## 1 Introduction  
As the underlying process for plant growth, photosynthesis serves as an essential indicator of plant efficiency and agricultural management practices. Spatially and temporally explicit estimations of photosynthesis at the ecosystem scale (Gross Primary Production, GPP) can provide important information used to study the terrestrial carbon budget [1; 2]. Traditionally, GPP studies have focused on field-based observations of specific species at individual sites [3; 4; 5], which are usually time-consuming and laborious. In addition, these sparsely distributed measurements are difficult to extend over large scales due to the spatial heterogeneity of the land surface. At the landscape or regional scale, GPP is generally obtained using models and algorithms that integrate ground observations with remotely sensed data [6]. Over the past several decades, a variety of approaches have been developed to estimate the GPP of terrestrial ecosystems, and they can be grouped into four categories [7]: (1) light use efficiency (LUE) models; (2) process-based models; (3) data-driven models upscaled from eddy covariance (EC) data; and (4) models based on sun-induced chlorophyll fluorescence (SIF). In general, LUE models are well-known for their simplicity and efficiency when using remotely sensed data, but they lose the links to some critical ecological processes by using empirical relationships and constants. These models are represented by Carnegie, Ames, Stanford approach (CASA) [8], global production efficiency model (GLO-PEM) [9], vegetation photosynthesis model (VPM) [10], moderate resolution imaging spectroradiometer (MODIS) GPP [11], and multi-source data synergized quantitative (MuSyQ) GPP [12]. Process-based biogeochemical models usually employ the enzyme kinetics theory encapsulated by [PERSON] et al. [13] for C3 plants and its modification for C4 plants [14]. However, these process-based models generally suffer from high complexity, large computational demands, and calibration difficulties. The boreal ecosystem productivity simulator (BEPS) [15; 16], terrestrial ecosystem model (TEM) [17], simple biosphere (SiB) model [18], and biome biogeochemical cycle (Biome-BGC) model [19] can be categorized as process-based models. By employing EC-derived GPP data, data-driven models use various algorithms, such as model tree ensembles (MTE) [20] or regression tree [21; 22] approaches, and provide a practical way to upscale GPP from individual sites to regional scales. These data-driven methods establish a series of rules using data mining that relate in situ flux observations to satellite-based indices and climate data. SIF provides a new perspective to assess plant photosynthesis and estimate regional GPP due to its inherent link to the photosynthesis process [23; 24; 25]. However, the application of SIF is limited because of the difficulty in obtaining high quality SIF data, including proper spatial and temporal resolution, and because of the difficulty in quantitatively assessing the factors that affecting the relationship between SIF and GPP. Although various methods have been developed to model GPP, it should be noted that large uncertainties remain regarding the spatial distribution and seasonal dynamics of GPP [26; 27], and model comparison is also necessary to address to what extent the GPP models can capture the spatial and temporal patterns of GPP. Remote sensing has provided fundamental data to support the study and monitoring of GPP over regional and global scales in recent years. It usually provides reflectance-based spectral measurements of vegetation conditions. However, reflectance-based indices are proxies for vegetation \"greenness\" and photosynthetic capacity, they often have a relatively low sensitivity to short term variation in plant photosynthetic functioning and cannot be used to reflect the actual photosynthesis [28]. In addition, vegetation parameters derived from surface reflectance have little link to actual plant photosynthetic functions [29].