Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques
Abstract
:1. Introduction
2. Search Engines and Overview of Parameters
2.1. Overview of TDS and TSS
2.1.1. Overview of TDS
2.1.2. Overview of TSS
3. TDS and TSS Related Issues
3.1. TDS Issues and Implications
3.2. TSS Issues and Implications
4. Measurement and Monitoring of TDS and TSS
4.1. Measuring TDS and TSS Using Conventional Approaches
4.1.1. Measurement of TDS
4.1.2. Measurement of TSS
4.1.3. Strengths and Limitations of the Conventional Method for Measuring TDS and TSS
Strengths
Limitations
4.2. Monitoring of TDS and TSS Using RS
4.2.1. Concept of TDS and TSS Interactions and Measurements Using RS
4.2.2. Optical Characterization of TDS and TSS
4.2.3. RS Sensors for Monitoring TDS and TSS
4.2.4. RS Spectral Indices Used in Estimating TDS and TSS
TDS and Salinity Indices
TSS and Sediment Indices
4.2.5. Summary of Studies on TDS and TSS Estimation with RS Applications
4.2.6. Strengths and Drawbacks of RS Methods for Measuring TDS and TSS
Strengths
Drawbacks
5. Conclusions and Recommendations
- Fusion or combination of bands from different sensors: the fusion of microwave and optical bands should be explored in the estimation of TDS and TSS in water bodies. The fusion of data from optical bands and ERS-2 SAR bands could increase the performance in the retrieval of WQPs such as TDS and TSS concentrations in water systems. There have been significant successes in the use of microwave domain radiometers and synthetic aperture radars in the estimation of surface salinity in coastal water systems. In a study [179] comparing the MODIS-Landsat fusion results to single-band algorithms for TSS estimations, the fusion model performed better in TSS estimations in dynamic river systems than the single-band MODIS model because it had finer spatial resolution. The reported R2 for the MODIS-based model increased from 65–77% to 85–89% for the Landsat-MODIS fused model [179].
- Utilization of ML and AI Algorithms for the retrieval and estimation of WQPS: instead of utilizing empirical approaches, estimation, retrieval, and interpretation of TDS and TSS concentrations from RS data should use ML techniques. Despite being simple to use and requiring less computation time and effort than other methods, empirical methods of retrieval may not be able to distinguish these WQPs. To increase the accuracy of WQPs retrieved using RS, ML models including ANN and SVM that have the potential to reflect complex nonlinear models through training and testing should be utilized [2,24,192].
- Observed or ground-truth data from conventional field sampling should be used to complement RS measurements: this is important to ensure proper calibration and validation of RS models. Statistical or evaluation metrics such as the R2, Percent Bias (PBIAS), MAE, NSE, RMSE, and the ratio of the RMSE to standard deviation which have been widely used should be utilized to evaluate developed models to improve their accuracy and robustness [2,128,145,271,272,273,274,275,276,277].
- Remote sensed retrieval and estimation of TDS and TSS should be using high-resolution images where practical and possible: for accurate and effective detection of these WQPs by remote sensing, a high-resolution image with frequent revisit times should be used to ensure a timely change in these parameters are adequately captured. Images from GeoEye IKONOS, which has a spatial resolution of 0.82–5 m with a revisit interval of 2–3 days, and Digital Globe WorldView-1, which has a resolution of 0.5 m and a revisit period of 1.7 days, are two examples of high-resolution satellite images which could be used for the effective retrieval and estimations of TDS and TSS. Hyperspectral and high-resolution imageries such as images from the Hyperspectral Digital Imagery Collection Experiment by the Naval Research Lab, Airborne Imaging Spectrometer Multispectral by Spectral Imaging, and the AVIRIS by the NASA Jet Propulsion Laboratory could aid in the elimination of issues of discrete spectral signatures associated with other images. Hyperspectral and high-resolution images have a high potential of effectively discriminating changes in TSS and TDS in water bodies [2].
- Applying atmospheric corrections when using level 1 satellite images: atmospheric corrections such as DOS, Polymer, Sen2Cor, iCOR, NASA-AC, and ACOLITE should be employed in minimizing errors associated with atmospheric interferences on the satellite images to ultimately improve the accuracy of the WQP retrievals [2,270].
