This paper introduces the surface energy balance system (SEBS) and its application for spatial-temporal estimation of actual evaporation using remote sensing data. Two case studies are presented, one for a large inland basin, the Urumqi River Basin, in Northwest China and the other for the whole Netherlands. After the presentation of the theory and processing procedures, NOAA/AVHRR scenes are used to derive land surface parameters as the inputs of SEBS. The HANTS (harmonic analysis of time series) algorithm is then used for reconstructing regional and temporal monthly and annual evaporation over the study areas. Time series of evaporative fraction and actual evaporation are successfully reconstructed for the two study areas and the estimated values are verified with the in-situ data. The results of this study demonstrate that SEBS algorithm can be used for spatial-temporal estimation of actual evaporation with an acceptable accuracy.

To suit actual situations and to adapt to new environments, robots should learn by experience to perform dynamic analysis of a sequence of pictures. For research on adaptive dynamic analyses, the reaction timing of the robots has been chosen in an important parameter in dynamic analyses. For this purpose, a robot system has been built. The robot returns a ball which rolls to it in a table. An algorithm provided by the vision system, called the ruler, is used to locate the ball in the pictures. Combined with the physical dynamic model of the robot and the ball, a feedforward neural net can improve the reaction timing. The neural net was trained via supervised learning. With correction by the neural net, the reaction timing of the robot has been improved.>