Unified, high resolution water quality retrievals from Earth Observation satellites

2021 ◽  
Author(s):  
James Harding
Keyword(s):  
Water Quality ◽  
Water Resource Management ◽  
State Of The Art ◽  
Earth Observation ◽  
Water Quality Parameter ◽  
Landsat 8 ◽  
Optical Parameters ◽  
Atmospheric Conditions ◽  
Mixture Density ◽  
Wide Range

<p>Earth Observation (EO) satellites are drawing considerable attention in areas of water resource management, given their potential to provide unprecedented information on the condition of aquatic ecosystems. Despite ocean colours long history; water quality parameter retrievals from shallow and inland waters remains a complex undertaking. Consistent, cross-mission retrievals of the primary optical parameters using state-of-the-art algorithms are limited by the added optical complexity of these waters. Less work has acknowledged their non- or weakly optical parameter counterparts. These can be more informative than their vivid counterparts, their potential covariance would be regionally specific. Here, we introduce a multi-input, multi-output Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in shallow and inland water bodies. The model is trained and validated using a sizeable historical database in excess of 1,000,000 samples across 38 optical and non-optical parameters, spanning 20 years across 500 surface waters in Scotland. The single network learns to predict concurrently Chlorophyll-a, Colour, Turbidity, pH, Calcium, Total Phosphorous, Total Organic Carbon, Temperature, Dissolved Oxygen and Suspended Solids from real Landsat 7, Landsat 8, and Sentinel 2 spectra. The MDN is found to fully preserve the covariances of the optical and non-optical parameters, while known one-to-many mappings within the non-optical parameters are retained. Initial performance evaluations suggest significant improvements in Chl-a retrievals from existing state-of-the-art algorithms. MDNs characteristically provide a means of quantifying the noise variance around a prediction for a given input, now pertaining to real data under a wide range of atmospheric conditions. We find this to be informative for example in detecting outlier pixels such as clouds, and may similarly be used to guide or inform future work in academic or industrial contexts. </p>

Spatio-temporal Variations in Water Quality Parameter Trends in River Waters

2018 ◽  
Vol 69 (10) ◽  
pp. 2940-2952 ◽  
Author(s):  
Martina Zelenakova ◽  
Pavol Purcz ◽  
Radu Daniel Pintilii ◽  
Peter Blistan ◽  
Petr Hlustik ◽  
...  
Keyword(s):  
Water Quality ◽  
Chemical Oxygen Demand ◽  
Quality Indicators ◽  
Water Resource Management ◽  
Oxygen Demand ◽  
Temporal Variations ◽  
Water Quality Parameter ◽  
Agricultural Activity ◽  
Water Quality Indicators ◽  
Nitrite Nitrogen

Evaluating trends in water quality indicators is a crucial issue in integrated water resource management in any country. In this study eight chemical and physical water quality indicators were analysed in seven river profiles in the River Laborec in eastern Slovakia. The analysed water quality parameters were biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), pH, temperature (t), ammonium nitrogen (NH4+-N), nitrite nitrogen (NO2--N), nitrate nitrogen (NO3--N), and total phosphorus (TP). Data from the monitored indicators were provided by the Ko�ice branch of the Slovakian Water Management Company, over a period of 15 years from 1999 to 2013. Mann�Kendall non-parametric statistical test was used for the trend analysis. Biochemical and chemical oxygen demand, ammonium and nitrite nitrogen content exhibit decreasing trends in the River Laborec. Decreasing agricultural activity in the area has had a significant impact on the trends in these parameters. However, NO2--N was the significant parameter of water quality because it mostly exceeds the limit value set in Slovak legislation, Regulation No. 269/2010 Coll. In addition, water temperature revealed an increasing trend which could be caused by global increase in air temperature. These results indicate that human activity significantly impacts the water quality.

scholarly journals WATER CLARITY MAPPING IN KERINCI AND TONDANO LAKE WATERS USING LANDSAT 8 DATA

2017 ◽  
Vol 12 (2) ◽  
pp. 117 ◽  
Author(s):  
Bambang Trisakti ◽  
Nana Suwargana ◽  
I Made Parsa
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Atmospheric Correction ◽  
Total Suspended Solid ◽  
Water Clarity ◽  
Suspended Solid ◽  
Lake Ecosystem ◽  
Landsat 8 ◽  
Wide Range ◽  
Lake Waters

