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In the research area Panjin city, visible and near-infrared remote sensing images by Tiangong-2 wide-band imaging spectrometer was used to extracte wetland. According to the established remote sensing interpretation signs, the wetland types of Panjin city in 2018 are extracted by using the method of partition classification. The thermal infrared imaging spectrometer of Tiangong-2 was used to invert the land surface temperature based on the split-window algorithm. This paper comprehensively analyzed the distribution of wetland cover types of Panjin city in 2018 and their impact on land surface temperature. The results show that in 2018, Panjin wetland covers an area of 3,184.89 km2 and the major type of wetland is artificial wetland. The area is reduced successively according to paddy field, reed swamp, mudflat wetland, suaeda salsa swamp, aquaculture farm, reservoir/pond and river wetland. The overall accuracy of partition classification method is 89.97%, kappa coefficient is 0.8761. The applicability of Tiangong-2 wide-band imaging spectrometer in wetland monitoring is verified. The split-window algorithm can be used to effectively invert the land surface temperature of Tiangong-2 data, and the land surface temperature results obtained by inversion are in good consistency with those obtained by MODIS inversion, the correlation coefficient reached 0.79. Through counting, the land surface temperature of different wetland types is different, among which the land surface temperature of mudflat wetland is the highest and that of reservoir is the lowest.

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Drought is a natural disaster caused by long-term water deficit. Because the growth of crops and vegetation is closely related to soil moisture environment, it is of great significance to study the soil drought and vegetation response. In this paper, the soil moisture availability index (SMAI) was developed for quantifying soil drought conditions. The effectiveness and the ability of SMAI to recognize drought events were analyzed, while the vegetation condition index (VCI) was used to characterize the vegetation status. Temporal and spatial variations of soil drought and vegetation condition as well as the impacts of drought on vegetation in North China during 2001–2015 were comprehensively examined. We firstly concluded that SMAI related well with standardized precipitation evapotranspiration index (SPEI), and drought events can be detected by SMAI. Next, the mean value of SMAI in North China showed a decreasing trend in recent 15 years. Finally, the SMAI positively correlated with VCI in most areas of North China, and the response of four types of vegetation to SMAI differed over time. The results of SMAI on vegetation would assist drought research and application in North China.