Improved time sequence vegetation index data synthesis method

Abstract

The invention discloses an improved time sequence vegetation index data synthesis method. The method comprises the steps that A, surface reflectance data quality is classified; B, vegetation indexes of all picture elements are calculated, wherein the vegetation indexes comprise the NDVI and the EVI; C, the number N of clean picture elements is counted one by one according to a surface reflectance data quality assessment result; D, different operations are selected according to the difference of the number N of the clean picture elements, so that a final synthesis value is obtained. By the adoption of the method, the defects in an existing CV-MVC synthetic method can be overcome, and the fineness and the limitation are improved.

Description

technical field [0001] The invention relates to remote sensing image processing technology, in particular to an improved time-series vegetation index data synthesis method. Background technique [0002] The vegetation index value is an index that can reflect the growth status of surface vegetation based on remote sensing surface albedo data through certain calculations. Common vegetation indices mainly include Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). However, due to a series of reasons, it is difficult to obtain vegetation data that meets the application requirements from the remote sensing data of a single imaging. As a result, the quality of the vegetation index obtained once is not good. 2) For most remote sensing satellites, a single imaging is often difficult to cover all areas in the target area. [0003] At present, satellites such as AVHRR, SeaWiFS, and Spot all provide multi-day synthetic vegetation index products. The s...

AI Technical Summary

Problems solved by technology

However, due to a series of reasons, it is difficult to obtain vegetation data that meets the application requirements from the remote sensing data of a single imaging. The quality of the vegetation index obtained in a single time is poor

2) For most remote sensing satellites, it is often difficult for a single imaging to cover all areas in the target area

CV-MVC is a more reasonable and feasible synthesis method, which is currently used in MODIS 16-day synthetic vegetation index products, but this method may have the following two problems: 1) When there is a large difference between the largest vegetation indices When there is a large difference, you may choose a point with a smaller vegetation index; 2) When the difference between the largest vegetation indices is small, you may choose a point with a larger imaging angle

The BRDF synthesis scheme is also flawed. It not only needs 5 days to clean the pixels, but also depends on the accuracy of the cloud mask. Since the iso-nadir value is interpolated from 5 or more pixels, one is polluted by clouds ( residual cloud) will jeopardize the entire calculated nadir value, unfortunately vegetation is the most sensitive to clouds (rain), which limits the BRDF inversion procedure to only be applied to drought and less cloud area

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