Abstract
The leaf area index (LAI) is an important parameter for vegetation monitoring and land surface ecosystem research. Although a variety of LAI products have been generated, the moderate to coarse spatial resolution and low temporal resolution of these products are insufficient for regional-scale analysis. In this study, a modified ensemble Kalman filter model (MEnKF) was proposed to generate spatio-temporal complete 30 m LAI data. High-quality, filtered historical Moderate-resolution Imaging Spectroradiometer (MODIS) LAI data were used to obtain the LAI background, and an LAI temporal dynamic model was constructed based on it. An improved back-propagation (BP) neural network based on a simulated annealing algorithm (SA-BP) was constructed with paired Landsat surface reflectance data and field LAI data to generate a 30 m LAI. The MEnKF was used to estimate the spatio-temporal complete LAI beginning from the LAI peak value position where Landsat observations were available. The spatio-temporal 30 m LAI was estimated in farmland (Pshenichne), grassland (Zhangbei), and woodland (Genhe) sites. The results indicate that the MEnKF-estimated LAI is consistent with the field measurements for all sites (the coefficient of determination ( R 2 ) = 0.70; root mean squared error (RMSE) = 0.40) and is better than that of the conventional sequence data assimilation algorithm ( R 2 = 0.40; RMSE = 0.78). The regional LAI captures the vegetation growth pattern and is consistent with the Landsat LAI, with an R 2 larger than 0.65 and an RMSE less than 0.51. The proposed MEnKF algorithm, which effectively avoids error accumulation in the data assimilation scheme, is an efficient method for spatio-temporal complete 30 m LAI estimation.
Highlights
The leaf area index (LAI), defined as the sum of green leaf area per unit of land area, is an important parameter for land surface ecosystems [1]
This study aims to propose a Modified Ensemble Kalman Filter (MEnKF) model to improve the accuracy of the 30 m LAI time series estimation
MEnKF starts estimation from the point closest to the annual maximum of the background; in these sites, the date of Julian day 189 was set as the starting point of MEnKF
Summary
The leaf area index (LAI), defined as the sum of green leaf area per unit of land area, is an important parameter for land surface ecosystems [1]. It provides dynamic information on vegetation growth and provides environment-related policy decision support [2]. A more convenient and rapid method is needed to obtain the regional time-series LAI [7]. Remote sensing technology, which has a series of advantages, such as wide coverage, low cost of human and material resources, and fine spatial and temporal resolution, is the most effective means to obtain long time series LAI at regional and global scales [8,9]
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