Feng Gao of USDA-ARS Hydrology and Remote Sensing Laboratory

The suite of available remote sensing instruments varies widely in terms of sensor characteristics, spatial resolution and acquisition frequency. For example, the Moderate-resolution Imaging Spectroradiometer (MODIS) provides daily global observations at 250m to 1km spatial resolution. While imagery from coarse resolution sensors such as MODIS are typically superior to finer resolution data in terms of their revisit frequency, they lack spatial detail to capture surface features for many applications. The Landsat satellite series provides medium spatial resolution (30m) imagery which is well suited to capturing surface details, but a long revisit cycle (16-day) has limited its use in describing daily surface changes. Data fusion approaches provide an alternative way to utilize observations from multiple sensors so that the fused results can provide higher value than can an individual sensor alone. In this presentation, I will review several data fusion models built based on the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) concept. Applications in agriculture such as crop phenology mapping, water use monitoring, and soil tillage classification at 30-m spatial resolution will be presented and discussed. Limitations for satellite data fusion will be discussed. 

Biography:

Dr. Feng Gao received the B.A. degree in geology and the M.E. degree in remote sensing from Zhejiang University, Hangzhou, China, in 1989 and 1992, respectively, the Ph.D. degree in geography from Beijing Normal University, Beijing, China, in 1998, and the M.S. degree in computer science from Boston University, Boston, MA, USA, in 2003. From 1992 to 1998, he was a Research Assistant at the Nanjing Institute of Geography and Limnology, Chinese Academy of Science, Nanjing, China. From 1998 to 2004, he was a Research Associate Professor at the Department of Geography and a Researcher in the Center for Remote Sensing, Boston University. From 2004 to 2011, he was a Research Scientist with Earth Resources Technology, Inc. and the NASA Goddard Space Flight Center. Since 2011, he has been a Research Scientist with the Hydrology and Remote Sensing Laboratory, USDA Agricultural Research Service. His recent research interests include remote sensing modeling, multi-sensor data fusion, and vegetation biophysical parameter retrieving for crop and ecosystem condition monitoring. He has been a member of Landsat Science Team since 2006.

Email:

Address:10300 Baltimore Ave, , Beltsville, Maryland, United States, 20705

Feng Gao of USDA-ARS Hydrology and Remote Sensing Laboratory

Biography:

Email:

Address:Beltsville, Maryland, United States