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Abstract

A robust retrieval algorithm to estimate concentrations of total suspended sediments (TSS) in Poyang Lake (the largest freshwater lake in China) was developed using Moderate Resolution Imaging Spectroradiometer (MODIS) medium-resolution (250 m) data from 2000 to 2010 and in situ data collected during two cruise surveys. The algorithm was based on atmospherically corrected surface reflectance at 645 nm, with 1240 nm data serving as a reference for aerosols and a nearest-neighbor method was used to avoid land adjacency effect. The algorithm showed an uncertainty of 30-40% for TSS ranging between 3 and 200 mg L-1. Long-term TSS distribution maps derived from MODIS data and the customized TSS algorithm showed significant variations in both space and time, with low TSS (<10 mg L-1) in wet seasons and much higher TSS (>15-20 mg L-1) in dry seasons for the south lake, and generally higher TSS in the north lake. The TSS difference between the north and the south increased significantly after 2002, with mean TSS often reaching >40 mg L-1in the north. While the TSS seasonality was attributed to the seasonal changes of the lake's circulation, the inter-annual variations were primarily driven by sand dredging activities, regulated by management policies. The case study here provides baseline water quality information for future restoration efforts in Poyang Lake, and more generally, an approach to assess water quality changes in similar water bodies, which have resulted from either climate variability or human activities.

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... The remote sensing data selected in this study was derived from MODIS-Terra data, which was launched in 2000. Therefore, we can perform long-term time series analysis using this data, which is the case for previous studies that have successfully applied this data to analyze turbid off shore and inland waters (Feng et al., 2012;Li et al., 2017;Wang et al., 2018). The MODIS surface refl ectance product (MOD09) provided 500-m spatial resolution data at seven bands across the visible, near-infrared, and short-wave infrared wavelengths. ...
... The reason why MOD09A1 was used in this paper is that it can provide data over a longer time range than MYD09A1. Also, it has been used for long-term and large-area water quality monitoring research as this dataset is well georeferenced, synthesized, and cloud marked (Feng et al., 2012;Wu et al., 2013;Li et al., 2016;Hou et al., 2017;Klein et al., 2017;Wang et al., 2018). The MOD09A1 data, which covered Jiaozhou Bay from 2000-2018, was obtained from the U.S. NASA Goddard Space Flight Center (GSFC, http:// oceancolor.gsfc.nasa.gov/). ...
... More measured data can further improve the Z sd retrieval model. At the same time, the satellite transit time may be diff erent from the measurement time (e.g., more than 3 h), which will also lead to certain errors in the Z sd retrieval results (Feng et al., 2012). ...
Article
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The Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data were used to analyze the temporal and spatial distribution characteristics of water clarity (Zsd) in the Jiaozhou Bay, Qingdao, China, in the Yellow Sea from 2000 to 2018. Zsd retrieval models were regionally optimized using in-situ data with coincident MODIS images, and then were used to retrieve the Zsd products in Jiaozhou Bay from 2000–2018. The analysis of the Zsd results suggests that the spatial distribution of relative Zsd spatial characteristics in Jiaozhou Bay was stable, being higher Zsd in the southeast and a lower Zsd in the northwest. The annual mean Zsd in Jiaozhou Bay showed a significant upward trend, with an annual increase of approximately 0.02 m. Water depth and wind speed were important factors affecting the spatial distribution and annual variation of Zsd in Jiaozhou Bay, respectively.
... Shi et al. [17] integrated MODIS data from 2003-2013 and in situ observations from a number of boat-based surveys to estimate the concentrations of TSM in Lake Taihu. Semi-analytical and empirical algorithms provide indices that are sensitive with Chl-a and TSM [13][14][15][16][17][18][19]. The retrieval of Chl-a and TSM concentration are mainly achieved through regression relationships between the measured parameter and sensitive indices while using linear [20,21], quadratic polynomial [22], exponential [16,17], and power-law [23] regression approaches. ...
... Semi-analytical and empirical algorithms provide indices that are sensitive with Chl-a and TSM [13][14][15][16][17][18][19]. The retrieval of Chl-a and TSM concentration are mainly achieved through regression relationships between the measured parameter and sensitive indices while using linear [20,21], quadratic polynomial [22], exponential [16,17], and power-law [23] regression approaches. In recent years, the emergence and application of new satellite sensors, such as Landsat-8 and Sentinel-2, have further promoted the development of the assessment of inland water quality while using remote sensing data [23][24][25]. ...
... These observations can be attributed to the transport of various pollutants into the lake by the Ganjiang, Fuhe, Xinjiang, Raohe, and Xiushui rivers, and to human activities in waters near to the lake shore [37]. Sand mining occurs during the whole year in the northern channel that connects the lake with the Yangtze River [16]. Consequently, a large amount of sewage is discharged from sand dredgers, and sand mining activities disturb the lake bottom, releasing large amounts of nutrients; both of these result in a high level of nutrients in the lake water, which in turn leads to an increase in the concentration of Chl-a [38]. ...
Article
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Poyang Lake is the largest freshwater lake in China. Its ecosystem services and functions, such as water conservation and the sustaining of biodiversity, have significant impacts on the security and sustainability of the regional ecology. The lake and wetlands of the Poyang Lake are among protected aquatic ecosystems with global significance. The Poyang Lake region has recently experienced increased urbanization and anthropogenic disturbances, which has greatly impacted the lake environment. The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are important indicators for assessing the water quality of lakes. In this study, we used data from the Gaofen-1 (GF-1) satellite, in situ measurements of the reflectance of the lake water, and the analysis of the Chl-a and TSM concentrations of lake water samples to investigate the spatial and temporal variation and distribution patterns of the concentrations of Chl-a and TSM. We analyzed the measured reflectance spectra and conducted correlation analysis to identify the spectral bands that are sensitive to the concentration of Chl-a and TSM, respectively. The study suggested that the wavelengths corresponding to bands 1, 3, and 4 of the GF-1 images were the most sensitive to changes in the concentration of Chl-a. The results showed that the correlation between the reflectance and TSM concentration was the highest for wavelengths that corresponded to band 3 of the GF-1 satellite images. Based on the analysis, bands 1, 3, and 4 of GF-1 were selected while using the APPEL (APProach by ELimination) model and were used to establish a model for the retrieval of Chl-a concentrations. A single-band model that was based on band 3 of GF-1 was established for the retrieval of TSM concentrations. The modeling results revealed the spatial and temporal variations of water quality in Poyang Lake between 2015 and 2016 and demonstrated the capacities of GF-1 in the monitoring of lake environment.
... Among these empirical models, few models were developed based on the prior information and behavior of the OAC at the certain bands acquired at a specific wavelength; thus, these models were considered semi-empirical models (Ogashawara et al., 2017). For instance, the presence of suspended sediments in the water bodies is known to amplify the spectral reflectance, and this phenomenon is more pronounced in the red band of the electromagnetic spectrum (Ritchie et al., 1976;Ritchie and Cooper, 1988;Feng et al., 2012;Dogliotti et al., 2015;Shi et al., 2015). Based on these properties, the RS-based semi-empirical models have been developed for TSS and Turbidity estimation in static water bodies, viz., lakes, estuaries, reservoirs, and lagoons He et al., 2013). ...
... Generally, the satellite signals of the water pixels adjacent to the land pixels are always contaminated by the surface reflectance generated from the nearby lands (Santer and Schmeching, 2000;Sterckx et al., 2010Sterckx et al., , 2015. For addressing the issue of land adjacency effect in the red band wavelength region, one pixel near to the river boundary can be removed as advocated by Feng et al. (2012) and Hou et al. (2017) before applying the ESTARFM. The ESTARFM was set up based on this concept for the homogenous and heterogeneous pixels in the MODIS and Landsat images. ...
... The performance measures given inset of Fig. 10d-10e reveal that Eq. (22) could provide reliable turbidity estimates corresponding to the RS-based CFUS T modelsimulated TSS concentrations at the three virtual monitoring stations along the Hooghly River reach. For addressing the issue of land adjacency effect in the red band wavelength region, one pixel near to the river boundary was removed before simulating the CFUS T model for TSS and the CFUS Tu model for Turbidity mapping along the Hooghly River as advocated by Feng et al. (2012) and Hou et al. (2017). Following this principle, the spatiotemporal variability of the TSS and Turbidity were derived along the Hooghly River as illustrated in Fig. 11. ...
Article
Riverine ecosystem management along an urban stretch mostly depends on high-frequent (daily-scale) monitoring of water quality at finer spatial resolutions. However, with the decrease in the number of in-situ monitoring stations owing to their expensive maintenance cost, there is a need to develop the next-generation remote sensing (RS) tools as an alternate approach with better synoptic coverage of river water quality assessment. This study advocates three novel model variants to estimate the total suspended solids (TSS) concentration at daily-scale using the public-domain MODIS and Landsat satellite datasets. The MODT model variant uses the 1-day×250 m MODIS public domain datasets, and the FUST model is based on the 1-day×30 m MODIS-Landsat fusion datasets, whereas the CFUST model integrates the Frank Copula with the FUST model. These hierarchical model variants are assessed in the urban-waste-dominated lower Ganges, namely the Hooghly River, in eastern India using the measured in-situ TSS datasets at five monitoring stations from 2016 to 2019. The results reveal that the CFUST is the best TSS estimation model variant that performs with the average coefficient of determination of 0.93, mean absolute error of 0.17, and normal root mean square error of 0.05. Conclusively, the proposed CFUST and CFUSTU stochastic models can be used as potential tools for TSS and turbidity assessment along the dynamic river systems, respectively.
... Generally, several quality control criteria should be applied to generate reliable matching pairs (Feng et al., 2012): (1) a ±3-h time window between satellite images and field data should be used to assure concurrent observations. Because the field turbidity measurements are high-frequency (see Table 1), this requirement was modified to select the closest temporal matching pairs (<2 h); (2) a 3 × 3-pixel box is needed to perform a homogeneity test to avoid potential influence of water patchiness and the variance in a 3 × 3-pixel box should be <10%. ...
... In addition to the significant seasonality, the inter-annual variability can better illustrate human impacts on water turbidity (Cao et al., 2017;Feng et al., 2012). The annual mean values of the Landsat-derived turbidity maps in Wuhan are plotted in Fig. 8; illustrating inter-annual variations in turbidity from 1987 to 2019. ...
... The turbidity in Wuhan shows significant annual and seasonal variations that could be explained by meteorological and/or human indicators (Cao et al., 2017;Feng et al., 2012). By dividing Wuhan into 10 sections by latitude, we found that the mean turbidity value of the entire area of Wuhan is consistent with those of each section (see Fig. 10 (a)); thus, the variations in mean turbidity values can be used to represent waters throughout Wuhan. ...
Article
Understanding of turbidity, an indicator of water quality, is of great importance in cities and can have significant implications for human society. Many users are interested in mapping turbidity using remote sensing tools for long-term and large-scale monitoring. This study aims to derive turbidity maps in an urbanizing city and to identify the driving factors for better decision-making and water quality management. Taking Wuhan, the most rapidly urbanizing metropolis in central China, as an instance, the water turbidity is monitored using Landsat observations from 1987 to 2019, and the relationships of turbidity and climatic/human factors are examined. Climatic factors are represented by meteorological conditions (rainfall, wind speed, temperature, and water vapor pressure) and human factors are characterized by the normalized difference vegetation index (NDVI) and impervious surface area (ISA). The results demonstrated that: (1) the seasonal mean turbidity increased from spring to summer (34.28 NTU to 36.27 NTU), decreased in autumn (25.04 NTU), and increased again in winter (37.20 NTU), and the variations were related to changes in rainfall; and (2) the annual mean turbidity was fluctuatingly stable during 1987–2004 and decreased by 1 NTU/yr. since 2005. The decline of water turbidity was highly correlated to the increase of NDVI and ISA with p values <0.01. The study indicates that meteorological conditions affect seasonal variations in turbidity, while human factors have long-term impacts. A cautious approach to human activities during urbanization is needed to achieve a balance between water quality protection and the city’s developments.
... The EPL zone belongs to the subtropical monsoon climate [81,82] and has the most representative and largest concentration of freshwater lakes in China [83]. These lakes are shallow with average depths from 1.1 to 8.4 m [21], providing sufficient water resources and promoting the development of the local economy [84]. Due to the influence of human activities, the water quality of the EPL zone is deteriorating and facing serious eutrophication with frequent occurrence of algal blooms [2,85,86]. ...
... The reflectance spectra of turbid waters and high chlorophyll-a waters showed significant differences in the red range and short-wave infrared range, which can be used to identify areas of high turbidity [84,106]. Therefore, the turbid water index (TWI) was used to extract turbid water and can be calculated from (3) [106]. ...
... The highly turbid water of Lake Poyang appears in the north lake because of the sand dredging in the Yangtze River. These results are consistent with the previous studies [84,95]. ...
