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Advancements in remote sensing technology have significantly contributed to the improvement of models for estimating terrestrial gross primary productivity (GPP). However, discrepancies in the spatial distribution and interannual variability within GPP datasets pose challenges to a comprehensive understanding of the terrestrial carbon cycle. In contrast to previous models that rely on remote sensing and environmental variables, we developed an ensemble model based on the random forest method (denoted ERF model). This model used GPP outputs from established models: Eddy Covariance Light Use Efficiency (EC-LUE), GPP estimate model based on Kernel Normalized Difference Vegetation Index (GPP-kNDVI), GPP estimate model based on Near-Infrared Reflectance of Vegetation (GPP-NIRv), Revised-EC-LUE, Vegetation Photosynthesis Model (VPM), and GPP estimate model based on the Moderate Resolution Imaging Spectroradiometer (MODIS). These outputs were used as inputs to estimate GPP. The ERF model demonstrated superior performance, explaining 85.1 % of the monthly GPP variations at 170 sites and surpassing the performance of selected GPP estimate models (67.7 %–77.5 %) and an independent random forest model using remote sensing and environmental variables (81.5 %). Additionally, the ERF model improved accuracy across each month and with various subranges, mitigating the issue of "high-value underestimation and low-value overestimation" in GPP estimates. Over the period from 2001 to 2022, the global GPP estimated by the ERF model was 132.7 PgC yr -1 , with an increasing trend of 0.42 PgC yr -2 , which is comparable to or slightly better than the accuracy of other mainstream GPP datasets in terms of validation results of GPP observations independent of FLUXNET (i.e., ChinaFLUX). Importantly, for a growing number of GPP datasets, our study provides a way to integrate these GPP datasets, which may lead to a more reliable estimate of global GPP. [ABSTRACT FROM AUTHOR]

Understanding and accurately predicting how the sensitivity of terrestrial vegetation productivity to rising atmospheric CO2 concentration (β) is crucial for assessing carbon sink dynamics. However, the temporal characteristics and driving mechanisms of β remain uncertain. Here, observational and CMIP6 modeling evidence suggest a decreasing trend in β at the Northern Middle and High Latitudes during the historical period of 1982–2015 (−0.082 ± 0.005% 100 ppm−1 year−1). This decreasing trend is projected to persist until the end of the 21st century (−0.082 ± 0.005% 100 ppm−1 year−1 under SSP370 and −0.166 ± 0.006% 100 ppm−1 year−1 under SSP585). The declining β indicates a weakening capacity of vegetation to mitigate warming climates, posing challenges for achieving the temperature goals of the Paris Agreement. The rise in vapor pressure deficit (VPD), that triggers stomata closure and weakens photosynthesis, is considered as the dominated factor contributing to the historical and future decline in β, accounting for 62.3%–75.2% of the effect. Nutrient availability and water availability contribute 15.7%–21.4% and 8.5%–16.3%, respectively. These findings underscore the significant role of VPD in shaping terrestrial carbon sink dynamics, an aspect that is currently insufficiently considered in many climate and ecological models. [ABSTRACT FROM AUTHOR]

The continuous advancement of remote sensing technology has been instrumental in improving models for estimating terrestrial gross primary productivity (GPP). However, challenges arise from inconsistent spatial distributions and interannual variations in GPP datasets, impeding our comprehensive understanding of the entire terrestrial carbon cycle. In contrast to previous models relying on remote sensing and environmental variables, we developed a an ensemble model based on random forest named GPPERF. This model utilized the GPP outputs from established remote sensing-based models (EC-LUE, GPP-kNDVI, GPP-NIRv, Revised-EC-LUE) as inputs for GPP estimations. GPPERF demonstrated significant effectiveness by explaining 83.7 % of the monthly variation in GPP across 171 sites. This performance surpassed the selected remote sensing models (72.4 %–77.1 %) and an independent random forest model using remote sensing and environmental variables (77.7 %). Over the period from 2001 to 2022, the global estimated GPP value using the ensemble model based on random forest was 131.2 PgC yr-1, exhibiting a trend of 0.45 PgC yr-2. Furthermore, evaluation results utilizing flux sites from ChinaFlux indicated that the dateset exhibited good generalization. In summary, the machine learning-based ensemble method helps to reduce the uncertainty in the estimation of a single remote sensing model and provides a more reliable estimation of global GPP. [ABSTRACT FROM AUTHOR]

Understanding and accurately predicting how the sensitivity of terrestrial vegetation productivity to rising atmospheric CO 2 concentration ( β ) is crucial for assessing carbon sink dynamics. However, the temporal characteristics and driving mechanisms of β remain uncertain. Here, observational and CMIP6 modeling evidence suggest a decreasing trend in β at the Northern Middle and High Latitudes during the historical period of 1982-2015 (-0.082 ± 0.005% 100 ppm -1 year -1 ). This decreasing trend is projected to persist until the end of the 21st century (-0.082 ± 0.005% 100 ppm -1 year -1 under SSP370 and -0.166 ± 0.006% 100 ppm -1 year -1 under SSP585). The declining β indicates a weakening capacity of vegetation to mitigate warming climates, posing challenges for achieving the temperature goals of the Paris Agreement. The rise in vapor pressure deficit (VPD), that triggers stomata closure and weakens photosynthesis, is considered as the dominated factor contributing to the historical and future decline in β , accounting for 62.3%-75.2% of the effect. Nutrient availability and water availability contribute 15.7%-21.4% and 8.5%-16.3%, respectively. These findings underscore the significant role of VPD in shaping terrestrial carbon sink dynamics, an aspect that is currently insufficiently considered in many climate and ecological models., Competing Interests: The authors declare no competing interests., (© 2024 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)