The Study on the Behavior of the Slop by VITs Relationship Derived from 10 Day ’ s AVHRR Composite Dataset in GAME Regions

For better understanding of the land surface processes, especially the surface moisture in the border of wet – dry region plays an important role on the nature and/or characteristics of atmospheric boundary layer (ABL). Satellite remote sensing technology is essential tool to monitor or detect the surface moisture status on the regional scale. Several method/algorithm have been developed to detect the surface/soil moisture, this study focus on optical sensor generally boarded on operational satellites. This study was carried out as a crosscutting investigation for GEWEX Asian Monsoon Experiment (GAME) regions (from Tropics to Siberia). We evaluated that the “slope” estimated from the scatter diagram of Normalize Difference Vegetation Index (NDVI) with surface temperature, as a diagnostic indicator to represent the surface status. The 10-day’s composite dataset compiled by U.S. Geological Survey (USGS) was used. Target area expanded from N 20 to N 80 in latitude and E 60 – E 150 in longitude, can be covered mostly the GAME regional study area. Results could be summarized as follows: 1) Basically, slope works as an indicator of surface moisture status in whole GAME regions, 2) however in Tibet, it was opposite sense reaction in slope from previous study, and 3) considering of scatter plot of NDVI and Ts, different phenomena detected with the threshold value of NDVI ranged roughly from 0.25 to 0.35. Introduction Better understanding of the interactive process between the land and atmosphere is one of the important issues for both atmospheric and hydrological studies. Modeling and/or field experimental studies help us improvements of knowledge, however, to state and/or monitor the “current” surface condition in regional/ global scales, satellite remote sensors is powerful tool. Sensors boarded on the space platform can detect some characteristics of the target, which is largely depended on the wave-length/frequency of sensors. Sensors with microwave bands can detect status of clouds, rain drop, ice chips, snow, liquid water in land surface, by solving radiative transfer equations (Ulaby et al., 1981). On the other hands, from visible to thermal infrared bands sensors can measure the radiance of each measured wave region (Asrar, 1989). Normalized difference vegetation index (NDVI) is one of a famous product derived from optical remote sensor, which can represent the vegetation activities (e.g., White et al., 1997; Hashimoto et al., 2001), vegetation related physical status (e.g., Kondoh and Higuchi, 2001; Higuchi and Kondoh, 2000). Most of vegetation indices were expressed as a combination of visible and near infrared bands, and use characteristics of spectral reflectance of plants. Generally, visible region’s radiance of the short wave radiation is absorbed by leaves for the activities of photosynthesis, on the other hand, leaves reflect well in near infrared ration’s radiance for protecting the leaf temperature increase. Lots of studies have been published using vegetation indices derived from satellite, for example, White et al. (1997) detect the phenology (seasonality of vegetation) in United States of America from time series of NDVI by Advanced High Resolution Radiometer (AVHRR) on boarded NOAA series polar orbit satellites. Needless to say, surface temperature derived from satellite is one of dominant parameters to determine the sensible heat fluxes (e.g., Seguin et al., 1999; Matsushima and Kondo, 2000). From the end of 1980’s, researchers focused on information by combination of vegetation index with surface temperature. Figure 1 shows an example of scatter diagram of NDVI with surface temperature (Ts). As a frontier research, Nemani and Running (1989) Figure 1 Scatter plot of NDVI with surface temperature (Ts) (after Nemani and Running, 1989, but slightly modified). Dry Condition