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Accurate remote assessment of phytoplankton chlorophyll-a (Chla) concentration in turbid case-2 waters is a challenge, owing largely to terrestrial substances (such as minerals and humus) that are optically significant but do not co-vary with phytoplankton. Here, we propose an improved Quasi-Analytical Algorithm (QAA) (denoted as TC2) for retrieving Chla concentrations from remote sensing reflectance (Rrs(λ)) which can be applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in turbid case-2 waters. TC2 has two main extensions when compared with QAA. First, TC2 makes an additional assumption to separate the total non-water absorption at 665 nm (anw(665)) into phytoplankton absorption (aph(665)) and yellow matter (aym(665)), which is the sum of colored dissolved matter (CDOM) and detritus. Second, for selecting the position of the near-infrared (NIR) band which is used to estimate the signal of total backscattering coefficient (bb(λ0)) at QAA reference band (λ0), we take into account the assumption that the absorption of pure water should be dominant at this band, as well as the impact of the signal-to-noise ratio (SNR) in the NIR band on the Chla concentration estimating model. When applied to in situ Rrs(λ) and OLCI match-up Rrs(λ) data in this study, TC2 provided more accurate Chla estimation than previous Cha concentration retrieval algorithms for turbid case-2 waters. TC2 has the potential for use as a simple and effective algorithm for monitoring Chla concentrations in the turbid case-2 waters at a global scale from space.

Estimating the proportions of particulate organic carbon (POC) endmembers is essential to fully understand the carbon cycle, the function of aquatic ecosystems, and the migration of contaminants in eutrophic lakes. There is currently no effective remote sensing optical algorithm in the literature to solve this problem. In this study, a POC-source color index (SPOC) was constructed based on the terrestrial and endogenous POC ratios calculated from field-measured stable isotope (δ13CPOC) values. The SPOC algorithm traces the sources of POC by utilizing three spectral bands centered approximately at 560 nm, 674 nm, and 709 nm, covering the intrinsic optical information of different POC sources. At the same time, the SPOC algorithm shows good applicability to Ocean and Land Color Instrument (OLCI), Medium-Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Geostationary Ocean Color Imager (GOCI) image data. The POC sources estimated using the algorithm and monthly OLCI data showed that from March 2018 to January 2019, the POC at the surface of Lake Taihu was mainly terrigenous. In addition, due to multiple factors such as algal blooms, plant physiology, river transport, regional rainfall, and carbon cycling, the distribution of POC sources exhibited strong spatial and temporal heterogeneity. Compared with other methods, it is more convenient to use remote sensing to identify the proportion of POC in different endmembers, which offers a more comprehensive understanding of the energy flows and material circulation in lakes.

The diffuse attenuation coefficient Kd(λ) is an important optical parameter that can be determined remotely. It plays a meaningful role in inland and coastal biogeochemical processes and aids in determining water quality. Four data-gathering expeditions were conducted in Lake Hongze (HZL), China, between 2015 and 2017. Based on the in situ optical and biological data, we constructed and evaluated a band ratio model for retrieving Kd(490) of HZL from MODIS/Aqua images between 2002 and 2016. Our results are as follows: (1) The developed algorithm, using the visible bands (Rrs(red)/Rrs(green)), performs robustly for Kd(490) estimation, with R2 of 0.82 and p

Knowledge workers' innovative behavior is an important driving force for enterprise development. The paper explores the influencing factors of knowledge worker's innovation behavior through the self-control ability and achievement motivation of knowledge workers. In the paper we introduce achievement motivation in the relationship between self-control ability and innovative behavior, built a relationship model for knowledge workers' influencing factors of innovation, and the model is validated by regression analysis of questionnaire data. The results show that the self-management ability of knowledge workers has a significant positive effect on innovation behavior, and the achievement motivation plays an intermediate role between them. Enterprises can influence knowledge workers’ innovative behavior by interfering with self-management and achievement motivation of them.

Woody vegetation is a central component of savanna ecosystems providing ecosystem services for local livelihoods. Accurate monitoring of woody vegetation in savannas is therefore desirable, yet large scale mapping approaches rely on relatively coarse spatial resolution satellite data, which cannot directly capture the scattered nature of savanna trees. Studies at regional scale thus estimate the fractional cover of woody plants for a given area, whereas binary tree/no-tree estimates are restricted to the use of very high-resolution (VHR) images at local scales. With the launch of the Sentinel satellite systems (Sentinel-1 and Sentinel-2), the spatial resolution of images approaches the size of medium/large tree crowns, providing the opportunity to map the presence/absence of tree canopies, rather than the fraction of woody cover or forested areas. Here, we used a support vector machine (SVM) to classify the presence/absence of woody canopies from Sentinel-1 and Sentinel-2 data at a 10-m spatial resolution for the entire African Sahel. Training samples for the SVM classifier were collected from VHR images provided by Google Earth and Sentinel satellite data were processed in Google Earth Engine. Accuracy assessment was performed based on independent VHR images, showing an overall accuracy of 93% (71% and 98% for producer’s accuracy of woody and non-woody pixels, 91% and 93% for user’s accuracy of woody and non-woody pixels) when combining Sentinel-1 and Sentinel-2 data (overall accuracy of 89% using Sentinel-1 only and 91% using Sentinel-2 only). The combined use proved to perform significantly better (p

Due to the increasing demand for military and commercial applications, magnetic metal-based core@shell nanostructures have attracted extensive attention in the field of electromagnetic wave (EMW) absorption materials. To further improve the overall performance, herein, an effective strategy is designed to fabricate Co

A reconstruction method was developed for hyperspectral remote sensing reflectance (Rrs)data in the visible domain (400–700 nm) based on in situ observations. A total of 2,647 Rrs spectra were collected over a wide variety of water environments including open ocean, coastal and inland waters. Ten schemes with different band numbers (6 to 15) were tested based on a nonlinear model. It was found that the accuracy of the reconstruction increased with the increase of input band numbers. Eight of these schemes met the accuracy criterion with the mean absolute error (MAE) and mean relative error (MRE)values between reconstructed and in situ Rrs less than 0.00025 sr-1 and 5%, respectively. We chose the eight-band scheme for further evaluation because of its decent performance. The results revealed that the parameterization derived by the eight-band scheme was efficient for restoring Rrs spectra from different water bodies. In contrast to the previous studies that used a linear model with 15 spectral bands, the nonlinear model with the eight-band scheme yielded a comparable reconstruction performance. The MAE andMRE values were generally less than 0.00016 sr-1 and 3% respectively; much lower than the uncertainties in satellite-derived Rrs products. Furthermore, a preliminary experiment of this method on the data from the Hyperspectral Imager for the Coastal Ocean (HICO) showed high potential in the future applications for reconstructing Rrs spectra from space-borne optical sensors. Overall, the eight-band scheme with our non-linear model was proven to be optimal for hyperspectral Rrs reconstruction in the visible domain.

