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

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.

Comparative analysis of k-nearest neighbors distance metrics for retrieving coastal water quality based on concurrent in situ and satellite observations

It is time consuming and expensive to monitor extensive areas of coastal waters with sufficient frequency using in situ (ship based) methods. Satellite remote sensing is much more cost effective. Satellites can detect Optically Active Constituents (OACs) in water. Therefore, it is crucial to know the concentrations of OACs in the study area in order to develop and validate remote sensing methods suitable for assessing water quality in this region. The Pars Special Economic Energy Zone (PSEEZ), a major hub of natural gas extraction in the Persian Gulf, has undergone rapid industrial expansion since 1998, intensifying environmental pressures and necessitating high-resolution, frequent water quality assessments. However, a structured, long-term monitoring framework is absent despite the significance of this region. In order to develop satellite-based remote sensing methods for this region we carried out measurements of different OACs (chlorophyll-a, coloured dissolved organic matter (CDOM) and turbidity) and tested Landsat 8, Sentinel-2, and Sentinel-3 performance in retrieving the OACs. We tested the k-Nearest Neighbors machine learning algorithm. The selection of distance metrics demonstrated a significant influence on the accuracy of retrieving OACs. In turbidity retrieval, the Euclidean Distance (ED) enhanced the regression slope to 0.90 (a 55.17 % improvement over Fuzzy Mahalanobis Distance (FD)) and reduced the RMSLE to 0.51, corresponding to an approximate 160 % enhancement in precision. For CDOM, RMSLE values for ED and FD were 0.39 and 0.48, respectively, indicating an 18.75 % improvement favoring ED. Furthermore, bias analysis revealed deviations of 1-6 % compared to reference data, with the lowest values observed for Mahalanobis Distance (MD) with MSI and FD with OLCI. In chlorophyll-a retrieval, the choice of distance metric directly impacted the accuracy of the OLCI sensor, inducing bidirectional bias, comprising both overestimation and underestimation, which varied depending on the selected metric. These results underscore the critical importance of optimizing distance metric selection to enhance prediction accuracy and mitigate systematic errors in remote sensing applications. Furthermore, the results revealed that the implementation of this algorithm exhibited substantially superior performance compared to other evaluated algorithms within the study area, achieving significantly higher accuracy metrics. Thereby establishing k-NN as the optimal framework for satellite-based water quality monitoring in environmentally sensitive regions like PSEEZ.

Satellite-ground synchronous in-situ dataset of water optical parameters and surface temperature for typical lakes in China

Remote sensing technology has the potential to enhance the lake's large-scale and long-term dynamic monitoring capabilities significantly. High-quality in-situ datasets are essential for improving the accuracy and reliability of remote sensing retrieval of lake ecosystems. This dataset provides satellite-ground synchronized in-situ data on water multi-parameters for typical lakes in China spanning the period between 2020 and 2023. It includes quality-checked water optical parameters (remote sensing reflectance (Rrs), chlorophyll-a (Chl-a), total suspended matter (TSM) and Secchi disk depth (SDD)), and water surface temperature (WST) data. It encompasses 586 sampling points across 18 lakes. The dataset exhibits two significant highlights: Firstly, synchronous observations from multiple satellites are coordinated during the data collection effectively supporting the retrieval and validation of water remote sensing products. Secondly, it encompasses diverse data types, collecting synchronous measurements of Rrs and various parameters. This dataset will continuously update, substantially enhancing regional and global lake monitoring capabilities through satellite remote sensing data.

A dataset of trophic state index for nation-scale lakes in China from 40-year Landsat observations

Trophic state index (TSI) serves as a key indicator for quantifying and understanding the lake eutrophication, which has not been fully explored for long-term water quality monitoring, especially for small and medium inland waters. Landsat satellites offer an effective complement to facilitate the temporal and spatial monitoring of multi-scale lakes. Landsat surface reflectance products were utilized to retrieve the annual average TSI for 2693 lakes over 1 km2 in China from 1984 to 2023. Our method first distinguishes lake types by pixels with a decision tree and then derives relationships between trophic state and algal biomass index. Validation with public reports and existing datasets confirmed the good consistency and reliability. The dataset provides reliable annual TSI results and credible trends for lakes under different area scales, which can serve as a reference for further research and provide convenience for lake sustainable management.

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 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.

Mapping particulate organic carbon in lakes across China using OLCI/Sentinel-3 imagery

Remote sensing monitoring of particulate organic carbon (POC) concentration is essential for understanding phytoplankton productivity, carbon storage, and water quality in global lakes. Some algorithms have been proposed, but only for regional eutrophic lakes. Based on in-situ data (N = 1269) in 49 lakes across China, we developed a blended POC algorithm by distinguishing Type-I and Type-II waters. Compared to Type-I, Type-II waters had higher reflectance peak around 560 nm (>0.0125 sr-1) and mean POC (4.65 ± 4.11 vs. 2.66 ± 3.37 mg/L). Furthermore, because POC was highly related to algal production (r = 0.85), a three-band index (R2 = 0.65) and the phytoplankton fluorescence peak height (R2 = 0.63) were adopted to estimate POC in Type-I and Type-II waters, respectively. The novel algorithm got a mean absolute percent difference (MAPD) of 35.93 % and outperformed three state-of-the-art formulas with MAPD values of 40.56-76.42 %. Then, the novel algorithm was applied to OLCI/Sentinel-3 imagery, and we first obtained a national map of POC in 450 Chinese lakes (> 20 km2), which presented an apparent spatial pattern of "low in the west and high in the east". In brief, water classification should be considered when remotely monitoring lake POC concentration over a large area. Moreover, a process-oriented method is required when calculating water column POC storage from satellite-derived POC concentrations in type-II waters. Our results contribute substantially to advancing the dynamic observation of the lake carbon cycle using satellite data.

