Remote sensing of diffuse attenuation coefficient patterns from Landsat 8 OLI imagery of turbid inland waters: A case study of Dongting Lake

A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters

It can be challenging to accurately estimate the Chlorophyll-a (Chl-a) concentration in inland eutrophic lakes due to lakes' extremely complex optical properties. The Orbita Hyperspectral (OHS) satellite, with its high spatial resolution (10 m), high spectral resolution (2.5 nm), and high temporal resolution (2.5 d), has great potential for estimating the Chl-a concentration in inland eutrophic waters. However, the estimation capability and radiometric performance of OHS have received limited examination. In this study, we developed a new quasi-analytical algorithm (QAA716) for estimating Chl-a using OHS images. Based on the optical properties in Dianchi Lake, the ability of OHS to remotely estimate Chl-a was evaluated by comparing the signal-to-noise ratio (SNR) and the noise equivalent of Chl-a (NEChl-a). The main findings are as follows: (1) QAA716 achieved significantly better results than those of the other three QAA models, and the Chl-a estimation model, using QAA716, produced robust results with a mean absolute percentage difference (MAPD) of 11.54 %, which was better than existing Chl-a estimation models; (2) The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model (MAPD = 22.22 %) was more suitable for OHS image compared to the other three atmospheric correction models we tested; (3) OHS had relatively moderate SNR and NEChl-a, improving its ability to accurately detect Chl-a concentration and resulting in an average SNR of 59.47 and average NEChl-a of 72.86 μg/L; (4) The increased Chl-a concentration in Dianchi Lake was primarily related to the nutrients input, and this had a significant positive correlation with total nitrogen. These findings expand existing knowledge of the capabilities and limitations of OHS in remotely estimating Chl-a, thereby facilitating effective water quality management in eutrophic lake environments.

Long-term remote observations of particulate organic phosphorus concentration in eutrophic Lake Taihu based on a novel algorithm

Monitoring the long-term spatiotemporal variations in particulate organic phosphorus concentration (C_POP) is imperative for clarifying the phosphorus cycle and its biogeochemical behavior in waters. However, little attention has been devoted to this owing to a lack of suitable bio-optical algorithms that allow the application of remote sensing data. In this study, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, a novel absorption-based algorithm of C_POP was developed for eutrophic Lake Taihu, China. The algorithm yielded a promising performance with a mean absolute percentage error of 27.75% and root mean square error of 21.09 μg/L. The long-term MODIS-derived C_POP demonstrated an overall increasing pattern over the past 19 years (2003-2021) and a significant temporal heterogeneity in Lake Taihu, with higher value in summer (82.06 ± 3.81 μg/L) and autumn (78.74 ± 3.8 μg/L), and lower C_POP in spring (79.52 ± 3.81 μg/L) and winter (81.97 ± 3.8 μg/L). Spatially, relatively higher C_POP was observed in the Zhushan Bay (85.87 ± 7.5 μg/L), whereas the lower value was observed in the Xukou Bay (78.95 ± 3.48 μg/L). In addition, significant correlations (r > 0.6, P < 0.05) were observed between C_POP and air temperature, chlorophyll-a concentration and cyanobacterial blooms areas, demonstrating that C_POP was greatly influenced by air temperature and algal metabolism. This study provides the first record of the spatial-temporal characteristics of C_POP in Lake Taihu over the past 19 years, and the C_POP results and regulatory factors analyses could provide valuable insights for aquatic ecosystem conservation.

A classification-based approach to mapping particulate organic matter (POM) in inland water using OLCI images

Particulate organic matter (POM) plays a major role in freshwater ecosystems by serving as a bridge for the conversion of various nutrients. The composition and sources of POM in inland lakes are complex, making it difficult to estimate its concentration accurately via remote sensing. Therefore, a classification-based method based on the sources and composition of POM is proposed for estimating POM concentrations in inland lakes. In this study, 379 samples were collected from ten lakes in the Yangtze River Delta (YRD) at different times. A water-type classification method based on OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] was developed for POM estimation based on biological and optical characteristics. Water type 1 is relatively clear, and POM may originate from aquatic vegetation or sediment. Water type 2 was dominated by inorganic suspended matter, and POM mainly originated from the attachment and entrainment of inorganic minerals. Water type 3 is an algae-dominated water body, and POM is mainly derived from fresh algal particles and the microbial degradation of phytoplankton. Therefore, specific POM estimation algorithms were developed for each water type. OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] were used for water type 1; [Formula: see text], [Formula: see text], and [Formula: see text] were adopted for water type 2; and [Formula: see text], [Formula: see text], and [Formula: see text] were selected for water type 3. Using an independent dataset to evaluate the estimation accuracy of the developed algorithm, the results show that the estimation performance of this algorithm is significantly improved compared to the two other algorithms used; the mean absolute percentage errors (MAPE) decreased from 72.56% and 52.21% to 32.61%, and the root mean square errors (RMSE) decreased from 3.05 mg/L and 2.24 mg/L to 1.75 mg/L. A random error analysis of the atmospheric correction demonstrated that this algorithm is robust and can still perform well within a random error of 30%. Finally, this method was successfully applied to map the POM concentrations in the YRD using OLCI images acquired on November 12, 2020.

