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5 January 2017 Empirical fitting of forward backscattering models for multitemporal retrieval of soil moisture from radar data at L-band
Fabio Fascetti, Nazzareno Pierdicca, Luca Pulvirenti
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Journal of Applied Remote Sensing, Vol. 11, Issue 1, 016002 (January 2017). https://doi.org/10.1117/1.JRS.11.016002
Abstract
A multitemporal algorithm, originally conceived for the C-band radar aboard the Sentinel-1 satellite, has been updated to retrieve soil moisture from L-band radar data, such as those provided by the National Aeronautics and Space Administration Soil Moisture Active/Passive (SMAP) mission. This type of algorithm may deliver more accurate soil moisture maps that mitigate the effect of roughness and vegetation changes. Within the multitemporal inversion scheme based on the Bayesian maximum a posteriori probability (MAP) criterion, a dense time series of radar measurements is integrated to invert a forward backscattering model. The model calibration and validation tasks have been accomplished using the data collected during the SMAP validation experiment 12 spanning several soil conditions (pasture, wheat, corn, and soybean). The data have been used to update the forward model for bare soil scattering at L-band and to tune a simple vegetation scattering model considering two different classes of vegetation: those producing mainly single scattering effects and those characterized by a significant multiple scattering involving terrain surface and vegetation elements interaction. The algorithm retrievals showed a root mean square difference (RMSD) around 5% over bare soil, soybean, and cornfields. As for wheat, a bias was observed; when removed, the RMSD went down from 7.7% to 5%.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Fabio Fascetti, Nazzareno Pierdicca, and Luca Pulvirenti "Empirical fitting of forward backscattering models for multitemporal retrieval of soil moisture from radar data at L-band," Journal of Applied Remote Sensing 11(1), 016002 (5 January 2017). https://doi.org/10.1117/1.JRS.11.016002
Received: 4 August 2016; Accepted: 9 December 2016; Published: 5 January 2017
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Cited by 4 scholarly publications.
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KEYWORDS
Soil science
Backscatter
Data modeling
Vegetation
Radar
L band
Polarization
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