| In the research work, by analyzing the NOAA/AVHRR (resolution is 1. 1km) data(from April to August in 2000) and soil humidity(0-100cm) of the corresponding period of NOAA/AVHRR data observed by more than 250 meteorologic stations , the statistical models have been made out between the NOAA/AVHRR data and the soil moisture index(W) according to different periods, different soil deepness, different surface cover attributes, different methods and different regressive functions. And the NOAA/AVHRR data cover most provinces, cities and municipalities of China that include large scale and many kinds of climates and vegetations.This study discusses the applying scopes and the characteristics of the apparent soil inertia method and the vegetation supply water index method, and then values the two methods. Because of the shortages of the two methods, and on the basis of the former investigator' s study, single phase product method is put forward in the study first. The method considering surface temperature and reflection attributes and was built between the value of product of channel 1 and channel 4 of single phase NOAA/AVHRR and the soil moisture index, and then the result of single phase product method was compared with the result of the apparent soil inertia method and vegetation supply water index method. The conclusions are as follows:1 ) L.inear, logarithmic and exponential empirical models were applied to develop the relation ship between soil moisture index (0-100cm) and apparent soil inertia and between soil moisture index (0-100cm) and vegetation supply water index and between soil moisture index (0-100cm) and the value of the product of channel 1 and channel 4 of single phase NOAA/AVHRR respectively. The result indicates that the logarithmic models of apparent soil inertia method has better performance in estimating soil surface(10-20cm) moisture index than others for bare soil or lowly covered soil. And the logarithmic models of vegetation supply water index method has better performance in estimating the deepness of 20~50cm of soil moisture than others for highly covered soil. And the linear model of single phase product method has better performance in estimating the deepness of 10-100cm of soil moisture than others for any level covered soil, and the mean relative error is controlled within 20% on the whole, it offers a new idea for using variable estimate deep soil moisture directly.2) The result indicated that the precision of the model built by single phase product method is better than the models built by apparent soil inertia method and by vegetation supply water index method. It not only improve the precision for lowly soil and highly soil, but also offers a new method for partly covered soil creationaryly. The retrieved values of single phase product method is 3% and 4%lower than that of apparent soil inertia method and the mean is reduced 3. 5% for lowly covered soil at the deepness of 10cm and 20cm respectively; it is 1. 5%, 3. 5% and 2% lower than that of vegetation supply water method and the mean was reduced 2.4% at the deepness of 10cm, 20cm and 50cm for the partly covered soil respectively; it is 1. 3%, 1. 4% and 0.3% lower than that of vegetation supply water method and the mean is reduced 1% at the deepness of 10cm, 20cm and 50cm for the highly covered soil respectively.3) It can resolve those questions about efficiency and continuity which can' tbe resolved by apparent soil inertia method because of its ordered two-phases cloudless remote sensing data in 12 hours and by Crop Water Stress Index method and so on because of its ordered too many ground weather data. In addition it could be used to monitor soil moisture in large scale with any level covered soil.4) Currency model for April to August was built by using single phase product method after analyzing the replaceable between each model. The currency model was as follows: 10cm: W=113.69-1.44Q; 20cm: ff=113.13-1.36Q; 50cm: 1=110.74-1.230, all of them were qualified in terms of statistic test and had certain degree...
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