## 1 Introduction
Note that most cropland fields are small in size [53], so the monitoring and assessment of agriculture at the field scale (tens of meters) would need satellite images at a high spatial resolution (tens of meters). Furthermore, agricultural management practices [54; 55; 56] and land use changes have driven large spatial variation in the GPP in sugarcane plantations [57; 58; 32]. The uncertainty of GPP estimates could increase when the GPP is calculated from MSR images [51; 59; 60]. Therefore, there is a need to generate GPP data products at a high spatial resolution (HSR, tens of meters) as, to date, no global GPP data products derived from HSR images are available. Transpiration (T) is a pivotal component of evapotranspiration (ET) in agricultural fields, playing a crucial role in assessing crop growth performance [61]. Data products that partition ET into T and E (evaporation) offer valuable insights into water use efficiency. However, the practical application of T and ET data at the field level faces multiple challenges, including the course spatial resolution of current remote-sensing products [59; 62], the high costs associated with field-scale ground T data, and the intricacies of ET partitioning methods [63]. Multiple models have successfully integrated the Penman-Monteith (PM) method [64; 65] with successful results [66], but the scalability limitations and structural uncertainties in the vegetation phenology complicate its application in commercial crops [67; 68]. Satellite-derived ET products, like MOD16 [69], are popular for agricultural studies over large regions due the coarse spatial resolution but face challenges in water-rich crops (e.g., sugarcane and rice) and more extensively irrigated fields [70; 71]. Newer products (e.g., ECOSTRESS, [72]) offer finer resolutions but have strong limitations, including infrequent data capture and validation issues [73]. Several studies have pinpointed inaccuracies in ECOSTRESS data across various ecosystems [74; 75]. Such inconsistencies compromise the data's reliability for precise water management and crop yield forecasting. Given these challenges, there is a pressing need for a straightforward, accurate, and adaptable method to derive field-level T estimates in water-rich crops like sugarcane. The performance of the VPM in estimating the GPP of sugarcane plantations has only been evaluated at a moderate spatial resolution (MSR) [35]. Therefore, its performance at a high spatial resolution (HSR) for individual sugarcane plantations still needs to be assessed and better understood. On the other hand, the vegetation transpiration model (VTM) has not been tested on sugarcane croplands, and this study evaluated VTM's potential as a tool to estimate water use efficiency in this crop system. The study employed an integrated approach utilizing both MSR 8-day MODIS optical data and a combined time series of HSR data from Landsat and Sentinel-2. This methodology was specifically designed to derive vegetation indices at both the MSR and HSR levels. The vegetation indices used in our analysis included the enhanced vegetation index (EVI) and the land surface water index (LSWI), which served as critical inputs for our modeling efforts. The primary focus was on leveraging these indices to comprehensively analyze the seasonal dynamics of the sugarcane crop. By systematically collecting and processing these time-series data, we were able to effectively utilize them as an input for predicting the temporal dynamics of GPP and T in sugarcane plantations over two different sites. This approach underscores the significance of time-series analysis in agricultural studies, where HSR data availability is limited and an MSR does not represent field patterns well [76], particularly for understanding and modeling the phenological and physiological changes in crop systems over time.