## 4 Discussion  
### Biophysical Performance of Vegetation Indices for the Sugarcane Plantations  
Satellite-based vegetation indices (VIs), including greenness-associated VIs (e.g., NDVI, EVI) and water-associated VIs (e.g., NDWI or LSWI) [66; 67; 68], have been used as proxies for several biochemical and biophysical variables including leaf area index, canopy chlorophyll content, and gross primary production [69]. Our results show that at the sugarcane croplands, the linear relationship between the GPP\({}_{\text{EC}}\) and EVI is slightly stronger than that between the GPP\({}_{\text{EC}}\) and NDVI. Similar results were reported in other studies of paddy rice fields [44], maize croplands [42], grasslands [41], and forests [36; 37; 38]. Crop phenology or calendar information (e.g., planting date, harvest date) are useful for crop management, crop yield estimation, and carbon cycle study [70; 71]. Several studies used reflectance and vegetation indices data to track or classify the sugarcane area over several years and predict the cane stalk yield [72; 73; 74; 75; 76; 77]. Time series MODIS-derived EVI data were used to classify sugarcane croplands in Brazil [72]. The land surface phenology of croplands can be tracked and delineated by the seasonal dynamics of vegetation indices (NDVI, EVI, and/or LSWI) from the perspective of ecosystem structure (leaf area index, greenness) [39; 38]. Our study shows that NDVI, EVI, and LSWI track the phenological dynamics of sugarcane croplands. According to the detailed information on sugarcane cultivation history at the two sites [16; 18; 50; 78], the fields have been used to grow sugarcane for at least a few decades (Table S1). The time series vegetation index data from MODIS during 2000-2018 (Figure S4) show that the LSWI dropped to \(<\)0 when the sugarcane fields were at the harvest stage at both the sites, which clearly showed that in the fall/winter season the date after the first negative  
Figure 9: Interannual variation in the annual mean air temperature (\({}^{\circ}\)C), annual precipitation (mm yr\({}^{-1}\)), annual PAR (mol m\({}^{-2}\) yr\({}^{-1}\)), and annual GPP as predicted by the VPM model with the NCEP climate data (GPP\({}_{\text{VPM-NCEP}}\), g C m\({}^{-2}\) yr\({}^{-1}\)) during the plant growing season at two sugarcane plantation sites. (a) Brazil site; (b) Louisiana, USA site. LSWI values (<0) corresponds to the harvesting date or the ending date in the plant growing season. As shown in Figure 3 and Figure S4, the LSWI-based algorithms accurately identified the harvest dates at the Brazil site and the USA site. This is consistent with our previous studies that used the LSWI to delineate phenological metrics [42; 44; 79]. In addition, the land surface phenology of croplands can also be tracked and delineated by the seasonal dynamic of GPP\({}_{\text{EC}}\) and NEE\({}_{\text{EC}}\) data at the EC flux tower sites from the perspective of ecosystem function (physiology) [36; 37; 41]. Our study shows that GPP\({}_{\text{EC}}\) tracks well the carbon uptake period using a simple criterion (GPP\({}_{\text{EC}}\geq 1\) g C/m\({}^{2}\)/day). It is important to note that this study demonstrates the temporal consistency in the land surface phenology derived from these two approaches. A Comparison of GPP Estimates from Multiple Data Products (GPP\({}_{\text{EC}}\), GPP\({}_{\text{VPM-Site}}\), GPP\({}_{\text{VPM-NCEP}}\), GPP\({}_{\text{MOD17A2}}\), and GPP\({}_{\text{VPM-Globe}}\))  
The simulations of the LUE-based GPP models driven by in situ climate and known C3 or C4 maximum LUE parameters were assessed at multiple crop EC flux tower sites [44; 45; 80; 81; 82]. The results of this study show that our VPM model has a good performance in modelling the GPP of sugarcane croplands when using a C4 maximum LUE parameter, site-specific T\({}_{\text{opt}}\) parameter, and local climate data (GPP\({}_{\text{VPM-Site}}\)) or global climate data (GPP\({}_{\text{VPM-NCEP}}\)).