HR: 0830h
AN: B21E-0767    [PDF]
TI: Current Status Of MOD17 And What Influence Its Results
AU: * Zhao, M
EM: zhao@ntsg.umt.edu
AF: NTSG, College of Forestry and Conservation, University of Montana, Missoula, MT 59812 United States
AU: Running, S W
EM: swr@ntsg.umt.edu
AF: NTSG, College of Forestry and Conservation, University of Montana, Missoula, MT 59812 United States
AU: Nemani, R R
EM: nemani@ntsg.umt.edu
AF: NTSG, College of Forestry and Conservation, University of Montana, Missoula, MT 59812 United States
AB: With the validation of collection3 MODIS data for 2001 and 2002, EOS science team is improving some MODIS algorithms and related ancillary data sets to enhance the accuracy of MODIS products. Now collection4 MODIS data sets are being reprocessed. Here we compare MOD17 collection4 with collection3 globally to see which aspects are improved and what make this happen. Changes in daily meteorological data (DAO) inputs and MOD15A2 (8-day Fpar \& LAI) have great impacts on MOD17 results. Globally, latest GEOS4.02 DAO data sets have higher accuracy than previous GEO3.0, which contributes much to MOD17 improvements. Improved MOD15A2 plays an important role in MOD17 improvements for some biomes, such as grass and crop. Furthermore, we find some uncertainties in MOD17, and propose some schemes for future improvements. For example, 1) the DAO boundary lines existing in MOD17 image due to its coarse spatial resolution can be eliminated by spatial non-linear interpolation, 2) the short missing-period and cloud contaminated pixels can be filled by using temporal linear interpolation, 3) unrealistic negative NPP can be solved by using a relatively constant ratio of NPP to GPP. In the end, the improved MOD17 can enhance our abilities to monitor ecological conditions, natural resources, and environment changes.
UR: http://www.ntsg.umt.edu
DE: 1615 Biogeochemical processes (4805)
DE: 1640 Remote sensing
DE: 1694 Instruments and techniques
SC: Biogeosciences [B]
MN: 2003 Fall Meeting