Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

Song, Kaishan; Li, Lin; Wang, Zongming; Liu, Dianwei; Zhang, Bai; Xu, Jun; Du, Juan; Li, Linhai; Li, Shuai; Wang, Yuandong

doi:10.1007/s10661-011-2053-3

Cited by 70 publications

(46 citation statements)

References 66 publications

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“…There have been many successful cases in which the TSS of turbid waters has been retrieved from satellite sensor data in the past 40 years [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. In the 1970s, just after the successful launch of Landsat-1, researchers built a quantitative statistical model for suspended sediment retrieval using multispectral scanner (MSS) remotely sensed images [13].…”

Section: Introductionmentioning

confidence: 99%

“…In the 1970s, just after the successful launch of Landsat-1, researchers built a quantitative statistical model for suspended sediment retrieval using multispectral scanner (MSS) remotely sensed images [13]. Subsequently, MODIS, MERIS, SPOT and Landsat TM/ETM+ have been used to assess the TSS concentration in different study areas [8,[10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, although many empirical regression models have been proposed for retrieving TSS effectively [6,8,[11][12][13][14][15][16][17][18][19][20][21][23][24][25][26][27][28][29], such models established in other study areas may not fit Deep Bay. Moreover, most of these models are based on in-situ spectra, requiring a high accuracy of atmospheric correction results when applied to satellite data.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

et al. 2014

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Using Deep Bay in China as an example, an effective method for the retrieval of total suspended sediment (TSS) concentration using HJ-1A/1B satellite images is proposed. The factors driving the variation of the TSS spatial distribution are also discussed. Two field surveys, conducted on August 29 and October 26, 2012, showed that there was a strong linear relationship (R 2 = 0.9623) between field-surveyed OBS (optical backscatter) measurements (5-31NTU) and laboratory-analyzed TSS concentrations (9.89-35.58 mg/L). The COST image-based atmospheric correction procedure and the pseudo-invariant features (PIF) method were combined to remove the atmospheric effects from the total radiance measurements obtained with different CCDs onboard the HJ-1A/1B satellites. Then, a simple and practical retrieval model was established based on the relationship between the satellite-corrected reflectance band ratio of band 3 and band 2 (Rrs3/Rrs2) and in-situ TSS measurements. The R 2 of the regression relationship was 0.807, and the mean relative error (MRE) was 12.78%, as determined through in-situ data validation. Finally, the influences of tide cycles, wind factors (direction and speed) and other factors on the OPEN ACCESSRemote Sens. 2014, 6 9912 variation of the TSS spatial pattern observed from HJ-1A/1B satellite images from September through November of 2008 are discussed. The results show that HJ-1A/1B satellite CCD images can be used to estimate TSS concentrations under different tides in the study area over synoptic scales without using simultaneous in-situ atmospheric parameters and spectrum data. These findings provide strong informational support for numerical simulation studies on the combined influence of tide cycles and other associated hydrologic elements in Deep Bay.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

et al. 2014

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“…8(b)]. 41,42 The remote sensing spectrum of Lake Qiandaohu showed a low magnitude (ranging from 0 to 0.014 sr −1 ), high variability in the blue and green regions, and a near-zero value in the near-infrared red (NIR) region. Furthermore, there was a weak trough around 440 nm, which is a typical characteristic of oligotrophic waters.…”

Section: Remote Sensing Reflectancementioning

confidence: 99%

Retrieval of absorption coefficients for a drinking water source using a green–red band quasianalytical algorithm

Shi

Tao

Mao

et al. 2018

J. Appl. Rem. Sens.

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"Retrieval of absorption coefficients for a drinking water source using a green-red band quasianalytical algorithm," J. Appl. Remote Sens. 12(4), 042802 (2018), doi: 10.1117/1.JRS.12.042802. Abstract. This study proposed a green-red band quasianalytical algorithm, QAA-GRI, and evaluates its performance using an in situ dataset from Lake Qiandaohu, a drinking water source, in China. First, by shifting the reference wavelength from 555 to 510 nm, a green-red index (GRI) can be calculated from remote sensing reflectance at 510, 560, and 620 nm, and the index was then used to retrieve the total absorption coefficients at the reference band, að510Þ. Second, a semianalytical model based on að510Þ and the GRI was deduced to establish the QAA-GRI, replacing the empirical model in quasianalytical algorithm version 5 (QAA-v5). The QAA-GRI was applied to retrieve absorption coefficients from an in situ dataset of Lake Qiandaohu, and the QAA-GRI's performance was compared with that of QAA-v5 and another red-green QAAbased approach (QAA-RGR). The results demonstrate that, for this dataset, the QAA-GRI exhibited better performance (R 2 ¼ 0.81, mean absolute percentage error, MAPE ¼ 15.7%), which indicated a clear improvement in the accuracy of absorption coefficient retrieval, compared with QAA-v5 (R 2 ¼ 0.56, MAPE ¼ 21.2%) and QAA-RGR (R 2 ¼ 0.67, MAPE ¼ 22.6%). In addition, to illustrate the QAA-GRI's assumptions and its applicability, the QAA-GRI was also applied to an in situ dataset from Taihu Lake, a highly turbid and eutrophic water source. As predicted, when applied to Taihu Lake, the QAA-GRI did not perform as well as it did when applied to Lake Qiandaohu because of the former's extremely high turbidity, which can cause greater uncertainty with regard to backscattering coefficients. This study suggests that the QAA-GRI is better suited to the retrieval of absorption coefficients from drinking water resources. Our algorithm will also have potential applications to satellite data from the Medium Resolution Imaging Spectrometer or Ocean and Land Color Instrument. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Traditionally, water quality parameters (WQPs), such as total nitrogen (TN) and total phosphorus (TP), are obtained by routine monitoring methods of field sampling and laboratory analyses [6]. In addition to cost and labor intensiveness, these traditional approaches are not suitable for monitoring a large number of water bodies at a regional or national scale because of spatial heterogeneity and temporal changes of water quality across aquatic ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery

Liu

Zhang

Yuan

et al. 2015

Water

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Abstract:Measuring total nitrogen (TN) and total phosphorus (TP) is important in managing heavy polluted urban waters in China. This study uses high spatial resolution IKONOS imagery with four multispectral bands, which roughly correspond to Landsat/TM bands 1-4, to determine TN and TP in small urban rivers and lakes in China. By using Lake Cihu and the lower reaches of Wen-Rui Tang (WRT) River as examples, this paper develops both multiple linear regressions (MLR) and artificial neural network (ANN) models to estimate TN and TP concentrations from high spatial resolution remote sensing imagery and in situ water samples collected concurrently with overpassing satellite. The measured and estimated values of both MLR and ANN models are in good agreement (R 2 > 0.85 and RMSE < 2.50). The empirical equations selected by MLR are more straightforward, whereas the estimated accuracy using ANN model is better (R 2 > 0.86 and RMSE < 0.89). Results validate the potential of using high resolution IKONOS multispectral imagery to study the chemical states of small-sized urban water bodies. The spatial distribution maps of TN and TP concentrations generated by the ANN model can inform the decision makers of variations in water quality in Lake Cihu and lower reaches of WRT River. The approaches and equations developed in this study could be applied to other urban water bodies for water quality monitoring. OPEN ACCESSWater 2015, 7 6552

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Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

Cited by 70 publications

References 66 publications

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

Retrieval of absorption coefficients for a drinking water source using a green–red band quasianalytical algorithm

Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery

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