This study analyzes the accuracy of the Particle Filter (PF) assimilation algorithm to retrieve Leaf Area Index (LAI) from remotely sensed observations using the crop growth model CERES_Maize as a dynamic system, the radiative transfer model SAIL as the observation equation, and MOD09 for external observations. Nonlinearity of the crop growth and radiative models makes the posterior probability of retrieved LAI non-Gaussian. The advantage of PF is its ability to estimate accurately the non-Gaussian posterior probability of retrieved LAI by the particles system. We retrieve LAI by the bootstrap particle filter algorithm whenever a remotely sensed observation was available. By comparing our filtered results to measured LAI at the Yushu area of Jilin province, China, we found that this algorithm greatly improved LAI retrieval. The crop growth model's constraint information and accurate estimation of posterior probability contributed to the improvement in retrieved LAI. We validated the accuracy of maize yield estimation by field measurements.
['Dongwei Wang', 'Jindi Wang', 'Yongmei Chen', 'Haobo Lin', 'Shunlin Liang', 'Zhiqiang Xiao']
Crop LAI Retrieval from MODIS Bidirectional Reflectance Observations using the Particle Filter Algorithm and a Crop Growth Model