scholarly journals Extending Landsat 8: Retrieval of an Orange contra-Band for Inland Water Quality Applications

2020 ◽  
Vol 12 (4) ◽  
pp. 637 ◽  
Author(s):  
Alexandre Castagna ◽  
Stefan Simis ◽  
Heidi Dierssen ◽  
Quinten Vanhellemont ◽  
Koen Sabbe ◽  
...  
Keyword(s):  
Water Quality ◽  
Atmospheric Correction ◽  
Cyanobacterial Bloom ◽  
Quality Parameters ◽  
Retrieval Algorithm ◽  
Percentage Error ◽  
Landsat 8 ◽  
Base Line ◽  
Band Ratio ◽  
Water Types

The Operational Land Imager (OLI) onboard Landsat 8 has found successful application in inland and coastal water remote sensing. Its radiometric specification and high spatial resolution allows quantification of water-leaving radiance while resolving small water bodies. However, its limited multispectral band set restricts the range of water quality parameters that can be retrieved. Identification of cyanobacteria biomass has been demonstrated for sensors with a band centered near 620 nm, the absorption peak of the diagnostic pigment phycocyanin. While OLI lacks such a band in the orange region, superposition of the available multispectral and panchromatic bands suggests that it can be calculated by a scaled difference. A set of 428 in situ spectra acquired in diverse lakes in Belgium and The Netherlands was used to develop and test an orange contra-band retrieval algorithm, achieving a mean absolute percentage error of 5.39% and a bias of −0.88% in the presence of sensor noise. Atmospheric compensation error propagated to the orange contra-band was observed to maintain about the same magnitude (13% higher) observed for the red band and thus results in minimal additional effects for possible base line subtraction or band ratio algorithms for phycocyanin estimation. Generality of the algorithm for different reflectance shapes was tested against a set of published average coastal and inland Optical Water Types, showing robust retrieval for all but relatively clear water types (Secchi disk depth > 6 m and chlorophyll a < 1.6 mg m − 3 ). The algorithm was further validated with 79 matchups against the Ocean and Land Colour Imager (OLCI) orange band for 10 globally distributed lakes. The retrieved band is shown to convey information independent from the adjacent bands under variable phycocyanin concentrations. An example application using Landsat 8 imagery is provided for a known cyanobacterial bloom in Lake Erie, US. The method is distributed in the ACOLITE atmospheric correction code. The contra-band approach is generic and can be applied to other sensors with overlapping bands. Recommendations are also provided for development of future sensors with broad spectral bands with the objective to maximize the accuracy of possible spectral enhancements.

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 Modelling Reservoir Chlorophyll-a, TSS, and Turbidity Using Sentinel-2A MSI and Landsat-8 OLI Satellite Sensors with Empirical Multivariate Regression

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Yashon O. Ouma ◽  
Kimutai Noor ◽  
Kipkemoi Herbert
Keyword(s):  
Water Quality ◽  
Optical Sensors ◽  
Multivariate Regression ◽  
Atmospheric Correction ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Band Adjustment ◽  
Sentinel 2A

Sentinel-2A/MSI (S2A) and Landsat-8/OLI (L8) data products present a new frontier for the assessment and retrieval of optically active water quality parameters including chlorophyll-a (Chl-a), suspended particulate matter (TSS), and turbidity in reservoirs. However, because of their differences in spatial and spectral samplings, it is critical to evaluate how well the sensors are suited for the seamless generation of the water quality parameters (WQPs). This study presents results from the retrieval of the WQP in a reservoir from L8 and S2A optical sensors, after atmospheric correction and standardization through band adjustment. An empirical multivariate regression model (EMRM) algorithmic approach is proposed for the estimation of the water quality parameters in correlation with in situ laboratory measurements. From the results, both sensors estimated Chl-a concentrations with R2 of greater than 70% from the visible green band for L8 and a combination of green and SWIR-1 bands for S2A. While the NMSE% was nearly the same for both sensors in Chl-a estimation, the RMSE was <10 μg/L and >10 μg/L for L8 and S2A estimations of Chl-a, respectively. For TSS retrieval, L8 outperformed S2A by 31% in accuracy with R2>0.9 from L8’s red, blue, and green bands, as compared to 0.47≤R2≥0.61 from S2A’s red and NIR bands. The RMSE were the same as for Chl-a, and the NMSE% were both in the same range. Both sensors retrieved turbidity with high and nearly equal accuracy of R2>70% from the visible and NIR bands, with equal RMSE at <10% NTU and NMAE% from S2A being higher by more than 30% as compared to L8’s NMAE% at 15%. The study concluded that the higher performance accuracy of L8 is attributed to its higher SNR and spectral bandwidth placement as compared to S2A bands. Comparatively, S2A overestimated Chl-a and turbidity but performed equally well compared to OLI in the estimation of TSS. The results show that while absolute accuracy of retrieval of the WQPs still requires improvements, the developed algorithms are broadly able to discern the biooptical water quality in reservoirs.

scholarly journals Detecting spatiotemporal variability in the physicochemical properties of water in the Lower Mearim using remote sensing data

2020 ◽  
Vol 42 ◽  
pp. e32
Author(s):  
George Colares Silva Filho ◽  
Juliana Martins dos Santos ◽  
Paulo Cesar Mendes Villis ◽  
Ingrid Santos Gonçalves ◽  
Isael Coelho Correia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Physicochemical Properties ◽  
Monitoring Program ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatiotemporal Variability ◽  
Landsat 8 ◽  
Solids Content

