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Remote Sensing
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Earth Observation Satellites for Soil Moisture Monitoring
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Earth Observation Satellites for Soil Moisture Monitoring

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 7027

Special Issue Editors

Dr. Jaromir Krzyszczak

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Guest Editor
Department of Metrology and Modelling of Agrophysical Processes, Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
Interests: climate change; climate change adaptation; crop growth and yield prediction; Earth observation; remote sensing in agriculture; spectral data analysis; multifractality of time series; forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Siedliska

E-Mail Website
Guest Editor
Department of Metrology and Modelling of Agrophysical Processes, Institute of Agrophysics Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
Interests: spectral imaging; remote sensing; precision agriculture; supervised classification; plant quality control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil moisture plays a crucial role in the exchange of water and energy within the soil–plant–atmosphere system, impacting ecosystems, water resources, and climate patterns. Its significance is acknowledged across a spectrum of environmental disciplines, encompassing meteorology, hydrology, agriculture, and climate change studies. Consequently, the precise monitoring and estimation of the spatial and temporal fluctuations in soil moisture are of special importance. Conventional point-based ground measurements fall short in capturing the spatiotemporal distribution of soil moisture on a large scale, due to their time- and labor-intensive nature. Satellite-based remote sensing offers a global-scale perspective with continuous spatiotemporal resolution, making it a cornerstone for soil moisture estimation. Through active and passive remote sensing methods, researchers have developed various soil moisture products such as SMAP, ESA CCI, and AMSR2. Despite challenges in data coverage and inversion results, continuous efforts in algorithm development and data evaluation have enhanced the quality and applicability of these products. However, there are still knowledge gaps and challenges, as well as opportunities associated with data interpretation and algorithm development.

Therefore, the journal Remote Sensing (ISSN: 2072-4292, IF 5.0, Citescore 7.9) has decided to run a Special Issue entitled “Earth Observation Satellites for Soil Moisture Monitoring”, which I am guest editing. This Special Issue aims to explore the latest developments in remote sensing techniques, from optical and thermal infrared to passive and active microwave measurements, for soil moisture monitoring. This Special Issue invites contributions focusing on the following topics:

  • Advances in satellite-based soil moisture estimation techniques;
  • Evaluation and validation of existing soil moisture products;
  • Applications of satellite-derived soil moisture data in climate change studies, drought monitoring, and environmental management;
  • Comparative studies between different observation networks and satellite products;
  • Multiscale analysis of soil moisture variations and their implications.

Authors are encouraged to submit original research articles, reviews, and methodological studies that contribute to our understanding of satellite-based soil moisture monitoring, addressing sensor technologies, data processing methodologies, and applications of satellite-derived soil moisture data. We welcome interdisciplinary approaches that integrate remote sensing, hydrology, climatology, and environmental science.

We look forward to receiving your valuable contributions.

Dr. Jaromir Krzyszczak
Dr. Anna Siedliska
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 250 words) can be sent to the Editorial Office for assessment.

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

  • remote sensing
  • soil moisture
  • hydrology
  • agriculture
  • meteorology
  • climate change
  • drought monitoring
  • algorithm development
  • data validation

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Published Papers (5 papers)

