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Special Issue "Satellite Remotely Sensed Soil Moisture"

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

Deadline for manuscript submissions: closed (15 July 2019).

Special Issue Editors

Dr. José Darrozes
Website1 Website2 SciProfiles
Guest Editor
Géosciences Environnement Toulouse (GET), UMR CNRS5563, CNRS/IRD/UPS, Observatoire Midi-Pyrénées (OMP), 14 Avenue Edouard Belin, 31400 Toulouse, France
Interests: earth observation; river morphology; near surface geophysics; soil moisture; GNSS-R; water cycle; soil contamination; remote sensing
Special Issues and Collections in MDPI journals
Dr. Frédéric Frappart
Website
Guest Editor
Laboratoire d'Etudes en Géophysique et Océanographie Spatiales, UMR 5566, CNES/CNRS/IRD/UPS, Observatoire Midi-Pyrénées, 14 Avenue Edouard Belin, 31400 Toulouse, France
Interests: hydrology; land surface processes; remote sensing
Special Issues and Collections in MDPI journals
Dr. Ernesto Lopez-Baeza
Website
Guest Editor
Director of the Climatology from Satellites Group (GIUV2018-407) Earth Physics & Thermodynamics Dept. Faculty of Physics. University of Valencia C/ Dr Moliner, 50. Burjassot. 46100 Valencia, Spain
Interests: remote sensing; soil moisture; earth observation; validation; vegetation biophysical parameters; water resources management and sustainability
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Soil moisture is a key component of the water cycle. It is also one of the main actor of the critical zone for conducting climate studies, weather predictions, flood monitoring and aquifer recharge. The different current measurement techniques have a wide range of characteristics in terms of spatial resolution, time resolution and precision. The optimization of one of these three parameters is unfortunately often to the detriment of the other two.

With the advent of remote sensing techniques, soil moisture is measured at a global scale, but to the detriment of temporal and spatial resolutions.

This Special Issue is dedicated to highlight the new downscaling/fusing researches (VNIS, SWIR, thermal IR, GNSS-R) and development of new sensors (from in situ to space-based sensors) for soil moisture retrieval.

Dr. José Darrozes
Dr. Frédéric Frappart
Dr. Ernesto Lopez-Baeza
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 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

  • GNSS-R
  • active and passive microwave remote sensing
  • thermal imagery
  • in situ gauge
  • fusing model
  • downscaling model
  • upscaling techniques
  • VNIR
  • SWIR
  • Multispectral/Hyperspectral imagery
  • GRACE
  • SMOS
  • SMAP
  • Sentinel-1

Published Papers (6 papers)

