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Precisely estimating the grassland biomass carbon storage is vital for evaluating grassland carbon sequestration potential and the monitoring and management of grassland resources. With the increasing intensity of climate change (CC) and human activities (HA), it is necessary to explore spatiotemporal variations in biomass carbon storage and its response to CC and HA. In this study, we focused on the Hulunbuir Grassland, utilizing sample plots data, MODIS data, environmental factors (terrain, soil, and climate), location factor, and texture characteristics to assess the performance of four machine learning algorithms: random forest, support vector machine, gradient boosting decision tree, and extreme gradient boosting in estimating grassland aboveground biomass (AGB). Based on the optimal model combined with root-shoot ratio data, grassland distribution data, and carbon content coefficients, the spatiotemporal characteristics and driving factors of biomass carbon storage from 2001–2022 were analyzed. The results showed that (1) the random forest achieved the highest prediction accuracy for grassland AGB, making it appropriate for AGB estimation in the Hulunbuir Grassland. (2) The spectral indices were the key variables of the grassland AGB, especially the enhanced vegetation index and difference vegetation index. (3) The 22-year average total biomass (TB) of the study area was 1037.10 gC/m2, of which the 22-year average AGB was 48.73 gC/m2 and 22-year average belowground biomass was 988.37 gC/m2, showing a spatial distribution feature of gradual increase from west to east. (4) From 2001–2022, TB carbon storage showed an insignificant growth trend (p > 0.05). The 22-year average carbon storage of TB was 72.34 ± 18.07 gC. (5) Climate factors were the main driving factors for the spatial pattern of grassland TB carbon density, while the combined effects of CC and HA were the main contributors to the interannual increase in grassland TB carbon density. [ABSTRACT FROM AUTHOR]

Cladophora qinghaiensis, an endemic species of Cladophora in saltwater lakes, was scientifically named in 2021 (hereafter referred to as Cladophora). Cladophora exists in different morphologies, including attached submerged Cladophora (AC), grown floating Cladophora (GFC), and death floating Cladophora (DFC). Previous satellite remote sensing has mainly focused on identifying floating algae. In this study, Qinghai Lake served as a case study, and a classification decision tree model (CDTM) was proposed. The model employed the chlorophyll spectral index (CSI) and the normalized difference vegetation index (NDVI) to differentiate AC, Floating Cladophora (FC), and water. Additionally, the floating Cladophora index (FCI) was introduced to further distinguish GFC and DFC within FC. The method was applicable to Sentinel-2 images from 2016–2023. Visual interpretation methods were used for Landsat series images from the summer months (July to September) to obtain the AC and FC. The results demonstrate that over the past 30 years, the areas inhabited by AC and FC have increased gradually. The three morphologies of Cladophora also exhibited seasonal variations, with growth observed annually in May–June, reaching peaks in August–September, and gradually declining in October. In addition, by combining factors such as water surface area and climatic factors, we analyzed the driving forces influencing the changes in Cladophora. In this research, AC and FC showed significant correlations with the water surface area, with correlation coefficients (r) of approximately 0.9 and 0.7, respectively. These new findings provide valuable insights regarding the spatiotemporal changes and underlying causes for different morphologies of Cladophora in global saline lakes. [ABSTRACT FROM AUTHOR]

Remote sensing reflectance (Rrs), which is currently measured mainly using the above-water approach, is the most crucial parameter in the remote sensing inversion of plateau inland water colors. It is very difficult to measure the Rrs of plateau inland unmanned areas; thus, we provide a measurement solution using a micro-spectrometer. Currently, commercial micro-spectrometers are not factory calibrated for radiation, and thus, a radiometric calibration of the micro-spectrometer is an essential step. This article uses an Ocean Optics micro-spectrometer (STS-VIS) and a traditional water spectrometer (Trios) to simultaneously measure the irradiance and radiance of diffuse reflectance plates with different reflectance values for field calibration. The results show the following: (1) different fiber types have different calibration coefficients, and the integration time is determined according to the diameter of the fiber and the type of fiber, and (2) by comparing the simultaneous measurement results of STS-VIS with Trios, the mean absolute percentage difference (MAPD) of both reached 18.64% and 5.11% for Qinghai Lake and Golmud River, respectively, which are accurate Rrs measurements of water bodies. The Rrs of the Hoh Xil and Qarhan Salt Lake water bodies in unmanned areas of China was measured, and this was the first collection of in situ spectral information with a micro-spectrometer. This article shows that the micro-spectrometer can perform the in situ measurement of water Rrs in unmanned inland areas. With this breakthrough in the radiometric performance of the micro-spectrometer, we are able to obtain more accurate remote sensing reflectance results of unmanned water bodies. [ABSTRACT FROM AUTHOR]

Cyanobacterial harmful algal blooms (CyanoHABs) in inland waters are now among the most pressing environmental issues worldwide, especially in China. Satellite remote sensing has limitations in monitoring CyanoHABs in small water bodies due to spatial and temporal resolution limitations. While literature and news media have the potential to supplement satellite remote sensing in monitoring CyanoHABs, they have currently not received sufficient attention. In this study, we combined information on the distributions of CyanoHABs from literature and media for the first time to comprehensively assess the spatiotemporal variation in CyanoHABs in China. We collected, cleaned, validated, and organized data from literature and media on CyanoHABs in China, resulting in the establishment of a comprehensive database on CyanoHABs in China's inland waters (ChinaCyanoDB) covering 198 water bodies, 525 records for 1950–2021. The majority of water bodies with CyanoHABs (CyanoWaters) are located in the eastern China, mainly concentrated in the middle and lower Yangtze region, with a clear upward trend in their number over the last four decades. The ChinaCyanoDB and analytical results can provide valuable data support for monitoring and managing CyanoHABs in China while the database construction method may also be applied to other countries and regions. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is a region sensitive to global climate change and has been experiencing substantial environmental changes in the past decades. Lake ice phenology (LIP) is a perceptible indicator reflecting changes of lake thermodynamics in response to global warming. Lake ice phenology over the Tibetan Plateau is however rarely observed and recorded. This research presents a dataset containing 39-year (1978–2016) lake ice phenology data of 132 lakes (each with area >40 km2) over the Tibetan Plateau by combining the strengths of both remote sensing (MOD11A2, MOD10A1) and numerical modelling (air2water). Data validation shows that the ice phenology data derived by our method is highly consistent with that based on existing approaches (with R2 > 0.75 for all phenology index and RMSE < 5d). The dataset is valuable to investigate the lake-atmosphere interactions and long-term hydrothermal change of lakes across the Tibetan Plateau. Measurement(s) lake ice phenology Technology Type(s) remote sensing and numerical modeling [ABSTRACT FROM AUTHOR]