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Research

18 pages, 6496 KB  
Article
Leveraging Limited ISMN Soil Moisture Measurements to Develop the HYDRUS-1D Model and Explore the Potential of Remotely Sensed Precipitation for Soil Moisture Estimates in the Northern Territory, Australia
by Muhammad Usman and Christopher E. Ndehedehe
Remote Sens. 2025, 17(22), 3723; https://doi.org/10.3390/rs17223723 - 14 Nov 2025
Viewed by 326
Abstract
Soil moisture plays a key role in the critical zone of the Earth and has extensive value in the understanding of hydrological, agricultural, and environmental processes (among others). Long-term (in situ) monitoring of soil moisture measurements is generally not practical; however, short-term measurements [...] Read more.
Soil moisture plays a key role in the critical zone of the Earth and has extensive value in the understanding of hydrological, agricultural, and environmental processes (among others). Long-term (in situ) monitoring of soil moisture measurements is generally not practical; however, short-term measurements are often found. Limited soil moisture measurements can be employed to develop a numerical model for long-term and accurate soil moisture estimations. A key input variable to the model is precipitation, which is also not easily accessible, particularly at a finer spatial resolution; hence, publicly available remote sensing data can be used as an alternative. This study, therefore, aims to develop a numerical model HYDRUS-1D to estimate soil moisture in the data-scarce state of the Northern Territory, Australia, with a land cover of shrubland and a Tropical-Savannah type climate. The HDYRUS-1D is based on the numerical solution of Richards’ equation of variably saturated flow that relies on information about the soil water retention characteristics. This study utilized the van Genuchten model parameters, which were optimized (against measured soil moisture) through parameter optimization with initial estimates obtained from the HYDRUS catalogue. Initial estimates from different sources can differ for the same soil texture (e.g., loamy sand) and can induce uncertainties in the calibrated model. Therefore, a comprehensive uncertainty analysis was conducted to address potential uncertainties in the calibration process. The HYDRUS-1D was calibrated for a period between March 2012 and February 2013 and was independently validated against three different periods between March 2013 and October 2016. Root Mean Square Error (RMSE), Pearson’s correlation coefficient (R), and Mean Absolute Error (MAE) were used to assess the efficiency of the model in simulating the measured soil moisture. The model exhibited good performance in replicating measured soil moisture during calibration (RMSE = 0.00 m3/m3, MAE = 0.005 m3/m3, and R = 0.70), during validation period 1 (RMSE = 0.035 m3/m3 and MAE = 0.023 m3/m3, and R = 0.72), validation period 2 (RMSE = 0.054 m3/m3 and MAE = 0.039 m3/m3, and R = 0.51), and validation period 3 (RMSE = 0.046 m3/m3 and MAE = 0.032 m3/m3, and R = 0.61), respectively. Remotely sensed precipitation data were used from the CHRS-PERSIANN, CHRS-CCS, and CHRS-PDIR-Now to assess their capabilities in estimating soil moisture. Efficiency evaluation metrics and visual assessment revealed that these products underestimated the soil moisture. The CHRS-CCS outperformed other products in terms of overall efficiency (average RMSE of 0.040 m3/m3, average MAE of 0.023 m3/m3, and an average R of 0.68, respectively). An integrated approach based on numerical modelling and remote sensing employed in this study can help understand the long-term dynamics of soil moisture and soil water balance in the Northern Territory, Australia. Full article
(This article belongs to the Special Issue Earth Observation Satellites for Soil Moisture Monitoring)
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24 pages, 6994 KB  
Article
Satellite-Based Machine Learning for Soil Moisture Prediction and Land Conservation Practice Assessment in West African Drylands
by Meron Lakew Tefera, Ethiopia B. Zeleke, Mario Pirastru, Assefa M. Melesse, Giovanna Seddaiu and Hassan Awada
Remote Sens. 2025, 17(21), 3651; https://doi.org/10.3390/rs17213651 - 5 Nov 2025
Viewed by 1013
Abstract
In semiarid, fragmented landscapes where data scarcity challenges effective land management, accurate soil moisture monitoring is critical. This study presents a high-resolution analysis that integrates remote sensing, in situ data, and machine learning to predict soil moisture and evaluate the impact of land [...] Read more.
