A Fusion Model of High Spatiotemporal Normalized Difference Vegetation Index NDVI Based on Histogram Clustering

Abstract

The invention relates to a fusion model of a high spatio-temporal normalized difference vegetation index NDVI based on histogram clustering, comprising: obtaining t 0 Time High Spatial Resolution NDVI Data HS t0 , and Gaussian filtering is performed on it, and the classification map 1 is obtained according to the local histogram features; using the classification map 1, the linear mixed model is used to analyze t 0 with t p The low-spatial-resolution NDVI data obtained at different times are up-sampled to obtain t0 HS at the same resolution u0 and HS up ; Based on the classification diagram 1, the HS t0 、HS u0 and HS up As input data, a linear mixed model is used to first predict t p High spatial resolution NDVI data HS at the moment tp1 ;according to HS u0 with HS tp1 The local histogram features of the generated classification map 2; based on the classification map 2, the HS t0 、HS u0 and HS up As input data, predict t again using a linear mixed model p High spatial resolution NDVI data HS at the moment tp2 ;Based on the size of different new coarse pixels, perform multiple HS tp2 predict, and finally generate t p High resolution NDVI at all times.

Description

technical field [0001] The invention relates to the field of remote sensing image data processing or data analysis, in particular to a fusion model of high spatio-temporal normalized difference vegetation index NDVI based on histogram clustering. Background technique [0002] The normalized difference vegetation index (NDVI) enhances the absorption and reflection characteristics of vegetation, and provides a method to estimate the greenness and vitality of vegetation canopy. One of the commonly used indicators. NVDI time series products have received extensive attention and applications from regional to global scales. At present, it is mainly used in various aspects such as monitoring vegetation cover changes, crop growth status, object type identification, crop yield estimation, biomass estimation, surface evapotranspiration, soil moisture monitoring, and climate change. Therefore, long-term series NDVI products are powerful tools for understanding the past of vegetation,...

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Problems solved by technology

The above models are limited in the following aspects: (1) auxiliary data or prior knowledge are required, such as the disclosed model of CN 102831310B and the IFSDAF model need to input classification diagrams, and the prediction results will differ with classification diagrams, and increase Increased workload; (2) Two or more pairs of cloud-free benchmark NDVI data are required. For example, although the NDVI-LMGM model can complete NDVI data fusion through a pair of image reuse, it will affect the prediction accuracy; (3) It is difficult to be accurate at the same time Predict phenological changes and land cover changes, such as the NDVI-LMGM model is difficult to predict land cover changes or the prediction effect is poor

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