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 (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 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 PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. 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.

No abstract

While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China’s grasslands. The four models were trained with two strategies: training for all of northern China’s grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China’s grasslands fairly well, while the SAE model performed best (R2 = 0.858, RMSE = 0.472 gC m−2 d−1, MAE = 0.304 gC m−2 d−1). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.

Backgrounds Vegetation dynamic plays a dominant role in the global carbon cycle and climate, especially in vulnerable karst ecosystem. Many studies have examined past several decades changes in vegetation greenness and the associated with climate drivers. Yet, few studies have analyzed the vegetation change in global karst regions particularly in the last decades when climate change and anthropogenic disturbance widely occurred.Methods In this study, we investigated the spatio-temporal variations of vegetation dynamic using the Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) and examined their relationship to climate changes by correlation analysis, the ordinary least squares method investigate the variation trends and the Mann-Kendal test to detect the turning point from 2001 to 2020. ResultsAs expected, there have greening trend in global karst SINDVI from 2001 to 2020, with significant increasing trend in China (range = 0.836, P < 0.05), Europe (range = 0.456, P < 0.05) and many other regions.According to correlation analyses, SINDVI is water-limited in arid and semi-arid regions, such as Middle East and central Asia, and temperature-limited in northern high-latitude.Conclusions consistent with previous studies, our results suggest that anthropogenic activities are mainly responsible for increasing vegetation greenness in tailoring management measures (e.g., Ecological Engineering, the Grain to Green Project) of China and Europe, intensive farmed in Middle East. Coupling warming temperature and increasing precipitation, southeastern Asia and Russia showed an increasing trend in SINDVI. In general, climate factors were the dominant drivers of the variation in vegetation greenness in globally karst regions during research period.

Backgrounds Vegetation dynamic plays a dominant role in the global carbon cycle and climate, especially in vulnerable karst ecosystem. Many studies have examined past several decades changes in vegetation greenness and the associated with climate drivers. Yet, few studies have analyzed the vegetation change in global karst regions particularly in the last decades when climate change and anthropogenic disturbance widely occurred. Methods In this study, we investigated the spatio-temporal variations of vegetation dynamic using the Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) and examined their relationship to climate changes by correlation analysis, the ordinary least squares method investigate the variation trends and the Mann-Kendal test to detect the turning point from 2001 to 2020. Results As expected, there have greening trend in global karst SINDVI from 2001 to 2020, with significant increasing trend in China (range = 0.836, P < 0.05), Europe (range = 0.456, P < 0.05) and many other regions. According to correlation analyses, SINDVI is water-limited in arid and semi-arid regions, such as Middle East and central Asia, and temperature-limited in northern high-latitude. Conclusions consistent with previous studies, our results suggest that anthropogenic activities are mainly responsible for increasing vegetation greenness in tailoring management measures (e.g., Ecological Engineering, the Grain to Green Project) of China and Europe, intensive farmed in Middle East. Coupling warming temperature and increasing precipitation, southeastern Asia and Russia showed an increasing trend in SINDVI. In general, climate factors were the dominant drivers of the variation in vegetation greenness in globally karst regions during research period.

&lt;p&gt;Water stress factor is utilized to describe drought effects on plant growth in land surface models (LSMs). Accurately representing water stress is critical to understand the impact of climate change on plant and ecosystem. Models use various approaches to describe the responses of vegetation to water stress. Some models assumed water stress causes stomata closure to attenuate gas exchange process, while others assumed water stress reduces the maximum rate of carboxylation (Vcmax) to slow photosynthesis. Only a few models considered both constraints. However, which parameterization can better capture the dry condition is still controversial. A reliable detection and attribution of the impact of water stress on plant is necessary for understanding the consequence of climate change on the ecosystem from a mechanism aspect. In this study, an empirical stomatal conductance scheme (proposed by Ball et al. in1987, called &amp;#8220;BB_gs&amp;#8221;) and a unified stomatal conductance model (proposed by Medlyn et al. 2011, called &amp;#8220;ME_gs&amp;#8221;) were coupled into STEMMUS-SCOPE model to explore the discrepancy between empirical and optimal approaches. Three scenarios were designed to represent the effect of water stress on gas exchange (gs_w), photosynthesis (Vcmax_w) and both processes (gs &amp; Vcmax_w). The coupled model was implemented for three sites with different plant function types, including C3 grassland, C3 shrub, and C4 cropland. Results showed that the optimal stomatal conductance scheme has better performance than the empirical approach because the optimal method considers the realistic stomata regulation. The Vcmax_w scheme captured the drought effects better than other schemes. The results improved our understanding on regional ecosystem functioning under the context of climate change. &amp;#160;&amp;#160;&lt;/p&gt;