Monitoring spatiotemporal variations in nutrients in a large drinking water reservoir and their relationships with hydrological and meteorological conditions based on Landsat 8 imagery

Li, Yuan; Zhang, Yunlin; Shi, Kun; Zhu, Guangwei; Zhou, Yongqiang; Zhang, Hongjie; Guo, Yulong

doi:10.1016/j.scitotenv.2017.05.075

Cited by 84 publications

(28 citation statements)

References 61 publications

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“…Taking R 2 as the comparison standard of retrieval accuracy, this research surveyed most existing publications regarding the retrieval of TP, TN and COD from 2000 to present and compared the performance of the proposed method in this research with previous research works, Figure 12 shows the results of comparison. Since the average R 2 of TP in the existing publications [21,22,[24][25][26][27]29] was 0.653, the performance of TP retrieval was improved by about 44% over the average of the past research works. For TN, the average R 2 in the existing publications [21, [23][24][25][26][27] was 0.613, and the performance of TN retrieval in this research was improved by 43% over the average of the past research works.…”

Section: Comparison Of Retrieval Accuracymentioning

confidence: 99%

“…Since the average R 2 of TP in the existing publications [21,22,[24][25][26][27]29] was 0.653, the performance of TP retrieval was improved by about 44% over the average of the past research works. For TN, the average R 2 in the existing publications [21, [23][24][25][26][27] was 0.613, and the performance of TN retrieval in this research was improved by 43% over the average of the past research works. For COD, the average R 2 in the past research works [17][18][19][20] was 0.722, and the performance of retrieval COD in this research was slightly higher than the upper quartile of the past research works.…”

Section: Comparison Of Retrieval Accuracymentioning

confidence: 99%

“…Over the past several decades, scholars have carried out extensive research on water quality monitoring by remote sensing, and have achieved good results in estimating optically active parameters, such as Chlorophyll-a (Chl-a), suspended solids (SS), colored dissolved organic matter (CDOM), turbidity and transparency [11][12][13][14][15][16]. There are however few studies on non-optically active parameters, such as chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN) [17,18,[27][28][29][19][20][21][22][23][24][25][26]. Estimating non-optically active parameters directly from spectral characteristics is difficult.…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

Huang¹,

Guo²,

Chen³

et al. 2020

Preprint

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Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R 2 of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.

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Section: Comparison Of Retrieval Accuracymentioning

confidence: 99%

Section: Comparison Of Retrieval Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

Huang¹,

Guo²,

Chen³

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…From each water sample, 1 L was extracted to determine the TSS concentration, and sample filtration was performed with pre-weighed filters of 45-µm porosity and 47-mm diameter. The TSS concentration of every water sample was obtained as a function of the filtered water volume in mg/L by comparing the weight before and after filtration [27].…”

Section: Collection Of Water Samples and Laboratory Analysismentioning

confidence: 99%

Exploring the Influence Mechanism of Meteorological Conditions on the Concentration of Suspended Solids and Chlorophyll-a in Large Estuaries Based on MODIS Imagery

Yao

et al. 2019

Water

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In estuary areas, meteorological conditions have become unstable under the continuous effects of climate change, and the ecological backgrounds of such areas have strongly been influenced by anthropic activities. Consequently, the water quality of these areas is obviously affected. In this research, we identified periods of fluctuation of the general meteorological conditions in the Yangtze River Estuary using a wavelet analysis. Additionally, we performed a spatiotemporal evaluation of the water quality in the fluctuating period by using remote sensing modeling. Then, we explored how the fluctuating meteorological factors affect the distribution of total suspended solids (TSS) and chlorophyll-a (Chla) concentration. (1) The results show that from 2000 to 2015, temperature did not present significant fluctuations, while wind speed (WS) and precipitation (PR) presented the same fluctuation period from January 2012 to December 2012. (2) Based on the measured water sample data associated with Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, we developed a water quality algorithm and depicted the TSS and Chla concentrations within the WS and PR fluctuating period. (3) We found that the TSS concentration decreased with distance from the shore, while the Chla concentration showed an initially decreasing trend followed by an increasing trend; moreover, these two water quality parameters presented different inter-annual variations. Then, we discussed the correlation between the changes in the TSS and Chla concentrations and the WS and PR variables. The contribution of this research is reflected in two aspects: 1. variations in water quality parameters over a wide range of water bodies can be evaluated based on MODIS data; 2. data from different time periods showed that the fluctuations of meteorological elements had different impacts on water bodies based on the distance from the shore. The results provide new insights for the management of estuary water environments.

