A large portion of Central-Western Asia is made up of contiguous closed basins, collectively termed as the Asian Endorheic Basins (AEB). As these retention basins are only being replenished by the intermittent precipitation, increasing droughts in the region and a growing demand for water have been presumed to jointly contributed to the land degradation. To understand the impact of climate change and human activities on dryland vegetation over the AEB, we conducted trend and partial correlation analysis of vegetation and hydroclimatic change from 2001 to 2021 using multi-satellite observations, including vegetation greenness, total water storage anomalies (TWSA) and meteorological data. Here we show that much of the AEB (65.53%) exhibited a greening trend over the past two decades. Partial correlation analyses indicated that climatic factors had varying effects on vegetation productivity as a function of vegetation types and aridity. In arid AEB, precipitation dominated the vegetation productivity trend. Such a rainfall dominance gave way to TWSA dominance in the hyper-arid AEB. We further showed that the decoupling of rainfall and hyper-arid vegetation greening was largely due to a significant expansion (17.3%) in irrigated cropland across the hyper-arid AEB. Given the extremely harsh environment in the hyper-arid AEB, our results therefore raised the concerns on the ecological and societal sustainability in this region, where a mild increase in precipitation might not be able to catch up the rising evaporative demand and water consumption resulted from global warming and irrigation intensification.

Remote sensing capabilities to monitor evergreen broadleaved vegetation are limited by the low temporal variability in the greenness signal. With canopy greenness computed from digital repeat photography (PhenoCam), we investigated how canopy greenness related to seasonal changes in leaf age and traits as well as variation of trees’ water fluxes (characterized by sap flow and canopy conductance). The results showed sprouting leaves, with significantly different leaf traits compared to mature and old leaves, are mainly responsible for the rapid increase in canopy green chromatic coordinate (GCC) in spring. Thus, the temporal dynamics of canopy GCC can be explained by leaf spectral properties and leaf age. Air temperature and vapor pressure deficit (VPD) explained most of sap flow and canopy conductance variance, respectively. Besides, GCC is an important explanatory variable for variation of canopy conductance may because GCC can represent the leaf ontogeny information. We conclude that PhenoCam GCC can be used to identify the new leaf flushing for evergreen broadleaved trees, which carries important information about leaf ontogeny and traits thus can better estimate of water fluxes such as canopy conductance.

Vegetation is an important component of terrestrial ecosystem as it supports other biological activities through the photosynthetic production. The biophysical and biochemical parameters of vegetation retrieved from satellite observations have been used extensively in studying the physiological states and growing conditions of vegetation that enabling global vegetation monitoring. Most of vegetation remote sensing applications using data from MODIS, Landsat, and Sentinel, though it would be beneficial, from the user perspective, to have an even more diverse data sources that not only secure data sustainability in case satellite retirement or sensor failure, but also enables research opportunities such as multi-sensor data fusion/integration and multi-angle remote sensing that can take advantage of observations acquired from different spaceborne sensors. In this regard, it would be worth to explore the potential of the large number of Chinese Earth Observation Satellites (CEOS) that have been put into orbit over past decade. Here we summarized the recent advances in applying CEOS remote sensing of vegetation and its associated applications. We focused on the uncertainty and limitations for retrieving several commonly-used vegetation parameters by critically examining the case studies conducted over different vegetation types. Suggestions for research opportunities that can benefit from the additional data from CEOS are also provided. The hope is to provide the community an overview of what could be useful to their specific ecological, environmental and global change studies by leveraging the growing data volume from the orbiting CEOS sensors.