| 摘要: |
| 【目的】评价乌梁素海多个季度浮游植物生物量反演模型的适用性。【方法】利用乌梁素海Landsat8-OLI遥感数据，结合实测水体的叶绿素a数据，采用回归分析方法，构建乌梁素海浮游植物生物量反演模型，对反演模型精度和普适性进行验证。【结果】研究结果表明；春季以b5(近红外)/b4 (红光)为自变量的二次多项式回归方程拟合度较差，决定系数为0.463，实测数据与预测数据的均方根误差为6.88 mg/m3；夏季以b5(近红外)/b4 (红光)为自变量的二次多项式回归方程拟合度最优，决定系数为0.86，实测数据与预测数据的均方根误差为3.67 mg/m3；秋季以（b5-b4）/ b3为自变量的二次多项式回归方程拟合度适中，决定系数为0.602，实测数据与预测数据的均方根误差为4.67 mg/m3。【结论】同步数据和恰当的反演方法是提高遥感反演模型精度的重要前提条件。 |
| 关键词: 浮游植物；生物量；遥感反演；叶绿素a；乌梁素海 |
| DOI： |
| 分类号:X524 |
| 基金项目:国家自然科学基金 |
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| Retrieval of phytoplankton biomass in Lake Wuliangsuhai based on OLI remote sensing data |
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Yue chengpeng, Li Xing
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Inner Mongolia Normal University
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| Abstract: |
| 【Objective】In order to evaluate the applicability of the phytoplankton biomass inversion model in spring, summer and autumn in Lake Wuliangsuhai .【Method】The paper used the landsat 8-OLI remote sensing data,combined with actual chlorophyll a data , constructed a inversion model of phytoplankton biomass in Lake Wuliangsuha,and verified the accuracy and universality of the inversion model.【Result】The results showed that the fitting degree of the quadratic polynomial regression equation in spring was poor, the decisive factor was 0.463 and the root-mean-square error of the data was 2.71 mg/m3;the fitting degree of the quadratic polynomial regression equation in summer was optimal, the decisive factor was 0.86 and the root-mean-square error of the data was 3.67 mg/m3;the fitting degree of the quadratic polynomial regression equation in autumn was relatively poor , the decisive factor was 0.602 and the root-mean-square error of the data was 4.67 mg/m3.【Conclusion】Synchronous data and proper inversion method are important prerequisites for improving the accuracy of remote sensing inversion models. |
| Key words: Phytoplankton;Biomass; Chlorophyll a; Remote sensing invertion; Lake Wuliangsuhai |
