Unmanned Aerial Vehicle (UAV) Hyperspectral Imagery Mining to Identify New Spectral Indices for Predicting the Field-Scale Yield of Spring Maize

Yue Zhang (), Yansong Wang, Hang Hao, Ziqi Li, Yumei Long, Xingyu Zhang and Chenzhen Xia
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Yue Zhang: College of Resources and Environment, Jilin Agricultural University, Changchun 130118, China
Yansong Wang: Songliao Basin Soil and Water Conservation Monitoring Center, Songliao Water Resources Commission of the Ministry of Water Resources, Changchun 130021, China
Hang Hao: College of Resources and Environment, Jilin Agricultural University, Changchun 130118, China
Ziqi Li: College of Resources and Environment, Jilin Agricultural University, Changchun 130118, China
Yumei Long: College of Resources and Environment, Jilin Agricultural University, Changchun 130118, China
Xingyu Zhang: Jilin Emergency Warning Information Dissemination Center, Changchun 130062, China
Chenzhen Xia: College of Resources and Environment, Jilin Agricultural University, Changchun 130118, China

Sustainability, 2024, vol. 16, issue 24, 1-20

Abstract: A nondestructive approach for accurate crop yield prediction at the field scale is vital for precision agriculture. Considerable progress has been made in the use of the spectral index (SI) derived from unmanned aerial vehicle (UAV) hyperspectral images to predict crop yields before harvest. However, few studies have explored the most sensitive wavelengths and SIs for crop yield prediction, especially for different nitrogen fertilization levels and soil types. This study aimed to investigate the appropriate wavelengths and their combinations to explore the ability of new SIs derived from UAV hyperspectral images to predict yields during the growing season of spring maize. In this study, the hyperspectral canopy reflectance measurement method, a field-based high-throughput method, was evaluated in three field experiments (Wang-Jia-Qiao (WJQ), San-Ke-Shu (SKS), and Fu-Jia-Jie (FJJ)) since 2009 with different soil types (alluvial soil, black soil, and aeolian sandy soil) and various nitrogen (N) fertilization levels (0, 168, 240, 270, and 312 kg/ha) in Lishu County, Northeast China. The measurements of canopy spectral reflectance and maize yield were conducted at critical growth stages of spring maize, including the jointing, silking, and maturity stages, in 2019 and 2020. The best wavelengths and new SIs, including the difference spectral index, ratio spectral index, and normalized difference spectral index forms, were obtained from the contour maps constructed by the coefficient of determination (R 2 ) from the linear regression models between the yield and all possible SIs screened from the 450 to 950 nm wavelengths. The new SIs and eight selected published SIs were subsequently used to predict maize yield via linear regression models. The results showed that (1) 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; (2) the new SIs established here were different across the three experimental fields, and their performance in maize yield prediction was generally better than that of the published SIs; and (3) the new SIs presented different responses to various N fertilization levels. This study demonstrates the potential of exploring new spectral characteristics from remote sensing technology for predicting the field-scale crop yield in spring maize cropping systems before harvest.

Keywords: hyperspectral imagery; spectral indices; contour map; maize; yield (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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