Abstract

C-band polarimetric synthetic aperture radar (PolSAR) data has been previously explored for estimating the leaf area index (LAI) of rice. Although the rice-growing cycle was partially covered in most of the studies, details for each phenological phase need to be further characterized. Additionally, the selection and exploration of polarimetric parameters are not comprehensive. This study evaluates the potential of a set of polarimetric parameters derived from multi-temporal RADARSAT-2 datasets for rice LAI estimation. The relationships of rice LAI with backscattering coefficients and polarimetric decomposition parameters have been examined in a complete phenological cycle. Most polarimetric parameters had weak relationships (R2 < 0.30) with LAI at the transplanting, reproductive, and maturity phase. Stronger relationships (R2 > 0.50) were observed at the vegetative phase. HV/VV and RVI FD had significant relationships (R2 > 0.80) with rice LAI for the whole growth period. They were utilized to develop empirical models. The best LAI inversion performance (RMSE = 0.81) was obtained when RVI FD was used. The acceptable error demonstrated the possibility to use the decomposition parameters for rice LAI estimation. The HV/VV-based model had a slightly lower estimation accuracy (RMSE = 1.29) but can be a practical alternative considering the wide availability of dual-polarized datasets.

Highlights

  • The staple food for approximately one-half of the global population is rice [1]

  • The leaf area index (LAI) of the crop is crucial in the quantitative description of the canopy structure and photosynthetic processes [2,3]

  • We study the quad-pol backscattering coefficients, Cloude–Pottier decomposition components, Freeman–Durden decomposition parameters, as well as their combinations

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Summary

Introduction

The staple food for approximately one-half of the global population is rice [1]. The development and health status of the rice crop are of great significance. The leaf area index (LAI) of the crop is crucial in the quantitative description of the canopy structure and photosynthetic processes [2,3]. The ground LAI measurements using digital photography [4,5] or integrated sensors [6,7] are time-consuming and laborious, restricting their practical application over a large region. Spaceborne or airborne remote sensing techniques and datasets are a possible alternative for the LAI monitoring at a regional scale [8,9,10]. Several vegetation indices (VIs) are used to estimate the LAI base on the contrast in the reflectance among different spectral bands of optical data [11,12,13,14]. The application of optical datasets to estimate the rice LAI becomes unpractical

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