关键词: 粒子滤波；LAI；同化；时间序列重构

Abstract:

Leaf area index (LAI) time-series data retrieved from remote sensing images have been widely used in monitoring crop growth. However, affected by atmosphere and other conditions, the data might be underestimated and even missing. Therefore, an algorithm was designed in this paper to reconstruct the remote sensing LAI time-series data by particle filter. Particle filter was used for the reconstruction. LAI was used as a variable to localize WOFOST model. Reconstruction of LAI time-series data can be done through assimilation of the remote sensing LAI data and WOFOST-LAI data. The algorithm simplified WOFOST model to be the nonlinear evolution of LAI changing with time, which was used as the state transition equation of re-sampling particle filter. And it got the observation equation with measured LAI data and the remote sensing LAI time-series data, to establish assimilation model of LAI time-series data. And the weighted particles represented the posterior distribution of LAI, then re-sampled the particles in the iteration in order to reconstruct LAI time-series data at the point and in the region. We reconstructed the LAI time-series data of Hebei Province in 2010 with this algorithm. According to the results, we could get better LAI time-series data at the point and in the region with this particle filter-based algorithm, which was closer to the actual growth conditions of crop. And it could also make up for the loss of remote sensing LAI time-series data. Therefore, the reconstructed LAI data could be very supportive for monitoring crop growth.

Key words: particle filter, LAI, assimilation, time-series data reconstruction