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

Inversion of lake transparency using remote sensing and deep hybrid recurrent models

Utilizing computer technology and remote sensing data, the extraction of water-related features of lakes has become a hot topic in lake ecological research. Addressing challenges like the high optical complexity of lake water bodies, the inadequacy of samples for capturing complex optical properties, and the difficulty of large - scale application of simplified lake water optical models, this study robustly integrated LSTM and GRU network structures to construct an accurate and efficient lake water transparency inversion model (WTIM). The model utilized Landsat - 8 remote sensing data, field measurements, and simulated data to form a sample set. This model is specifically designed for rapid, large-scale, and automated remote sensing inversion of lake transparency. The results show that the WTIM model can invert lake water transparency with good accuracy (R2=0.78, MAE=0.64, RMSE=0.84, MAPE=52.31 %), and the model has excellent robustness. Analysis of the time series characteristics of Chinese lakes from 2014 to 2021 reveals that lake water transparency in China first decreased and then increased over time, showing an overall decreasing trend. Analysis of spatial variation characteristics indicates that lake transparency in the Qinghai-Tibet Plateau lake region is increasing, mainly due to the inflow of glacial meltwater into lakes caused by global warming. In contrast, lake transparency in the eastern plain lake region and the northeast plain lake region is decreasing, likely due to intense human industrial and agricultural activities. Our research can provide a reference for lake transparency inversion.

Assessing the impacts of cascade reservoirs on Pearl River environmental status using machine learning and satellite-derived chlorophyll-a concentrations

Rivers play a crucial role in in global matter cycling and energy flow, contributing significantly to biogeochemical cycles and the development of human civilization. Reservoirs, as prevalent artificial water bodies, modify river flow and impact energy and environmental dynamics. These reservoirs can directly affect riverine ecosystems by retaining algal materials, thereby altering Chl-a concentrations in downstream water bodies. Nevertheless, the mechanisms by which reservoirs influence Chl-a concentrations in rivers remain poorly understood. This study utilized Landsat 8/9 images and in-situ measurements from the Pearl River to develop a machine learning model and generate a Chl-a concentration dataset spanning 2013-2022. We also examined the mechanisms through which reservoirs and the natural environmental factors affect Chl-a concentrations by regulating the Pearl River. The findings indicate that anthropogenic factors, primarily the construction of reservoirs and dams, play a significant role in shaping the spatial distribution of riverine Chl-a concentrations along the Pearl River. As the river traverses reservoirs in the upper and middle reaches, Chl-a concentrations in both the mainstem and tributary sections exhibit a distinct decrease. The highest Chl-a concentrations were observed in the headwaters of the Xijiang River, followed by a decline in the midstream, and a subsequent increase downstream. It also revealed that, river Chl-a levels are consistently lower before entering a reservoir, higher within it, and further decreased after exiting. Reservoirs, by intercepting and storing upstream sediment and nutrients, allow only a small amounts to pass through dams into downstream sections, thereby influencing riverine Chl-a concentrations. Furthermore, Chl-a concentrations in the Pearl River peak during summer and reach their lowest levels in winter, with water temperature being the dominant driver of seasonal and interannual Chl-a variations (r = 0.88, p < 0.01). Other environmental factors such as pH, dissolved oxygen (DO), total phosphorus (TP), total nitrogen (TN), and Chl-a concentrations were found to be positively correlated. Our findings indicate that cascade reservoirs have a more significant impact on river environmental status. To effectively address river water quality degradation and maximize the benefits of reservoirs, coordinated water diversion and protective measures between the reservoirs are required.

Landsat observations of total suspended solids concentrations in the Pearl River Estuary, China, over the past 36 years

Information on long-term trends in total suspended solids (TSS) is critical for assessing aquatic ecosystems. However, the long-term patterns of TSS concentration (CTSS) and its latent drivers have not been well investigated. In this study, we developed and validated three semi-analysis algorithms for deriving CTSS using Landsat images. Subsequently, the long-term trends in CTSS in the Pearl River Estuary (PRE) from 1987 to 2022 and the driving factors were clarified. The developed algorithms yielded excellent performance in estimating CTSS, with mean absolute percentage errors <25% and root mean square errors of <13 mg/L. Long-term Landsat observations showed an overall decreasing trend and significant spatiotemporal dynamics of the CTSS in the PRE from 1987 to 2022. The analysis of driving factors suggested that industrial sewage, cropland, forests and grasslands, and built-up land were the four potential driving forces that explained 87.81% of the long-term variation in CTSS. This study not only provides 36-year recorded datasets of CTSS in estuary water, but also offers new insights into the complex mechanisms that regulate CTSS spatiotemporal dynamics for water resource management.

