A new method for crop classification combining time series of radar images and crop phenology information

Agricultural land cover is characterized by strong variations within relatively short time intervals. These dynamics are challenging for land cover classifications on the one hand, but deliver crucial information that can be used to improve the classifiers performance on the other hand. Since up to date mapping of crops is crucial to assess the impact of agricultural land use on the ecosystems, an accurate and complete classification of crop types is of high importance. In the presented study, a new multitemporal data based classification approach was developed that incorporates knowledge about the phenological changes on crop lands. It identifies phenological sequence patterns (PSP) of the crop types based on a dense stack of Sentinel-1 data and accurate information about the plant's phenology. The performance of the developed methodology has been tested for two different vegetation seasons using over 200 ground truth fields located in northern Germany. The results showed that a dense time series of Sentinel-1 images allowed for high classification accuracies of grasslands, maize, canola, sugar beets and potatoes (F1-score above 0.8) using PSP as well as standard (Random Forest and Maximum Likelihood) classification method. The PSP approach clearly outperformed standard methods for cereal crops, especially for spring barley where the F1-score varied between zero and 0.43 for standard approaches, while PSP achieved values as high as 0.74 and 0.79 for both vegetation seasons. The PSP based approach also outperformed for oat, winter barley and rye. Furthermore, the PSP classification is more resilient to differences in farming management and conditions of growth since it takes information about each crop types' growing stage and its growing period into consideration. The results also indicate, that the PSP approach was more sensitive to subtle changes such as the proportion of weeds within a field. (C) 2017 Elsevier Inc. All rights reserved.