# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
###### Abstract
Optical images are constructed based on plants' spectral responses in different wavelengths of the ultraviolet, visible, and infrared part of the spectrum [15] and contain rich information about vegetation's status and biophysical properties [3,16]. As mentioned earlier, Sentinel-2 satellites provide freely available images of high spatial, spectral, and temporal characteristics and their great potential in accurate crop type mapping has been demonstrated by many studies [17, 18, 19, 20, 21, 22, 23, 24]. Although optical remote sensing systems offer numerous benefits for extracting valuable knowledge and implementing effective innovative solutions in crop classification research tasks, such systems suffer from an important limitation: the collection of clear, usable images depends on the atmospheric or weather conditions and, as a result, frequent cloud cover over an area of interest can render its monitoring challenging or unfeasible [25]. As opposed to the optical systems, satellite missions that are equipped with Synthetic Aperture Radar (SAR) sensors can acquire images day or night and under all-weather conditions [26], being an extremely valuable source of information for mapping agricultural fields in countries with frequent rain and cloud cover [27]. Additionally, backscattering signals of transmitted microwaves are sensitive to the geometric, structural, and dielectric properties of plants and soil [28], rendering SAR data very useful for effectively discriminating different types of vegetation cover. Since its launch, the Sentinel-1 mission has gained increasing attention by the remote sensing community thanks to its great capabilities, such as its short revisit time, high spatial resolution, dual polarization system, and open access policy. Specifically, many researchers have evaluated the potential of Sentinel-1 data in distinguishing different crop types and, in many cases, its contribution to accurate crop classification has been highlighted [9,29,30]. For example, refs. [29] and [9] both used Sentinel-1 VV and VH backscatter time-series for identifying tens of different crop types, achieving very promising results with overall accuracy of more than 70% and 88%, respectively. Ref. [30] used time-series of both Sentinel-1 coherence and backscattering coefficients to classify 17 crop types in Sevilla, Spain, and achieved an overall accuracy of more than 86%. At this point, it is worth mentioning that the majority of crop classification studies, making use of Sentinel-1 imagery, follow the typical approach of incorporating, as input data, only the backscatter intensity information in different polarization channels, while ignoring information about the phase changes of the microwave radiation. For more details on studies for crop classification using SAR data, in [2,26] the authors present detailed review analyses and in the recent work of [31] the authors have carried out an extensive review of deep learning applications on SAR data, which highlights the trend so far, as well as the future research directions. Compared to SAR data, containing information only for the amplitude of backscattered signals, polarimetric SAR (PolSAR) data are richer in information content, as they capture changes in both intensity and phase of the electromagnetic waves and are expected to have a greater contribution in remote crop-type identification applications [32]. As the scientific field of Radar Polarimetry progresses, many polarimetric decomposition techniques have been proposed for quantitatively analyzing PolSAR data and extracting information about the scattering characteristics of Earth's objects [33], and many of them have been successfully employed in crop classification studies [34]. H/\(A\)/\(\overline{\alpha}\) decomposition method, conceptualized by [PERSON] and [PERSON] (1997) [35], is one of the most popular among them [36] and it is based on the eigendecomposition of the multilooked polarimetric coherency or covariance matrix.