Satellite observations of suspended particulate matter concentration in Lake Gaoyou in the past four decades

Suspended Particulate Matter (SPM) concentration stands as a pivotal determinant of water quality within lake ecosystems. However, comprehension of the enduring dynamics of SPM within lakes is severely hindered due to a shortage of long-term records. Our research has developed a robust remote sensing algorithm to retrieve the SPM concentration in Lake Gaoyou, situated in the lower reaches of the Huai River basin in China. The algorithm demonstrates commendable performance, with an uncertainty of 28.68 %. Leveraging Landsat series sensors imagery, our investigation yields high spatial resolution SPM concentration maps, which first provide a four-decades record of the SPM distribution within Lake Gaoyou. Our findings unveil a significant annual reduction of 1.35 mg L-1 in SPM concentration over the past four decades. This notable decline is probably attributable to a series of ecological initiatives to enhancing the management of the eco-friendly within the basin. Furthermore, our research delineated the influence of environmental factors on the intra-annual SPM dynamics across distinct spatial domains, encompassing the natural inlet region, semi-obstructed inlet region and outlet areas within the lake The SPM concentration in the natural inlet region exhibits a conspicuous correlation with precipitation. Increased precipitation induces runoff within the basin, facilitating the transport of suspended solids and sediment into the lake, consequently augmenting SPM levels. Conversely, the semi-obstructed inlet and outlet areas are predominantly influenced by the wind field, with variations in SPM attributed to sediment resuspension caused by water mixing driven by wind forcing. Our research can be considered an important reference to the evaluation of the management of the lake over long periods.

Satellite imagery as a management tool for monitoring water clarity across freshwater ponds on Cape Cod, Massachusetts

Water clarity serves as both an indicator and a regulator of biological function in aquatic systems. Large-scale, consistent water clarity monitoring is needed for informed decision-making. Inland freshwater ponds and lakes across Cape Cod, a 100-km peninsula in Massachusetts, are of particular interest for water clarity monitoring. Secchi disk depth (SDD), a common measure of water clarity, has been measured intermittently for over 200 Cape Cod ponds since 2001. Field-measured SDD data were used to estimate SDD from satellite data, leveraging the NASA/USGS Landsat Program and Copernicus Sentinel-2 mission, spanning 1984 to 2022. Random forest machine learning models were generated to estimate SDD from satellite reflectance data and maximum pond depth. Spearman rank correlations (rs) were "strong" for Landsat 5 and 7 (rs = 0.78 and 0.79), and "very strong" for Landsat 8, 9, and Sentinel-2 (rs = 0.83, 0.86, and 0.80). Mean absolute error also indicated strong predictive capacity, ranging from 0.65 to 1.05 m, while average bias ranged from -0.20 to 0.06 m. Long- and recent short-term changes in satellite-estimated SDD were assessed for 193 ponds, selected based on surface area and the availability of maximum pond depth data. Long-term changes between 1984 and 2022 established a retrospective baseline using the Mann-Kendall test for trend and Theil-Sen slope. Generally, long-term water clarity improved across the Cape; 149 ponds indicated increasing water clarity, and 8 indicated deteriorating water clarity. Recent short-term changes between 2021 and 2022 identified ponds that may benefit from targeted management efforts using the Mann-Whitney U test. Between 2021 and 2022, 96 ponds indicated deteriorations in water clarity, and no ponds improved in water clarity. While the 193 ponds analyzed here constitute only one quarter of Cape Cod ponds, they represent 85% of its freshwater surface area, providing the most spatially and temporally comprehensive assessment of Cape Cod ponds to date. Efforts are focused on Cape Cod, but can be applied to other areas given the availability of local field data. This study defines a framework for monitoring and assessing change in satellite-estimated SDD, which is important for both local and regional management and resource prioritization.

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.