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Open AccessArticle
Toward High-Resolution Soil Moisture Monitoring by Combining Active-Passive Microwave and Optical Vegetation Remote Sensing Products with Land Surface Model
Sensors 2019, 19(18), 3924; https://doi.org/10.3390/s19183924 - 11 Sep 2019
Abstract
The assimilation of radiometer and synthetic aperture radar (SAR) data is a promising recent technique to downscale soil moisture products, yet it requires land surface parameters and meteorological forcing data at a high spatial resolution. In this study, we propose a new downscaling [...] Read more.
The assimilation of radiometer and synthetic aperture radar (SAR) data is a promising recent technique to downscale soil moisture products, yet it requires land surface parameters and meteorological forcing data at a high spatial resolution. In this study, we propose a new downscaling approach, named integrated passive and active downscaling (I-PAD), to achieve high spatial and temporal resolution soil moisture datasets over regions without detailed soil data. The Advanced Microwave Scanning Radiometer (AMSR-E) and Phased Array-type L-band SAR (PALSAR) data are combined through a dual-pass land data assimilation system to obtain soil moisture at 1 km resolution. In the first step, fine resolution model parameters are optimized based on fine resolution PALSAR soil moisture and moderate-resolution imaging spectroradiometer (MODIS) leaf area index data, and coarse resolution AMSR-E brightness temperature data. Then, the 25 km AMSR-E observations are assimilated into a land surface model at 1 km resolution with a simple but computationally low-cost algorithm that considers the spatial resolution difference. Precipitation data are used as the only inputs from ground measurements. The evaluations at the two lightly vegetated sites in Mongolia and the Little Washita basin show that the time series of soil moisture are improved at most of the observation by the assimilation scheme. The analyses reveal that I-PAD can capture overall spatial trends of soil moisture within the coarse resolution radiometer footprints, demonstrating the potential of the algorithm to be applied over data-sparse regions. The capability and limitation are discussed based on the simple optimization and assimilation schemes used in the algorithm. Full article
(This article belongs to the Special Issue Satellite Remotely Sensed Soil Moisture)
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Open AccessArticle
Evaluation of ESA Active, Passive and Combined Soil Moisture Products Using Upscaled Ground Measurements
Sensors 2019, 19(12), 2718; https://doi.org/10.3390/s19122718 - 17 Jun 2019
Cited by 2
Abstract
The European Space Agency (ESA) Climate Change Initiative (CCI) project combines multi-sensors at different microwave frequencies to derive three harmonized soil moisture products using active, passive and combined approaches. These long-term soil moisture products assist in understanding the global water and carbon cycles. [...] Read more.
The European Space Agency (ESA) Climate Change Initiative (CCI) project combines multi-sensors at different microwave frequencies to derive three harmonized soil moisture products using active, passive and combined approaches. These long-term soil moisture products assist in understanding the global water and carbon cycles. However, extensive validations are a prerequisite before applying the retrieved soil moisture into climatic or hydrological models. To fulfill this objective, we assess the performances of three CCI soil moisture products (active, passive and combined) with respect to in-situ soil moisture networks located in China, Spain and Canada. In order to compensate the scale differences between ground stations and the CCI product’s coarse resolution, we adopted two upscaling approaches of Inverse Distance Weighting (IDW) interpolation and simple Arithmetic Mean (AM). The temporal agreements between the satellite retrieved and ground-measured soil moisture were quantified using the unbiased root mean square error (ubRMSE), RMSE, correlation coefficients (R) and bias. Furthermore, the temporal variability of the CCI soil moisture is interpreted and verified with respect to the Tropical Rainfall Measuring Mission (TRMM) precipitation observations. The results show that the temporal variations of CCI soil moisture agreed with the in-situ ground measurements and the precipitation observations over the China and Spain test sites. In contrast, a significant overestimation was observed over the Canada test sites, which may be due to the strong heterogeneity in soil and vegetation characteristics in accordance with the reported poor performance of soil moisture retrieval there. However, despite a retrieval bias, the relatively temporal variation of the CCI soil moisture also followed the ground measurements. For all the three test sites, the soil moisture retrieved from the combined approach outperformed the active-only and passive-only methods, with ubRMSE of 0.034, 0.050, and 0.050–0.054 m3/m3 over the test sites in China, Spain and Canada, respectively. Thus, the CCI combined soil moisture product is suggested to drive the climatic and hydrological studies. Full article
(This article belongs to the Special Issue Satellite Remotely Sensed Soil Moisture)
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Open AccessArticle
Intercomparison of Soil Moisture Retrieved from GNSS-R and from Passive L-Band Radiometry at the Valencia Anchor Station
Sensors 2019, 19(8), 1900; https://doi.org/10.3390/s19081900 - 22 Apr 2019
Cited by 2
Abstract
In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an [...] Read more.
In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and a dual-pol down-looking antenna for receiving LHCP (left-hand circular polarization) and RHCP (right-hand circular polarization) reflected signals from the soil surface. Data were collected from the three different antennas through the two channels of Oceanpal GNSS-R receiver and, in addition, calibration was performed to reduce the impact from the differing channels. Reflectivity was thus measured, and soil moisture could be retrieved. The ESA (European Space Agency)-funded ELBARA-II (ESA L Band Radiometer II) is an L-band radiometer with two channels with 11 MHz bandwidth and respective center frequencies of 1407.5 MHz and 1419.5 MHz. The ELBARAII antenna is a large dual-mode Picket horn that is 1.4 m wide, with a length of 2.7 m with −3 dB full beam width of 12° (±6° around the antenna main direction) and a gain of 23.5 dB. By comparing GNSS-R and ELBARA-II radiometer data, a high correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical reflectivity from the radiometer (with correlation coefficients ranging from 0.83 to 0.91). Neural net fitting was used for GNSS-R soil moisture inversion, and the RMSE (Root Mean Square Error) was 0.014 m3/m3. The determination coefficient between the retrieved soil moisture and in situ measurements was R2 = 0.90 for Oceanpal and R2 = 0.65 for Elbara II, and the ubRMSE (Unbiased RMSE) were 0.0128 and 0.0734 respectively. The soil moisture retrievals by both L-band remote sensing methods show good agreement with each other, and their mutual correspondence with in-situ measurements and with rainfall was also good. Full article
