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Remote Sensing
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Root-Zone Soil Moisture Retrieval and Applications from Remote Sensing Measurements
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Root-Zone Soil Moisture Retrieval and Applications from Remote Sensing Measurements

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 7098

Special Issue Editors

Dr. David Fairbairn

E-Mail Website
Guest Editor
European Centre for Medium Range Weather Forecasts, Reading RG2 9AX, UK
Interests: data assimilation; NWP; soil moisture; machine learning
Dr. Bertrand Bonan

E-Mail Website
Guest Editor
CNRM, Université de Toulouse, Météo-France, CNRS, UMR3589 Toulouse, France
Interests: data assimilation; soil moisture; vegetation; fluxes; machine learning
Dr. Luca Brocca

grade E-Mail Website
Guest Editor
Research Institute for Geo-Hydrological Protection, National Research Council, Via della Madonna Alta, 126, 06128 Perugia, Italy
Interests: soil moisture; flood; drought; irrigation; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Satellite-derived SM measurements are mainly confined to a thin surface layer with a depth of a few centimetres. However, the root-zone SM, with a depth of up to 3 metres, is a significant driver of latent and sensible heat fluxes, vegetation phenology, and the water cycle. Fortunately, root-zone SM is connected to variables that can be detected by satellites, including surface SM, vegetation optical depth, solar-induced fluorescence, and ground water storage, amongst others.

Over the last few decades, land surface data assimilation systems have become established at various institutes, including NWP centres and universities. Both active (e.g., Metop-ASCAT) and passive (e.g., SMOS, SMAP) satellite-derived SM observations can be assimilated in land surface models. Furthermore, observed vegetation and agriculture from satellites have a strong relationship with root-zone SM, especially during the growing season. Traditionally, radiative transfer observation operators transform the SM model state to the observed quantity (e.g., brightness temperature for SMOS/SMAP) prior to assimilation. More recently, efficient machine learning-based observation operators have been advocated, including neural networks and gradient boosting trees.

This Special Issue aims to collect studies on innovative methods to derive root-zone SM using remote sensing measurements and applications of root-zone SM derived from remote sensing.

Themes include but are not limited to the following:

  • Data assimilation;
  • Machine learning;
  • Drought monitoring;
  • Flood monitoring;
  • Agriculture monitoring;
  • Irrigation;
  • SM validation;
  • NWP.

Both research and review articles are relevant.

Dr. David Fairbairn
Dr. Bertrand Bonan
Dr. Luca Brocca
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

  • root-zone
  • soil moisture
  • data assimilation
  • machine learning
  • remote sensing
  • observations
  • vegetation

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Published Papers (3 papers)

