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首页> 外文期刊>Journal of Remote Sensing & GIS >Study on Spatiotemporal Variability of Water Quality Parameters in Florida Bay Using Remote Sensing
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Study on Spatiotemporal Variability of Water Quality Parameters in Florida Bay Using Remote Sensing

机译：佛罗里达湾水质参数时空变化的遥感研究

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In this study, the bio-physical parameters associated with water quality of Florida Bay were investigated based on atmospherically corrected data. The principal objective of this study was to monitor and assess the spatial and temporal changes of four water quality parameters: turbidity, chlorophyll-a (chl-a), total phosphate, and total nitrogen (TN), using the application of integrated remote sensing, GIS data, and statistical techniques. For this purpose, two dates of Landsat Thematic Mapper (TM) data in 2000 (February 13), 2007 (January 31), and one date of Landsat Operational Land Imager (OLI) in 2015 (January 5) in the dry season, and two dates of TM data in 2000 (August 7), 2007 (September 28), and one date of OLI data in 2015 (September 2) in the wet season of the subtropical climate of South Florida, were used to assess temporal and spatial patterns and dimensions of studied parameters in Florida Bay, USA. The simultaneous observed data of four studied parameters were obtained from 20 monitoring stations and were used for the development and validation of the models. The optical bands in the region from blue to near infrared and all the possible band ratios were used to explore the relation between the reflectance of waterbody and observed data. The predictive models to estimate chl-a and turbidity concentrations were developed through the use of stepwise multiple linear regression (MLR) and gave high coefficients of determination in dry season (R2=0.86 for chl-a and R2=0.84 for turbidity) and moderate coefficients of determination in wet season (R2=0.66 for chl-a and R2=0.63 for turbidity). Values for total phosphate and TN were correlated with chl-a and turbidity concentration and some bands and their ratios. Total phosphate and TN were estimated using best-fit multiple linear regression models as a function of Landsat TM and OLI, and ground data and showed a high coefficient of determination in dry season (R2=0.74 for total phosphate and R2=0.82 for TN) and in wet season (R2=0.69 for total phosphate and R2=0.82 for TN). The MLR models showed a good trustiness to monitor and predict the spatiotemporal variations of the studied water quality parameters in Florida Bay.
机译：在这项研究中，基于大气校正数据，研究了与佛罗里达湾水质相关的生物物理参数。这项研究的主要目的是利用集成遥感技术监测和评估四个水质参数的时空变化：浊度，叶绿素-a（chl-a），总磷酸盐和总氮（TN）。 ，GIS数据和统计技术。为此，在干旱季节，2000年（2月13日），2007年（1月31日）的两个Landsat Thematic Mapper（TM）数据和2015年（1月5日）的Landsat Operational Imager（OLI）的两个日期，以及利用南佛罗里达亚热带气候湿季的2000年（8月7日），2007年（9月28日）的两个TM日期和2015年（9月2日）的OLI数据的日期来评估时空格局美国佛罗里达湾研究参数的大小和尺寸。从20个监测站​​获得了四个研究参数的同时观测数据，并将其用于模型的开发和验证。使用从蓝色到近红外区域的光学波段以及所有可能的波段比率来探索水体反射率与观测数据之间的关系。通过使用逐步多元线性回归（MLR）建立了估算chl-a和浊度浓度的预测模型，并给出了旱季的高测定系数（对于chl-a的R2 = 0.86和对于浊度的R2 = 0.84）和中等潮湿季节的测定系数（对于chl-a，R2 = 0.66，对于浊度，R2 = 0.63）。总磷酸盐和总氮的值与chl-a和浊度浓度以及某些谱带及其比率相关。总磷和总氮使用最佳拟合多元线性回归模型作为Landsat TM和OLI的函数进行估算，并得到地面数据，并显示出在旱季的高测定系数（总磷的R2 = 0.74，总氮的R2 = 0.82）在雨季（总磷酸盐的R2 = 0.69，TN的R2 = 0.82）。 MLR模型显示出很好的信任度，可以监视和预测佛罗里达湾研究水质参数的时空变化。

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