scholarly journals Assessing the Potential of Geostationary Himawari-8 for Mapping Surface Total Suspended Solids and Its Diurnal Changes

2021 ◽  
Vol 13 (3) ◽  
pp. 336
Author(s):  
Sidrah Hafeez ◽  
Man Sing Wong ◽  
Sawaid Abbas ◽  
Guangjia Jiang
Keyword(s):  
Diurnal Variation ◽  
Temporal Resolution ◽  
Suspended Solids ◽  
Atmospheric Correction ◽  
Total Suspended Solids ◽  
Coastal Areas ◽  
Japan Meteorological Agency ◽  
Spatial And Temporal Variation ◽  
Diurnal Changes

Ocean color sensors, typically installed on polar-orbiting satellites, have been used to monitor oceanic processes for last three decades. However, their temporal resolution is not considered to be adequate for monitoring highly dynamic oceanic processes, especially when considering data gaps due to cloud contamination. The Advanced Himawari Imager (AHI) onboard the Himawari-8, a geostationary satellite operated by the Japan Meteorological Agency (JMA), acquires imagery every 10 min at 500 m to 2000 m spatial resolution. The AHI sensor with three visible, one near-infrared (NIR), and two shortwave-infrared (SWIR) bands displays good potential in monitoring oceanic processes at high temporal resolution. This study investigated and identified an appropriate atmospheric correction method for AHI data; developed a model for Total Suspended Solids (TSS) concentrations estimation using hyperspectral data and in-situ measurements of TSS; validated the model; and assessed its potential to capture diurnal changes using AHI imagery. Two image-based atmospheric correction methods, the NIR-SWIR method and the SWIR method were tested for correcting the AHI data. Then, the new model was applied to the atmospherically corrected AHI data to map TSS and its diurnal changes in the Pearl River Estuary (PRE) and neighboring coastal areas. The results indicated that the SWIR method outperformed the NIR-SWIR method, when compared to in-situ water-leaving reflectance data. The results showed a good agreement between the AHI-derived TSS and in-situ measured data with a coefficient of determination (R²) of 0.85, mean absolute error (MAE) of 3.1 mg/L, a root mean square error (RMSE) of 3.9 mg/L, and average percentage difference (APD) of 30% (TSS range 1–40 mg/L). Moreover, the diurnal variation in the turbidity front, using the Normalized Suspended Material Index (NSMI), showed the capability of AHI data to track diurnal variation in turbidity fronts, due to high TSS concentrations at high temporal frequency. The present study indicates that AHI data with high image capturing frequency can be used to map surface TSS concentrations. These TSS measurements at high frequency are not only important for monitoring the sensitive coastal areas but also for scientific understanding of the spatial and temporal variation of TSS.

Uncertainties of spectral in situ measurements in wastewater using different calibration approaches

2006 ◽  
Vol 53 (12) ◽  
pp. 187-197 ◽  
Author(s):  
L. Rieger ◽  
G. Langergraber ◽  
H. Siegrist
Keyword(s):  
Suspended Solids ◽  
Wastewater Treatment Plants ◽  
Total Suspended Solids ◽  
Data Sets ◽  
Global Calibration ◽  
Nitrite Nitrogen ◽  
Calibration Methods ◽  
Using Data ◽  
Nitrate And Nitrite

Three calibration methods were applied to UV/VIS spectra recorded in the influent of six wastewater treatment plants (WWTPs) to measure total COD (CODtot), filtered COD (CODfil), nitrate and nitrite nitrogen (NOx-N) and total suspended solids (TSS). It could be shown that a calibration of the sensor using data sets from four Swiss WWTPs leads to an improvement of the precision in comparison to the global calibration provided by the manufacturer. A calibration to the specific wastewater matrix always improves the results and gives the highest accuracy. For CODtot a mean coefficient of variation CVx of 12.5% could be reached, whereas for NOx-N only weak results were achieved (average CVx=36%).

scholarly journals Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay

2018 ◽  
Vol 10 (9) ◽  
pp. 1393 ◽  
Author(s):  
Nicole DeLuca ◽  
Benjamin Zaitchik ◽  
Frank Curriero
Keyword(s):  
Water Quality ◽  
Random Forest ◽  
Chesapeake Bay ◽  
Suspended Solids ◽  
Total Suspended Solids ◽  
Single Band ◽  
Random Forest Model ◽  
Validation Dataset ◽  
Forest Model

Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate.

scholarly journals Turbidez y profundidad de disco de Secchi con Sentinel-2 en embalses con diferente estado trófico en la Comunidad Valenciana

2019 ◽  
pp. 15 ◽  
Author(s):  
J. Delegido ◽  
P. Urrego ◽  
E. Vicente ◽  
X. Sòria-Perpinyà ◽  
J.M. Soria ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Field Data ◽  
Suspended Solids ◽  
Atmospheric Correction ◽  
Secchi Disc ◽  
Secchi Disc Depth ◽  
Spectral Bands ◽  
The Relationship ◽  
Sentinel 2

<p>Transparency or turbidity is one of the main indicators in studies of water quality using remote sensing. Transparency can be measured <em>in situ</em> through the Secchi disc depth (SD), and turbidity using a turbidimeter. In recent decades, different relationships between bands from different remote sensing sensors have been used for the estimation of these variables. In this paper, several indices and spectral bands have been calibrated in order to estimate transparency from Sentinel-2 (S2) images from field data, obtained throughout 2017 and 2018 in Júcar basin reservoirs with a great variety of trophic states. Three atmospheric correction methods developed for waters have been applied to the S2 level L1C images taken at the same day as the field data: Polymer, C2RCC and C2X. From the spectra obtained from S2 and the SD field data, it has been found that the smallest error is obtained with the images atmospherically corrected with Polymer and a potential adjustment of the reflectivities’ ratio of the blue and green bands (R<sub>490</sub>/R<sub>560</sub>), which allow the estimation of SD with a relative error of 13%. Also the C2X method presents good adjustment with the same bands ratio, although with a greater error, while the correction C2RCC shows the worst correlation. The relationship between SD (in m) and turbidity (in NTU) has also been obtained, which provides an operational method for estimating turbidity with S2. The relationship for the different reservoirs between SD and chlorophyll-a concentration, suspended solids and dissolved organic matter, is also shown.</p>

scholarly journals Kualitas perairan Natuna pada musim transisi

DEPIK ◽  
2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Mariska Astrid Kusumaningtyas ◽  
Rikha Bramawanto ◽  
August Daulat ◽  
Widodo S. Pranowo
Keyword(s):  
Water Quality ◽  
Suspended Solids ◽  
Total Suspended Solids ◽  
Secchi Disk

Abstrak. Perairan Natuna, khususnya pada Kawasan Konservasi Laut Daerah (KKLD) I diprioritaskan untuk mendukung kegiatan perikanan berkelanjutan, sehingga penting diketahui kualitasperairannya. Penelitian ini bertujuan untuk mengetahui karakteristik kualitas air berdasarkan parameter fisika maupun kimia sebagai basis data terkini mengenai kualitas perairan Natuna pada musim transisi. Penelitian dilakukan di 31 stasiun pada bulan November 2012. Parameter kualitas air yang diukur antara lain kecerahan, derajat keasaman (pH), oksigen terlarut, suhu, salinitas, padatan tersuspensi total atau Total Suspended Solids (TSS) dan nutrien (nitrat, fosfat, silikat). Parameter pH, oksigen terlarut, suhu, dan salinitas diukur secara in-situ menggunaan alat water quality meter (TOA-DKK), kecerahan diukur menggunakan secchi disk, sedangkan sampel air di bawa ke laboratorium untuk dianalisis konsentrasi nutrien dan TSS. Hasil penelitian menunjukkan nilai kisaran kecerahan yaitu 2-20,9 (m), pH 8,09-8,27, oksigen terlarut 6,34-7,96 (mg/l), suhu 29,2-30,6 (°C), salinitas 27,9-30,4 (PSU), TSS <3-26 (mg/l), nitrat 0,005-0,078 (mg/l), fosfat <0,005-0,015 (mg/l) dan silikat 0,045-0,704 (mg/l). Hasil penelitian dibandingkan dengan baku mutu air laut untuk biota laut berdasarkan Keputusan Menteri Lingkungan Hidup Nomor 51 Tahun 2004. Berdasarkan hasil penelitian, kondisi perairan Natuna masih tergolong baik untuk menunjang kehidupan biota laut.Kata kunci: Parameter kimia; Parameter fisika; Natuna; musim transisi

scholarly journals A Landsat-based model for retrieving total suspended solids concentration of estuaries and coasts

