HR: 1340h
AN: GC23A-1300    [Abstracts]
TI: Development of a complete rice paddy map dataset over Asia using MODIS data
AU: * Takeuchi, W
EM: wataru@iis.u-tokyo.ac.jp
AF: Institute of Industrial Science, University of Tokyo, 6-1, Komaba 4-chome, Meguro, Tokyo, 153-8505
AU: Oki, T
EM: taikan@iis.u-tokyo.ac.jp
AF: Institute of Industrial Science, University of Tokyo, 6-1, Komaba 4-chome, Meguro, Tokyo, 153-8505
AU: Baruah, P J
EM: pjbaruah@iis.u-tokyo.ac.jp
AF: Institute of Industrial Science, University of Tokyo, 6-1, Komaba 4-chome, Meguro, Tokyo, 153-8505
AU: Yasuoka, Y
EM: yyasuoka@iis.u-tokyo.ac.jp
AF: Institute of Industrial Science, University of Tokyo, 6-1, Komaba 4-chome, Meguro, Tokyo, 153-8505
AB: Two thirds of the rice-growing areas in the World are in Asian countries and hundreds of millions of people depend on rice as their staple food source. At the same time, paddy fields have been considered to be one of the likely and most important sources of atmospheric methane since the rapid increase in atmospheric methane was recognized in the early 1980's. The improved understanding of paddy field distribution at large spatial scales has increased the interest in deriving crop yield and methane emission estimations. Nevertheless, the collection of such data through field surveys is time-consuming and expensive in Asian regions. Remotely sensing data from satellite images provide an alternative means of obtaining paddy field distribution. In this study, the patterns observed in metrics calculated for one year of MODIS over Asia is examined. Four analytical approached are used; calculation of temporal mean, maximum and minimum layers for selected metrics showing significant spatial variability of channel 1-7, NDXI; linear discriminant for input into the spectral mixture analysis is derived from the same multi-temporal metrics used for the classification product using ASTER; the continuous percentage of paddy field is generated using spectral mixture analysis with the training data derived from the above mentioned ASTER data. The derived metrics are not sensitive to time of year or the seasonal cycle and can limit the inclusion of atmospheric contamination. The comparison of 500m MODIS product with the past efforts on 1km AVHRR derived AARS, BU, JRC, UMD, USGS product shows that the finer resolution and its un-mixing played a crucial role in depicting the paddy field cover over Asia.
DE: 1632 Land cover change
DE: 1640 Remote sensing (1855)
SC: Global Climate Change [GC]
MN: Fall Meeting 2005