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. Landsat Band Designations
Band | S/N | Wavelength (µm) | Spatial Resolution (m) |
---|---|---|---|
Band 1–Coastal aerosol | 284 | 0.43–0.45 | 30 |
Band 2–Blue | 321 | 0.45–0.51 | 30 |
Band 3–Green | 223 | 0.53–0.59 | 30 |
Band 4–Red | 113 | 0.64–0.67 | 30 |
Band 5–NIR * | 45 | 0.85–0.88 | 30 |
Band 6–SWIR 1 * | 10.1 | 1.57–1.65 | 30 |
Band 7–SWIR 2 * | 7.4 | 2.11–2.29 | 30 |
Band 8–Panchromatic | 0.50–0.68 | 15 | |
Band 9–Cirrus | 1.36–1.38 | 30 |
Band | Wavelength (µm) | Spatial Resolution (m) |
---|---|---|
Band 1–Blue | 0.45–0.52 | 30 |
Band 2–Green | 0.52–0.60 | 30 |
Band 3–Red | 0.63–0.69 | 30 |
Band 4-NIR * | 0–77–0.90 | 30 |
Band 5–SWIR * 1 | 1.55–1.75 | 30 |
Band 6–Thermal | 10.40–12.50 | 60 (*30) |
Band 7–SWIR * 2 | 2.09–2.35 | 30 |
Band 8–Panchromatic | 0.52–0.90 | 15 |
Band | Wavelength (µm) | Spatial Resolution (m) |
---|---|---|
Band 1–Blue | 0.45–0.52 | 30 |
Band 2–Green | 0.53–0.61 | 30 |
Band 3–Red | 0.63–0.69 | 30 |
Band 4-NIR * | 0.76–0.90 | 30 |
Band 5–SWIR * 1 | 1.55–1.75 | 30 |
Band 6–Thermal | 10.40–12.50 | 120 |
Band 7–SWIR * 2 | 0.08–2.35 | 30 |
Appendix B. Sentinel-2 MSI Band Designations
Band | S2A | S2B | ||||
---|---|---|---|---|---|---|
S/N | Wavelength (nm) | Bandwidth (nm) | Wavelength (nm) | Bandwidth (nm) | Spatial Resolution (m) | |
B2-Blue | 102 | 492.4 | 66 | 492.1 | 66 | 10 |
B3-Green | 79 | 559.8 | 36 | 559.0 | 36 | |
B4-Red | 45 | 664.6 | 31 | 664.9 | 31 | |
B8-NIR * | 20 | 832.8 | 106 | 832.9 | 106 | |
B5-Red Edge 1 | 45 | 704.1 | 15 | 703.8 | 16 | 20 |
B6-Red Edge 2 | 34 | 740.5 | 15 | 739.1 | 15 | |
B7-Red Edge 3 | 26 | 782.8 | 20 | 779.7 | 20 | |
B8a-Red Edge 4 | 16 | 864.7 | 21 | 864.0 | 22 | |
B11-SWIR * 1 | 2.8 | 1613.7 | 91 | 1610.4 | 94 | |
B12-SWIR * 2 | 2.2 | 2202.4 | 175 | 2185.7 | 185 | |
B1-Aerosols | 439 | 442.7 | 21 | 442.2 | 21 | 60 |
B9-Water vapor | 945.1 | 20 | 943.2 | 21 | ||
B10-Cirrus | 1373.5 | 31 | 1376.9 | 30 |
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Strengths | Limitations |
---|---|
Well-established standardized methods | Labor and cost intensive and time-consuming |
Associated with a high degree of precision and accuracy | Accuracy may be impacted by errors associated with field samplings, transportation, storage, and lab analysis |
Can provide WQP measurements at varying depth | Impossible to monitor the entire water body at the same time due to inaccessibility issues due to topography |
Does not involve the knowledge of big data processing and the complexity of data analysis | Knowledge of specialized measuring devices or equipment may be required |
Provides direct measurement of WQPs | Limited spatial and temporal coverages |
Satellite Sensor | Year Launched | Spatial Res. (m) | Temporal Res. (Days) | Spectral Resolution | Parameter Measured | |