Increasing global oceanic wind speed partly counteracted water clarity management effectiveness: A case study of Hainan Island coastal waters

A sustainable coastal "blue economy" is one of the most significant opportunities and challenges in the new era. However, the management and conservation of marine ecosystems must recognize the interdependence in the coupled human and natural systems. In this study, we employed satellite remote sensing to map the spatial and temporal distribution of Secchi disk depth (SDD) in Hainan coastal waters, China for the first time, and quantitatively revealed the impacts of environmental investments on the coastal water environment in the context of global climate change. Based on the moderate resolution imaging spectroradiometer (MODIS) in situ concurrent matchups (N = 123), a simple green band (555 nm)-based quadratic algorithm was first developed to estimate the SDD for the coastal waters of Hainan Island in China (R² = 0.70, root mean square error (RMSE) = 1.74 m). The long time-series SDD dataset (2001-2021) for Hainan coastal waters was reconstructed from MODIS observations. Spatially, SDD showed a pattern of high water clarity in eastern and southern coastal waters and low water clarity in the western and northern coastal areas. This pattern is attributed to unbalanced distributions of bathymetry and pollution from seagoing rivers. Seasonally, the humid tropical monsoon climate drove the SDD into a general pattern of high in the wet season and low in the dry season. Annually, the SDD in Hainan coastal waters improved significantly (p < 0.1), benefiting from environmental investments over the last 20 years. However, the increasing global oceanic wind speed in recent years has exacerbated sediment resuspension and deep ocean mixing, counteracting approximately 14.14% of the remedial management's effectiveness in protecting and restoring the coastal ecosystem. This study offers ways to improve the ecological and environmental regulations under global changes and to strengthen the public service capacity for aquatic management authorities with methods that support the sustainable development of coastal areas.

Retrieval and validation of the Secchi disk depth values (Zsd) from the Sentinel-3/OLCI satellite data in the Persian Gulf and the Gulf of Oman

In this study, the Secchi disk depth (Z_sd) values as an indicator of seawater clarity/transparency were estimated using the ESA (European Space Agency) Sentinel-3A and Sentinel-3B OLCI (S3/OLCI) satellite data in the Persian Gulf and the Gulf of Oman (PG&GO). To do so, two procedures were evaluated including an existing methodology developed by Doron et al. (J Geophys Res: Oceans 112(C6) 2007 and (Remote Sens Environ 115:2986-3001 2011) and an empirical model proposed in this research formed by employing the blue (B₄) and green (B₆) bands of S3/OLCI data. In this regard, a total number of 157 field-measured Z_sd values (114 training points for calibration of the models and 43 control points for accuracy assessment of them) were observed during eight research cruises conducted by the research vessel, the Persian Gulf Explorer, in the PG&OS between 2018 and 2022. The optimum methodology was then selected based on the statistical indicators including R² (coefficient of determination), RMSE (root mean square error), and MAPE (mean absolute percentage error). However, after the indication of the optimal model, the data of all 157 observations were utilized for the calculation of unknown parameters of the model. The final results demonstrated that compared to the existing empirical model proposed by Doron et al. (J Geophys Res: Oceans 112(C6) 2007 and (Remote Sens Environ 115:2986-3001 2011), the developed model in this study which was formed based on the linear and ratio terms of B₄ and B₆ bands, has more efficiency in the PG&GO. Consequently, a model in form of Z_sd = e^1.638B₄^/B₆^-8.241B₄^-12.876B₆^+1.26 was suggested for the estimation of Z_sd values from S3/OLCI in the PG&GO (R² = 0.749, RMSE = 2.56 m, and MAPE = 22.47%). The results also showed that the annual oscillation of the Z_sd values in the GO (5-18 m) is evidently higher compared with those in the PG (4-12 m) and the SH (7-10 m) regions.

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.

Monitoring multi-temporal and spatial variations of water transparency in the Jiaozhou Bay using GOCI data

Water transparency, commonly measured as Secchi disk depth (SDD), is essential for describing the optical properties of coastal waters. We proposed a regional linear corrected SDD estimation model based on the North Sea Mathematical Models for GOCI and the mechanical model developed by Lee et al. (2015) in the Jiaozhou Bay. Combined with the multiple variable linear regression analysis, the diurnal SDD variations of the bay inside and the bay mouth are controlled by the solar zenith angle (SZA) and tides. The bay outside mainly varies with SZA. From GOCI observations between 2011 and 2021, wind force influenced the entire area on the inner-annual SDD variations. It exhibits an increasing trend in the inter-annual dynamics, which was more stable inside the bay with an annual increase of 0.035 m, and air temperature was the most significant contribution. However, human activities cannot be ignored in causing water environment changes.

Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with Landsat OLI images

Information regarding water clarity at large spatiotemporal scales is critical for understanding comprehensive changes in the water quality and status of ecosystems. Previous studies have suggested that satellite observation is an effective means of obtaining such information. However, a reliable model for accurately mapping the water clarity of global lakes (reservoirs) is still lacking due to the high optical complexity of lake waters. In this study, by using gated recurrent units (GRU) layers instead of full-connected layers from Artificial Neural Networks (ANNs) to capture the efficient sequence information of in-situ datasets, we propose a novel and transferrable hybrid deep-learning-based recurrent model (DGRN) to map the water clarity of global lakes with Landsat 8 Operational Land Imager (OLI) images. We trained and further validated the model using 1260 pairs of in-situ measured water clarity and surface reflectance of Landsat 8 OLI images with Google Earth Engine. The model was subsequently utilized to construct the global pattern of temporal and spatial changes in water clarity (lake area>10 km²) from 2014 to 2020. The results show that the model can estimate water clarity with good performance (R² = 0.84, MAE = 0.55, RMSE = 0.83, MAPE = 45.13%). The multi-year average of water clarity for global lakes (16,475 lakes) ranged from 0.0004 to 9.51 m, with an average value of 1.88 ± 1.24 m. Compared to the lake area, elevation, discharge, residence time, and the ratio of area to depth, water depth was the most important factor that determined the global spatial distribution pattern of water clarity. Water clarity of 15,840 global lakes between 2014 and 2020 remained stable (P ≥ 0.05); while there was a significant increase in 243 lakes (P < 0.05) and a decrease in 392 lakes (P < 0.05). However, water clarity in 2020 (COVID-19 period) showed a significant increase in most global lakes, especially in China and Canada, suggesting that the worldwide lockdown strategy due to COVID-19 might have improved water quality, espically water clarity, dueto the apparent reduction of anthropogenic activities.

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.

A unified model for high resolution mapping of global lake (>1 ha) clarity using Landsat imagery data

Lake clarity, usually measured by Secchi disc depth (SDD), is a reliable proxy of lakes trophic status due to its close link with total suspended matter, chlorophyll-a, and nutrients. Trained with in-situ measured SDD and match-up Landsat images, we established various regression models to estimate SDD for global lakes. We selected a unified model which demonstrated good spatiotemporal transferability, and has potential to map SDD in different years with good quality of Landsat top-of-atmosphere (TOA) images embedded in Google Earth Engine (GEE). The unified model was successfully calibrated (n = 3586 data points, R² = 0.84, MAPE = 29.8%) against SDD measured in 2235 lakes across the world, and the validation (n = 1779, R² = 0.76, MAPE = 38.8%) also exhibited stable performance. The unified model was tuned to historical SDD measurements coincident with different Landsat sensors (L5-TM, L7-ETM+, L8-OLI) launched over the past four decades (1984-2020), thus confirming its temporal stability. Global SDD was mapped using GEE with OLI TOA products mainly acquired in 2019 to examine the spatial variation of lake water clarity (lake surface area ≥ 1 ha) all over the world. Worldwide, lake water clarity averaged 3.13 ± 1.71 m in 2019, but exhibited remarkable spatial variability due to catchment hydrological and landscape settings, lake morphology, elevation and anthropogenic impact. Inland waters in Europe (4.18 ± 1.82 m) and North America (3.84 ± 1.77 m) had the highest clarity due to greater water depth combined with less human disturbance in the high latitude regions. Lakes in South America (2.50 ± 2.33 m), Asia (2.44 ± 1.63 m) and Africa (2.36 ± 0.72 m) displayed intermediate clarity. Lakes in Oceania (1.97 ± 1.48 m) exhibited the lowest clarity for all continents except Antarctica. Further, we used the mapped SDD to evaluate water trophic status using the Carlson trophic state index. Our results indicate that, in 2019, about 63.6% of the lake areas and 47.8% of total lake numbers (2,219,627/4,646,056) were oligotrophic for global lakes, while about 23.6% areal percent and 37.1% of lake numbers are eutrophic mostly as a result of their being located in agricultural and urban-dominated drainage basins. This study, for the first time, provides water clarity information for lakes with area ≥ 1 ha all over the world with 30-m resolution and facilitates the understanding of the water clarity relevant to TSM (r = 0.95), Chl-a (r = 0.73), total phosphorus (r = 0.75), total nitrogen (r = 0.60), which could further provide water clarity data and technical support for trophic level evaluations as well. This unified model could serve as a powerful research tool for long-term monitoring of aquatic ecosystems and assessing their resilience to anthropogenic disturbance and climate change-related stressors.

