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Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/38081
Title: Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images
Authors: Vicente Serrano, Sergio M. ; Pérez-Cabello, Fernando; Lasanta Martínez, Teodoro
Keywords: Landsat time series
TM-ETM+ cross-calibration
Atmospheric correction
Relative normalization
Vegetation change
Ebro Valley
Issue Date: 15-Oct-2008
Publisher: Elsevier
Citation: Remote Sensing of Environment 112(10): 3916-3934 (2008)
Abstract: The homogeneity of time series of satellite images is crucial when studying abrupt or gradual changes in vegetation cover via remote sensing data. Various sources of noise affect the information received by satellites, making it difficult to differentiate the surface signal from noise and complicates attempts to obtain homogeneous time series. We compare different procedures developed to create homogeneous time series of Landsat images, including sensor calibration, atmospheric and topographic correction, and radiometric normalization. Two seasonal time series of Landsat images were created for the middle Ebro Valley (NE Spain) covering the period 1984–2007. Different processing steps were tested and the best option selected according to quantitative statistics obtained from invariant areas, simultaneous medium-resolution images, and field measurements. The optimum procedure includes cross-calibration between Landsat sensors, atmospheric correction using complex radiative transfer models, a non-lambertian topographic correction, and a relative radiometric normalization using an automatic procedure. Finally, three case studies are presented to illustrate the role of the different radiometric correction procedures when analyzing and explaining gradual and abrupt temporal changes in vegetation cover, as well as temporal variability. We have shown that to analyze different vegetation processes with Landsat data, it is necessary to accurately ensure the homogeneity of the multitemporal datasets by means of complex radiometric correction procedures. Failure to follow such a procedure may mean that the analyzed processes are non-recognizable and that the obtained results are invalid.
Description: 19 páginas, 9 figuras, 4 tablas.-- El documento en word es la versión post-print del artículo.
URI: http://hdl.handle.net/10261/38081
DOI: 10.1016/j.rse.2008.06.011
ISSN: 0034-4257
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