Estimation of crop leaf area index using MODIS directional reflectances data

Leaf area index (LAI) is an essential parameter for monitoring crop growth dynamic. An algorithm, which is based on physical model and neural networks to derive crop LAI from MODIS land surface reflectance, is presented. This algorithm utilizes the directional reflectances instead of the BRDF normalized data to avoid complex BRDF normalization and the error from it. The estimated LAI is compared with existing LAI products. Results show that it is consistent with MODIS (RMSE = 0.4994) and CYCLOPES (RMSE = 0.6658) LAI products in temporal and spatial patterns. The algorithm is validated against ground measurements of annual crop LAI in 2004 in Hengshui, China. The neural network derived LAI could represent the spatial pattern of the field LAI. However, all these LAI products are lower than field measurements. It would be suggested that the physical model should be modified to adapt to the dense crop in Northern China.