---|---|---|---|---|---|---|
Number of Bands | Wavelength Range (µm) | |||||
Sentinel-2 A/B MSI | 2015 | 10–60 | 5 | 12 | 0.44–2.19 | TSS, TSM, TDS |
SPOT 6 | 2012 | 1.5–6.0 | 26 | 5 | 0.45–0.89 | TSS, TSM |
Landsat-4, 5 TM | 1982 | 30–120 | 16 | 7 | 0.50–2.35 | TSS, SSC, TSM, TDS, Salinity |
Landsat-7 ETM+ | 1993 | 15–60 | 16 | 8 | 0.45–0.90 | TSS, SM, SSC |
Landsat-8 OLI/TIRS | 2013 | 15–100 | 16 | 11 | 0.43–12.51 | TSS, TSM, TDS, Salinity |
Landsat-9 OLI-2/TIRS | 2021 | 15–100 | 16 | 11 | 0.43–12.51 | TSM |
RapidEye | 2008 | 5 | 5.5 | 5 | 0.44–0.85 | TSS, SSC |
Geostationary Ocean Color Imager (GOCI) | 2010 | 500 | 1 | 8 | 0.41–0.87 | SS, Salinity |
ALOS AVNIR-2 | 2006 | 2.5–10 | 2 | 5 | 0.42–0.89 | TSS |
MERIS | 2002 | 300–1200 | 1 | 15 | 0.39–1.04 | TSM, TSS, |
Terra ASTER | 1999 | 15–90 | 16 | 14 | 0.52–11.65 | TSS, TDS, Salinity |
Terra MODIS | 1999 | 250–1000 | 1–2 | 36 | 0.41–14.5 | TSM, TSS |
Aqua MODIS | 2002 | 250–1000 | 1–2 | 36 | 0.41–14.5 | TSM, TSS |
Visible Infrared Imaging Radiometer Suite (VIIRS) | 2011 | 375–750 | 0.5 | 22 | 0.50–12.01 | TSS |
Hyperspectral Imager for the Coastal Ocean (HICOTM) | 2009 | 100 | 10 | 128 | 0.35–1.08 | SPM |
Earth-Observing One satellite (EO-1) Hyperion | 2000 | 30 | 16 | 242 | 0.35–2.57 | SM |
EO-1 ALI | 2000 | 10–30 | 16 | 10 | 0.43–2.35 | TSS, SSC |
NOAA AVHRR | 1978 | 1000 | 1 | 5 | 0.60–1.20 | TSS |
OrbView-2 SeaWiFS | 1997 | 1130 | 16 | 8 | 0.41–0.87 | SS |
Satellite Sensor | Year Launched/ Deployed | Spatial Res. (km) | Temporal Res. (Days) | Spectral Resolution | Parameter Measured | |
---|---|---|---|---|---|---|
No. of Bands | Wavelength Range (µm) | |||||
NIMBUS-7 Scanning Multichannel Microwave Radiometer (SMMR) | 1978 | 2.7–8.5 | 4 | 5 | 0.008–0.045 | SS, Salinity |
European Remote Sensing (ERS-2) SAR | 1995 | ≤0.03 | 35 | 1 | 0.057 | SSC |
Soil Moisture and Ocean Salinity (SMOS) MIRAS | 2009 | 3.5–50 | 3 | 1 | 0.212 | Salinity |
Scientific Application Satellite-D (SAC-D) Aquarius | 2011 | 100 | 7 | 1 | 0.212 | Salinity |
(Airborne) Electronically Scanning Thinned-Array (ESTAR) | 1990 | 100 | - | - | 0.212 | Salinity |
(Airborne) Scanning Low-Frequency Microwave Radiometer (SLFMR) | 1999 | 0.5–1 | - | - | 0.212 | Salinity |
(Airborne) Salinity, Temperature, and Roughness Remote Scanner (STARRS) | 2001 | 1 | - | - | Up to 0.212 | Salinity |
(Airborne) Passive Active L- and S-band Sensor | 1999 | 0.350–1 | - | - | 0.212 | Salinity |
(Airborne) Two-Dimensional Electronically Scanning Thinned-Array Radiometer | 2003 | 0.800 | - | - | 0.212 | Salinity |
NASA Aquarius and Soil Moisture Active Passive mission (SMAP) (L-band) | 2015 | ~40 | 2–3 | - | 0.214 | Salinity |
Index | Image/Data | Equation | Metrics | Study Area (Country/Region) | References |
---|---|---|---|---|---|
