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 αand the concentration of marine suspended particulate matter and between Hand 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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