A neural-network model to retrieve CDOM absorption from <i>in situ</i> measured hyperspectral data in an optically complex lake: Lake Taihu case study

Sun, De Yong; Li, Yun Mei; Wang, Qiao; Lü, Heng; Le, C.; Huang, Chang Chun; Gong, Shao Qi

doi:10.1080/01431161.2010.481297

Cited by 23 publications

(4 citation statements)

References 28 publications

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“…Taihu Lake, the third largest freshwater lake in China (area ~ 2428 km 2 including islands or 2338 km 2 without islands), is a typical shallow inland lake with average depth of 1.9 m (maximum depth is about 2.6 m) [37][38][39][40]. It is located in the downstream of Yangtze River (Figure 1), on the southern Yangtze River Delta [41,42].…”

Section: Study Areamentioning

confidence: 99%

Automatic Extraction of Open Water Using Imagery of Landsat Series

Zhang

Shi

et al. 2020

Water

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Open surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodology based on the concept of spatial autocorrelation was developed for automatic water extraction from Landsat series images using Taihu Lake in south-eastern China as an example. The results show that this method has great potential to extract continuous open surface water automatically, even when the water surface is covered by floating vegetation or algal blooms. The results also indicate that the second shortwave-infrared band (SWIR2) band performs best for water extraction when water is turbid or covered by surficial vegetation. Near-infrared band (NIR), first shortwave-infrared band (SWIR1), and SWIR2 have consistent extraction success when the water surface is not covered by vegetation. Low filter image processing greatly overestimated extracted water bodies, and cloud and image salt and pepper issues have a large impact on water extraction using the methods developed in this study.

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Section: Study Areamentioning

confidence: 99%

Automatic Extraction of Open Water Using Imagery of Landsat Series

Zhang

Shi

et al. 2020

Water

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“…Considering the optical complexity of the studied water regions, this study proposed an empirical region-specific CDOM algorithm. This developed algorithm essentially utilized three band ratios to relate with the CDOM concentration (C CDOM ) based on the following assumptions: (1) reflectances at 412 nm and 443 nm were mostly affected by CDOM [47,48]; (2) reflectances at 488 nm were mostly affected by the phytoplankton, which are a source of CDOM; thus, adding this band into the CDOM algorithm would improve the quantification of photooxidation [9]; and (3) variations in the reflectance near 555 nm was mostly due to suspended particles; thus, adding Rrs(555) may remove the influence of particles [49]. Thus, empirical coefficients of Equation (7) were determined by nonlinear regression with cross-validation by using the in situ data set.…”

Section: Development Of the Cdom Algorithmmentioning

confidence: 99%

Long-Term Changes in Colored Dissolved Organic Matter from Satellite Observations in the Bohai Sea and North Yellow Sea

et al. 2018

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Spatial and temporal variations in colored dissolved organic matter (CDOM) are of great importance to understanding the dynamics of the biogeochemical properties of water bodies. This study proposed a remote sensing approach for estimating CDOM concentrations (C CDOM) based on in situ observations from the Bohai Sea (BS) and the North Yellow Sea (NYS). Cross-validation demonstrated that the accuracy of the CDOM algorithm is R 2 = 0.78, APD = 15.9%, RMSE = 0.92 (ppb). The CDOM algorithm was applied to estimate the 14-year (2003-2016) sea surface C CDOM in the BS and NYS areas using Moderate Resolution Imaging Spectroradiometer (MODIS) monthly products. The results showed a significant fluctuation in CDOM variations on a long-term scale. The highest values of CDOM were observed in the BS, the middle values were observed in the Bohai Strait, and the lowest values were observed in the NYS. Seasonal variations were observed with long-lasting high CDOM values from June to August in coastal waters, while relatively low values were observed in the NYS in the summer. In the spring and fall, a distinct increase appeared in the NYS. High CDOM values in the nearshore coastal waters were mostly related to terrestrial inputs, while CDOM in the offshore regions was mainly due to autochthonous production. Furthermore, ocean currents played an important role in the variations in CDOM in the BS and NYS areas, especially for variations in CDOM in the Bohai Strait.

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“…It was used as a unified algorithm in coastal waters for Medium Resolution Imaging Spectrometer (MERIS; Doerffer & Schiller, 1997). Its capability to predict CDOM absorption from ocean color measurements in case II waters has been evaluated by different studies (Chen, He, et al, 2017;D'Alimonte et al, 2004;Kishino et al, 2005;Sun et al, 2011;Zhan et al, 2001;Zhang & Frank, 2004). But this method suffers from issues such as the danger of overfitting and being trapped in a local minimum (Zhan, 2005).…”

Section: Introductionmentioning

confidence: 99%

Estimating CDOM Concentration in Highly Turbid Estuarine Coastal Waters

Zhao

Cao

et al. 2018

JGR Oceans

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We proposed a support vector machine (SVM) algorithm to retrieve colored dissolved organic matter (CDOM) concentration (using ag(443) as a proxy) in the highly turbid Pearl River estuary. Two band ratios, namely, Rrs(443)/Rrs(547) and Rrs(488)/Rrs(547), were used as inputs. Comparisons between the estimated and measured ag(443) illustrated high accuracy of the SVM algorithm, yielding R2s of 0.98 and 0.89, mean absolute percentage errors of 5.18% and 13.1%, and root‐mean‐square deviations of 0.012 and 0.034 m−1 for the training and validation data sets, respectively. The SVM algorithm was also evaluated against existing ones for the study area and gave the best accuracy with a R2 of 0.9, a mean absolute percentage error of 10.23%, and a root‐mean‐square deviation of 0.025 m−1. The diurnal dynamics of CDOM in the Pearl River estuary was revealed showing complicated variations and influenced by the combined effects of wind, tide, circulation, and river discharge. As for remote sensing applications, the SVM‐based CDOM product exhibited great potential to trace the Pearl River plume and the satellite‐derived plume area agreed well with the FVCOM model simulation result. SVM is an accurate and fast tool for retrieving CDOM concentration, especially in highly turbid estuarine coastal waters, and thus, river plume dynamics can be traced.

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A neural-network model to retrieve CDOM absorption from in situ measured hyperspectral data in an optically complex lake: Lake Taihu case study

Cited by 23 publications

References 28 publications

Automatic Extraction of Open Water Using Imagery of Landsat Series

Automatic Extraction of Open Water Using Imagery of Landsat Series

Long-Term Changes in Colored Dissolved Organic Matter from Satellite Observations in the Bohai Sea and North Yellow Sea

Estimating CDOM Concentration in Highly Turbid Estuarine Coastal Waters

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