On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance

TLDR

The 16‑day revisit cycle of Landsat and the coarse 500‑m resolution of MODIS limit the ability to monitor rapidly evolving, heterogeneous biophysical processes, especially in cloudy regions where only a few clear images are available annually. This study introduces the STARFM algorithm, a spatial‑temporal adaptive reflectance fusion model designed to blend Landsat and MODIS surface reflectance. By combining MODIS daily 500‑m surface reflectance with Landsat ETM+ 30‑m data, STARFM generates a synthetic daily surface reflectance product at ETM+ spatial resolution, leveraging MODIS’s high‑frequency temporal information and Landsat’s high‑resolution spatial detail. Results show that STARFM accurately reproduces Landsat‑level reflectance, but its performance declines on extremely heterogeneous fine‑grained landscapes, with accuracy depending on landscape patch size.

Abstract

The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS surface reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m surface reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m surface reflectance are used to produce a synthetic "daily" surface reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts surface reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes