Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images

作者：Vicente Serrano Sergio M^*; Perez Cabello Fernando; Lasanta Teodoro

来源：Remote Sensing of Environment, 2008, 112(10): 3916-3934.

DOI：10.1016/j.rse.2008.06.011

• Landsat time series
• TM-ETM plus cross-calibration
• atmospheric correction
• relative normalization
• vegetation change
• Ebro Valley
• Spain

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.

• 出版日期2008-10-15