A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine

Obtaining high quality remote sensing vegetation index that is not noticeably affected by abiotic factors is critical for agricultural, ecological, climate and hydrological studies. Here we developed a computationally efficient and well-performed denoising method for reconstructing remote sensing vegetation indices, namely wWHd (weighted Whittaker with dynamic parameter lambda in spatial). The single parameter lambda is automatically estimated for every pixel by a multiple linear regression. Weights updating and the inherit nature of Whittaker make Whittaker robust for contaminations. We applied the wWHd on the Google Earth Engine (GEE) platform for reconstructing 500 m resolution enhanced vegetation index (EVI) time series from moderate-resolution imaging spectroradiometer (MODIS) at global scale and for the period of 2000-2017. To demonstrate its robustness, wWHd was compared with four well-known denoising methods, i.e. Fourier-based approach (Fourier), Savitzky-Golay filter (SG), Asymmetric Gaussian (AG) and double logistic (DL) at 16,000 randomly selected sites. All approaches were evaluated using two indices at each site: (1) root mean square error (RMSE) between observed best quality EVI and gap-filled EVI series, and (2) roughness of gap-filled EVI series. Results show that wWHd has an RMSE (indicating fidelity) of similar to 0.032, which is similar to Fourier and SG at similar to 90% sampled sites, but outperforms (similar to 0.02 less in the RMSE) AG and DL at similar to 45% and similar to 25% sampled sites. Among the four, wWHd has the lowest (best) roughness of similar to 0.003. These performances demonstrate that wWHd balances fidelity and roughness well. Another advantage is that the wWHd is computationally more efficient than others, and is currently the only one denoising method deployed on the GEE. Our results suggest that it is promising to use the proposed wWHd method for processing remote sensing vegetation indices with high spatiotemporal resolution and the reconstructed EVI product should be widely used by global community.