Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images

Abstract

a b s t r a c tRadiometric correction is a prerequisite for generating high-quality scientific data, making it possible to discriminate between product artefacts and real changes in Earth processes as well as accurately produce land cover maps and detect changes. This work contributes to the automatic generation of surface reflectance products for Landsat satellite series. Surface reflectances are generated by a new approach developed from a previous simplified radiometric (atmospheric + topographic) correction model. The proposed model keeps the core of the old model (incidence angles and cast-shadows through a digital elevation model [DEM], Earth-Sun distance, etc.) and adds new characteristics to enhance and automatize ground reflectance retrieval. The new model includes the following new features: (1) A fitting model based on reference values from pseudoinvariant areas that have been automatically extracted from existing reflectance products (Terra MODIS MOD09GA) that were selected also automatically by applying quality criteria that include a geostatistical pattern model. This guarantees the consistency of the internal and external 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 detailed DEM and MODTRAN simulations. (3) It is designed so that large time-series of images can be processed automatically to produce consistent Landsat surface reflectance time-series. (4) The approach can handle most images, acquired now or in the past, regardless of the processing system, with the exception of those with extremely high cloud coverage. The new methodology has been successfully applied to a series of near 300 images of the same area including MSS, TM and ETM+ imagery as well as to different formats and processing systems (LPGS and NLAPS from the USGS; CEOS from ESA) for different degrees of cloud coverage (up to 60%) and SLC-off. Reflectance products have been validated with some example applications: time series robustness (for a pixel in a pseudoinvariant area, deviations are only 1.04% on average along the series), spectral signatures generation (visually coherent with the MODIS ones, but more similar between dates), and classification (up to 4 percent points better than those obtained with the original manual method or the CDR products). In conclusion, this new approach, that could also be applied to other sensors with similar band configurations, offers a fully automatic and reasonably good procedure for the new era of long time-series of spatially detailed global remote sensing data.

Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy

“…Hadjimitsis et al [21] used pseudoinvariant targets to atmospherically correct time series with high quality results. Pons et al [43] presented a hybrid technique between DOS and pseudoinvariant areas (PIA), fitting spectral TOD (τ 0λ ), and spectral atmospheric radiance (Latm λ ) unknowns to match reference values of radiometrically stable areas, which were extracted from existing 10-year surface reflectance TERRA-MODIS products (MOD09GA). The method was able to automatically generate surface reflectance products from MSS, TM, and ETM+ imagery, being validated throughout spectral signature comparisons with Landsat and MODIS official surface reflectance products to evaluate the signature coherence in several land covers (built-up areas, Aleppo pine forests, Scots pine forests, holm oak forests), while the time-series robustness was evaluated with testing PIA not used the radiometric correction procedure (σ blue = 0.54%; σ green = 0.68%; σ red = 0.69%; σ NIR = 1.81%; σ SWIR1 = 1.56%; and, σ SWIR2 = 0.96%).…”

“…The objective of this paper is to validate the performance of the PIA-based radiometric correction for the current ETM+, OLI, and MSI imagery (since for MSS-TM-ETM+ has already been validated in Pons et al [43]). …”

Developing spatially and thematically detailed backdated maps for land cover studies

“…Radiometric correction was applied to convert digital numbers to reflectance values following Pons et al (2014b) using the MiraMon CorRad module. This tool integrates, among other parameters, pseudo-invariants areas (PIA) derived from MODIS reference values, an illumination model (cast-shadows and self-shadows), sun elevation, sensor calibration parameters, DEM, etc.…”

Desarrollo de productos avanzados para la misión SEOSAT/Ingenio

“…Estos métodos de intercalibración mediante PPI han sido aplicados anteriormente con éxito a la homogeneización de series temporales Landsat, entre otras (VicenteSerrano et al, 2008, Pons et al, 2014. Dichos PPI se seleccionan automáticamente con los criterios de NDVI constantemente bajo a lo largo de todo el año y variaciones pequeñas en los ND relativas a sus vecinos.…”