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“…Although the influence of the TP concentration on the observed spectrum and remote sensing monitoring procedures is not yet clear, TP is closely related to many substances that affect the reflection spectra [9][10][11][12]. The TP could affect the growth and reproduction of plankton in the water [13], in addition, TP is associated with optical active constituents (OACs) in the water, including the SPM, Chla and chromogenic dissolved organic matter (CDOM) [12,14,15]. These three substances are the main substances that determine the optical characteristics of the water bodies [16][17][18].…”

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confidence: 99%

Remote Sensing Estimation of Lake Total Phosphorus Concentration Based on MODIS: A Case Study of Lake Hongze

Xiong

Lin

et al. 2019

Remote Sensing

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Phosphorus (P) is an important substance for the growth of phytoplankton and an efficient index to assess the water quality. However, estimation of the TP concentration in waters by remote sensing must be associated with optical substances such as the chlorophyll-a (Chla) and the suspended particulate matter (SPM). Based on the good correlation between the suspended inorganic matter (SPIM) and P in Lake Hongze, we used the direct and indirect derivation methods to develop algorithms for the total phosphorus (TP) estimation with the MODIS/Aqua data. Results demonstrate that the direct derivation algorithm based on 645 nm and 1240 nm of the MODIS/Aqua performs a satisfied accuracy (R2 = 0.75, RMSE = 0.029mg/L, MRE = 39% for the training dataset, R2 = 0.68, RMSE = 0.033mg/L, MRE = 47% for the validate dataset), which is better than that of the indirect derivation algorithm. The 645 nm and 1240 nm of MODIS are the main characteristic band of the SPM, so that algorithm can effectively reflect the P variations in Lake Hongze. Additionally, the ratio of the TP to the SPM is positively correlated with the accuracy of the algorithm as well. The proportion of the SPIM in the SPM has a complex effect on the accuracy of the algorithm. When the SPIM accounts for 78%, the algorithm achieves the highest accuracy. Furthermore, the performance of this direct derivation algorithm was examined in two inland lakes in China (Lake Nanyi and Lake Chaohu), it derived the expected P distribution in Lake Nanyi whereas the algorithm failed in Lake Chaohu. Different water properties influence significantly the accuracy of this direct derivation algorithm, while the TP, Chla, and suspended particular inorganic matter (SPOM) of Lake Chaohu are much higher than those of the other two lakes, thus it is difficult to estimate the TP concentration by a simple band combination in Lake Chaohu. Although the algorithm depends on the dataset used in the development, it usually presents a good estimation for those waters where the SPIM dominated, especially when the SPIM accounts for 60% to 80% of the SPM. This research proposed a direct derivation algorithm for the TP estimation for the turbid lake and will provide a theoretical and practical reference for extending the optical remote sensing application and the TP empirical algorithm of Lake Hongze’s help for the local government management water quality.

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Monitoring spatiotemporal variations in nutrients in a large drinking water reservoir and their relationships with hydrological and meteorological conditions based on Landsat 8 imagery

Cited by 84 publications

References 61 publications

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

Exploring the Influence Mechanism of Meteorological Conditions on the Concentration of Suspended Solids and Chlorophyll-a in Large Estuaries Based on MODIS Imagery

Remote Sensing Estimation of Lake Total Phosphorus Concentration Based on MODIS: A Case Study of Lake Hongze

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