Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM_2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM_2.5 increased by 0.17 K, 0.04, and 1.02 μg/m³ in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72°N and 48°S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM_2.5 on LST was more complex. On the whole, LST increased with a small increase in PM_2.5 concentrations but decreased with a marked increase in PM_2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature.

With advantages of multispatial resolutions, a high retrieval accuracy, and a high temporal resolution, the satellite-derived land surface temperature (LST) products are very important LST sources. However, the greatest barrier to their wide application is the invalid values produced by large quantities of cloudy pixels, especially for regions frequently swathed in clouds. In this study, an effective method based on the land energy balance theory and similar pixels (SP) method was developed to reconstruct the LSTs over cloudy pixels for the widely used MODIS LST (MOD11A1). The southwest region of China was selected as the study area, where extreme drought has frequently occurred in recent years in the context of global climate change and which commonly exhibits cloudy and foggy weather. The validation results compared with in situ LSTs showed that the reconstructed LSTs have an average error < 1.00 K (0.57 K at night and −0.14 K during the day) and an RMSE < 3.20 K (1.90 K at night and 3.16 K in the daytime). The experiment testing the SP interpolation indicated that the spatial structure of the LST has a greater effect on the SP performance than the size of the data-missing area, which benefits the LST reconstruction in the area frequently covered by large clouds.

Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region.

Revegetation is one of the most effective ways to combat desertification and soil erosion in semiarid and arid regions. However, the impact of the perturbation of revegetation on ecohydrological processes, particularly its effects on the interplay between hydrological processes and vegetation growth under water stress, requires further investigation. This study evaluated the effects of revegetation on the energy, water, and carbon fluxes in a desert steppe in Yanchi County, Ningxia Province, northwest China, by simulating two vegetated scenarios (shrub–grassland ecosystem and grassland ecosystem) using the STEMMUS–SCOPE (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil–Soil Canopy Observation of Photosynthesis and Energy fluxes) model. The model was validated by field observations from May to September of 2016–2019. The evaluation of revegetation effects relied on comparing simulated fluxes between two vegetated scenarios in 2016 and 2019. In both scenarios, turbulent energy was dominated by latent heat flux, which was stronger in the shrub–grassland ecosystem (+7 %). A higher leaf area index and root water uptake of C₃ shrubs (Caragana intermedia) resulted in increased carbon fixation (+83 %) and transpiration (+72 %) of the shrub–grassland ecosystem compared to the C₃ grassland ecosystem. Accompanied by a marked increase in root water uptake (+123 %), revegetation intensified water consumption beyond the levels of received precipitation. These results highlight the critical importance of considering both energy and water budgets in water-limited ecosystems during ecological restoration to avert soil water depletion.

The rapid advancement of remote sensing technology has given rise to numerous global- and regional-scale medium- to high-resolution land cover (LC) datasets, making significant contributions to the exploration of worldwide environmental shifts and the sustainable governance of natural resources. Nonetheless, owing to the inherent uncertainties embedded within remote sensing imagery, LC datasets inevitably exhibit inaccuracies. In this study, a local accuracy assessment of LC datasets in Southwest China was conducted. The datasets utilized in our analysis include ESA WorldCover, CLCD, Esri Land Cover, CRLC, FROM-GLC10, GLC_FCS30, GlobeLand30, and SinoLC-1. This study employed a sampling approach that combines proportional allocation and stratified random sampling (SRS) to gather sample points and compute confusion matrices to validate eight LC products. The local accuracy of the eight LC maps differs significantly from the overall accuracy provided by the original authors in Southwest China. ESA WorldCover and CLCD demonstrate higher local accuracy than other products in Southwest China, with their overall accuracy (OA) values being 87.1% and 85.48%, respectively. Simultaneously, we computed the area for each LC map based on categories, quantifying uncertainty through the reporting of confidence intervals for both accuracy and area parameters. This study aims to validate and compare eight LC datasets and assess precision and area of diverse spatial resolution datasets for mapping and monitoring across Southwest China.