Validation of Four Coarse-Resolution Leaf Area Index Products Over Croplands in China Using Field Measurements

The accuracy assessment of the global leaf area index (LAI) products is an indispensable step before applications. Four popular LAI products, namely MCD15A2H, GLASS, GEOV2, and GLOBMAP, were assessed over croplands in China using field measurements. All of these four products were validated with upscaled reference LAI maps. The validation results revealed uncertainties in these products for crops with RMSE ranging from 0.49 to 1.37 over the validation sites in Beijing, in Zhoukou and Jiaozuo counties in Henan province, Youyi farm in Heilongjiang province, and in Longkang farm in Anhui province. GEOV2 gave the highest accuracy (R-2 = 0.85, RMSE = 0.49, relative bias = -7.2%) compared to MCD15A2H (R-2 = 0.54, RMSE = 0.91, and relative bias = -24.4%), GLASS (R-2 = 0.80, RMSE = 0.73, and relative bias = -23.3%), and GLOBMAP (R-2 = 0.25, RMSE = 1.37, and relative bias = -55.4%). The LAI products overestimated over the croplands with dark soil-backgrounds in Youyi farm while they underestimated over other validation sites. The seasonal variation of these products was assessed with continuous in situ measurements at Daman Station, Gansu province. All of these four products showed good temporal consistency at Daman Station, while GEOV2 showed the highest accuracy (R-2 = 0.92, RMSE = 0.41, relative bias = -10.3%). The influence of scaling effect on the products' accuracy was also investigated, and the scaling differences of these LAI products contributes overestimation at coarse-resolution for croplands with dark soil-backgrounds, while underestimation at coarse-resolution in other validation sties.

Automated summary

The study assessed the accuracy of four global leaf area index (LAI) products over croplands in China, finding that GEOV2 had the highest accuracy compared to the other products. There were uncertainties in different regions, with the products showing overestimation or underestimation. The seasonal variation was minor, but scaling effects led to varying degrees of overestimation or underestimation in different areas.