# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 1 Introduction
The complexity is in the fact that in non-irrigated fields, the cane leaves may dry out and differ slightly in the optical images from the harvested stalks. Moreover, along with the development of mechanization technologies, harvest may be carried out during the rainy periods when optical images of the fields are impossible to obtain within the weeks or even months due to the cloudiness. In those cases, SAR images are the only source of information. Recent papers show the examples of SAR data successful use to find changes in sugarcane state, including the ones related to the harvest. The C-band SAR (Sentinel-1 and Radarsat-2), L-band SAR (ALOS-2), and X-band (TerraSAR-X) data are used successfully to map the state and evaluate the sugarcane productivity [7; 10; 12]. The possibility to determine the harvest data using SAR images is connected with the fact that SAR images are sensitive to the changing vegetation height and field surface texture due to harvest. The cane harvest may be carried out throughout a year. In addition, several parcels planted and harvested at different periods of time are possible within one cadastral field. Consequently, the main requirements for SAR selection data are as follows: rather high spatial and temporal resolution compatible with the dimensions of field fragments and time interval of harvest [12]. Open access SAR images from Sentinel-1 A/B satellite constellation allow the ability to monitor harvest every 6-12 days with a spatial resolution of up to 10 m. Interaction of radar signal with the surface is determined by the backscattering coefficient (\(\sigma^{0}\)) and coherence--a module of the complex correlation coefficient between two Single Look Complex (SLC) images containing the data on amplitude and phase of radar signal [20]. Harvest is identified by the accompanying abrupt decrease of \(\sigma^{0}\)[3]. It is pointed out that along with the cane stalks growth, \(\sigma^{0}\) values increase as well. When stalks are cut, \(\sigma^{0}\) falls sharply. Changes in \(\sigma^{0}\) related to the cutting of an above-ground part of a plant are the most characteristic feature of crop harvest in terms of SAR data [21; 22; 23]. During that period, coherence is low since SAR-images before and after harvest correlate weakly between each other [24; 25]. However, since the SAR-images regularity is several days (6-12 days for Sentinel-1) and mechanized harvest may be carried out during rainy periods, there is a high possibility of false detection of harvest dates due to high \(\sigma^{0}\) sensitivity for precipitation at minor and medium incidence angles, especially for seedlings (precipitation provokes \(\sigma^{0}\) bursts) [12]. Thus, such an approach requires the available additional data on precipitation within the monitoring parcels that complicate its use. Elimination of precipitation effect is possible, for instance, by averaging of \(\sigma^{0}\) data at a grid scale 10 km \(\times\) 10 km: if the \(\sigma^{0}\) burst is observed within all grid cells--it is caused by precipitation; otherwise, it is caused by the crop growing [26]. Precipitation can also be detected by the data from local meteorological services and Global Precipitation Measurement (GPM) data [27]. [PERSON] et al. [12] emphasize that possibility of harvest date determining using the TerraSAR-X data is also influenced by the incidence angle. They claim that the best results of harvest mapping may be obtained with an incidence angle of 37\({}^{\circ}\). The Sentinel-1 SLC C-band SAR data have been used successfully to determine crop harvest dates [24]. However, natural conditions and technologies of sugarcane growing differ considerably from the ones described in [24]. Thus, the algorithm [24] applicability for sugarcane needs additional studies. Numerous papers deal with the application of segmentation, Machine Learning, and Data Mining methods to estimate the sugarcane state using classification maps [28; 29; 30; 31].