Article
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Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland waters. First, the turbid water index was applied for the determination of optical water types on each pixel, and water bodies are divided into two categories: algae-dominated water (Type I) and turbid water (Type II). The algal biomass index (ABI) was then established based on water classification to derive the trophic state index (TSI) proposed by Carlson (1977). The results showed a considerable precision in Type I water (R2 = 0.62, N = 282) and Type II water (R2 = 0.57, N = 132). The ABI-derived TSI outperformed several band-ratio algorithms and a machine learning method (RMSE = 4.08, MRE = 5.46%, MAE = 3.14, NSE = 0.64). Such a model was employed to generate the trophic state index of 146 lakes (> 10 km2) in eastern China from 2013 to 2020 using Landsat-8 surface reflectance data. The number of hypertrophic and oligotrophic lakes decreased from 45.89% to 21.92% and 4.11% to 1.37%, respectively, while the number of mesotrophic and eutrophic lakes increased from 12.33% to 23.97% and 37.67% to 52.74%. The annual mean TSI for the lakes in the lower reaches of the Yangtze River basin was higher than that in the middle reaches of the Yangtze River and Huai River basin. The retrieval algorithm illustrated the applicability to other sensors with an overall accuracy of 83.27% for moderate-resolution imaging spectroradiometer (MODIS) and 82.92% for Sentinel-3 OLCI sensor, demonstrating the potential for high-frequency observation and large-scale simulation capability. Our study can provide an effective trophic state assessment and support inland water management.
... Dramatic changes have occurred in lakes driven by climate change and human activities in the past several decades [1][2][3][4]. As habitats of aquatic organisms, lake ecosystems are facing serious Although spectrum and WCPs could be measured by the above-mentioned instruments, there is still a lack of an integrated system to automatically collect and broadcast data, which is important for fixed observation station. ...
... Influenced by subtropical monsoons, the inundation area of Poyang Lake varies from approximately 3000 km 2 during the wet season to approximately 1000 km 2 during the dry season [19]. Existing studies primarily focus on water quality monitoring from MODIS [1], Landsat TM/ETM+ [20], HJ-1 CCD [21], or GF-1 WFV [22] at various spatial and temporal scales. Although the long-term (inter-annual and seasonal) dynamics of the lake, including the increased SPM (suspended particulate matter) concentration and declining water quality, have been well-documented [1,[23][24][25], stationary high-frequency spectral and water constituents concentration observations, especially Chl-a, NTU, and CDOM measurements, are rare and urgently needed. ...
... Existing studies primarily focus on water quality monitoring from MODIS [1], Landsat TM/ETM+ [20], HJ-1 CCD [21], or GF-1 WFV [22] at various spatial and temporal scales. Although the long-term (inter-annual and seasonal) dynamics of the lake, including the increased SPM (suspended particulate matter) concentration and declining water quality, have been well-documented [1,[23][24][25], stationary high-frequency spectral and water constituents concentration observations, especially Chl-a, NTU, and CDOM measurements, are rare and urgently needed. ...
Article
Full-text available
Measurements of the above-water spectrum and concerned water color parameters (WCPs) are crucial for research and applications in water environment remote sensing. Due to the lack of system integration and automatization, conventional methods are labor-intensive, time-consuming, and prone to subjective influences. To obtain a highly accurate and long-term consistent spectrum and concurrent WCPs (Chl-a (chlorophyll-a), turbidity, and CDOM (Colored Dissolved Organic Matter)) data with a relatively low cost, an Automatic Stationary Water Color Parameters Observation System (AFWCPOS) was developed. Controlled by an automatic platform, the spectral and WCPs data were collected by TriOS RAMSES hyperspectral spectroradiometers and WETLabs ECO (Environmental Characterization Optics) fluorometers following the measurement protocol. Experiment and initial validations of AFWCPOS were carried out in Poyang Lake, the largest freshwater lake in China, from 20 to 28 July 2013. Results proved that the spectral data from AFWCPOS were highly consistent with the commonly used portable SVC (Spectra Vista Corporation) HR-1024 field spectroradiometer, with the coefficient of determination (R2) of 0.96, unbiased percent difference (UPD) of 0.14, and mean relative difference (MRD) of 0.078. With advantages of continuous and high degrees of automation monitoring, the AFWCPOS has great potential in capture diurnal and inter-diurnal variations in the test site of Poyang Lake, as well as another high-dynamic shallow coastal and inland waters, which will benefit routine water quality monitoring with high quality and high-frequency time-series observations. In addition, a successful case based on Landsat 8 OLI (Operational Land Imager) image and in-situ data collected by AFWCPOS showed it’s potential in remote sensing applications. The spatial distribution of Chl-a, turbidity, and CDOM were mapped, which were explainable and similar to previous researches. These results showed our system was able to obtain reliable and valuable data for water environment monitoring.
... In general, only 19 sampling stations are set around the lake (Guo and Wang, 2014), where data are collected and then interpolated to represent the water quality of the whole lake. Extensive research primarily focused on remote sensing of water quality monitoring using Terra/Aqua MODIS (Feng et al., 2012), Landsat TM/ETM+/OLI (Wu et al., 2013), or HJ-1 CCD, GF-1 at different scales. The lake's long-term variations have been well documented (Feng et al., 2012;Wu and Cui, 2008), while how the sampling should be designed and optimized are still blank. ...
... Extensive research primarily focused on remote sensing of water quality monitoring using Terra/Aqua MODIS (Feng et al., 2012), Landsat TM/ETM+/OLI (Wu et al., 2013), or HJ-1 CCD, GF-1 at different scales. The lake's long-term variations have been well documented (Feng et al., 2012;Wu and Cui, 2008), while how the sampling should be designed and optimized are still blank. ...
... The sharpen method was used to re-sample 500 m data to 250 m for comparison and elimination of bad quality pixels (Pohl and Van Genderen, 1998). Then, the land adjacency effects on the water pixels were removed by calculating relative differences between adjacent pixels using MODIS surface reflectance bands at 555 and 645 nm along with the land-water profiles (Feng et al., 2012). The NDWI (Normalized Difference Water Index, NDWI = (R green − R nir ) / (R green + R nir )) was used to build the water mask since it enlarges the signal contrast between water and land. ...
Article
An efficient and precise spatial sampling design is critical to capture water quality spatial and temporal variations under cost and labor constraints. Therefore, it is practically essential to optimize the sampling locations using limited sampling numbers to obtain the most comprehensive water quality monitoring results considering both the spatial and temporal dynamics. Existing sampling methods were restricted due to lacking pre-information and specific sampling objective function. This paper proposed an optimal sampling strategy using remote sensing (RS) big data and spatial sampling annealing (SSA) integrated approach for sampling design. The proposed method involved spatial-temporal clustering of the total suspended sediment (TSS) using long-term remote sensing data (Terra/Aqua MODIS, 2000–2014), determining the required sampling numbers using geostatistical analysis, and SSA simulation following the objective function of minimization of the spatial-temporal mean estimation error using remote sensing data as references. Taking total suspended sediment (TSS) observations at Poyang Lake, China, as the case study and application region. Results showed that the RS + SSA sampling approach is superior to conventional sampling methods such as systematic, stratified, and expert sampling, concerning spatial and temporal sampling accuracy. TSS estimation errors of the whole lake were reduced by 18.11% and 29.34% on average compared to systematic and stratified sampling under the same sample size. The annual TSS estimation errors were dropped by approximately 50%. The sampling accuracy was affected by the synthetic effects of sampling strategy (station numbers and spatial distributions) and water quality variations (coefficient of variation, CV). Sampling optimization is more efficient to improve the sampling accuracy than increasing sampling size, which requires more cost and human resources. Remote sensing showed great potential as ideal means to provide spatially contiguous and comprehensive data as prior-knowledge for efficient sampling design. This paper provides solutions and recommendations for evaluating existing monitoring stations in their representation of water quality or optimizing a new sampling network for future implications of more efficient and precise water quality sampling and routine monitoring.
... Traditional field sampling methods, limited by spatial and temporal coverage, are often insufficient to develop robust models and to obtain statistically meaningful results [7]. Remote sensing techniques are widely used to obtain spatiotemporal information of SPM concentrations (C SPM ) because they can monitor the water ecosystems at a large scale and a high frequency [8]. ...
... Generally, an N*N window (e.g. 3*3) is needed to obtain the matching pairs between satellite images and in situ measurements [8]. However, in this study, the pseudo-field measurements (MODIS, VIIRS, and GOCI) were coarser than the satellite images (OLI and WFV), and no N*N window was applied at the "in-situ" measurements because (1) the pseudo-field SPM measurements matched the mean values of satellite images, indicating that a window was implicitly applied at the satellite images and (2) there were two more parameters (gain and offset a MODIS-Aqua rather than MODIS-Terra was used in this study for two reasons: (1) the MODIS-Aqua was used in the reference to yield "in-situ" SPM data [30]; and (2) the data from MODIS-Terra in 250 m resolutions are not well calibrated and are noisier than the MODIS-Aqua data [37,38]. ...
... In the applications for SPM retrieval over turbid inland/coastal waters, L path may be calculated using different methods such as RC-AC [8], UV-AC [26], and 6S-AC [27]. After L path is determined, the R rs becomes a function of DN and RC coefficients. ...
Article
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High-precision radiometric calibration (RC) coefficients are required to retrieve reliable water quality parameter products in turbid inland/coastal waters. However, unreliable RC coefficients when satellite sensors lack accurate and in-time RC may lead to pronounced uncertainties in the products through error propagation. To address this issue, a novel approach for estimating water quality parameters, taking suspended particulate matter (SPM) as a case, was proposed by coupling the procedures of RC and SPM model development. The coupled models were established using digital numbers (DNs) from target sensors and “in-situ” SPM measurements from concurrent well-calibrated reference sensors, with the RC coefficients introduced as unknown model parameters. The approach was tested and validated in varied Chinese inland/coastal regions, including the Hongze lake (HL), Taihu lake (TL), and Hangzhou bay (HB). The results show: (1) the DN-based SPM models can achieve a degree of accuracy comparable to reflectance-based SPM models with determination coefficients (R 2 ) of 0.94, 0.92, and 0.72, and root-mean-square errors (RMSE) of 7.02 mg/L, 15.73 mg/L, and 619.2 mg/L for the HL, TL, and HB, respectively, and the biases less than 3% between the derived and official gain RC coefficients; (2) the uncertainty of SPM products increases exponentially as the RC uncertainty increases for exponential reflectance-based SPM models; (3) the DN-based SPM models are less sensitive to the uncertainties of atmospheric correction and RC coefficients, while the reflectance-based models suffer deeply. This study provides encouraging results to the improvement of SPM retrieval using the DN-based models by coupling RC and SPM retrieving processes, especially for sensors without precise RC coefficients
... The dark-spectrumfunction method in ACOLITE (Vanhellemont and Ruddick, 2018) may result in large uncertainties in MLRY lakes (e.g., Lake Taihu) due to excessive algae particles and strongly absorptive aerosols (Wang et al., 2019). Therefore, the following procedure was used to create pseudoreflectance products (Cao et al., 2017;Feng et al., 2012). First, Rayleigh-corrected reflectance (R rc ) was derived after correction for Rayleigh scattering and gaseous absorption effects using SeaDAS 7.5 with ancillary data (such as meteorological data) (Franz et al., 2015). ...
... To remove, at least partially, the aerosol signal, the following algorithm was used: (2201). This method may retain residual aerosol signals in other bands; however it partially removes the bulk aerosol, haze, or glint signal (Cao et al., 2017;Feng et al., 2012). ...
Article
Landsat-8 Operational Land Imager (OLI) provides an opportunity to map chlorophyll-a (Chla) in lake waters at spatial scales not feasible with ocean color missions. Although state-of-the-art algorithms to estimate Chla in lakes from satellite-borne sensors have improved, there are no robust and reliable algorithms to generate Chla time series from OLI imageries in turbid lakes due to the absence of a red-edge band and issues with atmospheric correction. Here, a machine learning approach termed the extreme gradient boosting tree (BST) was employed to develop an algorithm for Chla estimation from OLI in turbid lakes. This model was developed and validated by linking Rayleigh-corrected reflectance to near-synchronous in situ Chla data available from eight lakes in eastern China (N = 225) and three coastal and inland waters in SeaWiFS Bio-optical Archive and Storage System (N = 97). The BST model performed well on a subset of data (N = 102, R² = 0.79, root mean squared difference = 7.1 μg L⁻¹, mean absolute percentage error = 24%, mean absolute error = 1.4, Bias = 0.9), and had better Chla retrievals than several band-ratio algorithms and a random forest approach. The performance of BST model was judged as appropriate only for the range of conditions in the training data. Given these limitations, spatial and temporal variations of Chla in hundreds of lakes larger than 1 km² in eastern China for the period of 2013–2018 were mapped using the BST model. OLI-derived Chla indicated that small lakes (<50 km²) had greater Chla than the larger lakes. This research suggests that machine-learning models provide practical approaches to estimate Chla in turbid lakes via broadband instruments like OLI and that extending to other regions requires training with a representative dataset.
... All studied lakes in the EPL were identified as type T1 (Fig. 6). This seasonal cycle was reported previously in some case studies (Feng et al., 2012(Feng et al., , 2019Shi et al., 2018). Feng et al. (2019) had reported that many lakes in the EPL had peaked SDDs in summer. ...
... Low wind speed and high water level led to weak sediment resuspension and high SDD in summer. For the Dongting and Poyang lakes, the stable lake flow conditions also contributed to the high SDDs in summer (Feng et al., 2012). ...
Article
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Water transparency, usually denoted by Secchi disk depth (SSD), represents the first-order description of water quality and has important implications for the diversity and productivity of aquatic life. In China, lakes supply freshwater and ecosystem services to nearly a billion people. Therefore, real time monitoring of lake transparency is of great significance. Moreover, understanding how and why transparency varies in space and time in response to different driving forces is needed to understand, manage, and predict lake water quality. Based on the time-saving and low-cost Google Earth Engine cloud platform, this study developed a new algorithm for quickly mapping SDDs in Chinese lakes. SDDs were retrieved for 412 Chinese lakes (> 20 km²) for the period 2000–2018. Results demonstrated that lake water depth spatially differentiated transparency. Deep lakes usually had high transparency and water depth explained 88.81 % of the spatial variations. With increasing catchment vegetation coverage and lake water depth, 70.15 % of lakes witnessed increasing transparency during 2000–2018. Of these 42.72 % were significant (p < 0.05). Transparency of deep lakes was generally determined by phytoplankton density not sediment resuspension. Minimum transparency occurred in summer. Future increases in lake water levels in response to factors such as climate change may contribute to further improvements in transparency. Management should focus on controlling eutrophication and increasing vegetation cover in catchments.
