基于贝叶斯最大熵与先验知识的土壤水分空间预测方法 (2019)
本发明是引入贝叶斯最大熵BME方法框架，在这个框架内将土壤水分地面测量值和环境先验知识融合起来，以地面采集数据作为硬数据，以土壤水分环境数据作为软数据，生成高分辨率土壤水分数字地图，使估算结果既包含了地面样点之间的空间相关性，又兼顾了土壤水分与先验知识的关系。该方法不仅对丰富遥感真实性检验学科理论和技术具有重要的意义，而且能够降低土壤水分产品的反演误差，提高在相关行业领域的实用价值，同时也为其他低分辨率遥感产品的真实性检验提供借鉴。 A Bayes maximum entropy (BME) method framework is introduced, soil moisture ground measured values and environment priori knowledge are fused in the framework, ground acquired data serves as hard data, soil moisture environmental data serves as soft data, a high-resolution soil moisture digital map is generated, and the estimated result contains the spatial correlation between ground sampling points and gives consideration to the relationship between soil moisture and the priori knowledge. According to the method, great significance is achieved for enriching remote sensing authenticity inspection subject theories and technologies, inversion errors of soil moisture products can be reduced, the practical value in relevant industry fields is improved, and meanwhile reference is provided for authen