Improved Data Products Research Articles

THE UDF05 FOLLOW-UP OF THE HUBBLE ULTRA DEEP FIELD. III. THE LUMINOSITY FUNCTION ATz∼ 6

In this paper, we present a derivation of the rest-frame 1400A luminosity function (LF) at redshift six from a new application of the maximum likelihood method by exploring the five deepest HST/ACS fields, i.e., the HUDF, two UDF05 fields, and two GOODS fields. We work on the latest improved data products, which makes our results more robust than those of previous studies. We use un-binned data and thereby make optimal use of the information contained in the dataset. We focus on the analysis to a magnitude limit where the completeness is larger than 50% to avoid possibly large errors in the faint end slope that are difficult to quantify. We also take into account scattering in and out of the dropout sample due to photometric errors by defining for each object a probability that it belongs to the dropout sample. We find the best fit Schechter parameters to the z~6 LF are: alpha = 1.87 +/- 0.14, M* = -20.25 +/- 0.23, and phi*=1.77^{+0.62}_{-0.49} * 10^{-3} Mpc^{-3}. Such a steep slope suggests that galaxies, especially the faint ones, are possibly the main sources of ionizing photons in the universe at redshift six. We also combine results from all studies at z~6 to reach an agreement in 95% confidence level that -20.45<M*<-20.05 and -1.90<alpha<-1.55. The luminosity density has been found not to evolve significantly between z~6 and z~5, but considerable evolution is detected from z~6 to z~3.

Multitemporal, multichannel AVHRR data sets for land biosphere studies—Artifacts and corrections

Temporal compositing of daily optical satellite data has become an accepted methodology for obtaining frequent images of large areas for studies of the land surface. However, such composite data sets sometimes contain large “artifacts” (i.e., errors due to sources unrelated to the land surface itself). The goal of this study was to develop a series of operations and algorithms that would identify and remove as many of these errors as possible. The specific objective was to obtain, every ten days, surface reflectance in advanced very high resolution radiometer (AVHRR) channels 1 and 2 and normalized difference vegetation index (NDVI)—all referenced to a constant viewing geometry—and surface temperature. The processing steps of the resulting methodology, dubbed “ABC3” for atmospheric, bidirectional, and contamination corrections of the Canada Centre for Remote Sensing (CCRS), includes atmospheric corrections; the identification of pixels “contaminated” by clouds, snow (including subpixel), and shadows from optically thick clouds; bidirectional reflectance and thermal emissivity corrections; and the replacement of the contaminated pixels in the composites through interpolation. The resulting procedures yield surface reflectance and NDVI fully corrected for bidirectional effects; a version of NDVI corrected for solar zenith only; and surface temperature corrected for atmospheric and surface emissivity effects. An evaluation of the resulting data set shows that the procedures provide significantly improved data products compared with the raw composites, but they do not approximate a single-date image sufficiently closely, especially for AVHRR channel 1, whose reflectance values are generally low. This is attributed to the limitations of the input data and knowledge of atmospheric (and partly bidirectional) characteristics applicable to each composite pixel.