... Sand mining influences sediment budgets in a second way, as dredging and barge overflow involve the stirring of important quantities of water and silt. This leads to resuspension of deposited sediments, driving a localised rise in suspended sediment concentrations and water turbidity (Kim and Lim, 2009;Feng et al., 2012;Zheng et al., 2016;Li et al., 2019). Besides the impacts on flora and fauna, along with pollution impacts, increased turbidity is linked to water quality degradation and modifications in temperature gradientsthat is the difference between surface and subsurface water temperatures (Sharip and Zaki, 2014;Lusiangustin and Kusratmoko, 2017). ...
... Actors exploit poor regulatory frameworks, but also gather support from various segments of the population including real estate developers and authorities (Tejpal et al., 2014;Rege, 2016;Rege and Lavorgna, 2017). In areas with an active sand mafia presence, intensive sand mining activities are perpetuated through violence, intimidation, and corrupting vast networks of government representatives and authorities (Chen et al., 2006;Feng et al., 2012;Rege, 2016;Rege and Lavorgna, 2017), hinting at broader societal consequences. Bisht and Gerber (2017: 549) refer to these phenomena as 'predatory extractivism', namely a form of exploitation where local populations are left to cope with the social and environmental externalities of extractive industries such as sand mining (see also Al-Awadhi et al., 2013). ...
... Therefore, these atmospheric correction algorithms are mainly used for ocean remote sensing, so that the correction accuracy of the inland turbid water is insufficient [41,42]. A large number of studies have proved that lake water properties can be estimated successfully by the Rayleigh-corrected reflectance (R rc , dimensionless) [43], for example, SPM can be estimated by the MODIS R rc in Lake Hongze [5] and Lake Poyang [44], and Chla can be estimated by the MODIS R rc in Lake Taihu [45]. R rc was derived after correction for the Rayleigh scattering and gaseous absorption effects following [43]: ...
... Since the existing atmospheric correction methods are mainly aimed at relatively clean ocean waters, their applicability is not very appropriate to the study of the inland water quality parameters [5,66,69]. According to the conclusions of other researchers, the required algorithm can be constructed only by the use of Rayleigh-scattering correction results [5,43,44], thus there is a lack of aerosol correction for the time being. However, the two bands ratio and normalization algorithm can reduce the influence of the atmosphere [70,71]. ...
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Phosphorus (P) is an important substance for the growth of phytoplankton and an efficient index to assess the water quality. However, estimation of the TP concentration in waters by remote sensing must be associated with optical substances such as the chlorophyll-a (Chla) and the suspended particulate matter (SPM). Based on the good correlation between the suspended inorganic matter (SPIM) and P in Lake Hongze, we used the direct and indirect derivation methods to develop algorithms for the total phosphorus (TP) estimation with the MODIS/Aqua data. Results demonstrate that the direct derivation algorithm based on 645 nm and 1240 nm of the MODIS/Aqua performs a satisfied accuracy (R2 = 0.75, RMSE = 0.029mg/L, MRE = 39% for the training dataset, R2 = 0.68, RMSE = 0.033mg/L, MRE = 47% for the validate dataset), which is better than that of the indirect derivation algorithm. The 645 nm and 1240 nm of MODIS are the main characteristic band of the SPM, so that algorithm can effectively reflect the P variations in Lake Hongze. Additionally, the ratio of the TP to the SPM is positively correlated with the accuracy of the algorithm as well. The proportion of the SPIM in the SPM has a complex effect on the accuracy of the algorithm. When the SPIM accounts for 78%, the algorithm achieves the highest accuracy. Furthermore, the performance of this direct derivation algorithm was examined in two inland lakes in China (Lake Nanyi and Lake Chaohu), it derived the expected P distribution in Lake Nanyi whereas the algorithm failed in Lake Chaohu. Different water properties influence significantly the accuracy of this direct derivation algorithm, while the TP, Chla, and suspended particular inorganic matter (SPOM) of Lake Chaohu are much higher than those of the other two lakes, thus it is difficult to estimate the TP concentration by a simple band combination in Lake Chaohu. Although the algorithm depends on the dataset used in the development, it usually presents a good estimation for those waters where the SPIM dominated, especially when the SPIM accounts for 60% to 80% of the SPM. This research proposed a direct derivation algorithm for the TP estimation for the turbid lake and will provide a theoretical and practical reference for extending the optical remote sensing application and the TP empirical algorithm of Lake Hongze’s help for the local government management water quality.
... The lake has a storage capacity of 27.6 billion m 3 and an average water depth of 8.4 m [32]. As a unique inland freshwater lake, there is a high variability in the water level, and the inundation area fluctuates from less than 1000 km 2 in the dry season to over 3000 km 2 in the wet season [33]. ...
... In the most recent two decades, sand dredging has been rapidly increasing in the Poyang Lake since it was banned in the Yangtze River in 2001. Since then, sand dredging activities have been continuously carried out, though there was a ban on dredging in 2008, which has led to a significant increase in the SSC in the lake and serious negative impacts on the Poyang Lake ecosystem [33]. Figure 1. ...
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As the largest freshwater lake in China, Poyang Lake plays an important role in the ecosystem of the Yangtze River watershed. The high suspended sediment concentration (SSC) has been an increasingly significant problem under the influence of extensive sand dredging. In this study, a hydrodynamic model integrated with the two-dimensional sediment transport model was built for Poyang Lake, considering sand dredging activities detected from satellite images. The sediment transport model was set with point sources of sand dredging, and fully calibrated and validated by observed hydrological data and remote sensing results. Simulations under different dredging intensities were implemented to investigate the impacts of the spatiotemporal variation of the SSC. The results indicated that areas significantly affected by sand dredging were located in the north of the lake and along the waterway, with a total affected area of about 730 km2, and this was one of the main factors causing high turbidity in the northern part of the lake. The SSC in the northern area increased, showing a spatial pattern in which the SSC varied from high to low from south to north along the main channel, which indicated close agreement with the results captured by remote sensing. In summary, this study quantified the influence of human induced activities on sediment transport for the lake aquatic ecosystem, which could help us to better understand the water quality and manage water resources.
... Sand mining influences sediment budgets in a second way, as dredging and barge overflow involve the stirring of important quantities of water and silt. This leads to resuspension of deposited sediments, driving a localised rise in suspended sediment concentrations and water turbidity (Kim and Lim, 2009;Feng et al., 2012;Zheng et al., 2016;Li et al., 2019). Besides the impacts on flora and fauna, along with pollution impacts, increased turbidity is linked to water quality degradation and modifications in temperature gradientsthat is the difference between surface and subsurface water temperatures (Sharip and Zaki, 2014;Lusiangustin and Kusratmoko, 2017). ...
... Actors exploit poor regulatory frameworks, but also gather support from various segments of the population including real estate developers and authorities (Tejpal et al., 2014;Rege, 2016;Rege and Lavorgna, 2017). In areas with an active sand mafia presence, intensive sand mining activities are perpetuated through violence, intimidation, and corrupting vast networks of government representatives and authorities (Chen et al., 2006;Feng et al., 2012;Rege, 2016;Rege and Lavorgna, 2017), hinting at broader societal consequences. Bisht and Gerber (2017: 549) refer to these phenomena as 'predatory extractivism', namely a form of exploitation where local populations are left to cope with the social and environmental externalities of extractive industries such as sand mining (see also Al-Awadhi et al., 2013). ...
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Sand, gravel, and crushed rock – known as construction aggregates – are in high demand in the Asian region. Such demand is driven by high rates of urbanization, infrastructure development, and dam building: an unprecedented amount of sand is being extracted from the region's river, delta and estuary areas, only to be transported for infill or construction purposes elsewhere. This systematic scoping review examines the state of knowledge in the peer review literature on sand ecologies, livelihoods, and governance in Asia. We find that the literature mainly focuses on the ecological implications of sand mining, namely biotic and abiotic components: sand mining is linked with many forms of ecological degradation, although partial ecosystemic recovery may be possible when sand mining stops. In contrast, the limited analysis on livelihoods suggests that violence, work-related injuries, and precarious jobs are common for those working in the sand industry, with sand mining producing different types of work depending on the level of mechanization. We conclude by noting several gaps in the literature, including the narrow geographical focus (mainly India and China), the lack of attention to the intersection between sand mining and other anthropogenic disturbances, and the need to establish transparent sand governance processes within this region.
... Using semi-empirical and semi-analytical approach, various studies had demonstrated the use of OLI for estimation of different water quality parameters in coastal and inland waters (Lymburner et al., 2016, Braga et al., 2016, Dogliotti et al., 2015, Feng et al., 2012, He and Chen, 2014. The accuracy of these satellitederived water quality parameters highly depends not only on the empirical/semi-analytical model that relates the apparent optical property (AOP) and concentration of a particular biophysical parameter but also to the atmospheric correction technique implemented to retrieve the AOP often referred as waterleaving or remote sensing reflectance (Rrs) (Jamet et al., 2011, Dogliotti et al., 2015. ...
... The ACOLITE algorithm uses a power function to extrapolate the aerosol contribution in the visible bands, whereas, the SWIRE technique employs an exponential model. The efficacy of these algorithms have been proven in previous studies, however, the respective accuracy of these models largely varies in complex turbid inland waters due to several factors such as high aerosol optical depth (AOD), high backscattering from total suspended solids (TSS) and high concentration of colored dissolved organic matter (CDOM) (Feng et al., 2012). ...
Article
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Spatiotemporal monitoring of water quality parameters such as turbidity in inland waters is desirable to better understand productivity and mitigate the negative impacts of pollution induced by increasing anthropogenic activities. However, precise retrieval of water quality parameters in complex turbid waters from the remote sensing reflectance Rrs, remains a challenging task due to the varying optical complexity of the water body. In this study, a modified version of the Atmospheric Correction for OLI-lite (ModACO) scheme for turbid inland waters, which implements a linear extrapolation of NIR aerosol reflectance in the visible bands instead of a nonlinear function, is presented. The performance of the proposed method and other existing algorithms such as the Atmospheric Correction for OLI-lite (ACOLITE), Management Unit of the Noth Seas Mathematical Models (MUMM) scheme, SeaDAS standard processing, and SWIRE were evaluated. The Rrs retrievals from these models were then used as input for turbidity estimation and mapping of Laguna de Bay. Results show lowest Rrs error in all five spectral bands (443, 482, 561, 655, and 865 nm) on ModACO-based retrievals. Relative to the ACOLITE and other atmospheric correction schemes, the proposed method reduced the Rrs retrieval errors in terms of RMSE and MAPE by more than 50%. Similarly, significant improvements in turbidity retrievals were achieved from ModACO-based Rrs values, wherein comparable accuracy was observed from red/green ratio and the single NIR band turbidity models. Turbidity maps of Laguna de Bay show elevated values from the mid of dry season, which may be associated with point source discharge and wind-induced resuspension of bottom sediments. The lake turbidity then drops by the end of dry season, which is linked to the absence of prevailing strong winds that may increase in-water mixing. Using the aforementioned method, accurate monitoring of turbidity can be done to determine and mitigate possible degradation on the water quality of Laguna de Bay and other productive turbid inland waters.
... Previous studies have estimated Chla and SPM in several large lakes in the MLYHR basin, including Lake Taihu (Qi et al., 2014;Shi et al., 2015;K. Shi et al., 2018), Lake Chaohu , Lake Hongze , and Lake Poyang (Feng et al., 2012). Moreover, Hou et al. (2017) used MODIS data to evaluate the long-term SPM dynamics (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) for lakes and reservoirs in the middle and lower reaches of the Yangtze River. ...
... The algal blooms with a coverage ≥10% were masked due to the large uncertainties in the atmospheric correction of waters with an algal bloom (Xue et al., 2019a;Zhang et al., 2014a). Forty-nine match-up pairs of OLCI data and field measurements were acquired using a time window of ±3 h and a coefficient of variation (CV) test (3 × 3 pixels with a CV b 10%) Feng et al., 2012). Note that the field R rs (λ) was averaged using the spectral response function (SRF) of OLCI. ...
Article
The concentration and composition of suspended particulate matter provide important information for evaluating water quality and understanding the variability in the underwater light field in lakes. In this study, inherent optical property (IOP)-centered algorithms were developed to estimate the concentrations of chlorophyll-a (Chla, [mg/m³]) and suspended particulate matter (SPM, [g/m³]) and the Chla/SPM ratio (an indicator of the suspended particulate composition) of 118 lakes in the middle and lower reaches of the Yangtze and Huai Rivers (MLYHR) of China using Sentinel-3A/OLCI (Ocean and Land Colour Instrument) data collected from August 2016 to July 2018. The mean Chla concentration and Chla/SPM ratio were high in summer and low in winter, while the mean SPM peaked in winter and decreased in summer. The 94 lakes in the Yangtze River basin had a higher mean Chla concentration (30.94 ± 14.84) and Chla/SPM ratio (0.97 × 10⁻³ ± 0.60 × 10⁻³), but a lower mean SPM (44.87 ± 12.61) than the 24 lakes in the Huai River basin (Chla: 27.35 ± 12.18, Chla/SPM: 0.79 × 10⁻³ ± 0.48 × 10⁻³, SPM: 47.31 ± 13.40). Regarding the mean values of each lake, Chla and Chla/SPM ratio correlated well with temperature, whereas the wind speed and precipitation had little effect on the variations of suspended particulate matter. Moreover, shipping transportation and sand dredging activities affected the spatial distribution of Chla, SPM, and Chla/SPM in several large lakes (e.g., Lake Poyang and Lake Dongting). Chla/SPM related well with other proxies that express the suspended particulate composition, and had a significant correlation with the Chla-specific absorption coefficient of phytoplankton at 443 nm (aph⁎(443)). The remotely sensed concentration and composition of suspended particulate matter can provide a comprehensive reference for water quality monitoring and expand our knowledge of the trophic status of the lakes.
