# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 1 Introduction  
Sugarcane is the key industrial crop for Brazil and a number of other countries (e.g., India, China, Thailand, etc.) [1]. Monitoring of sugarcane harvest is important to schedule and optimize logistic operations as well as forecast the crop productivity. The latter is applied to forecast the production indicators of sugar industry enterprises, biofuel (ethanol), etc. [2; 3; 4; 5; 6]. In Brazil, 60% of the sugarcane fields are located in the state of Sao Paulo [7]. Recently, Brazil has been demonstrating the rapid transition from the low-productive, costly, and environmentally unfriendly (due to preliminary burning of sugarcane leaves) technologies of manual harvest to mechanized harvest technologies (harvest without burning--green harvest) [5; 8]. That makes it possible to accelerate considerably the rate of operations and harvest throughout a year including the rainy season. Along with the growing Earth population and increasing rates of global economic development, the need for sugar industry products is rising as well. That requires the development and application of innovative technologies for sugarcane growing and harvest, and as a result--the implementation of a global harvest monitoring to evaluate the production output and to control certain changes in the fields. Ground-based harvest monitoring is possible by installing the navigation equipment and receiving GPS data from harvesters, tractors, and semi-trucks [9]. The equipment transmits in real-time mode thesignal about the machinery location (latitude and longitude) to a remote receiving station. An operator transforms the geographical coordinates into a spatial data file for their visualization in a GIS viewer and evaluation of the harvested parcel area. The methods based on the processing of digital mosaics of optical and SAR (Synthetic Aperture Radar) images of sub-meter spatial resolution, obtained from the Unmanned Aerial Vehicle (UAV), allow the ability to construct a detailed map of plants within the territory under study with an area of several hectares [10; 11]. Such approaches are efficient on a field and farming enterprise level; however, they require considerable costs while monitoring on regional, state or the whole country level. The solution here is to use the global satellite monitoring data with a spatial resolution compatible with the size of sugarcane fields [12]. To estimate sugarcane vegetation conditions and evaluate the content of nitrogen, moisture, and salts in the plant stalks, recent studies of the modern authors often involve different spectral indices, such as _NDVI_ (Normalised Difference Vegetation Index) [13], _RVI_ (Ratio Vegetation Index) [14], _EVI_ (Enhanced Vegetation Index) [15], _NDWI_ (Normalized Difference Water Index) [16]. The studies also pay much attention to optical remote sensing of the crop residue and tillage that is related to the problems of the research [17]. [PERSON] et al. [18] highlights considerable differences in spectral reflectance of green vegetation, bare soil, and crop residue. In particular, it is shown that the residue has much higher spectral reflectance in the Short-wave Infra-Red (SWIR) range than the green vegetation or soil. [PERSON] et al. [19] stresses that harvest is characterised by the sharp increase of spectral reflectance in SWIR 1 and SWIR 2 bands of Sentinel-2 satellite. In terms of mechanized harvest, sugarcane is not burnt; thus, during the harvest period, the field has thick green vegetation. When sugarcane is harvested, roots and parts of stalks are left in the soil. Later, new plants will grow from them (ratoon). The residual stalks differ considerably in their spectral characteristics from the plants before harvest. That allows using spectral indices to record the harvest date. For instance, the cane growth and harvest correlates with _NDVI_, which increases smoothly during the plant growth and decreases sharply after harvest.