| 摘要: |
| 河口区的悬浮物浓度受陆源输入和水动力等其他因素的影响严重，具有重要研究意义。本文利用黄河口及其邻近区域采集的145个站位光谱数据和悬浮物（Suspended Particulate Matter，SPM）浓度数据，检验了Nechad模型、多波段准分析算法（Quasi-Analytical Algorithm，QAA）、最优化模型和半经验辐射传输模型（Semi-EmpiricalRadiativeTransfer，SERT）在黄河口水域的适用性。结果表明，QAA561模型和Nechad561模型在低浓度水域反演结果较好，QAA655模型反演结果较差，Nechad665模型适合于低浓度水域，但反演精度低。QAA865模型和Nechad865模型在中高浓度水域的反演结果较好。SERT655模型反演精度较低。最优化模型和SERT滑动模型的反演精度较高。由于黄河口水域悬浮物浓度变化范围较大，因此，本文建立了分段QAA模型和Nechad模型。分段模型的反演结果均优于单一模型。根据误差敏感性分析可知，Nechad分段模型和QAA分段模型对于遥感反射率的50%以内的误差不敏感，稳定可靠。将分段模型应用于Landsat-8 OLI数据，获取了悬浮物浓度时空分布图。结果表明，反演结果与已有研究具有相似的分布特征。两种模型得到的悬浮物浓度在数值上存在差异，然而分布趋势在中高浓度季节有非常好的一致性。 |
| 关键词: 悬浮物浓度 半分析方法 遥感反演 黄河口 Landsat-8 OLI |
| DOI：10.11759/hykx20190414002 |
| 分类号:X87 |
| 基金项目:国家自然科学基金项目（61265008，61461034）；内蒙古自治区高等学校青年科技英才支持计划项目（NJYT-17-B04） |
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| Remote sensing retrieval of suspended-particulate-matterconcentrations in Yellow River estuary based on semi-analytical method |
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SHUN Bu-ri1, QING Song1, HAO Yan-ling2
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1.College of geographical sciences, Inner Mongolia Normal University, Hohhot 010022, China;2.The School of Ecology and Environment of Inner Mongolia University, Hohhot 010021, China
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| Abstract: |
| The concentration of suspended particulate matter (SPM) in estuarine areas is seriously affected by land source input, hydrodynamics and other factors, which is of significant concern. In this study, we used 145 groups of remotely sensed reflectance and SPM concentration data collected from the Yellow River estuary and its adjacent waters to evaluate the applicability of various models, including the Nechad model, quasi-analytical algorithm (QAA) model, optimization model, and semi-empirical radiative transfer (SERT) model, to the Yellow River estuary. The QAA561 and Nechad561 models exhibited higher retrieval accuracy in low-concentration waters. The QAA655 model retrieved a poor result. In low-concentration waters, the Nechad655 model also retrieved a bad result, whereas the results retrieved by both the QAA865 and Nechad865 models were suitable for middle-high-concentration waters. The SERT and optimization models also demonstrated high inversion accuracy. The use of an individual algorithm was not suitable for SPM inversion in the Yellow river estuary due to the wide range of SPM concentrations in this area (3.8~2 301 mg/L).Therefore, in this study, we established a segmented model, which according to the validation results obtained, performed better than an individual model. According to the error sensitivity analysis, the Nechad and QAA segmentation models are not sensitive to error within 50% of the remote sensing reflectivity and are robust and reliable, and the QAA segmentation model is less error sensitive. We applied the proposed segmented model to atmospheric-corrected Landsat-8 OLI data to obtain a spatio-temporal SPM map of the Yellow river estuary, and the inversion results showed similar distribution characteristics to those reported in previous studies. There are some differences in the magnitudes of the SPMs obtained by the two models, which implies that the Nechad and QAA models have high applicability and transferability. |
| Key words: suspended particulate matter semi-analytical method remote sensing retrieval Yellow River estuary Landsat-8 OLI |
