Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/15/2011
Publication Date: 8/18/2011
Citation: Gao, F.N. 2011. Developing consistent time series landsat data products [abstract]. Landsat Science Team Meeting. 2011 CDROM.
Technical Abstract: The Landsat series satellite has provided earth observation data record continuously since early 1970s. There are increasing demands on having a consistent time series of Landsat data products. In this presentation, I will summarize the work supported by the USGS Landsat Science Team project from 2006 to 2011. I will present the approaches that were developed in the past several years on producing a consistent time series of Landsat data products. These approaches cover three aspects: location consistency, radiometric consistency and product level consistency. For location consistency, we developed an automated registration and orthorectification package (AROP) for Landsat and Landsat-like data processing. The AROP package has been released for public use since 2008 and has been tested on Landsat (MSS, TM, ETM+), ASTER, AWiFS and CBERS data. For radiometric consistency, we developed a normalization approach which uses MODIS data product as a reference to normalize remote sensing data from multiple sensors, thus data from different sensors are consistent for time-series analysis. The normalization approach has also been used to normalize data from same sensor but with different acquisition dates; this allows making a consistent image mosaic for a large area application. For the product level consistency, we have developed approaches to build MODIS-consistent LAI, and albedo products from Landsat surface reflectance. A new efficient algorithm to map the continuous expansion of impervious surface area using a time series of four decades medium resolution satellite images will be presented. The approach allows extracting a complete and consistent time series of impervious surface maps from a corresponding times series of images collected from multiple sensors with minimal amount of image preprocessing efforts. I will discuss the results and the lessons learned from this project and talk about the future directions for Landsat data products.