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Abstract. Leaf Area Index (LAI) with an explicit biophysical meaning is a critical variable to characterize terrestrial ecosystems. Long-term global datasets of LAI have served as fundamental data support for monitoring vegetation dynamics and exploring its interactions with other Earth components. However, current LAI products face several limitations associated with spatiotemporal consistency. In this study, we employed the Back Propagation Neural Network (BPNN) and a data consolidation method to generate a new version of the half-month 1/12° Global Inventory Modeling and Mapping Studies (GIMMS) LAI product, i.e., GIMMS LAI4g, for the period 1982−2020. The significance of the GIMMS LAI4g was the use of the latest PKU GIMMS NDVI product and 3.6 million high-quality global Landsat LAI samples to remove the effects of satellite orbital drift and sensor degradation and to develop spatiotemporally consistent BPNN models. The results showed that the GIMMS LAI4g exhibited higher accuracy than its predecessor (GIMMS LAI3g) and two mainstream LAI products (Global LAnd Surface Satellite [GLASS] LAI and Long-term Global Mapping [GLOBMAP] LAI), with an R2 of 0.95, mean absolute error of 0.18 m2/m2, and mean absolute percentage error of 15 % which meet the accuracy target proposed by the Global Climate Observation System. It outperformed other LAI products for most vegetation biomes in a majority area of the land. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presented a better temporal consistency before and after the year 2000 and a more reasonable global vegetation trend. The GIMMS LAI4g product could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and could also benefit the model development in Earth and environmental sciences.

As the largest source of uncertainty in carbon cycle studies, accurate quantification of gross primary productivity (GPP) is critical for the global carbon budget in the context of global climate change. Numerous vegetation indices (VIs) based on satellite data have participated in the construction of GPP models. However, the relative performance of various VIs in predicting GPP and what additional factors should be combined with them to reveal the photosynthetic capacity of vegetation mechanistically better are still poorly understood. We constructed two types of models (universal and plant functional type [PFT]-specific) for solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation (NIRv), and Leaf Area Index (LAI) based on two widely used machine learning algorithms, i.e., the random forest (RF) and back propagation neural network (BPNN) algorithms. A total of thirty plant traits and environmental factors with legacy effects are considered in the model. We then systematically investigated the ancillary variables that best match each vegetation index in estimating global GPP. Four types of models (universal and PFT-specific, RF and BPNN) consistently show that SIF performs best when modeled using a single vegetation index (R2 = 0.67, RMSE = 2.24g C·m–2·d–1); however, NIRv combined with CO2, plant traits, and climatic factors can achieve the highest prediction accuracy (R2 = 0.87, RMSE = 1.40g C·m–2·d–1). Plant traits effectively enhance all prediction models' accuracy, and climatic variables are essential factors in improving the accuracy of NIRv- or LAI-based GPP models, but not the accuracy of SIF-based models. Our findings provide valuable information for the configuration of the data-driven models to improve the accuracy of predicting GPP and provide insights into the physiological and ecological mechanisms underpinning GPP prediction.