Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model

Song, Kaishan; Li, Lin; Tedesco, Lenore P.; Li, Shuai; Duan, Hongtao; Liu, Dianwei; Hall, Bob; Du, Juan; Li, Zuchuan; Shi, Kun; Zhao, Ying

doi:10.1016/j.rse.2013.05.017

Cited by 90 publications

(64 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our PLS analyses, results using ISE-PLS models with the FDR dataset showed higher R 2 and lower RMSECV values than those of the reflectance dataset. These results are consistent with the research of Han and Rundquitst (1997) [22], who noted that FDR was better correlated with chlorophyll concentration than raw reflectance, and that random noise and the effects of suspended matter could be reduced by FDR [46]. After eliminating outliers and useless predictors, ISE-PLS calibrated more potential models than FS-PLS, both for Chl-a and TSS, with the wavelengths relevant to water quality.…”

Section: Evaluation Of the Predictive Abilities Of Fs-pls And Ise-plssupporting

confidence: 90%

“…As we expected, the PLS models exhibited better predictive abilities than models that use single wavebands or the index-based (RSI and NDSI) approaches, which shows the PLS method is potentially useful in retrieval of inland water quality parameters [46,47]. In our PLS analyses, results using ISE-PLS models with the FDR dataset showed higher R 2 and lower RMSECV values than those of the reflectance dataset.…”

Section: Evaluation Of the Predictive Abilities Of Fs-pls And Ise-plssupporting

confidence: 67%

See 1 more Smart Citation

Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan

et al. 2017

View full text Add to dashboard Cite

Abstract:Concentrations of chlorophyll-a (Chl-a) and total suspended solids (TSS) are significant parameters used to assess water quality. The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds in Higashihiroshima, Japan, using field hyperspectral measurements and statistical analysis. Field experiments were conducted in six ponds and spectral readings for Chl-a and TSS were obtained from six field observations in 2014. For statistical approaches, we used two spectral indices, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI), and a partial least squares (PLS) regression. The predictive abilities were compared using the coefficient of determination (R 2 ), the root mean squared error of cross validation (RMSECV) and the residual predictive deviation (RPD). Overall, iterative stepwise elimination based on PLS (ISE-PLS), using the first derivative reflectance (FDR), showed the best predictive accuracy, for both Chl-a (R 2 = 0.98, RMSECV = 6.15, RPD = 7.44) and TSS (R 2 = 0.97, RMSECV = 1.91, RPD = 6.64). The important wavebands for estimating Chl-a (16.97% of all wavebands) and TSS (8.38% of all wavebands) were selected by ISE-PLS from all 501 wavebands over the 400-900 nm range. These findings suggest that ISE-PLS based on field hyperspectral measurements can be used to estimate water Chl-a and TSS concentrations in irrigation ponds.

show abstract

Section: Evaluation Of the Predictive Abilities Of Fs-pls And Ise-plssupporting

confidence: 90%

Section: Evaluation Of the Predictive Abilities Of Fs-pls And Ise-plssupporting

confidence: 67%

Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan

et al. 2017

View full text Add to dashboard Cite

show abstract

“…In addition, with the strong spatial dependence between water and vegetation, the vegetation may be the key component of the land-water pixels. Taken together, these factors mean that the green band reflectance of mixed land-water pixels is higher than the blue band [44][45][46]; and (2) most of the land covers that show higher reflectance in the green band than the blue band possess similar characteristics to water bodies; however, dark buildings present opposite characteristics. Above all, the green band and the blue band can be used for separation of mixed land-water pixels and dark buildings.…”

Section: Mixed Land-water Pixel Extractionmentioning

confidence: 99%

Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery

Xie

Luo

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

Abstract:Water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. Remote sensing has increasingly been used for water mapping in rural areas; however, when applied to urban areas, this spatially-explicit approach is a challenging task due to the fact that the water bodies are often of a small size and spectral confusion is common between water and the complex features in the urban environment. Water indexes are the most common method of water extraction at the pixel level. More recently, spectral mixture analysis (SMA) has been widely employed in analyzing the urban environment at the subpixel level. The objective of this study is to develop an automatic subpixel water mapping method (ASWM) which can achieve a high accuracy in urban areas. Specifically, we first apply a water index for the automatic extraction of mixed land-water pixels, and the pure water pixels that are generated in this process are exported as the final result. Secondly, the SMA technique is applied to the mixed land-water pixels for water abundance estimation. As for obtaining the most representative endmembers, we propose an adaptive iterative endmember selection method based on the spatial similarity of adjacent ground surfaces. One classical water index method (the modified normalized difference water index (MNDWI)), a pixel-level target detection method (constrained energy minimization (CEM)), and two widely used SMA methods (fully constrained least squares (FCLS) and multiple endmember spectral mixture analysis (MESMA)) were chosen for the water mapping comparison in the experiments. The results indicate that the proposed ASWM was able to detect water pixels more efficiency than other unsupervised water extraction methods, and the water fractions estimated by the proposed ASWM method correspond closely to the reference fractions with the slopes of 0.97, 1.02, 1.04, and 0.98 and the R-squared values of 0.9454, 0.9486, 0.9665, and 0.9607 in regression analysis corresponding to different test regions. In the quantitative accuracy assessment, the ASWM method shows the best performance in water mapping with the mean kappa coefficient of 0.862, mean producer's accuracy of 82.8%, and mean user's accuracy of 91.8% for test regions.

show abstract

“…The Chl-a concentration can be estimated using remote sensing data due to its unique spectral properties [4,6]. For Case I water (i.e., open oceans), algorithms based on blue and green spectral reflectances can be used due to the relatively simple optical properties [4,7].…”

Section: Introductionmentioning

confidence: 99%

A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data

2015

View full text Add to dashboard Cite

Due to the spatiotemporal variations of complex optical characteristics, accurately estimating chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing techniques remains challenging. In this study, a weighted algorithm was developed to estimate the Chl-a concentrations based on spectral classification and weighted matching using normalized mutual information (NMI). Based on the NMI algorithm, three water types (Class 1 to Class 3) were identified using the in situ normalized spectral reflectance data collected from Taihu Lake. Class-specific semi-analytic algorithms for the Chl-a concentrations were established based on the GOCI data. Next, weighted factors, which were used to determine the matching probabilities of different water types, were calculated OPEN ACCESS Remote Sens. 2015, 7 11732 between the GOCI data and each water type using the NMI algorithm. Finally, Chl-a concentrations were estimated using the weighted factors and the class-specific inversion algorithms for the GOCI data. Compared to the non-classification and hard-classification algorithms, the accuracies of the weighted algorithms were higher. The mean absolute error and root mean square error of the NMI weighted algorithm decreased to 22.63% and 9.41 mg/m 3 , respectively. The results also indicated that the proposed algorithm could reduce discontinuous or jumping effects associated with the hard-classification algorithm.

show abstract

Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model

Cited by 90 publications

References 61 publications

Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan

Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan

Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery

A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data

Contact Info

Product

Resources

About