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Data assimilation as an approach for crop Leaf Area Index (LAI) estimation has been rapidly developed in the field of agricultural remote sensing. Many studies have attempted to integrate sequential remotely sensed observations in the dynamical operation of physical models, aiming to improve model performance of LAI estimation by using various data assimilation schemes. In this study, a new data assimilation...
Estimating crop leaf area index (LAI) based on remotely sensed observations is a normal way for regional crop monitoring and yield estimation in precision agriculture. However, the spatial heterogeneity in crop canopies and the nonlinearity of model for LAI estimation make differences between estimated-upscaled LAI (baseline data) and upscaled-estimated LAI. Then, aiming to reduce these differences,...
Plant growth modeling is one of the key issues in virtual plant research. In this paper, we mainly focus on the morphogenetic models of virtual plant. The procedures for simulation and visualization of plant morphological structure during the period of plant growth are summarized first. The techniques for data acquisition of plant morphological structure are introduced since it is the initial work...
A new forest leaf area index (LAI) inversion method from multisource and multi-angle data combined with radiative transfer model and the strategy of k-means clustering and artificial neural network (ANN) was discussed. The four different temporal satellite images of Landsat-5 TM (L5TM) and Beijing-1 microsatellite multispectral sensors (BJI) were selected to construct multisource and multi-angle data...
Data assimilation is an advanced and innovative set of techniques for variable estimation, especially in agriculture research in recent years, which integrates not only remote sensing data products, other measurements, but also land dynamic models. It can provide more abundant and precise data set, and extend data's spatiotemporal scale. In this paper, firstly composition and assimilation methods...
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