Special Issue "Satellite Soil Moisture Validation and Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Dr. John J. Qu
Website1 Website2
Guest Editor
Professor and Director, GENRI & ESTC, Department of Geography and GeoInformation Science (GGS), Global Environment and Natural Resources Institute (GENRI), College of Science, George Mason University, Fairfax, VA 22030, USA
Interests: remote sensing; Earth system and climate science; soil moisture and drought monitoring; water-energy-food nexus; environment and fire science
Special Issues and Collections in MDPI journals
Dr. Luca Brocca
grade Website SciProfiles
Guest Editor
Research Institute for Geo-Hydrological Protection, National Research Council, Italy
Interests: soil moisture; rainfall; river discharge; flood; landslide; drought; water resources management, and agriculture
Special Issues and Collections in MDPI journals
Dr. Michael H. Cosh

Guest Editor
Research Hydrologist, Hydrology and Remote Sensing Laboratory, Rm 104 Bldg 007 BARC-West,10300 Baltimore Ave, Beltsville, MD 20705, USA
Interests: In-situ soil moisture network validation, scaling of land surface parameters to satellite scale, validation of satellite soil moisture products
Dr. Andreas Colliander
Website
Guest Editor
Research Scientist, Terrestrial Hydrology Group, Jet Propulsion Laboratory, 4800 Oak Grove Drive, M/S 300-233, Pasadena, CA91109, USA
Interests: observation and analysis of hydrologic and other Earth system phenomena utilizing microwave remote sensing techniques and their combination with other techniques
Special Issues and Collections in MDPI journals
Dr. Xianjun Hao
Website1 Website2
Guest Editor
Research Professor, Department of Geography and GeoInformation Science (GGS) & Environmental Science and Technology Center (ESTC)/ Global Environment and Natural Resources Institute (GENRI), College of Science, George Mason University, Fairfax, VA 22030, USA
Interests: satellite remote sensing applications; earth sciences and climate change; soil moisture and drought monitoring; data science and high performance computing

Special Issue Information

Dear Colleagues,

Soil moisture is a key element of our Earth’s system and an important indicator of climate change. Soil is the medium for plant growth and the substrate for all biogeochemical and biogeophysical processes. Its unique natural organization forms the foundation of any food–water–energy nexus system. In addition, soil is a very large reservoir for water and carbon with strong influences on local, regional, and global climate. The purpose of the 6th Satellite Soil Moisture and Application Workshop (15-17 Sept. 2020 in Perugia, Italy) is to discuss and reconcile recent methodological advances in the development, validation and application of satellite soil moisture observations. The workshop series is unique as it brings together users and developers of satellite soil moisture measurements with the aim of deriving and exploiting soil moisture from passive and active microwave satellite missions\sensors as well as optical instruments. The topics of the Special Issue include:

 

1) Soil Moisture Data Product Validation;
2) Space and in situ soil moisture measurements;
3) Soil Moisture Applications in Natural Hazards Monitoring;
4) Weather and Climate Modeling;
5) The Role of Soil Moisture in Carbon Cycle and Ecology;
6) Hydrology and Water Resources;
7) Agriculture and Food Security.
8) Soil-Ecosystem-Carbon-Climate (SECC) Nexus
9) Water-Energy-Food Nexus
10) WMO Soil Moisture Demonstration Project (SMDP)
11) International Soil Moisture Standard and Guidelines

Dr. John J. Qu
Dr. Eng. Luca Brocca
Dr. Michael H. Cosh
Dr. Andreas Colliander
Dr. Xianjun Hao
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. 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 2200 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

  • satellite
  • soil moisture
  • validation
  • applications
  • standard
  • guideline
  • demonstration

Published Papers (4 papers)