Long-term dynamics and drivers of particulate phosphorus concentration in eutrophic lake Chaohu, China

Particulate phosphorus (PP) plays an important biological role in the eutrophication process, and is thus an important water quality parameter for assessing climatic change and anthropogenic activity factors that affect aquatic ecosystems. Here, we used 20-year Moderate Resolution Imaging Spectroradiometer (MODIS) data to explore the patterns and trends of PP concentration (C_PP) in eutrophic Lake Chaohu based on a new empirical model. The validation results indicated that the developed model performed satisfactorily in estimating C_PP, with a mean absolute percentage error of 31.89% and root mean square error of 0.022 mg/L. Long-term MODIS observations (2000-2019) revealed that the C_PP of Lake Chaohu has experienced an overall increasing trend and distinct spatiotemporal heterogeneity. The driving factor analysis revealed that the chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were the five potential driving factors and collectively explained more than 81% of the long-term variation in C_PP. This study provides the long-term datasets of C_PP in inland waters and new insights for future water eutrophication control and restoration efforts.

An improved algorithm for estimating the Secchi disk depth of inland waters across China based on Sentinel-2 MSI data

Accurate remote sensing of the Secchi disk depth (Z_SD) in waters is beneficial for large-scale monitoring of the aquatic ecology of inland lakes. Herein, an improved algorithm (termed as Z_SD20 in this work) for retrieving Z_SD was developed from field measured remote sensing data and is available for various waters including clear waters, slightly turbid waters, and highly turbid waters. The results show that Z_SD20 is robust in estimating Z_SD in various inland waters. After further validation with an independent in situ dataset from 12 inland waters (0.1 m < Z_SD < 18 m), the developed algorithm outperformed the native algorithm, with the mean absolute square percentage error (MAPE) reduced from 32.8 to 19.4%, and root mean square error (RMSE) from 0.87 to 0.67 m. At the same time, the new algorithm demonstrates its generality in various mainstreaming image data, including Ocean and Land Color Instrument (OLCI), Geostationary Ocean Color Imager (GOCI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Finally, the algorithm's application was implemented in 410 waters of China based on Sentinel-2 MSI imagery to elucidate the spatiotemporal variation of water clarity during 2015 and 2021. The new algorithm reveals great potential for estimating water clarity in various inland waters, offering important support for protection and restoration of aquatic environments.

Variation of satellite-derived total suspended matter in large lakes with four types of water storage across the Tibetan Plateau, China

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.

Characteristics of the Total Suspended Matter Concentration in the Hongze Lake during 1984–2019 Based on Landsat Data

The Hongze Lake is the fourth largest freshwater lake in China and an important lake for the East Route of the South-to-North Water Diversion Project. The water quality of the lake affects social development and the lives of residents. To assess the impacts of environmental changes and human activities on the distribution of the total suspended matter (TSM) in the Hongze Lake, we developed an algorithm that utilizes the near-infrared (NIR) band to estimate TSM based on in situ measurements. The algorithm was applied to Landsat images to derive TSM distribution maps from 1984 to 2019, revealing significant inter-annual, seasonal, and spatial variability. The relationship between TSM, precipitation, and wind speed was analyzed, and we found that: (1) The estimation model of TSM concentration in the Hongze Lake constructed for TM and OLI has a high accuracy, and it can be used to jointly monitor TSM concentration in the Hongze Lake for long-term series; (2) From 1984 to 2019, the concentration of TSM in the Hongze Lake showed a trend of first rising and then falling, with the maximum value in 2010 at 100.18 mg/L mainly caused by sand mining activities. Precipitation and wind speed weakly influence the inter-annual variation of TSM concentration; (3) The concentration of TSM in the Hongze Lake in summer is easily affected by flooding in the Huai River, and the concentration of TSM in other seasons is significantly negatively correlated with precipitation; (4) TSM is highest in the Huaihe Bay, followed by the Lihe Bay and Chengzi Bay. The main reason for this is that the input of the Huaihe Bay flows directly into this lake area and is also the main navigation channel. The results of this study are of great significance for the protection and management of the water environment of the Hongze Lake.

Remote monitoring of total dissolved phosphorus in eutrophic Lake Taihu based on a novel algorithm: Implications for contributing factors and lake management

Understanding the spatiotemporal dynamics of total dissolved phosphorus concentration (C_TDP) and its regulatory factors is essential to improving our understanding of its impact on inland water eutrophication, but few studies have assessed this in eutrophic inland lakes due to a lack of suitable bio-optical algorithms allowing the use of remote sensing data. We developed a novel semi-analytical algorithm for this purpose and tested it in the eutrophic Lake Taihu, China. Our algorithm produced robust results with a mean absolute square percentage error of 29.65% and root mean square error of 9.54 μg/L. Meanwhile, the new algorithm demonstrates good portability to other waters with different optical properties and could be applied to various image data, including Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Ocean and Land Color Instrument (OLCI). Further analysis based on Geostationary Ocean Color Imager observations from 2011 to 2020 revealed a significant spatiotemporal heterogeneity of C_TDP in Lake Taihu. Correlation analysis of the long-term trend between C_TDP and driving factors demonstrated that air temperature is the dominant regulating factor in variations of C_TDP. This study provides a novel algorithm allowing remote-sensing monitoring of C_TDP in eutrophic lakes and can lead to new insights into the role of dissolved phosphorus in water eutrophication.

