Estimation of Total Phosphorus Concentration in Lakes in the Yangtze-Huaihe Region Based on Sentinel-3/OLCI Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. In Situ Data
2.2.2. Satellite Data and Preprocessing
2.2.3. Auxiliary Data
2.3. Model Development
2.3.1. Modeling Set Construction
2.3.2. Algorithm
2.4. Model Evaluation
2.4.1. K-Fold Cross-Validation
2.4.2. Algorithm Accuracy Evaluation
2.5. Spatial-Temporal Distribution Analysis
3. Results
3.1. Model Accuracy Evaluation
3.2. Spatial Distribution of TP Concentration in the Yangtze-Huaihe Region
3.3. Temporal Variation of TP Concentration in Lakes of Different Sizes
4. Discussion
4.1. Comparison with Other Algorithms
4.2. Drivers Analysis
4.3. Comparison of TP Concentration Estimation in Typical Lakes
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Ho, L.T.; Goethals, P.L.M. Opportunities and Challenges for the Sustainability of Lakes and Reservoirs in Relation to the Sustainable Development Goals (SDGs). Water 2019, 11, 1462, Erratum in Water 2021, 13, 3207. [Google Scholar] [CrossRef]
- Roy, S.; Ojha, S.R.; Reddy, N.N.; Samal, R.N.; Das, B.S. Suspended particulate matter and secchi disk depth in the Chilika Lagoon from in situ and remote sensing data: A modified semi-analytical approach. Int. J. Remote Sens. 2022, 43, 3628–3654. [Google Scholar] [CrossRef]
- Hu, Z.F.; Li, D.S.; Guan, D.T. Water quality retrieval and algae inhibition from eutrophic freshwaters with iron-rich substrate based ecological floating beds treatment. Sci. Total Environ. 2020, 712, 135584. [Google Scholar] [CrossRef] [PubMed]
- Dona, C.; Sanchez, J.M.; Caselles, V.; Dominguez, J.A.; Camacho, A. Empirical Relationships for Monitoring Water Quality of Lakes and Reservoirs Through Multispectral Images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 1632–1641. [Google Scholar] [CrossRef]
- Maciel, D.A.; Barbosa, C.C.F.; Novo, E.; Flores, R.; Begliomini, F.N. Water clarity in Brazilian water assessed using Sentinel-2 and machine learning methods. Isprs J. Photogramm. Remote Sens. 2021, 182, 134–152. [Google Scholar] [CrossRef]
- Cao, Z.G.; Ma, R.H.; Melack, J.M.; Duan, H.T.; Liu, M.; Kutser, T.; Xue, K.; Shen, M.; Qi, T.C.; Yuan, H.L. Landsat observations of chlorophyll-a variations in Lake Taihu from 1984 to 2019. Int. J. Appl. Earth Obs. Geoinf. 2022, 106, 102642. [Google Scholar] [CrossRef]
- Liu, X.Y.; Zhang, Z.; Jiang, T.; Li, X.H.; Li, Y.Y. Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China. J. Spectrosc. 2021, 2021, 5957376. [Google Scholar] [CrossRef]
- Ruescas, A.B.; Hieronymi, M.; Mateo-Garcia, G.; Koponen, S.; Kallio, K.; Camps-Valls, G. Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data. Remote Sens. 2018, 10, 786. [Google Scholar] [CrossRef]
- Somasundaram, D.; Zhang, F.F.; Ediriweera, S.; Wang, S.L.; Yin, Z.Y.; Li, J.S.; Zhang, B. Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine. Remote Sens. 2021, 13, 2193. [Google Scholar] [CrossRef]
- Balasubramanian, S.V.; Pahlevan, N.; Smith, B.; Binding, C.; Schalles, J.; Loisel, H.; Gurlin, D.; Greb, S.; Alikas, K.; Randla, M.; et al. Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters. Remote Sens. Environ. 2020, 246, 111768. [Google Scholar] [CrossRef]
- Sun, X.; Zhang, Y.L.; Zhang, Y.B.; Shi, K.; Zhou, Y.Q.; Li, N. Machine Learning Algorithms for Chromophoric Dissolved Organic Matter (CDOM) Estimation Based on Landsat 8 Images. Remote Sens. 2021, 13, 3560. [Google Scholar] [CrossRef]
- Niu, C.; Tan, K.; Jia, X.P.; Wang, X. Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery. Environ. Pollut. 2021, 286, 117534. [Google Scholar] [CrossRef] [PubMed]
