Recently USGS has announced the availability of provisional Landsat surface reflectance products, through the EarthExplorer website (see here).
In particular, the Landsat Surface Reflectance High Level Data Products for Landsat 8 is generated from the L8SR algorithm (for more information read http://landsat.usgs.gov/CDR_LSR.php). You can download the product guide from here.
These high level data products are very useful for environmental analysis, especially for supervised classifications. In fact, classification of images converted to surface reflectance can improve accuracy, for instance when several images are used for land cover change assessment.
The Semi-Automatic Classification Plugin (SCP) for QGIS allows for the conversion of Landsat images to TOA (Top Of Atmosphere) reflectance, which does not correct the atmospheric effects. Also, the SCP implements the image based method DOS1 (i.e. Dark Object Subtraction 1) (Chavez, 1996) for converting Landsat images from DN to surface reflectance. Of course, DOS1 method is very simple because it doesn't require any information about atmospheric conditions, but the results are not as accurate as the Landsat Surface Reflectance High Level Data Products.
In this post I try to compare DOS1 surface reflectance to Landsat Surface Reflectance High Level Data Products, calculating the spectral signature, NDVI, and spectral angle of several samples.
In this post I try to compare DOS1 surface reflectance to Landsat Surface Reflectance High Level Data Products, calculating the spectral signature, NDVI, and spectral angle of several samples.
In order to assess the results of DOS1 correction, I converted a Landsat 8 image acquired over central Italy on 12th June 2014 (LANDSAT SCENE ID = LC81910312014163LGN00). Also, the Landsat Surface Reflectance High Level Data Product of the same scene was downloaded from the EarthExplorer website (data available from the U.S. Geological Survey) which is shown in the following figure.
The Landsat 8 Surface Reflectance image (data available from the U.S. Geological Survey)
With SCP, the original Landsat image was converted to surface reflectance using the method DOS1 and to TOA reflectance (see my previous post for further information).
Then, several ROIs of 1 pixel size were created in a random fashion over different land cover classes, and I have calculated the spectral signatures thereof (using the SCP functions). I exported the spectral signatures to csv files, which were imported into a spreadsheet for comparing DOS1 reflectance to the Landsat Surface Reflectance High Level Data Product .
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Then, several ROIs of 1 pixel size were created in a random fashion over different land cover classes, and I have calculated the spectral signatures thereof (using the SCP functions). I exported the spectral signatures to csv files, which were imported into a spreadsheet for comparing DOS1 reflectance to the Landsat Surface Reflectance High Level Data Product .