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This paper presents a dynamic gene and transcriptional regulatory network inferring method by using the time-varying autoregressive (TVAR) model. It employs the Li-based regularization terms with spatial sparsity, temporal continuity and proposed multi-Laplacian prior (MLP) for key transcriptional factors (TFs) and their interactions identification. The MLP regularization allows the connections of...
Vegetation is the most important part of the terrestrial ecosystems which results in a large proportion of studies on vegetation parameters, such as coverage, biomass, water content and so on. Since the ultimate goal of remote sensing is to accurately and efficiently inverse land surface parameters, it is of great significance to find a good forward vegetation model with simple form and high accuracy...
Vegetation biomass is an important parameter in the carbon cycle study. In this paper, a new technique to estimate aboveground vegetation wet biomass based on the Microwave Vegetation Indices (MVIs), which are computed through the observed brightness temperature of AMSR-E/Aqua under two adjacent frequencies, has been developed. The MVIs can provide significant new information compared with the conventional...
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