2017 ◽  
Author(s):  
Chongyang Wang ◽  
Shuisen Chen ◽  
Dan Li ◽  
Wei Liu ◽  
Ji Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Suspended Solids ◽  
Near Infrared ◽  
River Estuary ◽  
Total Suspended Solids ◽  
Logarithmic Transformation ◽  
Pearl River ◽  
Retrieval Models ◽  
Model Based

Abstract. Retrieving total suspended solids (TSS) concentration accurately is essential for sustainable management of estuaries and coasts, which plays a key role in the interaction of hydrosphere, pedosphere and atmosphere. Although many TSS retrieval models have been published, the general inversion method that is applicable to different field conditions is still under research. In order to obtain a TSS remote sensing model that is suitable for estimating the TSS concentrations with wide range in estuaries and coasts by Landsat imageries, this study recalibrated and validated a number of regression-techniques-based TSS retrieval models using 129 in-situ samples collected from five regions of China during the period of 2006–2013. It was found that the optimized Quadratic model using the Ratio of Logarithmic transformation of red band and near infrared band and logarithmic transformation of TSS concentration (QRLTSS) works well and shows a relatively satisfactory performance. The adjusted QRLTSS model based on Landsat sensors explain about 72 % of the TSS concentration variation (TSS: 4.3–577.2 mg/L, N = 84) in the study and have an acceptable validation accuracy (TSS: 4.5–474 mg/L, RMSE: 21.5–25 mg/L, MRE: 27.2–32.5 %, N = 35). The QRLTSS model based on Landsat OLI is better than TM and ETM+ (R2: 0.7181 vs. 0.7079, 0.708) because of the optimization of OLI sensor's design. A threshold of red band reflectance (OLI: 0.032, ETM+ and TM: 0.031) was proved capable to help solve the QRLTSS model and retrieve TSS concentration from Landsat remote sensing imageries. After 6S model-based atmospheric correction of Landsat imageries, the TSS concentrations of three regions (Moyangjiang River Estuary, Pearl River Estuary and Hanjiang River Estuary) in Guangdong Province of China by OLI and ETM+ imageries were retrieved by the optimized QRLTSS model. As a result, the Landsat imagery inversed TSS concentrations showed good validation accuracies with the synchronous in-situ observation (TSS: 7–160 mg/L, RMSE: 11.06 mg/L, MRE: 24.1 %, N = 22). The TSS concentrations retrieved from Landsat imageries in the three estuaries showed large variation (0.295–370.4 mg/L). The further validation from EO-1 Hyperion imagery showed good performance of the model (In site synchronous measurement of TSS: 106–220.7 mg/L, RMSE: 26.66 mg/L, MRE: 12.6 %, N = 13) for the area of high TSS concentrations in Lingding Bay of Pearl River Estuaries as well. Evidently, the QRLTSS model can be potentially applied to simulate high-dynamic TSS concentrations of other estuaries and coasts in the world by Landsat imageries, improving the understanding of the spatial and temporal variation of TSS concentrations on regional and global scales. We believe that the optimized QRLTSS model can hopefully be further adjusted to establish a regional or unified TSS retrieval model of estuaries and coasts for different satellite sensors similar to Landsat OLI-ETM+-TM sensors or with similar red and near infrared bands, such as ALI, HJ-1 A/B, LISS, CBERS, ASTER, ALOS, RapidEye, Kanopus-V, GF, etc.

scholarly journals A Landsat-based model for retrieving total suspended solids concentration of estuaries and coasts in China

2017 ◽  
Vol 10 (12) ◽  
pp. 4347-4365 ◽  
Author(s):  
Chongyang Wang ◽  
Shuisen Chen ◽  
Dan Li ◽  
Danni Wang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Suspended Solids ◽  
Near Infrared ◽  
Landsat Imagery ◽  
River Estuary ◽  
Total Suspended Solids ◽  
Pearl River Estuary ◽  
Thematic Mapper ◽  
Pearl River ◽  
Retrieval Models