Atmospheric correction is an essential prerequisite for obtaining accurate inland water color information. An inland water atmospheric correction algorithm, ACbTC (Atmospheric Correction based on Turbidity Classification), was proposed in this study by using OLCI (Ocean and Land Color Instrument) and SLSTR (Sea and Land Surface Temperature Radiometer) synergistic observations for the first time. This method includes two main steps: (1) water turbidity classification by the GRA index (GRAdient of the spectrum index); and (2) atmospheric correction by synergistic use of OLCI and SLSTR images. The algorithm was validated with 72 in situ sampling sites in Lake Erhai, Lake Hongze, and Lake Taihu, and compared with other atmospheric correction methods, i.e., C2RCC (Case 2 Regional Coast Colour processor), MUMM (Management Unit of the North Seas Mathematical Models), FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes), POLYMER (POLYnomial based algorithm applied to MERIS), and BPAC (Bright Pixel Atmospheric Correction). The results show that (1) the GRA index performed better than the proposed turbidity classification indices, i.e., the Diff (spectral difference index) and the Tind (turbid index), in inland lakes by using the reflectance peak at 1020 nm in clean water; (2) the synergistic use of OLCI and SLSTR performed feasibly for atmospheric correction, and the ACbTC algorithm achieved full-band average values of the mean absolute percentage error (MAPE) = 29.55%, mean relative percentage error (MRPE) = 13.98%, and the root mean square of error (RMSE) = 0.0039 sr−1, which were more reliable than C2RCC, MUMM, FLAASH, POLYMER, and BPAC; and (3) the synergistic use of the 17th band (865 nm) on OLCI and the 5th band (1613 nm) on SLSTR are suitable for clean inland lakes, while both the 5th band (1613 nm) and 6th band (2250 nm) on SLSTR are advisable for the turbidity.

Manipulation of magnons opens an attractive direction in the future energy-efficient information processing devices. Such quasi-particles can transfer and process information free from the troublesome Ohmic loss in conventional electronic devices. Here, we propose to realize topologically protected magnon modes using the interface between the patterned ferrimagnetic insulator thin films of different configurations without the Dzyaloshinskii-Moriya interaction. The interface thus behaves like a perfect waveguide to conduct the magnon modes lying in the band gap. These modes are immune to backscattering even in sharply bent tracks, robust against the disorders, and maintain a high degree of coherence during propagation. We design a magnonic Mach-Zehnder interferometer, which realizes a continuous change of magnon signal with varying external magnetic field or driving frequency. Our results pave a new way for realizing topologically protected magnon waveguide and finally achieving a scalable low-dissipation spintronic devices and even the magnonic integrated circuit.

Tunable transport properties and Fano resonances are predicted in a circular bilayer phosphorene nanoring. The conductance exhibits Fano resonances with varying incident energy and applied perpendicular magnetic field. These Fano resonance peaks can be accurately fitted with the well known Fano curves. When a magnetic field is applied to the nanoring, the conductance oscillates periodically with magnetic field which is reminiscent of the Aharonov-Bohm effect. Fano resonances are tightly related to the discrete states in the central nanoring, some of which are tunable by the magnetic field.

Definition of HEART score [29]: There were five elements: history, ECG, age,risk factors and troponin in HEART score. Each variable was scored as 0, 1 or 2 points. Each patient will receive a score of 1-10. (DOCX 32 kb)

Asphaltine and heavy oil were extracted by the Soxhlet extraction method from the coliquefaction residue of coal and catalytic cracking slurry oil. The asphaltine was carbonized with different proportions of heavy oil to explore the co-carbonization effect for the sake of performance optimization of the mesophase products. Experimental results indicate that the H/C atomic ratio and alkyl group of polymerized samples gradually increase while insoluble content and softening point of polymerized samples gradually decrease as the percentage of heavy oil in the precursor blend increases. With about 20 wt% of heavy oil, a large number of orderly mesophase flow domains with low softening point (~210 °C) can be obtained and the polymerized sample yield reaches the maximum of 76.68 wt%. When the percentages of heavy oil are higher than about 20 wt%, the mesophase is insufficiently developed due to the higher H/C ratio, lower aromaticity, and molecular weight of raw materials. Moreover, when the percentage of heavy oil is about 80 wt%, discrete mesophase microspheres with relatively uniform size (~9 μm) and good sphericity can be generated. This work indicates that it is indispensable to adjust the ratio of asphaltine to heavy oil for effectively controlling the performance of mesophase.

Absorption and backscattering coefficients, two important inherent optical properties of water, play a significant role in environmental monitoring and the study of biogeochemical cycles. In this study, the optical classification of turbid eutrophic inland Case II water was the basis for the development of an IOP-retrieval algorithm. Results show that the water can be divided into two types with different optical characteristics using the spectral slope defined as the spectrum slope of remote sensing reflectance between a 677 nm absorption valley and a 701 nm reflection peak at a threshold value of 0.32. A method of segmented simulation was proposed to model a backscattering coefficient at wavelength 400–800 nm, in which the backscattering coefficient at wavelength 400–685 nm was simulated using a power function and the backscattering coefficient at a wavelength greater than 685 nm was regarded as constant. The simulation precision of this proposed segmented simulation method was much higher than the precision using a power function. Based on the quasi-analytical algorithm (QAA) algorithm proposed by Lee et al. , two algorithms using reference bands 550 and 675 nm separately for estimating a backscattering coefficient were developed. Weight coefficients of the two retrieval algorithms were also calculated based on their estimation errors. The optimal backscattering coefficient was determined through a multimodel collaborative retrieval method. Finally, the total absorption coefficient was derived from a bio-optical model introduced by Lee et al. Accuracy assessment results demonstrated that the proposed IOP-retrieval algorithm using double reference bands (QAA-DB) and based on the optical classification algorithm can be successfully applied to optically complex eutrophic inland waters with a mean absolute percentage error (MAPE) of 19.71% and root-mean-square error (RMSE) of 1.3933.

This study examines the cleavage of covalent bonds and the radical behavior in the pyrolysis of lignin, cellulose, and hemicellulose at 350, 400, and 440 °C. In the presence of a sufficient amount of a hydrogen donor solvent, i.e., 9,10-dihydrophenanthrene (DHP), the quantity of covalent bonds cleaved is determined, and the quantity of stable radicals formed is measured. It is found that more than 99.9% of the radicals generated in the pyrolysis are coupled to hydrogen donated by DHP. The pyrolysis of the biomass can be characterized as cleavage of two types of covalent bonds, weak and strong. The cleavage of the weak bonds dominates the pyrolysis in the early stage, less than 10 min at 440 °C, whereas that of the strong bonds becomes significant in the later stage, and its rate is promoted by DHP. The amounts of the weak bonds are 0.89 × 10–2, 0.63 × 10–2, and 0.68 × 10–2 mol/g in lignin, cellulose, and hemicellulose, respectively. These values correspond to the sum of the Cal–O and Cal–Cal bonds linking...

To accurately observe the dynamic concentration of total suspended matter CTSM by geostationary satellite, Geostationary Ocean Color Imager GOCI, a new, regionally empirical combination algorithm, was developed based on the in situ measurement of water properties. Also, the ability of GOCI with a 1 h temporal resolution to capture dynamic change in CTSM was evaluated using in situ buoy-measured data. The validation results indicate that the combination algorithm combination of two-and three-band ratio algorithms can achieve acceptable retrieval results for CTSM, with 34.52% root mean square error percentage RMSP for validation data and 45.01% RMSP for GOCI match-up points. The depth of reflectance valley at 680 nm Rrs-depth,680 is considered as an index for deciding whether to utilize a two-Rrs-depth,680 ≤ 0.005 or three-band Rrs-depth,680 > 0.005 algorithm in the combination algorithm. This combination algorithm overcomes some shortcomings of two-and three-band ratio algorithms of CTSM in turbid productive water. The results show that sediment resuspension will significantly change the optical properties of water and decrease the universality of the CTSM retrieval algorithm in the presence of a large area of algae. The influence of sediment resuspension on the CTSM retrieval algorithm is significant in the central lake but less so in the other four lakes studied due to lake morphology and algal biomass.

