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.

Research hotspots and frontiers in agricultural multispectral technology: Bibliometrics and scientometrics analysis of the Web of Science

“…At present, remote sensing in the field of agriculture mainly focuses on: crop classification, crop coverage, and precise identification. Classification of weeds and crops in the field and accurate management of weeds, crop cover and vegetation coverage in agricultural fields, accurate identification of biomass and trace element content are the current research priorities in remote sensing ( Huang et al, 2018 ; Näsi et al, 2018 ; Memon et al, 2019 ; Wijesingha et al, 2021 ). Remote sensing has made a great contribution to the development of precision agriculture.…”

Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits

“…Active sensors also use more operational energy and are more common in proximal or airborne platforms than satellite platforms. Although several studies compare the interchangeability of passive satellite sensors [31][32][33], the ability to estimate biophysical traits using proximal active and passive sensors [34][35][36][37][38], satellite passive and uncrewed aerial vehicle passive sensors [39,40], or a passive satellite sensor with passive proximal sensor [41], there is currently a gap in research comparing multiple passive satellite sensors with proximal active and passive sensors. Additionally, most sensor intercomparison studies focus on summer crops with higher biomass conditions rather than detecting green biomass under the low biomass conditions associated with monitoring cover crop performance.…”

Yield Prediction of Four Bean (Phaseolus vulgaris) Cultivars Using Vegetation Indices Based on Multispectral Images from UAV in an Arid Zone of Peru

“…Therefore, improving crops yield represents a great challenge [5], and under climate change conditions, the quantitative evaluation of variables that improve yield is becoming a high priority [6]. Among these traits, biomass, plant height, chlorophyll content and other variables might be an alternative to manage available resources and reduce inputs for production (precision agriculture) [7]. However, crop evaluating techniques must be adapted to different growth stages (phenology) and should involve a low amount of time and resources.…”