Secchi depth(SD,m)is a direct and intuitive measure of water's transparency,which is also an indicator of water quality.In 2015,a semi-analytical model was developed to derive SD from remote sensing reflectance,th...Secchi depth(SD,m)is a direct and intuitive measure of water's transparency,which is also an indicator of water quality.In 2015,a semi-analytical model was developed to derive SD from remote sensing reflectance,thus able to provide maps of water's transparency in satellite images.Here an in-situ dataset(338 stations)is used to evaluate its potential ability to monitor water quality in the coastal and estuarine waters,with measurements covering the Zhujiang(Pearl)River Estuary,the Yellow Sea and the East China Sea where measured SD values span a range of 0.2–21.0 m.As a preliminary validation result,according to the whole dataset,the unbiased percent difference(UPD)between estimated and measured SD is 23.3%(N=338,R^2=0.89),with about 60%of stations in the dataset having relative difference(RD)≤20%,over 80%of stations having RD≤40%.Furthermore,by excluding the field data which with relatively larger uncertainties,the semi-analytical model yielded the UPD of 17.7%(N=132,R^2=0.92)with SD range of 0.2–11.0 m.In addition,the semi-analytical model was applied to Landsat-8 images in the Zhujiang River Estuary,and retrieved high-quality mapping and reliable spatial-temporal patterns of water clarity.Taking into account the uncertainties associated with both field measurements and satellite data processing,and that there were no tuning of the semi-analytical model for these regions,these findings indicate highly robust retrieval of SD from spectral techniques for such turbid coastal and estuarine waters.The results suggest it is now possible to routinely monitor coastal water transparency or visibility at high-spatial resolutions from measurements,like Landsat-8 and Sentinel-2 and newly launched Gaofen-5.展开更多
This paper presents a novel approach for predicting the water quality indicator-Secchi disk depth(Z_(SD)).Z_(SD)indirectly reflects water clarity and serves as a proxy for other quality parameters.This study utilizes ...This paper presents a novel approach for predicting the water quality indicator-Secchi disk depth(Z_(SD)).Z_(SD)indirectly reflects water clarity and serves as a proxy for other quality parameters.This study utilizes Deep Neural Network(DNN)trained on satellite remote sensing and measured data from three sources:two datasets obtained from official agencies in Croatia and Slovenia,and one citizen science data source,all covering the northern coastal region of the Adriatic Sea.The proposed model uses 1D Convolutional Neural Network(CNN)in the spectral dimension to predict Z_(SD).The model’s performance indicates a strong fit to the observed data,proving capability of 1D-CNN to capture changes in water transparency.On the test dataset,the model achieved a high R-squared value of 0.890,a low root mean squared error(RMSE)of 0.023 and mean absolute error(MAE)of 0.014.These results demonstrate that employing a 1D-CNN in the spectral dimension of Sentinel-3 OLCI data is an effective approach for predicting water quality.These findings have significant implications for monitoring Z_(SD)in coastal areas.By integrating diverse data sources and leveraging advanced machine learning algorithms,a more accurate and comprehensive assessment of water quality can be achieved.展开更多
湖水透明度能直观反映湖水清澈和混浊程度,是水体能见程度的一个量度,同时也是评价湖泊富营养化,衡量水质优劣的一个重要指标。传统地表水透明度观测主要采用塞克盘(Secchi Disk)法,这种方法不仅费时费力,而且只具有局部的代表意义。遥...湖水透明度能直观反映湖水清澈和混浊程度,是水体能见程度的一个量度,同时也是评价湖泊富营养化,衡量水质优劣的一个重要指标。传统地表水透明度观测主要采用塞克盘(Secchi Disk)法,这种方法不仅费时费力,而且只具有局部的代表意义。遥感技术具有快速、大面积和周期性的特点,可以有效地解决这种局限性。该文通过查干湖高光谱数据,建立透明度(Secchi Disk Depth)单波段和比值高光谱估测遥感模型,并进行验证。结果表明:利用高光谱遥感监测模型对查干湖透明度进行估算和监测,能够获取较为准确的评价结果,相对于传统监测方法具有省时省力的特点。通过对单波段估测模型和比值估测模型进行比较发现,单波段模型估测结果好于比值模型,而对数比值模型又强于单纯的比值模型。查干湖透明度高光谱定量估测模型的建立,有利于今后利用遥感影像,对查干湖水体透明度进行全面估测,对于研究和监测查干湖水体水质状况有重要意义。展开更多
通过实测查干湖高光谱数据,建立透明度(Secchi Disk Depth,SDD)单波段估测模型、比值估测模型以及神经网络高光谱估测模型,并以确定性系数R2以及剩余残差RMSE为指标进行了验证。通过对单波段估测模型和比值估测模型进行比较发现,单波段...通过实测查干湖高光谱数据,建立透明度(Secchi Disk Depth,SDD)单波段估测模型、比值估测模型以及神经网络高光谱估测模型,并以确定性系数R2以及剩余残差RMSE为指标进行了验证。通过对单波段估测模型和比值估测模型进行比较发现,单波段模型估测结果与比值模型相差无几,而水体透明度经对数处理有利于模型精度提高,但是神经网络模型是三者中最优的。查干湖透明度高光谱定量估测模型的建立,有利于今后利用遥感影像,对查干湖水体透明度进行全面估测,对于研究和监测查干湖水体水质状况有重要意义。展开更多
基金The National Natural Science Foundation of China under contract No.61527810the Marine Science and Technology Fund from Director of South China Sea Branch+1 种基金State Oceanic Administration of China under contract No.180101the Key Laboratory Open Project Fund of Technology and Application for Safeguarding of Marine Rights and Interests,State Oceanic Administration of China under contract No.1720。
文摘Secchi depth(SD,m)is a direct and intuitive measure of water's transparency,which is also an indicator of water quality.In 2015,a semi-analytical model was developed to derive SD from remote sensing reflectance,thus able to provide maps of water's transparency in satellite images.Here an in-situ dataset(338 stations)is used to evaluate its potential ability to monitor water quality in the coastal and estuarine waters,with measurements covering the Zhujiang(Pearl)River Estuary,the Yellow Sea and the East China Sea where measured SD values span a range of 0.2–21.0 m.As a preliminary validation result,according to the whole dataset,the unbiased percent difference(UPD)between estimated and measured SD is 23.3%(N=338,R^2=0.89),with about 60%of stations in the dataset having relative difference(RD)≤20%,over 80%of stations having RD≤40%.Furthermore,by excluding the field data which with relatively larger uncertainties,the semi-analytical model yielded the UPD of 17.7%(N=132,R^2=0.92)with SD range of 0.2–11.0 m.In addition,the semi-analytical model was applied to Landsat-8 images in the Zhujiang River Estuary,and retrieved high-quality mapping and reliable spatial-temporal patterns of water clarity.Taking into account the uncertainties associated with both field measurements and satellite data processing,and that there were no tuning of the semi-analytical model for these regions,these findings indicate highly robust retrieval of SD from spectral techniques for such turbid coastal and estuarine waters.The results suggest it is now possible to routinely monitor coastal water transparency or visibility at high-spatial resolutions from measurements,like Landsat-8 and Sentinel-2 and newly launched Gaofen-5.
基金supported through project CAAT(Coastal Auto-purification Assessment Technology)funded by the European Union from European Structural and Investment Funds 2014-2020,Contract Number:KK.01.1.1.04.0064the Slovenian Research Agency(research core funding P2-0406 and P2-0180,and projects J2-3055 and J1-3033).
文摘This paper presents a novel approach for predicting the water quality indicator-Secchi disk depth(Z_(SD)).Z_(SD)indirectly reflects water clarity and serves as a proxy for other quality parameters.This study utilizes Deep Neural Network(DNN)trained on satellite remote sensing and measured data from three sources:two datasets obtained from official agencies in Croatia and Slovenia,and one citizen science data source,all covering the northern coastal region of the Adriatic Sea.The proposed model uses 1D Convolutional Neural Network(CNN)in the spectral dimension to predict Z_(SD).The model’s performance indicates a strong fit to the observed data,proving capability of 1D-CNN to capture changes in water transparency.On the test dataset,the model achieved a high R-squared value of 0.890,a low root mean squared error(RMSE)of 0.023 and mean absolute error(MAE)of 0.014.These results demonstrate that employing a 1D-CNN in the spectral dimension of Sentinel-3 OLCI data is an effective approach for predicting water quality.These findings have significant implications for monitoring Z_(SD)in coastal areas.By integrating diverse data sources and leveraging advanced machine learning algorithms,a more accurate and comprehensive assessment of water quality can be achieved.
文摘湖水透明度能直观反映湖水清澈和混浊程度,是水体能见程度的一个量度,同时也是评价湖泊富营养化,衡量水质优劣的一个重要指标。传统地表水透明度观测主要采用塞克盘(Secchi Disk)法,这种方法不仅费时费力,而且只具有局部的代表意义。遥感技术具有快速、大面积和周期性的特点,可以有效地解决这种局限性。该文通过查干湖高光谱数据,建立透明度(Secchi Disk Depth)单波段和比值高光谱估测遥感模型,并进行验证。结果表明:利用高光谱遥感监测模型对查干湖透明度进行估算和监测,能够获取较为准确的评价结果,相对于传统监测方法具有省时省力的特点。通过对单波段估测模型和比值估测模型进行比较发现,单波段模型估测结果好于比值模型,而对数比值模型又强于单纯的比值模型。查干湖透明度高光谱定量估测模型的建立,有利于今后利用遥感影像,对查干湖水体透明度进行全面估测,对于研究和监测查干湖水体水质状况有重要意义。
文摘通过实测查干湖高光谱数据,建立透明度(Secchi Disk Depth,SDD)单波段估测模型、比值估测模型以及神经网络高光谱估测模型,并以确定性系数R2以及剩余残差RMSE为指标进行了验证。通过对单波段估测模型和比值估测模型进行比较发现,单波段模型估测结果与比值模型相差无几,而水体透明度经对数处理有利于模型精度提高,但是神经网络模型是三者中最优的。查干湖透明度高光谱定量估测模型的建立,有利于今后利用遥感影像,对查干湖水体透明度进行全面估测,对于研究和监测查干湖水体水质状况有重要意义。
