In part I of this research, it was shown that the simplified bucket method in the PSU/NCAR MM4 system had an apparent tendency to overestimate surface evapotranspiration (ET) when the long-term observational data from the Atmospheric Radiation Measurement program are used for verification. It was demonstrated that a Penman-Monteith (PM) method could effectively reduce the degree of overestimating surface ET. An examination of the impact of satellite data insertion, using a variational Four-Dimensional Data Assimilation (FDDA) technique proposed by Gal-Chen (1983, 1986), on the model's estimation of surface ET is performed in the second part of this research. It shows that when the bucket method is in use the assimilation of the Geostationary Operational Environmental Satellite (GOES) temperature measurements helps the model make better estimation of surface ET owing to a significant decrease of potential ET resulting from a pronounced decrease of skin temperature and the associated moisture gradient at the ground surface. When the PM method is in use, the assimilation of GOES data tends to decrease the temperature and the associated mixing ratio depression at the lowest model level during the data assimilation period, and thus, the potential ET is decreased during the succeeding simulation period. Therefore, the model using the PM method is able to more correctly estimate latent heat flux after the data assimilation period. It reveals that Gal-Chen's FDDA algorithm of assimilating GOES data provides the model with the PM method a greater possibility of yielding the most accurate estimation of surface ET. The GOES data insertion would allow the model using the bucket method to gain a higher probability of making a more accurate estimation of latent heat flux than the model using the PM method without GOES data insertion. Even only satellite data insertion will enable the model to show a better estimation of surface ET. A nudging technique is shown to enhance the advantages of the proposed FDDA algorithm by making the model generate a more realistic estimation of surface ET. The nudging technique results in a further decrease of the skin temperature, temperature at the lowest model level and the accompanying moisture content at the ground surface and at the lowest model level during the data assimilation period