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Spatial or Temporal Analysis of Soil Moisture from Space

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

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 4174

Special Issue Editors

Interactions Sol Plante Atmosphère, UMR 1391 INRAE/Bordeaux Science Agro 71, Avenue Edouard Bourlaux, 33882 Villenave d'Ornon, France
Interests: remote sensing; water cycle; carbon cycle; wetlands
Special Issues, Collections and Topics in MDPI journals
INRAE, UMR1391 ISPA, F-33140 Villenave d'Ornon, France
Interests: microwave soil moisture modeling; validation; carbon cycle estimation
Special Issues, Collections and Topics in MDPI journals
INRAE, UMR 1114 EMMAH, UMT CAPTE, F-84000 Avignon, France
Interests: microwave/optical remote sensing; soil moisture; vegetation water/biomass; L-MEB; PROSAIL; hydro-ecological applications
Special Issues, Collections and Topics in MDPI journals
Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: microwave remote sensing; soil moisture; vegetation optical depth
French National Centre for Scientific Research | CNRS, Centre d’études spatiales de la biosphère (CESBIO), Universite Paul Sabatier Toulouse III, Toulouse, France
Interests: airborne instrumentation for land surfaces; microwave remote sensing; GNSS-R; GNSS; land surfaces; spatial hydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil moisture (SM) is an essential hydrological variable that connects terrestrial water, energy, and carbon cycles. It is highly essential for the regulation of both land–atmosphere interactions and hydrological cycles. High-quality, long-term, and large-scale SM products are crucial for understanding, modeling, and forecasting climate change, flood/drought early warnings, and agricultural monitoring. Accurate SM information is necessary in all of these fields at fine spatial resolutions (~1 km) with continuous temporal coverage.

This Special Issue aims to present reviews and recent advances in the general interest in the use of remote sensing observations for monitoring soil moisture.

Manuscripts can be related to any aspect of soil moisture estimates using satellite or AUV observations. They can be related to either new methodological developments or new advances in sensors as well as original studies based on the spatial and/or temporal monitoring of soil moisture from local to global scales.

Dr. Frédéric Frappart
Dr. Nicolas Baghdadi
Dr. Xiaojun Li
Dr. Hongliang Ma
Dr. Zanpin Xing
Dr. Mehrez Zribi
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • surface and root-zone soil moisture
  • passive and active microwaves
  • downscaling
  • data assimilation
  • eco-hydrological applications
  • evaluation/comparison

Published Papers (2 papers)

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Research

17 pages, 5649 KiB  
Article
Dual-Frequency Retrieval of Soil Moisture from L- and S-Band Radar Data for Corn and Soybean
by Tien-Hao Liao and Seung-Bum Kim
Remote Sens. 2022, 14(22), 5875; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225875 - 19 Nov 2022
Cited by 1 | Viewed by 1547
Abstract
Radar backscattering responds differently to soil moisture due to vegetation effects depending on the microwave frequency. The retrieval of soil moisture using a single frequency has been common. In this paper, we study how soil moisture retrieval performs using dual-frequency radar backscattering (L- [...] Read more.
Radar backscattering responds differently to soil moisture due to vegetation effects depending on the microwave frequency. The retrieval of soil moisture using a single frequency has been common. In this paper, we study how soil moisture retrieval performs using dual-frequency radar backscattering (L- and S-bands) compared with using L-band only. The dual-frequency inputs increase the amount of independent information, which is expected to reduce the uncertainty in estimating soil moisture. Forward scattering models for corn and soybean fields were previously generated and validated with the L-band for the SMAPVEX12 campaign: they are inverted as an independent test for the retrieval of soil moisture using the SMEX02 campaign data in this paper. It is demonstrated that L-band modeling of forward scattering processes is scalable at the S-band, in that the physics and parameters behind modeling the vegetation effects remain the same between L- and S-bands. Either L- or S-band single-frequency retrieval has reliable performance for soil moisture retrieval. Furthermore, averaging the retrieved soil moisture from both frequencies further improves the retrieval performance. The averaging avoids the determination of the weights of the L- and S-band sigma0 during the cost function minimization. The dual frequency retrieval is evaluated with the unbiased RMSE of 0.031 and 0.057 m3/m3 for corn and soybean, respectively, which are improvements by up to 0.010 and 0.004 m3/m3, compared with single-frequency cases. The findings here can apply to the upcoming NISAR mission featuring L- and S-bands. Full article
(This article belongs to the Special Issue Spatial or Temporal Analysis of Soil Moisture from Space)
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19 pages, 5247 KiB  
Article
Spatiotemporal Characteristics of Soil Moisture and Land–Atmosphere Coupling over the Tibetan Plateau Derived from Three Gridded Datasets
by Huimin Wang, Beilei Zan, Jiangfeng Wei, Yuanyuan Song and Qianqian Mao
Remote Sens. 2022, 14(22), 5819; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225819 - 17 Nov 2022
Cited by 2 | Viewed by 1642
Abstract
Soil moisture is a crucial component of the water cycle and plays an important role in regional weather and climate. However, owing to the lack of In Situ observations, an accurate understanding of the spatiotemporal variations of soil moisture (SM) on the Tibetan [...] Read more.
Soil moisture is a crucial component of the water cycle and plays an important role in regional weather and climate. However, owing to the lack of In Situ observations, an accurate understanding of the spatiotemporal variations of soil moisture (SM) on the Tibetan Plateau (TP) is still lacking. In this study, we used three gridded SM products to characterize the spatiotemporal features of SM on the TP during the warm season (May to August). We analyzed the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5), Global Land Data Assimilation System (GLDAS), and Soil Moisture Active Passive (SMAP) datasets and used station observation data and triple collocation to quantify product accuracy and consistency. Results of the evaluation based on observation data show that both ERA5 and GLDAS overestimate SM, while the accuracy of SMAP is high. In terms of capturing the temporal variations of SM measured at stations, the performance of ERA5 and that of SMAP are superior to that of GLDAS. According to the evaluation based on triple collocation, SMAP exhibits the smallest random error over the TP and the highest temporal correlation with the unknown true SM in eastern TP. For SMAP, SM variability is the largest in the southern TP. For ERA5 and GLDAS, variability in the western TP is substantially larger than that for SMAP. Low-frequency (30–90 days) variations are the largest contributor to TP SM intraseasonal variability. Relative to SMAP, the contribution of high-frequency variations is low in ERA5 and GLDAS. Land-atmosphere coupling is stronger (weaker) in the western (southeastern) TP, which is relatively dry (wet). Our evaluation of SM product performance over the TP may facilitate the use of these products for disaster monitoring and climate and hydrological studies. Full article
(This article belongs to the Special Issue Spatial or Temporal Analysis of Soil Moisture from Space)
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