Please join the department for a seminar with Dr. Feng Gao from the USDA-ARS Hydrology and Remote Sensing Laboratory this Thursday afternoon (4-5 PM). All are invited to meet the speaker before the seminar starts (3:45-4:00 PM) and light refreshments will be served.
To support the department's sustainability effort, please bring your own coffee mugs.
The abstract of the seminar can be found below:
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 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 data fusion models built based on the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) concept. Applications in vegetation phenology mapping, crop water use monitoring, and crop yield estimation at 30-m spatial resolution will be presented and discussed. Added values by combining Landsat and Sentinel-2 for crop yield prediction will be discussed.