# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 1 Introduction
The simplest approaches involve classification of the sugarcane state and stages of its growth (including the harvested areas), i.e., with the use of statistic methods (such as a maximum likelihood estimation) or Object-Based Image Analysis (OBIA) [32]. The \(\sigma^{0}\) values in different polarizations as well as polarimetric coefficients can be used as inputs in SAR classification tasks [33]. To single out the sugarcane plantations at different growth stages, one can use regression trees (Classification and Regression Trees--CART), support vector machine classifiers (Polynomial-SVM and RBF-SVM), a random forest classifier (RF), an artificial neural network classifier (ANN), and a decision tree classifier (CART-DT) [29; 34]. The Random Forest and XGBoost methods may identify the sugarcane plantations to high precision, including the early growth stages (that indicates indirectly the preceding harvest) [33]. Early identification of the agricultural plantings (including the early sugarcane emerging) is possible from Sentinel-1 time series using such neural network classifiers as convolutional neural networks (CNNs), long short-term memory recurrent neural networks (LSTM RNNs), and gated recurrent unit RNNs (GRU RNNs), etc. [35]. The disadvantage of those methods is in the requirement for the available ground-based training sample that is not always possible in the case of regional and global monitoring. Such methods are sensitive to the composition of reference objects and may need additional retraining while transferring to other parcels of the fields being classified. They are often complicated for their implementation and require considerable computational resources. The objective of the research is to develop the algorithms for determining the harvesting dates suitable for large sugarcane areas monitoring. The algorithm should provide correct results in the lack of a ground-based training sample and be computationally simple, thereby overcoming the limitations of existing methods. To do that, open-access data from Sentinel-1 and Sentinel-2 were used. There are the following main practical implications of the research results: control of the harvesting operations and determination of the harvested fields to estimate the harvested crop. This paper is organized as follows. Section 2 analyzes the climatic conditions and technologies of sugarcane growing in the state of Sao Paulo, Brazil. Methods for constructing time series of optical (Sentinel-2 _NDVI_) and SAR (Sentinel-1 coherence) data are described. Section 3 involves the formulation of the algorithm for determining the harvest dates from NDVI time series and the algorithm based on both SAR and optical data. The latter is the development of the algorithm proposed for grain-crops in [24]. Section 4 represents the accuracy analysis of harvest dates and harvested areas determination. In Section 5 some examples of errors in harvest dates determining dates are discussed. The error causes and possible approaches to their elimination are analyzed. In particular, the effect of clouds on the accuracy of harvest date determination according to _NDVI_ is investigated. Finally, the possibility of further development of the proposed algorithms is demonstrated. Section 6 contains the conclusions of the research.