# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 6 Conclusions  
The algorithms of sugarcane harvest monitoring based on the time series of the optical and SAR data are developed.  
The _NDVI_ time series have been constructed based on the data from optical sensor Sentinel-2 MSI. Sharp and continuous decrease of the _NDVI_ values is the main feature of harvest.  
The _NDVI_ time series allows the ability to record more than half of the harvest dates. Cloudiness is the main problem for accurate identification of harvest dates according to the optical data. In the case of light cloudiness, the harvest dates may be time-delayed relative to real dates by not more than five days, being the characteristic interval between Sentinel-2 surveys within that specific area. Short-term dense clouds result in the omission of harvest dates. A long-term cloudy period may cause the early false response of the algorithm with the following omission of the real harvest date.  
The best estimates of the sugarcane harvested areas per month have been obtained for the period of March-August 2018 when a cloudy pixel percentage was less than 45% of the image area.  
To monitor harvest according to the SAR data, the coherence time series generated from Sentinel-1 IW SLC images have been used. Low coherence corresponds to the harvest period; the coherence grows sharply upon the harvest completion due to the bare soil or sparse vegetation. False responses of the SAR-based algorithm may be connected with the non-harvest field operations, that alter the signal reflection. To reduce the number of false responses, trends of the _NDVI_ time series were used. It is supposed that the harvest is carried out on the decreasing _NDVI_ trend. Note that in this case, the spatial resolution and/or periodicity of the optical sensors survey used for the construction of _NDVI_ trends may be much lower than the ones used for detecting the harvest dates in the optical algorithm.  
Accuracy of the harvest dates identification from Sentinel-1 SAR data is clearly inferior to the algorithm based on the Sentinel-2 optical data due to a considerably larger interval between surveys (12 days compared to 5 days). However, the SAR data may be used to clarify the harvest dates obtained according to the optical data and, in the long view, to specify the harvest dates during the periods of dense clouds.  
Both algorithms have demonstrated the best performance while working with the fields where sugarcane is planted approximately simultaneously over the whole area. For the fields consisting of several sugarcane parcels at different stages of growth, it is recommended to perform preliminary segmentation into the homogeneous parcels.  
All authors contributed significantly to this manuscript. [PERSON] designed this study. O.K., K.S. and D.K. were responsible for the data processing, analysis, and paper writing. D.K. developed and implemented the software. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to express their sincere gratitude to the Earth Observing System company (eos.com) and its Chief Executive Officer, President [PERSON] for his support, development and kind assistance with this study. We are also grateful to reviewers and editors for their valuable comments, recommendations, and attention to the work.  
The authors declare no conflict of interest.