Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany

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 fo...

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Vydané v:	Remote sensing of environment Ročník 269; s. 112831
Hlavní autori:	Blickensdörfer, Lukas, Schwieder, Marcel, Pflugmacher, Dirk, Nendel, Claas, Erasmi, Stefan, Hostert, Patrick
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Elsevier Inc 01.02.2022 Elsevier BV
Predmet:	Agricultural land Agricultural land cover Agricultural products agricultural statistics Agriculture Analysis-ready data Big data Biodiversity Biodiversity loss Cereals Climate change Cropping sequence Crops Cultivated lands data collection Decision trees Drought Environmental conditions Environmental impact Environmental management Environmental policy Environmental testing Farming systems Germany Land cover Landsat Large-area mapping leaves Mapping Meteorological conditions Monoculture Multi-sensor Optical remote sensing Population growth radar Radar data Remote sensing SAR Sequences Summer Sustainable agriculture Time series time series analysis Workflow Germany SAR Analysis-ready data Time series Optical remote sensing Multi-sensor Big data Agricultural land cover Large-area mapping
ISSN:	0034-4257, 1879-0704
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Abstract	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 non-drought 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. •Large-area crop type mapping without region−/class-specific feature selection.•Integration of data describing local and seasonal environmental conditions.•24 agricultural land cover classes at national scale and for multiple years.•High accuracy despite strong inter-annual meteorological variability.•Combined crop type maps enable crop sequence analysis at national scale.
AbstractList	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 non-drought 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. 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 non-drought 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. •Large-area crop type mapping without region−/class-specific feature selection.•Integration of data describing local and seasonal environmental conditions.•24 agricultural land cover classes at national scale and for multiple years.•High accuracy despite strong inter-annual meteorological variability.•Combined crop type maps enable crop sequence analysis at national scale.
ArticleNumber	112831
Author	Blickensdörfer, Lukas Erasmi, Stefan Schwieder, Marcel Pflugmacher, Dirk Nendel, Claas Hostert, Patrick
Author_xml	– sequence: 1 givenname: Lukas surname: Blickensdörfer fullname: Blickensdörfer, Lukas email: lukas.blickensdoerfer@thuenen.de organization: Thünen Institute of Forest Ecosystems, Alfred-Moeller-Straße 1, 16225 Eberswalde, Germany – sequence: 2 givenname: Marcel surname: Schwieder fullname: Schwieder, Marcel organization: Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany – sequence: 3 givenname: Dirk surname: Pflugmacher fullname: Pflugmacher, Dirk organization: Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany – sequence: 4 givenname: Claas surname: Nendel fullname: Nendel, Claas organization: Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany – sequence: 5 givenname: Stefan surname: Erasmi fullname: Erasmi, Stefan organization: Thünen Institute of Farm Economics, Bundesallee 63, 38116 Braunschweig, Germany – sequence: 6 givenname: Patrick surname: Hostert fullname: Hostert, Patrick organization: Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
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Copyright	2021 The Authors Copyright Elsevier BV Feb 2022
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Keywords	SAR Analysis-ready data Time series Optical remote sensing Multi-sensor Big data Agricultural land cover Large-area mapping
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SubjectTerms	Agricultural land Agricultural land cover Agricultural products agricultural statistics Agriculture Analysis-ready data Big data Biodiversity Biodiversity loss Cereals Climate change Cropping sequence Crops Cultivated lands data collection Decision trees Drought Environmental conditions Environmental impact Environmental management Environmental policy Environmental testing Farming systems Germany Land cover Landsat Large-area mapping leaves Mapping Meteorological conditions Monoculture Multi-sensor Optical remote sensing Population growth radar Radar data Remote sensing SAR Sequences Summer Sustainable agriculture Time series time series analysis Workflow
Title	Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany
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