TY - JOUR A1 - Blickensdörfer, Lukas A1 - Schwieder, Marcel A1 - Pflugmacher, Dirk A1 - Nendel, Claas A1 - Erasmi, Stefan A1 - Hostert, Patrick T1 - Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany JF - Remote sensing of environment : an interdisciplinary journal N2 - Monitoring agricultural systems becomes increasingly important in the context of global challenges like climate change, biodiversity loss, population growth, and the rising demand for agricultural products. High-resolution, national-scale maps of agricultural land are needed to develop strategies for future sustainable agriculture. However, the characterization of agricultural land cover over large areas and for multiple years remains challenging due to the locally diverse and temporally variable characteristics of cultivated land. We here propose a workflow for generating national agricultural land cover maps on a yearly basis that accounts for varying environmental conditions. We tested the approach by mapping 24 agricultural land cover classes in Germany for the three years 2017, 2018, and 2019, in which the meteorological conditions strongly differed. We used a random forest classifier and dense time series data from Sentinel-2 and Landsat 8 in combination with monthly Sentinel-1 composites and environmental data and evaluated the relative importance of optical, radar, and environmental data. Our results show high overall accuracy and plausible class accuracies for the most dominant crop types across different years despite the strong inter-annual meteorological variability and the presence of drought and nondrought years. The maps show high spatial consistency and good delineation of field parcels. Combining optical, SAR, and environmental data increased overall accuracies by 6% to 10% compared to single sensor approaches, in which optical data outperformed SAR. Overall accuracy ranged between 78% and 80%, and the mapped areas aligned well with agricultural statistics at the regional and national level. Based on the multi-year dataset we mapped major crop sequences of cereals and leaf crops. Most crop sequences were dominated by winter cereals followed by summer cereals. Monocultures of summer cereals were mainly revealed in the Northwest of Germany. We showcased that high spatial and thematic detail in combination with annual mapping will stimulate research on crop cycles and studies to assess the impact of environmental policies on management decisions. Our results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping. KW - agricultural land cover KW - analysis-ready data KW - time series KW - large-area mapping KW - optical remote sensing KW - SAR KW - big data KW - multi-sensor Y1 - 2022 U6 - https://doi.org/10.1016/j.rse.2021.112831 SN - 0034-4257 SN - 1879-0704 VL - 269 PB - Elsevier CY - New York ER - TY - JOUR A1 - Schwieder, Marcel A1 - Wesemeyer, Maximilian A1 - Frantz, David A1 - Pfoch, Kira A1 - Erasmi, Stefan A1 - Pickert, Jürgen A1 - Nendel, Claas A1 - Hostert, Patrick T1 - Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series JF - Remote sensing of environment N2 - Spatially explicit knowledge on grassland extent and management is critical to understand and monitor the impact of grassland use intensity on ecosystem services and biodiversity. While regional studies allow detailed insights into land use and ecosystem service interactions, information on a national scale can aid biodiversity assessments. However, for most European countries this information is not yet widely available. We used an analysis-ready-data cube that contains dense time series of co-registered Sentinel-2 and Landsat 8 data, covering the extent of Germany. We propose an algorithm that detects mowing events in the time series based on residuals from an assumed undisturbed phenology, as an indicator of grassland use intensity. A self-adaptive ruleset enabled to account for regional variations in land surface phenology and non-stationary time series on a pixelbasis. We mapped mowing events for the years from 2017 to 2020 for permanent grassland areas in Germany. The results were validated on a pixel level in four of the main natural regions in Germany based on reported mowing events for a total of 92 (2018) and 78 (2019) grassland parcels. Results for 2020 were evaluated with combined time series of Landsat, Sentinel-2 and PlanetScope data. The mean absolute percentage error between detected and reported mowing events was on average 40% (2018), 36% (2019) and 35% (2020). Mowing events were on average detected 11 days (2018), 7 days (2019) and 6 days (2020) after the reported mowing. Performance measures varied between the different regions of Germany, and lower accuracies were found in areas that are revisited less frequently by Sentinel-2. Thus, we assessed the influence of data availability and found that the detection of mowing events was less influenced by data availability when at least 16 cloud-free observations were available in the grassland season. Still, the distribution of available observations throughout the season appeared to be critical. On a national scale our results revealed overall higher shares of less intensively mown grasslands and smaller shares of highly intensively managed grasslands. Hotspots of the latter were identified in the alpine foreland in Southern Germany as well as in the lowlands in the Northwest of Germany. While these patterns were stable throughout the years, the results revealed a tendency to lower management intensity in the extremely dry year 2018. Our results emphasize the ability of the approach to map the intensity of grassland management throughout large areas despite variations in data availability and environmental conditions. KW - Analysis-ready data KW - Big data KW - Large-area mapping KW - Germany KW - Common agricultural policy KW - Time series KW - Land use intensity KW - Optical remote sensing KW - Multi-spectral data KW - PlanetScope Y1 - 2022 U6 - https://doi.org/10.1016/j.rse.2021.112795 SN - 0034-4257 SN - 1879-0704 VL - 269 PB - Elsevier CY - New York ER -