EGU21-14869, updated on 04 Mar 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

A new MODIS-Landsat fusion method to reconstruct Landsat NDVI time-series data

Xiaofang Ling and Ruyin Cao
Xiaofang Ling and Ruyin Cao
  • University of Electronic Science and Technology of China, School of Resources and Environment, Chengdu, China (

The Normalized Difference Vegetation Index (NDVI) data provided by the satellite Landsat have rich historical archive data with a spatial resolution of 30 m. However, the Landsat NDVI time-series data are quite discontinuous due to its 16-day revisit cycle, cloud contamination and some other factors. The spatiotemporal data fusion technology has been proposed to reconstruct continuous Landsat NDVI time-series data by blending the MODIS data with the Landsat data. Although a number of spatiotemporal fusion algorithms have been developed during the past decade, most of the existing algorithms usually ignore the effective use of partially cloud-contaminated images. In this study, we presented a new spatiotemporal fusion method, which employed the cloud-free pixels in the partially cloud-contaminated images to improve the performance of MODIS-Landsat data fusion by Correcting the inconsistency between MODIS and Landsat data in Spatiotemporal DAta Fusion (called CSDAF). We tested the new method at three sites covered by different vegetation types, including deciduous forests in the Shennongjia Forestry District of China (SNJ), evergreen forests in Southeast Asia (SEA), and the irrigated farmland in the Coleambally irrigated area of Australia (CIA). Two experiments were designed. In experiment I, we first simulated different cloud coverages in cloud-free Landsat images and then used both CSDAF and the recently developed IFSDAF method to restore these “missing” pixels for quantitative assessments. Results showed that CSDAF performed better than IFSDAF by achieving the smaller average Root Mean Square Error (RMSE) values (0.0767 vs. 0.1116) and the larger average Structural SIMilarity index (SSIM) values (0.8169 vs. 0.7180). In experiment II, we simulated the scenario of “inconsistence” between MODIS and Landsat by simulating different levels of noise on MODIS and Landsat data. Results showed that CSDAF was able to reduce the influence of the inconsistence between MODIS and Landsat data on MODIS-Landsat data fusion to some extent. Moreover, CSDAF is simple and can be implemented on the Google Earth Engine. We expect that CSDAF is potentially to be used to reconstruct Landsat NDVI time-series data at the regional and continental scales.

How to cite: Ling, X. and Cao, R.: A new MODIS-Landsat fusion method to reconstruct Landsat NDVI time-series data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14869,, 2021.

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