ArticleRemote sensing2021
Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine.
Article in Remote sensing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed.
- PyEOGPR: A Python package for vegetation trait mapping with Gaussian Process Regression on Earth observation cloud platforms.Ecological informatics · 2025Article
- Predicting nepetalactone accumulation in Nepeta persica using machine learning algorithms and geospatial analysis.Scientific reports · 2025Article
- Tower-to-global upscaling of terrestrial carbon fluxes driven by MODIS-LAI, Sentinel-3-LAI and ERA5-Land data.Ecological indicators · 2025Article
- Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles.Remote sensing · 2022Article
- Retrieval of carbon content and biomass from hyperspectral imagery over cultivated areas.ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) · 2022Article
- Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery.Remote sensing · 2022Article
- Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data.Remote sensing · 2022Article
- Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data.Remote sensing of environment · 2022Article
- Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery.ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) · 2022Article
- Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI.Remote sensing · 2022Article
- Evaluation of Hybrid Models to Estimate Chlorophyll and Nitrogen Content of Maize Crops in the Framework of the Future CHIME Mission.Remote sensing · 2022Article
- Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine.Remote sensing · 2022Article
- Monitoring migratory birds of India's largest shallow saline Ramsar site (Sambhar Lake) using geospatial data for wetland restoration.Wetlands ecology and management · 2022Article
- Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression.Remote sensing · 2021Article
- Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery.Remote sensing · 2021Article
- Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform.Remote sensing · 2021Article
- Mapping landscape canopy nitrogen content from space using PRISMA data.ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) · 2021Article
- A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data.Remote sensing · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.