Salinity Index 1 | Landsat 8 OLI | R2 = 0.72 | Colorado River (USA) | [127] | |
R2 ≥ 0.72 | Shatt al-Arab River (Iraq) | [189] | |||
Salinity Index 2 | Landsat 8 OLI | R2 = 0.73 | Colorado River (USA) | [127] | |
R2 > 0.79 | Shatt al-Arab River (Iraq) | [189] | |||
Salinity Index 3 | Landsat 8 OLI | R2 = 0.72 | Colorado River (USA) | [127] | |
Salinity Index 4 | Landsat 8 OLI | R2 = 0.70 | Colorado River (USA) | [127] | |
Salinity Index 5 | Landsat 8 OLI | R2 = 0.73 | Colorado River (USA) | [127] | |
R2 ≥ 0.44 | Shatt al-Arab River (Iraq) | [189] | |||
Landsat 5 TM Landsat 8 OLI | R2 > 0.65 R2 > 0.49 | Coastal surface water (Bangladesh) | [212] | ||
Salinity Index 6 | Landsat 8 OLI | R2 = 0.71 | Colorado River (USA) | [127] | |
Salinity Index 7 | Landsat 5 TM Landsat 8 OLI | R2 > 0.06 R2 > 0.46 | Coastal surface water (Bangladesh) | [212] | |
- | ASTER | R2 > 0.57 | Qaroun Lake (Egypt) | [206] | |
NDSI | Landsat 8 OLI | R2 > 0.51 | Mekong Delta (Vietnam) | [213] | |
TDS 1 TDS 2 TDS 3 TDS 4 TDS 5 TDS 6 TDS 7 | Sentinel-2 MSI | r ≥ 0.79 r ≥ 0.00 r ≥ 0.52 r ≥ 0.79 r ≥ 0.79 r ≥ 0.00 r ≥ 0.00 | Guartinaja and Momil wetlands (Colombia) | [54] |
Index | Image/Data | Equation | Metrics | Study Area (Country/Region) | References |
---|---|---|---|---|---|
NSMI | Landsat 7 ETM+ | R2 ≥ 0.96 | Lake Mead (USA) | [190] | |
- | Cabo Rojo (Puerto Rico) | [198] | |||
R2 > 0.51 | Barito Delta (Indonesia) | [199] | |||
Landsat 8 OLI | R2 > 0.70 | Dams (South Africa) | [200] | ||
NDSSI | Landsat 7 ETM+ | - | Lake Mead (USA) | [190] | |
- | Cabo Rojo (Puerto Rico) | [198] | |||
R2 = 0.01 | Barito Delta (Indonesia) | [199] | |||
R2 > 0.66 | Mississippi River Lake Pontchartrain (USA) | [214] | |||
BR | Landsat 7 ETM+ | - | Lake Mead (USA) | [190] | |
Cabo Rojo (Puerto Rico) | [198] | ||||
R2 = 0.05 | Barito Delta (Indonesia) | [199] | |||
WSRI | Landsat 8 OLI | R2 > 0.70 | Dams (South Africa) | [200] | |
EGRI | Landsat 8 OLI | R2 > 0.70 | Dams (South Africa) | [200] | |
NDVI | Landsat 8 OLI | R2 > 0.70 | Dams (South Africa) | [200] |
Sensor/Data | Model/Algorithms | Metrics | Study Area (Country/Region) | References |
---|---|---|---|---|
ASTER | Empirical | R2 > 0.50 | Qaroun Lake (Egypt) | [206] |
Landsat 8 OLI | ML | R2 = 0.79 | Lake Tana (Ethiopia) | [26] |
Landsat 8 OLI | Empirical | R2 > 0.00 | Coastal Surface Water (Bangladesh) | [243] |
Landsat 5 TM, 8 OLI | Empirical | R2 ≥ 0.76 | Coastal Surface Water (Bangladesh) | [212] |
Landsat 8 OLI | Empirical | R2 ≥ 0.62 | Colorado River (USA) | [127] |
Landsat 8 OLI | Empirical | R2 ≥ 0.83 | Lake Al-Habbaniyah (Iraq) | [231] |
Sentinel-2 MSI | Empirical | R2 > 0.70 | Guartinaja, Momil wetlands (Columbia) | [54] |
Landsat 8 OLI | Empirical | R2 > 0.84 | Gorano Dam (Pakistan) | [244] |
Landsat 8 OLI, Sentinel-2 MSI, Göktürk-2 | ML | R2 ≥ 0.51 | Lake Gala (Turkey) | [245] |
