Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

Yu Bai,Shunlin Liang,Wenping Yuan

doi:10.3390/rs13050963

Abstract

The gross primary production (GPP) is important for regulating the global carbon cycle and climate change. Recent studies have shown that sun-induced chlorophyll fluorescence (SIF) is highly advantageous regarding GPP monitoring. However, using SIF to estimate GPP on a global scale is limited by the lack of a stable SIF-GPP relationship. Here, we estimated global monthly GPP at 0.05° spatial resolution for the period 2001–2017, using the global OCO-2-based SIF product (GOSIF) and other auxiliary data. Large amounts of flux tower data are not available to the public and the available data is not evenly distributed globally and has a smaller measured footprint than the GOSIF data. This makes it difficult to use the flux tower GPP directly as an input to the model. Our strategy is to scale in situ measurements using two moderate-resolution satellite GPP products (MODIS and GLASS). Specifically, these two satellite GPP products were calibrated and eventually integrated by in situ measurements (FLUXNET2015 dataset, 83 sites), which was then used to train a machine learning model (GBRT) that performed the best among five evaluated models. The GPP estimates from GOSIF were highly accurate coefficient of determination (R2) = 0.58, root mean square error (RMSE) = 2.74 g C·m−2, bias = –0.34 g C·m−2) as validated by in situ measurements, and exhibited reasonable spatial and seasonal variations on a global scale. Our method requires fewer input variables and has higher computational efficiency than other satellite GPP estimation methods. Satellite-based SIF data provide a unique opportunity for more accurate, near real-time GPP mapping in the future.

Highlights

State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
By comparing the accuracy of the model training dataset with the validation dataset and the efficiency of the model, we found that the gradient boosting regression trees (GBRT) model was more suitable for estimating global gross primary production (GPP) based on global OCO-2-based SIF product (GOSIF), and so we used this model to estimate global GPP from 2001 to 2017
It is worth noting that the R2, root-mean-square error (RMSE), and the bias of wetlands (WET) did not perform well, meaning that more work is needed before the GPP of this vegetation type can be estimated using GOSIF datasets

Summary

Introduction

GPP at 0.05◦ spatial resolution for the period 2001–2017, using the global OCO-2-based SIF product (GOSIF) and other auxiliary data. Large amounts of flux tower data are not available to the public and the available data is not evenly distributed globally and has a smaller measured footprint than the GOSIF data This makes it difficult to use the flux tower GPP directly as an input to the model. Our strategy is to scale in situ measurements using two moderate-resolution satellite GPP products (MODIS and GLASS). The GPP estimates from GOSIF were highly accurate coefficient of determination (R2 ) = 0.58, root mean square error (RMSE) = 2.74 g C·m−2 , bias = –0.34 g C·m−2 ) as validated by in situ measurements, and exhibited reasonable spatial and seasonal variations on a global scale. Satellite-based SIF data provide a unique opportunity for more accurate, near real-time

Objectives

Results

Discussion

Conclusion