Singh Raj, Saritha Vara, Pande Chaitanya B
Department of Environmental Science, GITAM Deemed to be University, Visakhapatnam, 530045, India.
Department of Environmental Science, GITAM Deemed to be University, Visakhapatnam, 530045, India.
Environ Res. 2024 Jan 15;241:117638. doi: 10.1016/j.envres.2023.117638. Epub 2023 Nov 14.
Satellite imagery has emerged as the predominant method for performing spatial and temporal water quality analyses on a global scale. This study employs remote sensing techniques to monitor the water quality of the Bisalpur wetland during both the pre and post-monsoon seasons in 2013 and 2022. The study aims to investigate the prospective use of Landsat-8 (L8) and Landsat-9 (L9) data acquired from the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the temporal monitoring of turbidity. Concurrently, the study examines the relationship of turbidity with water surface temperature (WST) and chlorophyll-a (Chl-a) concentrations. We utilized visible and near-infrared (NIR) bands to conduct a single-band spectral response analysis of wetland turbidity. The results reveal a notable increase in turbidity concentration in May 2022, as this timeframe recorded the highest reflectance (0.28) in the NIR band. Additionally, the normalized difference turbidity index (NDTI) formula was used to assess the overall turbidity levels in the wetland. The results indicated that the highest concentration was observed in May 2013, with a value of 0.37, while the second-highest concentration was recorded in May 2022, with a value of 0.25. The WST was calculated using thermal band-10 in conjunction with Chlorophyll-a, utilizing the normalized difference chlorophyll index (NDCI). The regression analysis shows a positive correlation between turbidity and WST, as indicated by R2 values of 0.41 in May 2013 and 0.40 in May 2022. Furthermore, a robust positive relationship exists between turbidity and Chl-a, with a high R2 value of 0.71 in May 2022. These findings emphasize the efficacy of the L8 and L9 datasets for conducting temporal analyses of wetland turbidity, WST, and Chl-a. Additionally, this research underscores the critical role of satellite imagery in assessing and managing water quality, particularly in situations where in-situ data is lacking.
卫星图像已成为在全球范围内进行空间和时间水质分析的主要方法。本研究采用遥感技术，在2013年和2022年的季风前和季风后季节监测比萨尔布尔湿地的水质。该研究旨在调查从陆地成像仪（OLI）和热红外传感器（TIRS）获取的Landsat-8（L8）和Landsat-9（L9）数据在浊度时间监测方面的潜在用途。同时，该研究考察了浊度与水面温度（WST）和叶绿素a（Chl-a）浓度之间的关系。我们利用可见光和近红外（NIR）波段对湿地浊度进行单波段光谱响应分析。结果显示，2022年5月浊度浓度显著增加，因为该时间段在近红外波段记录到最高反射率（0.28）。此外，使用归一化差异浊度指数（NDTI）公式评估湿地的整体浊度水平。结果表明，2013年5月观测到最高浓度，值为0.37，而第二高浓度记录在2022年5月，值为0.25。利用归一化差异叶绿素指数（NDCI），结合热波段10和叶绿素a计算水面温度。回归分析表明，浊度与水面温度之间存在正相关，2013年5月的R2值为0.41，2022年5月为0.40。此外，浊度与叶绿素a之间存在强烈的正相关关系，2022年5月的R2值高达0.71。这些发现强调了L8和L9数据集在进行湿地浊度、水面温度和叶绿素a时间分析方面的有效性。此外，本研究强调了卫星图像在评估和管理水质方面的关键作用，特别是在缺乏现场数据的情况下。