Monitoring the particulate phosphorus concentration of inland waters on the Yangtze Plain and understanding its relationship with driving factors based on OLCI data

Tracking the spatiotemporal dynamics of particulate phosphorus concentration (C_PP) and understanding its regulating factors is essential to improve our understanding of its impact on inland water eutrophication. However, few studies have assessed this in eutrophic inland lakes, owing to a lack of suitable bio-optical algorithms allowing the use of remote sensing data. Herein, a novel semi-analytical algorithm of C_PP was developed to estimate C_PP in lakes on the Yangtze Plain, China. The independent validations of the proposed algorithm showed a satisfying performance with the mean absolute percentage error and root mean square error less than 27% and 27 μg/L, respectively. The Ocean and Land Color Instrument observations revealed a remarkable spatiotemporal heterogeneity of C_PP in 23 lakes on the Yangtze Plain from 2016 to 2020, with the lowest value in December (62.91 ± 34.59 μg/L) and the highest C_PP in August (114.9 ± 51.69 μg/L). Among the 23 examined lakes, the highest mean C_PP was found in Lake Poyang (124.58 ± 44.71 μg/L), while the lowest value was found in Lake Qiandao (33.51 ± 4.71 μg/L). Additionally, 13 lakes demonstrated significant decreasing or increasing trends (P < 0.05) of annual mean C_PP during the observation period. The driving factor analysis revealed that four natural factors (wind speed, air temperature, precipitation, and sunshine duration) and two anthropogenic factors (the normalized difference vegetation index and nighttime light) combined explained more than 91% of the variation in C_PP, while the impacts of these factors on C_PP showed considerable differences among lakes. This study offered a novel and scalable algorithm for the study of the spatiotemporal variation of C_PP in inland waters and provided new insights into the regulating factors in water eutrophication.

Inter-annual and intra-annual variations in water quality and its response to water-level fluctuations in a river-connected lake, Dongting Lake, China

The hydrological conditions of river-connected lakes are complex primarily owing to their considerable water-level fluctuations (WLFs). Water quality in such lakes varies with hydrodynamic variations; however, their relationship is not clear. To identify the unique relationship between water level and water quality in river-connected lakes, we used the comprehensive pollution index (CPI) and regression analysis to analyze the spatiotemporal variation in water quality in Dongting Lake from 2015 to 2018 and the effects of water level on water quality. Four water quality parameters were selected: total nitrogen (TN), total phosphorus (TP), permanganate index (CODMn), and chlorophyll a (Chl-a). The results showed significant spatial variation in the lake water quality, with relatively high concentrations of TN, TP, CODMn, and Chl-a in East Dongting Lake. TN and TP decreased by 12.15% and 37.61%, respectively, from 2015 to 2018, whereas CODMn increased from 1.781 to 2.009 mg/L. Seasonally, TN and TP concentrations were low in the summer and autumn, with high concentrations in the winter and spring. In contrast, CODMn and Chl-a concentrations exhibited opposite trends. The pollution level in Dongting Lake ranged between slightly and moderately polluted, with a CPI ranging from 0.76 to 1.32 across all sampling sites during 2015-2018. The water level in Dongting Lake initially increased and, then, decreased in a year, with marked WLFs owing to seasonal shifts in precipitation and human activities. The water level had significant negative relationships with TN and TP concentrations and a significant positive relationship with CODMn concentration (p < 0.05). Based on the results, strict control of excessive external nutrient loading should be actively implemented in Dongting Lake, in addition to hydrological regulation for effective lake water quality management.

Changes in suspended sediments in the Yangtze River Estuary from 1984 to 2020: Responses to basin and estuarine engineering constructions

Suspended sediments in the estuary play an important role in regulating the erosion-accretion and shaping the geomorphological patterns. Yet, it can be strongly impacted by the human activities. Constructions of basin and estuarine engineering in the Yangtze River Estuary (YRE), including constructions of Three Gorges Dam (TGD) further upstream of the Yangtze River and Deep Waterway Project (DWP) in the estuary, have greatly modified the distribution of estuarine suspended sediment concentration (SSC). In this study, we retrieved the surface water SSC in the YRE from a total of 149 Landsat-TM/ETM+/OLI images (from 1984 to 2020) and 515 GOCI images (from 2011 to 2020) to obtain its long-term dynamics. The results indicate that the SSC estimation model performed well with a mean absolute percentage error of 12.83% and a root mean square error of 0.027 g/L. The SSC in the YRE demonstrated an overall declining pattern over the past 37 years, particularly during the flood season, which was related to the decrease in the sediment discharge of Yangtze River. The SSC in different sub-regions across the entire YRE responded differently to the reduced sediment discharge caused by the impoundment of the TGD. The SSC in the inner estuary responded promptly with a 40.3% decline noted immediately after the impoundment of the TGD, whereas hysteresis of SSC changes was found in the estuarine turbidity maximum zone (TMZ) and have begun to decline by 20.0-30.0% in the recent 5 years. Constructions of local estuarine engineering, such as the DWP also exert important controls on the estuarine SSC dynamics by blocking the lateral transport of suspended sediments between the shoal and the river channel. Our results highlight the strong influence of engineering constructions on the SSC dynamics in the world large river-influenced estuaries.

Four-decade dynamics of the water color in 61 large lakes on the Yangtze Plain and the impacts of reclaimed aquaculture zones

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, 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) from two field trips. 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.

