Time series correction method of vegetation index of high resolution remote sensing image based on MODIS remote sensing image

Abstract

The invention discloses a high-resolution remote sensing image vegetation index time sequence correcting method based on a moderate resolution imaging spectroradiometer (MODIS) remote sensing image. The method comprises the four stages of construction of an HJ normalized difference vegetation index (NDVI) time sequence and a low-middle spatial resolution remote sending image NDVI time sequence, co-integration testing of an HJ time sequence and a time sequence of a corresponding pixel of a low-middle spatial resolution remote sensing image, testing of an abnormal section of the HJ NDVI time sequence, and correction of the abnormal section of the HJ NDVI time sequence. According to the method, the abnormity, caused by climate reasons, of a sequence curve form of the HJ NDVI time sequence can be corrected, the accuracy and the usability are improved, and effective data is provided for research on a biogeochemistry model under high temporal-spatial resolution. Meanwhile, the method is applied to the time sequence of a certain vegetation index from other high-middle-resolution remote sensing images.

Description

technical field [0001] The invention relates to a high-resolution remote sensing image vegetation index time series correction method based on MODIS remote sensing images, belonging to the technical field of geographic information systems. Background technique [0002] The time series of remote sensing images is an image sequence that uses a series of original images of remote sensing image data or their derived index images to arrange them in chronological order so that they can express the changing characteristics of remote sensing objects in time. It provides a data source for analyzing and using remote sensing images from the time dimension. [0003] Currently, the most widely used method is the vegetation index time series of remote sensing images with medium and low spatial resolution. The NDVI (Normalized Difference Vegetation Index, Normalized Difference Vegetation Index in English) time series product provided by the medium and low spatial resolution remote sensing...

AI Technical Summary

Problems solved by technology

However, the HJNDVI time series still has the following problems: (1) The number of images acquired by the HJ satellite per unit time is much smaller than that of MODIS, and the maximum value synthesis (MVC) method cannot be used to eliminate the influence of noise on the reduction of NDVI values; (2) HJ-1A / 1B Remote sensing imagery does not provide products for pixel quality evaluation, and cannot adjust the generated NDVI time series according to its own platform to reduce the influence of noise-polluted phases

(3) The downloaded image has not undergone radiation correction and atmospheric correction, which also causes the instability of the HJNDVI time series

But none of these can solve the above problems of HJNDVI time series very well

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