... are cited from[26,[41][42][43][44][45][46][47]. ...
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The inland aquaculture environment is an artificial ecosystem, where the water quality is a key factor which is closely related to the economic benefits of inland aquaculture and the quality of aquatic products. Compared with marine aquaculture, inland aquaculture is normally smaller and susceptible to pollution, with poor self-purification capacity. Considering its low cost and large-scale monitoring ability, many researches have developed spectrum sensor on-board satellite platforms to allow remote monitoring of inland water surface. However, there remain many problems, such as low image resolution, poor flexible data acquisition, and anti-interference. Apart from that, the conventional forecasting model is of weak generalization ability and low accuracy. In our study, we combine unmanned aerial vehicles system (UAVs) with the wireless sensor network (WSN) to design a new ground water quality parameter and drone spectrum information acquisition approach, and to propose a novel dynamic network surgery-deep neural networks (DNS-DNNs) model based on multi-source feature fusion to forecast the distribution of dissolved oxygen (DO) and turbidity (TUB) in inland aquaculture areas. The result of using fused features, including characteristic spectrum, Gray-level co-occurrence matrix (GLCM) texture feature, and convolutional neural network (CNN) texture feature to build a model is that the characteristic spectrum+ CNN texture fusion features were the best input items for DNS-DNNs when forecasting DO, with the determination coefficient R2 of the vertical set arriving at 0.8741, while the characteristic spectrum+ GLCM texture+ CNN texture fusion features were the best for TUB, with the R2 reaching 0.8531. Compared with a variety of conventional models, our model had a better performance in the inversion of DO and TUB, and there was a strong correlation between predicted and real values: R2 reached 0.8042 and 0.8346, whereas the root mean square error (RMSE) were only 0.1907 and 0.1794, separately. Our study provides a new insight about using remote sensing to rapidly monitor water quality in inland aquaculture regions.
... The presence of TSS and consequently turbidity in water bodies increases reflectance from red and NIR regions of the electromagnetic spectrum (Dogliotti et al., 2015;Feng et al., 2012; Chawla et al. Journal of Hydrology xxx (xxxx) xxx-xxx al., 1976;Ritchie and Cooper, 1988). ...
Article
Water resources are critical to the sustainability of life on Earth. With a growing population and climate change, it is imperative to assess the security of these resources. Over the past five decades, satellite remote sensing has become indispensable in understanding the Earth and atmospheric processes. Satellite sensors have the capability of providing data at global scales, which is economical compared to the ground or airborne sensor acquisitions. The science community made significant advances over recent years with the help of satellite remote sensing. In view of these efforts, the current review aims to present a comprehensive review of the role of remote sensing in assessing water security. This review highlights the role of remote sensing applications to assess water quality, quantity, and hydroclimatic extreme events that play an important role in improving water security. Four water quality parameters, namely, chlorophyll-a, turbidity and Total Suspended Solids (TSS), Secchi Disk Depth (SDD), and Colored Dissolved Organic Matter (CDOM), are considered. Under water quantity assessment, we review three aspects, streamflow estimation, terrestrial water storage, and reservoir operations. Remote sensing applications in quantifying floods and droughts extremes are reviewed in this work. We present how satellite sensor information acquired from different spectral bands, including optical, thermal, and microwave ranges, along with gravity field measurements, have contributed towards the applications in the above areas. We also assess the role of physical models, empirical models, and data assimilation strategies, among others, in the above areas. Finally, possible future research pathways needed to address the issues faced by the science community are discussed. This work is the second of the two-part review series, wherein the first part deals with the applications of satellite remote sensing for agriculture management.
... Recently many researchers have identified LWFs based directly on water masks extracted from satellite images, such as those of Landsat [19]- [22] and Moderate Resolution Imaging Spectroradiometer (MODIS) [17], [23]- [25]. To improve the accuracy of identifying LWFs, some studies have conducted buffer analyses based on water masks extracted from satellite images. ...
Article
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Determining the accuracy of lake water levels calculated based on Ice, Cloud, and land Elevation Satellite (ICESat) data mainly relies on identifying lake water footprints (LWFs), which are obtained using an overlay analysis of lake water masks (LWMs) and ICESat tracks. However, most previous studies that have conducted a buffer analysis based on LWMs have set the buffer size subjectively without providing a detailed explanation for this or conducting a system analysis. In this study, the effects of using inside and outside buffers to obtain LWMs for seven lakes are analyzed. The Modified Normalized Difference Water Index (MNDWI) was applied to extract LWMs from Thematic Mapper (TM) images. The boxplot was used to remove footprints with abnormal elevations, and then the average of the remaining footprints was calculated as the ICESat water level. To compare with the in situ measured data, the root mean square error (RMSE) was used for accuracy evaluation. Results show the following: (1) for Yamzhog Yumco, which is a narrow lake, the altimetry accuracy is higher when using the outside buffer than for the inside buffer or with no buffer, and the highest accuracy is obtained with an outside buffer of approximately 100 m. (2) For other relatively wide lakes, such as Lake Michigan, Lake Erie, Lake Huron, Lake Ontario and Lake Superior, the inside buffer method does not always improve altimetry accuracy, and this result differs from those presented previously. (3) For different lakes, the range of change in altimetry accuracy is affected by the number of LWFs. This study is of value for use in studies that apply ICESat altimetry data to obtain changes in lake water levels, especially for relatively narrow lakes, and the results imply that the altimetry accuracy can be improved by using the outside buffer.
... Remote sensing overcomes the problem of traditional methods and provides an appropriate understanding of the SWT transport (Petus et al., 2010;Shang et al., 2016). The red and near-infrared (NIR) reflectances are obviously elevated, as revealed by the level of SSS accumulation on the water surface and enhancive backscatter associated with SWT (Feng et al., 2012;Neukermans et al., 2012). Thus, satellite images, including Terra & Aqua/moderate resolution imaging spectroradiometer, Landsat/enhanced thematic mapper plus & operational land imager, ENVISAT/medium resolution imaging spectrometer, COMS/geostationary ocean color imager, and HJ-1/charge-coupled device (CCD) imaging, have been widely applied to estimate SWT or SSS since the 1970s (Holyer, 1978;Wu et al., 2008;Chang et al., 2009;Sun et al., 2013;Alikas and Kratzer, 2017;Lei et al., 2020). ...
Article
Surface water turbidity (SWT), as a low-cost proxy of surface suspended sediment, is important for characterizing the hydro-ecological process and light availability in the lake or reservoir ecosystem. In this study, we proposed the combined use of HJ-1 charge-coupled device imaging and field observation to track the long-term SWT dynamics with environmental changes in Lakes Gaoyang, Hanfeng, and Changshou of the Three Gorges Reservoir, China. In situ remote sensing reflectance spectra were utilized to develop the characteristic spectral indexes for the SWT estimation in different water optical classes separated by a density peaks-based classification. Significant correlations were found between the red-, four-band, band ratio spectral indexes and SWT (determination coefficient >0.71 and root-mean-square error <8.32 nephelometric turbidity unit), suggesting a crucial role of the class-specific retrieval models for the SWT estimation in optically complex waters. The proposed method was further used to monitor the spatio-temporal SWT dynamics over the three lakes from 2008 to 2019, demonstrating that the significant SWT decline in Lakes Gaoyang and Hanfeng and the relatively stable trend in Lake Changshou during the 11-year period. Specifically, the SWT decreasing trends may be attributed to the water level linkage mechanism of Three Gorges and Wuyang Dams. In addition, analyses with simultaneous environmental factors showed that the seasonal and inter-annual variations of SWT appear to be closely correlated with water level and rainfall. Long-term remote tracking of the SWT dynamics presented in this study could provide new insight and reference for reservoir management in the post-Three Gorges Project Era.
... The Yangtze Plain lakes were generally formed by the sea level rise during the postglacial period when the water level of the Yangtze River elevated to inundate the lowland regions, resulting in shallow lakes (a mean water depth of < 5 m) (Yang et al., 2008). Furthermore, the ecosystems of the lakes have been disturbed by extensive human activities (Du et al., 2011;Fang et al., 2005;Feng et al., 2012b;Ma et al., 2010a) and accelerated urbanization (Wang et al., 2014;Zhao et al., 2005). These lakes suffer from a range of severe environmental issues, including reduced inundation (Feng et al., 2012a;Feng et al., 2013;Yin et al., 2007), water quality decline Hou et al., 2017), and wetland degradation (Feng et al., 2016;Han et al., 2015). ...
Article
The eutrophication problems in lakes on the Yangtze Plain of China have attracted global concern. However, a comprehensive assessment of the eutrophication status and its evolution is still lacking for these regional lakes, mostly because of technical difficulties and/or insufficient data to cover the large region. Our study attempts to fill this knowledge gap by using the entire archive of remote sensing images from two satellite ocean color missions (MEdium Resolution Imaging Spectrometer, or MERIS (2003−2011), and Ocean and Land Color Instrument, or OLCI (2017–2018)), together with in situ data on remote sensing reflectance and chlorophyll-a (Chla) concentrations across various lakes on the Yangtze Plain. A machine learning-based piecewise Chla algorithm was developed in this study, with special considerations to improve algorithm performance under lower Chla conditions. Remotely sensed Chla and algal bloom areas were then used to classify the eutrophication status of 50 large lakes on the Yangtze Plain, and the frequent satellite observations enabled us to estimate the probability of eutrophication occurrence (PEO) for each examined lake. The long-term mean Chla ranged from 17.58 mg m⁻³ to 43.86 mg m⁻³ on the Yangtze Plain, and severe floating algal blooms were found in 7 lakes. All 50 lakes had high climatological PEO values (50%) during the study period, indicating a generally high probability of eutrophication in lakes on the Yangtze Plain. However, 21 out of 51 lakes exhibited statistically significant (p < .05) decreasing trends in PEO during the observation period, suggesting an overall improvement in the water quality of lakes on the Yangtze Plain in recent years. The methods developed here are expected to contribute to real-time monitoring of drinking water safety for local regions, and the long-term results provide valuable baseline information for future lake conservation and restoration efforts.
... Surrounding Lake Poyang, about 10 million people and 10 thousand km 2 of farmland in the peripheral floodplain are protected by about 6400 km of levees (Shankman et al., 2006;Shankman and Liang, 2003). In response to local precipitation changes, the inundation area in Lake Poyang varies significantly in short-and long-term time scales (Feng et al., 2012c(Feng et al., , 2013aHan et al., 2015;Wang et al., 2014). In addition, water regulation from the Three Gorges Dam (TGD) affects the water flow and level at the Yangze-Poyang confluence, which further complicates accurate estimation of water storage changes in Lake Poyang (Guo et al., 2008;Wang et al., 2011;Wang et al., 2017;Zhang et al., 2015b). ...
Article
Understanding water storage changes in Lake Poyang, the largest freshwater lake in China, is essential for local hydro-ecological assessments and water resource management. The integration of multi-mission satellite data, hydrological models, and in situ measurements allows for a comprehensive estimate of Lake Poyang’s storage variations. We here estimated Lake Poyang water storage changes during the recent decade by using inundation areas mapped from optical satellite imagery and water levels measured by satellite radar altimetry and gauging stations. The amplitudes of seasonal variation from altimetry data are smaller than those from station measurements. This is likely attributed to their low temporal resolutions and limited footprint coverage, together with a complex surface gradient over Lake Poyang. The residual fields between land water storage changes assessed by the GRACE satellites and simulated by two hydrological models (GLDAS Noah and WGHM) were applied to estimate Poyang water storage changes. Leakage errors in the GRACE-model residuals are further corrected by a constrained forward modeling method, resulting in recovered water storage trends ∼66 times of the uncorrected signals. Water level changes estimated by different methods are then compared. Results show that level changes inverted from recovered storage variations by GRACE-GLDAS and GRACE-WGHM are significantly larger than those from satellite altimetry and in situ measurements. This indicates that the combination of GRACE observation and global hydrological modeling is likely insufficient to estimate accurate water storage changes in Lake Poyang. Our methods and results provide a valuable example of using integrated methods for monitoring water storage changes in highly dynamic fluvial lake systems.
... Most of the lakes are turbid with low Secchi disk depths; for example, the mean Secchi disk depths for the five largest freshwater lakes range from 17.1 to 53.7 cm. Frequent algal blooms, resuspended sediments, dredging activities, and river inflows are the main causes for IOP variations in these lakes [41][42][43][44]. ...
