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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 10115

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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
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Dr. Anna Siedliska

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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: 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

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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 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.

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Keywords

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

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Published Papers (7 papers)

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Research

46 pages, 13316 KB

Open AccessArticle

Assessing the Spatial Similarity of Soil Moisture Patterns and Their Environmental and Observational Drivers from Remote Sensing and Earth System Modeling Across Europe

by Thomas Jagdhuber, Lisa Jach, Anke Fluhrer, David Chaparro, Florian M. Hellwig, Gerard Portal, Hans-Stefan Bauer and Harald Kunstmann

Remote Sens. 2026, 18(4), 608; https://doi.org/10.3390/rs18040608 - 15 Feb 2026

Cited by 1 | Viewed by 379

Abstract

Soil moisture is an essential climate variable exhibiting strong spatio-temporal dynamics, especially in the topsoil. Therefore, it is assessed multiple times by sensors within in situ networks, satellites, and by modeling of the Earth system. The resulting soil moisture fields from all methods are individual and non-congruent due to the imperfection of the methods and retrievals. But their spatial patterns have valuable similarities that call for investigation to foster intercomparison or even fusion of soil moisture products. In this research study, the similarity of spatial soil moisture patterns between passive microwave remote sensing products and Earth system modeling is investigated. We configure and apply spatial similarity metrics to enable a spatial comparison of the operational SMAP Dual Channel Algorithm (DCA) radiometer soil moisture product with the soil moisture output from IFS model runs of the ECMWF. The pattern assessment spans over the whole of Europe and aims to find the drivers behind the spatial soil moisture distributions at scales ranging from single grid cells (minimum) to continental (maximum) spatial scales, and between growing periods of wet (2021) and dry (2022) years. The two specifically configured metrics, total disagreement and mean category distance, showcase the opportunities and challenges when assessing spatial similarity in soil moisture fields across different scales. In addition, the potential drivers of the spatial moisture patterns were screened. Here, soil texture is the most influential single driver of spatial patterns in the IFS soil moisture runs, when analyzed in absolute terms [m³ m⁻³]. In relative terms of soil moisture [-] (soil wetness index), precipitation and soil temperature explain most of the variability of the IFS soil moisture for Europe. The SMAP retrievals are predominantly driven by the brightness temperatures, mostly influenced by surface temperature, vegetation water content, and soil roughness. These differences in drivers, as well as in methodology, culminate in an inherent discrepancy between the two soil moisture products. However, the assessment of their spatial patterns reveals the underlying similarity from the local to the continental scale. Full article

(This article belongs to the Special Issue Earth Observation Satellites for Soil Moisture Monitoring)

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34 pages, 12397 KB

Open AccessArticle

Comparing Temporal Dynamics of Soil Moisture from Remote Sensing, Modeling, and Field Observations Across Europe

by Lisa Jach, Anke Fluhrer, Hans-Stefan Bauer, David Chaparro, Florian M. Hellwig, Gerard Portal and Thomas Jagdhuber

Remote Sens. 2026, 18(3), 445; https://doi.org/10.3390/rs18030445 - 1 Feb 2026

Cited by 1 | Viewed by 336

Abstract

This study evaluates temporal variability and algorithm differences in soil moisture estimates over Europe using the European Center for Medium-range Weather Forecasts (ECMWF) operational analysis and the passive Soil Moisture Active Passive (SMAP) soil moisture product. While models and satellite retrievals have improved in capturing the timing of soil moisture dynamics, absolute accuracy and temporal variability magnitudes still diverge. This study compares the representation of short-term and seasonal variability of soil moisture in absolute and normalized terms over two different hydrometeorological growing periods (2021 and 2022). Both datasets exhibit intermediate to high temporal correlations with in situ measurements at selected stations (median Pearson correlation coefficients of all stations range between 0.65 and 0.79), confirming previous studies. However, they overestimate the magnitude of absolute soil moisture variability at most stations (median interquartile range of all stations at 0.085 (0.10) m³m⁻³ for ECMWF and 0.072 (0.079) m³m⁻³ for SMAP opposed to 0.063 (0.072) m³m⁻³ for in situ in 2021 (2022)) due to an overestimation of short-term fluctuations, especially at dry stations in southern France and Eastern Europe. The soil wetness index is underestimated, particularly within SMAP estimates. The performance of both is sensitive to hydrometeorological conditions, with the 2022 European drought causing strong seasonal and weak short-term fluctuations. This is easier to capture than conditions with pronounced short-term and weaker seasonal fluctuations, as in 2021. Overall, SMAP and ECMWF time series show considerable coincident timing, whereas the magnitude of temporal variability and accuracy depend on site-specific characteristics and the pre-processing of the data. Full article

(This article belongs to the Special Issue Earth Observation Satellites for Soil Moisture Monitoring)

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18 pages, 6496 KB

Open AccessArticle

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 602

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 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

Open AccessArticle

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 1699

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 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

Open AccessArticle

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

Cited by 3 | Viewed by 1530

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 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

Open AccessArticle

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 7 | Viewed by 2329

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 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

Open AccessEditor’s ChoiceArticle

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 3 | Viewed by 1856

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 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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