Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters

Abstract

Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.

Plant phenotyping relevance

宇宙borne・UAVスペクトルデータと回帰手法を比較し、LAI、葉クロロフィル濃度、群落含水量という植物形質の推定精度を評価しており、センシングによる表現型取得が研究の中心です。

Code and data availability

The supplied blocks describe field phenotyping (LAI, LCC, CWC), WorldView-3 and UAV Cubert hyperspectral data, and mlr3/ranger/Boruta-based modelling, but contain no public dataset deposit, no author code repository or availability statement, and no supplement (pmc-prop-has-supplement no). The only URLs are ORCID IDs,

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