Version 1
: Received: 11 August 2024 / Approved: 12 August 2024 / Online: 12 August 2024 (11:41:11 CEST)
How to cite:
Zhang, Y.; Wang, Y.; Hao, H.; Li, Z.; Long, Y.; Zhang, X.; Xia, C. Uav Hyperspectral Imagery Mining to Identify New Spectral Indices for Predicting Field-Scale Yield of Spring Maize. Preprints2024, 2024080804. https://doi.org/10.20944/preprints202408.0804.v1
Zhang, Y.; Wang, Y.; Hao, H.; Li, Z.; Long, Y.; Zhang, X.; Xia, C. Uav Hyperspectral Imagery Mining to Identify New Spectral Indices for Predicting Field-Scale Yield of Spring Maize. Preprints 2024, 2024080804. https://doi.org/10.20944/preprints202408.0804.v1
Zhang, Y.; Wang, Y.; Hao, H.; Li, Z.; Long, Y.; Zhang, X.; Xia, C. Uav Hyperspectral Imagery Mining to Identify New Spectral Indices for Predicting Field-Scale Yield of Spring Maize. Preprints2024, 2024080804. https://doi.org/10.20944/preprints202408.0804.v1
APA Style
Zhang, Y., Wang, Y., Hao, H., Li, Z., Long, Y., Zhang, X., & Xia, C. (2024). Uav Hyperspectral Imagery Mining to Identify New Spectral Indices for Predicting Field-Scale Yield of Spring Maize. Preprints. https://doi.org/10.20944/preprints202408.0804.v1
Chicago/Turabian Style
Zhang, Y., Xingyu Zhang and Chenzhen Xia. 2024 "Uav Hyperspectral Imagery Mining to Identify New Spectral Indices for Predicting Field-Scale Yield of Spring Maize" Preprints. https://doi.org/10.20944/preprints202408.0804.v1
Abstract
Non-destructive, accurate, and timely approach for crop yield prediction at field scale is vital for precision agriculture. This study aimed to investigate the appropriate wavelengths and their combinations to explore the new SIs derived from UAV hyperspectral images in predicting yield during the growing season of spring maize. The best wavelengths and new SIs, including the difference spectral index, ratio spectral index, and normalized difference spectral index forms, were obtained by the contour maps constructed by the coefficient of determination (R2) from the linear regression models between the yield and all possible SIs screening out from the 450-950 nm wavelengths. The results showed that the most sensitive wavelengths were 640-714 nm at WJQ, 450-650 nm and 750-950 nm at SKS, and 450-700 nm and 750-950 nm at FJJ. The new SIs established here were different across the three experimental fields, and their performance on maize yield prediction were generally better than that of the published SIs. In addition, the new SIs presented different response to various N fertilization levels. This study demonstrated the potential of exploring new spectral characteristics from remote sensing technology for predicting field-scale crop yield in spring maize cropping systems before harvest.
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.
