## 1 Introduction  
Sugarcane (_Saccharatum_ spp.) croplands supply cane feedstock for biofuel (ethanol) and sugar production [1]. Sugarcane represents nearly 70% of the sugar production worldwide and approximately 60% of the global bioethanol production [2, 3, 4]. Brazil and the United States of America (USA) rank first and ninth among the global sugarcane-producing countries [5]. Brazil produced an average of 538 million metric tons (mnt) of sugar from 1994 to 2019 [6], and through the Renovabio policy it aims to produce 50 billion liters of ethanol per year by 2030 by improving production and investment infrastructure [7]. The USA produced an average of 29 mmt of sugar from 1994 to 2019 [6]. Sugarcane crop growth monitoring and assessment provide necessary information for crop management and sustainable production, as the changes in crop variety, field size and rotation, management, and climate may affect crop growth, water use efficiency, and yield prediction [8]. Among the many metrics of crop growth, gross primary production (GPP), which is the amount of CO\({}_{2}\) fixed by vegetation photosynthesis, representing the largest carbon flux in the terrestrial ecosystem [9], is one useful metric, but it has received less attention in the crop production community. GPP has been used to calculate net primary production (NPP), aboveground biomass, and crop yield [10; 11; 12; 13]. GPP is used to monitor crop growing conditions and improve crop management practices and crop production estimates [13; 14; 15; 16]. GPP has previously been utilized to improve crop yield assessments, which are important for agricultural resiliency and food security [17; 18; 19]. There is no method for directly measuring GPP at the ecosystem and landscape scales [20; 21]. The eddy covariance (EC) method is widely applied to measure the net ecosystem exchange (NEE) between the atmosphere and the land surface [22], and the half-hourly NEE data are then partitioned into ecosystem respiration (ER) and GPP [23; 24]. Several algorithms have been used for the partitioning of the NEE into ER and GPP [25; 26]. The resultant GPP (hereafter GPP\({}_{\text{EC}}\)) is utilized as the standard data to evaluate vegetation phenology, as well as GPP estimates derived from process-based models and data-driven models over multiple spatial and temporal resolutions [27; 28; 29] (p. 200). However, because of the high cost and complexity associated with the operation of EC sites, to date, the carbon and water fluxes of sugarcane plantations have only been measured at a few sites worldwide [30; 31; 32; 33; 34; 35]. Time-series data of the carbon and water fluxes from these sites provide information on sugarcane crop phenology, GPP, and evapotranspiration. Satellite-based remote-sensing data are widely available and are often used to monitor crop growth [36; 37; 38; 39] and estimate carbon fluxes [40; 41; 42]. Light use efficiency (LUE) models, first applied in agriculture [43; 44], can be fed with vegetation indices from remote-sensing data (surface reflectance) and climate data to calculate GPP [17; 45; 46; 47]. These LUE models gained popularity thanks to their simplicity and data availability [47]. The Vegetation Photosynthesis Model (VPM) [29; 48] calculates daily GPP (hereafter GPP\({}_{\text{VPM}}\)) as the product of light absorption by chlorophyll in the canopy (APAR\({}_{\text{chl}}\)) and LUE [29; 47; 48]. The GPP estimates from the VPM have been widely evaluated among multiple vegetation types and across various spatial scales (local, regional, and global) [17; 19; 29; 49; 50]. To date, only a few studies have presented information on GPP estimates of sugarcane plantations from data-driven models with satellite images [35; 51]. Multiple global GPP data products from LUE models are now available to the public [52], driven by climate data and satellite images at a moderate spatial resolution (MSR)-for example, the Moderate Resolution Imaging Spectroradiometer (MODIS) at a 500 m spatial resolution. These GPP data products at an MSR are useful.