Article
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Inherent optical properties play an important role in understanding the biogeochemical processes of lakes by providing proxies for a variety of biogeochemical quantities, including phytoplankton pigments. However, to date, it has been difficult to accurately derive the absorption coefficient of phytoplankton [𝑎𝑝ℎ(𝜆)] in turbid and eutrophic waters from remote sensing. A large dataset of remote sensing of reflectance [𝑅𝑟𝑠(𝜆)] and absorption coefficients was measured for samples collected from lakes in the middle and lower reaches of the Yangtze River and Huai River basin (MLYHR), China. In the process of scattering correction of spectrophotometric measurements, the particulate absorption coefficients [𝑎𝑝(𝜆)] were first assumed to have no absorption in the near-infrared (NIR) wavelength. This assumption was corrected by estimating the particulate absorption coefficients at 750 nm [𝑎𝑝(750)] from the concentrations of chlorophyll-a (Chla) and suspended particulate matter, which was added to the 𝑎𝑝(𝜆) as a baseline. The resulting mean spectral mass-specific absorption coefficient of the nonalgal particles (NAPs) was consistent with previous work. A novel iterative IOP inversion model was then designed to retrieve the total nonwater absorption coefficients [𝑎𝑛𝑤(𝜆)] and backscattering coefficients of particulates [𝑏𝑏𝑝(𝜆)], 𝑎𝑝ℎ(𝜆), and 𝑎𝑑𝑔(𝜆) [absorption coefficients of NAP and colored dissolved organic matter (CDOM)] from 𝑅𝑟𝑠(𝜆) in turbid inland lakes. The proposed algorithm performed better than previously published models in deriving 𝑎𝑛𝑤(𝜆) and 𝑏𝑏𝑝(𝜆) in this region. The proposed algorithm performed well in estimating the 𝑎𝑝ℎ(𝜆) for wavelengths >500nm for the calibration dataset [N=285, unbiased absolute percentage difference (UAPD)=55.22%, root mean square error (RMSE)=0.44m−1] and for the validation dataset (N=57, UAPD=56.17%, RMSE=0.71m−1). This algorithm was then applied to Sentinel-3A Ocean and Land Color Instrument (OLCI) satellite data, and was validated with field data. This study provides an example of how to use local data to devise an algorithm to obtain IOPs, and in particular, 𝑎𝑝ℎ(𝜆), using satellite 𝑅𝑟𝑠(𝜆) data in turbid inland waters.
... This new approach allows for the synchronous determination of the distributions of Cyanobacteria blooms and aquatic macrophytes in eutrophic shallow lakes. The classification tree uses 3 indices: a Cyanobacteria and macrophyte index (Liang et al. 2017), a turbid water index (Feng et al. 2012), and a floating algae index (Hu 2009). The spatial resolution of MODIS images is coarse, so we combined emergent and floating macrophytes into a single group. ...
... However, in recent decades, Poyang Lake is under considerable threats from intensive human activities and dramatic climate change. For example, urban sewage inputs have led to the eutrophication of the lake water, and sand dredging has resulted in highly turbid lake water, and flood and drought events are becoming increasingly frequent [21,65,66]. As shown in Figure 3, there were two serious drought events that occurred in early 2017 and late 2019, which lasted for several months. ...
Article
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Accurately quantifying spatiotemporal changes in surface water is essential for water resources management, nevertheless, the dynamics of Poyang Lake surface water areas with high spatiotemporal resolution, as well as its responses to climate change, still face considerable uncertainties. Using the time series of Sentinel-1 images with 6-or 12-day intervals, the Sentinel-1 water index (SWI), and SWI-based water extraction model (SWIM) from 2015 to 2020 were used to document and study the short-term characteristics of southwest Poyang Lake surface water. The results showed that the overall accuracy of surface water area was satisfactory with an average of 91.92%, and the surface water area ranged from 129.06 km 2 on 2 March 2017 to 1042.57 km 2 on 17 July 2016, with significant intra-and inter-month variability. Within the 6-day interval, the maximum change of lake area was 233.42 km 2 (i.e., increasing from 474.70 km 2 up to 708.12 km 2). We found that the correlation coefficient between the water area and the 45-day accumulated precipitation reached to 0.75 (p < 0.001). Moreover, a prediction model was built to predict the water area based on climate records. These results highlight the significance of high spatiotemporal resolution mapping for surface water in the erratic southwest Poyang Lake under a changing climate. The automated water extraction algorithm proposed in this study has potential applications in delineating surface water dynamics at broad geographic scales.
... Some media reported that sand mining activities (also called sand stealing) had induced the loss of 300 thousand tons of sand every day from 2012 to 2014 (Wu, 2015). In addition, evidence of sand mining have been detected by satellite images like Landsat (USA) and Gaofen (Chinese high-resolution satellite) (Feng et al., 2012;Li et al., 2014). Studies indicate that a large number of dredging boats emerged in HZL starting in early 2012 Lei et al., 2019b). ...
... The total lake surface area of the Yangtze Plain lakes has dramatically decreased in recent decades, mainly due to human water consumption, long-term drought, rapid reclamation and extensive water regulation after the impoundment of the Three Gorges Dam (Du et al., 2011;Wang et al., 2014a;Wang et al., 2017). The high turbidity in Poyang Lake and the harmful cyanobacteria blooms in Taihu Lake and Chaohu Lake (all of these lakes are located on the Yangtze Plain) have started to threaten the supply of drinking water and the survival of wildlife, and these blooms have been linked to excessive nutrient loading, sand mining activities and climatic factors (e.g., temperature, wind, ambient light, etc.) Feng et al., 2012;Hu et al., 2010). ...
Article
The lakes on the Yangtze Plain, a critical source of freshwater and fisheries for hundreds of millions of people in China, have lost a considerable portion of their surface area due to reclamation since the 1950s. Landsat satellites can provide long-term collections of high-resolution images and thus offer great potential for hindcasting the lake reclamations of aquaculture zones and their long-term impacts on the lacustrine water color. Using Landsat observations from 1984 to 2018 and a Forel-Ule index (FUI) model, we studied the water color dynamics of 61 lakes on the Yangtze Plain. Three distinct change patterns were found among the 61 examined lakes, and 25 of the 61 lakes showed statistically significant changes in the annual hue angle values (P < 0.05). We further collected environmental parameter datasets (runoff, a normalized difference vegetation index (NDVI), and wind speed) and a lacustrine reclamation dataset, and measured the concentrations of chlorophyll-a (Chl-a) and dissolved organic carbon (DOC) in two field works. We investigated their correlations with water color change from different facets. The results showed that the long-term water color in 33 of the 61 lakes exhibited significant correlations with environmental factors. The reclaimed aquaculture zones in this region have caused differences in the water color between the reclaimed area and that in adjacent natural waters. The Chl-a and DOC levels derived from field surveys further confirmed that reclaimed aquaculture zones increased light-absorbing materials in the water and may deteriorate water quality. This study is an important step forward in understanding the water quality changes in lake ecosystems affected by human impacts and natural variability.
... Various SPM retrieval models have been proposed, ranging from semi-analytical to empirical regression models [45][46][47][48]. Although analytical or semi-analytical models may have been more suitable for this lake, it would have been expensive to measure the many parameters required to run these models. ...
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Secchi disk depth (SDD) has long been considered as a reliable proxy for lake clarity, and an important indicator of the aquatic ecosystems. Meteorological and anthropogenic factors can affect SDD, but the mechanism of these effects and the potential control of climate change are poorly understood. Preliminary research at Lake Khanka (international shallow lake on the China-Russia border) had led to the hypothesis that climatic factors, through their impact on suspended particulate matter (SPM) concentration, are key drivers of SDD variability. To verify the hypothesis, Landsat and MODIS images were used to examine temporal trend in these parameters. For that analysis, the novel SPM index (SPMI) was developed, through incorporation of SPM concentration effect on spectral radiance, and was satisfactorily applied to both Landsat (R2 = 0.70, p < 0.001) and MODIS (R2 = 0.78, p < 0.001) images to obtain remote estimates of SPM concentration. Further, the SPMI algorithm was successfully applied to the shallow lakes Hulun, Chao and Hongze, demonstrating its portability. Through analysis of the temporal trend (1984-2019) in SDD and SPM, this study demonstrated that variation in SPM concentration was the dominant driver (explaining 63% of the variation as opposed to 2% due to solar radiation) of SDD in Lake Khanka, thus supporting the study hypothesis. Furthermore, we speculated that variation in wind speed, probably impacted by difference in temperature between lake surface and surrounding landscapes (greater difference between 1984-2009 than after 2010), may have caused varying degree of sediment resuspension, ultimately controlling SPM and SDD variation in Lake Khanka.
... However, frequent sun glint and thick aerosols, together with the strong adjacency effects of the SWIR bands from river banks (Bulgarelli & Zibordi, 2018;Sanders et al., 2001), often result in algorithm failure or incorrect spectral shapes. Due to the strong backscattering signals of suspended sediments on the red and near-infrared bands, Rayleigh-corrected reflectance has also been widely used as alternative source data to estimate the TSS concentrations in highly turbid inland water bodies (Feng et al., 2012;Guo et al., 2020). Therefore, we used Rayleigh-corrected reflectance for retrieval of TSS concentration in the Yangtze and Mekong rivers in this study. ...
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Global river systems are experiencing rapid changes in sediment transport under growing anthropogenic and climatic stresses. However, the response of sediment discharge to the coupled influence of anthropogenic and natural factors and the associated impacts on the fluvial geomorphology in the Yangtze and Mekong rivers are not comprehensively assessed. Here, we recalibrated a seamless retrieval algorithm of the total suspended sediment (TSS) concentrations using in situ data and concurrent satellite data sets to analyze spatiotemporal patterns of the TSS concentrations in the lower Yangtze and Mekong rivers. Combined with soil erosion rates estimated by the Revised Universal Soil Loss Equation for the past 20 years, we examined the contributions of different factors to TSS trends. The results show that TSS concentrations in the Yangtze River decreased from 0.47 g L⁻¹ in 2000 to 0.23 g L⁻¹ in 2018 due to the construction of the Three Gorges Dam (TGD), especially in the Jingjiang reach, with a declining magnitude of 0.3 g L⁻¹ (∼56%) since the TGD began operating. The Mekong River experienced increasing TSS concentration trends upstream and decreasing trends downstream from 2000 to 2018, possibly attributed to increased upstream soil erosion and decreased downstream water discharge. Declining TSS concentrations in both rivers have driven varying degrees of river channel erosion over the past two decades. This study investigated long‐term changes in the TSS concentrations and soil erosion in the Yangtze and Mekong rivers, and the results provide baseline information for the sustainable development of river sediment delivery.
... The framework design and application in our case study demonstrated its high value in water turbidity prediction. Different from satellite images widely used for large-scale investigation of water turbidity/clarity (Bi et al., 2018;Feng et al., 2012;Hou et al., 2017;Zhao et al., 2011), the framework aimed to provide a cost-effective way to predict water turbidity. Compared with previous studies in turbidity prediction/measurement (Bayram et al., 2018;Koydemir et al., 2019;Leeuw and Boss, 2018), the developed framework had several advantages including model update, uncertainty quantification, RFE algorithm for variable selection, and cross-validation to achieve an adequately evaluated model. ...
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High water turbidity in aquatic ecosystems is a global challenge due to its harmful impacts. A cost-effective manner to rapidly and accurately measure water turbidity is thus of particular useful in water management with limited resources. This study developed a novel framework aiming to predict water turbidity in various aquatic ecosystems. The framework predicted water turbidity and quantified the uncertainty of the prediction through Bayesian modelling. To improve model performance, a model-update method was implemented in the framework to update the model structure and parameters once more measured data were available. 120 paired records (an image from smartphone and a measured water turbidity value by standard turbidimeters for each record) were collected from rivers, lakes and ponds across China to evaluate the performance of the developed framework. Our cross-validation results revealed a well prediction of water turbidity with Nash-Sutcliffe efficiency (NS) >0.87 (p<0.001) during the training period and NS>0.73 (p<0.001) during the validation period. The model-update method (in case of more measured data) for the developed Bayesian models in the framework resulted in a decreasing trend of model uncertainty and a stable mode fit. This study demonstrated a high value of the Bayesian-based framework in predicting water turbidity in a robust and easy manner.
... Jiang and Liu (2011) constructed nonlinear functions based on MODIS in the dry, average, and wet seasons, respectively. Feng et al. (2012) and Wu et al. (2013a) found that the exponential functions of the red minus infrared band provided stable SPM estimation models via the use of MODIS. Li et al. (2015) suggested an exponential regression C SPM model from the 16-m GF-1 WFV data that resolved more than 75% of the spatial variability in highly dynamic turbid waters. ...
