Abstract
Abstract. In this study, we analyze Sentinel-1 time series data to characterize the observed seasonality of different land cover classes in eastern Thuringia, Germany and to identify multi-temporal metrics for their classification. We assess the influence of different polarizations and different pass directions on the multi-temporal backscatter profile. The novelty of this approach is the determination of phenological parameters, based on a tool that has been originally developed for optical imagery. Furthermore, several additional multitemporal metrics are determined for the different classes, in order to investigate their separability for potential multi-temporal classification schemes. The results of the study show a seasonality for vegetation classes, which differs depending on the considered class: whereas pastures and broad-leaved forests show a decrease of the backscatter in VH polarization during summer, an increase of the backscatter in VH polarization is observed for coniferous forest. The observed seasonality is discussed together with meteorological information (precipitation and air temperature). Furthermore, a dependence of the backscatter of the pass direction (ascending/descending) is observed particularly for the urban land cover classes. Multi-temporal metrics indicate a good separability of principal land cover classes such as urban, agricultural and forested areas, but further investigation and use of seasonal parameters is needed for a distinct separation of specific forest sub-classes such as coniferous and deciduous.
Highlights
AND STATE-OF-THE-ARTDue to their weather and daylight independence, SAR satellites are optimal instruments for regular monitoring of the Earth’s surface
Thiel et al, (2009) investigated the potential of Sentinel-1 for land cover classification using multi-temporal metrics defined based on ERS and ENVISAT time series
This last approach is used in agricultural applications, either to distinguish irrigated and non-irrigated crops and trees (Gao et al, 2018) or to develop crop models based on the temporal evolution of specific crops during a full season (Whelen et al, 2018)
Summary
AND STATE-OF-THE-ARTDue to their weather and daylight independence, SAR satellites are optimal instruments for regular monitoring of the Earth’s surface. While some authors directly use multi-temporal Sentinel-1 images in a machine learning approach using Random Forest to improve classification (Karlson et al, 2019), others explicitly calculate multi-temporal metrics of Sentinel-1 backscatter based on the time-series before using them in their classification scheme This last approach is used in agricultural applications, either to distinguish irrigated and non-irrigated crops and trees (Gao et al, 2018) or to develop crop models based on the temporal evolution of specific crops during a full season (Whelen et al, 2018). Rüetschi et al, (2018) used two years of Sentinel-1 data for classifying deciduous and coniferous forests in Northern Switzerland, improving the Random Forest algorithm using extracted phenological information from Sentinel-1 data For all those approaches, Sentinel-1 time series are used in a particular context for improving the classification of one particular land cover type and its subclasses.
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