Cyanobacterial harmful algal blooms (CyanoHABs) in inland water have emerged as a major global environmental challenge. Although satellite remote sensing technology has been widely used to monitor CyanoHABs, there are also some automatic extraction methods of CyanoHABs based on spectral indices (such as gradient mode, fixed threshold, and the Otsu method, etc.), the accuracy is generally not very high. This study developed a high-precision automatic extraction model for CyanoHABs using a deep learning (DL) network based on Sentinel-2 multi-spectral instrument (MSI) data of Chaohu Lake, China. First, we generated the CyanoHABs "ground truth" dataset based on visual interpretation. Thereafter, we trained the CyanoHABs extraction model based on a DL image segmentation network (U-Net) and extracted CyanoHABs. Then, we compared three previous automatic CyanoHABs extraction methods based on spectral index threshold segmentation and evaluated the accuracy of the results. Based on "ground truth", at the pixel level, the F1 score and relative error (RE) of the DL model extraction results are 0.90 and 3%, respectively, which are better than that of the gradient mode (0.81,40%), the fixed threshold (0.81, 31%), and the Otsu method (0.53, 62%); at CyanoHABs area level, the R2 of the scatter fitting between DL model result and the "ground truth" is 0.99, which is also higher than the other three methods (0.90, 0.92, 0.84, respectively). Finally, we produced the annual CyanoHABs frequency map based on DL model results. The frequency map showed that the CyanoHABs on the northwest bank are significantly higher than in the center and east of Chaohu Lake, and the most serious CyanoHABs occurred in 2018 and 2019. Furthermore, CyanoHAB extraction based on this model did not cause cloud misjudgment and exhibited good promotion ability in Taihu Lake, China. Hence, our findings indicate the high potential of the CyanoHABs extraction model based on DL in further high-precision and automatic extraction of CyanoHABs from large-scale water bodies. [ABSTRACT FROM AUTHOR]

Crowdsourced data from smart devices play an increasingly important role in water quality monitoring. However, guaranteeing and evaluating crowdsourced data quality is a key issue. This study aims to extract more accurate water reflectance data from smartphone photographs with variable exposure parameters, and to test the usability of these data in deriving water quality parameters. A set of low–cost reference cards was designed to be placed in the center of the photograph near the water surface, and a calculation model was proposed to convert the photograph digital numbers (DNs) to water reflectance. A nonlinear DN–to–reflectance model was constructed using the inherent reflectance and DN of the reference card in the photograph. Then, the reflectance of the water surface in the same photograph was estimated. During the evaluation of this scheme in seven different waterbodies with 112 sampling sites, small differences were observed between the estimated and measured remote sensing reflectance; the average unbiased relative errors (AUREs) for the red, green, and blue bands were 25.7%, 29.5%, and 35.2%, respectively, while the RMSEs for the three bands were 0.0032, 0.0051, 0.0031, respectively. The derived water reflectance data were used to retrieve the Secchi–disk depth (Zsd) and turbidity, with accuracies of 72.4% and 60.2%, respectively. The results demonstrate that the proposed method based on the smartphone camera can be used to derive the remote sensing reflectance and water quality parameters effectively with acceptable accuracy. [ABSTRACT FROM AUTHOR]

The Guanting Reservoir supplied drinking water to Beijing until 1997, following which the water quality of the reservoir deteriorated. The chlorophyll-a concentration (Cchl-a) of water is an important indicator of eutrophication. Therefore, changes in the Cchl-a of the Guanting Reservoir should be monitored and analysed. For more than 30 years, the monitoring of Cchl-a in inland waterbodies has only been possible using the Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM), and Landsat 8 Operational Land Imager (OLI). However, there are data consistency problems in monitoring Cchl-a using these sensors. To address this issue, this study utilized inter-sensor normalization models of the different sensors and a unified Cchl-a estimation model for Landsat series data with a long time span. After inter-sensor normalization, the mean relative error (MRE) of remote sensing reflectance (Rrs) between OLI and TM/ETM+ was corrected to 1–5%. The unified model of Cchl-a estimation employed the normalized ratio index of blue RB and near-infrared RNIR remote sensing reflectance: (RNIR – RB): (RNIR + RB). The MRE for estimating Cchl-a was 25.7% and the root-mean-square error (RMSE) was 5.65 mg m–3. The Cchl-a of the Guanting Reservoir was then estimated for years between 1985 and 2019. During this time, Cchl-a had a distinct seasonal distribution trend; the annual changes go through four stages, and each period shows different change characteristics. This study is the first to systematically understand the history of chlorophyll-a changes in the Guanting Reservoir over the past 30 years and provides references data for its eutrophication process and management. [ABSTRACT FROM AUTHOR]

Water colour is the result of its constituents and their interactions with solar irradiance; this forms the basis for water quality monitoring using optical remote sensing data. The Forel-Ule Index (FUI) is a useful comprehensive indicator to show the water colour variability and water quality change in both inland waters and oceans. In recent decades, lakes around the world have experienced dramatic changes in water quality under pressure from both climate change and anthropogenic activities. However, acquiring consistent water colour products for global lakes has been a challenge. In this paper we present the first time series FUI dataset for large global lakes from 2000–2018 based on MODIS observations. This dataset provides significant information on spatial and temporal changes of water colour for global large lakes during the past 19 years. It will be valuable to studies in search of the drivers of global and regional lake colour change, and the interaction mechanisms between water colour, hydrological factors, climate change, and anthropogenic activities. Measurement(s) color Technology Type(s) satellite imaging Factor Type(s) temporal interval Sample Characteristic - Environment water body • lake Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13257218 [ABSTRACT FROM AUTHOR]

