Method for removing cloud noise effects in normalized difference vegetation index (NDVI) time sequence image

Abstract

The invention discloses a method for removing cloud noise effects in a normalized difference vegetation index (NDVI) time sequence image. The method comprises the following steps of: S1, generating an annual NDVI time sequence image according to NDVI data of every ten days; S2, carrying out least square fitting on all time sequence pixels in the NDVI time sequence image, and assigning same values to weights of all points relative to a curve; S3, comparing an observed value with a fitted value, and eliminating points under negative cloud action; S4, repeating the steps S2 and S3, eliminating all the points under the negative cloud action, and generating a new curve; and S5, post-processing the curve obtained in the S4 to obtain a finally fitted curve. In the method disclosed by the invention, by utilizing an NDVI time sequence file and a harmonic function analysis method based on the least square fitting, cloud shielding and atmospheric effects on a sensor in a data acquisition process are effectively removed, the time sequence image with cloud contamination removed can be generated, the obtained curve has the advantages of obvious trend, strong relative property among years and high precision, and the method has a wide applicable range.

Description

technical field [0001] The invention relates to the technical field of remote sensing monitoring of crop growth periods, in particular to a method for removing the influence of cloud noise in time-series images of Normalized Difference Vegetation Index (NDVI). Background technique [0002] The time-series vegetation index can reflect the dynamic process in which soil albedo is gradually replaced by crop albedo. Ideally, the time-series vegetation index obtained by remote sensing monitoring has a dynamic change process, especially for crops. After crop emergence, as the crop grows, the leaf area index increases gradually, and the NDVI value also increases; when the crop is in the flowering stage, the leaf area index reaches the maximum, and the NDVI value reaches the maximum value during the crop growth period; As the crops enter the mature stage, the leaves begin to turn yellow, the near-infrared band reflectance begins to decrease gradually, and the NDVI value decreases ac...

AI Technical Summary

Problems solved by technology

Average and median filtering can closely fit the original data, eliminate short-term fluctuations, and form a time series of smooth changes, but the problem is that the maximum and minimum points in the curve are changed

Many time-series NDVI data processing methods can describe the characteristics of the curve in detail and fit a smooth curve, but these methods usually do not consider the periodicity of the time-series NDVI caused by the periodic characteristics of surface plant growth, especially the crop growth process on cultivated land. There is obvious periodicity, and these characteristics are good indicators for curve fitting

Images