# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 5 Discussion
### Possible Development of the Algorithms  
The division into small parcels has made it possible to reduce nonhomogeneity of the fields and record better the harvest-related _NDVI_ drop. That division was performed from geometrical considerations without taking into account vegetation state. Preliminary field segmentation according to the optical images would allow singling out the homogeneous parcels. In particular, according to [PERSON] and [PERSON] [46], the use of k-means clustering to single out homogeneous fields, within which the harvest was monitored based on Sentinel-1 \(\sigma^{0}_{VH}\), \(\sigma^{0}_{VV}\), and AVHRR _NDVI_ time series, provided the overall 82.17% accuracy of the result. It should be emphasized that in this case, the segmentation would result in the increased volume of the processed data. While supposing a certain duration of the vegetation period of sugarcane, the simplest model of its growth was used implicitly. The use of more advanced mathematical models will be the natural development of that approach. Such a model could forecast a trend, being required for operational monitoring. Gaussian function may be applied to model the NDVI change in time [47]. There were no data concerning the dates of planting or preceding harvest for the parcels under analysis. If there is information about the dates of growth beginning, local climatic and meteorological parameters, and state of soils, it is possible to apply biophysical modeling of the plant growth and development as well as the harvest at different vegetation stages. For instance, [PERSON] and [PERSON] [48] construct a dynamic simulation model of sugarcane growth in Brazil basing on the analysis of biophysical processes in terms of 27 variables for Photosynthesis, Phenology, Leaf Development, Biomass Partitioning, Sucrose Partitioning, Plant Extension, and Root and Water Stress. Extreme learning machine was applied by [PERSON] et al. [49] to predict the sugarcane growth based on such ground measurements as amount of daily water irrigation, soil electrical conductivity, daily maximum temperature, evaporation, sunshine hours, rainfall precipitation, humidity percentage, and mean wind speed at 2 m above the ground. Such observations would help narrow down the time intervals of harvest dates identification and clarify the results of satellite monitoring, having excluded the deliberately false dates. Time delay of the harvest dates detected by the SAR algorithms are mostly related to the post-harvest operations. A calendar of agrotechnical operations changing soil surface or vegetation cover state, as well as data on plant growth stage makes it possible to single out the harvest among the operations changing the nature of the radar signal reflection. A much shorter revisit time for the study area is the important advantage of the optical data compared to the SAR one. An interval between surveying is 5 days in the case of Sentinel-2 in comparison with 12 days for Sentinel-1. Increasing revisit frequency may improve considerably the algorithm based on SAR data. The use of textural features in harvest monitoring is one more tendency in the algorithm development. It will require machine learning methods capable of efficient identification and use of such features. Significant advantages can be achieved using high-resolution data (about 1 m), allowing to track changes in field texture associated with the agricultural machinery operation. Classification methods and artificial neural networks used to detect harvested fields during the harvest monitoring within certain scenes are the alternative for the threshold methods and solution tree. Such algorithms require a large reference sample, which completeness and representativeness affect the accuracy of the results. However, once adjusted decision rule of a classifier will help reduce the following computational costs owing to the processing of individual scenes and no need to construct a long time series.