Modeling Water Quality Parameters Using Landsat Multispectral Images: A Case Study of Erlong Lake, Northeast China

Erlong Lake is considered one of the largest lakes in midwest Jilin, China, and one of the drinking water resources in neighboring cities. The present study aims to explore the usage of Landsat TM5, ETM7, and OLI8 images to assess water quality (V-phenol, dissolved oxygen (DO), NH4-N, NO3-N) in Erlong Lake, Jilin province, northeast China. Thirteen multispectral images were used in this study for May, July, August, and September in 2000, 2001, 2002, and October 2020. Radiometric and atmospheric corrections were applied to all images. All in situ water quality parameters were strongly correlated to each other, except DO. The in situ measurements (V-phenol, dissolved oxygen, NH4-N, NO3-N) were statistically correlated with various spectral band combinations (blue, green, red, and NIR) derived from Landsat imagery. Regression analysis reported that there are strong relationships between the estimated and retrieved water quality from the Landsat images. Moreover, in calibrations, the highest value of the coefficient of determination (R2) was ≥0.85 with (RMSE) = 0.038; the lowest value of R2 was >0.30 with RMSE= 0.752. All generated models were validated in different statistical indices; R2 was up to 0.95 for most cases, with RMSE ranging from 1.390 to 0.050. Finally, the empirical algorithms were successfully assessed (V-phenol, dissolved oxygen, NH4-N, NO3-N) in Erlong Lake, using Landsat images with very good accuracy. Both in situ and model retrieved results showed the same trends with non-significant differences. September of 2000, 2001, and 2002 and October of 2020 were selected to assess the spatial distributions of V-phenol, DO, NH4-N, and NO3-N in the lake. V-phenol, NH4-N, and NO3-N were reported low in shallow water but high in deep water, while DO was high in shallow water but low in deep water of the lake. Domestic sewage, agricultural, and urban industrial pollution are the most common sources of pollution in the Erlong Lake.

A Prediction Model of Water In Situ Data Change under the Influence of Environmental Variables in Remote Sensing Validation

Validation is an essential process to evaluate the quality of waterbody remote sensing products, and the reliability and effective application of the in situ data of waterbody parameters are an important part of validation. Based on the in situ data of chlorophyll-a (Chl-a), total suspended solids (TSS) and other environmental variables (EVs) measured at the fixed station in Taihu Lake, we attempt to develop a prediction model to determine whether the in situ measurement has enough representativeness for validating waterbody remote sensing products. Key EVs that affect the changes of Chl-a and TSS are firstly identified by using correlation analysis, which participate in modeling as variables. In addition, three multi-parameter modeling approaches are selected to simulate the daily changes of Chl-a and TSS under different EVs configurations. The results indicate that the highest prediction accuracy can be achieved through the generalized regression neural network (GRNN) based model. In the all-valid dataset, the testing absolute average relative errors (AEs) of GRNN-based Chl-a and TSS prediction model are 11.4% and 11.3%, respectively, and in the sunny-day dataset, the testing AEs are 8.6% and 8.2%, respectively. Meanwhile, the application example proves that the prediction model in this paper can be effectively used to screen the in situ data and determine the time window for satellite-ground data matching.

Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South China

Accurate and quantitative assessment of the impact of natural environmental changes and human activities on total suspended solids (TSS) concentration is one of the important components of water environment protection. Due to the limits of traditional cross-sectional point monitoring, a novel water quality evaluation method based on the Markov model and remote sensing retrieval is proposed to realize the innovation of large-scale spatial monitoring across administrative boundaries. Additionally, to explore the spatiotemporal characteristics and driving factors of TSS, a new three-band remote sensing model of TSS was built by regression analysis for the inland reservoir using the synchronous field spectral data, water quality samples and remote sensing data in the trans-provincial Hedi Reservoir in the Guangdong and Guangxi Provinces of South China. The results show that: (1) The three-band model based on the OLI sensor explained about 82% of the TSS concentration variation (R2=0.81, N=34,  p value<0.01) with an acceptable validation accuracy (RMSE=6.24 mg/L,MRE=18.02%, N=15), which is basically the first model of its kind available in South China. (2) The TSS concentration has spatial distribution characteristics of high upstream and low downstream, where the average TSS at 31.54 mg/L in the upstream are 2.5 times those of the downstream (12.55 mg/L). (3) Different seasons and rainfall are important factors affecting the TSS in the upstream cross-border area, the TSS in the dry season are higher with average TSS of 33.66 mg/L and TSS are negatively correlated with rainfall from upstream mankind activity. Generally, TSS are higher in rainy seasons than those in dry seasons. However, the result shows that TSS are negatively correlated with rainfall, which means human activities have higher impacts on water quality than climate change. (4) The Markov dynamic evaluation results show that the water quality improvement in the upstream Shijiao Town is the most obvious, especially in 2018, the improvement in the water quality level crossed three levels and the TSS were the lowest. This study provided a technical method for remote sensing dynamic monitoring of water quality in a large reservoir, which is of great significance for remediation of the water environment and the effective evaluation of the river and lake chief system in China.