- Sagan, V.; Peterson, K.T.; Maimaitijiang, M.; Sidike, P.; Sloan, J.; Greeling, B.A.; Maalouf, S.; Adams, C. Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing. Earth-Sci. Rev. 2020, 205, 103187. [Google Scholar] [CrossRef]
- Hecky, R.E.; Kilham, P. Nutrient limitation of phytoplankton in fresh-water and marine environments—A review of recent-evidence on the effects of enrichment. Limnol. Oceanogr. 1988, 33, 796–822. [Google Scholar] [CrossRef]
- Baban, S.M.J. Detecting water-quality parameters in the norfolk broads, uk, using landsat imagery. Int. J. Remote Sens. 1993, 14, 1247–1267. [Google Scholar] [CrossRef]
- Wang, L.Y.; Li, C.Y.; Sun, B. Determination total phosphorus concentration and evaluation eutrophication of Hulun Lake with MODIS data. Chin. J. Environ. Eng. 2014, 8, 5527–5534. [Google Scholar]
- Chen, J.; Quan, W.T. Using Landsat/TM Imagery to Estimate Nitrogen and Phosphorus Concentration in Taihu Lake, China. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 273–280. [Google Scholar] [CrossRef]
- Zhang, L.S.; Zhang, L.F.; Cen, Y.; Wang, S.; Zhang, Y.; Huang, Y.; Sultan, M.; Tong, Q.X. Prediction of Total Phosphorus Concentration in Macrophytic Lakes Using Chlorophyll-Sensitive Bands: A Case Study of Lake Baiyangdian. Remote Sens. 2022, 14, 3077. [Google Scholar] [CrossRef]
- Liu, Y.; Jiang, H. Retrieval of Total Phosphorus Concentration in the Surface Waters of Poyang Lake Based on Remote Sensing and Analysis of Its Spatial-Temporal Characteristics. J. Nat. Resour. 2013, 28, 2169–2177. [Google Scholar] [CrossRef]
- Nieto, P.J.G.; Garcia-Gonzalo, E.; Fernandez, J.R.A.; Muinz, C.D. Water eutrophication assessment relied on various machine learning techniques: A case study in the Englishmen Lake (Northern Spain). Ecol. Model. 2019, 404, 91–102. [Google Scholar] [CrossRef]
- Lee, H.W.; Kim, M.; Son, H.W.; Min, B.; Choi, J.H. Machine-learning-based water quality management of river with serial impoundments in the Republic of Korea. J. Hydrol. -Reg. Stud. 2022, 41, 101069. [Google Scholar] [CrossRef]
- ** of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters. Remote Sens. Environ. 2021, 256, 112284. [Google Scholar] [CrossRef]
- Hu, C.; Lee, Z.; Ma, R.; Yu, K.; Li, D.; Shang, S. Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China. J. Geophys. Res.-Ocean. 2010, 115, 5511. [Google Scholar] [CrossRef]
- Qi, L.; Hu, C.M.; Visser, P.M.; Ma, R.H. Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations. Limnol. Oceanogr. 2018, 63, 1711–1726. [Google Scholar] [CrossRef]
- Liang, Q.C.; Zhang, Y.C.; Ma, R.H.; Loiselle, S.; Li, J.; Hu, M.Q. A MODIS-Based Novel Method to Distinguish Surface Cyanobacterial Scums and Aquatic Macrophytes in Lake Taihu. Remote Sens. 2017, 9, 133. [Google Scholar] [CrossRef]
- Chen, T.; Guestrin, C.; Assoc Comp, M. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA, 13–17 August 2016; pp. 785–794. [Google Scholar]
- Brooks, W.; Corsi, S.; Fienen, M.; Carvin, R. Predicting recreational water quality advisories: A comparison of statistical methods. Environ. Model. Softw. 2016, 76, 81–94. [Google Scholar] [CrossRef]
- Xu, H.; Paerl, H.W.; Zhu, G.W.; Qin, B.Q.; Hall, N.S.; Zhu, M.Y. Long-term nutrient trends and harmful cyanobacterial bloom potential in hypertrophic Lake Taihu, China. Hydrobiologia 2017, 787, 229–242. [Google Scholar] [CrossRef]
- Hossen, H.; Mahmod, W.E.; Negm, A.; Nakamura, T. Assessing Water Quality Parameters in Burullus Lake Using Sentinel-2 Satellite Images. Water Resour. 2022, 49, 321–331. [Google Scholar] [CrossRef]
- Xu, L.J.; Huang, C.C.; Li, Y.M.; Chen, X. Deriving Concentration of TN,TP based on Hyper Spectral Reflectivity. Remote Sens. Technol. Appl. 2013, 28, 681–688. [Google Scholar]
