Publications

Vuolo, Francesco; Mattiuzzi, Matteo; Atzberger, Clement (2015). Comparison of the Landsat Surface Reflectance Climate Data Record (CDR) and manually atmospherically corrected data in a semi-arid European study area. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 42, 1-10.

Abstract
This study contributes to the quality assessment of atmospherically corrected Landsat surface reflectance data that are routinely generated by the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS).This dataset, named Landsat Surface Reflectance Climate Data Record (Landsat CDR), is available at global scale and offers unprecedented opportunities to land monitoring and management services that require atmospherically corrected Earth observation (EO) data. Our assessment is based on the comparison of the Landsat CDR data against a set of Landsat and DEIMOS-1 images processed to a high degree of accuracy using an industry-standard atmospheric correction algorithm (ATCOR-2). The software package has been used for many years and its correction procedures can be considered consolidated and well-established. The dataset of Landsat and DEIMOS-1 images was acquired over a semi-arid agricultural area located in Lower Austria and was independently corrected by using a manual fine-tuning of ATCOR-2 parameters to reach the highest possible accuracy. Results show a very good correspondence of the surface reflectance in each of the six reflective spectral channels as well as for the NDVI (Normalized Difference Vegetation Index). An additional comparison against a NDVI time series from MODIS revealed also a good correspondence. Coefficients of determination (R-2) between the two multi-year and multi-seasonal Landsat/DEIMOS datasets range between 0.91 (blue band) and 0.98 (nIR, SWIR-1 and SWIR-2). The results obtained for our semi-arid test site in Austria confirm previous findings and suggest that automatic atmospheric procedures, such as the one implemented by LEDAPS are accurate enough to be used in land monitoring services that require consistent multi-temporal surface reflectance data. (C) 2015 Elsevier B.V. All rights reserved.

DOI:
10.1016/j.jag.2015.05.003

ISSN:
0303-2434