@article{4e1767563db54ffa8f17d1fdc321a3dd,
title = "STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product",
abstract = "Remote sensing datasets with both high spatial and high temporal resolution are critical for monitoring and modeling the dynamics of land surfaces. However, no current satellite sensor could simultaneously achieve both high spatial resolution and high revisiting frequency. Therefore, the integration of different sources of satellite data to produce a fusion product has become a popular solution to address this challenge. Many methods have been proposed to generate synthetic images with rich spatial details and high temporal frequency by combining two types of satellite datasets—usually frequent coarse-resolution images (e.g., MODIS) and sparse fine-resolution images (e.g., Landsat). In this paper, we introduce STAIR 2.0, a new fusion method that extends the previous STAIR fusion framework, to fuse three types of satellite datasets, including MODIS, Landsat, and Sentinel-2. In STAIR 2.0, input images are first processed to impute missing-value pixels that are due to clouds or sensor mechanical issues using a gap-filling algorithm. The multiple refined time series are then integrated stepwisely, from coarse-to fine-and high-resolution, ultimately providing a synthetic daily, high-resolution surface reflectance observations. We applied STAIR 2.0 to generate a 10-m, daily, cloud-/gap-free time series that covers the 2017 growing season of Saunders County, Nebraska. Moreover, the framework is generic and can be extended to integrate more types of satellite data sources, further improving the quality of the fusion product.",
keywords = "Fusion, Landsat, MODIS, Sentinel-2",
author = "Yunan Luo and Kaiyu Guan and Jian Peng and Sibo Wang and Yizhi Huang",
note = "Funding Information: This research was funded in part by the NASA New Investigator Award (NNX16AI56G) and NASA Carbon Monitoring System (80NSSC18K0170), managed by the NASA Terrestrial Ecology Program, the Blue Waters Professorship from National Center for Supercomputing Applications of University of Illinois at Urbana-Champaign (UIUC), the DOE Center for Advanced Bioenergy and Bioproducts Innovation awarded to UIUC, and the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Acknowledgments: We thank the U.S. Landsat project management and staff at USGS Earth Resources Observation and Science (EROS) Center South Dakota for providing the Landsat data free of charge. We thank European Space Agency COPERNICUS program for the free use of Sentinel-2 data. We also thank NASA for freely sharing the MODIS products. Funding Information: Funding: This research was funded in part by the NASA New Investigator Award (NNX16AI56G) and NASA Carbon Monitoring System (80NSSC18K0170), managed by the NASA Terrestrial Ecology Program, the Blue Waters Professorship from National Center for Supercomputing Applications of University of Illinois at Urbana-Champaign (UIUC), the DOE Center for Advanced Bioenergy and Bioproducts Innovation awarded to UIUC, and the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois.",
year = "2020",
month = oct,
day = "1",
doi = "10.3390/rs12193209",
language = "English (US)",
volume = "12",
pages = "1--21",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "19",
}