Land conversion occurred in the lake catchment area caused the decreasing of water quality in many lakes of Indonesia. According to Lake Ecosystem Management Guidelines from Ministry of Environment, tropic state of lake water is one of parameters for assessing the lake ecosystem status. Tropic state can be indicated by the quantity of nitrogen, phosphorus, chlorophyll, and water clarity. The objective of this research is to develop the water quality algorithm and map the water clarity of lake water using Landsat 8 data. The data were standardized for sun geometry correction and atmospheric correction using Dark Object Subtraction method. In the first step, Total Suspended Solid (TSS) distributions in the lake were calculated using a semi empirical algorithm (Doxaran et al., 2002), which can be applied to a wide range of TSS values. Secchi Disk Transparency (SDT) distributions were calculated using our water clarity algorithm that was obtained from the relationship between TSS and SDT measured directly in the lake waters. The result shows that the water clarity algorithm developed in this research has the determination coefficient that reaches to 0,834. Implementation of the algorithm for Landsat 8 data in 2013 and 2014 showed that the water clarity in Kerinci Lake waters was around 2 m or less, but the water clarity in Tondano Lake waters was around 2 – 3 m. It means that Kerinci Lake waters had lower water clarity than Tondano Lake waters which is consistent with the field measurement results.

scholarly journals Colour Classification of 1486 Lakes across a Wide Range of Optical Water Types

2018 ◽  
Vol 10 (8) ◽  
pp. 1273 ◽  
Author(s):  
Moritz Lehmann ◽  
Uyen Nguyen ◽  
Mathew Allan ◽  
Hendrik van der Woerd
Keyword(s):  
Water Quality ◽  
Environmental Control ◽  
Anthropogenic Factors ◽  
Landsat 8 ◽  
Diverse Range ◽  
Water Colour ◽  
Water Types ◽  
Wide Range ◽  
Yellow Orange ◽  
Spatio Temporal

Remote sensing by satellite-borne sensors presents a significant opportunity to enhance the spatio-temporal coverage of environmental monitoring programmes for lakes, but the estimation of classic water quality attributes from inland water bodies has not reached operational status due to the difficulty of discerning the spectral signatures of optically active water constituents. Determination of water colour, as perceived by the human eye, does not require knowledge of inherent optical properties and therefore represents a generally applicable remotely-sensed water quality attribute. In this paper, we implemented a recent algorithm for the retrieval of colour parameters (hue angle, dominant wavelength) and derived a new correction for colour purity to account for the spectral bandpass of the Landsat 8 Operational Land Imager (OLI). We used this algorithm to calculate water colour on almost 45,000 observations over four years from 1486 lakes from a diverse range of optical water types in New Zealand. We show that the most prevalent lake colours are yellow-orange and blue, respectively, while green observations are comparatively rare. About 40% of the study lakes show transitions between colours at a range of time scales, including seasonal. A preliminary exploratory analysis suggests that both geo-physical and anthropogenic factors, such as catchment land use, provide environmental control of lake colour and are promising avenues for future analysis.

scholarly journals Assessing the potential of integrating Landsat sensors for estimating chlorophyll-a concentration in a reservoir

2017 ◽  
Vol 49 (5) ◽  
pp. 1608-1617 ◽  
Author(s):  
Matias Bonansea ◽  
Claudia Rodriguez ◽  
Lucio Pinotti
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Landsat Imagery ◽  
Quality Parameters ◽  
Landsat 8 ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Sensing Applications ◽  
Wide Range ◽  
Quality Assessments

Abstract Landsat satellites, 5 and 7, have significant potential for estimating several water quality parameters, but to our knowledge, there are few investigations which integrate these earlier sensors with the newest and improved mission of Landsat 8 satellite. Thus, the comparability of water quality assessing across different Landsat sensors needs to be evaluated. The main objective of this study was to assess the feasibility of integrating Landsat sensors to estimate chlorophyll-a concentration (Chl-a) in Río Tercero reservoir (Argentina). A general model to retrieve Chl-a was developed (R2 = 0.88). Using observed versus predicted Chl-a values the model was validated (R2 = 0.89) and applied to Landsat imagery obtaining spatial representations of Chl-a in the reservoir. Results showed that Landsat 8 can be combined with Landsat 5 and 7 to construct an empirical model to estimate water quality characteristics, such as Chl-a in a reservoir. As the number of available and upcoming sensors with open access will increase with time, we expect that this trend will certainly further promote remote sensing applications and serve as a valuable basis for a wide range of water quality assessments.

