Spectral Normalization to MODIS module

Description

The Spectral Normalization to MODIS module adjusts multispectral optical images to make them as spectrally similar as possible to the reference MODIS product MCD43A4, acquired at nearly the same time.

Parameters

Name	Caption
Input Orthos	The set of orthorectified imagery to process
MODIS Folder	Folder where the prepared MODIS files are located
Output Folder	Output folder
Overwrite Results	Overwrite existing results
Save Intermediate Files	Save intermediate files
DEM Source	DEM tile source
Regression Method	The regression method to use
With Spectral Classes	Whether to use spectral classes in the regression
Classification files	Folder where the classification files are located
Synthesize Blue Band For SPOT 1-5	Whether to synthesize blue band for SPOT 1-5 dataset

Parameter descriptions

Input Orthos

The name of the folder that contains valid, GDB-supported orthorectified images.

This parameter can be specified by using any of the following:

Path and name of a folder of input orthorectified images; for example, C:\data
Path and name of a folder with a wildcard to filter the imagery by file name or type; for example, C:\data\*ORTHO_MS.pix
You can also specify an MFILE as input. An MFILE is a text file with an .mfile file name extension. For more information on this type of file, see About the MFILE format.

MODIS Folder

The name of the folder that contains prepared MODIS files. The prepared files are created by the MODIS Preparation module.

Output Folder

The path and name of the folder to which to write the output files.

Overwrite Results

Select this check box to overwrite the existing output files, if any exist. If this check box is left clear, and an output file exists in the relevant folder, the status of the job displays a message informing you of the existence and name of the output file. The message is also written to the event log of the job.

Save Intermediate Files

Select this check box to save any supporting files created during processing.

By keeping the files, you can use them to analyze intermediate results and, if necessary, fix any minor issues. You can then restart the job without having to regenerate all the supporting data, thereby reducing processing time.

Note: Intermediate files can consume a large quantity of disk space, thus it is a good idea to delete them when they are no longer needed.

DEM Source

The name of a file or folder containing DEM tiles.

This value may be the name of a single DEM file, the name of a folder containing DEM tiles, or the name of an index.txt file.

If the name of an existing folder is specified, the module reads the folder for a file named index.txt, and a set of DEM raster tiles. The index.txt file lists the DEM files contained in the specified folder and provides information describing each raster tile.

For more information on the format of the index.txt file and specific requirements for the individual DEM tiles, see Format of the DEM index file.

Regression Method

There are two regression methods available:

Global: all valid pixels in the corresponding image and reference bands are used jointly to derive linear regression between the two bands. If classes are used, the regression is derived separately for every class, but still over the whole area of the image. The global regression is always derived.
Local: the regression is derived for every valid image pixel, using valid pixels in a small window centered on the current pixel. If classes are used, only pixels belonging to the same class as the current pixel are used. If a local regression cannot be derived at a pixel, the parameters to use are either interpolated spatially from the adjacent derived values (when the classes are not used), or replaced by the global per-class values (if classes are used). Local regressions may run slower, and use significantly more disk space for the intermediate results, but generally produce more accurate corrections.

With Spectral Classes

Whether to use spectral classes when performing the regression. The use of classes usually improves the accuracy of results, particularly for local regressions, but may reduce the overall number of valid pixels.

Classification files

The name of the folder that contains prepared classification files. The prepared files are created by the Spectral Classification module.

Synthesize Blue Band For SPOT 1-5

Option to perform blue band synthesis for SPOT 1-5 images using the green, red and NIR bands. Only applied to SPOT 1-5 images. All other sensors ignore this option and continue processing.

Details

General job details

Preprocessing requirements

Before running this module, the following requirements must be met to ensure the job processes successfully and produces accurate results:

The data in the MODIS Folder must have been prepared by the MODIS Preparation module.
All input files must be in PCIDSK format (.pix).
All exclusion masks, in the input orthorectified images, must be in bitmap segments.

Module details

The Spectral Normalization to MODIS module adjusts multispectral optical images to make them as spectrally similar as possible to the reference MODIS product MCD43A4, acquired at approximately the same time.

The adjustments are based on linear regression between the image and reference pixel values in corresponding spectral bands. Image bands that do not have corresponding reference bands have their corrections interpolated or extrapolated in wavelength domain from the adjacent bands with derived corrections. The regressions can be derived in the Global mode (using all pixels in a given band) or in the Local mode (using only pixels in a small neighbourhood of a current pixel). Both modes can derive regressions jointly for all valid pixels in every channel pair, or separately for every class.

The corrected image values are converted to the radiometric quantity of Surface NBAR (Nadir BRDF-Adjusted Reflectance). The process removes most of the atmospheric and topographic spectral `. If classes are used, the effects of BRDF properties of individual classes are also reduced.

The Spectral Normalization to MODIS module normalizes orthorectified images so that they are spectrally consistent with the MODIS data in the MODIS Folder. The following steps are performed:

If necessary, the input orthorectified images are converted to TOA Reflectance.
If requested, the classes are derived for all valid pixels of the image.
The image is reprojected and downsampled to the geometry of the MODIS image.
Image exclusion mask is combined with MODIS quality layer to derive the inclusion mask of valid pixels.
The regression for each channel is derived from valid pixels in the Global or Local mode, and using classes if requested.
The parameters for image bands without MODIS reference are derived from adjacent bands.
A Global regression without classes is performed on the corrected image at MODIS resolution, to provide estimates of the radiometric accuracy of the process.
In Local mode the regression rasters are reprojected and upsampled to the image resolution.
The corrections are applied to all spectral bands of the input image

Supported Sensors

The following table lists the supported sensors. Full support level means that all images of the sensor, or its listed delivery product types, can be spectrally normalized. Partial or limited support indicates that some products were implemented and tested, but not all of them. Any input orthorectified images prior to the earliest MODIS data, late 1999, are not supported.

Sensor	Support Level	Comments
SPOT 1 - 7	Full
RapidEye	Full	Corrections for the Red Eye bands are synthesized from the Red and NIR bands.
Sentinel-2	Partial	Images with 10, 20 and 60 m bands fused into a single 12-band image at 10 m are supported.
ZY3-1	Partial	Only Level-4 product was tested.

Job results

On successful completion, the Spectral Normalization to MODIS module creates, for each valid input orthorectified image file, a new orthorectified image spectrally normalized to the MODIS data. The output file is written to the specified output folder using the name of the original file. The derived radiometric accuracy values are written to the process report, and stored in the channel metadata of the corrected full-resolution image.