Abstract

Wide mode SAR images have an apparent incidence angle effect. The existing incident angle normalization methods assume that the relationship between the incident angle (θ) and the backscattering coefficient (σPQ) does not change with the growth stage of crops, which is in conflict with the real-life situation. Therefore, the normalization results of σPQ based on these existing methods will affect the accuracy of object classification, target recognition, and land surface parameter inversion. Here, the change in θ-σPQ relationship was investigated based on time-series (April to October) σPQ of maize canopies in northeast China, and a dynamic method based on normalized difference vegetation index (NDVI) was developed to normalize the effect of θ on σPQ. Through the accuracy evaluation, the following conclusions are obtained: (1) the dependence (referring to N) of Sentinel 1 C-band σPQ on θ varies with maize NDVI. In addition, the value of N changed from 9.35 to 0.66 at VV polarization from bare soil to biomass peak, and from 6.26 to 0.99 at VH polarization; (2) a dynamic method was proposed to quantify the change of N based on its strong correlation with NDVI, indicated by R2 of 0.82 and 0.80 for VV and VH polarization, respectively; and (3) the overall root mean square error of normalized σPQ based on the newly-developed dynamic method is 0.51 dB, and this accuracy outperforms the original first-order cosine method (1.37 dB) and cosine square law method (1.08 dB) by about 63% and 53% on the whole. This study provides a dynamic framework for normalizing radar backscatter coefficient, improving the retrieval accuracy of land surface parameters from radar remote sensing.

Highlights

  • The good correlation proves that normalized difference vegetation index (NDVI) can well characterize the dynamic change of N, which is beneficial to the effective dynamic normalization of ∆σ0 during the entire maize growth stage

  • A dynamic cosine method based on NDVI has been proposed, which is suitable for different growth stages of maize and the bare soil period (Figures 10 and 11)

  • The normalization accuracy of first-order cosine method and cosine square law method cannot meet the requirements for normalization of the radar incident angle at different growth stages of maize

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Summary

Introduction

SAR data are often used for land cover classification, target detection, and land surface parameter retrievals (leaf area index, snow water equivalent, and soil moisture) [1,2,3]. Radar backscattering coefficient (σ0 ) changes with incident angle (θ) [4,5], show significant θ effect. Radarsat σ0 of C-band Radarsat image changes 0.26 dB/◦. For a snow covered surface when θ ranges from 23◦ to 45◦ [6,7]. This incidence angle effect would reduce the accuracy of crop classification and soil moisture estimation based on change detection [8,9]

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