Mapping spatio-temporal dynamics of main water parameters and understanding their relationships with driving factors using GF-1 images in a clear reservoir

Due to eutrophication and water quality deterioration in clear reservoirs, it is necessary to monitor and manage the main water parameters: concentration of total phosphorus (CTP), chemical oxygen demand (CCOD), chlorophyll-a (CChla), total suspended matter (CTSM), and Secchi disk depth (SDD). Five random forest (RF) models are developed to estimate these parameters in Xin'anjiang Reservoir, which is a clear drinking water resource in Zhejiang, China. Then, the spatio-temporal distributions of the parameters over 7 years (2013-2019) are mapped using GaoFen-1 (GF-1) images and the relationships with driving factors are analyzed. Our study demonstrates that the parameters' distributions exhibited a significant spatio-temporal difference in Xin'anjiang Reservoir. Spatially, relatively high CTP, CCOD, CChla, and CTSM but low SDD appear in riverine areas, showing strong evidence of impact from the incoming rivers. Temporally, CChla and CTSM reached high values in summer and winter, whereas SDD and CTP were higher in the summer and autumn, respectively. In contrast, no significant seasonal variations of CCOD could be observed. This may be why CCOD is not sensitive to hydrological or meteorological factors. However, precipitation had a significant impact on CChla, CTP, SDD, and CTSM in riverine areas, though these parameters were less sensitive to meteorological factors. Moreover, the geomorphology of the reservoir and anthropogenic interference (e.g., tourism activities) also have a significant impact on the water quality parameters. This study demonstrates that coupling long-term GF-1 images and RF models could provide strong evidence and new insights to understand long-term dynamics in water quality and therefore support the development of corresponding management strategies for freshwater reservoirs.

Diffuse Attenuation of Clear Water Tropical Reservoir: A Remote Sensing Semi-Analytical Approach

The diffuse attenuation coefficient of downwelling irradiance (Kd) is an essential parameter for inland waters research by remotely sensing the water transparency. Lately, Kd semi-analytical algorithms substituted the empirical algorithms widely employed. The purpose of this research was to reparametrize a semi-analytical algorithm to estimate Kd and then apply it to a Sentinel-2 MSI time-series (2017–2019) for the Três Marias reservoir, Brazil. The results for the Kd semi-analytical reparametrization achieved good accuracies, reaching mean absolute percentage errors (MAPE) for bands B2, B3 and B4 (492, 560 and 665 nm), lower than 21% when derived from in-situ remote sensing reflectance (Rrs), while for MSI Data, a derived MAPE of 12% and 38% for B2 and B3, respectively. After the application of the algorithm to Sentinel-2 images time-series, seasonal patterns were observed in the results, showing high Kd values at 492 nm during the rainy periods, mainly in the tributary mouths, possibly due to an increase in the surface runoff and inflows and outflow rates in the reservoir watershed.

Monitoring dissolved organic carbon by combining Landsat-8 and Sentinel-2 satellites: Case study in Saginaw River estuary, Lake Huron

Dissolved organic carbon (DOC) in aquatic environments is an important cycled pool of organic matter on the Earth. Satellite remote sensing provides a useful tool to determine spatiotemporal distribution of water quality parameters. Previous DOC remote sensing studies in inland water suffered from either low spatial resolution or low temporal frequency. In this study, we evaluated the potential of jointly using Landsat-8 and Sentinel-2 with high spatial resolution to estimate DOC concentrations in Saginaw River plume regions of Lake Huron. Firstly, CDOM (colored dissolved organic matter) was estimated from images using the known models and then DOC can be derived in terms of the good correlations between DOC and CDOM. The results show that Landsat-8 and Sentinel-2 had acceptable accuracy and good consistency in DOC estimation so that jointly using them can improve the observation frequency. In different seasons from 2013 to 2018, DOC was typically higher in spring and autumn but lower in summer. Monthly spatiotemporal variations of DOC in 2018 were also observed. The image-derived DOC spatiotemporal variations show that DOC was covaried with Saginaw River discharge (r = 0.82) and also weakly and negatively correlated with water temperature (r = -0.6). This study demonstrated that using Landsat-8 and Sentinel-2 together can offer the potential applications for monitoring DOC and water quality dynamic in complex inland water.

Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm

The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as ZSDZ) for retrieving ZSD was developed from Rrs(λ), and it was applied to Geostationary Ocean Color Imager (GOCI) images in extremely turbid waters. Our results are as follows: (1) the ZSDZ performs well in estimating ZSD in turbid water bodies (0.15 m < ZSD < 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R2) is determined to be 0.89, with a mean absolute square percentage error (MAPE) of 22.39%, and root mean square error (RMSE) of 0.24 m. (2) The ZSDZ improved the retrieval accuracy of ZSD in turbid waters and outperformed the existing semi-analytical schemes. (3) The developed algorithm and GOCI data are in order to map the hourly variation of ZSD in turbid inland waters, the GOCI-derived results reveal a significant spatiotemporal variation in our study region, which are significantly driven by wind forcing. This study can provide a new approach for estimating water transparency in turbid waters, offering important support for the management of inland waters.

