Compositing, smoothing, and gap-filling techniques
Abstract
Because of the influences of cloud cover, seasonal snow, and many other factors, time series of land surface parameters extracted from remote sensing data often suffer from discontinuities and missing data, which have seriously restricted the application of extracted land surface parameters to research in global change and other fields. This chapter introduces several different techniques for compositing, smoothing, and gap filling of remotely sensed time series data, which generate land surface parameter products that are temporally and spatially continuous.
A multitemporal compositing method is designed to select the optimal remote sensing data as the pixel value ...
Get Advanced Remote Sensing, 2nd Edition now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
