Yang Yan

The spatial and temporal resolution of remote sensing products in land surface temperature (LST) studies can be improved using the downscaling method. This is a crucial area of research as it provides basic data for the study of climate change. However, there have been few studies evaluating the applicability of downscaling methods using underlying surfaces of varying complexities. In this study, we focused on the semi–homogeneous underlying surface of Gurbantunggut Desert and evaluated the applicability of five classical, passive microwave, downscaling methods based on the machine learning of Catboost, using 365 days of AMSR–2 and MODIS data in 2019, which can be scanned once during the day and night. Our results showed four main points: (1) The correlation coefficients between feature vectors and the LST of the semi–homogeneous underlying surface were clearly different from those of the surrounding oases. The correlation coefficient of the semi–homogeneous underlying surface was high, and that of the surrounding oases was low. (2) At the same frequency, the correlation coefficient between vertically polarized BT and LST was greater than that between horizontally polarized BT and LST. Considering the semi–heterogeneous underlying surface, 23.8 GHz and 36.5 GHz may be more suitable for passive microwave LST retrieval than 89 GHz according to physical mechanisms. (3) The fine–scale LST downscaling accuracy achieved with all BT channels of AMSR–2 was higher than that achieved with the other four classical models. The day and night RMSE values verified with MYD11A1 data were 2.82 K and 1.38 K, respectively. (4) The correlation coefficients between downscaled LST and the soil temperature of the top layer of the site were the highest, with daytime–nighttime R² values of 0.978 and 0.970, and RMSE values of 3.42 and 4.99 K, respectively. The all–channel–based LST downscaling method is very effective and can provide a theoretical foundation for the acquisition of all–weather, multi–layer soil temperature.

The purposes of this study are to reveal the spatial pattern and dynamic changes of NDVI in the northern slope of the Tianshan Mountains for an extended period and to explore whether the spatial and temporal evolution of NDVI in different spatial scales is consistent so as to provide a reasonable theoretical basis for the selection of appropriate remote sensing spatial resolution in the study area. The GIMMS NDVI remote sensing data set was used to resample the NDVI data with three spatial resolutions of 0.5 km × 0.5 km, 1 km × 1 km, and 8 km × 8 km. The Mann-Kendall method was used to analyze the spatial-temporal evolution characteristics of vegetation NDVI on the NTSM from 1981 to 2015. The results showed that the interannual variation trend and spatial distribution of vegetation NDVI were consistent at different spatial scales. The change of NDVI displayed an increasing trend with changes concentrated in the middle of the NTSM. Five distinct trends were observed: no significant change (35% of the area), significant positive change (26%), significant single peak change (15%), a significant U-shaped change relationship (12%), and significant negative change (11%). Remote sensing NDVI data with a spatial resolution of 8 km could be used to analyze the long-term interannual variation trend of vegetation NDVI on the NTSM.

Vegetation growth and its response to climatic factors have become one of the most pressing issues in ecological research. However, no consensus has yet been reached on how to resolve this problem in arid areas with a high-elevation gradient and complex underlying surface. Here, NOAA CDR AVHRR NDVI V5 for 1981–2018 and China’s regional surface meteorological faction-driven datasets were used. General linear regression, the Mann-Kendall test and sliding t-test, Pearson correlations, and the Akaike information criterion (AIC), on a grid-scale, were applied to analyze the annual normalized difference vegetation index (NDVI) and its relationship with temperature and precipitation in the Altay region. Results revealed that the temporal trend of NDVI for most grid cells was non-significant. However, mountains, coniferous forests, grasslands, and meadows in the high-elevation zone displayed a slow increasing trend in NDVI. Further, NDVI was positively correlated with the mean annual temperature and total annual precipitation, the latter playing a more significant role. Yet, for desert and shrub vegetation and coniferous forest, their NDVI had insignificant negative correlations with the mean annual temperature. Hence, both the trends and drivers of NDVI of high elevation are highly complex. This study’s findings provide a reference for research on vegetation responses to climate change in arid areas having a high-elevation gradients and complex underlying surfaces.

The atmospheric environmental quality problem is one of the important issues concerned by domestic and foreign researchers. In this paper, a fuzzy comprehensive evaluation method of atmospheric environmental quality is provided. SO 2 , NO 2 , PM 10 , PM 2.5 , CO and O 3 were taken as evaluation factors, and Daniel trend test method, fuzzy comprehensive evaluation method and Pearson correlation analysis method were used to evaluate the atmospheric environmental quality and change trend in Zhangjiajie City from 2015 to 2020. The results showed that: 1) The atmospheric quality was Class I and optimized year by year; 2) Except for NO 2 , the concentrations of PM 2.5 , PM 10 , O 3 , SO 2 and CO all showed a downward trend, and the main pollutions affecting the atmospheric quality are transferred to NO 2 and O 3 . 3 ) There was a correlation between each pollutant, and the pollution levels in the four seasons were in order of winter > autumn > spring > summer; This study provided a scientific basis for the prevention and protection of atmospheric environment and the sustainable development of tourism in Zhangjiajie city.