for SMC (soil-moisture content) estimation models by using the linear-fitting method. The results showed that the soil-moisture estimation model offers better accuracy by using multiple-resource remote-sensing data. Furthermore, the SMC predicted the results by using the new Erf-BP neural network with multiple-resource remote-sensing data and a good overall correlation coefficient of 0.6838. Compared with the linear model’s estimation results, the accuracy of the SMC estimation using the Erf-BP method was increased, and the RMSE decreased from 0.017 g/g to 0.0146 g/g, a decrease of 16.44%. These results also indicate that the improved algorithm of the Erf-BP artificial neural network has better fitting results and precision. This research provides a reference for multiple-resource remote-sensing data for soil-moisture estimation. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications)

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20 pages, 3109 KiB

Open AccessArticle

A Method of Soil Moisture Content Estimation at Various Soil Organic Matter Conditions Based on Soil Reflectance

by Tianchen Li, Tianhao Mu, Guiwei Liu, Xiguang Yang, Gechun Zhu and Chuqing Shang

Remote Sens. 2022, 14(10), 2411; https://doi.org/10.3390/rs14102411 - 17 May 2022

Cited by 18 | Viewed by 5561

Abstract

Soil moisture is one of the most important components of all the soil properties affecting the global hydrologic cycle. Optical remote sensing technology is one of the main parts of soil moisture estimation. In this study, we promote a soil moisture-estimating method with applications regarding various soil organic matters. The results indicate that the soil organic matter had a significant spectral feature at wavelengths larger than 900 nm. The existence of soil organic matter would lead to darker soil, and this feature was similar to the soil moisture. Meanwhile, the effect of the soil organic matter on its reflectance overlaps with the effect of soil moisture on its reflected spectrum. This can lead to the underestimation of the soil moisture content, with an MRE of 21.87%. To reduce this effect, the absorption of the soil organic matter was considered based on the Lambert–Beer law. Then, we established an SMC_g-estimating model based on the radiative transform theory while considering the effect of the soil organic matter. The results showed that the effect of the soil organic matter can be effectively reduced and the accuracy of the soil moisture estimation was increased, while MRE decreased from 21.87% to 6.53%. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications)

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17 pages, 5283 KiB

Open AccessArticle

Implementation of Two-Stream Emission Model for L-Band Retrievals on the Tibetan Plateau

by Xiaojing Wu

Remote Sens. 2022, 14(3), 494; https://doi.org/10.3390/rs14030494 - 20 Jan 2022

Cited by 2 | Viewed by 1760

Abstract

This study assesses the suitability of the two-stream microwave emission model in simulating brightness temperature (T_B^p) and retrieving liquid water content (θ_liq) at L-band in combination with the four-phase dielectric model for both thawed and frozen soil. Both single (SCA) and double (DCA) channel algorithms are adopted using both ground-based ELBARA-III and spaceborne SMAP measurements conducted in a Tibetan grassland site. The ELBARA-III measured T_B^p are smaller than the SMAP measurements in the warm season due to a lower value of average θ_liq presented within the ELBARA-III footprint. The two-stream emission model configured with SMAP vegetation and surface roughness parameterization underestimates both ELBARA-III and SMAP measured T_B^p at horizontal polarization in the cold season, and overestimates the vertical polarized measurements (T_B^V) in the warm season. Implementation of a new surface roughness and vegetation parameterization resolves above deficiency, and the simulations capture better large-scale SMAP measurements in comparison to these performed for the ELBARA-III footprint. The dynamics of in situ θ_liq are better reproduced by retrievals using the SCA based on T_B^V measurements (SCA-V), whereby the SCA-V retrievals using the SMAP ascending overpass measurements shows the best results with an unbiased root-mean-square error (ubRMSE) of 0.035 m³ m⁻³ that outperforms the SMAP mission specification. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications)

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17 pages, 4655 KiB

Open AccessArticle

Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau

by Mengying Yang, Hongquan Wang, Cheng Tong, Luyao Zhu, Xiaodong Deng, Jinsong Deng and Ke Wang

Remote Sens. 2021, 13(10), 1913; https://doi.org/10.3390/rs13101913 - 13 May 2021

Cited by 13 | Viewed by 3090

Abstract

This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness parameter based on the hypothesis that the roughness is invariant among SAR acquisitions. Afterward, the vegetation water content (VWC) in the WCM is calculated from the daily MODIS NDVI data obtained by temporal interpolation. To improve the performance of the model, the parameters A, B, and α of the WCM are analyzed and optimized using randomly selected half of the sampled dataset. Then, the soil moisture is retrieved by minimizing a cost function between the simulated and measured backscattering coefficients. The comparison of the retrieved soil moisture with the ground measurements shows the determination coefficient R² and the Root Mean Square Error (RMSE) are 0.46 and 0.08 m³/m³, respectively. These results demonstrate the capability and reliability of Sentinel-1 SAR data for estimating the soil moisture over the Tibetan Plateau. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications)

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