气候变化领域集成服务门户(CCPortal): Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany

CCPortal

DOI	10.1016/j.rse.2021.112831
	Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany
	Blickensdoerfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick
发表日期	2022
ISSN	0034-4257
EISSN	1879-0704
卷号	269
英文摘要	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.
英文关键词	Agricultural land cover; Analysis-ready data; Time series; Large-area mapping; Optical remote sensing; SAR; Big data; Multi-sensor
语种	英语
WOS研究方向	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000759682100002
来源期刊	REMOTE SENSING OF ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281134
作者单位	Johann Heinrich von Thunen Institute; Humboldt University of Berlin; Johann Heinrich von Thunen Institute; Leibniz Zentrum fur Agrarlandschaftsforschung (ZALF); University of Potsdam; Humboldt University of Berlin; Czech Academy of Sciences; Global Change Research Centre of the Czech Academy of Sciences
推荐引用方式 GB/T 7714	Blickensdoerfer, Lukas,Schwieder, Marcel,Pflugmacher, Dirk,et al. Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany[J],2022,269.
APA	Blickensdoerfer, Lukas,Schwieder, Marcel,Pflugmacher, Dirk,Nendel, Claas,Erasmi, Stefan,&Hostert, Patrick.(2022).Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany.REMOTE SENSING OF ENVIRONMENT,269.
MLA	Blickensdoerfer, Lukas,et al."Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany".REMOTE SENSING OF ENVIRONMENT 269(2022).