## 1 Introduction  
Vegetation is an essential component of the terrestrial ecosystems that interacts with the atmosphere through the carbon and water cycles. The leaf stomata regulate the release of water by transpiration and the uptake of atmospheric carbon dioxide by photosynthesis. The sum of the gross photosynthesis by all leaves measured at the ecosystem scale is known as gross primary production (GPP) [1]. GPP, together with respiration, governs the exchange of CO2 between the biosphere and atmosphere, thereby providing the capacity of the terrestrial ecosystems to offset anthropogenic CO2 emissions [2]. Reliable estimation of spatio-temporal GPP variation in various terrestrial ecosystems is crucial because the changes of GPP over time reflect vital processes in plants and provide information about the plant-atmosphere feedback resolved over a large area [3,4]. Spatio-temporal variations in GPP can be quantified using either data-driven or data-assimilation approaches [5]. Data-driven approaches establish an empirical model between ground measurements of GPP (e.g., GPP partitioned from flux tower measurements of net ecosystem exchange [6]) and other explanatory variables that are commonly derived from remote sensing data (satellite images). The empirical model extrapolates the spatio-temporal variations in GPP. Among the widely-used explanatory variables, vegetation indices (VIs) (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and photochemical reflectance index (PRI)) are the indices that have been mostly often used in various studies [7, 8, 9, 10, 11]. However, there are several limitations when using VIs as an explanatory variable connected with the upscaling of flux tower measurements [12]. For instance, the leaf greenness approximated from VIs (e.g., by means of NDVI and EVI) may not be necessarily proportional to the photosynthetic capacity and rate. Environmental stressors may affect the photosynthesis rate even during the early stages without the appearance of any significant visual symptoms at the leaf (e.g., leaf color change) or canopy levels (e.g., leaf area index change). This creates challenges to relating the maximum photosynthesis capacity to maximum vegetation greenness. The majority of VIs are derived at the leaf level (e.g., PRI as a proxy for the leaf conversion efficiency of absorbed light); therefore, further upscaling from the leaf to the canopy level is not always feasible due to the additional confounding effects of the canopy structure on VIs. Data-assimilation approaches calibrate a process-based model (PBM) against the data on the output variables, e.g., flux tower measurements. The calibrated PBM is then used to estimate the spatio-temporal variations in GPP [13, 14, 15, 16]. The limitations of VIs in the data-driven approach do not occur when adopting the data-assimilation approaches for GPP estimation. PBM can predict GPP by simulating various plant physiological responses to climatic and atmospheric conditions and stand structural properties [16, 17, 18, 19], which are not explicitly included in the data-driven approach. Overall, a PBM is required to effectively interpret remote sensing data and exploit their full potential for estimating GPP variations in various terrestrial ecosystems. Among the developed PBMs, the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE, [19]) model has been widely used in various applications, all attempting to contribute to a better understanding and interpretation of remote sensing data over vegetation, for instance, by: (1) simulating multiple satellite radiances through coupling with an atmosphere radiative transfer model [20, 21]; (2) retrieving biochemical and structural vegetation properties [22, 23, 24]; and (3) estimating vegetation functioning variations and their biases [20, 25, 26]. Further extensions of the SCOPE model have been recently proposed and examined in various ecosystems to improve their performance [27, 28, 29].