I spy with my little eye: automated detection of agricultural parcels and crops using satellite images
Assessing the potentials of remote sensing data in the fields of land use, agricultural economics and biodiversity
German-wide and plot-specific land use data is so far not public available. This can change due to the new data from satellite imagery from the Copernicus program (Sentinel), which is free and taken at a high frequency.
Background and Objective
So far, the resolution of freely available satellite remote sensing data in Germany and in the EU was too low and thus only of limited use for the description and analysis of land use dynamics. Since 2015, the European Earth observation programme “Copernicus” provides high resolution satellite remote sensing data regarding area and time dimensions. This new quality of data offers the opportunity to display up-to-date information on the land use and land use structures in Germany with a high degree of spatial accuracy for each single plot of land. Thus, this satellite remote sensing data could help to improve the explanation of the relationship between land use intensity and its regional distribution and to better answer questions regarding the protection of abiotic resources as well as the distribution and development of biodiversity.
The main aim is to extend the existing estimation approach for the Thünen-Atlas on agricultural use (www.thuenen.de/thuenen-atlas) with remote sensing information on cropping pattern and to develop appropriate inference statistics. Remote sensing data, provided by JKI, will be used in the statistical estimation approach developed for the Thünen-Atlas to improve the fit of the downscaling. Additionally, it should be assessed, how other data sources (data collection on soil use, IACS) could be complemented or substituted.
The following objectives should be achieved:
- Updating the “Thünen-Atlas on agricultural use” as well as the inclusion and analysis of field specific remote sensing data
- Development of methods and their applications to assess the quality of the estimation models (inference statistics)
- Map on agricultural plots for Germany
The following steps will be developed:
- Building a test environment for the use of Sentinel 1 and Sentinel 2 data
- Development of an approach to recover the optimal parameters for segmenting remote sensing data into parcels using multi-temporal S1/S2 stacks
- Development of an approach to process German-wide segmentation of parcels with the help of cloud services such as available in CODE-DE
- Development of methods to detect fields crop from satellite images
- Validation of methods using ground proof data from agricultural farms
Links and Downloads
Involved external Thünen-Partners
(Halle (Saale), Deutschland)
9.2017 - 8.2022
Tetteh G, Gocht A, Erasmi S, Schwieder M, Conrad C (2021) Evaluation of sentinel-1 and sentinel-2 feature sets for delineating agricultural fields in heterogeneous landscapes. IEEE Access 9:116702-116719, DOI:10.1109/ACCESS.2021.3105903
Schlund M, Erasmi S (2020) Sentinel-1 time series data for monitoring the phenology of winter wheat. Remote Sens Environ 246:111814, DOI:10.1016/j.rse.2020.111814
Tetteh G, Gocht A, Schwieder M, Erasmi S, Conrad C (2020) Unsupervised parameterization for optimal segmentation of agricultural parcels from satellite images in different agricultural landscapes. Remote Sensing 12(18):3096, DOI:10.3390/rs12183096