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.

Climate-driven variations in suspended particulate matter dominate water clarity in shallow lakes

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.

The Effect of Urban Land-Use Change on Runoff Water Quality: A Case Study in Hangzhou City

The main functions of this research are to guide the proportion of urban land that is used and the layout of the facilities on it, help understand the changes to surface runoff that are caused by land being used in urban development, and thus solve surface runoff pollution. Hangzhou City, China has been selected for the experiment, and the way in which its land is utilized as well as the grading of urban construction projects in the demonstration area are specifically analyzed. This study systematically distinguishes the definitions of impervious area based on the Sutherland equation and analyzes the impact of different impervious area subtypes on surface runoff water quality. Then, we compare the impact of impervious area subtypes with the impact of other land-use patterns on surface runoff water quality. This study shows the relationship between different land-use types and runoff water bodies: Land-use index can affect runoff water quality; Greening activities, impervious surface, and the water quality index are negatively correlated; the effective impervious area rate is positively correlated with the water quality index. The paper suggests that increasing the proportion of green spaces and permeable roads in build-up land reduces the effective impervious area (EIA) and thus controls land runoff pollution and improves runoff water quality.

Urban Water Quality Assessment Based on Remote Sensing Reflectance Optical Classification

With the acceleration of urbanization, increasing water pollution means that monitoring and evaluating urban water quality are of great importance. Although highly accurate, traditional evaluation methods are time consuming, laborious, and vastly insufficient in terms of the continuity of spatiotemporal coverage. In this study, a water quality assessment method based on remote sensing reflectance optical classification and the traditional grading principle is proposed. In this method, an optical water type (OWT) library was first constructed using the measured in situ remote sensing reflectance dataset based on fuzzy clustering technology. Then, comprehensive scoring rules were established by combining OWTs and 12 water quality parameters, and water quality was graded into different urban water quality levels (UWQLs) based on the scoring results. Using the proposed method, the relative water quality of urban waterbodies was qualitatively evaluated at the macro level based on images from the multispectral imager of Sentinel-2. In addition, there was a significant positive correlation between the UWQLs and the water quality index (WQI). These results indicate the potential of this method for quantitative assessment of urban water quality, providing a new way to evaluate water quality using remote sensing algorithms in the future.

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.

Analyzing the spatiotemporal pattern and driving factors of wetland vegetation changes using 2000-2019 time-series Landsat data

Probing the long-term spatiotemporal patterns of wetland vegetation changes and their response to climate change and human activities is critical to make informed decisions regarding ecosystem protection. Here, the spatiotemporal patterns and factors that drive vegetation changes in the Dongting Lake wetland from 2000 to 2019 were analyzed using monthly normalized difference vegetation index (NDVI) data at a 30 m spatial resolution. First, abrupt vegetation changes were identified using the breaks for additive season and trend approach. Moreover, the relative impacts of climatic factors on monthly vegetation changes were quantified using a partial correlation-based approach, and the effects of three specific climatic factors (temperature, precipitation, and solar radiation) and human factors on vegetation recovery and degradation were determined. Our study found that: 1) the study area is becoming greener, with NDVI increases of 0.006 per year; however, there was a pronounced interannual variation in the vegetation types; 2) more than 50% of the vegetation pixels exhibited at least two breakpoints, with ~5% of the vegetation pixels exhibiting eight breakpoints; 3) in the past 20 years, human activities have favored wetland vegetation recovery (58.85%), whereas climate change threatens wetland vegetation (59.19%). Regarding climate factors, the influence of solar radiation on vegetation was found to be stronger than that of temperature and precipitation.