In semiarid, fragmented landscapes where data scarcity challenges effective land management, accurate soil moisture monitoring is critical. This study presents a high-resolution analysis that integrates remote sensing, in situ data, and machine learning to predict soil moisture and evaluate the impact of land conservation practices. A Long Short-Term Memory (LSTM) model combined with Random Forest gap-filling achieved strong predictive performance (R2 = 0.84; RMSE = 0.103 cm3 cm−3), outperforming SMAP satellite estimates by approximately 30% across key accuracy metrics. The model was applied to 222 field sites in northern Ghana to quantify the effects of stone bunds on soil moisture retention. The results revealed that fields with stone bunds maintained 4–6% higher moisture than non-bunded fields, particularly on steep slopes and in areas with low to moderate topographic wetness. These findings demonstrate the capability of combining remote sensing and deep learning for fine-scale soil-moisture prediction and provide quantitative evidence of how nature-based solutions enhance water retention and climate resilience in dryland agricultural systems. Full article
(This article belongs to the Special Issue Earth Observation Satellites for Soil Moisture Monitoring)
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27 pages, 8010 KB  
Article
Deep Learning-Based Short- and Mid-Term Surface and Subsurface Soil Moisture Projections from Remote Sensing and Digital Soil Maps
by Saman Rabiei, Ebrahim Babaeian and Sabine Grunwald
Remote Sens. 2025, 17(18), 3219; https://doi.org/10.3390/rs17183219 - 18 Sep 2025
Viewed by 1057
Abstract
Accurate real-time information about soil moisture (SM) at a large scale is essential for improving hydrological modeling, managing water resources, and monitoring extreme weather events. This study presents a framework using convolutional long short-term memory (ConvLSTM) network to produce short- (1, 3, and [...] Read more.
Accurate real-time information about soil moisture (SM) at a large scale is essential for improving hydrological modeling, managing water resources, and monitoring extreme weather events. This study presents a framework using convolutional long short-term memory (ConvLSTM) network to produce short- (1, 3, and 7 days ahead) and mid-term (14 and 30 days ahead) forecasts of SM at surface (0–10 cm) and subsurface (10–40 and 40–100 cm) soil layers across the contiguous U.S. The model was trained with five-year period (2018–2022) datasets including Soil Moisture Active Passive (SMAP) level 3 ancillary covariables, North American Land Data Assimilation System phase 2 (NLDAS-2) SM product, shortwave infrared reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS), and terrain features (e.g., elevation, slope, curvature), as well as soil texture and bulk density maps from the Soil Landscape of the United States (SOLUS100) database. To develop and evaluate the model, the dataset was divided into three subsets: training (January 2018–January 2021), validation (2021), and testing (2022). The outputs were validated with observed in situ data from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN) soil moisture networks. The results indicated that the accuracy of SM forecasts decreased with increasing lead time, particularly in the surface (0–10 cm) and subsurface (10–40 cm) layers, where strong fluctuations driven by rainfall variability and evapotranspiration fluxes introduced greater uncertainty. Across all soil layers and lead times, the model achieved a median unbiased root mean square error (ubRMSE) of 0.04 cm3 cm−3 with a Pearson correlation coefficient of 0.61. Further, the performance of the model was evaluated with respect to both land cover and soil texture databases. Forecast accuracy was highest in coarse-textured soils, followed by medium- and fine-textured soils, likely because the greater penetration depth of microwave observations improves SM retrieval in sandy soils. Among land cover types, performance was strongest in grasslands and savannas and weakest in dense forests and shrublands, where dense vegetation attenuates the microwave signal and reduces SM estimation accuracy. These results demonstrate that the ConvLSTM framework provides skillful short- and mid-term forecasts of surface and subsurface soil moisture, offering valuable support for large-scale drought and flood monitoring. Full article
(This article belongs to the Special Issue Earth Observation Satellites for Soil Moisture Monitoring)