Due to the coarse spatial resolution and saturation issues associated with the 1-km ocean bands of MODerate-resolution Imaging Spectrometer (MODIS) instruments, the higher resolution (250 and 500 m) land bands are tended to be used for water color applications in coastal and inland waters. However, these wide spectral bands provide limited spectral information; therefore, resolving the chlorophyll-a concentration (Chla) signal in highly turbid waters poses a significant challenge. In this study, we present a classification-based algorithm to estimate Chla in a highly turbid eutrophic lake, Taihu Lake in Eastern China, using four visible to near-infrared land bands of MODIS observations. A threshold segmentation method of MODIS Rrs(555)/Rrs(645) was used to categorize the lake into two classes: Chla-dominated waters (Class 1) and suspended particulate matter (SPM)-dominated waters (Class 2). Then, a band ratio of Rrs(859)/Rrs(645) was applied to retrieve Chla in Class 1, and a newly proposed spectral index, the Anti-SPM Chlorophyll-a Index (ASCI), was used to estimate Chla in Class 2. Validation using the leave-one-out cross-validation (LOOCV) method showed that the average unbiased relative error (AURE) of the derived Chla is 44.4%, and the coefficient of determination (R2) is 0.55. The algorithm was further applied to MODIS data of Taihu Lake between 2000 and 2015 to obtain Chla time series maps, whose spatial and temporal patterns agreed well with previous studies. [ABSTRACT FROM AUTHOR]

Urban black and odorous water (BOW) is a serious global environmental problem. Since these waters are often narrow rivers or small ponds, the detection of BOW waters using traditional satellite data and algorithms is limited both by a lack of spatial resolution and by imperfect retrieval algorithms. In this paper, we used the Chinese high-resolution remote sensing satellite Gaofen-2 (GF-2, 0.8 m). The atmospheric correction showed that the mean absolute percentage error of the derived remote sensing reflectance ($R_{\mathrm {rs}}$) in visible bands is 25.19%. We first measured $R_{\mathrm {rs}}$ spectra of two classes of BOW [BOW with high concentrations of iron (II) sulfide, i.e., BOW1 and BOW with high concentrations of total suspended matter, i.e., BOW2] and ordinary water in Shenyang. Then, in situ $R_{\mathrm {rs}}$ data were converted into $R_{\mathrm {rs}}$ corresponding to the wide GF-2 bands using the spectral response functions. We used the converted $R_{\mathrm {rs}}$ data to calculate several band combinations, including the baseline height, [ $R_{\mathrm {rs}}$ (green) $- R_{\mathrm {rs}}$ (red))/($R_{\mathrm {rs}}$ (green) $+ R_{\mathrm {rs}}$ (red)], and the color purity on a Commission Internationale de L’Eclairage (CIE) chromaticity diagram. The color purity was found to be the best index to extract BOW from ordinary water. Then, $R_{\mathrm {rs}}$ (645) was applied to categorize BOW into BOW1 and BOW2. We applied the algorithm to two synchronous GF-2 images. The recognition accuracy of BOW2 and ordinary water are both 100%. The extracted river water type near Weishanhu Road was BOW1, which agreed well with ground truth. The algorithm was further applied to other GF-2 data for Shenyang and Beijing. [ABSTRACT FROM AUTHOR]

Abstract Eutrophication of inland waters is considered a serious global environmental problem. Satellite remote sensing (RS) has been established as an important source of information to determine the trophic state of inland waters through the retrieval of optically active water quality parameters such as chlorophyll-a (Chl-a). However, the use of RS techniques for assessment of the trophic state of inland waters on a global scale is hindered by the performance of retrieval algorithms over highly dynamic and complex optical properties that characterize many of these systems. In this study, we developed a new RS approach to assess the trophic state of global inland water bodies based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and the Forel-Ule index (FUI). First, the FUI was calculated from MODIS data by dividing natural water colour into 21 indices from dark blue to yellowish-brown. Then the relationship between FUI and the trophic state index (TSI) was established based on in-situ measurements and MODIS products. The water-leaving reflectance at 645 nm band was employed to distinguish coloured dissolved organic matter (CDOM)-dominated systems in the FUI-based trophic state assessment. Based on the analysis, the FUI-based trophic state assessment method was developed and applied to assess the trophic states of 2058 large inland water bodies (surface area >25 km2) distributed around the world using MODIS data from the austral and boreal summers of 2012. Our results showed that FUI can be retrieved from MODIS with a considerable accuracy (92.5%, R2 = 0.92) by comparing with concurrent in situ measurements over a wide range of lakes, and the overall accuracy of the FUI-based trophic state assessment method is 80.0% (R2 = 0.75) validated by an independent dataset. Of the global large water bodies considered, oligotrophic large lakes were found to be concentrated in plateau regions in central Asia and southern South America, while eutrophic large lakes were concentrated in central Africa, eastern Asia, and mid-northern and southeast North America. Highlights • Present a trophic state assessment method for global large inland waters based on MODIS. • Produce the trophic state map of global large inland water bodies. • Analyze the spatial distribution of the trophic states of global large inland water bodies. [ABSTRACT FROM AUTHOR]

During the atmospheric correction of remote sensing data in inland waters, the original Second Simulation of the Satellite Signal in the Solar Spectrum-Vector version (6SV) model does not eliminate the specular reflection of downward skylight radiance at the air-water interface. Thus, we propose a modified version of the 6SV model (M6SV) that does remove reflected skylight at the air-water interface. We apply the new model to the atmospheric correction of a Landsat 8 Operational Land Imager (OLI) image over Taihu Lake, China, where the aerosol optical depth is known. In situ reflectance measurements acquired concurrently with the L8/OLI image are used to validate the performance of the new M6SV algorithm. To further analyze the merits and demerits of M6SV, the model is compared with two short-wave infrared (SWIR)-based atmospheric correction models: the Sea-Viewing Wide Field-of-View Sensor Data Analysis System short-wave infrared (SD-SWIR) model and the Vanhellemont & Ruddick short-wave infrared with a per scene fixed aerosol type (VR-SWIR-F) model. Comparisons of results from all three L8/OLI image atmospheric corrections with the in situ remote sensing reflectance data show that M6SV produces reliable atmospheric corrections in the green and red spectral bands and is an effective alternative for Landsat 8 OLI atmospheric correction in inland waters. [ABSTRACT FROM AUTHOR]

