AM–GM Algorithm for Evaluating, Analyzing, and Correcting the Spatial Scaling Bias of the Leaf Area Index

The leaf area index (LAI) is a crucial variable in climate, ecological, and land surface modeling. However, the estimation of LAI from coarse-resolution remote sensing data can be affected by spatial scaling bias, which arises nonlinearity retrieval models heterogeneity surface. This study provides an algorithm named Arithmetic Mean Geometric (AM–GM) to correct bias. It established based on negative logarithmic functions avoids second-order stationarity. In this algorithm, relationships are derived between bias arithmetic geometric means directional gap probability for two commonly used models, Beer–Lambert law semi-empirical transfer function, respectively. According AM–GM expression representing model utilized analysis Furthermore, simplified linear relationship, constructed quantities related specific resolutions. Two scenes simulated LargE-Scale image Simulation framework (LESS) three sites evaluate proposed analyze LAI. validation results show that accurate correction analyses demonstrate increases with increase value, stronger coarser resolution. at Sud-Ouest site, absolute value estimated decreases 0.10, 0.22, 0.29, 0.31 0.04, 0.01, 0.05 200 m, 500 1000 1500 m resolutions, conclusion, effective