Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

Waldhoff, Guido; Lussem, Ulrike; Bareth, Georg

doi:10.1016/j.jag.2017.04.009

Cited by 84 publications

(78 citation statements)

References 63 publications

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“…In the radar images utilized in this study, the changes led to different backscattering mechanisms, which can be detected using radar polarimetry (Skriver et al, 1999). The TerraSAR-X acquisition dates match the AWs proposed by Waldhoff et al (2017). As can be seen in Table 1, in our study at least one image exists for every AW, except for AW5, which has the same purpose as AW4 and normally serves as a backup window when no image can be acquired in AW4.…”

Section: Separation Of Crops Using Acquisition Windowsmentioning

confidence: 84%

“…The spatial accuracy is at least ±3 m (AdV, 2006) for the ATKIS Basis DLM, which is sufficient for our investigation considering a spatial resolution of 15 m of the final classifications. PB were used for the delineation of agricultural areas (Waldhoff et al, 2017), they consist of land parcels of the same land use type (e.g. arable land, Grassland or permanent crops), which are always enclosed, e.g.…”

Section: Ancillary and Official Geodata For The Mdamentioning

confidence: 99%

“…For this region, Waldhoff et al (2017) studied the optimal point in time (AWs) to separate the crops of the region using optical remote sensing images. AWs are time periods during the growing season during which crop separation is most favorable in this specific region.…”

Section: Separation Of Crops Using Acquisition Windowsmentioning

confidence: 99%

“…LULC classifications enriched with missing information about annually grown crops is also the main focus of the Multi-Data Approach (MDA) (Bareth, 2008;Waldhoff, Curdt, Hoffmeister, & Bareth, 2012;Waldhoff, 2014;Waldhoff & Bareth, 2009;Waldhoff, Lussem, & Bareth, 2017). It combines multitemporal remote sensing images and external geodata to produce LULC maps, including information about the crops grown each year.…”

Section: Introductionmentioning

confidence: 99%

“…It combines multitemporal remote sensing images and external geodata to produce LULC maps, including information about the crops grown each year. It was successfully developed and applied to the Rur catchment in the west of Germany (Waldhoff et al, 2017) and, interestingly, provided ideal points of time for optical image acquisition. However, the existing MDA has a drawback: it has only been implemented using optical data.…”

Section: Introductionmentioning

confidence: 99%

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Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata

Hütt

Waldhoff

2017

European Journal of Remote Sensing

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Crop distribution information is essential for tackling some challenges associated with providing food for a growing global population. This information has been successfully compiled using the Multi-Data Approach (MDA). However, the current implementation of the approach is based on optical remote sensing, which fails to deliver the relevant information under cloudy conditions. We therefore extend the MDA by using Land Use/Land Cover classifications derived from six multitemporal and dual-polarimetric TerraSAR-X stripmap images, which do not require cloud-free conditions. These classifications were then combined with auxiliary, official geodata (ATKIS and Physical Blocks (PB)) data to lower misclassification and provide an enhanced LULC map that includes further information about the annual crop classification. These final classifications showed an overall accuracy (OA) of 75% for seven crop-classes (maize, sugar beet, barley, wheat, rye, rapeseed, and potato). For potatoes, however, classification does not appear to be as consistently accurate, as could be shown from repeated comparisons with variations of training and validation fields. When the rye, wheat, and barley classes were merged into a winter cereals class, the resultant five crop-class classifications had a high OA of about 90%. ARTICLE HISTORY

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Section: Separation Of Crops Using Acquisition Windowsmentioning

confidence: 84%

Section: Ancillary and Official Geodata For The Mdamentioning

confidence: 99%

Section: Separation Of Crops Using Acquisition Windowsmentioning

confidence: 99%