Abstract: Quantitative remote sensing is a significant and compulsory course for the major of remote sensing science and technology. Due to its extensive content, complex basic models, and various key algorithms, it is not only quite necessary but also challenging to select effective teaching methods for achieving teaching aims and demands successfully. In this paper, a knowledge conversion efficiency model (KCEM) is suggested for constructing effective teaching methods. The KCEM assumes that the amount of student's acquired knowledge per class hour is the product of the amount of teacher's output knowledge per class hour, the amount of student's received knowledge per class hour, and student's knowledge conversion efficiency. Only if each item reaches its optimal state, the final teaching effect attains the optimal level. The paper presents specific and classical teaching methods to optimize the above-mentioned single items. Subsequently, the KCEM model is practiced in teaching remotely sensed land surface temperature and fractional vegetation coverage (LST/FVC) space which is a knowledge point in quantitative remote sensing. This study is helpful to improve teaching quality and training level of remote sensing science and technology and also provides references to other courses.

Key words: quantitative remote sensing, undergraduate teaching, teaching methods, effective teaching, knowledge conversion efficiency model