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Remote Sens. 2013, 5(10), 4961-4976;

Comparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site

Istituto di Fisica Applicata "Nello Carrara", National Research Council (IFAC-CNR), I-50019 Florence, Italy
Institute for Applied Remote Sensing, EURAC Research, I-39100 Bolzano, Italy
Gecosistema, R&D Unit Trentino-Suedtirol, I-39100 Bolzano, Italy
Author to whom correspondence should be addressed.
Received: 11 August 2013 / Revised: 11 September 2013 / Accepted: 24 September 2013 / Published: 11 October 2013
(This article belongs to the Special Issue Hydrological Remote Sensing)
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In this paper, the results of a comparison between the soil moisture content (SMC) estimated from C-band SAR, the SMC simulated by a hydrological model, and the SMC measured on ground are presented. The study was carried out in an agricultural test site located in North-west Italy, in the Scrivia river basin. The hydrological model used for the simulations consists of a one-layer soil water balance model, which was found to be able to partially reproduce the soil moisture variability, retaining at the same time simplicity and effectiveness in describing the topsoil. SMC estimates were derived from the application of a retrieval algorithm, based on an Artificial Neural Network approach, to a time series of ENVISAT/ASAR images acquired over the Scrivia test site. The core of the algorithm was represented by a set of ANNs able to deal with the different SAR configurations in terms of polarizations and available ancillary data. In case of crop covered soils, the effect of vegetation was accounted for using NDVI information, or, if available, for the cross-polarized channel. The algorithm results showed some ability in retrieving SMC with RMSE generally <0.04 m3/m3 and very low bias (i.e., <0.01 m3/m3), except for the case of VV polarized SAR images: in this case, the obtained RMSE was somewhat higher than 0.04 m3/m3 (≤0.058 m3/m3). The algorithm was implemented within the framework of an ESA project concerning the development of an operative algorithm for the SMC retrieval from Sentinel-1 data. The algorithm should take into account the GMES requirements of SMC accuracy (≤5% in volume), spatial resolution (≤1 km) and timeliness (3 h from observation). The SMC estimated by the SAR algorithm, the SMC estimated by the hydrological model, and the SMC measured on ground were found to be in good agreement. The hydrological model simulations were performed at two soil depths: 30 and 5 cm and showed that the 30 cm simulations indicated, as expected, SMC values higher than the satellites estimates, with RMSE higher than 0.08 m3/m3. In contrast, in the 5-cm simulations, the agreement between hydrological simulations, satellite estimates and ground measurements could be considered satisfactory, at least in this preliminary comparison, showing a RMSE ranging from 0.054 m3/m3 to 0.051 m3/m3 for comparison with ground measurements and SAR estimates, respectively. View Full-Text
Keywords: SAR data; soil moisture; hydrological model; Artificial Neural Networks; inversion algorithms SAR data; soil moisture; hydrological model; Artificial Neural Networks; inversion algorithms
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Santi, E.; Paloscia, S.; Pettinato, S.; Notarnicola, C.; Pasolli, L.; Pistocchi, A. Comparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site. Remote Sens. 2013, 5, 4961-4976.

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