Datum
2021-04-20Schlagwort
580 Pflanzen (Botanik) 600 Technik IndienMonsunErnteBlattflächenindexChlorophyllkonzentrationWassergehaltFernerkundungMultispektraltechnikHyperspektraler SensorMetadata
Zur Langanzeige
Aufsatz
Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters
Zusammenfassung
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.
Zitierform
In: Senors Volume 21 / Issue 8 (2021-04-20) EISSN 1424-8220Förderhinweis
Gefördert durch den Publikationsfonds der Universität KasselZitieren
@article{doi:10.17170/kobra-202104293776,
author={Wijesingha, Jayan and Dayananda, Supriya and Wachendorf, Michael and Astor, Thomas},
title={Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters},
journal={Senors},
year={2021}
}
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2021-05-03T08:31:18Z 2021-05-03T08:31:18Z 2021-04-20 doi:10.17170/kobra-202104293776 http://hdl.handle.net/123456789/12775 Gefördert durch den Publikationsfonds der Universität Kassel eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ monsoon crops leaf area index leaf chlorophyll concentration crop water content multispectral hyperspectral 580 600 Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters Aufsatz 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. open access Wijesingha, Jayan Dayananda, Supriya Wachendorf, Michael Astor, Thomas doi:10.3390/s21082886 Indien Monsun Ernte Blattflächenindex Chlorophyllkonzentration Wassergehalt Fernerkundung Multispektraltechnik Hyperspektraler Sensor publishedVersion EISSN 1424-8220 Issue 8 Senors Volume 21 false 2886
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