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Quantifying the concentration of suspended particulate matter (CSPM) is necessary for the evaluation of the ecological processes, matter transfer, and environments in lakes. Traditional monitoring via the use of field measurements lacks the spatial coverage necessary for detailed analysis in large-area lakes. In contrast, the macroscopic, real-time monitoring of CSPM can be achieved using data from remote-sensing satellites. Due to the major limitations of the existing analytical and semi-analytical algorithms, namely the lake locations and limited data, regression algorithms have become important tools for the analysis of the CSPM values in lakes. The purpose of this study is the evaluation of both parametric and nonparametric regression algorithms for CSPM estimation based on the simulation of Gaofen-1 (GF-1) wide field-of-view (WFV) satellites for the imaging of turbid water. A total of 71 samples collected during four cruises were analyzed to determine the spectral reflectance and CSPM in Poyang Lake. The results indicate that the spectral ratio (SR) parametric regression model and the extreme learning machine (ELM) nonparametric regression model based on in situ SPM and spectral data perform relatively better than the other investigated models. A SR fitting model using band3 (λ = 660 nm)/band2 (λ = 555 nm) of the GF-1 WFV sensor was established to estimate the CSPM value. The evaluation results indicate that the SR model achieved a high retrieval accuracy (R2 = 0.901, RMSE = 8.22 mg·L−1); this is because the model was more sensitive and could effectively weaken, and even partially eliminate, the effects of external factors. The ELM model achieved a higher retrieval accuracy (R2 = 0.903, RMSE = 8.05 mg·L−1) than the other neural network-based algorithms, namely the backpropagation (BP), radial basis function (RBF), and cascade-forward neural network (CFNN) algorithms. The ELM model is well suited for both low and high turbidity, and has a robust ability to accurately retrieve the CSPM value. In Poyang Lake, the CSPM values were found to have a spatial characteristic and were higher in the northern waters than in the southern waters. Overall, this study demonstrates the applicability of GF-1 WFV sensors with an advanced spatial resolution and an increased spectral sensitivity for monitoring the CSPM values in high-turbidity water, and the ELM model is recommended for application to GF-1 images to satisfactorily produce quantitative CSPM maps.
... Each year, Poyang Lake water levels rise from March to May, wherein the highest water levels occur from June to August, reaching 17-22 m, and then recede from September to November, finally falling to its lowest levels (7-9 m) from December to February of the following year. And its water depth also varies from <6 m to 30 m with an average water depth of 8.4 m (Feng et al., 2012). This process leads to the alternation of lake water inundation (high water level) and bare soil and wetland vegetation exposure (low water level), namely, a lake phase during the rainy season and riverlike phase during the dry season ( Fig. 1c) (Lai, 2012). ...
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Phytoplankton contribute approximately 50% to the global photosynthetic carbon (C) fixation. However, our understanding of the corresponding C sequestration capacity and driving mechanisms associated with each individual phytoplankton taxonomic group is limited. Particularly in the hydrologically dynamic system with highly complex surface hydrological processes (floodplain lake systems). Through investigating seasonal monitoring data in a typical floodplain lake system and estimation of primary productivity of each phytoplankton taxonomic group individually using novel equations, this study proposed a phytoplankton C fixation model. Results showed that dominant phytoplankton communities had a higher gross carbon sequestration potential (CSP) (9.50 ± 5.06 Gg C each stage) and gross primary productivity (GPP) (65.46 ± 25.32 mg C m⁻² d⁻¹), but a lower net CSP (−1.04 ± 0.79 Gg C each stage) and net primary productivity (NPP) (−5.62 ± 4.93 mg C m⁻³ d⁻¹) than rare phytoplankton communities in a floodplain lake system. Phytoplanktonic GPP was high (317.94 ± 73.28 mg C m⁻² d⁻¹) during the rainy season and low (63.02 ± 9.65 mg C m⁻² d⁻¹) during the dry season. However, their NPP reached the highest during the rising-water stage and the lowest during the receding-water stage. Findings also revealed that during the rainy season, high water levels (p = 0.56**) and temperatures (p = 0.37*) as well as strong solar radiation (p = 0.36*) will increase photosynthesis and accelerate metabolism and respiration of dominant phytoplankton communities, then affect primary productivity and CSP. Additionally, water level fluctuations drive changes in nutrients (p = −0.57*) and metals (p = −0.68*) concentrations, resulting in excessive nutrients and metals slowing down phytoplankton growth and reducing GPP. Compared with the static water lake system, the floodplain lake system with a lower net CSP became a heterotrophic C source.
... The formula assumes that the contribution of whitecaps and sunlight can be neglected, and it is used to show the relationship between R rc and R rs in this study. The R rc data are re-projected through equidistance cylindrical (rectangular) projection (Feng et al., 2012). ...
Article
There are some uncertainties of using chlorophyll a (Chla) concentrations in water surface to address phytoplankton dynamics, especially in large shallow lakes, because of the dramatic vertical migration of phytoplankton. The column-integrated algal biomass (CAB) can reflect the whole water column information, so it is considered as a better indicator for phytoplankton total biomass. An algal biomass index (ABI) and an empirical algorithm were proposed previously to measure algal biomass inside and outside euphotic zone from the Moderate Resolution Imaging Spectrometer (MODIS) data. A long-term CAB time series was generated in this study to clarify the temporal and spatial changes in phytoplankton and address its sensitivity to climatic factors in Lake Chaohu, a shallow eutrophic lake in China, from 2000 to 2018. Overall, the CAB for Lake Chaohu showed significant temporal and spatial dynamics. Temporally, the annual average CAB (total CBA within the whole lake) was increased at rate of 0.569 t Chla/y, ranging from 62.06±8.89 t Chla to 76.03±10.01 t Chla during the 19-year period. Seasonal and periodic variations in total CAB presented a bimodal annual cycle every year, the total CAB was highest in summer, followed by that in autumn, and it was the lowest in winter. The pixel-based CAB (total CAB of a unit water column), ranging from 112.42 to 166.85 mg Chla, was the highest in the western segment, especially its northern part, and was the lowest in the central parts of eastern and central segments. The sensitivity of CAB dynamics to climatic conditions was found to vary by region and time scale. Specifically, the change of pixel-based algal biomass was more sensitive to the temperature change on the monthly and annual scales, while wind speed impacted directly on the short-term spatial-temporal redistribution of algal biomass. High temperature and low wind speed could prompt the growth of total CAB for the whole lake, and the hydrodynamic situations affected by wind and so on determined the spatial details. It also indicated that Lake Chaohu may face more severe challenges with the future climatic warming. This study may serve as a reference to support algal bloom forecasting and early warning management for other large eutrophic lakes with similar problems.
... Clouds and land pixels were identified using a threshold in the SWIR2 band (Vanhellemont and Ruddick 2015). To remove the pixels near the land possibly containmined by the land adjacency effect, five water pixels near the land were excluded following several attempts to examine the variations in R rc from nearshore to off-shore (Feng et al. 2012). Pixels with cyanobacteria scums in Landsat images were excluded using a threshold of the floating-algae index (FAI) (i.e., − 0.005), which was determined by visual interpretation of pixels with cyanobacterial scums (Hu et al. 2010). ...
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Long-term datasets of chlorophyll-a (Chla) are necessary to evaluate changes in eutrophication and to assist in lake management; however, current aquatic remote sensing datasets usually start after 2000. Here, a 36-year Chla dataset was assembled from Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Landsat Imager (OLI) imagery for Lake Taihu (China) over the period from 1984 to 2019. TM, ETM+ and OLI reflectances were compared to those using the MODerate resolution Imaging Spectroradiometer (MODIS) on Terra, and agreement was found within a mean absolute difference of 15%. An algorithm for Chla retrieval developed by a machine learning approach (XGBoost) had good performance (mean absolute percentage error = 35%, mean absolute error = 9%) and outperformed random forest and support vector machine regressors and existing empirical algorithms for Lake Taihu. Landsat-derived mean Chla ranged between 12.8 µg L-1 and 32.3 µg L-1 and indicates that Lake Taihu has been eutrophic from 1984 to 2019. Chla in the northern region was higher than that in other areas over the 36 years. With the limited number of Landsat images each year, we found that the annual variation in Chla had high values during the periods of 1984-1992 and 1994-1997 and significant increases in 1999-2009 and from 2012 to 2019. The spatial and temporal variations in Chla for Lake Taihu were correlated with dissolved nutrients and air temperature. This research illustrates the use of machine learning approaches to generate long-term datasets of water quality from multiple Landsat instruments , for extending watercolor archives for lakes.
... The value of Chla/TSM near the bloom area derived from OLCI was significantly higher than that derived from Sentinel-2/MSI. Compared with the MSI image with 20 m resolution, the pixels near the bloom on the OLCI image with 300 m resolution may be affected by the adjacent effect (Cao et al., 2019;Feng et al., 2012). Comparing the variation trend of Chla/ TSM along the transection from the P1 location to the P2 location, it can be found that the variation trend along the transection estimated by the two sensors was substantially consistent. ...
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The composition of suspended particles is a key factor in determining the underwater light field, which is of great significance for understanding the variability in the optical properties of water bodies. In this study, the ratio of the phytoplankton absorption coefficient to the backscattering coefficient at wavelength of 681 nm (aph(681)/bb(681)) was found to be an optimal optical indicator of the ratio of chlorophyll-a to the total suspended matter concentration (Chla/TSM), a parameter indicating the particulate composition. Therefore, a semianalytical algorithm was proposed to estimate Chla/TSM from remote sensing reflectance (Rrs(λ)) at 681 nm and 754 nm on Sentinel-3 Ocean and Land Color Instrument (OLCI) images. The validation dataset collected from 11 inland lakes and 3 reservoirs in China and 2 inland lakes in America was used to evaluate the algorithm’s performance. The evaluation results demonstrated that the proposed algorithm could have favorable performance in inland waters. Furthermore, comparison with two other state-of-the-art algorithms (Sun_13 and NTD675) showed that this proposed algorithm had higher estimation accuracy, with an overall winning rate (OWR) of 60%, an unbiased mean absolute percentage error (UMAPE) reduction from 72.95% to 46.44%, a root mean square error (RMSE) decline from 1.42 µg/mg to 0.83 µg/mg and a normalized root mean square error (NRMSE) decline from 11.36% to 6.62%. This algorithm was successfully applied to acquire the Chla/TSM tempo-spatial variation using the OLCI images of Lake Taihu from 2016 to 2019. It was found that the algorithm developed based on OLCI images can be applied to satellite sensors with similar bands, such as Medium-Resolution Imaging Spectrometer (MERIS), and Sentinel-2 multispectral instrument (MSI), etc. As a simple and effective algorithm, the proposed algorithm has the potential to monitor changes in Chla/TSM in inland waters on a global scale.
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Due to the difference of vertical distribution of algae in lakes, it is necessary to carry out remote sensing estimation of algal biomass based on the vertically heterogeneous distribution of chlorophyll in order to improve the accuracy of biomass inversion. A new algorithm is proposed and validated to measure algal biomass in Lake Chaohu based on the Moderate Resolution Imaging Spectrometer (MODIS) images. The algal biomass index (ABI) is defined as the difference in remote-sensing reflectance (Rrs, sr⁻¹) at 555 nm normalized against two baselines with one formed linearly between Rrs(859) and Rrs(469) and another formed linearly between Rrs(645) and Rrs(469). Both theory and model simulations show that ABI has a good relation with the algal biomass in the euphotic zone (R²=0.88, p<0.01, N=50). Field data were further used to estimate the biomass outside the euphotic layer through an empirical algorithm. The ABI algorithm was applied to MODIS Rayleigh-corrected reflectance (Rrc) data after testing the sensitivity to sun glint and thickness of aerosols, which showed an acceptable precision (root mean square error<21.31mg and mean relative error<16.08%). Spectral analyses showed that ABI algorithm was immune to concentration of colored dissolved organic matter (CDOM) but relatively sensitive to suspended particulate inorganic matter (SPIM), which can be solved by using Turbid Water Index (TWI) though in such a challenging environment. A long-term (2012–2017) estimation of algal biomass was further calculated based on the robust algorithm, which shows both seasonal and spatial variations in Lake Chaohu. Tests of ABI algorithm on Sentinel-3 OLCI demonstrates the potential for application in other remote sensors, which meets the need of observation using multi-sensor remote sensing in the future.
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Total suspended matter (TSM) is one of the most widely used water quality parameters, which can influence the light transmission process, planktonic algae, and ecological health. A comprehensive field expedition aiming at water quality assessment was conducted for Lake Qinghai in September 2019. The in-situ measurements were used to support the calibration and validation of TSM concentration using Landsat images. A regional empirical model was established using the top-of-atmosphere (TOA) radiance of Landsat image data at the red band with a wavelength range of 640–670 nm. The coefficient of determination (R2), mean relative error (MRE), and root mean square error (RMSE) of the TSM estimation model were 0.81, 17.91%, and 0.61 mg/L, respectively. The model was further applied to 87 images during the periods from 1986 to 2020. A significant correlation was found between TSM concentration and daily wind speed (r = 0.74, p < 0.01, n = 87), which revealed the dominance of wind speed on TSM concentration. In addition, hydrological changes also had a significant influence on TSM variations of lake estuaries.
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Total suspended matter (TSM), as an indicator of the concentration of fine materials in the water column including particulate nutrients, pollutants, and heavy metals, is widely used to monitor aquatic ecosystems. However, the long-term spatiotemporal variations of TSM in lakes across the Tibetan Plateau (TP) and their response to environmental factors are rarely explored. Accordingly, taking advantage of the Landsat top-of-atmosphere reflectance and in-situ data, an empirical model (R² = 0.83, RMSE = 1.08 mg/L, and MAPE = 19.49 %) was developed to estimate the average autumnal TSM in large TP lakes (≥50 km²) during the 1990–2020 period. For analyzing the spatiotemporal variability in TP lakes TSM, the examined lakes were classified into four types (Type A-D) based on their water storage changing in different periods. The results showed that the lakes in the southern and some northeastern parts of the TP exhibited lower TSM values than those situated in other regions. The assessment of TSM in each of these four lake types showed that more than half of them had a TSM value of <20 mg/L. Apart from Type D, the lakes with the TSM showing significantly decreasing trends were dominantly Types A-C. A relative contribution analysis involving five driving factors indicated that they contributed by >50 % to lake TSM interannual variation in 73 out of 114 watersheds, and the lakes area change demonstrated the greatest contribution (82.2 %), followed by wind speed (11.0 %). Further comparison between the entire lake and the non-expansive regions suggested that the expansive region played an indispensable role in determining the TSM value of the whole lake. This study can help to better understand the water quality condition and provide valuable information for policy-makers to maintain sustainable development in the TP region.