An operational approach for large-scale mapping of water clarity levels in inland lakes using landsat images based on optical classification

Water clarity is a critical parameter of water, it is typically measured using the setter disc depth (SDD). The accurate estimation of SDD for optically varying waters using remote sensing remains challenging. In this study, a water classification algorithm based on the Landsat 5 TM/Landsat 8 OLI satellite was used to distinguish different water types, in which the waters were divided into two types by using the ad(443)/ap(443) ratio. Water type 1 refers to waters dominated by phytoplankton, while water type 2 refers to waters dominated by non-algal particles. For the different water types, a specific algorithm was developed based on 994 in situ water samples collected from Chinese inland lakes during 42 cruises. First, the Rrs(443)/Rrs(655) ratio was used for water type 1 SDD estimation, and the band combination of (Rrs(443)/Rrs(655) - Rrs(443)/Rrs(560)) was proposed for water type 2. The accuracy assessment based on an independent validation dataset proved that the proposed algorithm performed well, with an R2 of 0.85, mean absolute percentage error (MAPE) of 25.98%, and root mean square error (RMSE) of 0.23 m. To demonstrate the applicability of the algorithm, it was extensively evaluated using data collected from Lake Erie and Lake Huron, and the estimation accuracy remained satisfactory (R2 = 0.87, MAPE = 28.04%, RMSE = 0.76 m). Furthermore, compared with existing empirical and semi-analytical SDD estimation algorithms, the algorithm proposed in this paper showed the best performance, and could be applied to other satellite sensors with similar band settings. Finally, this algorithm was successfully applied to map SDD levels of 107 lakes and reservoirs located in the Middle-Lower Yangtze Plain (MLYP) from 1984 to 2020 at a 30 m spatial resolution, and it was found that 53.27% of the lakes and reservoirs in the MLYP generally show an upward trend in SDD. This research provides a new technological approach for water environment monitoring in regional and even global lakes, and offers a scientific reference for water environment management of lakes in the MLYP.

Spatiotemporal changes and influencing factors of water clarity in the Yellow Sea over the past 20 years

Water transparency is an important parameter for describing the optical properties of water. It reflects changes in marine environment and is of great significance for guiding the development and protection of marine environment. In this study, based on the algorithm proposed by Lee et al. in 2015, water transparency in the Yellow Sea from 2003 to 2022 was inverted. The results revealed that, in terms of spatial distribution, the water in western region of the Yellow Sea had relatively low transparency, whereas the water in the central and southern regions had high transparency. Regarding temporal trends, declining transparency was observed throughout most of the study period, but the trends reversed and transparency began to increase in 2017. Empirical orthogonal function analysis confirmed that water transparency was primarily influenced by the optical constituents of water. Long-term monitoring of water clarity is of significant importance for the preservation of marine ecological environments.

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.

Ecological risk changes and their relationship with exposed surface fraction in the karst region of southern China from 1990 to 2020

Due to anthropogenic disturbances, the karst region in southern China is vulnerable to ecological problems such as soil erosion and surface exposure. However, limited studies on variations in large-scale ecological risk (ER) and their influencing factors, particularly the coupling/decoupling relationship with an exposed surface fraction (ESF), make ER regulations and ecological restoration challenging. The present study evaluates the ER of eight typical karst provinces in Southern China from 1990 to 2020 using the technique for order preference by similarity to an ideal solution (TOPSIS) model and ecosystem services (habitat quality, water yield, carbon storage, soil conservation, and food production), and extracts the contemporaneous ESF using Landsat satellite data in Google Earth Engine (GEE). The spatiotemporal change of ER and ESF are analyzed, and their coupling/decoupling relationship and driving mechanism are explored using coupling coordination degree (CCD) and multi-scale geographically weighted regression (MGWR) models. The results show that: (1) Over the past 30 years, the ER has increased until 2010 and subsequently declined, with an increasing mean value (0.463-0.503), except in Chongqing municipality. The ESF decreased significantly (the mean value dropped from 44.7% to 38.7%), except that in Sichuan province. (2) The average CCD between ER and ESF decreased with fluctuation of -0.017, with a decoupling relationship (58.18%). The coupling area is larger than the decoupling area in the Sichuan area, while other provinces are opposite. (3) The coupling/decoupling relationship in the study area is mainly driven by terrain (elevation, slope) and socio-economic (population density, per capita GDP) factors. More attention should be paid to the role of these factors in the continuous reduction and control of ESF and ER. This study can serve as a reference for similar studies in karst regions, such as risk assessment and surface monitoring, rocky desertification control, ecological engineering layout, and territorial planning.

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.