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SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product

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TLDR
An alternative SMOS product that was developed by INRA and CESBIO is presented, which is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry and considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets.
Abstract
The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product that was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphere). One of the main goals of this SMOS-INRA-CESBIO (SMOS-IC) product is to be as independent as possible from auxiliary data. The SMOS-IC product provides daily SM and τ at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A six-year (2010–2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ, SMOS-IC τ was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and the northern mid-latitudes.

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Citations
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Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations

TL;DR: In this article, in situ soil moisture data from more than 200 stations located in Africa, Australia, Europe and the United States are used to determine the reliability of three soil moisture products, one analysis from the ECMWF (European Centre for Medium-Range Weather Forecasts) numerical weather prediction system (SM-DAS-2) and two remotely sensed soil moisture product, namely ASCAT (Advanced scatterometer) and SMOS (Soil Moisture Ocean Salinity).
Journal ArticleDOI

Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology

TL;DR: The European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) merging algorithm generates consistent and quality-controlled long-term (1978-2018) climate data records for soil moisture, which serves thousands of scientists and data users worldwide as discussed by the authors.
Journal ArticleDOI

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

TL;DR: Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society.
References
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Book

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TL;DR: In this article, the authors present a model of a MICROWAVE REMOTE SENSING FUNDAMENTALS and RADIOMETRY, which is based on the idea of surface scattering.
Journal ArticleDOI

A Modified Soil Adjusted Vegetation Index

TL;DR: In this article, a modified SAVI (MSAVI) was proposed to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a vegetation signal to soil noise ratio.
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TL;DR: Monumental as discussed by the authors is a compilation of the present engineering state of the art of microwave remote sensing, presented as a survey of the state-of-the-art in the field.
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