Next Article in Journal
Drought and Flood Monitoring of the Liao River Basin in Northeast China Using Extended GRACE Data
Next Article in Special Issue
Mapping Mining Areas in the Brazilian Amazon Using MSI/Sentinel-2 Imagery (2017)
Previous Article in Journal
Inventory of Glaciers in the Shaksgam Valley of the Chinese Karakoram Mountains, 1970–2014
Previous Article in Special Issue
Multitemporal Cloud Masking in the Google Earth Engine
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(8), 1167;

Global Estimation of Biophysical Variables from Google Earth Engine Platform

Department of Earth Physics and Thermodynamics, Faculty of Physics, Universitat de València, Dr. Moliner 50, 46100 Burjassot, València, Spain
Numerical Terradynamic Simulation Group, College of Forestry & Conservation, University of Montana, Missoula, MT 59812, USA
Image Processing Laboratory (IPL), Universitat de València, Catedrático José Beltrán 2, 46980 Paterna, València, Spain
Max-Planck-Institute for Biogeochemistry, Hans-Knöll Straβe 10, 07745 Jena, Germany
Author to whom correspondence should be addressed.
Received: 6 June 2018 / Revised: 19 July 2018 / Accepted: 20 July 2018 / Published: 24 July 2018
(This article belongs to the Special Issue Google Earth Engine Applications)
Full-Text   |   PDF [10324 KB, uploaded 24 July 2018]   |  


This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R2 = 0.87, RMSE = 0.54 m2/m2 and ME = 0.03 m2/m2 in the case of LAI, and R2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R2 = 0.42 and R2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications. View Full-Text
Keywords: Google Earth Engine; LAI; FVC; FAPAR; CWC; plant traits; random forests; PROSAIL Google Earth Engine; LAI; FVC; FAPAR; CWC; plant traits; random forests; PROSAIL

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Campos-Taberner, M.; Moreno-Martínez, Á.; García-Haro, F.J.; Camps-Valls, G.; Robinson, N.P.; Kattge, J.; Running, S.W. Global Estimation of Biophysical Variables from Google Earth Engine Platform. Remote Sens. 2018, 10, 1167.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top