The apparent optimum air temperature for vegetation photosynthesis (T_opt) is a key temperature parameter in terrestrial ecosystem models estimating daily photosynthesis or gross primary production (GPP, g C/m²/day). To date, most models use biome-specific T_opt (T_opt-biome) parameter values. Given vegetation acclimation and adaptation to local climate, site-specific T_opt (T_opt-site) is needed to reduce uncertainties in estimating daily GPP across the scales from site to region and the globe. Previous studies have demonstrated using the Enhanced Vegetation Index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images and daytime air temperature data to estimate the T_opt-site at the eddy covariance tower sites. This study used MODIS-derived EVI and ERA5 climate data to estimate and generate global T_opt-site data products from 2000 to 2019. The T_opt-site of individual pixels within a biome has large variation, which clearly cannot be represented accurately by the widely used T_opt-biome. Therefore, using this global dataset of T_opt-site estimates might significantly affect GPP simulation in current ecosystem models.

Accurate estimation of gross primary production (GPP) of terrestrial vegetation is crucial for comprehending the carbon dynamics. To date, there is still no consensus on the magnitude and seasonality of global GPP among the major global GPP products, underscoring the necessity to improve GPP models for higher accuracy of global GPP estimates. Here, we introduce an improved Vegetation Photosynthesis Model (VPM v3.0), which incorporates site-specific apparent optimum temperature for photosynthesis, leaf-trait-based light absorption (flat leaf vs. needle leaf), and improved water stress estimation. The global VPM simulation is driven by Moderate Resolution Imaging Spectroradiometer images and the ERA5-Land climate dataset. We evaluate VPM v3.0 using GPP from 205 eddy flux tower sites across 11 land cover types (1,658 site-years) (GPP _EC ), as well as the TROPOspheric monitoring instrument (TROPOMI) solar-induced fluorescence (SIF) product for 2018 to 2021. The slope, R ² , and root mean square error between GPP from VPM v3.0 (GPP _VPM-v3 ) and GPP _EC are 0.97, 0.78, and 1.46 gC m ⁻² day ⁻¹ , respectively. GPP _VPM-v3 shows high temporal consistency with TROPOMI SIF. VPM v3.0 provides higher accuracy of GPP estimates at most evaluated sites than VPM v2.0. Comparisons of global GPP from VPM v3.0 with other major global GPP products reveal both spatial–temporal consistency and discrepancies. These findings clearly indicate the improved accuracy of VPM v3.0 in estimating GPP, making it suitable for generating global GPP datasets.

The carbon sink in pantropical biomes play a crucial role in modulating the inter‐annual variations of global terrestrial carbon balance and is threatened by extreme climate events. However, it has not been carefully examined whether an increase in tropical gross primary productivity (GPP) can compensate the decrease during precipitation anomalies. Using the asymmetry index (AI) and multiple GPP products, we assessed responses of pantropical GPP to precipitation anomalies during 2001–2022. Positive AI indicates that GPP increases are greater than GPP decreases during precipitation anomalies, and vice versa. Our results showed an average negative pantropical GPP asymmetry, that is, GPP decreases exceeded the GPP increases during precipitation anomalies. In addition, a positive AI was found in tropical hyper‐arid and arid regions, which is opposite to the negative AI observed in tropical semi‐arid, sub‐humid, and humid regions. This suggest that tropical GPP asymmetry changes from positive to negative as the moisture increases. Notably, a significant decreasing trend of negative AI was observed over the entire tropical region, indicating that the negative effect of inter‐annual precipitation variations on pantropical vegetation productivity has enhanced. Considering the model predicted increasing climate variability and extremes, the negative impact of precipitation variability on tropical carbon cycle may continue to intensify. Lastly, the divergence in AI estimates among multiple GPP products highlight the need to further improve our understanding of the response of tropical carbon cycle to climate changes, especially for the tropical humid regions.

Expansion of impervious surface area (ISA) in urbanizing regions often leads to vegetation area losses, a direct impact of urbanization. Many activities driven by economic growth, population increases, targeted urban greening investments, environmental policies, and major sports events change vegetation composition, structure, and function, leading to substantial indirect (positive or negative) impacts on vegetation in urban area. In this study, we analyzed the spatial‐temporal dynamics of ISA, enhanced vegetation index (EVI), and gross primary production (GPP) in the Yangtze River Delta (YRD), China, over 2000–2020. Positive indirect impacts of urbanization on EVI and GPP surged after 2011, coinciding with China's Ecological Civilization Strategy. The concurrent increases of ISA, EVI, and GPP in the YRD provide an example for our society to work and advance the UN's Sustainable Development Goal #11, “Make cities inclusive, safe, resilient, and sustainable.”

Accurate mapping of crop yields is essential for informed agricultural decision-making and optimal allocation of resources. Current crop yield datasets are deficient in large-scale, high-resolution information regarding the long-term spatial and temporal distribution of crop yields. To address this challenge, we developed a method of vegetation photosynthesis model combined with transition coefficient, producing a detailed dataset with 10 m resolution, covering major regions of maize, rice, and soybean in Northeast China from 2016 to 2021. The method introduces a dynamic observation index (\({{\rm{APAR}}}_{{\varepsilon }_{g}}\)) and a composite yield-conversion coefficient (a), which presents an innovative method for estimating crop yields without field measurements. Validation results show that, for maize, rice, and soybean, the model achieves r values of 0.39, 0.51, and 0.52; MREs of 12.14%, 11.96%, and 14.06%; and rRMSEs of 16.97%, 16.12%, and 17.26%, respectively. The dataset offers valuable insights into crop yield distribution, supporting better agricultural decision-making and resource optimization.

Water, as the source of life, is a key element of environmental education. Geography education is strongly linked to the three pillars of sustainable development (SD). Firstly, this paper used the water-related standards of China’s national geography curriculum (GCSs) as the object of textual analysis. Two coders categorized the water-related GCSs, coding them with two distinct dimensions: geography core competencies and the SD literacy framework. Then, the coding results were analyzed by word frequency analysis to compare them with U.S. high school GCSs. Finally, the distribution of core competencies and the degree of SD literacy in China’s water-related GCSs were summarized and described. Finally, this study showed that China’s sustainable water education content is rich in content, yet fragmented. China’s water-related GCSs focus on the three dimensions of ESD, with an emphasis on the environmental dimension. GCSs in both countries emphasize human–Earth relations effectively.