Landsat 8 OLI | Empirical | R2 > 0.55 | Shatt al-Arab River (Iraq) | [189] |
Landsat 8 OLI | Empirical, ML | R2 ≥ 0.68 | Karun River Basin (Iran) | [246] |
GOCI | Empirical | PMARE = 0.75% | Southern Yellow Sea (China, North and South Korea) | [247] |
Landsat 8 OLI | Empirical | R2 ≥ 0.60 | Arabian Gulf (Middle East) | [248] |
Landsat 5 TM | Empirical | R2 = 95 | Tigris and Euphrates Rivers (Iraq) | [238] |
Ship-borne microwave radiometer | - | RMSE ≈ 0.4 PSU | East China Sea (China) | [218] |
Landsat 8 OLI | Empirical | R2 > 0.96 | Tubay River (Philippines) | [237] |
IRS LISS III | Empirical | R2 > 0.46 | Gomti River (India) | [249] |
Landsat 8 OLI | Empirical | R2 > 0.13 | Mosul Dam Lake (Iraq) | [239] |
Sensor/Data | Model/Algorithms | Metrics | Study Area (Country/Region) | References |
---|---|---|---|---|
Unmanned Aerial Vehicle (AV) multispectral images, Landsat 8 OLI | ANN, Empirical | R2 > 0.60 | Lake at Unisinos University (South Brazil) | [202] |
Landsat 5 TM, Landsat 8 OLI, and Chinese GaoFen-1 (GF-1) Wide Field of View (WFV) | Wen, Nechad, and Novoa algorithms | R2 > 0.88 | Min River (China) (TSS) | [250] |
Landsat 5 TM | Empirical | R2 > 0.23 | Reelfoot Lake, Tennessee (USA) | [119] |
MERIS, OLCI | Semi-analytical | R2 ≥ 0.25 | Lakes Kasumigaura, Suwa, Akan (Japan), Lake Taihu (China), Lac Vieux Desert, Lakes Winnebago, Poygan, Winneconne, Green Bay of Lake Michigan (North America) | [251] |
Landsat 8 OLI | Empirical | R2 ≥ 0.71 | Dam impoundments (South Africa) | [200] |
Aqua MODIS | Empirical | R2 = 0.70 | Río de la Plata estuary (South America) | [100] |
Sentinel-2 MSI | Empirical | R2 = 0.85 | Negro River, Amazon Basin (Brazil) | [137] |
Landsat 8 OLI, MODIS | - | R2 ≥ 0.40 | Poyang Lake (China) | [232] |
Landsat 5 TM | Semi-analytical model | R2 ≥ 0.51 | Gulf of Bohai (China) | [135] |
Ocean Optic 2000 spectroradiometers (USB2000) | Empirical | R2 = 0.75 | Chesapeake Bay (USA) | [169] |
Landsat 8 OLI | Empirical | R2 ≥ 0.87 | Lake Al-Habbaniyah (Iraq) | [231] |
Landsat 7 ETM+ | Empirical | R2 > 0.66 | Mississippi River and Lake Pontchartrain (USA) | [214] |
SPOT 6 | Empirical | R2 = 0.65 | Didipio catchment (Philippines) | [234] |
MODIS | Empirical | R2 = 0.89 | Lake Pontchartrain, Mississippi River, Mississippi Sound (USA) | [226] |
MODIS | Empirical | PMARE = 25.5% | Yangtze river (China) | [134] |
Landsat 5 TM, Landsat 7 EM+, MODIS | Empirical | R2 ≥ 0.13 | Yangtze river (China) | [252] |
MODIS | Neural Network | R2 > 0.60 | Bohai Sea, Yellow Sea, East China Sea (China) | [253] |
Landsat 9 OLI-2, Sentinel-2 | Bio-Optical Model | R2 ≥ 0.17 | Lakes Trasimeno, Maggiore, and Mantova (Italy) | [181] |
MODIS, Landsat 8 OLI | Copula-based enhanced nonlinear | R2 ≥ 0.31 | Hooghly River (India) | [179] |
ALOS/AVNIR-2 | Empirical | R2 ≥ 0.71 | Monobe River (Japan), Altamaha River (USA), St. Mary’s River (USA) | [183] |