- Holloway, J.; Mengersen, K. Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review. Remote Sens. 2018, 10, 1365. [Google Scholar] [CrossRef]
- Du, C.G.; Wang, Q.; Li, Y.M.; Lyu, H.; Zhu, L.; Zheng, Z.B.; Wen, S.; Liu, G.; Guo, Y.L. Estimation of total phosphorus concentration using a water classification method in inland water. Int. J. Appl. Earth Obs. Geoinf. 2018, 71, 29–42. [Google Scholar] [CrossRef]
- Vu, H.L.; Ng, K.T.W.; Richer, A.; An, C. Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation. J. Environ. Manag. 2022, 311, 114869. [Google Scholar] [CrossRef]
- Hadjisolomou, E.; Stefanidis, K.; Herodotou, H.; Michaelides, M.; Papatheodorou, G.; Papastergiadou, E. Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks. Water 2021, 13, 1590. [Google Scholar] [CrossRef]
- Li, N.; Zhang, Y.L.; Shi, K.; Zhang, Y.B.; Sun, X.; Wang, W.J.; Qian, H.M.; Yang, H.Y.; Niu, Y.K. Real-Time and Continuous Tracking of Total Phosphorus Using a Ground-Based Hyperspectral Proximal Sensing System. Remote Sens. 2023, 15, 507. [Google Scholar] [CrossRef]
- Ding, L.; Qi, C.C.; Li, G.; Zhang, W.Q. TP Concentration Inversion and Pollution Sources in Nanyi Lake Based on Landsat 8 Data and InVEST Model. Sustainability 2023, 15, 9678. [Google Scholar] [CrossRef]
- Lei, S.H.; Xu, J.; Li, Y.M.; Li, L.; Lyu, H.; Liu, G.; Chen, Y.; Lu, C.Y.; Tian, C.; Jiao, W.Z. A semi-analytical algorithm for deriving the particle size distribution slope of turbid inland water based on OLCI data: A case study in Lake Hongze. Environ. Pollut. 2021, 270, 116288. [Google Scholar] [CrossRef]
- Janatian, N.; Olli, K.; Cremona, F.; Laas, A.; Noges, P. Atmospheric stilling offsets the benefits from reduced nutrient loading in a large shallow lake. Limnol. Oceanogr. 2020, 65, 717–731. [Google Scholar] [CrossRef]
- ** Long-Term Spatiotemporal Dynamics of Pen Aquaculture in a Shallow Lake: Less Aquaculture Coming along Better Water Quality. Remote Sens. 2020, 12, 1866. [Google Scholar] [CrossRef]
- Liao, R.; Wu, X.; Wang, Z.; Wang, B.; Ke, F.; Han, C.; Zhou, Q.; Ren, J. Spatial distribution and pollution assessment on the main nutrients and heavy metals in sediments of Lake Gehu, Taihu Basin after removing the aquaculture net. J. Lake Sci. 2021, 33, 1436–1447. [Google Scholar] [CrossRef]
- Zeng, S.; Du, C.G.; Li, Y.M.; Lyu, H.; Dong, X.Z.; Lei, S.H.; Li, J.D.; Wang, H.J. Monitoring the particulate phosphorus concentration of inland waters on the Yangtze Plain and understanding its relationship with driving factors based on OLCI data. Sci. Total Environ. 2022, 809, 151992. [Google Scholar] [CrossRef]
- **ong, J.F. Remote Sensing Estimation of Phosphorus Concentration in Lake Taihu and Its Response to Non-Point Source Phosphorus Load. Nan**g Institute of Geography and Limnology; Chinese Academy of Sciences: Bei**g, China, 2021. [Google Scholar]
- Shang, W.; **, S.G.; He, Y.; Zhang, Y.Y.; Li, J. Spatial-Temporal Variations of Total Nitrogen and Phosphorus in Poyang, Dongting and Taihu Lakes from Landsat-8 Data. Water 2021, 13, 1704. [Google Scholar] [CrossRef]
- Qian, W.H.; Gao, Y.X.; Zhang, Y.M.; Yang, F.; Zhang, Z.W.; Zhao, Y.; Liu, Y.; Xu, D.J. Analysis of characteristics and reasons of temporal and spatial changes of Gehu Lake water quality based on multivariate statistical analysis. Water Resour. Hydropower Eng. 2021, 52, 116–128. [Google Scholar] [CrossRef]
- **ong, G.H.; Wang, G.C.; Wang, D.Q.; Yang, W.L.; Chen, Y.Y.; Chen, Z.L. Spatio-Temporal Distribution of Total Nitrogen and Phosphorus in Dianshan Lake, China: The External Loading and Self-Purification Capability. Sustainability 2017, 9, 500. [Google Scholar] [CrossRef]
