As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPP_MOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPP_EC) from a global network of eddy covariance towers (FluxNet), we compared GPP_MOD and GPP_EC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPP_MOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R²), and root mean square errors (RMSE). We found that GPP_MOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (R² = 0.66 (CI, 0.63–0.69), RMSE = 2.46 (2.33–2.58) g Cm⁻² day⁻¹) for the three grassland biomes. GPP_MOD had varied performances with R² values of 0.72 (0.68–0.75) (temperate), 0.64 (0.59–0.68) (alpine), and 0.40 (0.27–0.52) (tropical). Thus, GPP_MOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPP_MOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (ε_max) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in ε_max, FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes.

As a key ecological parameter, NPP measures the photosynthetic efficiency of plants in capturing atmospheric carbon. With the warming of the climate, extreme temperature events are frequent, which has exerted a profound influence on NPP. Previous studies on the drivers of NPP have predominantly relied on linear approaches. This study innovatively employs deep neural networks (DNN) integrated with SHAP method, integrating nationwide daily meteorological data (2001–2020) and MODIS annual NPP products, to establish the first quantitative causal linkages between extreme temperature events and NPP dynamics across China. The findings indicate that: (1) The study period witnessed a pronounced increase in both occurrence rates and severity of extreme heat events across China, contrasted by a distinct decrease in extreme cold episodes, with notable regional variations in these trends. (2) NPP exhibits a distinct southeast-to-northwest decreasing gradient across China, and from 2001 to 2020, NPP in most parts of China showed an upward trend, but NPP in some mountainous and hilly areas in the south decreased slightly. (3) Regional analyses reveal contrasting NPP responses to temperature extremes. In southern region and Tibetan Plateau region, NPP shows a positive association with elevated temperature extremes, while moderate low temperature is more conducive to vegetation growth in northern region. (4) The DNN model constructed in this study performs better (R²≈0.89) than other models in simulating the spatiotemporal distribution of NPP in China. The SHAP analysis identifies frost days and annual precipitation as the primary drivers of NPP variation across China, with most factors exhibiting threshold-dependent effects on NPP.

How modeled ecosystem carbon balance varies across the contrasting hydroclimatic zones of the Yellow River Basin, and whether its relationships with climate extremes differ among these zones, remain insufficiently understood. To address this knowledge gap, this study examined the spatiotemporal variability of modeled net ecosystem productivity (NEP) and its associations with temperature- and precipitation-extreme indices across three major ecological zones of the basin. Using satellite-derived NDVI, reanalysis climate data, and extreme climate indices for 2000–2022, we estimated NEP from CASA-simulated net primary productivity and modeled heterotrophic respiration, and assessed its temporal trends and climatic associations across three ecological zones. Three principal findings were obtained: (1) Over the 23-year study period, basin-wide NEP showed a statistically significant increasing tendency (β = 2.699 g C m⁻² a⁻¹), with a well-defined spatial gradient characterized by relatively lower productivity in the northwestern areas and elevated productivity toward the southeastern regions. (2) Modeled NEP increased across all four seasons. Summer showed the largest positive trend, whereas autumn and winter remained net carbon-release seasons but became progressively less negative over the study period. (3) The statistical associations between modeled NEP and extreme climate indices varied markedly across ecological zones. Extreme-precipitation indices were generally positively associated with modeled NEP in the arid northwestern zone, whereas several extreme-temperature indices showed predominantly negative associations in the northeastern monsoon-influenced region. Collectively, these findings provide model-based evidence of spatially differentiated associations between ecosystem carbon balance and climate extremes across the YRB, supporting regional ecosystem assessment and climate-adaptation planning.

Based on MODIS NDVI and ERA5-Land reanalysis data for 2000–2022, we estimated net ecosystem productivity (NEP) at the eco-functional-zone scale in the Yellow River Basin (YRB)with an improved Carnegie-Ames-Stanford Approach (CASA) model. Theil–Sen slope estimation, Mann–Kendall testing, coefficient-of-variation (CV) stability assessment and Pearson correlation analysis were applied to quantify spatiotemporal variations in vegetation NEP and its response to climate change and extreme events. The main findings are: (1) Over the past 23 years basin-wide NEP exhibited a significant upward trend (β = 8.36 g C m⁻² a⁻¹) with a pronounced “low in the northwest, high in the southeast” spatial pattern; areas of high inter-annual variability (CV > 25%) were concentrated in desert regions. (2) NEP was positively correlated with temperature (TEM) and precipitation (PRE) but negatively correlated with vapor pressure deficit (VPD); correlations with solar radiation (RADI) varied among eco-functional zones. (3) Both the frequency of extreme-high-temperature days and that of extreme drought days are significantly and negatively correlated with regional vegetation NEP, with NEP in arid zones exhibiting markedly stronger sensitivity to these extremes than in humid zones. These findings furnish a scientific basis for carbon-accounting, eco-compensation policy design and climate-adaptation strategies across the YRB.