Abstract. Retrieving total suspended solids (TSS) concentration accurately is essential for sustainable management of estuaries and coasts, which plays a key role in the interaction between hydrosphere, pedosphere and atmosphere. Although many TSS retrieval models have been published, the general inversion method that is applicable to different field conditions is still under research. In order to obtain a TSS remote sensing model that is suitable for estimating TSS concentrations with wide range in estuaries and coasts by Landsat imagery, after reviewing a number of Landsat-based TSS retrieval models and improving a comparatively better one among them, this study developed a quadratic model using the ratio of logarithmic transformation of red band and near-infrared band and logarithmic transformation of TSS concentration (QRLTSS) based on 119 in situ samples collected in 2006–2013 from five regions of China. It was found that the QRLTSS model works well and shows a satisfactory performance. The QRLTSS model based on Landsat TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus) and OLI (Operational Land Imager) sensors explained about 72 % of the TSS concentration variation (TSS: 4.3–577.2 mg L−1, N = 84, P value  < 0.001) and had an acceptable validation accuracy (TSS: 4.5–474 mg L−1, root mean squared error (RMSE)  ≤ 25 mg L−1, N = 35). In addition, a threshold method of red-band reflectance (OLI: 0.032, ETM+ and TM: 0.031) was proposed to solve the two-valued issue of the QRLTSS model and to retrieve TSS concentration from Landsat imagery. After a 6S model-based atmospheric correction of Landsat OLI and ETM+ imagery, the TSS concentrations of three regions (Moyangjiang River estuary, Pearl River estuary and Hanjiang River estuary) in Guangdong Province in China were mapped by the QRLTSS model. The results indicated that TSS concentrations in the three estuaries showed large variation ranging from 0.295 to 370.4 mg L−1. Meanwhile we found that TSS concentrations retrieved from Landsat imagery showed good validation accuracies with the synchronous water samples (TSS: 7–160 mg L−1, RMSE: 11.06 mg L−1, N = 22). The further validation from EO-1 Hyperion imagery also showed good performance (in situ synchronous measurement of TSS: 106–220.7 mg L−1, RMSE: 26.66 mg L−1, N = 13) of the QRLTSS model for the area of high TSS concentrations in the Lingding Bay of the Pearl River estuary. Evidently, the QRLTSS model is potentially applied to simulate high-dynamic TSS concentrations of other estuaries and coasts by Landsat imagery, improving the understanding of the spatial and temporal variation of TSS concentrations on regional and global scales. Furthermore, the QRLTSS model can be optimized to establish a regional or unified TSS retrieval model of estuaries and coasts in the world for different satellite sensors with medium- and high-resolution similar to Landsat TM, ETM+ and OLI sensors or with similar red bands and near-infrared bands, such as ALI, HJ-1 A and B, LISS, CBERS, ASTER, ALOS, RapidEye, Kanopus-V, and GF.

Establishing an empirical equation for the relationship between total suspended solids and total phosphorus concentrations in the downstream Red river water

2020 ◽  
Vol 20 (3) ◽  
pp. 325-332
Author(s):  
Le Nhu Da ◽  
Le Thi Phuong Quynh ◽  
Phung Thi Xuan Binh ◽  
Duong Thi Thuy ◽  
Trinh Hoai Thu ◽  
...  
Keyword(s):  
Total Phosphorus ◽  
Suspended Solids ◽  
Empirical Relationship ◽  
Water Environment ◽  
Total Suspended Solids ◽  
River System ◽  
Red River ◽  
Reservoir Impoundment ◽  
Downstream Section ◽  
Reasonable Prediction

Recently, the Asian rivers have faced the strong reduction of riverine total suspended solids (TSS) flux due to numerous dam/reservoir impoundment. The Red river system is a typical example of the Southeast Asian rivers that has been strongly impacted by reservoir impoundment in both China and Vietnam, especially in the recent period. It is known that the reduction in total suspended solids may lead to the decrease of some associated elements, including nutrients (N, P, Si) which may affect coastal ecosystems. In this paper, we establish the empirical relationship between total suspended solids and total phosphorus concentrations in water environment of the Red river in its downstream section from Hanoi city to the Ba Lat estuary based on the sampling campaigns conducted in the dry and wet seasons in 2017, 2018 and 2019. The results show a clear relationship with significant coefficient between total suspended solids and total phosphorus in the downstream Red river. It is expressed by a simple equation y = 0.0226x0.3867 where x and y stand for total suspended solids and total phosphorus concentrations (mg/l) respectively with the r2 value of 0.757. This equation enables a reasonable prediction of total phosphorus concentrations of the downstream Red river when the observed data of total suspended solids concentrations are available. Thus, this work opens up the way for further studies on the calculation of the total phosphorus over longer timescales using daily available total suspended solids values.

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