Global Estimation of Biophysical Variables from Google Earth Engine Platform
"> Figure 1
<p>Work flow of the proposed retrieval chain over GEE.</p> "> Figure 2
<p>Constrained random samples of leaf chlorophyll (C<math display="inline"><semantics> <msub> <mrow/> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> </semantics></math>), leaf dry matter (C<math display="inline"><semantics> <msub> <mrow/> <mrow> <mi>d</mi> <mi>m</mi> </mrow> </msub> </semantics></math>), and leaf water (C<math display="inline"><semantics> <msub> <mrow/> <mi>w</mi> </msub> </semantics></math>) contents based upon prior knowledge of the TRY database, the MODIS land cover (MCD12Q1), kernel density estimators, and copulas.</p> "> Figure 3
<p>Theoretical performance of the Random forest regression over PROSAIL simulations of LAI, FAPAR, FVC and CWC. The colorbar indicates density of points in the scatter plots.</p> "> Figure 4
<p>LAI, FAPAR, FVC, and CWC global maps and latitudinal transects corresponding to the mean values estimated by the proposed retrieval chain for the period 2010–2015.</p> "> Figure 5
<p>LAI and FAPAR global maps and latitudinal transects corresponding to the mean values of the GEE MODIS reference product (MOD15A3H) for the period 2010–2015.</p> "> Figure 6
<p>Sites location of the BELMANIP-2.1 network used for intercomparison of LAI and FAPAR retrievals and MOD15A3H LAI/FAPAR product.</p> "> Figure 7
<p>Biome-dependent scatter plots of the retrieved LAI over BELMANIP2.1 sites for the period 2002–2017. The colorbar indicates density of points in the scatter plots.</p> "> Figure 8
<p>Biome-dependent scatter plots of the retrieved FAPAR over BELMANIP2.1 sites for the period 2002–2017. The colorbar indicates density of points in the scatter plots.</p> "> Figure 9
<p>LAI and FAPAR global maps and latitudinal transects corresponding to the difference of mean values between derived estimates by the proposed retrieval chain and the GEE MODIS reference product for the period 2010–2015.</p> "> Figure 10
<p>LAI (<b>left</b>) and FAPAR (<b>right</b>) latitudinal profiles over Africa (longitude 22°E) corresponding to the mean values of the GEE MODIS reference product and estimated by the proposed retrieval chain for the period 2010–2015.</p> ">
Abstract
:1. Introduction
- The development of a general methodology for global LAI/FAPAR estimation including FVC and CWC which are not provided by MODIS.
- The use of a global plant traits database (composed of thousands of data) for probability density function (PDF) estimation with copulas to be used for radiative transfer modeling leaf parameterization.
- The enforceability of biophysical parameter retrieval chain over GEE exploiting its capabilities to provide climate data records of global biophysical variables at computationally both affordable and efficient way.
2. Data Collection
2.1. MODIS Data
2.2. Global Plant Traits
3. Methodology
3.1. Creation of Leaf Plant Traits’ Distributions
3.2. Radiative Transfer Modeling
3.3. Random Forests Regression
4. Results and Validation
4.1. Random Forests Theoretical Performance
4.2. Obtained Estimates over GEE
4.3. Validation
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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MCD43A4 Band | Wavelength (nm) |
---|---|
Band 1 (red) | 620–670 |
Band 2 (NIR) | 841–876 |
Band 3 (blue) | 459–479 |
Band 4 (green) | 545–565 |
Band 5 (SWIR-1) | 1230–1250 |
Band 6 (SWIR-2) | 1628–1652 |
Band 7 (MWIR) | 2105–2155 |
Trait Name | Number of Samples | Number of Species |
---|---|---|
C | 19,222 | 941 |
C | 69,783 | 11,908 |
C | 32,020 | 4802 |
Parameter | Min | Max | Mode | Std | Type | |
---|---|---|---|---|---|---|
Leaf | N | 1.2 | 2.2 | 1.6 | 0.3 | Gaussian |
C (g·cm) | - | - | - | - | KDE * | |
C (g·cm) | 0.6 | 16 | 5 | 7 | Gaussian | |
C (g·cm) | - | - | - | - | KDE * | |
C | - | - | - | - | KDE * | |
C | 0 | 0 | 0 | 0 | - | |
Canopy | LAI (m/m) | 0 | 8 | 3.5 | 4 | Gaussian |
ALA (°) | 35 | 80 | 60 | 12 | Gaussian | |
Hotspot | 0.1 | 0.5 | 0.2 | 0.2 | Gaussian | |
vCover | 0.3 | 1 | 0.99 | 0.2 | Truncated Gaussian | |
Soil | 0.1 | 1 | 0.8 | 0.6 | Gaussian |
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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. https://doi.org/10.3390/rs10081167
Campos-Taberner M, Moreno-Martínez Á, García-Haro FJ, Camps-Valls G, Robinson NP, Kattge J, Running SW. Global Estimation of Biophysical Variables from Google Earth Engine Platform. Remote Sensing. 2018; 10(8):1167. https://doi.org/10.3390/rs10081167
Chicago/Turabian StyleCampos-Taberner, Manuel, Álvaro Moreno-Martínez, Francisco Javier García-Haro, Gustau Camps-Valls, Nathaniel P. Robinson, Jens Kattge, and Steven W. Running. 2018. "Global Estimation of Biophysical Variables from Google Earth Engine Platform" Remote Sensing 10, no. 8: 1167. https://doi.org/10.3390/rs10081167