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The water environment has experienced prominent changes worldwide in recent decades, especially in inland waters. As an important lake region in China, the water turbidity of lakes/reservoirs in the Northeast Plain-Mountainous Region (NPLR) remains less understood, especially the influencing mechanisms of regional climatic conditions and human activities. To enhance the knowledge of turbidity dynamics and explanatory variables of NPLR water bodies, long-term turbidity estimates of lakes/reservoirs were derived from Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua observations between 2003 and 2019. The results show that most lakes/reservoirs became less turbid in NPLR during the study period, as indicated by a decreasing turbidity trend of 0.05–2.4 nephelometric turbidity units (NTU)/year. Overall, the decreased turbidity was mainly attributed to the decreased wind speed (from 2003 to 2012, r = 0.85, p < 0.01), but also to the increased normalized difference vegetation index (NDVI) of the forest region (r = −0.80, p < 0.01), with the latter being a result of the forest protection efforts of the Chinese government. The increased snowmelt depth also contributed to a substantial turbidity decline in April. In addition, distinct seasonal patterns of turbidity were observed in the NPLR, with less turbid water in the wet season (i.e., from June to August) and turbid water in the dry season (i.e., October, November, and April in the next year). This result is attributed to the rapid seasonal dynamics of precipitation and runoff. This study provided a comprehensive analysis on the influencing mechanisms of turbidity dynamics in NPLR lakes; in particular, for the first time, revealed the impacts of the national policy-mandated forest development and the snowmelt depth on lake turbidity. These findings will provide vital information for government decision-making in water environment protection in NPLR and other similar areas with densely distributed lakes.
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Phosphorus is a limiting nutrient in freshwater ecosystems. Therefore, the estimation of total phosphorus (TP) concentration in eutrophic water using remote sensing technology is of great significance for lake environmental management. However, there is no TP remote sensing model for lake groups, and thus far, specific models have been used for specific lakes. To address this issue, this study proposes a framework for TP estimation. First, three algorithm development frameworks were compared and applied to the development of an algorithm for Lake Taihu, which has complex water environment characteristics and is a representative of eutrophic lakes. An Extremely Gradient Boosting (BST) machine learning framework was proposed for developing the Taihu TP algorithm. The machine learning algorithm could mine the relationship between FAI and TP in Lake Taihu, where the optical properties of the water body are dominated by phytoplankton. The algorithm exhibited robust performance with an R² value of 0.6 (RMSE = 0.07 mg/L, MRE = 43.33%). Then, a general TP algorithm (R² = 0.64, RMSE = 0.06 mg/L, MRE = 34.13%) was developed using the proposed framework and tested in seven other lakes using synchronous image data. The algorithm accuracy was found to be affected by aquatic vegetation and enclosure aquaculture. Third, compared with field investigations in other studies on Lake Taihu, the Taihu TP algorithm showed good performance for long-term TP estimation. Therefore, the machine learning framework developed in this study has application potential in large-scale spatio-temporal TP estimation in eutrophic lakes.
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En el presente artículo se muestra la elaboración de un modelo empírico aplicado a las imágenes MODIS MYD09 y MOD09 con base en muestras in situ de sólidos suspendidos totales (TSS) que se tomaron en las aguas del golfo de Urabá (Colombia) entre 2011 y 2015. Se analizó la variabilidad espacial y temporal a escala anual y mensual de los sedimentos, lo que mostró una diferencia marcada durante las dos temporadas climáticas (de lluvia y sequia), en la concentración y en la dirección de la pluma de sedimentación. El mejor modelo aplicado sobre las imágenes para la recuperación de los tss es la forma funcional polinómica de grado 3 que relaciona la sedimentación con la reflectancia de la banda 2, aplicadas en las imágenes MYD09. Dentro de los resultados obtenidos hay valores promedio anuales superiores a los 100mgL-1 en la bahía El Rotico, debido a los aportes dados por el río Atrato en la boca El Roto y de concentración alta de sedimentos en bahía Colombia durante el primer trimestre del año, sobre todo por la dirección de los vientos (≈150 mgL-1). La investigación permite mostrar las bondades del tratamiento digital de imágenes para recuperar la sedimentación a partir de datos de color del océano, con el propósito de obtener resultados pertinentes que ayuden a realizar un análisis de los flujos de sedimentación en el interior del golfo, sobre todo porque se constituye en un lugar de importancia ecológica por su gran biodiversidad marina y donde el conocimiento de la dinámica y concentración de los sedimentos es escaso.
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Food chain length is a significant ecosystem index and it greatly affects ecosystem processes and their outcomes. In recent decades, determinants or indicators of FCL have been investigated in different ecological contexts, but few studies analyzed the relationships between macrophyte community structure and the food chain length of freshwater ecosystems despite the crucial role of macrophyte community in determining food web structure. This study investigated the influence of macrophyte community structure on food chain length by conducting a field survey in Lake Poyang, the largest freshwater lake in China. Plant volume inhabited (PVI), Shannon-Wiener diversity index and Pielou’s evenness index were used to indicate macrophyte community structure. Our results suggested that intermediate levels of PVI and diversity (PVI 44% and diversity 0.61) with greater evenness could support longer food chain in sub-lakes of Lake Poyang. We proposed that macrophyte community structure indexes could be useful indicators in indicating food chain length in freshwater ecosystem. These novel findings also highlight that within suitable ranges of PVI and diversity, better restoration effect could be achieved by higher macrophyte evenness in lake management. Our findings provide new insights for the restoration and management of macrophytes in freshwater ecosystems.
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In this study, we empirically developed a robust model (the Root Mean Square Error (RMSE), bias, NSE and RE were 26.63 mg/L, −4.86 mg/L, 0.47 and 16.47%, respectively) for estimating the total suspended solids (TSS) concentrations in lakes and reservoirs (Hereinafter referred to as lakes) across the Eastern Plain Lake (EPL) Zone. The model was based on 700 in-situ TSS samples collected during 2007–2020 and logarithmic transformed red band reflectance of Landsat data. Based on the Google Earth Engine (GEE), the TSS concentrations in 16,804 lakes were mapped from 1984 to 2019. The results demonstrated a decreasing tendency of TSS in 82.2% of the examined lakes (72.5% of the basins) indicating that the pollutants carried by TSS flowing into the lakes were decreasing. Statistically significant variation (p < 0.05) was found in half of these lakes (28.6% of the basins). High TSS level (>100 mg/L) was observed in 0.31% of lakes (1.1% of the basins). The changing rates of TSS in 47.8% of the lakes (52.7% of the basins) ranged between −50 mg/L/yr and 0. We found high and significantly increased relative spatial heterogeneity of TSS in 4.6% and 6.5% of lakes, respectively. Likewise, the environmental factors, i.e., fertilizer usage, domestic wastewater, industrial wastewater, precipitation, wind speed and Normalized Difference Vegetation Index (NDVI) exhibited a significant correlation with interannual TSS in 38, 21, 20, 11, 17 and 15 of the 91 basins, respectively. This analysis indicated that only precipitation and fertilizer usage were significantly (p < 0.05) related to the spatial distribution of TSS. The relative contributions of the six factors to the interannual TSS changes were varied in different basins. Overall, the NDVI (the representation of vegetation cover) had a high mean contribution to the interannual TSS changes with an average contribution of 7.2%, and contributions of fertilizer were varied greatly among the basins (0.01%–68%). Human activities (fertilizer usage, domestic wastewater, industrial wastewater) and natural factors (precipitation, wind speed and NDVI) played relatively important roles to TSS changes in 14 and 15 of the 91 basins, respectively. Beyond the six factors in this study, other unanalyzed factors (such as lake depth and soil texture) also had some impacts on the distribution of TSS in the study area.
Chapter
Monitoring of acid mine drainage is an important ecological task. Remote sensing provides means of accurate operational observations of the Earth in a wide range of spectrum. Numerous remote indices of surface water state/quality have been developed based on remote sensing data. Monitoring of small-sized objects requires a fine spatial resolution which generally implies a coarser spectral resolution. Hence, only a qualitative knowledge of water optical features can be taken into account which makes it difficult to perform a quantitative analysis of water compounds. A possible approach is a comprehensive analysis of a numerous indicators, which can be sensitive not only to variations of water optical properties due to pollution but also to changes of the course of biological and sedimentation processes caused by it within next days. The aim of this work is selection and/or construction of a set of such indicators and presents the initial stage of the investigation. A list of used remote indicators (around 20 in total) is formed; an approach to their qualitative comparison is introduced and tested; the prospects of the future investigation are discussed; and some recommendations are given.
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A practical Atmospheric Correction algorithm for inLand and Nearshore Coastal waters (ACLANC) is proposed in this study. The ACLANC algorithm uses interpolated aerosol optical depth (AOD) products (AOD interp) from nearby land surfaces and simulates the corresponding aerosol reflectance spectrum using a combination of the continental model in the satellite signal in the solar spectrum-vector (6SV) radiative transfer code and an approximate aerosol model (r85f20) in the Sea-viewing Wide Field-of view Sensor (SeaWiFS) Data Analysis System (SeaDAS). Validations with worldwide insitu measurements show that the ACLANC-derived remote-sensing reflectance ( Rrs ) for nine Moderate Resolution Imaging Spectroradiometer (MODIS) bands agreed well with the insitu datasets, where the mean R² was 0.77 ± 0.09 and the mean unbiased percent difference was 28.7% ± 9.8%. ACLANC outperformed the existing atmospheric correction algorithms in not only the accuracy of the Rrs retrievals but also data coverage. Vicarious calibration over the ACLANC algorithm showed minor improvement in the derived Rrs products. Error budget analysis revealed that the uncertainties in AOD interp represent >50% of the errors for ACLANC and that this proportion increases with decreasing AOD. Further efforts can also be applied to improve the aerosol models, especially for turbid aerosol environments, where the fixed aerosol model in SeaDAS contributes up to 30% of the error budget. The ACLANC algorithm can potentially be implemented in ocean color missions other than MODIS to obtain Rrs with high accuracy and wide coverage for global inland and nearshore coastal waters.
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The estimation of total suspended matter (TSM) concentration is crucial in monitoring, evaluating, and protecting water quality. Many empirical and semi-analytical models have been established for clear or extremely turbid water bodies; however, only a few are applicable to inland, extremely turbulent deep rivers. Using in situ data from the water of the Manwan reservoir, we developed a robust algorithm to estimate the TSM concentration in the Manwan reservoir, with a root mean square error (RMSE) ≤ 4.43 mg L⁻¹ and a mean absolute percentage error (MAPE) of 23.2%, indicating the feasibility of the empirical model for estimating TSM. The empirical model was then applied to 251 Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting loaded with the Charge Coupled Device (HJ-CCD) images to derive TSM distribution maps from 2009 to 2018. The estimated TSM concentrations exhibited significant spatial and seasonal changes, revealing relationships among TSM, wind speed, and precipitation. The spatial heterogeneity was significantly higher downstream than upstream in the reservoir due to watershed inputs and anthropogenic dredging activity. The temporal heterogeneity of TSM, significantly higher in summer and autumn than in winter and spring, was mainly caused by seasonal rainfall. Our study shows that the empirical model for HJ-CCD images can be used to quantitatively monitor the TSM in inland rivers.
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With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research ® eld at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.
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Lake size is sensitive to both climate change and human activities, and therefore serves as an excellent indicator to assess environmental changes. Using a large volume of various datasets, we provide a first complete picture of changes in China's lakes between 1960s–1980s and 2005–2006. Dramatic changes are found in both lake number and lake size; of these, 243 lakes vanished mainly in the northern provinces (and autonomous regions) and also in some southern provinces while 60 new lakes appeared mainly on the Tibetan Plateau and neighboring provinces. Limited evidence suggested that these geographically unbalanced changes might be associated primarily with climate change in North China and human activities in South China, yet targeted regional studies are required to confirm this preliminary observation.
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Ariake Bay is located in the north of Kyushu, and surrounded by four prefectures (Saga, Nagasaki, Fukuoka and Kumamoto). It is one of the most important bays in Japan; since it comprises about 40% of the total prodn. of shellfish and Nori (seaweeds) culture growth in there. A point at which two rivers; "Kashima River" and "Shiota River", discharge into the bay, is called the Kashima area of the Bay. This area is characterized by a muddy nature of its sediments. The accumulation of metals (Al, As, Cd, Cr, Cu, Fe, Pb, Mn, Hg, Sr, Sn, and Zn) in the sediments of this area has been studied since the last few decades. The herein study investigates the relevant factors that control and affect these metals' accumulation in this area of the Bay. The main studied components of the sediments were Al, Fe, Mn, Acid Volatile Sulfide (AVS) and Total Org. Carbon (TOC). These components were found to control the accumulation of other metals, and were highly accumulated in the surface sediments than in 20 cm deep sediments, in the order of: TOC > Al > Fe > Mn > AVS. The higher concn. of such components in the surface sediments resulted in higher concn. of other metals. The controlling factors for metals were 6.13 (TOC), 4.99 (AVS), 6.83 (Al), 7.4 (Fe) and 7.45 (Mn). The significant correlation between TOC and AVS suggests a favorable decompn. of org. matter to sulfides in the sediments. Meanwhile, the max. AVS concn. in the surface sediments of the core reflected the formation of stable metal sulfides. High correlations between metals' concn. were obsd.; Cr and Fe (0.94), Cu and Pb (0.98), Cu and Mn (0.84), Pb and Mn (0.89), Cd and Zn (0.97), Cu and Zn (0.84), Pb and Zn (0.83). These high correlations suggest that metals correlated with each other, may have the same source, and/or the same chem. properties as stated in the HSAB (Hard Soft Acid Base Theory). So, Al and Fe, which are hard metals, were shown to accumulate Cr, Sr and other metals of the same hard nature. Also, minor components (Cd, Cu, Pb and Zn) were accumulated together for their similar chem. property as border metals. On the other hand, the poor correlation between As, Sn and Hg, and other studied metals, suggests that these metals might be released by some point source rather than the natural one. The enrichment factors (EF) of metals were detd. as normalized concn. ratios between the concn. of the metals in the samples and those in the bottom core, which have been accumulated 300-500 years ago. The EF values of all metals, AVS and TOC were higher than 1 but less than 3, suggesting a minor enrichment.
