海洋悬浮物浓度遥感监测对海洋环境监测与治理具有重要意义。以Radarsat-2全极化SAR(synthetic aperture radar)数据与珠江口东岸香港海域悬浮物实测数据为基础，利用微波散射机制与 Cloude-Pottier 理论探索平均散射角α和散射熵H等特征参数与海洋悬浮物浓度之间的关系，建立基于H-α细分类空间的海洋悬浮物浓度分布监测模型。研究结果表明，平均散射角α和散射熵H与海洋悬浮物浓度之间存在着正相关关系，其相关系数分别为0.79和0.88。由此构建的H-α空间细分类模型可将悬浮物浓度分为4类，分别对应20mg·L−1以上、10-20mg·L−1、5-10mg·L−1以及5mg·L−1以下的悬浮物浓度。经验证，该模型的分类精度达到95.6%，远高于单独利用α或 H的分类效果，可以较好应用于海洋悬浮物浓度探测领域。%Remote sensing of marine suspended particulate matter concentration is important for monitoring and managing marine environments. Based on the theory of Cloude-Pottier decomposition and backscattered radiation mechanism, the scattering angle (α) and entropy (H), decomposed from full polarimetric SAR (synthetic aperture radar) data of Radarsat-2, were used to build a model for retrieving suspended particulate matter concentration. The results show good positive relationships between α and the concentration of marine suspended particulate matter and between H and the concentration, with the coefficients of 0.79 and 0.88, respectively. Four categories of regions are constructed in the H-αspace, corresponding to the concentrations of suspended particulate matter of 20 mg·L−1 and above, 10−20 mg·L−1, 5−10 mg·L−1, and 5 mg·L−1 and below, respectively. The validation shows that the accuracy of the model was up to 95.6% for classifying different concentrations of marine suspended particulate matter, which would be well applied in the field of monitoring marine suspended particulate matter concentration.
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