Treatment of eutrophication and algal blooms in large shallow lakes has been constrained by the endogenetic release of nutrients from sediment resuspension despite the well-controlled input of nutrients from surrounding land in Taihu Lake, China. In this study, we propose a method to simplify regionally empirical algorithms for total suspended matter ( ${{C}_{{\rm{TSM}}}}$ ), chlorophyll-a ( ${{C}_{{\rm{Chl\hbox{-}a}}}}$ ), and phosphorus concentration ( ${{C}_{\rm{p}}}$ ) for the Geostationary Ocean Color Imager (GOCI) for Taihu Lake. The results show that the GOCI-derived ${{C}_{{\rm{TSM}}}}$ , ${{C}_{{\rm{Chl\hbox{-}a}}}}$ , and ${{C}_{\rm{p}}}$ match well with the in situ measured data. Based on the validated synchronous and timely observation data ( ${{C}_{{\rm{TSM}}}}$ , ${{C}_{{\rm{Chl\hbox{-}a}}}}$ and ${{C}_{\rm{p}}}$ ) from GOCI level-1b data, the interaction of phosphorus with algae and suspended sediment were analyzed from August 6 to August 9, 2013. The results indicate that phosphorus released from sediment resuspension is significant in Taihu Lake, with a high spatial–temporal variation. The effect of the restriction of nutrients (nitrogen and phosphorus) on algal growth varied temporally and spatially with the hydrodynamic characteristics of the lake. In most cases, restricted phosphorus in the lake is the primary reason for the algal growth, especially in open regions such as Center Lake (CL), Northwestern Lake (NWL), and Southwestern Lake (SWL). Both nitrogen and phosphorus are the restrictive factors to algal growth in Meiliang and Gonghu Bays. More attention should thus be focused on the endogenetic release of phosphorus in Taihu Lake when treating eutrophication and algal blooms in addition to controlling the input of nitrogen and phosphorus from surrounding land.

We have developed and validated a robust empirical model for estimating the concentrations of total suspended matter (TSM) in Lake Taihu (China), a large turbid inland water body. This model was generated using Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) medium-resolution (250 m) data gathered from 2003 to 2013 and in situ data collected from a number of cruise surveys. A strong significant correlate relationship between the in situ TSM data and the atmospherically corrected MODIS-Aqua remote sensing reflectance at the 645 nm band ( R rs (645)) was found (R 2 = 0.70, p n = 150). From these data, a local TSM model was developed for Lake Taihu. Long-term TSM distribution maps retrieved from the MODIS-Aqua data demonstrated marked temporal and spatial variations. Temporally, significant lower TSM was found in summer and autumn than in winter and spring ( p t -test). The significant seasonal variability could be attributed to sediment resuspension due to changes in the wind speed between different seasons. Lake Taihu also experiences large inter-annual variations that are primarily caused by changes in wind force over the region. In particular, the TSM in Lake Taihu from 2006 to 2008 was relatively lower than in other years, which could be explained by the lower mean wind speed during these years compared to the other years. Spatially, the TSM in the Open area, especially in the southern part of this region, was consistently higher than in other sub-regions of Lake Taihu. The coverage of submerged aquatic vegetation (SAV) generally characterized East Lake Taihu as a region with a relatively lower TSM. Lake topographic conditions, SAV, and runoff discharge jointly contributed to the spatial variations in TSM.

Phytoplankton bloom in a shallow inland eutrophic lake (Taihu Lake) is characterized by significant spatial and temporal variation and a high concentration of chlorophyll-a (Cchl-a). The observation of the rapidly changing dynamic characteristics of algae is limited by the insufficient temporal resolution of satellite data. The Geostationary Ocean Color Imager (GOCI), launched by Korea, can provide high temporal resolution satellite data to observe the hourly dynamics of algae. In this study, a simple regional NIR-red two-band empirical algorithm of Cchl-a for GOCI is proposed for Taihu Lake. Study results show that the GOCI-derived Cchl-a matches the in situ measured values well. Based on this validated algorithm of Cchl-a, we obtained the hourly maps of Cchl-a from GOCI level-1b data during the period August 6 to August 9, 2013. The spatial variation of GOCI-derived Cchl-a also matches synchronous in situ measured values well, and the temporal variation of GOCI-derived Cchl-a coincides with buoy-measured Cchl-a. The northwestern area of the lake and Meiliang Bay are worst hit by phytoplankton bloom. GOCI-derived Cchl-a revealed a clear evidence of hourly spatial and temporal variations of Cchl-a in Taihu Lake. The vertical current plays an important role in the hourly scale of spatial and temporal variations in phytoplankton. The horizontal current is important to the distribution of phytoplankton over the long term, but spatially and temporally limited in the short term.

Total suspended solid (TSS) concentration is an important water quality parameter. Mapping its varying distribution using satellite images with high temporal resolution is valuable for studying suspended sediment transportation and diffusion patterns in inland lakes. A total of 255 sites were used to make remote-sensing reflectance measurements and surface water sampling at four Chinese inland lakes, i.e. Taihu Lake, Chaohu Lake, Dianchi Lake, and the Three Gorges Reservoir, at different seasons. A two-step retrieval method was then developed to estimate TSS concentration for contrasting Chinese inland lakes, which is described in this article. In the first step, a cluster method was applied for water classification using eight Geostationary Ocean Colour Imager (GOCI) channel reflectance spectra simulated by spectral reflectance measured by an Analytical Spectral Devices (ASD) Inc. spectrometer. This led to the classification of the water into three classes (1, 2, and 3), each with distinct optical charac...

Although remote sensing technology has been widely used to monitor inland water bodies; the lack of suitable data with high spatial and spectral resolution has severely obstructed its practical development. The objective of this study is to improve the unmixing-based fusion (UBF) method to produce fused images that maintain both spectral and spatial information from the original images. Images from Environmental Satellite 1 (HJ1) and Medium Resolution Imaging Spectrometer (MERIS) were used in this study to validate the method. An improved UBF (IUBF) algorithm is established by selecting a proper HJ1-CCD image band for each MERIS band and thereafter applying an unsupervised classification method in each sliding window. Viewing in the visual sense—the radiance and the spectrum—the results show that the improved method effectively yields images with the spatial resolution of the HJ1-CCD image and the spectrum resolution of the MERIS image. When validated using two datasets; the ERGAS index (Relative Dimensionless Global Error) indicates that IUBF is more robust than UBF. Finally, the fused data were applied to evaluate the chlorophyll a concentrations (Cchla) in Taihu Lake. The result shows that the Cchla map obtained by IUBF fusion captures more detailed information than that of MERIS.

The successfully launched Huanjing-1 (HJ-1) satellite by China in 2008 provides a new source of data for monitoring the environment. In this article, we develop a new algorithm for retrieving the aerosol optical thickness (AOT) using HJ-1 charge-coupled device (CCD) data with the assistance of the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and the bidirectional reflectance distribution function (BRDF) data products. This algorithm is then used to retrieve AOT in a delta region of the Yangtze River. The retrieved results are assessed for their accuracy by comparison with ground-measured data using sun photometers. Comparison of such derived AOT with in situ AOT measured using sun photometers indicates a root mean squared error (RMSE) of 0.123, and their regression relation has a correlation coefficient of 0.896 that is statistically significant at the 0.01 level. Such a relatively high level of retrieval accuracy suggests that HJ-1 CCD data can be used competently and ef...

We investigate theoretically the electronic properties of two-dimensional electron gases (2DEGs) with regular and distorted triangular antidot lattices. We show that the triangular antidot lattices embedded in 2DEGs behave like artificial graphene and host Dirac fermions. By introducing the Wannier representation, we obtain a tight-binding Hamiltonian including the second-nearest-neighboring hopping, which agrees well with the numerically exact solutions. Based on the tight-binding model, we find that spatially nonuniform distortions of the antidot lattices strongly modify the electronic structures, generate pseudomagnetic fields and the well-defined Landau levels. In contrast to graphene, we can design the nonuniform distortions to generate various configurations of pseudomagnetic fields. We show that the snake orbital states arise by designing the $\ifmmode\pm\else\textpm\fi{}\mathbf{B}$ pseudomagnetic field configuration. We find that the disorders of antidot lattices during fabrication would not affect the basic feature of the Dirac electrons, but they lead to a reduction in conductance in strong disorder cases.

