Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Research on Estimation Model for Cotton Canopy Structure Parameters Based on Hyperspectral Vegetation Index

Yaqin Qi
* ORCID logo ,
Xi Chen
,
Zhengchao Chen
,
Xin Zhang
,
Congju Shen
,
Yan Chen
,
Yuanying Peng
ORCID logo ,
Bing Chen
,
Qiong Wang
,
Taijie Liu
,
Hao Zhang
*
Version 1 : Received: 3 January 2024 / Approved: 17 January 2024 / Online: 18 January 2024 (03:24:15 CET)

How to cite: Qi, Y.; Chen, X.; Chen, Z.; Zhang, X.; Shen, C.; Chen, Y.; Peng, Y.; Chen, B.; Wang, Q.; Liu, T.; Zhang, H. Research on Estimation Model for Cotton Canopy Structure Parameters Based on Hyperspectral Vegetation Index. Preprints 2024, 2024011329. https://doi.org/10.20944/preprints202401.1329.v1 Qi, Y.; Chen, X.; Chen, Z.; Zhang, X.; Shen, C.; Chen, Y.; Peng, Y.; Chen, B.; Wang, Q.; Liu, T.; Zhang, H. Research on Estimation Model for Cotton Canopy Structure Parameters Based on Hyperspectral Vegetation Index. Preprints 2024, 2024011329. https://doi.org/10.20944/preprints202401.1329.v1

Abstract

The hyperspectral vegetation index was defined based on the distinctive features of the spectral curve. In alignment with the growth and development characteristics of cotton, the spectral reflectivity of the cotton canopy was computed at different growth stages. The aim of this study was to clarify the association between cotton yield and canopy spectral indices and to develop yield estimation models utilizing hyperspectral imaging. The ASD Field Spec Pro VNIR 2500 spectrometer radiometer was employed to collect spectral reflectance data from cotton canopies at various growth stages. Using spectral analysis techniques, quantitative models were developed based on hyper-spectral vegetation indices, including Normalized Difference Vegetation Index (NDVI) (NDVI) and Radar Vegetation Index (RVI) to extract characteristic information from the cotton canopies. Following thorough testing and precise monitoring of the estimation models, the most optimal models representing cotton canopy structure parameters were identified. Results demonstrate that the power function model, relying on NDVI, provides the most accurate forecasting for Leaf Area Index (LAI)). Additionally, the exponential function model based on RVI proves to be the most effective for predicting cotton unit area above-ground fresh biomass, while another exponential function model based on RVI is identified as the best for predicting cotton unit area above-ground fresh biomass. Clearly, the application of hyper-spectral remote sensing technology enables the analysis, simulation, assessment, and providing scientific basis for precision cotton planting and cotton field management strategies.

Keywords

Hyper-spectral Remote Sensing; cotton canopy information; Vegetation Index; estimation model

Subject

Environmental and Earth Sciences, Remote Sensing

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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