Introduction

The normalized difference of the vegetation index (NDVI) is a non-linear transformation of the visible (red) and near-infrared bands of remotely sensed imagery. NDVI is defined as the ratio of the terms (NIR-Red) and (NIR+Red), where NIR is the spectral response in the near-infrared band, and Red is the spectral response in the red band (Tarpley et al., 1984). For vegetation monitoring, the NDVI obtained by the combination of Channels 1 (0.58-0.681#1m) and Channel 2 (0.725-1.11#1m) [ (CH2 - CH1)/(CH2 + CH1)], visible and near infrared respectively of AVHRR data, is commonly used (Jackson et al., 1983; Tucker, 1979; Justice et al., 1985; Tucker et al., 1991). NDVI represent the relative seasonal changes in vegetation rather than vegetation amount and is not a parameter for direct use in numerical models. NDVI has been commonly correlated with certain physical properties of the vegetation canopy such as leaf area index (LAI), fractional vegetation cover, vegetation condition, and biomass (Yin and Williams, 1997; Carlson and Ripley, 1997; Wiegand et al., 1979). In some research, NDVI was also related to components of the water balance equation such as soil moisture, precipitation, and evaporation (Choudhury and Golus, 1988; Szilagyi et al., 2000; Grist et al., 1997). In these studies, the clouds which often block the satellites' view of the surface entirely were considered as obstacles and many ways were performed to get the "true" NDVI, as would be measured at the surface. Maximum-value composite images from temporal satellitic data were used to remove "errors" caused by clouds (Holben, 1986). This compositing process is effective to provide a cloud free image as close as possible.

In this study we compare the monthly composite value of NDVI to daily NDVI data, and educe a daily NDVI Clouds Index (NCI). The daily NCI values are related to observed cloud amount and sunshine time index at 120 meteorological stations over a six year period. The results show that there is a significant relationship between daily NCI and observed amount and sunshine time. Then an application was performed to relate the NCI values to cloud amount and sunshine time index for a 776,900 km² study area with diverse land cover. The results show that the daily NDVI can represent the cloud status over large scale region. In particular, we suggest that the daily NDVI may be used to estimate the solar radiation where no actual solar radiation data are available.