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Assessment of sea surface temperature using Landsat-TM data

机译：使用Landsat-TM数据评估海面温度

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The Thane creek, forming the East Coast of Island city of Mumbai, has been used as a convenient dumping site for disposal of treated as well as untreated effluents from industries and municipal corporations. The study area (approximately 100 sq. kms) in the Thane creek, has an unusual co-existence of highly industrialised urban setting and a thick stand of mangrove forest at the land-water interface. Eight field trips between January 1997 to February 1998 were carried out in the study area for collection of surface samples synchronised with the Landsat satellite passes. Sea Surface Temperature (SST) values were also recorded simultaneously. Linear regression model and Linear regression model with sun angle correction, were developed by co-relating the field SST data with Digital Numbers (DNs) from thermal IR band (10.4μm to 12.5μm, Thematic Mapper band 6 data) which were extracted from the digital remotely sensed data for the corresponding dates and locations. In case of linear regression model, part of the data from each field trip was used for calibration of the model and the remaining data were utilised for validation of the model developed in the present study. A reasonable "goodness of fit" was obtained based on chi-square test while comparing field observed and model predicted SST values (α = 0.77 to 0.99). In case of linear regression with sun angle correction model, three field trips were utilised for calibration of model after sun angle correction. Data from remaining five field trips was utilised for validation of model after correcting for sun angle. "Goodness of fit" was assessed to be excellent based on high α values (0.99 to 1) obtained in chi-square test. Linear regression model with sun angle correction was found superior to the linear regression model developed in this study.
机译：Thane Creek，形成孟买岛市东海岸，已被用作处置处理的便利倾销，以及从业和市政公司的未经处理的污水。在溪边的研究区（大约100平方米），在土地水界面的高度工业化城市环境和美洲红树林的厚朴架上存在异常的共存。 1997年1月至1998年2月在1998年至2月之间进行了八次实地考察，该研究领域进行了与Landsat卫星通行量同步的表面样品。也同时记录海表面温度（SST）值。通过与来自热IR频段（10.4μm至12.5μm，主题映射器6数据）共同关联与数字数字（DNS）的字段SST数据共同关联SST数据而开发了线性回归模型和线性回归模型。用于相应日期和位置的数字远程感测数据。在线性回归模型的情况下，来自每个现场跳闸的部分数据用于模型的校准，并且剩余数据用于验证本研究中开发的模型。基于Chi-Square测试获得合理的“拟合的良好”，同时比较观察到的场景，模型预测的SST值（α= 0.77至0.99）。在与太阳角校正模型线性回归的情况下，在太阳角校正之后使用三个场比赛进行模型校准。剩余五个现场旅行的数据用于校正太阳角度后的模型。基于在Chi-Square试验中获得的高α值（0.99至1），评估“拟合的良好”。利用太阳角校正的线性回归模型优于本研究开发的线性回归模型。

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