Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity
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 decisi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-04-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/1/2015/isprsarchives-XL-7-W3-1-2015.pdf |
| _version_ | 1818524572525264896 |
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| author | H. Bach P. Klug T. Ruf S. Migdall F. Schlenz T. Hank W. Mauser |
| author_facet | H. Bach P. Klug T. Ruf S. Migdall F. Schlenz T. Hank W. Mauser |
| author_sort | H. Bach |
| collection | DOAJ |
| description | 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.
<br><br>
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.
<br><br>
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. |
| first_indexed | 2024-12-11T05:58:52Z |
| format | Article |
| id | doaj.art-021a019000a84c999ff75d2e59e12749 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-11T05:58:52Z |
| publishDate | 2015-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-021a019000a84c999ff75d2e59e127492022-12-22T01:18:34ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342015-04-01XL-7/W31710.5194/isprsarchives-XL-7-W3-1-2015Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensityH. Bach0P. Klug1T. Ruf2S. Migdall3F. Schlenz4T. Hank5W. Mauser6VISTA Geowissenschaftliche Fernerkundung GmbH, München, GermanyVISTA Geowissenschaftliche Fernerkundung GmbH, München, GermanyVISTA Geowissenschaftliche Fernerkundung GmbH, München, GermanyVISTA Geowissenschaftliche Fernerkundung GmbH, München, GermanyLudwig-Maximilians-Universität München, GermanyLudwig-Maximilians-Universität München, GermanyLudwig-Maximilians-Universität München, GermanyTo 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. <br><br> 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. <br><br> 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.http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/1/2015/isprsarchives-XL-7-W3-1-2015.pdf |
| spellingShingle | H. Bach P. Klug T. Ruf S. Migdall F. Schlenz T. Hank W. Mauser Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity |
| title_full | Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity |
| title_fullStr | Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity |
| title_full_unstemmed | Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity |
| title_short | Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity |
| title_sort | satellite image simulations for model supervised dynamic retrieval of crop type and land use intensity |
| url | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/1/2015/isprsarchives-XL-7-W3-1-2015.pdf |
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