scholarly journals DEVELOPMENT OF WATER QUALITY PARAMETER RETRIEVAL ALGORITHMS FOR ESTIMATING TOTAL SUSPENDED SOLIDS AND CHLOROPHYLL-A CONCENTRATION USING LANDSAT-8 IMAGERY AT POTERAN ISLAND WATER

2015 ◽  
Vol II-2/W2 ◽  
pp. 55-62 ◽  
Author(s):  
N. Laili ◽  
F. Arafah ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
...  
Keyword(s):  
Water Quality ◽  
Accurate Method ◽  
Water Area ◽  
Quality Parameters ◽  
Water Quality Parameter ◽  
Retrieval Algorithm ◽  
Acquisition Time ◽  
Landsat 8 ◽  
Landsat Images ◽  
Chl A

The Landsat-8 satellite imagery is now highly developed compares to the former of Landsat projects. Both land and water area are possibly mapped using this satellite sensor. Considerable approaches have been made to obtain a more accurate method for extracting the information of water area from the images. It is difficult to generate an accurate water quality information from Landsat images by using some existing algorithm provided by researchers. Even though, those algorithms have been validated in some water area, but the dynamic changes and the specific characteristics of each area make it necessary to get them evaluated and validated over another water area. This paper aims to make a new algorithm by correlating the measured and estimated TSS and Chla concentration. We collected in-situ remote sensing reflectance, TSS and Chl-a concentration in 9 stations surrounding the Poteran islands as well as Landsat 8 data on the same acquisition time of April 22, 2015. The regression model for estimating TSS produced high accuracy with determination coefficient (<i>R</i><sup>2</sup>), NMAE and RMSE of 0.709; 9.67 % and 1.705 g/m<sup>3</sup> respectively. Whereas, Chla retrieval algorithm produced <i>R</i><sup>2</sup> of 0.579; NMAE of 10.40% and RMSE of 51.946 mg/m<sup>3</sup>. By implementing these algorithms to Landsat 8 image, the estimated water quality parameters over Poteran island water ranged from 9.480 to 15.801 g/m<sup>3</sup> and 238.546 to 346.627 mg/m<sup>3</sup> for TSS and Chl-a respectively.

scholarly journals Using Landsat 8 Imagery for Remote Monitoring of Total Phosphorus as a Water Quality Parameter of Irrigation Ponds in Taiwan

2021 ◽  
Author(s):  
Po-Wen Su ◽  
Shang-Lien Lo
Keyword(s):  
Water Quality ◽  
Multiple Regression ◽  
Total Phosphorus ◽  
Satellite Imagery ◽  
Power Plants ◽  
Quality Data ◽  
Water Quality Parameter ◽  
Landsat 8 ◽  
Water Quality Data ◽  
Selection Of Variables

Abstract Monitoring water body quality parameters with high spatial and temporal resolutions is crucial because mitigation of pollution is usually costlier than early prevention/intervention. The existing monitoring methods for irrigation ponds in Taoyuan, Taiwan, are based on field measurements that have low spatial and temporal resolutions. In this study, using Landsat 8 satellite imagery, a multiple regression-derived relationship between the satellite band reflectance and the concentration of total phosphorus (TP) was established. The satellite imagery was atmospherically corrected with ACOLITE based on shortwave infrared (SWIR) bands. This method was used to select predictor variables in the multiple regression-derived equation based on forward selection of variables using a p-value and variation inflation factor (VIF) threshold. The derived equation yielded a coefficient of determination (R2) of 0.67. The near-infrared band (band 5) was found to be most significant. The Landsat 8 imagery retrieved for two of the three pond studies included only a few pixels from the ponds because parts of the pond surfaces are covered by floating photovoltaic power plants. The TP concentrations resulting from the derived equation indicate the feasibility of using satellite remote sensing methods to monitor the water quality. The derived relationships are potentially applicable to extend the availability of temporal and spatial water quality data for these irrigation ponds.

scholarly journals A Remote Sensing and Machine Learning Based Framework For The Assessment of Spatiotemporal Water Quality Along The Middle Ganga Basin

2021 ◽  
Author(s):  
Ashwitha Krishnaraj ◽  
Ramesh H
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Satellite Data ◽  
Water Resource Management ◽  
Geographical Area ◽  
Gradient Boosting ◽  
Landsat 8 ◽  
Physico Chemical ◽  
Extreme Gradient Boosting

