A Spectral Decomposition Algorithm for Estimating Chlorophyll-a Concentrations in Lake Taihu, China

Zhang, Yuchao; Ma, Ronghua; Duan, Hongtao; Loiselle, Steven; Xu, Jinduo

doi:10.3390/rs6065090

Cited by 27 publications

(13 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, compared to the conventional models, this approach is less sensitive to geographical and temporal variability. If the standard reflectance spectra of the respective endmember remain consistent with similar spectral sensitivity, this model could be applied to other satellite images [51,73]. The new model is capable of estimating the SAV biomass for the whole lake basin.…”

Section: Discussionmentioning

confidence: 99%

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

et al. 2017

View full text Add to dashboard Cite

Assessing the abundance of submerged aquatic vegetation (SAV), particularly in shallow lakes, is essential for effective lake management activities. In the present study we applied satellite remote sensing (a Landsat-8 image) in order to evaluate the SAV coverage area and its biomass for the peak growth period, which is mainly in September or October (2013October ( to 2016, in the eutrophic and shallow south basin of Lake Biwa. We developed and validated a satellite-based water transparency retrieval algorithm based on the linear regression approach (R 2 = 0.77) to determine the water clarity (2013)(2014)(2015)(2016), which was later used for SAV classification and biomass estimation. For SAV classification, we used Spectral Mixture Analysis (SMA), a Spectral Angle Mapper (SAM), and a binary decision tree, giving an overall classification accuracy of 86.5% and SAV classification accuracy of 76.5% (SAV kappa coefficient 0.74), based on in situ measurements. For biomass estimation, a new Spectral Decomposition Algorithm was developed. The satellite-derived biomass (R 2 = 0.79) for the SAV classified area gives an overall root-mean-square error (RMSE) of 0.26 kg dry weight (DW) m −2 . The mapped SAV coverage area was 20% and 40% in 2013 and 2016, respectively. Estimated SAV biomass for the mapped area shows an increase in recent years, with values of 3390 t (tons, dry weight) in 2013 as compared to 4550 t in 2016. The maximum biomass density (4.89 kg DW m −2 ) was obtained for a year with high water transparency (September 2014). With the change in water clarity, a slow change in SAV growth was noted from 2013 to 2016. The study shows that water clarity is important for the SAV detection and biomass estimation using satellite remote sensing in shallow eutrophic lakes. The present study also demonstrates the successful application of the developed satellite-based approach for SAV biomass estimation in the shallow eutrophic lake, which can be tested in other lakes.

show abstract

Section: Discussionmentioning

confidence: 99%

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Through photosynthesis, the phytoplankton converts CO 2 and H 2 O into O 2 and is responsible for primary production in the water column [6,12]. In addition, chl a is the main indicator of phytoplankton biomass [13][14][15] and can be used to determine the water clarity [9]. Phytoplankton blooms are natural processes in the water environment, which show the normal functioning of a water ecosystem [16].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Chlorophyll a from Sentinel-2 MSI Data for the European Union Water Framework Directive Reporting Purposes

2018

View full text Add to dashboard Cite

The European Parliament and The Council of the European Union have established the Water Framework Directive (2000/60/EC) for all European Union member states to achieve, at least, “good” ecological status of all water bodies larger than 50 hectares in Europe. The MultiSpectral Instrument onboard European Space Agency satellite Sentinel-2 has suitable 10, 20, 60 m spatial resolution to monitor most of the Estonian lakes as required by the Water Framework Directive. The study aims to analyze the suitability of Sentinel-2 MultiSpectral Instrument data to monitor water quality in inland waters. This consists of testing various atmospheric correction processors to remove the influence of atmosphere and comparing and developing chlorophyll a algorithms to estimate the ecological status of water in Estonian lakes. This study shows that the Sentinel-2 MultiSpectral Instrument is suitable for estimating chlorophyll a in water bodies and tracking the spatial and temporal dynamics in the lakes. However, atmospheric corrections are sensitive to surrounding land and often fail in narrow and small lakes. Due to that, deriving satellite-based chlorophyll a is not possible in every case, but initial results show the Sentinel-2 MultiSpectral Instrument could still provide complementary information to in situ data to support Water Framework Directive monitoring requirements.

