Study on the growth and spectral response of rice with different nitrogen morphologies in water-saving irrigation model

Zhifang Xiao; Xilin Fang; Yue Wang; Rongliang Zeng; Jianhui Li; Guanghui Chen

doi:10.1117/12.2521903

12 March 2019 Study on the growth and spectral response of rice with different nitrogen morphologies in water-saving irrigation model

Zhifang Xiao, Xilin Fang, Yue Wang, Rongliang Zeng, Jianhui Li, Guanghui Chen

Author Affiliations +

Proceedings Volume 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application; 1102332 (2019) https://doi.org/10.1117/12.2521903
Event: Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 2018, Xi'an, China

Abstract

Hyper-spectral remote sensing is now a frontier of the remote sensing technology, which can monitoring chlorophyll content rapidly and nondestructive. In this study, four nitrogen forms were set up：ammonium sulfate (ammonium nitrogen) B1, potassium nitrate (nitrate nitrogen) B2, urea (amide nitrogen) B3, and formaldehyde urea (slow effect nitrogen fertilizer) B4. The chlorophyll content of B4 treated with different nitrogen forms was significantly different. Spectral parameters also show good correlation. The chlorophyll content estimation model constructed with the spectral parameters DVI(755,690) and DVI(810,690) has a good fitting degree, and the linear model R2 has reached a significant level, respectively. Rice treated with traditional urea had a lower growth rate than the other three types of nitrogen, the slow effect nitrogen fertilizer and ammonium nitrogen were suggested to improve nitrogen utilization efficiency and eventually reach the goal of high yield.

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Zhifang Xiao, Xilin Fang, Yue Wang, Rongliang Zeng, Jianhui Li, and Guanghui Chen "Study on the growth and spectral response of rice with different nitrogen morphologies in water-saving irrigation model", Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 1102332 (12 March 2019); https://doi.org/10.1117/12.2521903

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KEYWORDS

Nitrogen

Reflectivity

Urea

Vegetation

Nondestructive evaluation

Remote sensing

Data modeling

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