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Remote Sens. 2015, 7(3), 3184-3205; doi:10.3390/rs70303184

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

1
DOTA-ONERA, 2 Avenue Edouard Belin, 31055 Toulouse Cedex 4, France
2
CESBIO, 18 avenue. Edouard Belin, 31401 Toulouse Cedex 9, France
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: George P. Petropoulos and Prasad S. Thenkabail
Received: 14 November 2014 / Revised: 19 January 2015 / Accepted: 15 February 2015 / Published: 20 March 2015
View Full-Text   |   Download PDF [2978 KB, uploaded 23 March 2015]   |  

Abstract

The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 µm) when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON). These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R2) between 0.72 and 0.92. A clay content (CC) dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOLCC and NINSONCC). The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² superior to 0.76 and Root mean square errors inferior to 8.3% m3∙m−3 in all cases) and using in-situ data, where WISOIL, NINSOLCC and NINSONCC criteria stand out among the NSMI and CH. View Full-Text
Keywords: soil moisture content; clay content; WISOIL; NSMI; NINSOL; NINSON; CH soil moisture content; clay content; WISOIL; NSMI; NINSOL; NINSON; CH
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Oltra-Carrió, R.; Baup, F.; Fabre, S.; Fieuzal, R.; Briottet, X. Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments. Remote Sens. 2015, 7, 3184-3205.

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