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

Abstract

Abstract:The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the radiometric correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based radiometric correction. The results show a high coherence between sensors (r 2 = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r 2 > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program's continuity, a goal of great interest for the environmental, scientific, and technical community.

A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

“…On the other hand, the thresholds were above the 4.5th percentile (i.e., >2250 pixels) for ten sites. The range of thresholds determined in this study was comparable to the threshold of 0.020-0.027 recommended for MODIS time series [37,40]. Regarding the regression lines, the average slope and intercept were 0.0017 and 0.017 for the NIR band, respectively, and 0.00092 and −0.013 for the red band, respectively.…”

“…It assumes that a linear relationship exists between digital numbers from a 1 m NAIP image and surface reflectance from a 30 m Landsat image for overlapping areas, if the two images are acquired simultaneously and the atmospheric condition is homogenous within the scene. The Landsat surface reflectance image is considered as a proxy of actual ground-level reflectance measurements for PIV pixels, which follows a strategy that has been used for the correction and validation of satellite images with different resolutions [36,37]. Landsat has been used as a reference radiometer against which many other satellite payloads have their performance gauged against.…”

“…For Landsat TM and ETM+ sensors, the visible bands record less variation and are more affected by residual clouds and snow, in comparison to the infrared bands [39]. Second, the number of PIV pixels varies based on the optimal threshold of each image, in contrast to the use of a fixed empirical threshold for all images [37,40]. The adaptive scheme of PINT could alleviate the disturbance of nonlinearity of the spectral variance and enhance the spectral diversity of PIV features (i.e., pixels are well spread other than concentrated in a small range of brightness), all amendable to the robustness of the linear regression.…”

Using object‐based image analysis with multi‐temporal aerial imagery and LiDAR to detect change in temperate intertidal habitats

“…The (Singh, 1989). The impact of within-class spectral variability due to environmental conditions on different flight dates was reduced by using training samples taken from each set of imagery, but future studies could attempt to reduce variability by pre-processing imagery, perhaps adapting methods developed for correction of satellite (Padro et al, 2017) or UAV imagery (Tu, Phinn, Johansen, & Robson, 2018 Imperfect reference data can affect the outcome of quality assessments (Foody, 2010), but the creation of training and validation data through visual interpretation of the imagery is unavoidable when neither ground truth data nor higher quality remote sensing data are available. Change is often clearly detectable using this approach, as was the case here, but it introduced operator-mediated selection into an otherwise automated process and could therefore be a source of bias or error.…”

A provenance metadata model integrating ISO geospatial lineage and the OGC WPS: Conceptual model and implementation

“…Pseudo-invariant areas (PIAs) are used to deduce atmospheric effects in images captured by passive sensors in the solar spectrum (Pons, Pesquer, Cristóbal, & González-Guerrero, 2014;Padró et al, 2017). The idea is that radiance captured by satellite sensors varies due to changes in the Earth's surface, such as land cover phenology dynamics, land cover changes, and so on, but also due to other conditions (illumination angle, atmospheric conditions, etc.).…”