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Research

Open AccessArticle
Assessment of Remotely Sensed and Modelled Soil Moisture Data Products in the U.S. Southern Great Plains
Remote Sens. 2020, 12(12), 2030; https://doi.org/10.3390/rs12122030 - 24 Jun 2020
Abstract
Soil moisture (SM) plays a crucial role in the water and energy flux exchange between the atmosphere and the land surface. Remote sensing and modeling are two main approaches to obtain SM over a large-scale area. However, there is a big difference between [...] Read more.
Soil moisture (SM) plays a crucial role in the water and energy flux exchange between the atmosphere and the land surface. Remote sensing and modeling are two main approaches to obtain SM over a large-scale area. However, there is a big difference between them due to algorithm, spatial-temporal resolution, observation depth and measurement uncertainties. In this study, an assessment of the comparison of two state-of-the-art remotely sensed SM products, Soil Moisture Active Passive (SMAP) and European Space Agency Climate Change Initiative (ESACCI), and one land surface modeled dataset from the North American Land Data Assimilation System project phase 2 (NLDAS-2), were conducted using 17 permanent SM observation sites located in the Southern Great Plains (SGP) in the U.S. We first compared the daily mean SM of three products with in-situ measurements; then, we decompose the raw time series into a short-term seasonal part and anomaly by using a moving smooth window (35 days). In addition, we calculate the daily spatial difference between three products based on in-situ data and assess their temporal evolution. The results demonstrate that (1) in terms of temporal correlation R, the SMAP (R = 0.78) outperforms ESACCI (R = 0.62) and NLDAS-2 (R = 0.72) overall; (2) for the seasonal component, the correlation R of SMAP still outperforms the other two products, and the correlation R of ESACCI and NLDAS-2 have not improved like the SMAP; as for anomaly, there is no difference between the remotely sensed and modeling data, which implies the potential for the satellite products to capture the variations of short-term rainfall events; (3) the distribution pattern of spatial bias is different between the three products. For NLDAS-2, it is strongly dependent on precipitation; meanwhile, the spatial distribution of bias represents less correlation with the precipitation for two remotely sensed products, especially for the SMAP. Overall, the SMAP was superior to the other two products, especially when the SM was of low value. The difference between the remotely sensed and modeling products with respect to the vegetation type might be an important reason for the errors. Full article
(This article belongs to the Special Issue Satellite Soil Moisture Validation and Applications)
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Open AccessArticle
Evaluation of Satellite-Derived Surface Soil Moisture Products over Agricultural Regions of Canada
Remote Sens. 2020, 12(9), 1455; https://doi.org/10.3390/rs12091455 - 04 May 2020
Abstract
Soil moisture is a critical indicator for climate change and agricultural drought, but its measurement is challenging due to large variability with land cover, soil type, time, space and depth. Satellite estimates of soil moisture are highly desirable and have become more widely [...] Read more.
Soil moisture is a critical indicator for climate change and agricultural drought, but its measurement is challenging due to large variability with land cover, soil type, time, space and depth. Satellite estimates of soil moisture are highly desirable and have become more widely available over the past decade. This study investigates and compares the performance of four surface soil moisture satellite datasets over Canada, namely, Soil Moisture and Ocean Salinity Level 3 (SMOS L3), versions 3.3 and 4.2 of European Space Agency Climate Change Initiative (ESA CCI) soil moisture product and a recent product called SMOS-INRA-CESBIO (SMOS-IC) that contains corrections designed to reduce several known sources of uncertainty in SMOS L3. These datasets were evaluated against in situ networks located in mostly agricultural regions of Canada for the period 2012 to 2014. Two statistical comparison methods were used, namely, metrics for mean soil moisture and median of metrics. The results suggest that, while both methods show similar comparisons for regional networks, over large networks, the median of metrics method is more representative of the overall correlation and variability and is therefore a more appropriate method for evaluating the performance of satellite products. Overall, the SMOS products have higher daily temporal correlations, but larger biases, against in situ soil moisture than the ESA CCI products, with SMOS-IC having higher correlations and smaller variability than SMOS L3. The SMOS products capture daily wetting and drying events better than the ESA CCI products, with the SMOS products capturing at least 75% of observed drying as compared to 55% for the ESA CCI products. Overall, for periods during which there are sufficient observations, both SMOS products are more suitable for agricultural applications over Canada than the ESA CCI products, even though SMOS-IC is able to capture soil moisture variability more accurately than SMOS L3. Full article
(This article belongs to the Special Issue Satellite Soil Moisture Validation and Applications)
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Open AccessArticle
Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau
Remote Sens. 2020, 12(1), 183; https://doi.org/10.3390/rs12010183 - 03 Jan 2020
Cited by 1
Abstract
Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements [...] Read more.
Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII. Full article
(This article belongs to the Special Issue Satellite Soil Moisture Validation and Applications)
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Open AccessArticle
Evaluation of Two SMAP Soil Moisture Retrievals Using Modeled- and Ground-Based Measurements
Remote Sens. 2019, 11(24), 2891; https://doi.org/10.3390/rs11242891 - 04 Dec 2019
Cited by 1
Abstract
A comprehensive evaluation of the performance of satellite-based soil moisture (SM) retrievals is undoubtedly very important to improve its quality and evaluate its potential application in hydrology, climate, and natural disasters (drought, flood, etc.). Since the release of the SMAP (Soil Moisture Active [...] Read more.
A comprehensive evaluation of the performance of satellite-based soil moisture (SM) retrievals is undoubtedly very important to improve its quality and evaluate its potential application in hydrology, climate, and natural disasters (drought, flood, etc.). Since the release of the SMAP (Soil Moisture Active Passive) mission data in April 2015, the associated SM retrieval algorithms have developed rapidly, and their improvement work is still in progress. However, some newly developed SM retrievals have not been fully assessed and inter-compared. One such product is the new multi-temporal dual-channel retrieval algorithm (MT-DCA) SM retrievals, which was recently retrieved using the so-called MT-DCA algorithm. To solve this, we aim to assess the MT-DCA SM retrievals along with the SMAP-enhanced level three SM products (SPL3SMP_E, version 2). More specifically, in this paper we evaluated and inter-compared the two SMAP SM retrievals with the ECMWF (European Centre for Medium-Range Weather Forecasts) modeled SM and ISMN (International Soil Moisture Network) in situ observations by applying four statistical scores: Pearson correlation coefficient (R), root mean square difference (RMSD), bias, and unbiased RMSD (ubRMSD). It was found that both SMAP SM retrievals can better capture the seasonal variations of ECMWF-modeled SM and ground-based measurements according to correlations, and MT-DCA SM was drier than SPL3SMP_E SM by ~0.018 m3/m3 on average on a global scale. With respect to the ISMN ground-based measurements, the performance of SPL3SMP_E SM compared better than the MT-DCA SM. The median ubRMSD of SPL3SMP_E SM and MT-DCA SM with ground measurements computed over all selected ISMN sites were 0.058 m3/m3 and 0.070 m3/m3, respectively. Full article
(This article belongs to the Special Issue Satellite Soil Moisture Validation and Applications)
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