In situ measured values of particulate organic carbon (POC) in Taihu Lake and remote sensing reflectance observed by three satellite courses from 2014 to 2015 were used to develop an near infrared-red (NIR-Red) empirical algorithm of POC for the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) satellite image. The performance of the POC algorithm is highly consistent with the in situ measured POC, with root mean square error percentage (RMSPs) of 38.9% and 31.5% for two independent validations, respectively. The MODIS-derived POC also shows an acceptable result, with RMSPs of 53.6% and 61.0% for two periods of match-up data. POC from 2005 to 2007 is much higher than it is from 2002 to 2004 and 2008 to 2013, due to a large area of algal bloom. Riverine flux is an important source of POC in Taihu Lake, especially in the lake’s bank and bays. The influence of a terrigenous source of POC can reach the center lake during periods of heavy precipitation. Sediment resuspension is also a source of POC in the lake due to the area’s high dynamic ratio (25.4) and wind speed. The source of POC in an inland shallow lake is particularly complex, and additional research on POC is needed to more clearly reveal its variation in inland water.

Summary Based on the bio-optical properties of highly turbid productive inland waterbody, Taihu Lake, a novel bio-optical optimization algorithm was developed to estimate chlorophyll-a concentration (Cchla) and total suspended matter concentration (CTSM) after optical classification. Between 2006 and 2013, 1080 in situ samples collected from four inland lakes in China were utilized to test this optimization algorithm. All data were classified into four classes based on a new bio-optical classification method. The retrieval results of CTSM and Cchla exhibit a good consistency with in situ measured CTSM and Cchla. CTSM retrieval accuracies (evaluated by the root mean square of percentage errors: RMSPs) of classes 1, 2, 3, and 4 were 35.77%, 16.09%, 28.42%, and 26.86% for data1 and were 32.15%, 33.14%, 47.71%, and 34.89% for data3, respectively. Cchla retrieval accuracies (RMSPs) of classes 1, 2, 3, and 4 were 32.49%, 20.05%, 42.01%, and 34.85% for data1, were 44.71%, 32.59%, 47.92%, and 38.11% for data2, and were 33.12%, 25.65%, 70.88%, and 23.57% for data3, respectively. The optimization algorithm was also tested by the simulated data of Medium Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer, and Sea-viewing Wide Field-of-view Sensor satellite sensors’ center wavelengths. The validation shows a good correlation with the measured CTSM and Cchla. All these examinations demonstrate that the bio-optical optimization algorithm and classification are valid and robust for both the in situ data and the simulated satellite data. The optical relationships of aph(4 4 0) to Cchla and bbp(4 4 0) to CTSM are reasonable and effective. In summary, the results present that the bio-optical optimization algorithm proposed in this study shows high potential application to various water types and satellite sensors. The retrieval accuracy of CTSM and Cchla derived by bio-optical optimization algorithm was significantly improved after classification.

Assessments of specific inherent optical properties (SIOPs) and their variability in highly turbid and productive inland waters are essential for the accurate estimation of water quality. A new optical classification method including two classification criteria [i.e., normalized remote sensing reflectance slope (NS), and normalized remote sensing reflectance depth (ND)] was developed to divide remote sensing reflectance into four classes, i.e., class 1 (NS 0.21) is low turbid and high productive water; class 3 (NS > −0.0017 and ND −0.0017 and ND > 0.009) is high turbid and high productive water. The relationships between phytoplankton absorption at 440 nm [a ph(440)] and chlorophyll-a concentration [C chla] as well as between particle backscattering coefficient at 440 nm [b bp(440)] and total suspended matter concentration (C TSM) after classification were obtained from a large number of in situ data in Lake Taihu. The measured specific phytoplankton absorption $$[ {a_{\text{ph}}^{*} \left( \lambda \right)} ]$$ and particle backscattering coefficient $$[ {b_{\text{bp}}^{*} \left( \lambda \right)} ]$$ show significant variations even within the same class. The mean values of $$a_{\text{ph}}^{*} \left( \lambda \right)$$ at 440 nm $$[ {a_{\text{ph}}^{*} \left( {440} \right)} ]$$ for each class are 0.048 ± 0.013, 0.060 ± 0.012, 0.083 ± 0.021, and 0.056 ± 0.017 m2/mg, respectively. The mean values of $$b_{\text{bp}}^{*} \left( \lambda \right)$$ at 440 nm $$[ {b_{\text{bp}}^{*} \left( {440} \right)} ]$$ for each class are 0.035 ± 0.01, 0.024 ± 0.004, 0.041 ± 0.009, and 0.038 ± 0.009 m2/g, respectively. The power functions of SIOPs and water constituents’ concentration indicate that $$a_{\text{ph}}^{*} \left( {440} \right)$$ and $$b_{\text{bp}}^{*} \left( {440} \right)$$ vary with C chla and C TSM. The validation results show that our proposed values for $$a_{\text{ph}}^{*} \left( {440} \right)$$ and $$b_{\text{bp}}^{*} \left( {440} \right)$$ cover a very wide range of water optical properties, which are characterized from clear water to highly turbid productive water. The validation results also suggest that the retrieval accuracy of C chla and C TSM bio-optical model was improved after classification. The root mean square error (RMSE) of C chla was improved from 14.18 to 7.43 μg/L (mean value of all classes) and RMSE of C TSM was improved from 32.98 to 26.10 mg/L (mean value of all classes). Thus, the temporal and spatial variation of $$a_{\text{ph}}^{*} \left( {440} \right)$$ and $$b_{\text{bp}}^{*} \left( {440} \right)$$ should be considered in the bio-optical retrieval model of water quality. In complex optical properties of inland water, retrieving the water constituents with high accuracy needs to classify the water optical properties from the remote sensing spectrum by optical classification method. The figure shows the water color examples of each class .

Data assimilation is a method to produce a description of the system state, as accurately as possible, under the control of observations by using all the available information and by taking into account the observation and model errors. We developed a framework for Observing System Simulation Experiments (OSSEs) based on the ensemble square root Klaman filter (EnSRF) technique, and the framework could assimilate two data sets of chlorophyll a retrieved from Environmental Satellite 1 (HJ-1) and moderate resolution imaging spectro-radiometer (MODIS) onboard the Terra platform, separately. We assumed that one of the retrieved results was the proxy “truth value” and the other one contained errors. Based on EnSRF technique, combined with the three dimensional numerical model of wind-driven circulation and pollutant transportation in a large-scale lake, we investigated the potential impact of location distributions of simulated observation stations in Taihu Lake (China) on the performance of data assimilation. In addition, the effectiveness of this method for evaluation and prediction of the concentration of chlorophyll a was validated. The results showed that the location of simulated observation stations not only influenced the accuracy of evaluating and forecasting results, but also the performance of data assimilation. We also discuss the impact of assimilation time and background error on the results. This study demonstrated that this method of data assimilation is effective for evaluation and prediction of the concentration of chlorophyll a in highly turbid case 2 waters.

