# Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data
###### Abstract  
Ecosystems modulate Earth's climate through the exchange of carbon and water fluxes. However, long-term trends in these terrestrial fluxes remain unclear due to the lack of continuous measurements on the global scale. This study combined flux data from 197 eddy covariance sites with satellite-retrieved solar-induced chlorophyll fluorescence (SIF) to investigate spatiotemporal variations in gross primary productivity (GPP), evapotranspiration (ET), and their coupling via water use efficiency (WUE) from 2001 to 2020. We developed six global GPP and ET products at 0.05\({}^{\circ}\) spatial and 8-day temporal resolution, using two machine learning models and three SIF products, which integrate vegetation physiological parameters with data-driven approaches. These datasets provided mean estimates of 128 \(\pm\) 2.3 Pg C yr\({}^{-1}\) for GPP, 522 \(\pm\) 58.2 mm yr\({}^{-1}\) for ET, and 1.8 \(\pm\) 0.21 g C kg\({}^{-1}\) H\({}_{2}\)O yr\({}^{-1}\) for WUE, with upward trends of 0.22 \(\pm\) 0.04 Pg C yr\({}^{-2}\) in GPP, 0.64 \(\pm\) 0.14 mm yr\({}^{-2}\) in ET, and 0.0019 \(\pm\) 0.0005 g C kg\({}^{-1}\) H\({}_{2}\)O yr\({}^{-2}\) in WUE over the past two decades. These high-resolution datasets are valuable for exploring terrestrial carbon and water responses to climate change, as well as for benchmarking terrestrial biosphere models. s 2025 17 2064 2(ET) accounts for the total water losses, including soil evaporation, canopy intercepted evaporation, and plant transpiration [4; 5]. As the representation of terrestrial carbon and water interactions, water use efficiency (WUE) is calculated as the ratio of GPP and ET to indicate coupling relationships between carbon gains and water losses. The spatiotemporal variations in GPP, ET, and WUE substantially influence carbon and water cycles from regional to global scales [6]. Therefore, the accurate quantification of GPP, ET, and WUE globally is vital for advancing our understanding of terrestrial carbon and water dynamics and their interactions under the changing climate. Moreover, understanding long-term trends in WUE is also critical for predicting ecosystem responses to global change, improving Earth system models, and informing policies on climate mitigation, adaptation, and water sustainability. Different approaches have been applied to quantify regional/global GPP and ET, including process-based models [7], semi-mechanistic models [8; 9], and machine learning approaches [10; 11; 12]. Several machine learning (ML) methods for estimating GPP already exist, such as cost-effective and scalable ML frameworks for GPP modeling that combine remote sensing and flux tower data; hybrid models that merge machine learning techniques with process-based or light-use efficiency (LUE) models; and automated machine learning (AutoML) [13; 14]. Each of these approaches has its advantages and disadvantages [2]. Process-based models, such as the Breathing Earth System Simulator [7] and Boreal Ecosystem Productivity Simulator [15], can simultaneously simulate regional GPP and ET based on land surface physical and chemical mechanisms [16]. However, their performance is highly dependent on parameterization schemes and model structures [17; 18]. Semi-mechanistic models, such as the Light Use Efficiency (LUE) model and the Penman-Monteith equations, simulate regional GPP and ET based on satellite observations and empirical relationships [8]. However, establishing uniform constraint equations across different ecosystems is challenging, and these models are limited in their application to climate or vegetation change scenarios. Machine learning (ML) algorithms have also been applied to derive GPP and ET using ground- and satellite-based observations [19; 20; 21; 22]. Validations have shown that these data-driven approaches often outperform traditional process-based and semi-mechanistic models [18; 23].