Fusion method of vegetation index NDVI with high temporal-spatial resolution

Abstract

The invention discloses a fusion method of a high-temporal-spatial-resolution vegetation index NDVI. The fusion method comprises the following steps of A, performing downscaling processing on a low-resolution vegetation index NDVI image by utilizing a pixel downscaling algorithm; B, calculating increment NDVI by utilizing the vegetation index NDVI image subjected to downscaling processing and thehigh-resolution vegetation index NDVI image, counting an increment NDVI cumulative distribution histogram, and clustering to construct an increment NDVI classification map; C, constructing a linear hybrid model of the high-resolution increment and the low-resolution increment delta NDVI by utilizing the increment delta NDVI classification diagram, and respectively calculating the high-resolution increment by utilizing a ridge regression method; and D, calculating a high-resolution vegetation index NDVI value at the moment tp. According to the method, the classification graph is generated through incremental NDVI clustering. The NDVI change characteristics are fully utilized. Compared with other methods, the fusion effect of the method is more satisfactory. An effective method is provided for constructing the vegetation index NDVI with the high temporal-spatial resolution.

Description

technical field [0001] The invention belongs to the field of remote sensing image data processing, and relates to a fusion method of vegetation index NDVI with high temporal-spatial resolution, in particular to a high-spatial-temporal resolution that takes time and is difficult to obtain high-resolution ground object classification data and the ground objects change during the fusion period Fusion method of vegetation index NDVI. Background technique [0002] Vegetation index is the product of satellite visible light and near-infrared bands combined with multiple bands, which can simply and effectively measure the vegetation condition on the ground surface. It is very sensitive to the coverage of surface vegetation and is one of the commonly used indicators to detect and indicate the status and dynamics of vegetation coverage. NDVI image data with high spatio-temporal resolution plays an important role in monitoring vegetation cover changes, crop growth status, identifying ...

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Problems solved by technology

[0010] The present invention aims at the problem that the NDVI-LMGM model needs high-resolution ground object classification images and does not consider the change of the ground object coverage during the fusion period. First, the low-resolution images are downscaled, and the t 0 , t p , t 1 Three-time vegetation index NDVI FC data and t 0 , t 1 Two-time high-resolution vegetation index NDVI F Data design three kinds of ΔNDVI increments, count the cumulative distribution histograms of the three vegetation index NDVI increments ΔNDVI for clustering to construct the classification map of the increment ΔNDVI, and then use the vegetation index NDVI increment ΔNDVI classification map to construct high and low resolution increments The linear mixed model of ΔNDVI, and the ridge regression method was used to calculate the high-resolution vegetation index NDVI increment and Finally, calculate t p High Resolution Vegetation Index value to achieve data fusion

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