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Research

17 pages, 4932 KB  
Article
Validation of Soil Temperature Sensing Depth Estimates Using High-Temporal Resolution Data from NEON and SMAP Missions
by Shaoning Lv, Edward Ayres and Yin Hu
Remote Sens. 2025, 17(23), 3845; https://doi.org/10.3390/rs17233845 - 27 Nov 2025
Viewed by 231
Abstract
Passive microwave remote sensing of soil moisture is crucial for monitoring the Earth’s water cycle and surface dynamics. The penetration depth during this process is significant, as it influences the accuracy of retrieved soil moisture data. Within L-band remote sensing, tools such as [...] Read more.
Passive microwave remote sensing of soil moisture is crucial for monitoring the Earth’s water cycle and surface dynamics. The penetration depth during this process is significant, as it influences the accuracy of retrieved soil moisture data. Within L-band remote sensing, tools such as the τ-z model interpret microwave emissions to estimate soil moisture, taking into account the complex interactions between soil and radiation. However, in validating these models against high-temporal-resolution, ground-based measurements, especially from extensive networks like the Terrestrial National Ecological Observatory Network (NEON), further research and validation efforts are needed. This study comprehensively validates the τ-z model’s ability to estimate the soil temperature sensing depth (zTeff) using data from the NEON and Soil Moisture Active Passive (SMAP) satellite missions. A harmonization process was conducted to align the spatial and temporal scales of the two datasets, enabling rigorous validation. We compared soil optical depth (τ)—a parameter capable of theoretically unifying sensing depth representations across wet soil (~0.05 m) to extreme dry/frozen conditions (e.g., up to ~1500 m in ice-equivalent scenarios)—and geometric depth (z) frameworks against outputs from the τ-z model and NEON’s in situ profiles. The results show that: (1) for the profiles that satisfy the monotonic assumption by the τ-z model, zTeff fits the prediction well at about 0.2 τ for the average; (2) Combining SMAP’s soil moisture, the τ-z model achieves high accuracy in estimating zTeff, with RMSD (0.05 m) and unRMSD (0.03 m), and correlations (0.67) between estimated and observed values. The findings are expected to advance remote sensing techniques in various fields, including agriculture, hydrology, and climate change research. Full article
(This article belongs to the Special Issue Root-Zone Soil Moisture Retrieval and Applications from Remote Sensing Measurements)
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17 pages, 3980 KB  
Article
Landscape Spatiotemporal Heterogeneity Decreased the Resistance of Alpine Grassland to Soil Droughts
by Yuxin Wang, Hu Liu, Wenzhi Zhao, Jiachang Jiang and Zhibin He
Remote Sens. 2025, 17(7), 1293; https://doi.org/10.3390/rs17071293 - 4 Apr 2025
Viewed by 973
Abstract
Alpine grasslands face increasing threats from soil droughts due to climate change. While extensive research has focused on the direct impacts of drought on vegetation, the role of landscape fragmentation and spatiotemporal heterogeneity in shaping the response of these ecosystems to drought remains [...] Read more.
Alpine grasslands face increasing threats from soil droughts due to climate change. While extensive research has focused on the direct impacts of drought on vegetation, the role of landscape fragmentation and spatiotemporal heterogeneity in shaping the response of these ecosystems to drought remains inadequately explored. This study aims to fill this gap by examining the Gannan alpine grassland in the northeastern Qinghai-Tibet Plateau. Using remote sensing data, indicators of spatial and temporal heterogeneity were derived, including spatial variance (SCV), spatial autocorrelation (SAC), and temporal autocorrelation (TAC). Two soil drought thresholds (Tr: threshold of rapid resistance loss and Tc: threshold of complete resistance loss) representing percentile-based drought intensities were identified to assess NDVI decline under drought conditions. Our findings indicate that the grassland has low resistance to soil droughts, with mean Tr and Tc of 8.93th and 7.36th percentile, respectively. Both increasing and decreasing spatiotemporal heterogeneity reduced vegetation resistance, with increasing SCV having a more pronounced effect. Specifically, increasing SCV increased Tr and Tc 1.4 times faster and 2.6 time slower than decreasing SCV, respectively. These results underscore the critical role of landscape heterogeneity in modulating grassland responses to drought, suggesting that managing vegetation patches could enhance ecosystem resilience. Full article
(This article belongs to the Special Issue Root-Zone Soil Moisture Retrieval and Applications from Remote Sensing Measurements)
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28 pages, 5528 KB  
Article
Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning
by Shukran A. Sahaar and Jeffrey D. Niemann
Remote Sens. 2024, 16(19), 3699; https://doi.org/10.3390/rs16193699 - 4 Oct 2024
Cited by 12 | Viewed by 4906
Abstract
This study explores machine learning for estimating soil moisture at multiple depths (0–5 cm, 0–10 cm, 0–20 cm, 0–50 cm, and 0–100 cm) across the coterminous United States. A framework is developed that integrates soil moisture from Soil Moisture Active Passive (SMAP), precipitation [...] Read more.
This study explores machine learning for estimating soil moisture at multiple depths (0–5 cm, 0–10 cm, 0–20 cm, 0–50 cm, and 0–100 cm) across the coterminous United States. A framework is developed that integrates soil moisture from Soil Moisture Active Passive (SMAP), precipitation from the Global Precipitation Measurement (GPM), evapotranspiration from the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), vegetation data from the Moderate Resolution Imaging Spectroradiometer (MODIS), soil properties from gridded National Soil Survey Geographic (gNATSGO), and land cover information from the National Land Cover Database (NLCD). Five machine learning algorithms are evaluated including the feed-forward artificial neural network, random forest, extreme gradient boosting (XGBoost), Categorical Boosting, and Light Gradient Boosting Machine. The methods are tested by comparing to in situ soil moisture observations from several national and regional networks. XGBoost exhibits the best performance for estimating soil moisture, achieving higher correlation coefficients (ranging from 0.76 at 0–5 cm depth to 0.86 at 0–100 cm depth), lower root mean squared errors (from 0.024 cm3/cm3 at 0–100 cm depth to 0.039 cm3/cm3 at 0–5 cm depth), higher Nash–Sutcliffe Efficiencies (from 0.551 at 0–5 cm depth to 0.694 at 0–100 cm depth), and higher Kling–Gupta Efficiencies (0.511 at 0–5 cm depth to 0.696 at 0–100 cm depth). Additionally, XGBoost outperforms the SMAP Level 4 product in representing the time series of soil moisture for the networks. Key factors influencing the soil moisture estimation are elevation, clay content, aridity index, and antecedent soil moisture derived from SMAP. Full article
(This article belongs to the Special Issue Root-Zone Soil Moisture Retrieval and Applications from Remote Sensing Measurements)
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