(This article belongs to the Special Issue Satellite Remotely Sensed Soil Moisture)
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Open AccessArticle
Soil Moisture Retrieval by Integrating TASI-600 Airborne Thermal Data, WorldView 2 Satellite Data and Field Measurements: Petacciato Case Study
Sensors 2019, 19(7), 1515; https://doi.org/10.3390/s19071515 - 28 Mar 2019
Cited by 6
Abstract
Soil moisture (SM) plays a fundamental role in the terrestrial water cycle and in agriculture, with key applications such as the monitoring of crop growing and hydrogeological management. In this study, a calibration procedure was applied to estimate SM based on the integration [...] Read more.
Soil moisture (SM) plays a fundamental role in the terrestrial water cycle and in agriculture, with key applications such as the monitoring of crop growing and hydrogeological management. In this study, a calibration procedure was applied to estimate SM based on the integration of in situ and airborne thermal remote sensing data. To this aim, on April 2018, two airborne campaigns were carried out with the TASI-600 multispectral thermal sensor on the Petacciato (Molise, Italy) area. Simultaneously, soil samples were collected in different agricultural fields of the study area to determine their moisture content and the granulometric composition. A WorldView 2 high-resolution visible-near infrared (VNIR) multispectral satellite image was acquired to calculate the albedo of the study area to be used together with the TASI images for the estimation of the apparent thermal inertia (ATI). Results show a good correlation (R2 = 0.62) between the estimated ATI and the SM of the soil samples measured in the laboratory. The proposed methodology has allowed us to obtain a SM map for bare and scarcely vegetated soils in a wide agricultural area in Italy which concerns cyclical hydrogeological instability phenomena. Full article
(This article belongs to the Special Issue Satellite Remotely Sensed Soil Moisture)
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Open AccessArticle
Spatial Evaluation of Soil Moisture (SM), Land Surface Temperature (LST), and LST-Derived SM Indexes Dynamics during SMAPVEX12
Sensors 2019, 19(5), 1247; https://doi.org/10.3390/s19051247 - 12 Mar 2019
Cited by 3
Abstract
Downscaling microwave soil moisture (SM) with optical/thermal remote sensing data has considerable application potential. Spatial correlations between SM and land surface temperature (LST) or LST-derived SM indexes (SMIs) are vital to the current optical/thermal and microwave fusion downscaling methods. In this study, the [...] Read more.
Downscaling microwave soil moisture (SM) with optical/thermal remote sensing data has considerable application potential. Spatial correlations between SM and land surface temperature (LST) or LST-derived SM indexes (SMIs) are vital to the current optical/thermal and microwave fusion downscaling methods. In this study, the spatial correlations were evaluated at the same spatial scale using SMAPVEX12 SM data and MODIS day/night LST products. LST-derived SMIs was calculated using NLDAS-2 gridded meteorological data with conventional trapezoid and two-stage trapezoid models. Results indicated that (1) SM agrees better with daytime LST than the nighttime or the day-night differential LST; (2) the daytime LSTs on Aqua and Terra present very similar spatial agreement with SM and they have very similar performances as downscaling factors in simulating SM; (3) decoupling effect among SM, LST, and LST-derived SMIs occurs not only in very wet but also in very dry condition; and (4) the decoupling effect degrades the performance of LST as a downscaling factor. The future downscaling algorithms should consider net surface radiation and soil type to tackle the decoupling effect. Full article
(This article belongs to the Special Issue Satellite Remotely Sensed Soil Moisture)
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Open AccessLetter
Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France
Sensors 2019, 19(4), 802; https://doi.org/10.3390/s19040802 - 16 Feb 2019
Cited by 5
Abstract
The objective of this paper is to present an analysis of Sentinel-1 derived surface soil moisture maps (S1-SSM) produced with high spatial resolution (at plot scale) and a revisit time of six days for the Occitanie region located in the South of France [...] Read more.
The objective of this paper is to present an analysis of Sentinel-1 derived surface soil moisture maps (S1-SSM) produced with high spatial resolution (at plot scale) and a revisit time of six days for the Occitanie region located in the South of France as a function of precipitation data, in order to investigate the potential of S1-SSM maps for detecting heavy rainfalls. First, the correlation between S1-SSM maps and rainfall maps provided by the Global Precipitation Mission (GPM) was investigated. Then, we analyzed the effect of the S1-SSM temporal resolution on detecting heavy rainfall events and the impact of these events on S1-SSM values as a function of the number of days that separated the heavy rainfall and the S1 acquisition date (cumulative rainfall more than 60 mm in 24 hours or 80 mm in 48 hours). The results showed that the six-day temporal resolution of the S1-SSM map doesn’t always permit the detection of an extreme rainfall event, because confusion will appear between high S1-SSM values due to extreme rainfall events occurring six days before the acquisition of S1-SSM, and high S1-SSM values due to light rain a few hours before the acquisition of Sentinel-1 images. Moreover, the monitoring of extreme rain events using only soil moisture maps remains difficult, since many environmental parameters could affect the value of SSM, and synthetic aperture radar (SAR) doesn’t allow the estimation of very high soil moistures (higher than 35 vol.%). Full article
(This article belongs to the Special Issue Satellite Remotely Sensed Soil Moisture)
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