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 (R 2 = 0.87, RMSE = 0.54 m 2 /m 2 and ME = 0.03 m 2 /m 2 in the case of LAI, and R 2 = 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 (R 2 = 0.42 and R 2 = 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.

Generating High Spatio-Temporal Resolution Fractional Vegetation Cover by Fusing GF-1 WFV and MODIS Data

“…Random Forest Regression (RFR) is insensitive to noise or overfitting, and less parameters need to be optimized in this method compared with other machine learning algorithms, such as Neural Networks (NNs) and Support Vector Regression (SVR). RFR also shows satisfactory accuracy in the FVC estimation using remote sensing data [34,35]. Therefore, RFR is a suitable choice for building the FVC estimation model for GF-1 WFV data.…”

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

“…Recent developments in machine learning and artificial intelligence algorithms, such as the deep learning algorithm, have shown potential and are worthwhile for further exploration (M. Campos‐Taberner et al, ; Lazaro‐Gredilla et al, ; L. P. Zhang et al, ). High‐performance cloud platforms, such as the Google Earth Engine, have shown the capability to improve the efficiency of global variable retrieval (Manuel Campos‐Taberner et al, ). Some locally optimized methods such as the Markov Chain Monte Carlo method (Q. Zhang, Xiao, et al, ) and the trust region method (J. Qin et al, ) warrant further examination before large‐scale operational application.…”

Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover

“…Except from NNs, support vector regression (SVR) is another common algorithm for FVC estimation, especially for hyperspectral data [51,52]. Moreover, RFR algorithm has also been applied for FVC estimation [53,54] and is often used for band selection. Considering the issues of FVC estimation and optimum band selection, RFR learning based on CRTM simulations was chosen for assessing the Sentinel-2 band performances on FVC estimation and was used to estimate FVC in this study.…”