It has been proven that empirical (two-band) and semi-analytical (three-band) algorithms based on near-infrared and red (NIR-red) wavelengths can be used for estimating Cchl-a in highly turbid productive waters with satisfactory performance. However, the optimal spectral bands and parameters of algorithms vary significantly because of the different optical properties of datasets. Using a comprehensive dataset, we validate and evaluate the applicability of empirical and semi-analytical algorithms for deriving Cchl-a for inland lakes in China. The comprehensive dataset contains 993 in situ samples collected from five inland lakes in China between 2006 and 2013. The optimal algorithms, Rrs(706)/Rrs(685) and [ R rs - 1 ( 685 ) - R rs - 1 ( 707 ) ] R rs ( 722 ) , are calibrated using an in situ dataset, with root mean square errors (RMSEs) of 10.66 mg/m3 and 8.47 mg/m3, respectively. The RMSEs of the NIR-red two- and three-band algorithms for the validation data are 11.1 mg/m3 and 8.82 mg/m3, respectively. The RMSEs increase to 13.17 mg/m3 and 12.58 mg/m3 when the algorithms are applied to Medium Resolution Imaging Spectrometer (MERIS) and Geostationary Ocean Color Imager (GOCI) centre wavelengths. The RMSEs for the validation data decrease to 8.80 mg/m3 and 7.78 mg/m3 when the optimal spectral band (λ1) shifts to 671 nm. The RMSEs decrease to 10.03 mg/m3 and 9.09 mg/m3 as a result of optimization of the model parameters when the algorithms are applied to MERIS and GOCI centre wavelengths. The shifting of the optimal spectral band (the difference between 671 nm and 685 nm) increases the RMSEs from 8.80 mg/m3 to 11.1 mg/m3 for the two-band algorithm, and slightly increases the RMSEs from 7.78 mg/m3 to 8.82 mg/m3 for the three-band algorithm. This indicates that the three-band algorithm is much more suitable for high-turbidity water than the two-band algorithm. Nevertheless, the two-band model can be used for extremely turbid and low Cchl-a waters for analysis of the retrieval results after cluster analysis of remote sensing reflectance. Meanwhile, shifting of the optimal spectral bands (λ1) is highly correlated with the total suspended matter concentration (CTSM) (the Pearson correlation coefficient between λ1 and CTSM can reach 0.95). In conclusion, the results indicate that both the two- and three-band algorithms have high potential applicability for derivation of Cchl-a in high-turbidity inland waters in China.

This study presents an investigation of the scattering and backscattering properties of the particulates in three Chinese inland lakes (the Taihu Lake, the Chaohu Lake and the Dianchi Lake) based on in situ measurements taken at 119 sites. We modeled the particulate scattering spectra using a wavelength-dependent power-law function, finding that the power-law exponents in the Taihu Lake and the Chaohu Lake differ from those in the Dianchi Lake but are similar to the values in the U.S. coastal waters. In contrast to the open ocean, the backscattering properties in the three lakes can not be determined only from chlorophyll-a concentration. The backscattering ratio spectra exhibit a wavelength dependence feature in all three lakes, generally decreasing with the increasing wavelength. Analysis results of the correlations between the backscattering ratio and the individual water quality parameters clearly show that there are distinctive relations among the three lakes, attributed primarily to different compositions of optically active materials in the three lakes. Analysis of the mass-specific scattering and backscattering coefficients shows that the coefficients at wavelength 532 nm in the Taihu Lake and Chaohu Lake are similar, but they are apparently different from those in the Dianchi Lake. Lastly, Model I multiple linear regressions were adopted to partition the mass-specific cross-sections for scattering and backscattering into organic and inorganic cross-sections to further interpret the scattering and backscattering properties. The relative contribution of organic and inorganic particulates to scattering and backscattering is clearly different among the three lakes. The scattering and backscattering properties of the particulates in the three inland lakes vary significantly based on our collected data. The results indicated that the existing semi-analytical water quality retrieval models of the Taihu Lake can not be applied perfectly to the Chaohu Lake and the Dianchi Lake.

We present the results from a study of the particulate scattering properties of three bodies of water that represent a wide range of optical properties found in inland waters. We found a positive linear relationship (R 2 = 0.45, P b p *(532)) and the ratio of the inorganic suspended material (ISM) to the total suspended material (TSM) in our study areas. In contrast to earlier studies in which b p *(532) was lower for inorganic particles than for organic particles, we found that the value of b p *(532) for ISM ( b p *(532) ISM = 0.71 m 2 /g) was approximately 1.6 times greater than the value found for organic suspended materials (OSM) ( b p *(532) OSM = 0.45 m 2 /g). We found that the dependence of the particle scattering coefficient ( b p ) on wavelength (λ) could be described accurately by a power law (with mean average percent error (MAPE) 0.1), especially in the spectral region associated with strong phytoplankton absorption. The errors could be reduced over this wavelength range by adding a term to the model to account for particle absorption, but the additional term tended to increase the error outside of this range. We conclude that information about the nature of the scattering particles in lake waters is necessary for the selection of an appropriate model for particle absorption and that a hybrid model that includes absorption over some wavelength ranges may be necessary.

The diffuse attenuation coefficient, Kd(λ), is an important water optical property. Detection of Kd(λ) by means of remote sensing can provide significant assistance in understanding water environment conditions and many biogeochemical processes. Even when existing algorithms exhibit good performance in clear open ocean and turbid coastal waters, accurate quantification of highly turbid inland water bodies can still be a challenge due to their bio-optical complexity. In this study, we examined the performance of two typical pre-existing Kd(490) models in inland water bodies from Lake Taihu, Lake Chaohu, and the Three Gorges Reservoir in China. On the basis of water optical classification, new Kd(490) models were developed for these waters by means of the support vector machine approach. The obtained results showed that the two pre-existing Kd(490) models presented relatively large errors by comparison with the new models, with mean absolute percentage error (MAPE) values above ~30%. More importantly, among...

Multiple bio-optical measurements of optically active substances were conducted in Taihu Lake, Chaohu Lake, Three Gorges Reservoir, and Dianchi Lake (China). A hierarchical cluster analysis was applied to remote sensing spectra (Rrs(λ)), by which those waters were clustered into three optically distinct types (Type I, Type II, and Type III). Absorption coefficients of phytoplankton were simulated using the linear function and the coefficients of the functions varied in different water types. The slope of colored dissolved organic matter (CDOM) absorption could be clustered into two classes, one class of high slope for Types I and II, whereas the other class of low slope for Type III. A uniform model was used for parameterization of the nonalgal particles absorption coefficients spectral between different water types. The power-law function was used to parameterize the scattering coefficient and the slope of scattering spectra could be classified into two groups of high slope for Types I and II, low slope for Type III. The composition of particles, particulate scattering, and nonalgal particulate absorption are the factors controlling the variability in Rrs(λ) for corresponding type waters. Those results support such a fact that it is indispensable to classify waters before developing a model to estimate water quality parameters for multi-type optical waters.

Supplemental Digital Content is available in the text, Four risk scores for stratifying patients with chest pain presenting to emergency departments (EDs) (namely Thrombolysis in myocardial infarction [TIMI], Global registry for acute coronary events [GRACE], Banach and HEART) have been developed in Western settings but have never been compared and validated in Chinese patients. We aimed to find out to the number of MACE within 7 days, 30 days, and 6 months after initial ED presentation, and also to compare the prognostic performance of these scores in Chinese patients with suspected cardiac chest pain (CCP) to predict 7-day, 30-day, and 6-month major adverse cardiac events (MACE). A prospective 2-center observational cohort study of consecutive patients presenting with chest pain to the EDs of 2 university hospitals in Guangdong and Hong Kong from 17 March 2012 to 14 August 2013 was conducted. Patients aged ≥18 years with suspected CCP but without ST-segment elevation myocardial infarction (STEMI) were recruited. Of 833 enrolled patients (mean age 65.1 years, SD14.5; 55.6% males), 121 (14.5%) experienced MACE within 6 months (4.8% with safety outcomes and 10.3% with effectiveness outcomes). The HEART score had the largest area under the receiver operating characteristic (ROC) curve for predicting MACE at 7-day, 30-day, and 6-month follow-up [area under curve (AUC) = 0.731, 0.726, and 0.747, respectively. The HEART score also had the largest AUC for predicting effectiveness outcome (AUC = 0.715, 0.704, and 0.721, respectively). However, there was no significant difference in AUC between HEART and TIMI scores. Banach had the largest AUC for predicting safety outcome (AUC = 0.856, 0.837, and 0.850, respectively). The HEART score performed better than the GRACE and Banach scores to predict total MACE and effectiveness outcome in Chinese patients with suspected CCP, whereas the Banach score best predicted safety outcomes.

