Study on the Automatic Selection of Sensitive Hyperspectral Bands for Rice Nitrogen Retrieval Based on a Maximum Inscribed Rectangle

Most existing studies on the optimal bandwidth selection for plant nitrogen are based on the sensitive band center, and determine the optimal bands by manually adjusting the bandwidth, step by step. However, this method has a high level of manual involvement and is time-consuming. This paper focused on rice as the research subject, based on determining the center of the rice plant nitrogen-sensitive bands and the maximum region Ω of the fitted R2 between the narrow-band vegetation indices (N-VIs) and plant nitrogen, a method was proposed to automatically select the optimal bandwidth by constructing inscribed rectangles. UAV hyperspectral images were used to carry out the spatial inversion and precision verification of the rice plant nitrogen, based on the optimal width of sensitive bands. The results revealed that the optimal bandwidths, automatically selected on the basis of N-VIs via the inscribed rectangle method, achieved good results in the remote sensing inversion of plant nitrogen at the rice jointing and flowering stages, with the coefficient of determination (R2) greater than 0.49 to satisfy the requirement of significance (p < 0.05) and the normalized root mean square error (NRMSE) and mean relative error (MRE) of less than 13%. These findings indicate that the method of crop plant nitrogen inversion band center screening and automatic search for the optimal bandwidth in this study has certain feasibility, which provides a new idea for screening the optimal bandwidth on the basis of the sensitive band center and provides technical support for the design of satellite band parameters.

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Publication Details

Journal
Agronomy
Published
2025-02-05
DOI
https://doi.org/10.3390/agronomy15020406
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
0.00

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article

Study on the Automatic Selection of Sensitive Hyperspectral Bands for Rice Nitrogen Retrieval Based on a Maximum Inscribed Rectangle

Wei Kuang, Shangrong Wu, Youping Fan, Youxing Chen et al.
Agronomy
Remote Sensing and Land Use
article

Study on the Automatic Selection of Sensitive Hyperspectral Bands for Rice Nitrogen Retrieval Based on a Maximum Inscribed Rectangle

Wei Kuang, Shangrong Wu, Youping Fan, Youxing Chen, Baohua Fang, Peng Yang, Yan Zha, Jieyang Tan
article en

Abstract

Most existing studies on the optimal bandwidth selection for plant nitrogen are based on the sensitive band center, and determine the optimal bands by manually adjusting the bandwidth, step by step. However, this method has a high level of manual involvement and is time-consuming. This paper focused on rice as the research subject, based on determining the center of the rice plant nitrogen-sensitive bands and the maximum region Ω of the fitted R2 between the narrow-band vegetation indices (N-VIs) and plant nitrogen, a method was proposed to automatically select the optimal bandwidth by constructing inscribed rectangles. UAV hyperspectral images were used to carry out the spatial inversion and precision verification of the rice plant nitrogen, based on the optimal width of sensitive bands. The results revealed that the optimal bandwidths, automatically selected on the basis of N-VIs via the inscribed rectangle method, achieved good results in the remote sensing inversion of plant nitrogen at the rice jointing and flowering stages, with the coefficient of determination (R2) greater than 0.49 to satisfy the requirement of significance (p < 0.05) and the normalized root mean square error (NRMSE) and mean relative error (MRE) of less than 13%. These findings indicate that the method of crop plant nitrogen inversion band center screening and automatic search for the optimal bandwidth in this study has certain feasibility, which provides a new idea for screening the optimal bandwidth on the basis of the sensitive band center and provides technical support for the design of satellite band parameters.

AgronomyVol. 15(2)
North University of China (CN), Institute of Agricultural Resources and Regional Planning (CN), Hunan Academy of Agricultural Sciences (CN), Hunan Rice Research Institute (CN), Chinese Academy of Agricultural Sciences (CN), Ministry of Agriculture and Rural Affairs (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 98%
Remote Sensing and Land Use
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