# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
###### Abstract
According to the findings of numerous studies [20; 70; 71; 72; 73; 74], integrating data from both Sentinel-1 and Sentinel-2 missions is of great value to discriminate different crop types, since better classification results are obtained than employing optical or SAR data alone. The rich potential of this approach is clearly demonstrated by the research of [PERSON] et al. (2018) [14] that examined multiple object-based classification scenarios, using as input data different combinations of Sentinel-1 VV and VH polarized backscattering values and Sentinel-2 images, and found that, in all cases, the joint use of both types of data resulted in higher overall accuracy. At this point, it should be noted that most of the above studies make use only of the amplitude information of the collected SAR backscattering signals, while ignoring the phase information contained in Sentinel-1 PolSAR data. Despite the fact that there are many recent publications suggesting the integration of these two types of satellite data in crop classification tasks [75; 32], research in this direction remains scarce. To the best of our knowledge, there is only one study focusing on evaluating both Sentinel-1 polarimetric data and Sentinel-2 optical data contribution in differentiating different types of crops [62], reporting some very promising results for accurately mapping crops. These findings are in agreement with another crop classification study carried out by [PERSON] et al. (2018) [16], who demonstrated that the integration of single date Gaofen-3 PolSAR an Sentinel-2A optical data resulted in an improved overall accuracy compared with the classification models built on optical or PolSAR data alone. The increasing scientific interest in utilizing various types of remotely sensed data combined with the continuous advancement in remote sensing systems has lead to the accumulation of dense multi-temporal imagery with richer information content and finer spatial resolution [76] and, as a result, to the formation of very large datasets with numerous features [77]. Regarding the task of remote identification of crops, such high dimensional datasets, not only demand for high computational resources, but also may contain redundant, irrelevant, or misleading information that could diminish significantly the performance of Machine Learning (ML) classifiers [78]. These negative aspects could be compensated by applying feature selection methods in order to extract an optimal subset of predictors [2]. Indeed, various feature selection and feature importance estimation strategies, especially Random Forest based importance estimation, have been implemented by a considerable number of crop classification studies [68; 79; 80; 81; 82; 83]. Another high quality and extensively used feature selection strategy is Genetic Algorithms (GA). This particular method exhibits numerous advantages, such as its wide applicability and effectiveness in dealing with complex problems [84; 85], rendering it a very attractive option for feature selection tasks. Despite the great popularity of GAs in numerous scientific fields [86], this technique has been moderately used in land-use/land-cover classification studies for feature selection [77; 87; 88; 89; 90; 91] and in optimizing the parameters of the employed classification algorithms [76; 91; 92; 93; 94]. The research objectives of this study are to explore the capability of PolSAR data to classify different crop types under diverse experimental setups, (i) as independent input and (ii) as complementary information combined both with cloud-free and with clouded Sentinel-2 imagery. Moreover, we aim to assess the importance of the different input time-series features in order to design a crop classification pipeline that is both accurate and efficient. To this end, an object-level crop classification approach was adopted in order to generate crop maps of an agricultural region in Navarre, Spain.