ValLAI_Crop: Validation dataset for coarse-resolution satellite LAI product over Chinese Cropland

Numerous validation campaigns have been conducted over the last decade to assess the accuracy of the global leaf area index (LAI) products. Accurate and comprehensive validations for coarse-resolution LAI products are still very difficult due to lack of enough high-quality field measurements. Here we developed a fine resolution LAI dataset, consisting of 80 sample plots with an area of 3 km × 3 km in four major agricultural regions in China collected from 2003 to 2017. Instead of the indirect optical measurement method employed in most validation campaigns, the direct destructive method was employed to measure LAI of cropland for all the field experiments to avoid the measurement uncertainties, especially for crops at early growth stages with low height. Fine resolution reference LAI maps were derived from Landsat-5 TM and Landsat-8 OLI surface reflectance products based on the semi-empirical inversion model, which were calibrated using field measurements for each growth stage with an RMSE ranging from 0.22 to 0.95, and a relative root mean square error (RRMSE) ranging from 7.58% to 44.42%. Then, 80 sample plots with an area of 3 km × 3 km were selected as the fine resolution validation dataset from the fine resolution reference LAI maps with a proportion of cropland larger than 75% and one or more in-situ samples were contained in each 3 km × 3 km reference map.