Remote Sensing Applications and Decision Support

Empirical fitting of forward backscattering models for multitemporal retrieval of soil moisture from radar data at L-band

[+] Author Affiliations
Fabio Fascetti, Nazzareno Pierdicca

Sapienza, DIET, via Eudossiana, 18, Rome 00184, Italy

Luca Pulvirenti

Cima Research Foundation, via Armando Magliotto, 2, Savona 17100, Italy

J. Appl. Remote Sens. 11(1), 016002 (Jan 05, 2017). doi:10.1117/1.JRS.11.016002
History: Received August 4, 2016; Accepted December 9, 2016
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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%.

© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

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", J. Appl. Remote Sens. 11(1), 016002 (Jan 05, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.016002


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