Global estimation of biophysical variables from Google Earth Engine platform

Manuel Campos-Taberner; Álvaro Moreno-Martínez; Francisco Javier García-Haro; Gustau Camps-Valls; Nathaniel P. Robinson; Jens Kattge; Steven W. Running

doi:10.3390/rs10081167

Global estimation of biophysical variables from Google Earth Engine platform

Manuel Campos-Taberner, Álvaro Moreno-Martínez, Francisco Javier García-Haro, Gustau Camps-Valls, Nathaniel P. Robinson, Jens Kattge, Steven W. Running

W. A. Franke College of Forestry and Conservation

Research output: Contribution to journal › Article › peer-review

104 Scopus citations

Abstract

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.

Original language	English
Article number	1167
Journal	Remote Sensing
Volume	10
Issue number	8
DOIs	https://doi.org/10.3390/rs10081167
State	Published - Aug 1 2018

Funding

Funding: The research leading to these results was funded by the European Research Council under Consolidator Grant SEDAL ERC-2014-CoG 647423, the NASA Earth Observing System MODIS project (Grant NNX08AG87A), and supported by the LSA SAF CDOP3 project, and the Spanish Ministry of Economy and Competitiveness (MINECO) through the ESCENARIOS (CGL2016-75239-R) project.

Funders	Funder number
National Aeronautics and Space Administration	NNX08AG87A
647423
CGL2016-75239-R

Keywords

CWC
FAPAR
FVC
Google Earth Engine
LAI
PROSAIL
Plant traits
Random forests

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/rs10081167

Cite this

@article{8c9659b762b04ce180c40475f89c31f0,

title = "Global estimation of biophysical variables from Google Earth Engine platform",

abstract = "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.",

keywords = "CWC, FAPAR, FVC, Google Earth Engine, LAI, PROSAIL, Plant traits, Random forests",

author = "Manuel Campos-Taberner and {\'A}lvaro Moreno-Mart{\'i}nez and Garc{\'i}a-Haro, \{Francisco Javier\} and Gustau Camps-Valls and Robinson, \{Nathaniel P.\} and Jens Kattge and Running, \{Steven W.\}",

note = "Publisher Copyright: {\textcopyright} 2018 by the authors.",

year = "2018",

month = aug,

day = "1",

doi = "10.3390/rs10081167",

language = "English",

volume = "10",

journal = "Remote Sensing",

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TY - JOUR

T1 - Global estimation of biophysical variables from Google Earth Engine platform

AU - Campos-Taberner, Manuel

AU - Moreno-Martínez, Álvaro

AU - García-Haro, Francisco Javier

AU - Camps-Valls, Gustau

AU - Robinson, Nathaniel P.

AU - Kattge, Jens

AU - Running, Steven W.

PY - 2018/8/1

Y1 - 2018/8/1

N2 - 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.

AB - 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.

KW - CWC

KW - FAPAR

KW - FVC

KW - Google Earth Engine

KW - LAI

KW - PROSAIL

KW - Plant traits

KW - Random forests

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U2 - 10.3390/rs10081167

DO - 10.3390/rs10081167

M3 - Article

AN - SCOPUS:85051632939

SN - 2072-4292

VL - 10

JO - Remote Sensing

JF - Remote Sensing

IS - 8

M1 - 1167

ER -

Global estimation of biophysical variables from Google Earth Engine platform

Abstract

Funding

Keywords

UN SDGs

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