Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 416887
Title Multitemporal unmixing of medium-spatial-resolution satellite images: A case study using MERIS images for land-cover mapping
Author(s) Zurita Milla, R.; Gómez-Chova, L.; Guanter, L.; Clevers, J.G.P.W.; Champs-Valls, G.
Source IEEE Transactions on Geoscience and Remote Sensing 49 (2011)11. - ISSN 0196-2892 - p. 4308 - 4317.
DOI https://doi.org/10.1109/TGRS.2011.2158320
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) spectral mixture analysis - modis data - aerosol - pixel
Abstract Data from current medium-spatial-resolution imaging spectroradiometers are used for land-cover mapping and land-cover change detection at regional to global scales. However, few landscapes are homogeneous at these scales, and this creates the so-called mixed-pixel problem. In this context, this study explores the use of the linear spectral mixture model to extract subpixel land-cover composition from medium-spatial-resolution data. In particular, a time series of MEdium Resolution Imaging Spectrometer (MERIS) full-resolution (FR; pixel size of 300 m) images acquired over The Netherlands is used to illustrate this study. The Netherlands was selected because of the following: 1) the fragmentation of its landscapes and 2) the availability of a high-spatial-resolution land-cover data set (LGN5) which can be used as a reference. The question then is to what extent a multitemporal unmixing of MERIS FR data delivers land-cover information comparable with the one provided by the LGN5. To this end, fully constrained linear spectral unmixing is applied to each individual MERIS image and to the multitemporal composite. The unmixing results are validated at both subpixel and per-pixel scales and at two thematic aggregation levels (12 and 4 land-cover classes). The obtained results indicate that the described unmixing approach yields moderate results for the 12-class case and good results for the 4-class case. These results might be explained by MERIS preprocessing steps, gridding effects, vegetation phenophases, and spectral class separability.
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