Interpolation and Gap Filling of Landsat Reflectance Time Series

TL;DR

This paper introduces an optimal interpolation method that fuses Landsat and MODIS data using Google Earth Engine to generate continuous, gap-free Landsat reflectance time series across diverse sites.

Contribution

It presents a novel, scalable interpolation approach leveraging GEE for global Landsat-MODIS data fusion, improving temporal continuity of reflectance data.

Findings

Effective gap filling in Landsat time series

Scalable approach using cloud-based GEE platform

Good performance across diverse vegetation and climates

Abstract

Products derived from a single multispectral sensor are hampered by a limited spatial, spectral or temporal resolutions. Image fusion in general and downscaling/blending in particular allow to combine different multiresolution datasets. We present here an optimal interpolation approach to generate smoothed and gap-free time series of Landsat reflectance data. We fuse MODIS (moderate-resolution imaging spectroradiometer) and Landsat data globally using the Google Earth Engine (GEE) platform. The optimal interpolator exploits GEE ability to ingest large amounts of data (Landsat climatologies) and uses simple linear operations that scale easily in the cloud. The approach shows very good results in practice, as tested over five sites with different vegetation types and climatic characteristics in the contiguous US.