Loading...
Please wait, while we are loading the content...
Similar Documents
Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine
| Content Provider | MDPI |
|---|---|
| Author | Pipia, Luca Amin, Eatidal Belda, Santiago Salinero-Delgado, MatÃas Verrelst, Jochem |
| Copyright Year | 2021 |
| Abstract | For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine learning techniques and convert them into gap-filled vegetation properties products. However, GPR is not yet part of the GEE ecosystem. To circumvent this limitation, this work proposes a general adaptation of GPR formulation to parallel processing framework and its integration into GEE. To demonstrate the functioning and utility of the developed workflow, a GPR model predicting green leaf area index (LAI |
| Starting Page | 403 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13030403 |
| Journal | Remote Sensing |
| Issue Number | 3 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-01-24 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Google Earth Engine (gee) Gaussian Process Regression (gpr) Machine Learning Sentinel-2 Gap Filling Leaf Area Index (lai) |
| Content Type | Text |
| Resource Type | Article |