Reforestation and afforestation have been implemented over large regions in China for decades, and this has significantly reduced dust storms, desertification and soil/water erosion in China. However, the quantificational evaluation of the reforestation program in China is still incomplete, especially in the southwest China, due to the limitation of survey data. In order to assess the effects of reforestation on soil erosion in southwest China, a subtropical monsoon region with acid red soil, a representative watershed (Dianchi, Yunnan, in southwest China), was selected to study the effect of reforestation on soil erosion over nearly five decades. The land surface soil samples and sedimentary core in this watershed were examined in 2012 to evaluate the soil erosion and sediment deposition during the period of deforestation and reforestation. The results from the multi-source indexes (nutrients, radionuclides, metallic and isotope elements) measurement from soil samples and analyses (composite fingerprinting, remote sensing and geographic information system) indicate that reforestation significantly reduced the presence of sand from surrounding basin via the sedimentary records. Natural forests had the greatest soil conservation capacity, followed by economic forests and farmland, according to a composite fingerprinting. The distribution of total phosphorus, total nitrogen (TN), total organic carbon (TOC) and isotope carbon (δ13CSOC) presents a high consistency to the results of the composite fingerprinting. The loss rate of TN and TOC in farmland is much greater than that in forest. Reforestation will thus increase the storage of TN and TOC in the long term, although this storage is lower than that in farmland during a short time due to the application of fertilizers. The logging of economic forests will significantly increase soil erosion in this region because of low grass coverage. Recommendations include limiting the period of logging in economic forests to periods outside the rainy season and intercropping other kinds of indigenous trees or grasses to reduce soil erosion.

Water optical clustering based on water color information is important for many ecological and environmental application studies, both regionally and globally. The fuzzy clustering method avoids the sharp boundaries in type-memberships produced by hard clustering methods, and thus presents its advantages. However, to make good use of the fuzzy clustering methods on water color spectra data sets, the determination of the fuzzifier parameter (m) of FCM (fuzzy c-means) is the key factor. Usually, the m is set to 2 by default. Unfortunately, this method assigned some membership degrees to non-belonging water type, failing to obtain the unitarity of cluster structure in some cases, especially in inland eutrophic water. To overcome this shortcoming, we proposed an improved FCM method (namely FCM-m) for water color spectra classification by optimizing the fuzzifier parameter. We collected an inland data set containing 1280 in situ spectral data and co-measured water quality parameters with a wide range of biogeochemical variability in China. Using FCM-m, seven spectrally distinct water optical clusters on Sentinel-3 OLCI (Ocean and Land Colour Imager) bands were obtained with the optimized fuzzifier (m=1.36), and the well-performed clustering result is assessed by the validated index (Fuzzy Silhouette Index=0.513). Also, the FCM-m-based soft classification framework was successfully applied to the atmospherically corrected OLCI images, which was evaluated by previous case studies. Besides, by testing FCM-m on three coastal and oceanic data sets, we verified that the optimized m should be adjusted based on the data set itself, and in general, the value gradually approaches 1 with the increase of the band number (or dimension). Finally, the effect of the improved method was tested by Chlorophyll-a concentration estimation. The results show that the algorithm------- blending by FCM-m performs better than that by original FCM, which is mainly because the FCM-m reduces the estimation error from non-belonging clusters by a stricter membership value assignation. To sum up, we believe that FCM-m is an adaptive algorithm, whose R codes are available at https://github.com/bishun945, and needs to be tested by more public data sets.

The Tiaoxi River is the main source of water for Lake Taihu and can result in plumes in the lake after heavy precipitation events. These plumes have played a crucial role in the water quality changes within the lake. High temporal resolution GOCI (Geostationary Ocean Color Imager) data were used to study the spatial distribution of the total suspended matter concentration in Lake Taihu after heavy precipitation events in the Tiaoxi River Basin via an empirical model. The plumes were analyzed after two heavy precipitation events in 2011 and 2013 using 16 GOCI images, which indicated that the Tiaoxi River had a great influence on the spatial distributions of total suspended matter and algal blooms. It was concluded that the main factors affecting the plumes in the Tiaoxi River were precipitation intensity, runoff, and total suspended matter concentration. Human activity, such as sand excavation also played a crucial role in sediment discharge. The results of this study demonstrate that the visualization of GOCI data makes it possible to use remote sensing technology to continuously monitor an inland water environment on an hourly scale, which is of great significance for studying the diffusion and evolution of river plumes.

With the rapid development of the economy in recent years, massive algal (blue-green algae in particular) blooms have often observed in Chinese eutrophic lakes. The concentration of the cyanobacterial pigment phycocyanin (PC), an accessory pigment unique to freshwater blue-green algae, is often used as a quantitative indicator of blue-green algae in eutrophic inland waters. The purpose of this study was to evaluate the semi-analytic PC retrieval algorithm proposed by Simis et al. and to explore the potential to improve this PC algorithm so that it is more suitable for eutrophic lakes, such as Taihu Lake. In this paper, we recalculated the correction coefficients γ and δ to calculate the absorptions of chlorophyll-a at 665 nm and the absorptions of phycocyanin at 620 nm in terms of in situ measurements and observed that the values of these coefficients differed from the values used by Simis et al. and Randolph et al. The two coefficients are site dependent due to the different bio-optical properties of lakes. We also observed that the specific PC absorption at 620 nm a pc *(620) decreases exponentially with an increase in PC concentrations. Therefore, a non-linear power–function of a pc *(620), instead of a constant value of a pc *(620) as used by Simis et al., was proposed for our improved PC retrieval algorithm in Taihu Lake, yielding a squared correlation coefficient (R 2 ) of 0.55 and a root mean square error (RMSE) of 58.89 μg/L. Compared with the original PC retrieval algorithm by Simis et al., the improved retrieval algorithm has generally superior performance. In evaluating the limitation of the PC retrieval algorithms, we observed that the ratio of the total suspended solids to phycocyanin can be used as a primary measure for retrieval performance. Validation in Dianchi Lake and an error analysis proved that the improved PC algorithm has a better universality and is more suitable for eutrophic lakes with higher PC concentrations.

Time-series MODIS data were used to extract and characterize algal blooms from the Taihu Lake study area. Water quality data including total nitrogen, total phosphorus, and dissolved oxygen; local meteorology data; and global climate trends were examined to reveal the factors influencing the formation of the algal blooms. Results for the 2000–2011 study period show that the annual algal bloom typically begins between March and May in Taihu Lake. All large-scale blooms originate from northern Taihu Lake (Meiliang Bay and Zhushan Bay). Some small-scale blooms originate from southwestern Taihu Lake, but the duration of these blooms is very brief because of episodes of turbulent mixing due to high wind speed. Nutrient supply is the main factor influencing algal mass propagation during bloom periods, and temperature changes may trigger algal recovery. The algal bloom area significantly decreased when wind speed is greater than 4 m/s, causing turbulence and changes in algal buoyancy. A strong East Asian summer monsoon transporting warm air to the lake is shown to extend the duration of algal blooms in Taihu Lake, as occurred in 2007.

