Comparison and analysis of hyperspectral vegetation indices for recognizing water stress of soybean

Rui Dai; Shengbo Chen; Xitong Xu; Zibo Wang

doi:10.1117/12.2668138

23 February 2023 Comparison and analysis of hyperspectral vegetation indices for recognizing water stress of soybean

Rui Dai, Shengbo Chen, Xitong Xu, Zibo Wang

Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 125512K (2023) https://doi.org/10.1117/12.2668138
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China

Abstract

Drought, which frequently occurs in the major soybean producing areas in China, has led to a serious reduction in soybean yields. The objective of the present paper was to study the spectral characteristics of soybean under water stress, and propose the Soybean Water Stress Index (SWSI) to monitor the extent and area of water stress through a field simulated experiment. The experiment was carried out in the Agricultural Experimental Base of Jilin University from May to September in 2020, and canopy spectral data were collected once a week. The result showed that the spectral reflectance of soybean canopy increased in the VIS and SWIR spectral regions and decreased in the NIR with an increase of the water stress. This paper selected NDVI, RDVI, PRI, MCARI, NDWI, WI and SWSI to identify different degrees of water stress of soybean. The result suggested that the RDVI and SWSI were suitable for identifying soybean under water stress. To seek the best identifiable vegetation index, the normalized average distance of vegetation indices under different water stress degrees were calculated. The result indicated that the distance of SWSI is more than that of other indices’ in the whole growth period, illustrated that the identifiable ability of SWSI for different water stress degrees of soybean is better than other indices, then SWSI has the strong sensitivity and stability. Therefore, SWSI can be used to monitor the area and extent of drought and provide information support for disaster relief and decisions.

Citation Download Citation

Rui Dai, Shengbo Chen, Xitong Xu, and Zibo Wang "Comparison and analysis of hyperspectral vegetation indices for recognizing water stress of soybean", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 125512K (23 February 2023); https://doi.org/10.1117/12.2668138

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