Spatiotemporal Variation of Siberian Crane Habitats and the Response to Water Level in Poyang Lake Wetland, China

The Poyang Lake wetland in China is the largest wintering destination for Siberian cranes worldwide. Understanding the spatiotemporal characteristics of crane habitats is of great importance for ecological environment governance and biodiversity protection. The shallow water, grassland, and soft mudflat regions of the Poyang Lake wetland are ideal habitats for wintering Siberian cranes. Based on Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) remote sensing images, habitat areas were extracted and associated with various water levels taken on multiple dates. Landscape metrics were applied to describe the spatial structural characteristics of the crane habitats, and spatial statistics are used to explore the cold and hot spots of their distribution. Moreover, three indicators including sustainability, stability, and variety were applied to evaluate the vulnerability of the crane habitats under different hydrological conditions. Our findings indicate: (a) The main crane habitats exhibit a gradual decreasing degree of fragmentation in time, an obvious uncertainty of shape complexity and a relatively stable connectivity. (b) The crane habitats have a consistent spatial pattern of highly aggregated distributions associated with various water levels. (c) The hot spots of the habitats formed multiple “sheet” belts centered on the “Lake Enclosed in Autumn” regions, while the cold spots indicate a spatial pattern of axial distributions. (d) The majority of the hot spots of the habitats were distributed in sub-lakes found in the southeast part of the Poyang Lake watershed and the Nanjishan and Wucheng nature reserves, while the cold spots were mainly distributed in the main channels of the basins of Poyang Lake. (e) The sustainable habitats were mainly distributed in the “Lake Enclosed in Autumn” regions and intensively aggregated in two national nature reserves. (f) Under conditions of extremely low to average water levels (5.3–11.46 m), an increase of water level causes a decrease of the stability and variety of the crane habitats and weakens the aggregation structure.

Evaluation and variation trends analysis of water quality in response to water regime changes in a typical river-connected lake (Dongting Lake), China

Lake water pollution has caused many serious ecological issues globally. An emerging public concern over water quality deterioration in lakes has heightened the need to evaluate the water quality of lakes at long-term scales, particularly for those with high hydrological alterations. This study combines the Mann-Kendall (M-K) test and self-organising map (SOM) to characterise and evaluate water quality trends in Dongting Lake, China, from 1991 to 2018, before and after the inauguration of the Three Gorges Dam (TGD). Herein, six water quality parameters were selected, namely pH, permanganate index (COD_Mn), ammonia nitrogen (NH₃-N), total nitrogen (TN), total phosphorus (TP), and the five-day biochemical oxygen demand (BOD₅). Our results show that the concentrations of TN and BOD₅ increase significantly throughout the study period (|Z| ≥ 1.96). The number of abrupt change points for the six water quality parameters in the post-TGD period was greater than that in the pre-TGD period, which indicates an increased risk of water deterioration in the post-TGD period. The SOM results show that the pH values ranged from 7.64 to 7.85 among the four clusters; besides, the concentrations of the remaining water quality parameters from 1991 to 1997 and 2000 to 2003 were relatively lower, suggesting that the water quality in the pre-TGD period was better. The classification of TN and TP ranged from Level Ⅳ-Ⅴ among the clusters, which did not satisfy the level Ⅲ standard for potable water, thereby posing a higher ecological risk to the Dongting Lake. These results indicate the deterioration of the water quality in Dongting Lake during the post-TGD period under the influences of pollution load and hydrological regulation. Therefore, strict controls on the external nutrient loading and hydrological regulations should be considered for water quality management.

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.

Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications

Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10–15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters.

A Review of Earth Observation-Based Analyses for Major River Basins

Regardless of political boundaries, river basins are a functional unit of the Earth’s land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream–downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins.

Quantifying the trophic status of lakes using total light absorption of optically active components

Eutrophication of lakes has become one of the world's most serious environmental problems, resulting in an urgent need to monitor and provide safeguards to control water quality. Results from analysis of lake trophic status based on calculated throphic state index (TSI) showed that 69.5% of the surveyed 277 lakes were in a state of eutrophication. Significant logarithmic relationships between light absorption of optically active components (a_OACs) and TSI (R² = 0.78) existed: TSI = 13.64 × ln(a_OACs)+43.24, and the regression relationship between a_OACs and TSI had a better degree of fit (R²) than the currently used reflectance-TSI relationship. a_OACs appeared to be a good predictor of TSI estimation in lake ecosystems. The relationship coefficient (a_OACs-TSI) slightly varied with lake type, and relationships in saline lakes and phy-type lakes were shown to be more robust than the relationship with the total lake data. This study highlights the quantification of the trophic status in lakes using a_OACs, which realized the monitoring of trophic status in lakes using inherent optical properties on a large-scale. To our knowledge this is the first investigation to assess the variability of trophic status in lakes across China. The assessment trophic state of lakes based on a_OACs provides a new way to monitor the trophic status of lakes, and findings may have applications for monitoring large-scale and long-term trophic patterns in lakes using remote sensing techniques.