Understanding how the dynamics of vegetation growth respond to climate change at different temporal and spatial scales is critical to projecting future ecosystem dynamics and the adaptation of ecosystems to global change. In this study, we investigated vegetated growth dynamics (annual productivity, seasonality and the minimum amount of vegetated cover) in China and their relations with climatic factors during 1982–2011, using the updated Global Inventory Modeling and Mapping Studies (GIMMS) third generation global satellite Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset and climate data acquired from the National Centers for Environmental Prediction (NCEP). Major findings are as follows: (1) annual mean NDVI over China significantly increased by about 0.0006 per year from 1982 to 2011; (2) of the vegetated area in China, over 33% experienced a significant positive trend in vegetation growth, mostly located in central and southern China; about 21% experienced a significant positive trend in growth seasonality, most of which occurred in northern China (>35°N); (3) changes in vegetation growth dynamics were significantly correlated with air temperature and precipitation (p < 0.001) at a region scale; (4) at the country scale, changes in NDVI was significantly and positively correlated with annual air temperature (r = 0.52, p < 0.01) and not associated with annual precipitation (p > 0.1); (5) of the vegetated area, about 24% showed significant correlations between annual mean NDVI and air temperature (93% positive and remainder negative), and 12% showed significant correlations of annual mean NDVI with annual precipitation (65% positive and 35% negative). The spatiotemporal variations in vegetation growth dynamics were controlled primarily by temperature and secondly by precipitation. Vegetation growth was also affected by human activities; and (6) monthly NDVI was significantly correlated with the preceding month’s temperature and precipitation in western, central and northern China. The effects of a climate lag of more than two months in southern China may be caused mainly by the abundance of precipitation. These findings suggest that continuing efforts to monitor vegetation changes (in situ and satellite observations) over time and at broad scales are greatly needed, and are critical for the management of ecosystems and adapting to global climatic changes. It is likewise difficult to predict well future vegetation growth without linking these observations to mechanistic terrestrial ecosystem processes models that integrate all the satellite and in situ observations.

The combination of Landsat-8, Landsat-9, Sentinel-2A and Sentinel-2B data provides a new perspective in remote sensing application for terrestrial monitoring. Jointly, these four sensors together offer global 10–30-m multi-spectral data coverage at a higher temporal revisit frequency. In this study, combinations of four sensors were used to examine the revisit interval by modelled orbit swath information. To investigate different factors that could influence data availability, an analysis was carried out for one year based on daytime surface observations of Landsat-8 and Sentinel-2A -2B. We found that (i) the global median average of revisit intervals for the combination of four sensors was 2.3 days; (ii) the global mean average number of surface observations was 141.4 for the combination of Landsat-8 and Sentinel-2A -2B; (iii) the global mean average cloud-weighted number of observations for the three sensors combined was 81.9. Three different locations were selected to compare with the cloud-weighted number of observations, and the results show an appropriate accuracy. The utility of combining four sensors together and the implication for terrestrial monitoring are discussed.

High spatiotemporal resolution satellite imagery is useful for natural resource management and monitoring for land-use and land-cover change and ecosystem dynamics. However, acquisitions from a single satellite can be limited, due to trade-offs in either spatial or temporal resolution. The spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM (ESTARFM) were developed to produce new images with high spatial and high temporal resolution using images from multiple sources. Nonetheless, there were some shortcomings in these models, especially for the procedure of searching spectrally similar neighbor pixels in the models. In order to improve these models’ capacity and accuracy, we developed a modified version of ESTARFM (mESTARFM) and tested the performance of two approaches (ESTARFM and mESTARFM) in three study areas located in Canada and China at different time intervals. The results show that mESTARFM improved the accuracy of the simulated reflectance at fine resolution to some extent.

There is a growing concern over change in vegetation dynamics and drought patterns with the increasing climate variability and warming trends in Africa, particularly in the semiarid regions of East Africa. Here, several geospatial techniques and datasets were used to analyze the spatio-temporal vegetation dynamics in response to climate (precipitation and temperature) and drought in Eritrea from 2000 to 2017. A pixel-based trend analysis was performed, and a Pearson correlation coefficient was computed between vegetation indices and climate variables. In addition, vegetation condition index (VCI) and standard precipitation index (SPI) classifications were used to assess drought patterns in the country. The results demonstrated that there was a decreasing NDVI (Normalized Difference Vegetation Index) slope at both annual and seasonal time scales. In the study area, 57.1% of the pixels showed a decreasing annual NDVI trend, while the significance was higher in South-Western Eritrea. In most of the agro-ecological zones, the shrublands and croplands showed decreasing NDVI trends. About 87.16% of the study area had a positive correlation between growing season NDVI and precipitation (39.34%, p < 0.05). The Gash Barka region of the country showed the strongest and most significant correlations between NDVI and precipitation values. The specific drought assessments based on VCI and SPI summarized that Eritrea had been exposed to recurrent droughts of moderate to extreme conditions during the last 18 years. Based on the correlation analysis and drought patterns, this study confirms that low precipitation was mainly attributed to the slowly declining vegetation trends and increased drought conditions in the semi-arid region. Therefore, immediate action is needed to minimize the negative impact of climate variability and increasing aridity in vegetation and ecosystem services.

In this paper we present an estimate of net ecosystem CO₂ exchange over China for the years 2001–2010 using the CarbonTracker Data Assimilation System for CO₂ (CTDAS). Additional Chinese and Asian CO₂ observations are used in CTDAS to improve our estimate. We found that the combined terrestrial ecosystems in China absorbed about −0.33 Pg C yr⁻¹ during 2001–2010. The uncertainty on Chinese terrestrial carbon exchange estimates as derived from a set of sensitivity experiments suggests a range of −0.29 to −0.64 Pg C yr⁻¹. This total Chinese terrestrial CO₂ sink is attributed to the three major biomes (forests, croplands, and grass/shrublands) with estimated CO₂ fluxes of −0.12 Pg C yr⁻¹ (range from −0.09 to −0.19 Pg C yr⁻¹), −0.12 Pg C yr⁻¹ (range from −0.09 to −0.26 Pg C yr⁻¹), and −0.09 Pg C yr⁻¹ (range from −0.09 to −0.17 Pg C yr⁻¹), respectively. The peak‐to‐peak amplitude of interannual variability of the Chinese terrestrial ecosystem carbon flux is 0.21 Pg C yr⁻¹ (~64% of mean annual average), with the smallest CO₂ sink (−0.19 Pg C yr⁻¹) in 2003 and the largest CO₂ sink (−0.40 Pg C yr⁻¹) in 2007. We stress that our estimate of terrestrial ecosystem CO₂ uptake based on inverse modeling strongly depends on a limited number of atmospheric CO₂ observations used. More observations in China specifically and in Asia in general are needed to improve the accuracy of terrestrial carbon budgeting for this region.