Traditional manual methods of extracting water bodies from remote sensing images cannot satisfy the requirements for mass processing of remote sensing data, and new automated methods are complicated and require a large amount of auxiliary data. The histogram bimodal method is a frequently used objective tool for threshold selection in image segmentation. However, automatically calculating the threshold is difficult because of complex surfaces and image noise, which lead to imperfect twin peaks. To overcome these difficulties, we developed an operational automated water extraction method. This method does not require the identification of twin histogram peaks but instead seeks minimum values in the threshold range to achieve an automated dynamic threshold. We calibrated the method for 18 lakes in China using Landsat 8 Operational Land Imager images, for which the relative error (RE) and coefficient of determination (R2) for threshold accuracy were 2.1% and 0.96, respectively. The RE of area accuracy was 0.59%. The advantages of the method lie in its simplicity and minimal requirements for auxiliary data while still achieving an accuracy comparable to that of other automatic water extraction methods. It can be applied to mass remote sensing data to calculate water thresholds and automatically extract large water bodies. [ABSTRACT FROM AUTHOR]

Abstract: Although designed for land surfaces, MODIS Aqua surface reflectance data products (MYD09, termed as R_Land in this work) have also been used for water applications. Yet to date their uncertainties and general suitability in such applications have rarely been documented. In this study, R_Land products of two regions (Chesapeake Bay and Taihu Lake) between July 2002 and December 2015 are evaluated against in situ measurements and against reflectance products derived by the MODIS Ocean Team using atmospheric correction schemes specifically designed for water applications, namely the default atmospheric correction method based on the near‐infrared (NIR) bands (denoted as R_NIR, data products available from NASA) and alternative atmospheric correction method based on the shortwave‐infrared (SWIR) bands (denoted as R_SWIR, data products not available from NASA but require customized processing by the user), respectively. Results suggest high accuracy in R_Land(645) and R_Land(645/555) for both Chesapeake Bay and Taihu Lake in terms of daily spatial distributions, seasonality, and long‐term trends. A sensitivity test also shows improved data quality in R_Land(645/555) when data are binned by 7 × 7 pixels in space and 32 days in time. Improved data quality can also be obtained for R_Land(645) when data are only binned in time to minimize the patchiness noise in R_Land daily images. Given the fact that most users do not have the capacity to process low‐level data to obtain R_SWIR and the standard NASA R_NIR products often lack coverage over inland waters because they are optimized for global oceans instead of inland waters, this study provides a general guide on the applicability of the widely available R_Land products in inland and estuarine water applications in the absence of customized R_NIR or R_SWIR data products for local regions. [ABSTRACT FROM AUTHOR]

Based on field measurements of water surface reflectance spectra in Lake Taihu, we construct a model for distinguishing between cyanobacterial bloom and floating leaf vegetation by combining a chlorophyll spectral index with a baseline of phycocyanin. In situ $R_{{\rm{rs}}}$ measurements validation results show that this model performs well in distinguishing cyanobacterial bloom from floating leaf vegetation in Lake Taihu. We apply this model to 52 remote sensing images from the Medium-Resolution Imaging Spectrometer (MERIS) from 2003 to 2011. Using two different accuracy evaluation methods, we find an average recognition accuracy of more than 80% for cyanobacterial bloom and floating leaf vegetation when using optimal index thresholds. Using an average index threshold to extract cyanobacterial bloom and floating leaf vegetation from the images, the relative accuracies are 78.8% and 74.6%, respectively. If more efficiency is desired, these average thresholds can be used, which is convenient for batch processing and automated extraction of cyanobacterial bloom and floating leaf vegetation from remote sensing data. The overall distribution of cyanobacterial bloom and floating leaf vegetation in Lake Taihu from 2003 to 2011 is determined by overlapping the distribution maps from individual images, and the results of our analysis are consistent with previously published results. In addition, our analysis shows that this model is immune to perturbations from thin clouds and aerosols. [ABSTRACT FROM PUBLISHER]

Remote sensing of water quality in turbid coastal and inland waters requires accurate atmospheric correction, which is technically challenging. While previous efforts have shown the advantage of using the short-wave infrared (SWIR) bands instead of near-infrared (NIR) bands for atmospheric correction, such an approach could only be applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite (MODISA). This is because MODIS data from the Terra satellite (MODIST) contain more noise and other sensor artifacts, thus this sensor has been generally regarded by the ocean color research community as not being able to provide science quality data. Here, we address this technical challenge through noise reduction and regional vicarious calibration adjustment, and demonstrate preliminary success using turbid Taihu Lake as an example. The noise in the three SWIR bands was evaluated first, and then reduced through a noise reduction method. The SWIR bands were adjusted over open-ocean waters using the well-calibrated NIR ocean bands (1-km resolution) and radiative transfer, which were then used to adjust the land bands (250-m and 500-m resolution) in the visible and NIR over turbid waters where concurrent field-measured reflectance spectra are available. Of all three combinations of SWIR bands, the combination of 1240 and 1640-nm bands was found to perform the best, showing significantly improved retrieval accuracy for Taihu Lake, leading to recovery of low-quality MODIST data to higher-quality data comparable to MODISA, and thus doubling valid data coverage. Testing of this approach on another highly turbid lake (Chaohu Lake, China) showed similar results. While the general application of this approach to turbid lakes still needs to be tested as local tuning of the calibration coefficients may be required, these results suggest that MODIST may be used as effectively as MODISA for monitoring Taihu Lake water quality. [ABSTRACT FROM AUTHOR]

Forel-Ule (FU) index of water color is an important parameter in traditional water quality investigations. We retrieved the FU index of the largest 10 lakes in China during 2000-2012 from MODerate-resolution Imaging Spectroradiometer surface reflectance product (MOD09) images. Since FU index is an optical parameter, it can be derived from optical remote sensing data by direct formulas, which is invariant with region and season. Based on validation by in situ measured reflectance data, the FU index products are reliable, with average relative error of 7.7%. FU index can be used to roughly assess water clarity: the clearer a water body is, and the bluer it is in color, the smaller its FU index is. FU index can also be used to roughly classify trophic state into three classes: oligotrophic, mesotrophic, and eutrophic. We analyzed the spatial, interannual, and seasonal variations of the FU index and its implications for water clarity and trophic state, and the findings are mostly consistent with the results from related literature. All in all, it might be a feasible way to roughly assess inland water quality by FU index in large region and over long time period. [ABSTRACT FROM AUTHOR]

