ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-5
https://doi.org/10.5194/isprs-annals-IV-5-215-2018
https://doi.org/10.5194/isprs-annals-IV-5-215-2018
15 Nov 2018
 | 15 Nov 2018

THE SENSITIVITY OF C-BAND HYBRID POLARIMETRIC RISAT-1 SAR DATA TO LEAF AREA INDEX OF PADDY CROP

H. S. Srivastava, T. Sivasankar, and P. Patel

Keywords: hybrid polarimetry, m-δ, m-χ, m-α space decompositions, leaf area index (LAI), paddy, RISAT-1 SAR

Abstract. Active microwave remote sensing data has become an important source to retrieve crop biophysical parameters due to its unique sensitivity towards geometrical, structural and dielectric properties of various crop components. The temporal variability of various crop biophysical parameters during crop cycle has significant impact on the overall crop yield. In this study, two RISAT-1 hybrid polarimetric temporal SAR datasets at ∼32° incidence angle were acquired during 2015 Kharif season. The in-situ leaf area index (LAI) values from seventeen paddy fields were measured in synchrony to the satellite passes during both the campaigns. Analysis observed the decreasing trend of backscattering coefficients (σ°RH, σ°RV) with increase in LAI. Results indicate that the sensitivity of hybrid polarimetric parameters towards LAI, also depends on the change in crop structure due to crop growth. This study investigate the sensitivity of backscattering coefficients (σ°RH, σ°RV) and polarimetric parameters (even bounce, odd bounce and volume component) generated from m-δ, m-χ and m-α space decompositions towards LAI using empirical analysis. An increase of 0.16 in R2 (from 0.63 to 0.79) clearly indicates that the polarimetric parameters (even bounce, odd bounce and volume component) are more sensitive to LAI of paddy crop than the backscattering coefficients (σ°RH, σ°RV). It has been identified that the combined use of backscattering coefficients as well as polarimetric parameters (even bounce, odd bounce and volume component) in the model, can significantly improve the accuracy of the LAI estimation.