Incidence Angle Normalization of C-Band Radar Backscattering Coefficient over Agricultural Surfaces Using Dynamic Cosine Method

The radar-backscattering coefficient (σ0) depends on surface characteristics and instrumental parameters (wavelength, polarization, and incidence angle). For Sentinel-1 (S1), with incidence angles ranging from 25° to 45°, σ0 for similar targets typically differs by a few dB depending on their localization in the S1 swath. Overcoming this angular dependence is crucial for the operational applications of radar data. In theory, σ0 follows a cosine function with an exponent “N” that represents the degree of dependence between σ0 and the incidence angle. In order to reduce the effect of the incidence angle on σ0, dynamic N normalizations based on vegetation descriptors, NDVI and SAR Ratio (VV/VH), were applied and then compared to the results obtained with temporally fixed N normalizations. N was estimated at each S1 date during the period of the study for three main summer crops: corn, soybean, and sunflower. Analysis shows that the angular dependence of the S1 σ0 is similar for all three crops. N varies from 3.0 for low NDVI values to 2.0 for high NDVI values (stage of maximal vegetation development) in the VV polarization and from 2.5 to 1.5 for the VH polarization. Furthermore, N fluctuates strongly during the periods before plant emergence and after harvesting, due to variations in the soil roughness. Finally, the results demonstrated that the dynamic normalization of σ0 significantly reduces its angular dependence compared to fixed N (N = 1 and N = 2), with SAR ratio-based normalization performing similarly to NDVI-based normalization.

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Publication Details

Journal
Remote Sensing
Published
2024-10-16
DOI
https://doi.org/10.3390/rs16203838
Citations
7
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
1.44
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article

Incidence Angle Normalization of C-Band Radar Backscattering Coefficient over Agricultural Surfaces Using Dynamic Cosine Method

Nicolas Baghdadi, Sami Najem, Mehrez Zribi, Hassan Bazzi
7 citations
Remote Sensing
Soil Moisture and Remote Sensing
1.44
article

Incidence Angle Normalization of C-Band Radar Backscattering Coefficient over Agricultural Surfaces Using Dynamic Cosine Method

Nicolas Baghdadi, Sami Najem, Mehrez Zribi, Hassan Bazzi
article en
7 citations

Abstract

The radar-backscattering coefficient (σ0) depends on surface characteristics and instrumental parameters (wavelength, polarization, and incidence angle). For Sentinel-1 (S1), with incidence angles ranging from 25° to 45°, σ0 for similar targets typically differs by a few dB depending on their localization in the S1 swath. Overcoming this angular dependence is crucial for the operational applications of radar data. In theory, σ0 follows a cosine function with an exponent “N” that represents the degree of dependence between σ0 and the incidence angle. In order to reduce the effect of the incidence angle on σ0, dynamic N normalizations based on vegetation descriptors, NDVI and SAR Ratio (VV/VH), were applied and then compared to the results obtained with temporally fixed N normalizations. N was estimated at each S1 date during the period of the study for three main summer crops: corn, soybean, and sunflower. Analysis shows that the angular dependence of the S1 σ0 is similar for all three crops. N varies from 3.0 for low NDVI values to 2.0 for high NDVI values (stage of maximal vegetation development) in the VV polarization and from 2.5 to 1.5 for the VH polarization. Furthermore, N fluctuates strongly during the periods before plant emergence and after harvesting, due to variations in the soil roughness. Finally, the results demonstrated that the dynamic normalization of σ0 significantly reduces its angular dependence compared to fixed N (N = 1 and N = 2), with SAR ratio-based normalization performing similarly to NDVI-based normalization.

Remote SensingVol. 16(20)
Centre National de la Recherche Scientifique (FR), Université de Montpellier (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Centre d'Études Spatiales de la Biosphère (FR), Institut de Recherche pour le Développement (FR)
Zero hunger
Openalex Percentile: Top 20%
Soil Moisture and Remote Sensing
1.44
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