# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
###### Abstract  
Crop classification is an important remote sensing task with many applications, e.g., food security monitoring, ecosystem service mapping, climate change impact assessment, etc. This work focuses on mapping 10 crop types at the field level in an agricultural region located in the Spanish province of Navarre. For this, multi-temporal Synthetic Aperture Radar Polarimetric (PolSAR) Sentinel-1 imagery and multi-spectral Sentinel-2 data were jointly used. We applied the Cloude-Pottier polarimetric decomposition on PolSAR data to compute 23 polarimetric indicators and extracted vegetation indices from Sentinel-2 time-series to generate a big feature space of 818 features. In order to assess the relevance of the different features for the crop mapping task, we run a number of scenarios using a Support Vector Machines (SVM) classifier. The model that was trained using only the polarimetric data demonstrates a very promising performance, achieving an overall accuracy over 82%. A genetic algorithm was also implemented as a feature selection method for deriving an optimal feature subset. To showcase the positive effect of using polarimetric data over areas suffering from cloud coverage, we contaminated the original Sentinel-2 time-series with simulated cloud masks. By incorporating the genetic algorithm, we derived a high informative feature subset of 120 optical and polarimetric features, as the corresponding classification model increased the overall accuracy by 5% compared to the model trained only with Sentinel-2 features. The feature importance analysis indicated that apart from the Sentinel-2 spectral bands and vegetation indices, several polarimetric parameters, such as Shannon entropy, second eigenvalue and normalised Shannon entropy are of high value in identifying crops. In summary, the findings of our study highlight the significant contribution of Sentinel-1 PolSAR data in crop classification in areas with frequent cloud coverage and the effectiveness of the genetic algorithm in discovering the most informative features. crop type mapping; time series; cloud cover; genetic algorithm; feature selection; radar polarimetry; common agricultural policy +
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Observation (EO) data acquired from increasingly improving space-borne remote sensing technologies, and the extended application of artificial intelligence techniques in monitoring and mapping crops offer practical and cost-effective solutions as an alternative to field visits [7]. The practical benefits of remotely detecting the type and status of crops are highlighted in the context of European's Union (EU) Common Agricultural Policy's (CAP) scheme. CAP is an initiative for supporting agricultural activities of European countries and providing subsidies, according to farmers' declarations about the location, area, and type of their cultivated fields [8]. European Commission has recently enacted several legal reforms for enabling the use of EU's Copernicus Sentinel-1 and Sentinel-2 satellite data, in order to optimize CAP's activities and to ensure farmers' compliance to CAP rules [9]. Each one of Sentinel-1 and Sentinel-2 missions was designed as a two-satellite constellation that acquire C-band radar and optical satellite images of Earth, respectively. Sentinel images are freely available [10,11], providing great opportunities for cost-effective research and application development in a variety of remote sensing domains. Additionally, the high temporal, spatial, and spectral resolution of the collected data, combined with the wide area and global coverage provided [12], render Sentinel satellites an extremely attractive option, specifically, for effective monitoring of agricultural land and the production of high-quality crop maps [13]. According to the scientific literature of recent decades, building crop classification models using optical imagery as input data is one of the most popular approaches [14].