Spectral shape-based temporal compositing algorithms for MODIS surface reflectance data

Spectral similarity metrics have previously been used to select representative spectra from a class for use in spectral mixture modeling. Since the tasks of spectral selection for spectral mixture modeling and spectral selection for temporal compositing are similar, these metrics may have utility for temporal compositing. This paper explores the use of two spectral similarity metrics, endmember average root mean square error (EAR) and minimum average spectral angle (MASA), for constructing temporal composites. EAR and MASA compositing algorithms were compared against four previously used algorithms, including maximum NDVI, minimum view zenith angle, minimum blue, and median red. A total of 10 different algorithms were used to create 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data over a 6-year period. Algorithm performance was assessed based on short-term temporal variability in spectral reflectance and in a selection of indices, both within a southwestern California study area and within five land-cover class subsets. EAR compositing produced the lowest variability for 4 out of 7 MODIS bands, as measured by the root mean square of time series residuals. MASA or EAR compositing produced the lowest root mean square residual values for all of the tested indices. To assess how compositing algorithms might affect remote sensing correlations with biophysical variables, correlations between indices calculated from different composites and live fuel moisture were compared. Correlations between indices and live fuel moisture were higher for shape-based composites compared with the standard composites.