The inherent optical properties (IOPs) of natural waters are the most significant factors affecting light propagation within water columns, and thus play indispensable roles on estimation of aquatic biomass, primary production, and carbon pools. Despite its importance, no IOPs retrieval model was specifically developed for inland water bodies, although significant efforts were made on oceanic inversion models. In addition, for inland waters, an IOPs-based model is often preferred for estimating chlorophyll-a (Chl-a) concentration, an application of IOPs, over empirical and some semi-empirical algorithms. Then developing a model for estimating both IOPs and Chl-a is of significance for understanding the bio-optical properties and occurrence of algal blooms in eutrophic reservoirs, lakes and estuaries. In this paper, an IOPs Inversion Model of Inland Waters (IIMIW) for deriving natural water IOPs and estimating Chl-a is proposed and validated. The results indicate that this model can be used to accurately retrieve absorption coefficients at 443 nm and 665 nm with R2 = 0.8347 and R2 = 0.7550 respectively for Indiana study sites, and to estimate Chl-a from the derived absorption coefficients at high accuracies (R2 = 0.9292 and a mean relative error 21.65%) with samples collected from eight different study sites in the world and in different seasons. The model was also applied on Airborne Imaging Spectrometer for Application (AISA) images to map IOPs and Chl-a. Through validation by in situ measured Chl-a, results directly show that IIMIW can predict Chl-a with good accuracy even using the AISA bands, to as well indirectly prove that non-water absorption coefficients are retrieved accurately, at least within red and near-infrared region. Further biogeochemical information can be derived from these maps as well. These promising mapping results reveal possible remote routine surveillance of bio-optical states of inland waters.

Pigment C-phycocyanin (C-PC) is a useful indicator for the presence of cyanobacteria in inland waters, which has been well known as a phytoplankton group with many negative effects on human, animal, and aquatic ecosystem health. In recent years, the remote detection of the C-PC concentrations for inland waters has received much attention. However, their accurate quantification by means of remote sensing is still a challenge due to the significant bio-optical complexity of turbid inland waters. In this paper, three typical turbid inland lakes in China were investigated through in situ observed data sets containing optical and water quality parameters. By using a recently proposed TD680 optical classification method, all collected samples were first classified into three types. For each type of water, we determined specific spectral sensitive regions for the pigment C-PC. Then, we developed three type-specific support vector regression (SVR) algorithms and an aggregated SVR algorithm. The performances of these algorithms were evaluated through the validation data sets. The results show that the type-specific algorithms generally have significantly improved performance over the aggregated SVR algorithm. Their assessment errors [mean absolute percentage error (MAPE) and root-mean-square error ( rmse)] were as follows: 1) MAPE = 15.6% and rmse = 30.6 mg·m-3 for Type 1 water; 2) MAPE = 47.1% and rmse = 61.5 mg·m-3 for Type 2 water; and 3) MAPE = 26.4% and rmse = 19.1 mg·m-3 for Type 3 water. The findings in this paper demonstrate that a prior water classification is needed for the development of accurate C-PC retrieval algorithms. This paper provides a valid strategy for improving C-PC estimation accuracy and enhancing algorithm commonality for optically complex turbid waters.

Remote sensing detection of suspended particulate composition is of importance for understanding variability of water optical properties, especially in complex-material loading turbid inland waters. To achieve this goal, this study carried out multiple in situ bio-optical investigations during the period of 2008 ~ 2010, covering Lake Taihu, Chaohu, Dianchi, and Three Gorges reservoir in China. Using the collected in situ observed datasets, a semi-analytical approach was developed based on theoretical derivation of the water radiative transfer theory. This algorithm showed relatively high calibration accuracy, and was further evaluated by means of an independent validation dataset, producing low predictive errors (mean absolute percentage error, MAPE = 29.5%, and root mean square error, RMSE = 0.23, 10− 3). Then, the developed algorithm was applied into a new hyperspectral satellite data, namely the HJ1A/HSI image, mapping spatio-temporal distribution of suspended particulate composition for Lake Taihu waters. The findings in the present study indicate that the proposed approach has a great potential in detecting particulate composition of water bodies. This study provides us improved understanding of particulate composition characteristics and corresponding spatio-temporal distribution for complex turbid inland waters.

[1] Multiple bio-optical measurements were conducted in inland waters of China, including Lake Taihu [spring and autumn], Lake Chaohu, Lake Dianchi, and Three Gorges Reservoirs. The variations in the absorption characteristics of chromophoric dissolved organic matter (CDOM), phytoplankton, and non-algal particles (NAP) and their relative contributions to total absorption among these waters were analyzed. The obtained results indicated that these areas are representative of the optically complex inland waters characterized by strong regional variations of their absorption properties. By means of the relative contributions of NAP and phytoplankton to the total water absorption at 550 and 675 nm, these waters were classified into three optical water types, each one having specific biogeochemical and optical properties. Two of the types were distinct and corresponded to waters that are optically controlled by NAP (Type I) and dominated by phytoplankton (Type III). Type II was related to relatively optically mixed waters where the absorption properties are controlled by NAP and phytoplankton. Additionally, the differences in remote-sensing reflectance (Rrs) spectra among the three classified water types were clarified to establish optical criteria for identifying these water types. On this basis, the classification criteria for MERIS images were developed, which allowed one to cluster every Rrs spectrum into one of the three water types by comparing the values from band 6, band 8, and band 9 of MERIS images. The proposed criteria were subsequently conducted to map the water types of Lake Taihu using MERIS images.

Relationships between water clarity, light attenuation, and concentration of suspended particulates are important in water optics and remote sensing, but are not well described yet, especially for optically complex turbid inland waters. In this study, based on 3-year data from Chinese lakes (Lake Taihu, Lake Chaohu, and Three Gorges reservoir), we propose a new approach to describe the inter-relationships of these bio-optical variables. This approach includes a pre-classification step of the waters into three types based on a semi-analytical parameter C s before establishing the relationships. Our results showed that the pre-classification of waters increased model accuracies both for Z SD (Secchi depth) versus K d (diffuse attenuation coefficient) and K d versus TSM (total suspended matter concentration). The quasi-theoretical model described better the relationship between Z SD and K d than the empirical model. For the K d versus TSM relationship, linear models proved suitable for the Type 2 and Type 3 waters, whereas the power function model gave a better fit for the Type 1 water. Testing of the proposed relations with an independent dataset showed mean absolute percentage errors (MAPE) mostly below 30%. The findings of this study clarify the relationships between Z SD, K d, and TSM, and improve our bio-optical understanding of complex turbid inland waters.

Background: Early identification of stroke is crucial to maximize early management benefits in emergency\ud departments. This study aimed to develop and validate a new stroke recognition instrument for differentiating\ud acute stroke from stroke mimics in an emergency setting.\ud Methods: A prospective observational cohort study among suspected stroke patients presenting to Emergency\ud Department in the Second Affiliated Hospital of Guangzhou Medical University was conducted from May 2012 to\ud March 2013. The symptoms and signs of suspected stroke patients were collected. Logistic regression analysis was\ud used to identify the factors associated with acute stroke. The symptoms and signs closely associated with acute\ud stroke were selected to develop the new stroke scale, Guangzhou Stroke Scale (GZSS). The diagnostic value of GZSS\ud was then compared with ROSIER, FAST and LAPSS. The primary outcome was confirmed stroke by CT within 24 h.\ud Results: Four hundred and sixteen suspected stroke patients (247 ischemia, 107 hemorrhage, 4 transient ischemic\ud attack, 58 non-stroke) were assessed. A new stroke scale, GZSS (total score from −1 to 8.5), was developed and\ud consisted of nine parameters: vertigo (−1), GCS ≤ 8 (+2), facial paralysis (+1), asymmetric arm weakness (+1),\ud asymmetric leg weakness (+1), speech disturbance (+0.5), visual field defect (+1), systolic blood pressure ≥145 mmHg\ud (+1) and diastolic blood pressure ≥95 mmHg (+1). Among the four scales, the discriminatory value (C-statistic) of GZSS\ud was the best (AUC: 0.871 (p < 0.001) when compared to ROSIER (0.772), LAPSS (0.722) and FAST (0.699). At an optimal\ud cut-off score of >1.5 on a scale from −1 to 8.5, the sensitivity and specificity of GZSS were 83.2 and 74.1 %, whilst the\ud sensitivities and specificities of ROSIER were 77.7 and 70.7 %, FAST were 76.0 and 63.8 %, LAPSS were 56.4 and 87.9 %.\ud Conclusion: GZSS had better sensitivity than existing stroke scales in Chinese patients with suspected stroke. Further\ud studies should be conducted to confirm its effectiveness in the initial differentiation of acute stroke from stroke mimics.\ud Keywords: Diagnosis, Stroke, Stroke mimics, ROSIER scale, FAST scale, LAPSS scale, Emergency department, China\ud Abbreviations: AUC, area under the ROC curve; CT, computed tomography; DWI, diffusion weighted imaging; FAST,\ud the face arm speech test; GCS, Glasgow Coma Scale; IQR, inter quartile range; LAPSS, the Los Angeles Prehospital Stroke\ud Screen; MRI, magnetic resonance imaging; NIHSS, National Institute of Health stroke scale; OR, odds ratio; ROC, receiver\ud operating characteristic; ROSIER, the Recognition of Stroke in the Emergency Room scale; TIA, transient ischemic attack

