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.
The dynamic detection of coastline is important for the study of sea level rising caused by global climate change. Therefore, it is especially meaningful to select an appropriate method to extract the coastline accurately. In this paper, the coastal zones of Reykjavik, Iceland was chosen for the study area, and the method of combination water indices (i.e., NDWI, MNDWI, EWI, and AWEI) and the maximum interclass variance method (Otsu) was used to automatically detect the coastline. The results show that the NDWI and EWI can accurately extract the coastlines in this region, and NDWI performs better than others (accuracy of 91%). The conclusions obtained in this paper have the potential to be applied to coastline extraction in the polar regions.
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