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Remote sensing of euphotic depth in shallow tropical inland waters of Lake Naivasha using MERIS data

机译:利用MERIS数据遥感奈瓦夏湖浅热带内陆水域的富营养深度

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Freshwater resources are deteriorating rapidly due to human activities and climate change. Remote sensing techniques have shown potential formonitoringwater quality in shallowinland lakes, especially in data-scarce areas. The purpose of this study was to determine the spectral diffuse attenuation coefficient (Kd(λ)) of the water column, in order to map the euphotic depth (Zeu) of Lake Naivasha, Kenya using the Medium Resolution Imaging Spectrometer (MERIS). Intensive in situ radiometric and limnological data collection was undertaken at Lake Naivasha. Atmospheric correction was done on the MERIS images using MERIS Neural Network algorithms, Case 2 Waters (C2R) and Eutrophic Lakes processors and the bright pixel atmospheric correction algorithm (BPAC). The Eutrophic Lakes processor gave the most accurate atmospherically corrected remote sensing reflectances at 490 nm compared to the other processors, with mean absolute percentage error (MAPE) of 47% and a root mean square error (RMSE) 43%, with BPAC giving negative reflectances in the blue spectral range. In situ Kd and Zeu models were calibrated and validated using above- and under-water radiometric measurements, and tested on the Neural Network atmospheric correction processed MERIS images. The Eutrophic Lakes estimates were the most accurate, with an RMSE of 0.26 m?1 and MAPE of 18% for Kd(490) and RMSE of 0.17 m and MAPE of 14% for Zeu. The Zeu maps produced from MERIS images clearly show the variation of euphotic depth both in space and time. These results indicate the suitability of MERIS to monitor euphotic depth and other water quality parameters of shallow inland water bodies.
机译:由于人类活动和气候变化,淡水资源迅速恶化。遥感技术已显示出监测浅水内陆湖泊水质的潜力,尤其是在数据稀少的地区。这项研究的目的是确定水柱的光谱扩散衰减系数(Kd(λ)),以便使用中分辨率成像光谱仪(MERIS)绘制肯尼亚奈瓦夏湖的共沸深度(Zeu)。在奈瓦沙湖进行了密集的原位辐射和林学数据收集。使用MERIS神经网络算法,案例2沃特世(C2R)和富营养湖处理器以及明亮像素大气校正算法(BPAC)对MERIS图像进行了大气校正。与其他处理器相比,富营养化的Lakes处理器在490 nm处提供了最准确的经大气校正的遥感反射率,平均绝对百分比误差(MAPE)为47%,均方根误差(RMSE)为43%,BPAC提供负反射率在蓝色光谱范围内。使用水上和水下辐射测量法对原位Kd和Zeu模型进行校准和验证,并在神经网络大气校正处理的MERIS图像上进行测试。富营养化湖泊的估算最准确,Kd(490)的RMSE为0.26 m?1,MAPE为18%,Zeu的RMSE为0.17 m,MAPE为14%。由MERIS图像产生的Zeu图清楚地显示了在时间和空间上共沸深度的变化。这些结果表明,MERIS适合监测浅水内陆水体的富营养深度和其他水质参数。

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