Reconstruction of MODIS NDVI time series in cloudy area based on Savitzky-Golay filter with spatial-temporal Kriging improvement

Juncheng Feng; Yuxiang Tao

doi:10.1117/12.2674378

28 April 2023 Reconstruction of MODIS NDVI time series in cloudy area based on Savitzky-Golay filter with spatial-temporal Kriging improvement

Juncheng Feng, Yuxiang Tao

Author Affiliations +

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126261M (2023) https://doi.org/10.1117/12.2674378
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

NDVI (Normalized Difference Vegetation Index) time series usually contain a lot of loud noise, which limits its further application. However, the existing filtering and reconstruction methods cannot effectively remove continuous loud noise, which is particularly obvious in cloudy areas. This paper proposes a Spatial-temporal Kriging improved Savitzky Golay filtering algorithm (TSK-SG) based on Spatial-temporal Kriging. By combining the quality factors in MODIS VI products to generate reference data, a Spatial-temporal Kriging variogram model is established and interpolated using the adjacent Spatial-temporal information. Finally, the fitting result is obtained by iterating Savitzky Golay (S-G) filtering based on the quality weight. The NDVI time series curve reconstructed by this method can effectively suppress noise and has a better spatial reconstruction effect, which can better reflect the phenological characteristics of different types of crops.

Citation Download Citation

Juncheng Feng and Yuxiang Tao "Reconstruction of MODIS NDVI time series in cloudy area based on Savitzky-Golay filter with spatial-temporal Kriging improvement", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126261M (28 April 2023); https://doi.org/10.1117/12.2674378

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KEYWORDS

Tunable filters

Vegetation

MODIS

Interpolation

Windows

Data modeling

Denoising

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