FY LAI: A Long-Term Global Leaf Area Index Dataset (2000-2020)

Leaf Area Index (LAI) is a cornerstone biophysical parameter for driving global climate and land surface models. However, existing global LAI products face persistent challenges: systematic underestimation in high-biomass regions, significant data gaps in cloud-persistent zones, and a heavy reliance on limited satellite platforms that restrict independent validation. To resolve these bottlenecks, we developed a global LAI dataset (FY LAI, 2000–2020) leveraging a recalibrated, cross-sensor consistent Fengyun (FY) NDVI data. Our retrieval framework employs a Random Forest algorithm that effectively captures complex non-linear canopy–reflectance relationships by integrating FY observations with geospatial geometry and key structural constraints, such as the clumping index. Direct validation against 46 GBOV sites demonstrates robust performance (R²=0.60, RMSE=0.87), with 50% of samples meeting GCOS accuracy requirements. Notably, the FY LAI product exhibits superior spatial completeness in challenging regions like tropical rainforests and the Tibetan Plateau. Crucially, our product effectively mitigates the chronic saturation-induced underestimation in dense forests (LAI ≥ 5), maintaining a minimal bias of −0.05 and substantially outperforming established products that exhibit severe negative biases. By bridging these observational gaps, this study establishes the FY LAI dataset as an independent benchmark that diversifies the global LAI portfolio. This research provides an autonomous data stream essential for reducing uncertainties in global carbon cycle monitoring and climate change assessments.