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About 10 yr of TOPEX/Poseidon (T/P) altimetry data have been used to compute time series of lake levels at six inland lakes in China. To verify our T/P data processing strategy, the T/P-derived lake levels at Bosten Lake (west China) and Lake Huron (north America) were compared with lake gauge records: good agreement is found between the T/P and the gauge results. Wavelet spectra indicate annual and interannual variations of these lake levels, which are also sensitive to climate variability. At the interannual timescale, the lake levels of Hulun (north China), Bosten (west China) and Ngangzi (east Tibet) are correlated with precipitation and El Niño Southern Oscillation (ENSO); in particular, they all respond to the 1997-1998 El Niño. The Bosten lake level has increased monotonically since 1993 due to the increased temperature on Tianshan Mountain, which feeds water into this lake. The lake levels of Hongze and Gaoyou (east China) show minor decreasing trends. The lake level of La'nga (west Tibet) decreased steadily from 1993 to 2001, with a total drop of 4 m. The Ngangzi lake level decreased from 1993 January to 1997 December, but after the peak of the 1997-1998 El Niño the slope was reversed and the lake level has increased monotonically since then. An example given at Bosten Lake shows that waveform contamination over Chinese lakes affects the quality of T/P-derived lake levels and retracking is necessary to mitigate the problem.
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The remote sensing of turbid waters (Case II) using the Medium Resolution Imaging Spectrometer (MERIS) requires new approaches for atmospheric correction of the data. Unlike the open ocean (Case I waters) there are no wavelengths where the water-leaving radiance is zero. A coupled hydrological atmospheric model is described here. The model solves the water-leaving radiance and atmospheric path radiance in the near-infrared (NIR) over Case II turbid waters. The theoretical basis of this model is described, together with its place in the proposed MERIS processing architecture. Flagging procedures are presented that allow seamless correction of both Case I waters, using conventional models, and Case II waters using the proposed model. Preliminary validation of the model over turbid waters in the Humber estuary, UK is presented using Compact Airborne Spectrographic Imager (CASI) imagery to simulate the MERIS satellite sensor. The results presented show that the atmospheric correction scheme has superior performance over the standard single scattering approach, which assumes that water-leaving radiance in the NIR is zero. Despite problems of validating data in such highly dynamic tidal waters, the results show that retrievals of sediments within 50% are possible from algorithms derived from the theoretical models.
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1] We measured the absorption properties of phytoplankton, nonalgal particles (NAP), and colored dissolved organic matter (CDOM) at about 350 stations in various coastal waters around Europe including the English Channel, Adriatic Sea, Baltic Sea, Mediterranean Sea, and North Sea. For comparison, we also collected data in the open ocean waters of North Atlantic. The exponential slope of the CDOM absorption spectrum varied within a narrow range around 0.0176 nm À1 (SD = 0.0020 nm À1). When data from all the regions were considered altogether, the relationship between phytoplankton absorption and chlorophyll concentration was generally similar to the one previously established for open oceanic waters. Our coastal data, however, show that significant departures from the general trend may occur due to peculiar pigment composition and cell size. In some coastal areas, high phaeopigment concentrations gave rise to especially high blue-to-red ratio of phytoplankton absorption. The NAP absorption covaried with the particle dry weight. Most absorption spectra of these particles were well described by an exponential function with a slope averaging 0.0123 nm À1 (SD = 0.0013 nm À1). In some highly turbid waters, the spectra exhibited a signature possibly associated with iron oxides. In the Baltic Sea, NAP absorption systematically showed lower values at wavelengths shorter than 440 nm than predicted from the fitted exponential function. Overall, the variability in the absorption properties of European coastal waters showed some consistent patterns despite the high diversity of the examined waters. Distinct features were identified in the phytoplankton and NAP components. An absorption budget is presented and parameterizations are proposed., Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe, J. Geophys. Res., 108(C7), 3211, doi:10.1029/2001JC000882, 2003.
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This letter presents an empirical relationship that may be used to estimate the suspended particulate matter concentrations in highly turbid waters from remote sensing re ectance measurements. Numerous measurements carried out in the Gironde estuarine waters (France) in 2000 and 2001 are presented and analysed. It was observed that the near-infrared (850 nm) re ectance was weakly correlated with the total suspended matter concentration (T SMc) measured in surface waters. A strong correlation (r=0.91) was obtained between the ratio of the near-infrared and visible (550 nm) re ectance and T SMc, which could provide an accurate calibration curve for data from Système Probatoire de l'Observation de la Terre (SPOT), Landsat and Indian Remote Sensing (IRS) satellite sensors. The re ectance ratio reduced the e V ects of changes in illumination conditions and sediment type (grain-size, refractive index). The calibration function obtained, successfully applied to the Gironde, should be applied in other sediment-dominated coastal waters.
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Poyang Lake in Jiangxi Province is the largest freshwater lake in China and is historically a region of significant floods. Annual events of peak lake stage and of severe floods have increased dramatically during the past few decades. This trend is related primarily to levee construction at the periphery of the lake and along the middle of the Changjiang (Yangtze River), which protects a large rural population. These levees reduce the area formerly available for floodwater storage resulting in higher lake stages during the summer flood season and catastrophic levee failures. The most severe floods in the Poyang Lake since 1950, and ranked in descending order of severity, occurred in 1998, 1995, 1954, 1983, 1992, 1973, and 1977. All of these floods occurred during or immediately following El Niño events, which are directly linked to rainfall in central China. The 2-year recurrence interval for maximum annual lake stage during El Niño years is 1.2 m higher than during non-El Niño years. The 10-year recurrence interval is 1.4 m higher during El Niño years than during non-El Niño years. Copyright © 2006 Royal Meteorological Society.
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Plumes of neritic sediment caused by the passage of Hurricane Gert near Bermuda in 1999, and by the passage of Hurricane Michelle over Cubas Gulf of Batabano in 2001, were observed by the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The mass of sediments in each of these plumes, which consist largely of neritic carbonate particles, was estimated using an algorithm for the calculation of suspended sediment concentrations. The Bermuda and Batabano plumes transported 0.22 and 1.2–1.35millionkg of sediment, respectively. The algorithm results were compared with the results from two other sediment mass algorithms and proved to be consistent. These results indicate the potential use of remote sensing to estimate carbonate flux from coral reefs and banks and atolls as an augmentation to in situ studies. In addition, the use of remote sensing data may improve estimates of the annual global carbonate sediment flux, a quantity important to models of global carbonate production and the global carbon cycle.
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The objective of this study was to examine the interaction between the Atchafalaya River and the Atchafalaya Delta estuarine complex. Measurements of suspended sediments, inorganic nutrients (NO3 −, NH4 +, PO4 3−), chlorophylla (chla), and-salinity were taken monthly from December 1996 to January 1998. These data were compiled by season, and the Atchafalaya River plume data were also analyzed using the Generalized Additive Model technique. There were significant decreases in NO3 − concentrations during summer, fall, and winter as river water passed through the estuary, that were attributable to chemical and biological processes rather than dilution with ambient water. In some regions there were higher chla concentrations during summer and fall compared to winter and spring, when river discharge and the introduction of inorganic nutrients were highest, suggesting biological processes were active during this study. The presence of NH4 +, as a percentage of available dissolved inorganic nitrogen, increased with distance from the Atchafalaya River, indicative of remineralization processes and NO3 − reduction. Mean PO4 3− concentrations were often higher in the estuarine regions compared to the Atchafalaya River. During summer total suspended solid (TSS) concentrations increased with distance from the river mouth, suggesting a turbidity maximum. Highest chla concentrations were found in the bayous and shallow water bodies of the Terrebonne marshes, as were the lowest TSS concentrations. The low chla concentrations found in other areas of this study, despite high inorganic nutrient concentrations, suggest light limitation as the major control of phytoplankton growth. Salinity reached near seawater concentrations at the outer edge of the Atchafalaya River plume, but much lower salinities (3 − concentrations before reaching Gulf waters.
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The light environment of one Chesapeake Bay tributary where seagrasses have decreased in abundance was described using both continuous and discrete measures of irradiance and related to the growth and survival of transplanted eelgrass (Zostera marina L.). After 8 months of continuous growth at an upriver site, a decline and eventual complete loss of eelgrass transplants began during a month long (May–June) period of increased turbidity (Kd>3.0). Transplant loss continued even after light conditions improved (Kd<2.0). At a downriver site where there has been some natural seagrass regrowth, the pulse of high turbidity was not as evident and transplants survived. Other than this spring period of high turbidity at the upriver site, the light environments of the two areas were similar with minimum turbidity in January and maximum in the spring and summer. Annual median daily attenuation coefficients (Kd) at the upriver and downriver sites were 1.77 and 1.96, respectively, and were not significantly different (P=0.49). Total downwelling quantum flux at transplant depths of 0.8 m below mean sea level were 2618 and 2556 mol·m−2·yr−1, or approximately 24.9 and 24.3% of annual solar PAR. The high spring turbidity pulse corresponded to an increase in non-chlorophyll particulate matter. Chlorophyll specific attenuation (Kc) accounted for 6.7–9.0% of Kd in June. Differences in attenuation were greatest in the 400–500 nm spectral region. Therefore, measures of total PAR attenuation can overestimate the usable irradiance available to the macrophytes. Scalar quantum fluxes during the period of elevated turbidity were 2.7 and 13.4 mols·m−2·day−1 at the upriver and downriver sites. The duration and intensity of total PAR measured upriver during this period were insufficient to support eelgrass growth and survival, and below literature estimates for eelgrass community light compensation at in situ temperatures (20–25°C). Therefore late spring, month-long pulses in turbidity, such as measured here can account for the loss of transplanted vegetation and, potentially, explain lack of successful recruitment into formerly vegetated upriver sites.
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In a 1984 report to Congress, the US Environmental Protection Agency concludes that nonpoint pollution was a leading cause of the nations water quality problems. Managers of nonpoint pollution abatement programs must identify land, and define land use activities that pose a threat to receiving waters. To define hazardous lands, information is needed not only on the pollution source, but on attenuation of pollutants between the source and receiving water body. Without this knowledge on delivery mechanisms abatement measures would be based on soil losses rather than on water quality impact. The article deals with these delivery processes rather than the sources of, and resultant effects, of pollution. -after Authors
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A model that can predict runoff and soil loss from a watershed is an important tool that can be used for planning and for watershed assessment and management. An application that combined the capabilities of remote sensing, Geographic Information Systems (GIS), and the Agricultural NonPoint Source Pollution (AGNPS) model was used to assess runoff and sediment yield from various sub-watersheds above Cheney Reservoir in Kansas. Remotely sensed Landsat thematic mapper (TM) images were used to obtain land cover and associated AGNPS model input parameters, including the Universal Soil Loss Equation's (USLE) cropping factors (C-factor), based on estimates of vegetative cover for rangeland and crop residue. Several input parameters of the AGNPS model were extracted from GIS layers using the AGNPS-ARC/INFO interface. C-factors and curve numbers (CNs) of agricultural crops were adjusted on the basis of management practices and hydrologic conditions of the watershed during various runoff events. Surface-water quantity and quality data, including total suspended solids (TSS) for major runoff events, were obtained from United States Geological Survey (USGS) gaging stations in the watershed and were used for evaluation of this AGNPS modeling process. Baseflow separation was done so that measured runoff and TSS levels could be compared directly with the AGNPS model output. Use of remote sensing along with GIS reduced the time to obtain input for the modeling process and added to the confidence in the representation of watershed conditions. The modeling process was effective for small watersheds (up to 145 sq km [56 sq mi]) with adequate available rainfall data. However, for larger watersheds with substantial variations of rainfall, this process was less satisfactory.
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Suspended matter in inland waters is related to total primary production and fluxes of heavy metals and micropollutants such as PCBs. Synoptic information on suspended matter cannot be obtained from an in situ monitoring network since suspended matter is a spatially inhomogeneous parameter. This problem can be solved by the integrated use of remote sensing data, in situ data and water quality models. To enable retrospective model and remote sensing data comparison of suspended matter concentration and distribution, a methodology is required for processing satellite images that is independent of in situ measurements. Analytical optical modelling, based on knowledge of the in situ inherent optical properties, leads to reliable multi-temporal algorithms for estimating suspended matter concentration in lakes for the data from the SPOT and Landsat TM sensors. This methodology allows multi-temporal, multi-site and multi-instrument comparison of TSM maps derived from satellite imagery. This means that satellite sensor data can now become an independent measurement tool for water management authorities. The remote sensing maps showed that large gradients in TSM were observed for the various lakes as well as temporal changes of these spatial gradients. In situ point samples are shown to be not representative for suspended matter in the lakes.