Assessments of specific inherent optical properties (SIOPs) and their variability in highly turbid and productive inland waters are essential for the accurate estimation of water quality. A new optical classification method including two classification criteria [i.e., normalized remote sensing reflectance slope (NS), and normalized remote sensing reflectance depth (ND)] was developed to divide remote sensing reflectance into four classes, i.e., class 1 (NS < −0.0017 and ND < 0.21) is low turbid and productive water; class 2 (NS < −0.0017 and ND > 0.21) is low turbid and high productive water; class 3 (NS > −0.0017 and ND < 0.09) is high turbid and low productive water; and class 4 (NS > −0.0017 and ND > 0.009) is high turbid and high productive water. The relationships between phytoplankton absorption at 440 nm [ a(440)] and chlorophyll- a concentration [ C] as well as between particle backscattering coefficient at 440 nm [ b(440)] and total suspended matter concentration ( C) after classification were obtained from a large number of in situ data in Lake Taihu. The measured specific phytoplankton absorption $$[ {a_{\text{ph}}^{*} \left( \lambda \right)} ]$$ and particle backscattering coefficient $$[ {b_{\text{bp}}^{*} \left( \lambda \right)} ]$$ show significant variations even within the same class. The mean values of $$a_{\text{ph}}^{*} \left( \lambda \right)$$ at 440 nm $$[ {a_{\text{ph}}^{*} \left( {440} \right)} ]$$ for each class are 0.048 ± 0.013, 0.060 ± 0.012, 0.083 ± 0.021, and 0.056 ± 0.017 m/mg, respectively. The mean values of $$b_{\text{bp}}^{*} \left( \lambda \right)$$ at 440 nm $$[ {b_{\text{bp}}^{*} \left( {440} \right)} ]$$ for each class are 0.035 ± 0.01, 0.024 ± 0.004, 0.041 ± 0.009, and 0.038 ± 0.009 m/g, respectively. The power functions of SIOPs and water constituents' concentration indicate that $$a_{\text{ph}}^{*} \left( {440} \right)$$ and $$b_{\text{bp}}^{*} \left( {440} \right)$$ vary with C and C. The validation results show that our proposed values for $$a_{\text{ph}}^{*} \left( {440} \right)$$ and $$b_{\text{bp}}^{*} \left( {440} \right)$$ cover a very wide range of water optical properties, which are characterized from clear water to highly turbid productive water. The validation results also suggest that the retrieval accuracy of C and C bio-optical model was improved after classification. The root mean square error (RMSE) of C was improved from 14.18 to 7.43 μg/L (mean value of all classes) and RMSE of C was improved from 32.98 to 26.10 mg/L (mean value of all classes). Thus, the temporal and spatial variation of $$a_{\text{ph}}^{*} \left( {440} \right)$$ and $$b_{\text{bp}}^{*} \left( {440} \right)$$ should be considered in the bio-optical retrieval model of water quality. Graphical Abstract: In complex optical properties of inland water, retrieving the water constituents with high accuracy needs to classify the water optical properties from the remote sensing spectrum by optical classification method. The figure shows the water color examples of each class[Figure not available: see fulltext.]. [ABSTRACT FROM AUTHOR]

Serious difficulties are present in the application of remote sensing techniques for optically complex waters, as retrieval of water quality parameters is generally based on detailed local knowledge of optical properties of water bodies for specific areas and at specific times. Water color is measured in traditional water quality observations and characterized by the Forel-Ule scale, as it is intimately related to water compositions. In this paper, a Moderate Resolution Imaging Spectroradiometer (MODIS) based water color extraction and classification approach is developed and applied to Lake Taihu. By using MODIS data together with field data, we attempted to 1) retrieve the dominant wavelength of water color and classify water color into FU-classes; 2) analyze the relationship between water color dominant wavelength and the abundance of optically active component (OACs) in water; and 3) discover seasonal variations of water color based on Lake Taihu. Our results show that the dominant wavelength exhibits some relationship with the three types of OAC concentrations under certain conditions, particularly TSM and Chl-a; inorganic suspended matter (ISM) can be retrieved by using MODIS derived dominant wavelength in appropriate water body. Moreover, differences in water quality for different seasons can be detected by dominant wavelength and FU-class with some prior knowledge of the studied water. Therefore, dominant wavelength may be used as a comprehensive and promising indicator of water quality situation even though much work has to be done in the future to optimize the analyses and verify it on diverse sites. [ABSTRACT FROM PUBLISHER]

Monitoring chlorophyll a (CHLA) by remote sensing is particularly challenging for turbid productive waters. Although several empirical and semianalytical algorithms have been developed for such waters, their accuracy varies significantly due to variability in optical properties. In this paper, we evaluated the performance of six CHLA concentration (${{\bm{{C}}}}_{{\bm{{chla}}}}$) estimation algorithms [e.g., two-band ratio algorithm (TBR), normalized difference chlorophyll index (NDCI), synthetic chlorophyll index (SCI), three-band algorithm (TBS), four-band algorithm (FBS), and improved four-band algorithm (IOC3 M)] for a highly turbid lake based on remote sensing reflectance classification. Remote sensing reflectance was classified using the iterative k-mean clustering method. We also developed four estimation schemes (S1–S4) for the six algorithms to assess the effect of the estimation scheme on the accuracy of the algorithms. The estimation schemes were developed based on classification methods (no, soft, or hard classification) and the optimization bands used. The six algorithms performed differently for different remote sensing reflectance classes and different estimation schemes. The optimal algorithms for Classes 1, 2, and 3 were TBS, NDCI, and TBR, respectively. For the four estimation schemes, TBS and NDCI outperformed the other four algorithms. The accuracy of TBS and NDCI was higher than FBS, IOC3 M, TBR, and SCI. The accuracy of all six algorithms was improved by remote sensing reflectance classification, particularly for Classes 2 and 3. Soft classification with recalibration of the bands for each class outperformed hard classification for all the three classes. [ABSTRACT FROM PUBLISHER]