Natural or anthropogenic chemical compounds of different origins often accumulate in estuarine regions. These compounds may alter the water quality. Therefore, It is important to constantly monitor the quality of estuarine regions. A combination of remote sensing and traditional sampling can lead to a better monitoring program for water quality parameters. The objective of this work is to assess the spatiotemporal variability of the physicochemical properties of water in the lower region of the Mearim River and estimate water quality parameters via remote sensing. Samples were collected at 16 points, from Baixo Arari to the mouth of the watershed, using a multiparameter meter and Landsat 8 satellite images. The physicochemical parameters of the water had high salinity levels, between 2.30 and 20.10 parts per trillion; a high total dissolved solids content, between 2.77 and 19.70 g/L; and minimum dissolved oxygen values. Estimating the physicochemical properties of the water via remote sensing proved feasible, particularly in the dry season when there is less cloud cover.

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 Using Landsat-8 Images for Quantifying Suspended Sediment Concentration in Red River (Northern Vietnam)

2018 ◽  
Vol 10 (11) ◽  
pp. 1841 ◽  
Author(s):  
Quang Pham ◽  
Nguyen Ha ◽  
Nima Pahlevan ◽  
La Oanh ◽  
Thanh Nguyen ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Suspended Sediment Concentration ◽  
Atmospheric Correction ◽  
Sediment Concentration ◽  
Suspended Load ◽  
Red River ◽  
Landsat 8 ◽  
Band Ratio ◽  
Radiometric Data

Analyzing the trends in the spatial distribution of suspended sediment concentration (SSC) in riverine surface water enables better understanding of the hydromorphological properties of its watersheds and the associated processes. Thus, it is critical to identify an appropriate method to quantify spatio-temporal variability in SSC. This study aims to estimate SSC in a highly turbid river, i.e., the Red River in Northern Vietnam, using Landsat 8 (L8) images. To do so, in situ radiometric data together with SSC at 60 sites along the river were measured on two different dates during the dry and wet seasons. Analyses of the in situ data indicated strong correlations between SSC and the band-ratio of green and red channels, i.e., r-squared = 0.75 and a root mean square error of ~0.3 mg/L. Using a subsample of in situ radiometric data (n = 30) collected near-concurrently with one L8 image, four different atmospheric correction methods were evaluated. Although none of the methods provided reasonable water-leaving reflectance spectra (ρw), it was found that the band-ratio of the green-red ratio is less sensitive to uncertainties in the atmospheric correction for mapping SSC compared to individual bands. Therefore, due to its ease of access, standard L8 land surface reflectance products available via U.S. Geological Survey web portals were utilized. With the empirical relationship derived, we produced Landsat-derived SSC distribution maps for a few images collected in wet and dry seasons within the 2013–2017 period. Analyses of image products suggest that (a) the Thao River is the most significant source amongst the three major tributaries (Lo, Da and Thao rivers) providing suspended load to the Red River, and (b) the suspended load in the rainy season is nearly twice larger than that in the dry season, and it correlates highly with the runoff (correlation coefficient = 0.85). Although it is demonstrated that the atmospheric correction in tropical areas over these sediment-rich waters present major challenges in the retrievals of water-leaving reflectance spectra, the study signifies the utility of band-ratio techniques for quantifying SSC in highly turbid river waters. With Sentinel-2A/B data products combined with those of Landsat-8, it would be possible to capture temporal variability in major river systems in the near future.

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 ESTIMATION OF WATER QUALITY IN A RESERVOIR FROM SENTINEL-2 MSI AND LANDSAT-8 OLI SENSORS

2020 ◽  
Vol V-3-2020 ◽  
pp. 401-408
Author(s):  
F. M. C. Pizani ◽  
P. Maillard ◽  
A. F. F. Ferreira ◽  
C. C. de Amorim
Keyword(s):  
Water Quality ◽  
Multiple Regression ◽  
Regression Models ◽  
Quality Parameters ◽  
Optically Active ◽  
Water Quality Parameters ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Multiple Regression Models ◽  
Sentinel 2

Abstract. The low operational cost of using freely available remote sensing data is a strong incentive for water agencies to complement their field campaigns and produce spatially distributed maps of some water quality parameters. The objective of this study is to compare the performance of Sentinel-2 MSI and Landsat-8 OLI sensors to produce multiple regression models of water quality parameters in a hydroelectric reservoir in Brazil. Physical-chemistry water quality parameters were measured in loco using sensors and also analysed in laboratory from water samples collected simultaneously. The date of sampling corresponded to the almost simultaneous overflight of Sentinel-2B and Landsat-8 satellites which provided a means to perform a fair comparison of the two sensors. Four optically active parameters were considered: chlorophyll-a, Secchi disk depth, turbidity and temperature (the latter using Landsat-8 TIR sensor). Other six optically non-active parameters were also considered. The multiple regression models used the spectral reflectance bands from both sensors (separately) as predictors. The reflectance values were based on averaging kernels of 30 m and 90 m. Stepwise variable selection combined with a priori knowledge based on other studies were used to optimize the choice of predictors. With the exception of temperature, the other optically active parameters yielded strong regression models from both the Sentinel and Landsat sensors, all with r2 > 0.75. The models for the optically non-active parameters produced less striking results with r2 as low as 0.03 (temperature) and as high or better than > 0.8 (pH and Dissolved oxygen).

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