Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Abstract

The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

Exploration of Flood Prediction in Watersheds Based on the Fusion Analysis of Remote Sensing Big Data with Multiple Physical Fields

“…After the reservoirs were built, some water conservancy projects were implemented later in their upstream basins, resulting in changing the runoff process, and if the flood prediction model did not reflect this change in a timely manner, the accuracy of the prediction model would not be up to the requirements [5]. However, flood control engineering treats the symptoms but not the root cause, and consumables are labor intensive, so the concept of flood treatment has changed from control to management, from relying on flood control engineering to combining flood control and management [6]. With the development of satellite remote sensing technology, especially the increase in the number of domestic remote sensing satellites launched and the significant reduction in the cost of use, the operational use of multiphysics field remote sensing technology means for regular flood monitoring has become possible [7].…”

Semantic Conceptual Framework for Environmental Monitoring and Surveillance—A Case Study on Forest Fire Video Monitoring and Surveillance

“…Images taken with airborne sensors often have a high spatial and spectral resolution, but limited coverage and discontinuous monitoring due to flight restrictions. [36,37]. Sensor data can be described by their features and trajectories.…”

Análise de parâmetros da qualidade da água no lago Descoberto no Distrito Federal, por meio de imagens do satélite Sentinel –2B

“…Magalhães e Xavier, (2018) corroboram com este estudo ao mensurar e analisar os valores de clorofila a e turbidez em corpos hídricos por meio de imagens Opticas do satélite Landsat -TM5 no município de Itapemirim no sul do estado do Espírito Santo. Cao et al, (2021) ao analisar oito parâmetros de qualidade da água no rio Haihe em Tianjin, China por meio de dados de sensoriamento remoto hiperespectral obtiveram boa correlação linear do coeficiente de Pearson para os parâmetros analisados em conjunto com as bandas hiperespectrais. Dessa maneira, comprovaram que uma maior resolução espectral e temporal das imagens de sensoriamento remoto para análise de qualidade da água geram mais precisão e constância no monitoramento de corpos hídricos.…”