An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze

There are a few studies working on the vertical distribution of TSM, however, understanding the underwater profile of TSM is of great benefit to the study of biogeochemical processes in the water column that still require further research. In this study, three data-gathering expeditions were conducted in Lake Hongze (HZL), China, between 2016 and 2018. Based on the in situ optical and biological data, a multivariate linear stepwise regression method was applied for retrieval of the surface horizontal distribution of TSM (TSM_0.2) using GOCI (Geostationary Ocean Color Imager) data. Then, the estimation model of vertical structure of underwater TSM was constructed using layer-by-layer recursion. This study drew several crucial findings: (1) the approach proposed in this paper generated very high goodness of fit results, with determination coefficients (R²) of 0.83 (p < 0.001, N = 54), and with smaller prediction errors (the mean absolute percentage error is determined to be 16.34%, the root mean square error is 9.01 mg l^-1, and the mean ratio is 1.00, N = 26). (2) The monthly surface TSM and the column mass of suspended matter (CMSM) are affected by both wind speed and precipitation in HZL. In addition, the hourly variation of surface TSM and CMSM are driven by local wind, most especially in spring and winter. (3) Compared with the non-uniform hypothesis, the CMSM derived by conventional vertical uniformity hypothesis was underestimated by almost 10% in HZL during 2016. This should warrant the attention of lake managers and lake environmental evolution researchers.

Tracking spatio-temporal dynamics of POC sources in eutrophic lakes by remote sensing

Estimating the proportions of particulate organic carbon (POC) endmembers is essential to fully understand the carbon cycle, the function of aquatic ecosystems, and the migration of contaminants in eutrophic lakes. There is currently no effective remote sensing optical algorithm in the literature to solve this problem. In this study, a POC-source color index (S_POC) was constructed based on the terrestrial and endogenous POC ratios calculated from field-measured stable isotope (δ¹³C_POC) values. The S_POC algorithm traces the sources of POC by utilizing three spectral bands centered approximately at 560 nm, 674 nm, and 709 nm, covering the intrinsic optical information of different POC sources. At the same time, the S_POC algorithm shows good applicability to Ocean and Land Color Instrument (OLCI), Medium-Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Geostationary Ocean Color Imager (GOCI) image data. The POC sources estimated using the algorithm and monthly OLCI data showed that from March 2018 to January 2019, the POC at the surface of Lake Taihu was mainly terrigenous. In addition, due to multiple factors such as algal blooms, plant physiology, river transport, regional rainfall, and carbon cycling, the distribution of POC sources exhibited strong spatial and temporal heterogeneity. Compared with other methods, it is more convenient to use remote sensing to identify the proportion of POC in different endmembers, which offers a more comprehensive understanding of the energy flows and material circulation in lakes.

Optical classification of inland waters based on an improved Fuzzy C-Means method

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

Estimation of Kd(PAR) in inland waters across China in relation to the light absorption of optically active components

The comprehensive analysis of the relationships between the attenuation of photosynthetic active radiation (K_d(PAR)) and light absorption is an imperative requirement to retrieve K_d(PAR) from remote sensing data for aquatic environments. The spatial distributions of the K_d(PAR) and light absorption of optically active components (a_OACs) were routinely estimated in China lakes and reservoirs. Spatial K_d(PAR) was relatively dependent on the inorganic particles (average relative contribution of 57.95%). The a_OACs could explain 70-87% of K_d(PAR) variations. A linear model is used to predict K_d(PAR), as a function of light absorption coefficient of phytoplankton (a_phy), colored dissolved organic matter (a_CDOM), and inorganic particles (a_NAP): K_d(PAR) = 0.41 + 0.57 × a_CDOM + 0.96 × a_NAP + 0.57 × a_phy (R² = 0.87, n = 741, p < 0.001). In the lakes with low TSM concentration and non-eutrophic lakes with high TSM, a_CDOM was the most powerful predicting factor on K_d(PAR). In eutrophic lakes with high TSM, a_NAP had the most significant impact on K_d(PAR). This study allowed K_d(PAR) to be predicted from a_OACs values in the inland waters.

Improved atmospheric correction algorithm for Landsat 8-OLI data in turbid waters: a case study for the Lake Taihu, China

Several atmospheric correction algorithms for turbid waters have been developed based on an assumption of zero reflection in short-wave infrared (SWIR) bands. However, for the Landsat8-Operational Land Imager (OLI), some water reflections are so strong in the 1609 nm band that they cannot be ignored. In this study, we developed a novel atmospheric correction algorithm based on a zero assumption for the short-wave infrared band (ACZI). The ACZI algorithm uses the black pixel index (BPI) and the floating algae index (FAI) to distinguish black pixels, which are used to estimate the aerosol scattering of non-black pixels based on the assumption of spatial homogeneity of aerosol types. In Lake Taihu, compared with the SeaDAS (SeaWiFS Data Analysis System) -SWIR algorithm, the ACZI algorithm achieved better precision for visible bands MAPE (the mean absolute percentage error), < 30%, RMSE (the root mean square error) < 0.0117 sr^-1) and provided more available water pixels. The accuracy of ACZI was close to that of the DSF (dark spectrum fitting) algorithm and was better than that of the EXP (exponential extrapolation) algorithm and L8SR (Landsat 8 OLI Surface Reflectance) product. The ACZI algorithm showed good applicability in turbid waters.

