To monitor sediment variations in the Chilika Lake, the Landsat-8 OLI data was used to calibrate suspended sediment concentration (SSC) model. The relationship between remote sensing reflectance of OLI bands and in-situ measured SSC were used to develop new site-specific algorithms. Four different models were calibrated in this study for retrieval of SSC using in-situ observation and remote sensing reflectance of OLI data. The multiband linear regression model provided better result (R² = 0.6) as compared to the single-band regression model (R² = 0.45, polynomial; R² = 0.38, exponential and R² = 0.39, linear). The Landsat-8 OLI image shows spatiotemporal variations of SSC during pre and post-monsoon season (2013–15) in the lake. It is observed that the SSC variation is predominantly influenced by three factors: monsoon effect, wind-induced re-suspension of bottom sediments and influx of river water into the lake. It is also observed that due to the impact of severe tropical cyclone Phailin, there was a rapid increase of SSC in the lake.

为了监测Chilika湖中的沉积物变化，Landsat-8 OLI数据用于校准悬浮沉积物浓度（SSC）模型。OLI波段的遥感反射率与现场测量的SSC之间的关系被用于开发新的针对特定地点的算法。在本研究中，使用OLI数据的原位观察和遥感反射率对四个不同的模型进行了校准，以检索SSC。 与单带回归模型（R ²  = 0.45，多项式；R ²  = 0.38，指数和R ²）相比，多波段线性回归模型提供了更好的结果（R ² = 0.6）。 = 0.39，线性）。Landsat-8 OLI图像显示了季风前后季风季节（2013-15年）湖中SSC的时空变化。可以看出，SSC的变化主要受三个因素的影响：季风效应，风引起的底部沉积物的再悬浮以及河水向湖中的涌入。还观察到，由于严重的热带气旋Phailin的影响，湖中的SSC迅速增加。