For decades, the satellite images acquired in visible and infrared bands have been used for environmental monitoring. In this purpose, the normal ized difference vegetation index (NDVI) is the commonly used vegetation index model for canopy monitoring and biomass assessment. However, due to the fact that the NDVI index is susceptible to various outside influencesmost notably the atmospheric disturbance and currently more bands are provided by satellite platforms-additional indexes have been developed to counter these effects. This paper explores two such indexes-the Aerosol Free Vegetation Index (AFRI) and the Atmospherically Resistant Vegeta tion Index (ARVI). Comparisons were made with the NDVI index to see if they indeed performed better. The relationship of the different outcomes exhibited• between the indexes with the aerosol optical depth (AOD) was analyzed and exploited to see if this scattering effect was more reduced than with NDVI. In general, the results showed that the AFRI and ARVI (with gamma= 1) indeed did perform better than their NDVI counterpart study with the related channels were employed.

Many investigators have used satellite data to derive rainfall intensity and to compare them with rain gauge data. However, there has always been a problem: what is the optimal time period for the two different types of data? A set of well-controlled data collected by ground-based dual-frequency microwave radiometers at the National Central University (24. 9ЊN, 121.1ЊE) in Taiwan between January of 1996 and December of 1997 was used to find the answer. The results show that a 1-h interval would be the optimal time period and that hourly data will provide a better accuracy than other options (5, 10, or 30 min or 2 h). Two algorithms, the differential and the brightness temperature, were established to estimate rainfall intensity using ground-based dual-frequency microwave brightness temperature and rain gauge data. The results show that the root-mean-square error and the correlation coefficient are 0.63 mm h Ϫ1 and 0.88, respectively, for the differential method, and 0.91 mm h Ϫ1 and 0.71 for the brightness temperature method. The analysis also shows that because the atmospheric background and environmental influence in the continuous observations are identical, the changes in brightness temperature are only caused from the changes in liquid water content in the air. That probably made the differential method a better choice for rainfall intensity estimation than the brightness temperature method. Moreover, ground-based radiometers measure downwelling radiation from bottom up, and little ice-particle scattering or horizontal inhomogeneity is involved. The results can be compared with retrievals from satellite microwave radiometers for a better understanding of the physics of microwave emission and scattering due to raindrops or ice particles.

No abstract

No abstract

Due to the fact that conventional data is insufficient over the ocean, satellite observations are the best tool for analyzing mesoscale convective systems (MCSs), which frequently grow and develop over the ocean. In this paper, an objective potential index (OPI) is developed from satellite data. Since the OPI includes two kinds of information-the magnitude and the evolution of the air-sea interaction over the ocean, it is suitable for surveying areas where MCSs may develop.Various air-sea parameters were derived from SSM/I microwave and GMS-5 infrared channels covering an area from 15 N to 30 N, and 110 E to 125 E in May and June 1997 to 1999. These parameters were then combined to produce the OPI. The results of this paper show that an OPI value larger (smaller) than 0.5 implies a higher probability that the GMS-5 IR1 brightness temperature will be smaller (larger) than 235 K. It may serve as a good reference in pointing out areas where convective clouds may occur and develop into MCSs.