To investigate the mechanisms and potential for the remote sensing of phenanthrene-induced vegetation stress, we measured field canopy spectra, and associated plant and soil parameters in the field controlled experiment in the Yellow River Delta of China. Two widely distributed plant communities, separately dominated by reed ( Phragmites australis ) and glaucous seepweed ( Suaeda salsa ), were treated with different doses of phenanthrene. The canopy spectral changes of plant community resulted from the decreases of biomass and foliar projective coverage, while leaf photosynthetic pigment concentrations showed no significance difference among treatments. The spectral response to phenanthrene included a flattened red edge, with decreased first derivative of reflectance. The red edge slope and area consistently responded to phenanthrene, showing a strong relationship with aboveground biomass, coverage and canopy pigments density. These results suggest the potential of remote sensing and the importance of field validation to correctly interpret the causes of the spectral changes. [ABSTRACT FROM AUTHOR]

Among water quality parameters, total suspended matter is important for the evaluation of inland waters. A recently launched satellite sensor, HJ-CCD, by China possesses high temporal resolution, medium spatial resolution, and wide swath, so it is convenient for monitoring this parameter in large inland waters. However, no operational method currently exists for retrieving the total suspended matter concentration of turbid inland waters from HJ-CCD data. Using Lake Taihu in Eastern China as a study area, we obtained and analyzed optical properties of the lake during all seasons, and found that the absorption coefficient of suspended matter, chlorophyll, and colored dissolved organic matter of the near-infrared band may approximate zero. Based on this analysis, we found that a single band method of retrieving concentration using the near-infrared band was suitable using HJ-CCD data. We parameterized the single band method with specific inherent optical properties of Lake Taihu, and validated it using the results retrieved from a HJ-CCD image taken on 14 March 2009, as well as water-surface quasi-synchronous measured data. The concentration retrieved from the image is precise and stable; the single band method uses the established specific optical property database in the study area for input parameters, and does not need support from synchronous data. With this method, HJ-CCD may be applied to retrieve total suspended matter of other highly turbid inland waters. [ABSTRACT FROM AUTHOR]

The bidirectional reflectance properties of the anisotropic light field above the water surface are important for a range of applications. The bidirectional reflectance distribution function of oceanic waters has been well characterized but there is a lack of information for turbid inland waters. In addition, there is a lack of bidirectional reflectance data measured in turbid inland waters partially due to the difficulty in collectingin situwater-surface multi-angle remote-sensing reflectance data. To facilitate bidirectional reflectance studies of turbid inland waters usingin situmulti-angular reflectance data, we have designed and developed a simple hand-held 3D positioning pole to position the spectrometer optical fibre probe and a specific method to collect the multi-angular reflectance data above the water surface with this pole. Using this device, we collected multi-angular reflectance data in Meiliang Bay, Taihu Lake, China, and analysed the uncertainties in this method. We analysed the bidirectional distribution characteristics of the data, and compared the findings to those in the literature. Both uncertainty analysis and bidirectional distribution characteristics analysis showed that our method is effective in collecting multi-angular reflectance above the water surface and can be applied to validate bidirectional correction models in the future. [ABSTRACT FROM PUBLISHER]

Beer's attenuation law is the basis for the retrieval of aerosol optical depth (AOD) from sunphotometer data. However, the filter band function causes uncertainty during the retrieval of AOD from sunphotometer data, particularly for channels covering spectral regions of strong gas absorption. In this work, the uncertainty in AOD retrieval due to the filter band function is systematically analyzed by employing fine spectral absorption cross sections obtained from the Molecular Spectroscopy and Chemical Kinetics Group and the line-by-line radiative transfer model (LBLRTM). The uncertainty in AOD retrieval includes the uncertainty due to the wings of the filter band function in the ultraviolet (UV) region and errors in the optical depth calculation for Rayleigh scattering and absorption of O3, NO2, H2O, CH4, and CO2. The results showed that 1) the uncertainty of AOD retrieval by this method, which is called the approximate AOD retrieval method, might become large when the filter band function is not well designed, particularly in the UV region; 2) in the case of a large zenith observation condition, the errors will be nonnegligible if the Rayleigh scattering optical depth is calculated at a central wavelength without including filter band function; 3) the band-weighted absorption coefficients of O3 and NO2 remain nearly constant when the gas amounts change, except in the case of questionably designed band filters; and 4) these weak-absorption optical depths for H2O, CH4, and CO2 cannot be ignored in the 1020- or 1640-nm channels, where an optical depth error of 0.01−0.02 may be introduced. [ABSTRACT FROM AUTHOR]

Abstract: In order to monitor red tide in Case 2 waters, this paper proposes a method of multi normalized difference indices combination to extractred tide information from multi-spectral remote sensing image, and uses HJ-CCD and MODIS data to extract red tide information in the Shenzhen near-shore waters and the Pearl River Estuary to verify its feasibility. Because the spatial resolution of HJ-CCD is better than MODIS, the extract result is better than MODIS. [Copyright &y& Elsevier]

Total suspended matter concentration (CTSM) is an important parameter in aquatic ecosystem studies. Compared with multispectral satellite images, the Advanced Hyperspectral Imager (AHSI) carried by the ZY1-02D satellite can capture finer spectral features, and the potential for CTSM retrieval is enormous. In this study, we selected seven typical Chinese inland water bodies as the study areas, and recalibrated and validated 11 empirical models and two semi-analytical models for CTSM retrieval using the AHSI data. The results showed that the semi-analytical algorithm based on the 697 nm AHSI-band achieved the highest retrieval accuracy (R2 = 0.88, average unbiased relative error = 34.43%). This is because the remote sensing reflectance at 697 nm was strongly influenced by CTSM, and the AHSI image spectra were in good agreement with the in-situ spectra. Although further validation is still needed in highly turbid waters, this study shows that AHSI images from the ZY1-02D satellite are well suited for CTSM retrieval in inland waters. [ABSTRACT FROM AUTHOR]

