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Applications and Downscaling of Remote Sensing Soil Moisture
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Applications and Downscaling of Remote Sensing Soil Moisture

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (16 December 2021) | Viewed by 3652

Special Issue Editors

Prof. Dr. Mercè Vall-llossera

E-Mail Website
Guest Editor
CommSensLab, Unidad de Excelencia María de Maeztu, and IEEC Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, E-08034 Barcelona, Spain
Interests: microwave remote sensing; soil moisture; VOD applications; wildfires; epidemiological diseases; SMOS; Sentinel
Dr. Miriam Pablos

E-Mail Website
Guest Editor
Institute of Marine Sciences, Spanish National Research Council, and Barcelona Expert Centre, E-08003 Barcelona, Spain
Interests: microwave remote sensing; soil moisture; retrieval and disaggregation algorithms; calibration and validation of satellite products; SMOS; SMAP
Dr. David Chaparro

E-Mail Website
Guest Editor
CommSensLab, Unidad de Excelencia María de Maeztu and IEEC Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, E-08034 Barcelona, Spain
Interests: Interests: microwave remote sensing; soil moisture; VOD applications; vegetation ecology; forests biomass; wildfires

Special Issue Information

Dear Colleagues,

Soil moisture is an essential variable in land, climate, and vegetation modelling. Remote sensing retrieval techniques and applications are a cutting-edge research topic due to the scientific and societal relevance of soil moisture products. In particular, new retrieval methods from both satellite and airborne platforms have been introduced; consequently, global and regional applications are being constantly developed. New downscaling algorithms using multi-sensor approaches are improving, which have, in turn, increased their unprecedented applicability.

This Special Issue is aimed at representing the most recent advances in soil moisture remote sensing. This includes, but is not limited to, the following topics:

  • Emerging global and regional applications
  • Soil moisture downscaling techniques
  • Multi-sensor approaches, including passive and active techniques
  • Product development and validation
  • New soil moisture retrieval algorithms
  • Other related topics

Prof. Dr. Mercè Vall-llossera
Dr. Miriam Pablos
Dr. David Chaparro
Guest Editors

Manuscript Submission Information

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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. Sensors 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 2600 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

  • soil moisture
  • downscaling algorithms
  • soil moisture applications
  • product validation
  • multi-sensor approaches
  • soil moisture retrieval

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Published Papers (1 paper)

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Research

26 pages, 4402 KiB  
Article
A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data
by Nitu Ojha, Olivier Merlin, Abdelhakim Amazirh, Nadia Ouaadi, Vincent Rivalland, Lionel Jarlan, Salah Er-Raki and Maria Jose Escorihuela
Sensors 2021, 21(21), 7406; https://doi.org/10.3390/s21217406 - 8 Nov 2021
Cited by 4 | Viewed by 2814
Abstract
Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite [...] Read more.
Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration. Full article
(This article belongs to the Special Issue Applications and Downscaling of Remote Sensing Soil Moisture)
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