Monitoring crop biochemical concentrations by high spectral remote sensing

Wen Wang; Jing Yan; Yonghua Chen; Zheng Niu; Changyao Wang

doi:10.1117/12.372896

16 December 1999 Monitoring crop biochemical concentrations by high spectral remote sensing

Wen Wang, Jing Yan, Yonghua Chen, Zheng Niu, Changyao Wang

Proceedings Volume 3854, Pattern Recognition, Chemometrics, and Imaging for Optical Environmental Monitoring; (1999) https://doi.org/10.1117/12.372896
Event: Photonics East '99, 1999, Boston, MA, United States

Abstract

High spectral remote sensing is a hopeful technology in diagnosing crop nutrition background. With surface spectral measurement and laboratory biochemical analysis, the relationship between crop properties and spectral remote sensing data has been established. Seven chemical components - total chlorophyll, water crude protein, soluble sugar, N, P, K - were analyzed by laboratory chemical analyzing instrument. Foliar spectral property was detected outdoors by surface spectrometer. Chemical concentrations have been related to foliar spectral properties through stepwise multiple regression. The statistical equations between the chemical concentrations and reflectance as well as its several transformations were established. They underscored good estimation performance for chlorophyll, water crude protein, N and K with high squared multiple correlation coefficients (R2) values and high believable level. Especially R2 value of the equation between crude protein concentration and the first derivative of reflectance is 0.9564, which is the best result in the study of the fresh leave biochemistry up to now. On the basis of field experiment, an airborne remote sensing for crop nutrition monitoring was conducted in Shunyi County, Beijing, PR China. The sensor, made by Chinese Academy of Sciences, is in visible and near IR band. By image processing, the crop biochemistry map is obtained.

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Wen Wang, Jing Yan, Yonghua Chen, Zheng Niu, and Changyao Wang "Monitoring crop biochemical concentrations by high spectral remote sensing", Proc. SPIE 3854, Pattern Recognition, Chemometrics, and Imaging for Optical Environmental Monitoring, (16 December 1999); https://doi.org/10.1117/12.372896

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