Monitoring the particulate phosphorus concentration of inland waters on the Yangtze Plain and understanding its relationship with driving factors based on OLCI data

Landsat observations of total suspended solids concentrations in the Pearl River Estuary, China, over the past 36 years

Information on long-term trends in total suspended solids (TSS) is critical for assessing aquatic ecosystems. However, the long-term patterns of TSS concentration (CTSS) and its latent drivers have not been well investigated. In this study, we developed and validated three semi-analysis algorithms for deriving CTSS using Landsat images. Subsequently, the long-term trends in CTSS in the Pearl River Estuary (PRE) from 1987 to 2022 and the driving factors were clarified. The developed algorithms yielded excellent performance in estimating CTSS, with mean absolute percentage errors <25% and root mean square errors of <13 mg/L. Long-term Landsat observations showed an overall decreasing trend and significant spatiotemporal dynamics of the CTSS in the PRE from 1987 to 2022. The analysis of driving factors suggested that industrial sewage, cropland, forests and grasslands, and built-up land were the four potential driving forces that explained 87.81% of the long-term variation in CTSS. This study not only provides 36-year recorded datasets of CTSS in estuary water, but also offers new insights into the complex mechanisms that regulate CTSS spatiotemporal dynamics for water resource management.

Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River

Quantitative prediction by unmanned aerial vehicle (UAV) remote sensing on water quality parameters (WQPs) including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a (Chl-a), total suspended solids (TSS), and turbidity provides a flexible and effective approach to monitor the variation in water quality. In this study, a deep learning-based method integrating graph convolution network (GCN), gravity model variant, and dual feedback machine involving parametric probability analysis and spatial distribution pattern analysis, named Graph Convolution Network with Superposition of Multi-point Effect (SMPE-GCN) has been developed to calculate concentrations of WQPs through UAV hyperspectral reflectance data on large scale efficiently. With an end-to-end structure, our proposed method has been applied to assisting environmental protection department to trace potential pollution sources in real time. The proposed method is trained on a real-world dataset and its effectiveness is validated on an equal amount of testing dataset with respect to three evaluation metrics including root of mean squared error (RMSE), mean absolute percent error (MAPE), and coefficient of determination (R²). The experimental results demonstrate that our proposed model achieves better performance in comparison with state-of-the-art baseline models in terms of RMSE, MAPE, and R². The proposed method is applicable for quantifying seven various WQPs and has achieved good performance for each WQP. The resulting MAPE ranges from 7.16% to 10.96% and R² ranges from 0.80 to 0.94 for all WQPs. This approach brings a novel and systematic insight into real-time quantitative water quality monitoring of urban rivers, and provides a unified framework for in-situ data acquisition, feature engineering, data conversion, and data modeling for further research. It provides fundamental support to assist environmental managers to efficiently monitor water quality of urban rivers.

Long-term remote observations of particulate organic phosphorus concentration in eutrophic Lake Taihu based on a novel algorithm

Monitoring the long-term spatiotemporal variations in particulate organic phosphorus concentration (C_POP) is imperative for clarifying the phosphorus cycle and its biogeochemical behavior in waters. However, little attention has been devoted to this owing to a lack of suitable bio-optical algorithms that allow the application of remote sensing data. In this study, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, a novel absorption-based algorithm of C_POP was developed for eutrophic Lake Taihu, China. The algorithm yielded a promising performance with a mean absolute percentage error of 27.75% and root mean square error of 21.09 μg/L. The long-term MODIS-derived C_POP demonstrated an overall increasing pattern over the past 19 years (2003-2021) and a significant temporal heterogeneity in Lake Taihu, with higher value in summer (82.06 ± 3.81 μg/L) and autumn (78.74 ± 3.8 μg/L), and lower C_POP in spring (79.52 ± 3.81 μg/L) and winter (81.97 ± 3.8 μg/L). Spatially, relatively higher C_POP was observed in the Zhushan Bay (85.87 ± 7.5 μg/L), whereas the lower value was observed in the Xukou Bay (78.95 ± 3.48 μg/L). In addition, significant correlations (r > 0.6, P < 0.05) were observed between C_POP and air temperature, chlorophyll-a concentration and cyanobacterial blooms areas, demonstrating that C_POP was greatly influenced by air temperature and algal metabolism. This study provides the first record of the spatial-temporal characteristics of C_POP in Lake Taihu over the past 19 years, and the C_POP results and regulatory factors analyses could provide valuable insights for aquatic ecosystem conservation.

Long-term dynamics and drivers of particulate phosphorus concentration in eutrophic lake Chaohu, China

Particulate phosphorus (PP) plays an important biological role in the eutrophication process, and is thus an important water quality parameter for assessing climatic change and anthropogenic activity factors that affect aquatic ecosystems. Here, we used 20-year Moderate Resolution Imaging Spectroradiometer (MODIS) data to explore the patterns and trends of PP concentration (C_PP) in eutrophic Lake Chaohu based on a new empirical model. The validation results indicated that the developed model performed satisfactorily in estimating C_PP, with a mean absolute percentage error of 31.89% and root mean square error of 0.022 mg/L. Long-term MODIS observations (2000-2019) revealed that the C_PP of Lake Chaohu has experienced an overall increasing trend and distinct spatiotemporal heterogeneity. The driving factor analysis revealed that the chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were the five potential driving factors and collectively explained more than 81% of the long-term variation in C_PP. This study provides the long-term datasets of C_PP in inland waters and new insights for future water eutrophication control and restoration efforts.

Classification of Geomorphic Units and Their Relevance for Nutrient Retention or Export of a Large Lowland Padma River, Bangladesh: A NDVI Based Approach

Geomorphic classification of large rivers identifies morphological patterns, as a foundation for estimating biogeochemical and ecological processes. In order to support the modelling of in-channel nutrient retention or export, the classification of geomorphic units (GUs) was done in the Padma River, Bangladesh, a large and geomorphically-complex lowland river. GUs were classified using the normalized difference vegetation index (NDVI) four times over a year, so as to cover the seasonal variation of water flows. GUs were categorized as primary and secondary channels (C & S); longitudinal bar (L); transverse bar (T); side bar (SB); unvegetated bank (EK); dry channel (ED); island (VI); and water depression (WD). All types of GUs were observed over the four distinct annual seasons, except ED, which was absent during the high flow, monsoon season. Seasonal variation of the surface area of GUs and discharge showed an inverse relation between discharge and exposed surface areas of VI, L, T, and SB. Nutrients mainly enter the river system through water and sediments, and during monsoon, the maximum portion of emergent GUs were submerged. Based on the assumption that nutrient retention is enhanced in the seasonally inundated portions of GUs, nutrient retention-/export-relevant geomorphic units (NREGUs) were identified. Seasonal variation in the area of NREGUs was similar to that of GUs. The mean NDVI values of the main identified NREGUs were different. The variation of NDVI values among seasons in these NREGUs resulted from changes of vegetation cover and type. The variation also occurred due to alteration of the surface area of GUs in different seasons. The changes of vegetation cover indicated by NDVI values across seasons are likely important drivers for biogeochemical and ecological processes.