Time series fusion construction of discrete Landsat normalized vegetation index

doi:10.14088/j.cnki.issn0439-8114.2019.12.012

Abstract

Abstract: In combination with the use of sliding windows, a time series construction method based on Fourier approximation and linear fusion downscaling is proposed. The Fourier approximation of discrete Landsat data is performed on a time-by-pixel basis and merged with the lower resolution AVHRR sensor data to realize the time series construction of the Landsat vegetation index in experimental area, combined with Landsat as the actual observation data. The data verifies the accuracy of the model results. The results show thatThe model results in the hue and texture, image features are consistent with the actual observation data, and the NDVI of the white snow-covered area is constructed. The construction results have very good spatial continuity with the surrounding surface, and there is no obvious blocky effect in the entire image; Model results and actual observed data has a high correlation （mean 0.869 2） and a low root mean square error （average of 0.043 5）, averaging up to 75.04% of the pixels between ±0.05, up to 97.64% of the pixel error between ±0.1, and the error is in a good normal distribution. The accuracy of the model has a certain dependence on the amount of input data, and there is still room for improvement in the processing of mixed pixels.

Key words: Landsat, NDVI, data fusion, Fourier analysis

CLC Number:

TP79

ZENG De-yu, LU Xiao-ning, HUANG Yue, YANG Liu-qing, MENG Cheng-zhen. Time series fusion construction of discrete Landsat normalized vegetation index[J]. HUBEI AGRICULTURAL SCIENCES, 2019, 58(12): 45-51.

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