Processing of MODIS Data
25 to 0.7 mm that a canopy absorbs. MOD15A2 FPAR has 1 km resolution and
provided in 8 day basis. MODIS FPAR are biophysical variables which describe canopy structure and are related to functional process rates of energy and mass
exchange. MODIS FPAR have been used extensively as satellite derived parameters for calculation of surface photosynthesis and annual net primary
production. These products are essential in calculating terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation.
For preprocessing the MODIS images in a format compatible with others software it was necessary to obtain software code from the US Geological Survey
USGS http:LDPAAC.usgs.gov, which are available for different users‟
operating systems. In the case, we used software programs MODIS Reprojection Tool MRT. The MODIS Reprojection Tool is software designed to help
individuals work with MODIS data by reprojecting MODIS images Level-2G, Level-3, and Level-4 land data products into more standard map projections. The
main function of the MODIS Reprojection Tool is the resampler and mrtmosaic, executable programs that may be run either from the command-line or from the
MRT Graphical User Interface GUI. Resampling is the mathematical technique used to create a new version of
the image with a different width andor height in pixels. Increasing the size of an image is called up-sampling; reducing its size is called down-sampling. Many
different resampling schemes are possible. Most techniques work by computing new pixels as a weighted average of the surrounding pixels. The weights depend
on the distance between the new pixel location and the neighboring pixels. The simplest methods consider only the immediate neighbors; more advanced methods
examine more of the surround pixels to attempt to produce a more accurate result. Following are the most common resampling methods:
Nearest neighbor: Each pixel in the output image receives its value from the nearest pixel in the input reference image.
Bilinear: Each estimated pixel value in the output image is based on a weighted average of the four nearest neighboring pixels in the input image.
26 Cubic convolution: Each estimated pixel value in the output image is
based on a weighted average of 16 nearest neighboring pixels in the input image. Cubic convolution is the slowest method, but it yields the
smoothest results Mosaicking images involves combining multiple images into a single
composite image. The MRT provides a mosaic tool mrtmosaic for mosaicking tiles together prior to resampling. The mosaic tool requires that all input files are
of the same product type and they must contain the same Scientific Data Set SDS names, SDS sizes number of lines and samples, SDS projection types and
projection information, SDS pixel size, etc. If the SDS characteristics for each input tile do not match, then the mosaic tool will exit with an error.
The mosaic tool requires an input parameter file which lists the full path and filename of each input file to be mosaicked. The input files can be listed in
any order and the mosaic tool will determine how they fit together in the mosaic. The mosaic tool also requires an output filename. The file type of the output file
must match that of the input files. Thus, if the input files are HDF-EOS then the output file extension must be .hdf. If the input files are raw binary then the output
file extension must be .hdr.