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Article

Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors

1
CESBIO, Université de Toulouse, CNRS/UPS/IRD/CNES/INRAE, 18 av. Edouard Belin, bpi 2801, CEDEX 9, 31401 Toulouse, France
2
Institut National Agronomique de Tunisie, Université de Carthage, LR 7AGR01 GREEN-TEAM. 43 Avenue Charles Nicolle, Tunis 1082, Tunisia
3
CIRAD, CNRS, INRAE, TETIS, University Montpellier, AgroParisTech, CEDEX 5, 34093 Montpellier, France
*
Author to whom correspondence should be addressed.
Academic Editor: John J. Qu
Remote Sens. 2021, 13(7), 1393; https://doi.org/10.3390/rs13071393
Received: 26 February 2021 / Revised: 28 March 2021 / Accepted: 31 March 2021 / Published: 4 April 2021
(This article belongs to the Special Issue Satellite Soil Moisture Validation and Applications)
This paper discusses the potential of L-band Advanced Land Observing Satellite-2 (ALOS-2) and C-band Sentinel-1 radar data for retrieving soil parameters over cereal fields. For this purpose, multi-incidence, multi-polarization and dual-frequency satellite data were acquired simultaneously with in situ measurements collected over a semiarid area, the Merguellil Plain (central Tunisia). The L- and C-band signal sensitivity to soil roughness, moisture and vegetation was investigated. High correlation coefficients were observed between the radar signals and soil roughness values for all processed multi-configurations of ALOS-2 and Sentinel-1 data. The sensitivity of SAR (Synthetic Aperture Radar) data to soil moisture was investigated for three classes of the normalized difference vegetation index (NDVI) (low vegetation cover, medium cover and dense cover), illustrating a decreasing sensitivity with increasing NDVI values. The highest sensitivity to soil moisture under the dense cover class is observed in L-band data. For various vegetation properties (leaf area index (LAI), height of vegetation cover (H) and vegetation water content (VWC)), a strong correlation is observed with the ALOS-2 radar signals (in HH(Horizontal-Horizontal) and HV(Horizontal-Vertical) polarizations). Different empirical models that link radar signals (in the L- and C-bands) to soil moisture and roughness parameters, as well as the semi-empirical Dubois modified model (Dubois-B) and the modified integral equation model (IEM-B), over bare soils are proposed for all polarizations. The results reveal that IEM-B performed a better accuracy comparing to Dubois-B. This analysis is also proposed for covered surfaces using different options provided by the water cloud model (WCM) (with and without the soil–vegetation interaction scattering term) coupled with the best accuracy bare soil backscattering models: IEM-B for co-polarization and empirical models for the entire dataset. Based on the validated backscattering models, different options of coupled models are tested for soil moisture inversion. The integration of a soil–vegetation interaction component in the WCM illustrates a considerable contribution to soil moisture precision in the HV polarization mode in the L-band frequency and a neglected effect on C-band data inversion. View Full-Text
Keywords: radar; ALOS-2; L-band; Sentinel-1; C-band; soil moisture; soil roughness; vegetation; water cloud model radar; ALOS-2; L-band; Sentinel-1; C-band; soil moisture; soil roughness; vegetation; water cloud model
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MDPI and ACS Style

Ayari, E.; Kassouk, Z.; Lili-Chabaane, Z.; Baghdadi, N.; Bousbih, S.; Zribi, M. Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors. Remote Sens. 2021, 13, 1393. https://doi.org/10.3390/rs13071393

AMA Style

Ayari E, Kassouk Z, Lili-Chabaane Z, Baghdadi N, Bousbih S, Zribi M. Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors. Remote Sensing. 2021; 13(7):1393. https://doi.org/10.3390/rs13071393

Chicago/Turabian Style

Ayari, Emna, Zeineb Kassouk, Zohra Lili-Chabaane, Nicolas Baghdadi, Safa Bousbih, and Mehrez Zribi. 2021. "Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors" Remote Sensing 13, no. 7: 1393. https://doi.org/10.3390/rs13071393

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