Surface Soil Moisture Retrievals Under Forest Canopy For L-band SAR Observations Across A Wide Range of Incidence Angles By Inverting A Physical Scattering Model

One of the limitations of presently available Synthetic Aperture Radar (SAR) surface soil moisture (SSM) products is their moderated temporal resolution (e.g., 3-4 days) that is non optimal for several applications, as most user requirements point to a temporal resolution of 1-2 days or less. A possible path to tackle this issue is to coordinate multi-mission SAR acquisitions with a view to the future Copernicus Sentinel-1 (C&D and Next Generation) and L-band Radar Observation System for Europe (ROSE-L).In this respect, the recent agreement between the Japanese (JAXA) and European (ESA) Space Agencies on the use of SAR Satellites in Earth Science and Applications provides a framework to develop and validate multi-frequency and multi-platform SAR SSM products. In 2019 and 2020, to support insights on the interoperability between C- and L-band SAR observations for SSM retrieval, Sentinel-1 and ALOS-2 systematic acquisitions over the TERENO (Terrestrial Environmental Observatories) Selhausen (Germany) and Apulian Tavoliere (Italy) cal/val sites were gathered. Both sites are well documented and equipped with hydrologic networks.The objective of this study is to investigate the integration of multi-frequency SAR measurements for a consistent and harmonized SSM retrieval throughout the error characterization of a combined C- and L-band SSM product. To this scope, time series of Sentinel-1 IW and ALOS-2 FBD data acquired over the two sites will be analysed. The short time change detection (STCD) algorithm, developed, implemented and recently assessed on Sentinel-1 data [e.g., Balenzano et al., 2020; Mattia et al., 2020], will be tailored to the ALOS-2 data. Then, the time series of SAR SSM maps from each SAR system will be derived separately and aggregated in an interleaved SSM product. Furthermore, it will be compared against in situ SSM data systematically acquired by the ground stations deployed at both sites. The study will assess the interleaved SSM product and evaluate the homogeneous quality of C- and L-band SAR SSM maps.&#160;&#160;ReferencesBalenzano. A., et al., &#8220;Sentinel-1 soil moisture at 1km resolution: a validation study&#8221;, submitted to Remote Sensing of Environment (2020).Mattia, F., A. Balenzano, G. Satalino, F. Lovergine, A. Loew, et al., &#8220;ESA SEOM Land project on Exploitation of Sentinel-1 for Surface Soil Moisture Retrieval at High Resolution,&#8221; final report, contract number 4000118762/16/I-NB, 2020.

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An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

10.5194/hess-2019-105 ◽

2019 ◽

Cited By ~ 3

Author(s):

Bouchra Ait Hssaine ◽

Olivier Merlin ◽

Jamal Ezzahar ◽

Nitu Ojha ◽

Salah Er-raki ◽

...

Keyword(s):

Soil Moisture ◽

Energy Balance ◽

Surface Temperature ◽

Land Surface Temperature ◽

Vegetation Cover ◽

Land Surface ◽

Surface Soil ◽

Surface Soil Moisture ◽

Wide Range ◽

The Mean

Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1 km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014–2018). The field was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015–2016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated αPT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved αPT remains at a mostly constant value (∼ 0.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62 W/m2 for S1, S2, S3 and B1 respectively.

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Soil Hydraulic Parameters and Surface Soil Moisture of a Tilled Bare Soil Plot Inversely Derived from L-Band Brightness Temperatures

Vadose Zone Journal ◽

10.2136/vzj2013.04.0075 ◽

2014 ◽

Vol 13 (1) ◽

pp. vzj2013.04.0075 ◽

Cited By ~ 10

Author(s):

M. Dimitrov ◽

J. Vanderborght ◽

K. G. Kostov ◽

K. Z. Jadoon ◽

L. Weihermüller ◽

...

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Bare Soil ◽

Hydraulic Parameters ◽

Surface Soil Moisture ◽

Brightness Temperatures ◽

Soil Hydraulic Parameters ◽

L Band ◽

Soil Hydraulic

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Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy

Water ◽

10.3390/w12030866 ◽

2020 ◽

Vol 12 (3) ◽

pp. 866 ◽

Cited By ~ 5

Author(s):

Myriam Foucras ◽

Mehrez Zribi ◽

Clément Albergel ◽

Nicolas Baghdadi ◽

Jean-Christophe Calvet ◽

...

Keyword(s):

Soil Moisture ◽

Optical Sensors ◽

Surface Soil ◽

Remote Sensing Data ◽

Climatic Conditions ◽

Optical Data ◽

Surface Soil Moisture ◽

Moisture Index ◽

Wide Range ◽

Moderate Resolution Imaging Spectroradiometer

The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). The proposed methodology is based on the change detection technique, applied to a series of measurements over a three-year period (2015 to 2018). The algorithm described here as “Soil Moisture Estimations from the Synergy of Sentinel-1 and optical sensors (SMES)” proposes different options, allowing information from vegetation densities and seasonal conditions to be taken into account. The output from this algorithm is a moisture index ranging between 0 and 1, with 0 corresponding to the driest soils and 1 to the wettest soils. This methodology has been tested at different test sites (South of France, Central Tunisia, Western Benin and Southwestern Niger), characterized by a wide range of different climatic conditions. The resulting surface soil moisture estimations are compared with in situ measurements and already existing satellite-derived soil moisture ASCAT (Advanced SCATterometer) products. They are found to be well correlated, for the African regions in particular (RMSE below 6 vol.%). This outcome indicates that the proposed algorithm can be used with confidence to estimate the surface soil moisture of a wide range of climatically different sites.

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