Rising atmospheric dryness [vapor pressure deficit (VPD)] can limit photosynthesis and thus reduce vegetation productivity. Meanwhile, plants can benefit from global warming and the fertilization effect of carbon dioxide (CO₂). There are growing interests to study climate change impacts on terrestrial vegetation. However, global vegetation productivity responses to recent climate and CO₂ trends remain to be fully understood. Here, we provide a comprehensive evaluation of the relative impacts of VPD, temperature, and atmospheric CO₂ concentration on global vegetation productivity over the last two decades using a robust ensemble of solar‐induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) data. We document a significant increase in global vegetation productivity with rising VPD, temperature, and atmospheric CO₂ concentration over this period. For global SIF (or GPP), the decrease due to rising VPD was comparable to the increase due to warming but far less than the increase due to elevated CO₂ concentration. We found that rising VPD counteracted only a small proportion (approximately 8.1%–15.0%) of the warming and CO₂‐induced increase in global SIF (or GPP). Despite the sharp rise in atmospheric dryness imposing a negative impact on plants, the warming and CO₂ fertilization effects contributed to a persistent and widespread increase in vegetation productivity over the majority (approximately 66.5%–72.2%) of the globally vegetated areas. Overall, our findings provide a quantitative and comprehensive attribution of rising atmospheric dryness on global vegetation productivity under concurrent climate warming and CO₂ increasing.

Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the North China Plain, East Asia, which has a stable cropping pattern and similar phenological stages across the region. Ground phenological observations acquired from a typical agro-meteorological station were used as a priori knowledge. A parallelepiped classifier processed VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving) backscatter signals in order to map the winter wheat planting area. An accuracy assessment showed that the total classification accuracy reached 84% and the Kappa coefficient was 0.77. Both the difference ( ${\sigma }_d$ ) between VH and VV and its slope were obtained to contrast with a priori knowledge and then used to extract the phenological metrics. Our findings from the analysis of the time series showed that the seedling, tillering, overwintering, jointing, and heading of winter wheat may be closely related to ${\sigma }_d$ and its slope. Overall, this study presents a generalizable methodology for mapping the winter wheat planting area and monitoring phenology using Sentinel-1 backscatter time series, especially in areas lacking optical remote sensing data. Our results suggest that the main change in Sentinel-1 backscatter is dominated by the vegetation canopy structure, which is different from the established methods using optical remote sensing data, and it is available for phenological metrics extraction.

Understanding spatio-temporal changes in winter wheat (Triticum aestivum L) phenology and its response to temperature will be vital for adapting to climate change in the coming years. For this purpose, the heading date (HD), maturity date (MD), and length of the reproductive growth period (LRGP) were detected from the remotely sensed leaf area index (LAI) data by a threshold-based method during the harvest year 2003 to 2018 across the North China Plain. The results show that there was high spatial heterogeneity of winter wheat phenology in pixel scale across the whole area, which could not be detected in previous site-based studies. The results also verified that climate warming could explain part of the change in the HD. However, for the LRGP, the potential impact of non-climate effects should be further investigated. This study presents the spatio-temporal changes both in winter wheat phenology and corresponding mean temperature and then analyzes their relationships in pixel scale. Additionally, this study further discusses the potential impact of non-climate effects on the LRGP.

With continued global warming, the frequency and severity of heat wave events increased over the past decades, threatening both regional and global food security in the future. There are growing interests to study the impacts of drought on crop. However, studies on the impacts of heat stress on crop photosynthesis and yield are still lacking. To fill this knowledge gap, we used both statistical models and satellite solar-induced chlorophyll fluorescence (SIF) data to assess the impacts of heat stress on wheat yield in a major wheat growing region, the Indo-Gangetic Plains (IGP), India. The statistical model showed that the relationships between different accumulated degree days (ADD) and reported wheat yield were significantly negative. The results confirmed that heat stress affected wheat yield across this region. Building on such information, satellite SIF observations were used to further explore the physiological basis of heat stress impacts on wheat yield. Our results showed that SIF had strong negative correlations with ADDs and was capable of monitoring heat stress. The SIF results also indicated that heat stress caused yield loss by directly impacting the photosynthetic capacity in wheat. Overall, our findings demonstrated that SIF as an effective proxy for photosynthetic activity would improve our understanding of the impacts of heat stress on wheat yield.

Multiple lines of evidence confirm a widespread increase in vegetation growth across China over the past few decades. The relationship between vegetation growth and water availability is thought to be becoming stronger under climate change, that is, water constraints on vegetation growth have been increasing. However, our understanding of how water constraints have influenced these vegetation greening trends, especially those climate change‐driven ones, remains limited. Here, we conduct a comprehensive evaluation of recent water constraints and their implications for vegetation growth in China between 1982 and 2015. By analyzing the spatiotemporal patterns of the relationship between vegetation growth and water availability, we reveal recent water constraints and their changes hidden within an overall greening trend in China. Further analysis demonstrates that two climate change‐related categories, defined broadly as “climate” (e.g., air temperature, precipitation, and so on) and “CO₂” (i.e., atmospheric carbon dioxide), have exerted varying levels of importance in regulating vegetation growth across different water constraints. With increasing water constraints, the proportion of the climate‐dominated area has significantly risen, while that of the CO₂‐dominated area has sharply declined. Our findings highlight that water constraints can mediate the dominance of climate and atmospheric CO₂ on vegetation growth. This has the great potential to exacerbate the uncertainty surrounding current and future sustainable vegetation greening trends.