Publications

Campos-Taberner, M; Gilabert, MA; Sánchez-Ruiz, S; Martínez, B; Jiménez-Guisado, A; García-Haro, FJ; Guanter, L (2024). Global Carbon Fluxes Using Multioutput Gaussian Processes Regression and MODIS Products. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 17, 11310-11321.

Abstract
The quantification of carbon fluxes (CFs) is crucial due to their role in the global carbon cycle having a direct impact on Earth's climate. In the last years, considerable efforts have been made to scale CFs from eddy covariance (EC) data to the globe. In this work, a data-driven approach that exploits a multioutput Gaussian processes regression algorithm (G-model) is proposed to jointly estimate gross primary production (GPP), terrestrial ecosystem respiration (TER), and net ecosystem exchange (NEE) at a global scale. The G-model not only provides an estimate of the CFs but also an uncertainty. Moreover, it derives the three fluxes jointly preserving their physical relationship. The predictors are selected from a set of the moderate-resolution imaging spectroradiometer (MODIS) products available on Google Earth engine. The performance of the model revealed high accuracies (R-2 reaching 0.82, 0.69, and 0.80 in the case of GPP, NEE, and TER, respectively), and low root mean square errors (1.55 g m(-2) d(-1) in the case of GPP, 1.09 g m(-2) d(-1) for the NEE, and 1.14 g m(-2) d(-1) for TER) over the FLUXNET2015 data set at eight-day time scale. The GPP estimates provided by G-model outperformed the MOD17A2 product, and a state-of-the-art GPP product (PML_V2) without using meteorological forcing data sets. The results reported mean annual amounts of 133.7, 114.8, and 18.9 Pg yr(-1) for GPP, TER, and NEE, respectively, during the 2002-2023 period. The proposed approach paves the way for the development of multioutput strategies that preserve the physical relationships among CFs in upscaling processes.

DOI:
10.1109/JSTARS.2024.3413184

ISSN:
2151-1535