Chlorophyll-a concentrations in water bodies are one of the most important environmental evaluation indicators in monitoring the water environment. Small water bodies include headwater streams, springs, ditches, flushes, small lakes, and ponds, which represent important freshwater resources. However, the relatively narrow and fragmented nature of small water bodies makes it difficult to monitor chlorophyll-a via medium-resolution remote sensing. In the present study, we first fused Gaofen-6 (a new Chinese satellite) images to obtain 2 m resolution images with 8 bands, which was approved as a good data source for Chlorophyll-a monitoring in small water bodies as Sentinel-2. Further, we compared five semi-empirical and four machine learning models to estimate chlorophyll-a concentrations via simulated reflectance using fused Gaofen-6 and Sentinel-2 spectral response function. The results showed that the extreme gradient boosting tree model (one of the machine learning models) is the most accurate. The mean relative error (MRE) was 9.03%, and the root-mean-square error (RMSE) was 4.5 mg/m3 for the Sentinel-2 sensor, while for the fused Gaofen-6 image, MRE was 6.73%, and RMSE was 3.26 mg/m3. Thus, both fused Gaofen-6 and Sentinel-2 could estimate the chlorophyll-a concentrations in small water bodies. Since the fused Gaofen-6 exhibited a higher spatial resolution and Sentinel-2 exhibited a higher temporal resolution. [ABSTRACT FROM AUTHOR]

Some lakes in China have undergone serious eutrophication, with cyanobacterial blooms occurring frequently. Dynamic monitoring of cyanobacterial blooms is important. At present, the traditional lake-survey-based cyanobacterial bloom monitoring is spatiotemporally limited and requires considerable human and material resources. Although satellite remote sensing can rapidly monitor large-scale cyanobacterial blooms, clouds and other factors often mean that effective images cannot be obtained. It is also difficult to use this method to dynamically monitor and manage aquatic environments and provide early warnings of cyanobacterial blooms in lakes and reservoirs. In contrast, ground-based remote sensing can operate under cloud cover and thus act as a new technical method to dynamically monitor cyanobacterial blooms. In this study, ground-based remote-sensing technology was applied to multitemporal, multidirectional, and multiscene monitoring of cyanobacterial blooms in Dianchi Lake via an area array multispectral camera mounted on a rotatable cloud platform at a fixed station. Results indicate that ground-based imaging remote sensing can accurately reflect the spatiotemporal distribution characteristics of cyanobacterial blooms and provide timely and accurate data for salvage treatment and early warnings. Thus, ground-based multispectral remote-sensing data can operationalize the dynamic monitoring of cyanobacterial blooms. The methods and results from this study can provide references for monitoring such blooms in other lakes. [ABSTRACT FROM AUTHOR]

The Yangtze River is the third longest river in the world. Monitoring and protecting its water quality are important for economic and social development. Water clarity (Secchi disk depth, SDD) is an important reference index for evaluating water quality. In this study, Sentinel-2 multispectral instrument (MSI) remote sensing images were utilized together with the Forel-Ule index (FUI) and hue angle α to construct an SDD retrieval model, which was applied to the Yangtze River from 2017 to 2020, which was used to describe color in the International Commission on Illumination (CIE) color space to construct an SDD retrieval model that was applied to the Yangtze River for the period 2017–2020. Further, the spatial distribution, seasonal variation, inter-annual variation, and driving factors of the observed SDD variations were analyzed. The spatial distribution pattern of the Yangtze River was high in the west and low in the east. The main driving factors affecting the Yangtze River SDD was sediment runoff, water level, and precipitation. The upstream and downstream Yangtze River SDD were negatively correlated with the change in water level and sediment runoff, whereas the midstream Yangtze River SDD was positively correlated with the change in water level and sediment runoff. The upper and lower reaches of the Yangtze River and overall SDD showed a weak downward trend, and the middle reaches of the Yangtze River remained almost unchanged. [ABSTRACT FROM AUTHOR]

Water clarity, commonly measured as the Secchi disk depth ( Z s d ), is an important parameter that depicts water quality in aquatic ecosystems. China's new generation Advanced HyperSpectral Imager (AHSI) on board the GF-5 satellite has significant potential for applications of more accurate water clarity estimation compared with existing multispectral satellite imagery, considering its high spectral resolution with a 30-m spatial resolution. In this study, we validate the semi-analytical model with various Quasi-Analytical Algorithms (QAA), including Q A A V 5 , Q A A V 6 , Q A A L 09 and Q A A M 14 , for the AHSI images with concurrent in situ measurements in four inland water bodies with a Z s d range of 0.3–4.5 m. The semi-analytical method with Q A A V 5 can yield the most accurate Z s d predictions with approximated atmospheric-corrected remote sensing reflectance. For 84 concurrent sampling sites, the estimated Z s d had a mean absolute error (MAE) of 0.35 m, while the mean relative error (MRE) was 25.3%. Specifically, the MAEs of estimated Z s d were 0.22, 0.46, and 0.24 m for Z s d of 0.3–1, 1–3, and 3–4.5 m, respectively. The corresponding MREs were 33.1%, 29.1% and 6.3%, respectively. Although further validation is still required, especially in terms of highly turbid waters, this study indicates that AHSI is effective for water clarity monitoring. [ABSTRACT FROM AUTHOR]

• Multiple ocean color satellite missions to study the water productivity in Qinghai Lake. • A substantial increase in phytoplankton growth was found in recent years. • The increase is likely due to the rapid decrease in the duration of the freeze-up period. • Provides the first comprehensive analysis of the biogeochemical properties in Qinghai Lake. Although previous research has focused on the inundation changes in Qinghai Lake, the largest lake in China, few studies have investigated the variations in primary production and correlated these changes with environmental transitions. In this study, this knowledge gap was filled using multiple ocean color satellite missions between 2003 and 2017. The results indicated a substantial increase in phytoplankton growth over recent years, during which the normalized fluorescence line height (nFLH) and algal bloom index (ABI) increased by approximately 45% and 61%, respectively, from the first (2003–2012) to the second period (2013–2017). Such a remarkable increase is likely associated with a rapid decrease in the duration of the freeze-up period, for which the 2014–2017 mean was >2 standard deviations below that of the previous years. High temperatures and a large number of sunshine hours could possibly explain the elevated nFLH and ABI in 2013. A multiple general linear model revealed that the freeze-up period, number of sunshine hours, and temperature explained 76.1%, 5.6%, and 10.2%, respectively, of the long-term changes in primary production in Qinghai Lake during the observed period. This study not only provides the first comprehensive analysis of the biogeochemical properties of Qinghai Lake but also demonstrates the capability of multiple remote sensing products in addressing environmental problems. Further, the method here is easily extendable to similar water bodies worldwide to study their potential responses to climate variability. [ABSTRACT FROM AUTHOR]

