Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers

Abstract

The remote sensing inversion of the water quality parameters of a complex river network in the absence of historical ground data is a difficult problem in the field of remote sensing. In this paper, a sub-regional inversion method for typical water quality parameters is presented for a complex river network using Gaofen-1 satellite data. Qidong’s rivers were selected as the survey region, and different band combination models and datasets on different river sub-regions were used to perform the remote sensing inversion, which realized the inversion of the permanganate index (CODMn), ammonia nitrogen (NH3-N), total phosphorus (TP), and total nitrogen (TN) in the rivers. The results show that all the coefficients of determination (R^2) of the inversion models are larger than 0.5, indicating an increase of about 0.4 when compared with the inversion method of the whole region, indicating good relevance. Water quality data and satellite data collected at different times were used for validation, which showed good results. On the basis of the water quality inversion, the key polluted areas were extracted in combination with on-site surveys to find the pollution source in order to verify the results of the inversion. The sub-region inversion method proposed in this paper can be used for the remote sensing inversion of the water quality parameters of complex river networks in the absence of historical ground data.

Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

“…In an example of studies focusing on small and medium rivers, Zhu et al [74] successfully inverted the permanganate index (CODMn), N-NH 3 (ammonia nitrogen), and TN in the intricate river network of Qidong (with rivers mostly ranging from 15 to 20 m in width) using Gaofen-1 satellite imagery. The inversion accuracy, represented by R 2 , slightly exceeded 0.5.…”

“…Firstly, although the CBR model was well applied in the field of water quality monitoring of medium-/small-sized rural water bodies in the current work, the applicability of the model over time and in different regions needs to be evaluated. The water quality inversion model is more likely to achieve much better accuracy because the individual water quality parameters may exhibit specific spectral characteristics for a fixed area within a fixed period with a relatively fixed source of effluent [74]. The development of a water quality inversion model for the study area in seasons and sub-basins and the applicability of the model in different years over a larger period should be conducted.…”

Sử dụng viễn thám ước tính nồng độ NO3- của hồ Trị An, tỉnh Đồng Nai

“…[16] (6) y = ax i b i [16] (7) Trong đó y là nồng độ của NO3 -; 𝑥 𝑖 là giá trị quang phổ của các kênh ảnh; a,b,c là các hệ số hồi quy.…”

Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning

“…Taking the estimation of total phosphorus (TP) as an example, Wu et al [17] constructed a linear regression equation for natural logarithmic TP concentration, and Gao et al [18] constructed a combination of linear regression models adapted to the different regions. This kind of method is easy to establish and has achieved high accuracy in several studies [19][20][21][22][23][24]. It may be because most of these studies focused on small areas and using only a small amount of data for model building, so the water type was relatively homogeneous.…”