Ms. Yunxia Han Profile

Yunxia Han

at China Agricultural Univ

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Proceedings Article | 16 September 2005 Paper

Estimating growth status of winter wheat based on aerial images and hyperspectral data

Yunxia Han, Minzan Li, Liangliang Jia, Xijie Zhang, Fusuo Zhang

Proceedings Volume 5909, 59091V (2005) https://doi.org/10.1117/12.615717

KEYWORDS: Nitrogen, Reflectivity, Remote sensing, Hyperspectral imaging, RGB color model, Vegetation, Agriculture, Soil science, Satellites, Data analysis

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The aim of this paper is to estimate the growth status and yield of winter wheat using aerial images and hyperspectral data obtained by unmanned aircraft, and then to perform precision management to the crop. The test farm was divided into 48 cells. Twenty-four cells were selected as variable rate fertilization area, and the other 24 cells were used as contrast area with low fertilization in growth season. In 2004, the aerial images of winter wheat canopy were measured from an unmanned aircraft. The SPAD value of crop leaf was acquired using a SPAD-502 chlorophyll meter, and then the hyperspectral reflectance of the crop canopy was measured by a handheld spectroradiometer. The vegetation indices, NDVI and DVI, were calculated from the hyperspectral data. The characteristics of the aerial images were used to evaluate the growth status. The RGB values of all cells were calculated from aerial images. The result showed that total nitrogen had better correlation with SPAD, NDVI, DVI, and RGB. NDVI and DVI had high correlation with the growth condition, and R/(R+G+B) and G/(R+G+B) had good correlation with the growth status and yield. The variable rate fertilization based on aerial images and NDVI was executed in the experimental cells. The yield map showed that the spatial variation of the yield was reduced and the total yield was increased. While in contrast cells, the spatial variation of the yield is greater than in experimental cells because of the spatial variation of the field nutrition. Therefore, it is practical to use aerial images and hyperspectral data of the crop canopy in estimation of the crop growth status.

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