## 1 Introduction  
Quantifying the spatio-temporal patterns of gross primary production (GPP), which is the total amount of carbon dioxide (CO\({}_{2}\)) absorbed through photosynthesis, is an important aspect of studying ecosystem function and the carbon cycle. While GPP cannot be directly measured on global scales, remote sensing observations of solar-induced chlorophyll fluorescence (SIF) provide a reliable proxy for quantifying GPP globally [1; 2; 3; 4]. SIF occurs when chlorophyll molecules return from excited to non-excited states and dissipate the light energy absorbed during photosynthesis, in part, through re-emission of fluorescent photons in the spectral range between 650 nm (red) and 800 nm (far red) with varying intensity [5]. Hence, there is a complex physical relationship between SIF and GPP [6]. An accurate characterization of this relationship relies on the availability of high-quality, spatially complete SIF data [7]. Measurements of SIF have been examined in laboratory and field studies, from the sub-cellular scale up to the leaf scale, for decades [8], although more recently it has become possible to measure SIF from satellite instruments at the global scale [9; 10; 11; 12]. Different satellites collect global remote-sensing observations of SIF at different spatial and temporal resolutions. Available records of these observations, sometimes called Level 2 data products (e.g., [13]), are of limited use in analyzing the physical relationship with GPP due to large measurement errors, coarse spatial resolutions, and/or sparse spatial observations [14]. These issues are less prevalent in the fine-resolution, high-density observations recently available from the Tropospheric Monitoring Instrument (TROPOMI), but gaps in coverage are still present [15; 16]. To examine the relationship between SIF and GPP across different spatial scales, de-noised and gap-filled (i.e., Level 3) data products with quantified uncertainties are required. Several studies have proposed methods to produce Level 3 SIF data products with enhanced spatial resolution, typically at 0.05 degrees and as fine as 500 m in longitude and latitude. These methods are based on a variety of Level 2 SIF data products, but they are categorized into two general approaches: (1) physiologically motivated approaches that leverage relationships between SIF and other explanatory variables to downscale SIF from coarser resolutions [17; 18; 19; 20; 21], and (2) machine learning approaches that either downscale coarse-resolution SIF using ancillary datasets available at a finer resolution [22; 23; 24], or gap-fill fine-resolution but spatially sparse SIF observations [25; 26; 27]. Though these Level 3 data products have demonstrated overall capability in capturing the spatial patterns in SIF [7], none are accompanied by formally quantified uncertainties of each gap-filled SIF value. As a consequence, validation procedures for estimated Level 3 SIF fields are lacking. Spatial statistical prediction (e.g., kriging) is another approach for producing Level 3 data products. As highlighted by [28], a key advantage of a statistical approach is the full quantification of uncertainty that is immediately available with the spatial predictions. Locally constructed kriging models were developed in [29; 30] to produce coarse-resolution Level 3 data products for multiple remotely sensed processes, including SIF. However, a problematic feature of local kriging is that the spatial union of multiple local models can result in an incoherent global probability structure and, hence, in invalid prediction uncertainties [28]. Instead, spatial-statistical methods that consider satellite observations as realizations from an underlying spatial stochastic process that covers the domain of interest result in coherent uncertainty quantifications of all upscaled SIF estimates.