- Zhang, M.S.; Li, W.; Zhang, W.G.; Li, Y.T.; Li, J.Y.; Gao, Y. Agricultural land-use change exacerbates the dissemination of antibiotic resistance genes via surface runoffs in Lake Tai Basin, China. Ecotoxicol. Environ. Saf. 2021, 220, 112328. [Google Scholar] [CrossRef] [PubMed]
- Gong, W.J. Evaluation of water quality and eutrophication status of Tuo Lake in Wuhe County, Anhui Province. Anhui Archit. 2021, 28, 101–103. [Google Scholar] [CrossRef]
- Li, X.F.; Xu, W.Z.; Song, S.; Sun, J. Sources and spatiotemporal distribution characteristics of nitrogen and phosphorus loads in the Haihe River Basin, China. Mar. Pollut. Bull. 2023, 189, 114756. [Google Scholar] [CrossRef] [PubMed]
Lake | Sampling Points | TP (mg/L) Mean ± STD | Sampling Time |
---|---|---|---|
Taihu Lake | 41 | 0.23 ± 0.16 | 29 April 2017/30 April 2017/14 November 2019/7 November 2020 |
Chaohu Lake | 24 | 0.18 ± 0.10 | 23 August 2017/16 July 2018 |
Gaoyou Lake | 12 | 0.11 ± 0.04 | 12 November 2021 |
Hongze Lake | 18 | 0.10 ± 0.06 | 14 October 2022 |
Gehu Lake | 8 | 0.10 ± 0.01 | 18 July 2018 |
Poyang Lake | 10 | 0.11 ± 0.05 | 14 April 2017/12 July 2018 |
Approaches | Input Variables |
---|---|
1 | AFAI, CMI, TWI, b1~b17 |
2 | AFAI, CMI, TWI, 1/b1~1/b17 |
3 | AFAI, CMI, TWI, eb1~eb17 |
4 | AFAI, CMI, TWI, (b1)2~(b17)2 |
5 | AFAI, CMI, TWI, ~ |
Year | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Total |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2017 | 16 | 15 | 14 | 17 | 17 | 8 | 10 | 3 | 4 | 12 | 21 | 15 | 152 |
2018 | 10 | 12 | 17 | 18 | 7 | 11 | 20 | 14 | 17 | 13 | 12 | 14 | 165 |
2019 | 12 | 1 | 11 | 7 | 4 | 2 | 12 | 23 | 27 | 18 | 28 | 28 | 173 |
2020 | 7 | 15 | 6 | 16 | 13 | 7 | 1 | 3 | 9 | 10 | 16 | 13 | 116 |
2021 | 16 | 16 | 12 | 11 | 11 | 7 | 5 | 4 | 16 | 13 | 11 | 14 | 136 |
2022 | 6 | 8 | 11 | 15 | 10 | 6 | 5 | 18 | 19 | 20 | 4 | 18 | 140 |
Algorithm Type | Reference | Lake | Factors Used | OLCI Bands | Fitted Model Equation |
---|---|---|---|---|---|
Empirical Model | Hossen, et al. [40] | Burullus Lake | 842/560 nm | b15/b6 | y = 36.201x2 + 92.078x + 230.74 |
Xu, et al. [41] | Taihu Lake | 671/680 nm | b9/b10 | y = 12.8265x2 − 26.86x + 14.0727 |
Lake Area | Lake | References | Sampled Time | Number of Samples |
---|---|---|---|---|
>500 km2 | Taihu Lake | (**ong, et al., 2021 [59]) | 8 March 2017/29 April 2017/ 30 April 2017/30 April 2017 | 55 |
(Shang, et al., 2021 [60]) | April 2018/July 2018 | 28 | ||
100–500 km2 | Gehu Lake | (Qian, et al., 2021 [61]) | 2019 monthly | 20 |
50–100 km2 | Dianshan Lake | (**ong, et al., 2017 [62]) | 2005–2015 monthly | 13 |
20–50 km2 | Tuohu Lake | (Gong, et al., 2021 [64]) | April 2018/August 2018/November 2018 /February 2019 | 8 |
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Wang, X.; Jiang, Y.; Jiang, M.; Cao, Z.; Li, X.; Ma, R.; Xu, L.; **ong, J. Estimation of Total Phosphorus Concentration in Lakes in the Yangtze-Huaihe Region Based on Sentinel-3/OLCI Images. Remote Sens. 2023, 15, 4487. https://doi.org/10.3390/rs15184487
Wang X, Jiang Y, Jiang M, Cao Z, Li X, Ma R, Xu L, **ong J. Estimation of Total Phosphorus Concentration in Lakes in the Yangtze-Huaihe Region Based on Sentinel-3/OLCI Images. Remote Sensing. 2023; 15(18):4487. https://doi.org/10.3390/rs15184487
Chicago/Turabian StyleWang, **aoyang, Youyi Jiang, Mingliang Jiang, Zhigang Cao, **ao Li, Ronghua Ma, Ligang Xu, and Junfeng **ong. 2023. "Estimation of Total Phosphorus Concentration in Lakes in the Yangtze-Huaihe Region Based on Sentinel-3/OLCI Images" Remote Sensing 15, no. 18: 4487. https://doi.org/10.3390/rs15184487