Automatic and improved radiometric correction of landsat imageryusing reference values from MODIS surface reflectance images

Radiometric correction is a prerequisite for generating high-quality scientific data, making it possibleto discriminate between product artefacts and real changes in Earth processes as well as accuratelyproduce land cover maps and detect changes. This work contributes to the automatic generation of surfacereflectance products for Landsat satellite series. Surface reflectances are generated by a new approachdeveloped from a previous simplified radiometric (atmospheric + topographic) correction model. Theproposed model keeps the core of the old model (incidence angles and cast-shadows through a digitalelevation model [DEM], Earth-Sun distance, etc.) and adds new characteristics to enhance and automatizeground reflectance retrieval. The new model includes the following new features: (1) A fitting model basedon reference values from pseudoinvariant areas that have been automatically extracted from existingreflectance products (Terra MODIS MOD09GA) that were selected also automatically by applying qualitycriteria that include a geostatistical pattern model. This guarantees the consistency of the internal andexternal series, making it unnecessary to provide extra atmospheric data for the acquisition date and time,dark objects or dense vegetation. (2) A spatial model for atmospheric optical depth that uses detailedDEM and MODTRAN simulations. (3) It is designed so that large time-series of images can be processedautomatically to produce consistent Landsat surface reflectance time-series. (4) The approach can handlemost images, acquired now or in the past, regardless of the processing system, with the exception ofthose with extremely high cloud coverage. The new methodology has been successfully applied to aseries of near 300 images of the same area including MSS, TM and ETM+ imagery as well as to differentformats and processing systems (LPGS and NLAPS from the USGS; CEOS from ESA) for different degreesof cloud coverage (up to 60%) and SLC-off. Reflectance products have been validated with some exampleapplications: time series robustness (for a pixel in a pseudoinvariant area, deviations are only 1.04% onaverage along the series), spectral signatures generation (visually coherent with the MODIS ones, butmore similar between dates), and classification (up to 4 percent points better than those obtained withthe original manual method or the CDR products). In conclusion, this new approach, that could also beapplied to other sensors with similar band configurations, offers a fully automatic and reasonably goodprocedure for the new era of long time-series of spatially detailed global remote sensing data. © 2014 The Authors.