Inversion of Lake Bathymetry through Integrating Multi-Temporal Landsat and ICESat Imagery

Lake bathymetry provides valuable information for lake basin planning and treatment, lake watershed erosion and siltation management, water resource planning, and environmental protection. Lake bathymetry has been surveyed with sounding techniques, including single-beam and multi-beam sonar sounding, and unmanned ship sounding. Although these techniques have high accuracy, most of them require long survey cycles and entail a high degree of difficulty. On the contrary, optical remote sensing inversion methods are easy to implement, but tend to provide less accurate bathymetry measures, especially when applied to turbid waters. The present study, therefore, aims to improve the accuracy of bathymetry measurements through integrating Landsat Thematic Mapper imagery, the Ice, Cloud, and Land Elevation Satellite’s Geoscience Laser Altimeter System (ICESat/GLAS) data, and water level data measured at hydrological stations. First, the boundaries of a lake at multiple dates were derived using water extraction, initial boundary extraction, and Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+) strip removal processing techniques. Second, ICESat/GLAS data were introduced to obtain additional topographic information of a lake. The striped topography of a lake’s area was then obtained through eliminating and correcting erroneous points and interpolating the values of unknown points. Third, the entire bathymetry of the lake was obtained through interpolating water level values of lake boundary points in various dates. Experiments show that accurate bathymetry (±1 m) can be successfully derived.

The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters

The OLI (Operational Land Imager) sensor on Landsat-8 has the potential to meet the requirements of remote sensing of water color. However, the optical properties of inland waters are more complex than those of oceanic waters, and inland atmospheric correction presents additional challenges. We examined the performance of atmospheric correction (AC) methods for remote sensing over three highly turbid or hypereutrophic inland waters in China: Lake Hongze, Lake Chaohu, and Lake Taihu. Four water-AC algorithms (SWIR (Short Wave Infrared), EXP (Exponential Extrapolation), DSF (Dark Spectrum Fitting), and MUMM (Management Unit Mathematics Models)) and three land-AC algorithms (FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes), 6SV (a version of Second Simulation of the Satellite Signal in the Solar Spectrum), and QUAC (Quick Atmospheric Correction)) were assessed using Landsat-8 OLI data and concurrent in situ data. The results showed that the EXP (and DSF) together with 6SV algorithms provided the best estimates of the remote sensing reflectance (Rrs) and band ratios in water-AC algorithms and land-AC algorithms, respectively. AC algorithms showed a discriminating accuracy for different water types (turbid waters, in-water algae waters, and floating bloom waters). For turbid waters, EXP gave the best Rrs in visible bands. For the in-water algae and floating bloom waters, however, all water-algorithms failed due to an inappropriate aerosol model and non-zero reflectance at 1609 nm. The results of the study show the improvements that can be achieved considering SWIR bands and using band ratios, and the need for further development of AC algorithms for complex aquatic and atmospheric conditions, typical of inland waters.

Remote observation of water clarity patterns in Three Gorges Reservoir and Dongting Lake of China and their probable linkage to the Three Gorges Dam based on Landsat 8 imagery

The Secchi disk depth (Z_SD) plays a critical role in describing water clarity. Several studies have shown linkages between Three Gorges Dam (TGD) and the downstream lacustrine ecosystem in the middle and lower Yangtze River basin. However, the potential influence on the Z_SD fluctuation in the entire anthropogenic reservoirs of Three Gorges (ER) and Dongting Lake (DTL) has not been reported, possibly due to technical obstacles in obtaining statistically significant spatial and temporal results. We addressed this challenge by using remote sensing technology: the Landsat 8 Operational Land Imager (OLI). We proposed a new, robust remote-sensing algorithm to estimate Z_SD from OLI imagery using red and green band-ratio, leading to MAPE of 21.68% and RMSE of 0.076m for Z_SD ranging from 0.1m to 1.05m. After satisfactory image-based validation, the algorithm was implemented on OLI data to derive Z_SD patterns over ER and DTL from 2013 to 2017. Several crucial findings can be drawn: 1) Spatial-temporal patterns of Z_SD exhibited notable fluctuations over both ER and DTL, and they also demonstrated a significant correlation with each other because of the opposite temporal cycle of Z_SD fluctuations between ER and DTL; 2) Temporally, monthly fluctuations of Z_SD between ER and DTL had opposite temporal cycles, which was mainly attributed to the surface runoff and sediment discharge driven by the outbound runoff variations of TGD. Spatially, the heterogeneity of the Z_SD pattern in ER might have resulted from the different geographical regions being divided by large anthropologic hydrological facilities, such as TGD; 3) The relationship between Z_SD and total suspended matter (TSM) showed a significant negative correlation, as did the relationship between Z_SD and K_d(490). These findings demonstrate that TSM often plays a principal role in light attenuation of extremely turbid inland waters; 4) An inversed phenomenon of water clarity was observed at the intersection of DTL and the Yangtze River around Chenglingji site (YRAC), which was due to the opposite temporal cycle of Z_SD fluctuations between DTL and ER after the impoundment of TGD; and 5) Owing to the analysis of noise-equivalent Z_SD, OLI data can be used to derive Z_SD, since the imagery uncertainty is 0.07m by means of our band-ratio algorithm, which demonstrates similar results to MODIS. The proposed Z_SD-derived algorithm in this study could be suitable for other turbid lakes or reservoirs to formulate related strategies of water quality management in the middle and lower Yangtze River basin, and the unveiled findings here improve our understanding of Z_SD spatiotemporal fluctuations in large river-connected lakes, such as Poyang Lake.

