定量遥感课程有效教学方法探索与实践

定量遥感是遥感科学与技术专业的核心必修课程之一，其涉及内容广泛、基础模型复杂、核心算法众多，采用何种有效教学方法才能顺利完成本科教学目标与要求，成为目前教学过程中的重点和难点。本文提出了一套“知识转化率模型”的教学方法，认为单位学时内学生习得的知识量为教师输出量、学生接受量及学生知识转化率三者的乘积，只有每一单项均达到最优，最终的教学效果才能达到最优。文中详细列举了典型的具体教学措施，以促使“知识转化率模型”中每个单项达到最优化状态。结合“遥感地表温度与植被盖度空间”模型这一定量遥感知识点，阐述了上述教学方法的应用实践。本文研究有助于提升遥感科学与技术专业的教学质量与人才培养水平，也可为其他相关课程的教学提供参考。

Exploring and practicing of effective teaching methods for quantitative remote sensing courses

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.