show abstract

“…It is specified here that these turbid water indices were tuned using the same data set used for GUC2 formulation ( n = 41). Several studies (Chen & Quan, ; Li et al, ; Zhang et al, , , ) in Asian case II waters, Moses et al () in Azov Sea, Gilerson et al () in Nebraska lakes, Kutser et al () in boreal and arctic lakes with high CDOM load, and Ligi et al () in the Baltic waters are few examples that have demonstrated the applicability of different NIR‐Red band ratio schemes in optically complex waters for the retrieval of CHL . For example, the resulting mean absolute error percentages were more than 100%, and RMSE as much as 32.55 μg/L for CHL ranging up to 30 μg/L (Li et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…It is specified here that these turbid water indices were tuned using the same data set used for GUC2 formulation (n = 41). Several studies (Chen & Quan, 2013;Li et al, 2011;Zhang et al, 2011Zhang et al, , 2014Zhang et al, , 2015 in Asian case II waters, Moses et al (2009) in Azov Sea, Gilerson et al (2010) in Nebraska lakes, Kutser et al (2016) (Li et al, 2012). The error in estimations is mostly consistent for CHL concentrations less than 10 μg/L while high CHL loads results in relatively decreased mean absolute error percentages, thereby different forms of turbid water algorithms are needed for different ranges, and that too, for specific water types.…”

Section: Performance Of Guc2 Algorithmmentioning

confidence: 99%

Remote Sensing of Chlorophyll‐A in Case II Waters: A Novel Approach With Improved Accuracy Over Widely Implemented Turbid Water Indices

Menon

Adhikari

2018

JGR Oceans

View full text Add to dashboard Cite

A new semianalytical algorithm was formulated to retrieve chlorophyll‐a (CHL) in optically complex waters using in situ data set of coastal waters of eastern Arabian Sea. The algorithm was derived using CHL index of the form, x = (Rrs(λ1)−1−Rrs(λ2)−1) × Rrs(λ3). The first wavelength (λ1) represents the secondary peak of CHL, while the second wavelength (λ2) and third wavelength (λ3) were delineated using a radiative transfer model and partial derivative analysis of hyperspectral remote sensing reflectance, respectively. Further iteration of three wavelengths between 600 and 700 nm resulted in a two‐wavelength index, x = (Rrs(λ1)−1−Rrs(λ2)−1) × Rrs(λ2). This was further regressed with CHL data initially used for three wavelength index. The final form of algorithm, Goa University Case II (GUC2), cMCHL=113.112x3−58.408x2+8.669x − 0.0384, was validated with in situ CHL ranging between 0.11 and 25.56 μg/L, resulted in a strong correlation r2 = 0.99, RMSE = 0.30, and bias = 0.03. A comparison with NIR‐Red two‐band, three‐band, four‐band, synthetic chlorophyll index, and normalized difference chlorophyll index pointed to the nonsuitability of turbid water indices in different water types of the study area. For the first time, a CHL algorithm has been tested successfully in water types outside the region of its formulation. A pixel‐to‐pixel validation of GUC2‐derived MERIS CHL with NASA bio‐Optical Marine Algorithm Dataset and Satellite Coastal and Oceanography Research data set resulted in correlation, bias, and RMSE of 0.90, −0.0013, and 1.2499, respectively. Furthermore, GUC2 was successfully tested in Chesapeake Bay for accurate retrieval of CHL from stations with varying turbidity levels.

show abstract

A Spectral Decomposition Algorithm for Estimating Chlorophyll-a Concentrations in Lake Taihu, China

Cited by 27 publications

References 47 publications

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

Retrieval of Chlorophyll a from Sentinel-2 MSI Data for the European Union Water Framework Directive Reporting Purposes

Remote Sensing of Chlorophyll‐A in Case II Waters: A Novel Approach With Improved Accuracy Over Widely Implemented Turbid Water Indices

Contact Info

Product

Resources

About