Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

138
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
138
Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

100
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
100
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

86
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
86
Levels of Use of a GIS01:29

Levels of Use of a GIS

86
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
86
Introduction to GIS01:28

Introduction to GIS

166
Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
166
Manipulation and Analysis01:21

Manipulation and Analysis

56
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
56

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A cloud-computing framework for downscaled global 300 m SIF retrieval from Sentinel-3 and TROPOSIF.

International journal of applied earth observation and geoinformation : ITC journal·2026
Same author

Assessment of PlanetScope Spectral Data for Estimation of Peanut Leaf Area Index Using Machine Learning and Statistical Methods.

Sensors (Basel, Switzerland)·2026
Same author

PyEOGPR: A Python package for vegetation trait mapping with Gaussian Process Regression on Earth observation cloud platforms.

Ecological informatics·2025
Same author

Bridging Gaps in Aquatic Remote Sensing Reflectance Validation: Pixel Boundary Effect and Its Induced Errors.

Sensors (Basel, Switzerland)·2025
Same author

Tower-to-global upscaling of terrestrial carbon fluxes driven by MODIS-LAI, Sentinel-3-LAI and ERA5-Land data.

Ecological indicators·2025
Same author

Driving variables to explain soil organic carbon dynamics: páramo highlands of the Ecuadorian Real mountain range.

Journal of soils and sediments·2025

Related Experiment Video

Updated: Aug 29, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K

Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine.

Luca Pipia1, Eatidal Amin2, Santiago Belda2

  • 1Institut Cartogràfic i Geològic de Catalunya (ICGC), Parc de Montjüic, 08038 Barcelona, Spain.

Remote Sensing
|September 9, 2022
PubMed
Summary

Gaussian process regression (GPR) is now integrated into Google Earth Engine (GEE) for large-scale satellite data processing. This enables cloud-free vegetation property mapping and time series analysis with high detail and on-the-fly processing.

Keywords:
Gaussian process regression (GPR)Google Earth Engine (GEE)Sentinel-2gap fillingleaf area index (LAI)machine learning

More Related Videos

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.6K
Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.0K

Related Experiment Videos

Last Updated: Aug 29, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K
Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.6K
Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.0K

Area of Science:

  • Remote Sensing
  • Machine Learning
  • Geospatial Analysis

Background:

  • Gaussian Process Regression (GPR) has been a valuable tool in Earth observation for its unique capabilities like uncertainty estimation and band relevance.
  • GPR excels at time series processing, effectively filling data gaps in optical imagery caused by cloud cover, crucial for continuous monitoring.
  • The Google Earth Engine (GEE) cloud platform offers unprecedented opportunities for processing vast amounts of satellite data using advanced machine learning.

Purpose of the Study:

  • To adapt Gaussian Process Regression (GPR) for parallel processing and integrate it into the Google Earth Engine (GEE) ecosystem.
  • To demonstrate the utility of GPR within GEE for generating gap-filled vegetation property products from satellite imagery.
  • To develop a GPR-based gap-filling strategy for creating high-resolution, cloud-free multi-orbit maps and pixel-level time series.

Main Methods:

  • Developed a general adaptation of the GPR formulation to a parallel processing framework suitable for cloud platforms.
  • Integrated the adapted GPR model into the Google Earth Engine (GEE) environment.
  • Utilized Sentinel-2 imagery to train and apply a GPR model for predicting green Leaf Area Index (LAI_G).

Main Results:

  • Successfully integrated GPR into GEE, enabling large-scale, on-the-fly mapping of LAI_G at 20m resolution globally.
  • Demonstrated the workflow with case studies over Western Europe, including detailed LAI_G maps of Spain.
  • Developed and showcased a GPR-based gap-filling strategy for generating detailed, cloud-free LAI_G maps and time series.

Conclusions:

  • The integration of GPR into GEE opens new possibilities for advanced remote sensing image processing.
  • This workflow allows for the seamless application of locally-trained GPR models to planetary-scale satellite data.
  • The developed approach enables efficient, high-resolution, and gap-free mapping of biophysical variables and time series extraction.