静止与极轨气象卫星监测沙尘的融合算法研究
Study on the Fusion of Dust Products of Geostationary and Polar-Orbiting Meteorological Satellites Based on Improved Evidence Theory Algorithm
中文摘要：
为了综合应用静止气象卫星与极轨气象卫星沙尘监测的结果，更好地进行沙尘信息的判识，采用改进的证据理论方法，进行静止卫星FY-2D/2E红外差值沙尘指数（infrared difference dust index，IDDI）产品与极轨气象卫星FY-3A
沙尘强度指数（dust strength index，DSI）沙尘监测产品的融合处理，划分沙尘发生过程中的有沙尘暴发生区、无沙尘暴发生区及可能沙尘暴发生区。融合结果与气象站点观测结果的对比分析表明，本算法可以将静止气象卫星与极轨气象卫星遥感沙尘监测结果进行较好地融合，更好地划分沙尘暴发生的区域，对于沙尘暴过程的监测、评估和分析有重要的参考价值和指导意义。
沙尘强度指数（dust strength index，DSI）沙尘监测产品的融合处理，划分沙尘发生过程中的有沙尘暴发生区、无沙尘暴发生区及可能沙尘暴发生区。融合结果与气象站点观测结果的对比分析表明，本算法可以将静止气象卫星与极轨气象卫星遥感沙尘监测结果进行较好地融合，更好地划分沙尘暴发生的区域，对于沙尘暴过程的监测、评估和分析有重要的参考价值和指导意义。
中文关键词：
静止气象卫星，极轨气象卫星，红外差值沙尘指数，沙尘强度指数，改进的证据理论，数据融合
KeyWords：
geostationary meteorological satellite, polar-orbit meteorological satellite, infrared difference dust index, dust strength index, improved evidence theory algorithm, data fusion
Abstract：
The objective of this study was to integrate the advantages of multi-source remote sensing to monitor dust storms and better discriminate between regions where dust storms occur. Firstly, The traditional evidence theory algorithm was improved by
not only considering the certainty of the evidence, but also considering the average level of support for the subsets of evidence in the discrimination framework in the process of evidence combination by reducing the conflict between synthesized data. Then the algorithm is applied to the FY-2D/2E infrared difference dust index (IDDI) and the FY-3A dust strength index (DSI) to categorize the study region as either a dust storm area, non-dust storm area, or possible dust storm area. Finally, the result was validated and analyzed using the monitored data from ground stations. Both the accuracy and reliability of the dust monitoring results were considerably improved using our method.
not only considering the certainty of the evidence, but also considering the average level of support for the subsets of evidence in the discrimination framework in the process of evidence combination by reducing the conflict between synthesized data. Then the algorithm is applied to the FY-2D/2E infrared difference dust index (IDDI) and the FY-3A dust strength index (DSI) to categorize the study region as either a dust storm area, non-dust storm area, or possible dust storm area. Finally, the result was validated and analyzed using the monitored data from ground stations. Both the accuracy and reliability of the dust monitoring results were considerably improved using our method.