The impact of channel capture on estuarine hydro-morphodynamics and water quality in the Amazon delta

Due to progressive erosion of the new Urucurituba Channel, the Amazon River has recently captured almost all discharge from the lower Araguari River (Amapá-AP, Brazil), which previously flowed directly to the Atlantic Ocean. These recent geomorphological changes have caused strong impacts on the landscape and hydrodynamic patterns near the Araguari River mouth, especially the alteration of the riverine drainage system and its water quality. Landsat images were used to assess the estuarine landscape morphodynamic, particularly the expansion of the Urucurituba Channel, 80km from the Araguari River mouth, chronicling its connection to the Amazon River. The results suggest that the Urucurituba developed by headward migration across the Amazon delta; this is perhaps the first observation of estuarine distributary network development by headward channel erosion. The rate of Urucurituba Channel width increase has been ≈5m/month since 2011, increasing drainage capacity of the channel. We also collected in situ hydrodynamic measurements and analyzed 17 water quality parameters. Having 2011 as baseline, the flowrate of Araguari River has been diverted by up to 98% through Urucurituba Channel, with substantial changes in net discharge recorded at 3 monitoring stations. Statistically significant differences in water quality (p<0.05) were observed between 2011 and 2015, associated with the change in the flow pattern. Estuarine salinity and solids concentrations have increased. Overall, we demonstrate changes in landscape, hydrodynamics and water quality of the lower Araguari River.

Monitoring spatiotemporal variations in nutrients in a large drinking water reservoir and their relationships with hydrological and meteorological conditions based on Landsat 8 imagery

Nutrient enrichment is a major cause of water eutrophication, and variations in nutrient enrichment are influenced by environmental changes and anthropogenic activities. Accurately estimating nutrient concentrations and understanding their relationships with environmental factors are vital to develop nutrient management strategies to mitigate eutrophication. Landsat 8 Operational Land Imager (OLI) data is used to estimate nutrient concentrations and analyze their responses to hydrological and meteorological conditions. Two well-accepted empirical models are developed and validated to estimate the total nitrogen (TN) and total phosphorus (TP) concentrations (C_TN and C_TP) in the Xin'anjiang Reservoir using Landsat 8 OLI data from 2013 to 2016. Spatially, C_TN decreased from the transition zone to the riverine zone and the lacustrine zone. On the other hand, C_TP decreased from the riverine zone to the transition zone and the lacustrine zone. Temporally, C_TN displayed elevated values during the late fall and winter and had lower values during the summer and early fall, whereas C_TP was higher during the spring and lower during the winter. Among the environmental factors, the rainfall and the inflow rate have strong positive correlations with the nutrient concentrations. TN is more sensitive to meteorological factors (wind speed, temperature, sunshine duration), and the spatial driving forces vary among the different sections of the reservoir. However, TP is more easily influenced by human activities, such as fishery and agricultural activities. Current results would improve our understanding of the drivers of nutrients spatiotemporal variability and the approach in this study can be applicable to other similar reservoir to develop related strategies to mitigate eutrophication.

Effects of river-lake interactions in water and sediment on phosphorus in Dongting Lake, China

As a large river connected lake, Dongting Lake is influenced by anthropogenic activities and the discharge from its upstream tributaries in the lake basin and by the water recharge via a connection to the Yangtze River (YR) outside the basin. This makes the lake phosphorous cycle more complex than that in other disconnected lakes. Here, we calculated section fluxes and ran a hydrodynamic model to investigate the phosphorus (P) variations in response to the changing interactions in the water and sediment between the YR, four tributaries, and the lake. Results show that particulate P was the dominant form with a significant linear relationship with suspended sediment (r ² = 0.906). The sediment input reduction from the YR through three water inlets, which is closely related to the Three Gorges Reservoir operation since 2003, led to a decrease in the total P (TP) concentration in the western Dongting Lake. However, the impact and range of this decrease were fairly limited. Compared with the limited effect of the YR, the raised TP flux from the Yuanjiang tributary controlled the TP concentration at the outlet of the western Dongting Lake. Apart from the influence of the YR and the tributaries, anthropogenic activities (sand dredging) in the eastern Dongting Lake also contributed to a high TP concentration around the S10 area through sediment resuspension. We suggest that, compared with the reduction in TP flux and sediment load from the connected Yangtze River outside the basin, the elements within the basin (increased TP input from tributaries and sand dredging) have a greater effect on the variations of TP in Dongting Lake.

Environmental effects of ozone depletion and its interactions with climate change: Progress report, 2016

The Parties to the Montreal Protocol are informed by three Panels of experts. One of these is the Environmental Effects Assessment Panel (EEAP), which deals with two focal issues. The first focus is the effects of UV radiation on human health, animals, plants, biogeochemistry, air quality, and materials. The second focus is on interactions between UV radiation and global climate change and how these may affect humans and the environment. When considering the effects of climate change, it has become clear that processes resulting in changes in stratospheric ozone are more complex than previously believed. As a result of this, human health and environmental issues will be longer-lasting and more regionally variable. Like the other Panels, the EEAP produces a detailed report every four years; the most recent was published as a series of seven papers in 2015 (Photochem. Photobiol. Sci., 2015, 14, 1-184). In the years in between, the EEAP produces less detailed and shorter Progress Reports of the relevant scientific findings. The most recent of these was for 2015 (Photochem. Photobiol. Sci., 2016, 15, 141-147). The present Progress Report for 2016 assesses some of the highlights and new insights with regard to the interactive nature of the direct and indirect effects of UV radiation, atmospheric processes, and climate change. The more detailed Quadrennial Assessment will be made available in 2018.