## 5 Conclusions  
This study successfully explored the use of Landsat and Sentinel-2 imagery to monitor phenology and as an input for gross primary production (GPP) estimation in sugarcane plantations in Sao Paulo, Brazil, and Louisiana, USA. Our findings contribute significantly to the field of remote sensing in agriculture, offering new perspectives and methodologies. The key conclusions and contributions include:  
* Potential of Landsat and Sentinel-2 over cloudy environments: We demonstrated the effective combination of Landsat and Sentinel-2 time-series images for monitoring phenology and as an input for GPP estimation in sugarcane plantations. This approach proved particularly effective in diverse environmental conditions, including cloudy scenarios where HSR images have the greatest limitations, thereby underscoring the robustness of these satellite images in capturing agricultural dynamics. Furthermore, HSR data better represented field vegetation carbon uptake at both sites compared to MSR data. * EVI as a proxy for estimating optimal air temperature: The study revealed a novel application of the enhanced vegetation index (EVI) in estimating site-specific optimal air temperature (T\({}_{\text{opt}}\)) for photosynthesis. This correlation between the GPP\({}_{\text{EC}}\), EVI, and air temperature variables opens up new avenues for understanding the biophysical performance of vegetation indices across different pixels and fields. * VPM efficacy: Our research highlighted the VPM's capabilities for accurately estimating the seasonal dynamics of GPP in sugarcane plantations at a high spatial resolution. The model's adaptability to varying environmental conditions was a key finding, showcasing its potential for broader application. Nonetheless, the field variability of the ECT footprint introduced some uncertainty into the ground data. * Transpiration modeling insights: The Vegetation Transpiration Model (VTM) effectively captured the seasonal dynamics of transpiration. However, its dependency on high-quality GPP data and the need for further research into time-scale dependency and initial water content impact were noted. The model showed promise in environments like Louisiana, but additional research is needed in settings like Brazil to refine its accuracy and address uncertainties. Future work will include assessments of the models at additional sugarcane plantations with EC flux systems, spatial yield data, and detailed field information including variety type and row spacing, as this could increase our knowledge regarding likely sources of uncertainty and the prospects of deploying the models as tools for precision agriculture. The authors [PERSON] and [PERSON] were responsible for the conceptualization, methodology, and outline design, with [PERSON] also handling the outline design, modeling, and analysis. P.M.W.J. managed the software aspect of the Louisiana sugarcane EC data processing, while O.M.R.C. and H.C.F. handled the software for processing the EC data from Brazil. All authors contributed to the discussion and identifying current knowledge gaps. Validation was conducted by [PERSON] and reviewed by [PERSON], [PERSON] and [PERSON]. All authors provided significant feedback on the review of the manuscript and editing. All authors have read and agreed to the published version of the manuscript. This study was supported by the USDA National Institute of Food and Agriculture (grant 2020-67104-30935) and NASA GeoCarb Mission (GeoCarb contract No. 80 LARC17C0001). The site in Brazil was funded by FAPESP (2014/24452-0) and Embrapa Meio Ambiente (03.15.00.117). Optical satellite data were obtained and are available from Google Earth Engine. EC data can be requested from the site administrators or the authors of this manuscript. Any additional data produced herein can be requested from the authors of this document. We thank the reviewers for their time and effort in reviewing the manuscript, as their comments and suggestions helped us to further improve this manuscript.