Retrival of bio-physical parameters in Sunflower crop (Helianthus annuus) using field based hyperspectral remote sensing

Abstract

Information on several crop bio-physical parameters is important as inputs for crop growth modelling, leaf stress analysis, crop health study and productivity point of view. Conventionally, biophysical parameters are measured in laboratory methods which are time consuming, laborious and destructive in nature. With the advent of remote sensing technology, the limitations of conventional methods can be overcome. Moreover, due to its narrow absorption bands at different wavelength, use of hyperspectral remote sensing becomes very useful in retrieving several bio-physical parameters. In the present study, field as well as laboratory based spectro-radiometer observations were carried out at Agronomy Department of VisvaBharati University, West Bengal, on Sunflower crop at its peak vegetation stage towards retrieving different bio-physical parameters, specifically leaf area index (LAI), chlorophyll content index (CCI), fluorescence etc. Different foliar boron (no boron, 0.15% and 0.20%) and irrigation (4-6 irrigations) treatments, i.e. total nine treatments with three replications, were applied on sunflower crop during different phenological stages to achieve maximum ranges of the bio-physical parameters. The LAI, CCI and fluorescence parameters were collected using canopy analyzer, chlorophyll content meter and portable gas exchange system, respectively. In each of the treatments, total four hyperspectral measurements were collected, which were further corrected for noise and smoothened using Savitzky-Golay filtering. Total thirty-four narrow band indices were computed based on the hyperspectral data, and the regression analysis was carried out among the indices and bio-physical parameters. The regression parameters were further deployed on the hyperspectral indices to retrieve the bio-physical parameters. The Gitelson& Merzylak-1 (GM-1) and Carter Indices-1 (CI-1) were found to the best indices for retrieving the LAI and CCI, respectively with correlation correlation (r) values of 0.87 and 0.80. On the other hand, Normalized Phaenophytinization Index (NPQI) and GM-1 were found to best for retrieving the Fv/Fm (dark) and Fv/Fm (light) with correlation (r) values of 0.92 and 0.76, respectively. Hence, the hyperspectral remote sensing be successfully utilized for retrieving several biophysical parameters both at field (canopy level) and laboratory (leaf level) conditions.