Investigation of crop growth condition with hyperspectral reflectance based on ground-based remote sensing

Minzan Li; Xijie Zhang; Yane Zhang; Peng Zhao; Jianping Zhang

doi:10.1117/12.580649

20 January 2005 Investigation of crop growth condition with hyperspectral reflectance based on ground-based remote sensing

Minzan Li, Xijie Zhang, Yane Zhang, Peng Zhao, Jianping Zhang

Author Affiliations +

Proceedings Volume 5655, Multispectral and Hyperspectral Remote Sensing Instruments and Applications II; (2005) https://doi.org/10.1117/12.580649
Event: Fourth International Asia-Pacific Environmental Remote Sensing Symposium 2004: Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2004, Honolulu, Hawai'i, United States

Abstract

Cucumber was selected as the experimental crop in greenhouse, and a spectroradiometer (ASD FieldSpec HH, 325-1075 nm measurable range with 1 nm resolution) was used to acquire hyperspectral reflectance of whole plants and leaves in growing status. The seedlings were grown in compound substrate composed of vermiculite and straw charcoal. In order to create nutrient stress to cucumber, five kinds of compound substrates were prepared with mixing vermiculite and straw charcoal in the ratios of 10:0, 8:2, 6:4, 4:6, and 2:8, respectively. Thirteen measurements were conducted in testing period continued from May to July in 2003. The correlation coefficient between hyperspectral reflectance and N-content of leaves and that between hyperspectral reflectance and growth condition of whole plants were analyzed in all wavelength bands. The results show that the hyperspectral reflectance based on ground-based remote sensing is available to predict N-content of leaves and to determine growth condition of whole plants.

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Minzan Li, Xijie Zhang, Yane Zhang, Peng Zhao, and Jianping Zhang "Investigation of crop growth condition with hyperspectral reflectance based on ground-based remote sensing", Proc. SPIE 5655, Multispectral and Hyperspectral Remote Sensing Instruments and Applications II, (20 January 2005); https://doi.org/10.1117/12.580649

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KEYWORDS

Reflectivity

Remote sensing

Agriculture

Calibration

Instrument modeling

Atmospheric modeling

Data modeling

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