The diffuse attenuation coefficient, Kd(λ), is an important optical property. Environmental change and anthropogenic activities, however, have made it challenging to accurately assess Kd(λ) patterns in the extremely turbid inland waters. We addressed this challenge by using new Landsat 8 Operational Land Imager (OLI) imagery. For the bio-optical complexity of water, we proposed an empirical band-ratio algorithm for estimating Kd(490) using our in situ measurements. Based on the acceptable performance of an OLI image-based atmospheric correction and Kd(490) validation, the algorithm was then applied to OLI images to estimate Kd(490) patterns from April 2013 to April 2016, leading to several key findings: (1) Spatial-temporal patterns of Kd(490) varied significantly in Dongting Lake. The temporal heterogeneity of Kd(490) could be explained primarily by surface-runoff changes driven by regional precipitation. The spatial heterogeneity was due to sediment resuspension, resulting from sand dredging and shipping activities; (2) Kd(490) values that were inversed at the intersection of Dongting Lake and Yangtze River were observed for the first time near the Chengliji site and resulted from the opposing temporal cycle of Kd(490) variations between Dongting Lake and the Yangtze River; (3) There was a significant positive correlation between Kd(490) and total suspended matter (TSM). This confirms that TSM often plays a principal role in the attenuation of light in extremely turbid water bodies; (4) The empirical band-ratio algorithm worked well, not only for the broader Landsat archives, but also for the narrower Sentinel-2/3 for Kd(490) estimation, which demonstrates that the algorithm could be used to quantitatively monitor multi-decade records of Landsat observations and future applications of inland water quality in turbid inland waters, such as Dongting Lake and Poyang Lake.

The classification criteria are established to classify the water of Taihu Lake into four classes based on above-water remote sensing reflectance (Rrs), i.e., types A to D. Among the four water types, type A spectra represented the case of waters where algal blooms or aquatic plants appeared, while type B is referred to the water with high suspended matter concentration and low chlorophyll a concentration (Cchla). Both types A and B were not suitable for retrieving Cchla from image data. Hence, three-band, four-band, and two-band ratio algorithms were constructed to retrieve Cchla from water types C and D. The obtained results showed that the relation trends between Cchla and Rrs were different between type C and type D waters. By using Medium Resolution Imaging Spectrometer images, acquired on November 11, 2007 and November 20, 2008, the Cchla of Taihu Lake was mapped by band 9/band 7 models; it could be concluded that the Cchla mainly ranged from 0 to 20 mg · m-3, accounting for 83.70% of the whole lake area in 2007 image, while the area was 86.63% in 2008 image. The estimation accuracies varied from different Cchla ranges. The mean absolute percent errors obtained by band 9/band 7 models were 106.23%, 56.79%, 38.04%, 33.80%, and 58.74% for the ranges 0 mg · m-3

This study develops a novel support vector regression (SVR) model for retrieving the specific cyanobacterial pigment C-phycocyanin (C-PC) concentrations in cyanobacteria-dominated large turbid lakes of China. Lake Taihu, Lake Chaohu, and Lake Dianchi in China were our study areas. Five field cruises were carried out to collect data sets of optical and water quality parameters. To retrieve the C-PC, three types of reflectance forms, including single band, band ratio, and three-band-combination, were compared. The band ratio was the best candidate to serve for algorithm development. On this basis, two types of models, including linear models and a SVR model, were originally established. The previous typical algorithms were also examined. The obtained results showed that the best-performing model was the SVR model. By our validation data set, the proposed SVR model also presented accurate prediction results, with the lowest errors among all methods. The novelty of the SVR model compared to the previous ones lies in the inclusion of band ratios that are located outside of the main pigment absorption peaks but hold information on inflection points, curvature, etc., into empirical optimization. The implications of these findings indicates the potential applicability of the SVR models in lakes of the similar type.

Microcystis aentginosa (MA), which is one kind of cyanobacteria, is the primary algal species in Taihu Lake. The MA bloom has a significantly negative effect on the human health and water environment ecosystem. The monitoring and prediction of MA bloom become more and more critical for the security of drinking water source and environment in the Taihu Lake area. In this paper, the percentage of MA was estimated from remote-sensing reflectance via a novel spectral shape genetic optimization algorithm. This algorithm focuses on the shape of remote-sensing reflectance, and it can remove the influence of the amplitude of remote-sensing reflectance from the retrieval result. The accuracy of this optimization algorithm is acceptable according to both simulated and in situ data. The percentage of mean square root (RMSP) of the percentage of the phytoplankton absorption coefficient to the total absorption coefficient at 440 nm [ar (440 nm)] between the retrieved and the simulated is 18.39%. The RMSP of the total absorption coefficient at 440 nm [a (440 nm)] between the retrieved and the simulated is 3.65%. The RMSP of the percentage of MA [Sf] between the retrieved and the simulated is 13.60%. The RMSP of the colored dissolved organic matter (CDOM) absorption coefficient slope [S] between the retrieved and the simulated is 5.89%. The RMSP of the particle backscatter coefficient slope [Y] between the retrieved and the simulated is 30.46%. In Taihu Lake, the RMSPs of ar (440 nm), a (440 nm), Sf , and S between the retrieved and the measured are 36.59%, 35.70%, 19.25%, and 16.80%, respectively. The retrieved percentage of MA (Sf) and Scenedesmus obliquus (1 - Sf) by this model from August 2006, October 2006, to November 2008 indicates the variation trend of algal species in different seasons. This trend accords with the results from previous studies and observations. This paper extends and advances the previous retrieval methods and confirms that the genetic optimization algorithm can be used to retrieve the information of water constituents in the high turbid and eutrophic inland water mass.

Although both algorithms enabled the establishment of a linear relationship between measured and predicted PC, the nested band-ratio algorithm did not have a satisfactory performance with either data set, having a high level of uncertainty. Its mean relative error stands at 51.07% and 51% for the 2007 and 2008 data sets, respectively. It accounted for 68% and 74% of the variation in PC in the 2007 and 2008 data sets, respectively. The four-band algorithm worked well in PC estimation. It accounted for 87% of the variation in PC for the 2007 data set and 86% of the variation in the 2008 data set. Furthermore, it decreased estimation uncertainty, compared with the nested band-ratio algorithm, by more than 20%. The values of mean relative error for the correspondence data sets are 29.1% and 30%. Therefore, the proposed four-band algorithm holds great potential in estimating PC in highly turbid waters.