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28 pages, 7756 KB  
Article
An Interpretable Machine Learning Framework for Unraveling the Dynamics of Surface Soil Moisture Drivers
by Zahir Nikraftar, Esmaeel Parizi, Mohsen Saber, Mahboubeh Boueshagh, Mortaza Tavakoli, Abazar Esmaeili Mahmoudabadi, Mohammad Hassan Ekradi, Rendani Mbuvha and Seiyed Mossa Hosseini
Remote Sens. 2025, 17(14), 2505; https://doi.org/10.3390/rs17142505 - 18 Jul 2025
Cited by 2 | Viewed by 1641
Abstract
Understanding the impacts of the spatial non-stationarity of environmental factors on surface soil moisture (SSM) in different seasons is crucial for effective environmental management. Yet, our knowledge of this phenomenon remains limited. This study introduces an interpretable machine learning framework that combines the [...] Read more.
Understanding the impacts of the spatial non-stationarity of environmental factors on surface soil moisture (SSM) in different seasons is crucial for effective environmental management. Yet, our knowledge of this phenomenon remains limited. This study introduces an interpretable machine learning framework that combines the SHapley Additive exPlanations (SHAP) method with two-step clustering to unravel the spatial drivers of SSM across Iran. Due to the limited availability of in situ SSM data, the performance of three global SSM datasets—SMAP, MERRA-2, and CFSv2—from 2015 to 2023 was evaluated using agrometeorological stations. SMAP outperformed the others, showing the highest median correlation and the lowest Root Mean Square Error (RMSE). Using SMAP, we estimated SSM across 609 catchments employing the Random Forest (RF) algorithm. The RF model yielded R2 values of 0.89, 0.83, 0.70, and 0.75 for winter, spring, summer, and autumn, respectively, with corresponding RMSE values of 0.076, 0.081, 0.098, and 0.061 m3/m3. SHAP analysis revealed that climatic factors primarily drive SSM in winter and autumn, while vegetation and soil characteristics are more influential in spring and summer. The clustering results showed that Iran’s catchments can be grouped into five categories based on the SHAP method coefficients, highlighting regional differences in SSM controls. Full article
(This article belongs to the Special Issue Earth Observation Satellites for Soil Moisture Monitoring)
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21 pages, 6227 KB  
Article
Evaluation of Satellite-Based Global Navigation Satellite System Reflectometry (GNSS-R) Soil Moisture Products in Complex Terrain: A Case Study of the Yunnan–Guizhou Plateau
by Yixiao Liu, Yong Wang, Jingcheng Lai, Yunjie Lin and Leyan Shi
Remote Sens. 2025, 17(5), 887; https://doi.org/10.3390/rs17050887 - 2 Mar 2025
Cited by 2 | Viewed by 1595
Abstract
Complex terrain is one of the main factors affecting the process of retrieving surface soil moisture using GNSS-R technology. This study evaluates the impact of complex terrain on surface soil moisture inversion using Cyclone Global Navigation Satellite System (CYGNSS) L3 SSM products, with [...] Read more.
Complex terrain is one of the main factors affecting the process of retrieving surface soil moisture using GNSS-R technology. This study evaluates the impact of complex terrain on surface soil moisture inversion using Cyclone Global Navigation Satellite System (CYGNSS) L3 SSM products, with Soil Moisture Active Passive (SMAP) SSM products as the true value. The errors in CYGNSS SSM are primarily attributed to med–high elevation and large relief. Compared with the Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products, CYGNSS exhibits superior performance in terms of AD and RMSE (median AD = −0.10 m3/m3, RMSE = 0.14 m3/m3). The ubRMSE of CYGNSS (median ubRMSE = 0.094 m3/m3) outperforms SMOS, but is slightly worse than AMSR2, with the differences mainly observed in med–high elevation and large-relief regions. The three satellites complement each other in detecting complex terrain. CYGNSS errors (AD, RMSE) are higher in the rainy season than in the dry season, with greater discrepancies observed in large-relief, high-elevation regions compared to flatter, lower-elevation areas. This study provides the first comprehensive analysis of CYGNSS in such a complex region, offering valuable insights for improving the application of GNSS-R inversion technology. Full article
(This article belongs to the Special Issue Earth Observation Satellites for Soil Moisture Monitoring)
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