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Article

Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model

1
Earth and Life Insitute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
2
Hydro-Climate Extremes Lab, Ghent University, 9000 Ghent, Belgium
3
Centre Spatial de Liège, Université de Liège, 4031 Angleur, Belgium
4
Signal and Image Centre, Royal Military Academy, 1000 Brussels, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: Kim Calders, Bas van Wesemael, Trissevgeni Stavrakou, Dimitry van der Zande, Hans Lievens, Jean-Christophe Schyns and Joost Vandenabeele
Remote Sens. 2022, 14(10), 2496; https://doi.org/10.3390/rs14102496
Received: 15 April 2022 / Revised: 13 May 2022 / Accepted: 21 May 2022 / Published: 23 May 2022
(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)
The green area index (GAI) and the soil moisture under the canopy are two key variables for agricultural monitoring. The current most accurate GAI estimation methods exploit optical data and are rendered ineffective in the case of frequent cloud cover. Synthetic aperture radar (SAR) measurements could allow the remote estimation of both variables at the parcel level, on a large scale and regardless of clouds. In this study, several methods were implemented and tested for the simultaneous estimation of both variables using the water cloud model (WCM) and dual-polarized radar backscatter measurements. The methods were tested on the BELSAR-Campaign data set consisting of in-situ measurements of bio-geophysical variables of vegetation and soil in maize fields combined with multi-polarized C- and L-band SAR data from Sentinel-1 and BELSAR. Accurate GAI estimates were obtained using a random forest regressor for the inversion of a pair of WCMs calibrated using cross and vertical co-polarized SAR data in L- and C-band, with correlation coefficients of 0.79 and 0.65 and RMSEs of 0.77 m2 m−2 and 0.98 m2 m−2, respectively, between estimates and in-situ measurements. The WCM, however, proved inadequate for soil moisture monitoring in the conditions of the campaign. These promising results indicate that GAI retrieval in maize crops using only dual-polarized radar data could successfully substitute for estimates derived from optical data. View Full-Text
Keywords: GAI retrieval; soil moisture; maize; SAR; L-band; multi-polarization; agriculture GAI retrieval; soil moisture; maize; SAR; L-band; multi-polarization; agriculture
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MDPI and ACS Style

Bouchat, J.; Tronquo, E.; Orban, A.; Neyt, X.; Verhoest, N.E.C.; Defourny, P. Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model. Remote Sens. 2022, 14, 2496. https://doi.org/10.3390/rs14102496

AMA Style

Bouchat J, Tronquo E, Orban A, Neyt X, Verhoest NEC, Defourny P. Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model. Remote Sensing. 2022; 14(10):2496. https://doi.org/10.3390/rs14102496

Chicago/Turabian Style

Bouchat, Jean, Emma Tronquo, Anne Orban, Xavier Neyt, Niko E.C. Verhoest, and Pierre Defourny. 2022. "Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model" Remote Sensing 14, no. 10: 2496. https://doi.org/10.3390/rs14102496

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