## 1 Introduction  
Sugarcane (complex hybrids of _Saccharum_ spp.) is one of the major cash crops in the world [1]. It accounts for 70% of the world's sugar production [2] and is also a major source of biomass feedstockfor biofuel (ethanol) production [3; 4]. Sugarcane is the largest crop produced in the world in terms of biomass. According to the agricultural statistics data from the United Nations Food and Agricultural Organization (FAO), 25.98 million ha of sugarcane produced 1.84 billion tons of sugarcane in 2017. Brazil and the United States of America (USA) were the top 1 st and 9 th producer countries of sugarcane in the world during the period 1994-2017 (Figure S1), accounting for approximately 50% and 2% of the harvested sugarcane area in 2017, respectively (Figure S2).  
Gross primary production (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) are the major carbon flux components in the terrestrial carbon cycle. The carbon fluxes of sugarcane plantations have large uncertainty across the spatial and temporal scales, driven by climate change [5], agricultural management practices [6; 7; 8; 9; 10], and land use change [11; 12]. Therefore, there is a need to observe and predict the phenology and carbon fluxes of sugarcane plantations under diverse weather and crop management practices with in situ measurement, remote sensing, and biogeochemical models across various temporal and spatial scales.  
The eddy covariance (EC) technique has been widely used to measure the NEE between the land and the atmosphere, which is then partitioned into GPP and ER based on day and night-time fluxes [13]. The measured NEE\({}_{\text{EC}}\) data and estimated ERE\({}_{\text{EC}}\) and GPP\({}_{\text{EC}}\) data have been used as standard or reference data for the assessment of vegetation phenology and biogeochemical models that estimate these CO\({}_{2}\) fluxes across site, regional, and global scales [14; 15]. To date, only a small number of EC tower sites have been operated to measure the CO\({}_{2}\) fluxes of sugarcane plantations [16; 17; 18; 19; 20; 21].  
Remote sensing data are extensively used to track crop phenology or land surface phenology [22; 23; 24], estimate CO\({}_{2}\) fluxes across space and time [25; 26], and scale up carbon fluxes across spatial scales [27; 28]. Many studies have developed and applied light use efficiency (LUE) models that use vegetation indices (VI) from optical images and climate data to estimate the daily GPP of vegetation [29; 30; 31; 32; 33]. A number of studies compared GPP data from the LUE models with GPP\({}_{\text{EC}}\) data estimated from EC flux tower sites [34; 32; 35]. These studies showed that the maximum light use efficiency parameter is essential for estimating the GPP in LUE models. The Vegetation Photosynthesis Model (VPM) calculates the daily GPP (GPP\({}_{\text{VPM}}\)) as the product of the light absorbed by chlorophyll in the canopy (APAR\({}_{\text{chl}}\)) and the light use efficiency [36]. The VPM model has been run to estimate the daily GPP at many EC flux tower sites [37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. To our limited knowledge, very few studies reported information on the phenology and GPP of sugarcane fields [47], and the VPM model has not been evaluated for sugarcane plantations yet. Therefore, there is a need to evaluate the VPM model at individual sugarcane EC flux tower sites, which is a critical step before we use it to estimate sugarcane GPP over years across site, regional, and global scales.  
This study has three objectives: (1) to assess the potential of vegetation indices (NDVI, EVI, LSWI) in understanding the land surface phenology and physiology of sugarcane crop at individual site, (2) to evaluate the VPM model's effectiveness at estimating the GPP of sugarcane croplands at individual sites, and (3) to quantify the effect of climate and management practices on the GPP of sugarcane croplands at individual sites over several years.