Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1-7, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-1-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
28 Apr 2015
Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity
H. Bach1, P. Klug1, T. Ruf1, S. Migdall1, F. Schlenz2, T. Hank2, and W. Mauser2 1VISTA Geowissenschaftliche Fernerkundung GmbH, München, Germany
2Ludwig-Maximilians-Universität München, Germany
Keywords: M4Land, land management classification system, Sentinel, canopy reflectance model SLC, crop growth model PROMET Abstract. To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status.

M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach.

This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2’s continuous and manifold data stream.

Conference paper (PDF, 1305 KB)


Citation: Bach, H., Klug, P., Ruf, T., Migdall, S., Schlenz, F., Hank, T., and Mauser, W.: Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1-7, https://doi.org/10.5194/isprsarchives-XL-7-W3-1-2015, 2015.

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