{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T23:10:15Z","timestamp":1785453015633,"version":"3.56.0"},"reference-count":85,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,24]],"date-time":"2021-01-24T00:00:00Z","timestamp":1611446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["755617"],"award-info":[{"award-number":["755617"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 (LAIG) from Sentinel-2 imagery was imported. Although by running this GPR model into GEE any corner of the world can be mapped into LAIG at a resolution of 20 m, here we show some demonstration cases over western Europe with zoom-ins over Spain. Thanks to the computational power of GEE, the mapping takes place on-the-fly. Additionally, a GPR-based gap filling strategy based on pre-optimized kernel hyperparameters is also put forward for the generation of multi-orbit cloud-free LAIG maps with an unprecedented level of detail, and the extraction of regularly-sampled LAIG time series at a pixel level. The ability to plugin a locally-trained GPR model into the GEE framework and its instant processing opens up a new paradigm of remote sensing image processing.<\/jats:p>","DOI":"10.3390\/rs13030403","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T09:59:40Z","timestamp":1611568780000},"page":"403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0759-4422","authenticated-orcid":false,"given":"Luca","family":"Pipia","sequence":"first","affiliation":[{"name":"Institut Cartogr\u00e0fic i Geol\u00f2gic de Catalunya (ICGC), Parc de Montj\u00fcic, 08038 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eatidal","family":"Amin","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, C\/Catedr\u00e1tico Jos\u00e9 Beltr\u00e1n 2, 46980 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3739-6056","authenticated-orcid":false,"given":"Santiago","family":"Belda","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, C\/Catedr\u00e1tico Jos\u00e9 Beltr\u00e1n 2, 46980 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mat\u00edas","family":"Salinero-Delgado","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, C\/Catedr\u00e1tico Jos\u00e9 Beltr\u00e1n 2, 46980 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, C\/Catedr\u00e1tico Jos\u00e9 Beltr\u00e1n 2, 46980 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. 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