Water use efficiency (WUE) measures the tradeoff between carbon uptake and water consumption in terrestrial ecosystems. It remains unclear how the responses of WUE to drought vary with drought severity. We assessed the spatio-temporal variations of ecosystem WUE and its responses to drought for terrestrial ecosystems in Southwest China over the period 2000–2017. The annual WUE values varied with vegetation type in the region: Forests (3.25 gC kg⁻¹H₂O) > shrublands (2.00 gC kg⁻¹H₂O) > croplands (1.76 gC kg⁻¹H₂O) > grasslands (1.04 gC kg⁻¹H₂O). During the period 2000–2017, frequent droughts occurred in Southwest China, and overall, drought had an enhancement effect on WUE. However, the effects of drought on WUE varied with vegetation type and drought severity. Croplands were the most sensitive to drought, and slight water deficiency led to the decline of cropland WUE. Over grasslands, mild drought increased its WUE while moderate and severe drought reduced its WUE. For forests and shrublands, mild and moderate drought increased their WUE, and only severe drought reduce their WUE, indicating that these ecosystems had stronger resistance to drought. Assessing the patterns and trends of ecosystem WUE and its responses to drought are essential for understanding plant water use strategy and informing ecosystem water management.

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.

Drought can cause severe agricultural economic losses and hinder social and economic development. To manage drought, the process of drought events needs to be described with the help of an effective drought indicator. As a comprehensive variable, soil moisture is an essential indicator for describing agricultural drought. In this work, the extreme drought events in southwest China were analysed by the Global Land Data Assimilation System (GLDAS) root zone soil moisture data set. To define the drought quantitatively, a Standardized Soil Moisture Drought Index (SSMI) was calculated using the soil moisture data, then used to get the duration, frequency, and severity of drought events in southwest China. The results showed that the frequency and intensity of drought in southwest China had an apparent upward trend before 2014 and an apparent downward trend since 2014. Moreover, there are apparent differences in the frequency and intensity of drought in various regions of southwest China. Yunnan Province is prone to spring drought events. Guangxi Province and Guizhou Province are prone to spring, autumn and winter droughts, and the intensity of autumn and winter droughts is significantly higher than that of spring droughts. The Sichuan-Chongqing border area is prone to summer drought. We found that the monthly variation of soil moisture in different provinces in southwest China is consistent, but the seasonal variation of drought is different. Meanwhile, the performance of the SSMI was compared to the commonly used drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI). The results showed that the SSMI is more sensitive to drought than both SPEI and PDSI in southwest China. The results also demonstrate that GLDAS soil moisture data can be used to study drought at a small regional scale.

Scarce water resources are available in the arid and semi-arid areas of Northwest China, where significant water-related challenges will be faced in the coming decades. Quantitative evaluations of the spatio-temporal dynamics in ecosystem water use efficiency (WUE), as well as the underlying environmental controls, are crucial for predicting future climate change impacts on ecosystem carbon-water interactions and agricultural production. However, these questions remain poorly understood in this typical region. By means of continuous eddy covariance (EC) measurements and time-series MODIS data, this study revealed the distinct seasonal cycles in gross primary productivity (GPP), evapotranspiration (ET), and WUE for both grassland and cropland ecosystems, and the dominant climate factors performed jointly by temperature and precipitation. The MODIS WUE estimates from GPP and ET products can capture the broad trend in WUE variability of grassland, but with large biases for maize cropland, which was mainly ascribed to large uncertainties resulting from both GPP and ET algorithms. Given the excellent biophysical performance of the MODIS-derived enhanced vegetation index (EVI), a new greenness model (GR) was proposed to track the eight-day changes in ecosystem WUE. Seasonal variations and the scatterplots between EC-based WUE and the estimates from time-series EVI data (WUE_GR) also certified its prediction accuracy with R² and RMSE of both grassland and cropland ecosystems over 0.90 and less than 0.30 g kg⁻¹, respectively. The application of the GR model to regional scales in the near future will provide accurate WUE information to support water resource management in dry regions around the world.

It is of great significance for the validation of remotely sensed evapotranspiration (ET) products to solve the spatial-scale mismatch between site observations and remote sensing estimations. To overcome this challenge, this paper proposes a comprehensive framework for obtaining the ground truth ET at the satellite pixel scale (1 × 1 km resolution in MODIS satellite imagery). The main idea of this framework is to first quantitatively evaluate the spatial heterogeneity of the land surface, then combine the eddy covariance (EC)-observed ET (ET_EC) to be able to compare and optimize the upscaling methods (among five data-driven and three mechanism-driven methods) through direct validation and cross-validation, and finally use the optimal method to obtain the ground truth ET at the satellite pixel scale. The results showed that the ET_EC was superior over homogeneous underlying surfaces with a root mean square error (RMSE) of 0.34 mm/d. Over moderately and highly heterogeneous underlying surfaces, the Gaussian process regression (GPR) method performed better (the RMSEs were 0.51 mm/d and 0.60 mm/d, respectively). Finally, an integrated method (namely, using the ET_EC for homogeneous surfaces and the GPR method for moderately and highly heterogeneous underlying surfaces) was proposed to obtain the ground truth ET over fifteen typical underlying surfaces in the Heihe River Basin. Furthermore, the uncertainty of ground truth ET was quantitatively evaluated. The results showed that the ground truth ET at the satellite pixel scale is relatively reliable with an uncertainty of 0.02–0.41 mm/d. The upscaling framework proposed in this paper can be used to obtain the ground truth ET at the satellite pixel scale and its uncertainty, and it has great potential to be applied in more regions around the globe for remotely sensed ET products’ validation.

An increase in the frequency and severity of droughts associated with global warming has resulted in deleterious impacts on forest productivity in Southwest China. Despite attempts to explore the response of vegetation to drought, less is known about forest’s resilience in response to drought in Southwest China. Here, the reduced resilience of the forest was found based on remotely sensed optical and microwave vegetation products. The spatial distribution and temporal variation of resilience-reduced forest were assessed using the standardized precipitation evapotranspiration index (SPEI) and vegetation optical depth (VOD). Our findings showed that 40–50% of the forest appeared to have abnormally low resilience approximately 6 months after the severe drought. The spatial distributions of abnormally low resilience had a good agreement with the regions affected by the 2009–2011 drought events. In particular, our results indicated that areas of afforestation were more susceptible to drought than natural forest, maybe due to the different water uptake strategy of the diverse root systems. Our findings highlight the vulnerability of afforestation areas to climate change, and recommend giving more attention to soil water availability.