## 1 Introduction  
As a key component of the global carbon cycle, the terrestrial gross primary production (GPP) in forests plays an important role in the global carbon, water, and energy cycles [1; 2; 3]. GPP is the amount of illumination energy from the sun that is transformed into biomass energy, and is dependent on the illumination, heat, and water in terrestrial ecosystems [4; 5]. Precisely estimating GPP is essential for evaluating terrestrial carbon cycles, which also influence climate change [6]. Approximately 40% of GPP is directly or indirectly used by humans [7]. Vegetation is the key component in the ability of humans to adjust the climate and mitigate increasing atmospheric CO\({}_{2}\)[8]. Currently, several GPP remote sensing products, such as MPI-BGC [9], MODIS [10], JULES [11; 12], BESS [13], and BEPS [14; 15], which estimate GPP over large regions, exhibit different estimation accuracies for different vegetation types. Forest ecosystems cover more than 30% of the land surface and represent 80% of the aboveground biomass on Earth [16; 17]. In primeval forest ecosystems, the aboveground parts of the forest system can be divided into two components, the overstory and understory. The overstory component is the tree layer that intercepts direct solar radiation. The understory is defined as the vegetation structures under the upper layer of the canopy, and it is mainly composed of species of vegetation that prefer shaded conditions. In tropical and subtropical dense forests, the understory component can be divided into shrub and grass layers, but in temperate forests, this component consists of mostly grasses and herbs [18; 19]. Both overstory and understory species are important contributors to the carbon uptake in terrestrial ecosystems [20; 21]. Some studies have shown that understory vegetation contributes 32% of the total GPP in tropical forests [22; 23], but this contribution may be 25% in temperate forests [24]. A recent study showed that the shaded GPP has increased by 1.1% over the past three decades [25]. This variability is due to variations in the light, temperature, and humidity conditions in the understory and overstory. Shaded leaves also have unique characteristics, with understory and overstory vegetation exhibiting different responses to the environment and various photosynthetic traits [26; 27; 28; 29; 30]. However, in regions with high biodiversity and a high leaf area index (LAI) values in the forest zone, such as evergreen broadleaf forests (EBF) and artificial forests, the estimation of GPP exhibits high variance [31; 32]. Furthermore, GPP estimations by remote sensing methods combined with canopy photosynthesis models do not consider the understory incident radiation, and thus may underestimate the total GPP [33; 34; 35]. In dense forest ecosystems, canopy species show multiple leaf functional traits that are important for GPP estimation. Leaf traits vary greatly among overstory and understory species. When scaling up different leaf traits to whole ecosystems [36; 37], the structural parameters of ecosystems are some of the most important controlling factors [38; 39]. However, species in the same vertical stratification, such as the tree and grass layers in temperate forests and the trees, shrubs, and grasses in tropical rainforests, have similar living environments, and thus similar leaf traits [40; 41; 42; 43; 44; 45; 46; 47; 48]. Remote sensing is a useful approach for estimating ecosystem GPP at larger spatial scales [8; 49]. In forest sites, many studies have demonstrated that micrometeorological parameters such as temperature, humidity, photosynthetically active radiation (PAR), and longwave incident radiation exhibit vertical gradients [50; 51; 52; 53; 54]. Moreover, the physiological parameters of vegetation such as chlorophyll contents also exhibit vertical gradients [42; 55; 56]. However, it is difficult to capture these signals from deep in the canopy.