LISST-200x and EXO2 Multiparameter Sonde sensors | Empirical | R2 ≥ 0.67 | Coastal regions | [254] |
Landsat 7 ETM+, Landsat 8 OLI | Empirical | R2 ≥ 0.16 | Qaraoun Reservoir (Lebanon) | [235] |
Landsat 8 OLI | Empirical | R2 ≥ 0.64 | Thirty-two sub-catchments (Zimbabwe) | [233] |
Landsat 5 TM | Neural Network | R2 > 0.90 | Beaver Reservoir (USA) | [229] |
MODIS | Empirical | R2 > 0.80 | Green Bay of Lake Michigan (USA) | [255] |
Sentinel-2 MSI | Semi-empirical | R2 ≥ 0.63 | Yangtze Main Stream (China) | [131] |
MODIS | Empirical, ML | R2 > 0.27 | Chesapeake Bay (USA) | [129] |
CASI | Empirical | R2 > 0.84 | Tamar estuary (UK) | [227] |
Landsat 8 OLI | Empirical | R2 > 0.73 | Orinoco River (Venezuela) | [256] |
OLCI | ||||
Landsat 4,5 TM, 7 ETM+, 8 OLI | Empirical | R2 ≥ 0.58 | Estuaries and Coasts (China) | [257] |
Chinese HJ-1A/CCD | Semi-analytical | R2 > 0.66 | Oujiang River Estuary (China) | [258] |
MODIS | Empirical | R2 = 0.90 | Tampa Bay (USA) | [225] |
Sentinel-2A MSI | Semi-empirical, ML | R2 = 0.80 | Water Reservoirs (Czech Republic) | [236] |
Landsat 8 OLI | AI | R2 > 0.93 | Saint John River (Canada and USA) | [259] |
Sentinel-2 A/B MSI | Empirical | R2 > 0.13 | Sado Estuary (Portugal) | [93] |
Landsat 8 OLI | Empirical | R2 > 0.10 | Mosul Dam Lake (Iraq) | [239] |
IRS LISS III | Empirical | R2 > 0.23 | Gomti River (India) | [249] |
Landsat 8 OLI, Sentinel-2 MSI, Göktürk-2 | ML | R2 ≥ 0.64 | Lake Gala (Turkey) | [245] |
Landsat 8 OLI, Sentinel-2 MSI | Empirical, Semi-empirical | R2 ≥ 0.81 | Hedi Reservoir (China) | [34] |
HICOTM | Semi-empirical | R2 = 0.85 | Northern Adriatic Sea | [260] |
Strengths | Limitations |
---|---|
Provides a synoptic overview of the entire water body | Accuracy may be limited |
Easy access and acquisition of open source | Image may be impacted by atmospheric interference |
Monitoring of WQPs on large spatiotemporal scale coverage | Accuracy may be impacted by the resolution |
Cost and labor effective | Requires knowledge of data processing and analysis |
Repository of historical images for water quality studies | Requires high-spec computer for large download and storage |
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Adjovu, G.E.; Stephen, H.; James, D.; Ahmad, S. Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques. Remote Sens. 2023, 15, 3534. https://doi.org/10.3390/rs15143534
Adjovu GE, Stephen H, James D, Ahmad S. Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques. Remote Sensing. 2023; 15(14):3534. https://doi.org/10.3390/rs15143534
Chicago/Turabian StyleAdjovu, Godson Ebenezer, Haroon Stephen, David James, and Sajjad Ahmad. 2023. "Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques" Remote Sensing 15, no. 14: 3534. https://doi.org/10.3390/rs15143534