Abstract Exploring qualitative measures of any waterbody is as vital as quantitative analysis for the sustainability of our water resources. Thus, examining the dynamics of spatiotemporal behaviour of dominant Water Quality Parameters (WQPs) along any waterbody is indeed critical for proposing the appropriate water resource management. This study aims to create a Machine learning model for mapping the dominant optical and non-optical WQPs such as Electrical Conductivity (EC), pH, Temperature (Temp), Total Dissolved Solids (TDS), Silicon Dioxide (SiO2) and Dissolved Oxygen (DO) using satellite data. However, the association between WQPs and satellite data is strenuous to model precisely using simple regression theory. In this context, we developed remote sensing-based Extreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) regressor with optimized Hyperparameters to understand the spatiotemporal variations of WQPs using Landsat-8 imageries. We evaluated six years of satellite data for the geographical area covering from Ankinghat to Chopan (20 sampling stations under Central Water Commission (CWC), Middle Ganga Division (MGD) II) for characterizing the trends of dominant Physico-chemical WQPs across the four clusters identified in our previous study. Through the developed XGBoost and MLP regression models between measured WQPs and the remote sensing reflectance for the pixels corresponding to the sampling stations, a significant coefficient of determination (R2) in the range of 0.88- 0.98 for XGBoost and 0.72-0.97 for MLP have generated with bands B1-B4 and their ratios more consistent. Indeed, our findings recommend fewer in-situ measurements to generate reliable Landsat-8 based ML models to estimate Spatio-temporal variations of Physico-chemical and biological WQPs to facilitate better management of our waterbodies.

scholarly journals Potential impacts of climate change on water quality and ecology in six UK rivers

2009 ◽  
Vol 40 (2-3) ◽  
pp. 113-122 ◽  
Author(s):  
P. G. Whitehead ◽  
A. J. Wade ◽  
D. Butterfield
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Time Series ◽  
Soluble Reactive Phosphorus ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
Climate Change Scenarios ◽  
Wide Range ◽  
The Uk ◽  
Impacts Of Climate Change

A modelling study has been undertaken to assess the likely impacts of climate change on water quality across the UK. A range of climate change scenarios have been used to generate future precipitation, evaporation and temperature time series at a range of catchments across the UK. These time series have then been used to drive the Integrated Catchment (INCA) suite of flow, water quality and ecological models to simulate flow, nitrate, ammonia, total and soluble reactive phosphorus, sediments, macrophytes and epiphytes in the Rivers Tamar, Lugg, Tame, Kennet, Tweed and Lambourn. A wide range of responses have been obtained with impacts varying depending on river character, catchment location, flow regime, type of scenario and the time into the future. Essentially upland reaches of river will respond differently to lowland reaches of river, and the responses will vary depending on the water quality parameter of interest.

scholarly journals An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China

2021 ◽  
Vol 13 (9) ◽  
pp. 1663
Author(s):  
Daniel Druce ◽  
Xiaoye Tong ◽  
Xia Lei ◽  
Tao Guo ◽  
Cecile M.M. Kittel ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Resource Management ◽  
Information Source ◽  
Mainland China ◽  
Earth Observation ◽  
Water Bodies ◽  
Water Dynamics ◽  
Multivariate Logistic Regression Model ◽  
Mapping Technique ◽  
Landsat 8

Earth Observation (EO) data is a critical information source for mapping and monitoring water resources over large inaccessible regions where hydrological in-situ networks are sparse. In this paper, we present a simple yet robust method for fusing optical and Synthetic Aperture Radar (SAR) data for mapping surface water dynamics over mainland China. This method uses a multivariate logistic regression model to estimate monthly surface water extent over a four-year period (2017 to 2020) from the combined usages of Sentinel-1, Sentinel-2 and Landsat-8 imagery. Multi-seasonal high-resolution images from the Chinese Gaofen satellites are used as a reference for an independent validation showing a high degree of agreement (overall accuracy 94%) across a diversity of climatic and physiographic regions demonstrating potential scalability beyond China. Through inter-comparison with similar global scale products, this paper further shows how this new mapping technique provides improved spatio-temporal characterization of inland water bodies, and for better capturing smaller water bodies (< 0.81 ha in size). The relevance of the results is discussed, and we find this new enhanced monitoring approach has the potential to advance the use of Earth observation for water resource management, planning and reporting.

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