Abstract: Traditionally, a monitoring network is set up to collect soil moisture information for a large area. The construction of monitoring networks is rather expensive in terms of both time and materials. However, the monitoring result is only point representativeness, and cannot satisfy a large area soil moisture mapping demand. Compared to traditional in-situ monitoring network results, information retrieved from spaceborne or airborne instruments is area representative. Moreover, the remote sensing method is much more timely and low cost. ERS (European remote sensing satellites) series satellites and METOP (meteorological operational satellite) series satellites provide global coverage, continuous, long-term, high revisit rate (2-5 days, determined by different latitudes) datasets. SCAT (scatterometer) and ASCAT (advanced catterometer) are the main scatterometer instruments onboard them respectively. Finding a practical retrieving method tailored for SCAT and ASCAT is very urgent. Referring to the TU-WIEN presented by Wolfgang in 1999, a practical method base on multi-angle long-term series change detection was developed in this paper. TU-WIEN takes full advantages of the multi-viewing capabilities of the sensor, the availability of several years of backscatter data, and a high temporal sampling rate. Taking the roughness, inhomogeneity, and vegetation cover of the land surface into account, soil moisture is retrieved by analyzing long time mass data with statistical techniques. However, there is still some weakness in the algorithm. An improvement was proposed in this paper, in which two key model parameters σ′(θ, t) and σ″(θ, t) are generated by adaption learning functions by changing a time moving window in sequence, instead of experience functions as used in the prior version. The proposed method can perform more stably and can be transplanted to different areas more easily. Besides, abnormal observations are removed from the long-term huge amounts of data to avoid fatal damage for the final output. In the experiment in the Iberian peninsula, the new function of σ′(θ, t) which were generated by a new adapting time moving window, represented the seasonal variation of σ′(θ). In addition, the experiment showed that the new adaption learning function could successfully take the place of the old experience one. Furthermore, the improved method was applied in Tibet Plateau area, where soil moisture is urgent needed. To validate the proposed algorithm, the result retrieved from remote sensing method was compared with in-situ observations which were collected in Maqu monitoring network in the Tibet-Obs plan. A good consistent relationship was found between the retrieval results and in-situ observations. The RMSE (root mean square error) was 0.0155, and the related coefficient R2 was 0.8361. The applicability of the algorithm was validated preliminarily. The algorithm is worthy of being applied to more needed areas to help take the advantages of satellite monitoring into practical use.