Water color remote sensing requires accurate atmospheric correction but this remains a significant challenge in highly turbid waters. In this respect, the shortwave infrared (SWIR) band-based atmospheric correction approach has proven advantageous when applied to the moderate resolution imaging spectroradiometer (MODIS) onboard the Aqua satellite. However, even so, uncertainties affect its accuracy. We performed a regional vicarious calibration of the MODIS-Aqua SWIR (1240, 2130)-based atmospheric correction using in situ water surface reflectance data measured during different seasons in Lake Taihu, a highly turbid lake. We then verified the accuracy of the (1240, 2130)-based atmospheric correction approach using these results; good results were obtained for the remote sensing reflectance retrievals at the 555, 645, and 859 nm, with average relative errors of 15%, 14%, and 22%, respectively, and no significant bias. Comparisons with the (1240, 2130)-based iterative approach and (1640, 2130)-based approach showed that the vicarious calibrated (1240, 2130)-based approach has the best accuracy and robustness. Thus, it is applicable to the highly turbid Lake Taihu. It may also be applicable to other highly turbid inland waters with similar optical and aerosol optical properties above water, but such applications will require further validation. [ABSTRACT FROM AUTHOR]

In the Tibetan Plateau (TP), the changes of lake ice phenology not only reflect regional climate change, but also impose substantial ecohydrological impacts on the local environment. Due to the limitation of ground observation, remote sensing has been used as an alternative tool to investigate recent changes of lake ice phenology. However, uncertainties exist in the remotely sensed lake ice phenology owing to both the data and methods used. In this paper, three different remotely sensed datasets are used to investigate the lake ice phenology variation in the past decade across the Tibetan Plateau, with the consideration of the underlying uncertainties. The remotely sensed data used include reflectance data, snow product, and land surface temperature (LST) data of moderate resolution imaging spectroradiometer (MODIS). The uncertainties of the three methods based on the corresponding data are assessed using the triple collocation approach. Comparatively, it is found that the method based on reflectance data outperforms the other two methods. The three methods are more consistent in determining the thawing dates rather than the freezing dates of lake ice. It is consistently shown by the three methods that the ice-covering duration in the northern part of the TP lasts longer than that in the south. Though there is no general trend of lake ice phenology across the TP for the period of 2000–2015, the warmer climate and stronger wind have led to the earlier break-up of lake ice. [ABSTRACT FROM AUTHOR]

Water clarity is a comprehensive indicator of the water environment status. Studies have shown that in recent years the clarity of inland and ocean water has decreased in many parts of the world owing to the influence of climate change and human activities. However, changes in inland water clarity exclusively resulting from climate change are largely ignored because most inland water is being utilized by human beings for applications such as waterworks or aquaculture. Therefore, understanding the trends and reasons of inland water clarity under natural conditions is important to recognize the inland water environment shifts under the global changes. Because of the harsh environment, the lakes on the Tibetan Plateau are less disturbed by human activities and are ideal objects for studying water clarity variations attributed to climatic changes. Here, we describe water clarity changes as measured using the Secchi Depth (SD) in 152 big lakes with an area greater than 50 km2 based on in-situ investigations and the retrieval results from a high accuracy retrieval model (r = 0.94, P < 0.01). Most of the lakes had an SD of 3–10 m, although there was marked spatial variation and a significant positive correlation with lake area (P < 0.01). The mean SD of all 152 lakes significantly increased at a rate of 0.033 m/year (95% CI, 0.021–0.045; P < 0.01) between 2000 and 2019. Variations in lake SD were linked with temporal changes in precipitation, and related to spatial changes of water optical components including suspended matter (0.13 < R2 < 0.40, P < 0.01 or P < 0.05), fDOM (R2 = 0.24, P < 0.01) and chlorophyll-a concentration (R2 = 0.12, P < 0.01).The reconstructed lake water clarity in our study will improve the understanding of inland water clarity changes and provide basic data to study the heat exchanges between lake water and atmosphere. • Evaluation of MODIS ocean reflectance product and Lee 2015 water clarity inversion model. • Spatial-temporal characters of lake clarity over the Tibetan Plateau. • The Secchi depth of the Tibet lakes increased between 2000 and 2019. • The Secchi depth in different years were correlated with precipitation. • The Secchi depth correlated differently to suspended matter, fluorescent dissolved organic matter, and chlorophyll-a. [ABSTRACT FROM AUTHOR]

Summary: Nam Co Lake is the highest lake in the central Tibetan Plateau, and existing research on water storage and water level variations are lacking. This paper provides a method for estimating the lake water storage based on historical meteorological records from 1976 to 2009, remote sensing images scattered in this period, in situ bathymetric survey, and GIS techniques, and presents a comprehensive 34-year analysis of intra-annual and inter-annual variations of Nam Co Lake water storage. The multi-year mean water storage of Nam Co Lake is 842.36×108 m3, with the maximum water depth of about 98m. During 1976–2009, the lake water storage increased from 786.06×108 m3 to 870.30×108 m3, with a tendency value of 2.67×108 m3/a; the lake area enlarged from 1927.48km2 to 2015.12km2, with a tendency value of 2.71km2/a. The lake area fluctuations annually, increasing from April of each year until late September and early October, then decreasing until March of the next year. Climate change has a significant impact on the water storage variation of the lake. A general pattern of warming temperature is evident with the regional annual mean air temperature increasing significantly by 0.404°C/10a. Preliminary analysis indicates that the enlarging status of Nam Co Lake water storage is closely related to increasing of precipitation and stream runoff especially coming from the input of glacial meltwater. By combining this data with other research, it can be presented that under the trend of global warming, on Tibetan Plateau, the inland lakes which depend on the rainfall and river supply in the basin are shrinking, while the lakes which depend on glacial meltwater supply are enlarging. Climate change is an important factor promoting the lake variation. [ABSTRACT FROM AUTHOR]