Exploring the Optimized Leaf Area Index Retrieval Strategy Based on the Look-up Table Approach for Decametric-Resolution Images

Abstract

Leaf area index (LAI) is a pivotal biophysical parameter for characterizing canopy structure and monitoring vegetation growth. Although the look-up table (LUT) method has been widely employed for LAI retrieval, the optimization of key retrieval processes remains to be explored. Here, we proposed a generic optimization strategy for LUT-based inversion based on Landsat -8 imagery and global ground LAI measurements. Specifically, based on the LUT generated by the PROSAIL model, LAI inversion was optimized by introducing several functions, including band selection, artificial noise addition, cost function (CF) substitution, and multiple solutions. Furthermore, the optimized LUT-based inversion method was compared to the Simplified Level 2 Product Prototype Processor (SL2P) method and the ground-measurement-derived (GMD) regression method to comprehensively evaluate its performance over various vegetation types. Results showed that the combination of Red, near-infrared (NIR), and shortwave infrared-1 (SWIR1) bands was well suited to capture LAI dynamics. In terms of accuracy and efficiency, the best performance was achieved by the optimal band combination and retrieval parameter settings (i.e., root-mean-square error (RMSE) as CF, noise level of 20%, and multiple solutions of 5%), with the RMSE and ${R} ^{2}$ of 0.817 and 0.740, respectively. In addition, the optimized LUT-based inversion was superior to SL2P method in accuracy and to GMD regression method in efficiency. Overall, the optimized LUT-based inversion strategy can be applied for estimating decametric-resolution LAI with high accuracy over different regions and observation dates at a global scale, exhibiting high adaptability and generalization capability, especially for crops, and requiring no ground LAI measurements.