Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region.

Chlorophyll a (Chl-a) in lakes serves as an effective marker for assessing algal biomass and the nutritional level of lakes, and its observation is feasible through remote sensing methods. HJ-1 (Huanjing-1) satellite, deployed in 2008, incorporates a CCD capable of a 30 m resolution and has a revisit interval of 2 days, rendering it a superb choice or supplemental sensor for monitoring trophic state of lakes. For effective long-term and regional-scale mapping, both the imagery and the evaluation of machine learning algorithms are essential. The several typical machine learning algorithms, i.e., Support Vector Regression (SVR), Gradient Boosting Decision Trees (GBDT), XGBoost (XGB), Random Forest (RF), K-Nearest Neighbor (KNN), Kernel Ridge Regression (KRR), and Multi-Layer Perception Network (MLP), were developed using our in-situ measured Chl-a. A cross-validation grid to identify the most effective hyperparameter combinations for each algorithm was used, as well as the selected optimal superparameter combinations. In Chl-a mapping of three typical lakes, the R2 of GBDT, XGB, RF, and KRR all reached 0.90, while XGB algorithm also exhibited stable performance with the smallest error (RMSE = 3.11 µg/L). Adjustments were made to align the Chl-a spatial-temporal patterns with past data, utilizing HJ1-A/B CCD images mapping through XGB algorithm, which demonstrates its stability. Our results highlight the considerable effectiveness and utility of HJ-1 A/B CCD imagery for evaluation and monitoring trophic state of lakes in a cold arid region, providing the application cases contribute to the ongoing efforts to monitor water qualities.

Analysis of taiga and tundra lake browning trends from 2002 to 2021 using MODIS data.

Lakes in taiga and tundra regions may be silently undergoing changes due to global warming. One of those changes is browning in lake color. The browning interacts with the carbon cycle, ecosystem dynamics, and water quality in freshwater systems. However, spatiotemporal variabilities of browning in these regions have not been well documented. Using MODIS remote sensing reflectance at near ultraviolet wavelengths from 2002 to 2021 on the Google Earth Engine platform, we quantified long-term browning trends across 7616 lakes (larger than 10 km2) in taiga and tundra biomes. These lakes showed an overall decreased trend in browning (Theil-Sen Slope = 0.00015), with ∼36% of these lakes showing browning trends, and ∼1% of these lakes showing statistically significant (p-value <0.05) browning trends. The browning trends more likely occurred in small lakes in high latitude, low ground ice content regions, where air temperature increased and precipitation decreased. While temperature is projected to increase in response to climate change, our results provide one means to understand how biogeochemical cycles and ecological dynamics respond to climate change.

Effects of cropland reclamation on soil organic carbon in China's black soil region over the past 35 years.

The long-term use of cropland and cropland reclamation from natural ecosystems led to soil degradation. This study investigated the effect of the long-term use of cropland and cropland reclamation from natural ecosystems on soil organic carbon (SOC) content and density over the past 35 years. Altogether, 2140 topsoil samples (0-20 cm) were collected across Northeast China. Landsat images were acquired from 1985 to 2020 through Google Earth Engine, and the reflectance of each soil sample was extracted from the Landsat image that its time was consistent with sampling. The hybrid model that included two individual SOC prediction models for two clustering regions was built for accurate estimation after k-means clustering. The probability hybrid model, a combination between the hybrid model and classification probabilities of pixels, was introduced to enhance the accuracy of SOC mapping. Cropland reclamation results were extracted from the land cover time-series dataset at a 5-year interval. Our study indicated that: (1) Long-term use of cropland led to a 3.07 g kg-1 and 6.71 Mg C ha-1 decrease in SOC content and density, respectively, and the decrease of SOC stock was 0.32 Pg over the past 35 years; (2) nearly 64% of cropland had a negative change in terms of SOC content from 1985 to 2020; (3) cropland reclamation track changed from high to low SOC content, and almost no cropland was reclaimed on the "Black soils" after 2005; (4) cropland reclamation from wetlands resulted in the highest decrease, and reclamation period of years 31-35 decreased when SOC density and SOC stock were 16.05 Mg C ha-1 and 0.005 Pg, respectively, while reclamation period of years 26-30 from forest witnessed SOC density and stock decreases of 8.33 Mg C ha-1 and 0.01 Pg, respectively. Our research results provide a reference for SOC change in the black soil region of Northeast China and can attract more attention to the area of the protection of "Black soils" and natural ecosystems.

China's wetland soil organic carbon pool: New estimation on pool size, change, and trajectory.

Robust estimates of wetland soil organic carbon (SOC) pools are critical to understanding wetland carbon dynamics in the global carbon cycle. However, previous estimates were highly variable and uncertain, due likely to the data sources and method used. Here we used machine learning method to estimate SOC storage and their changes over time in China's wetlands based on wetland SOC density database, associated geospatial environmental data, and recently published wetland maps. We built a database of wetland SOC density in China that contains 809 samples from 181 published studies collected over the last 20 years as presented in the published literature. All samples were extended and standardized to a 1-m depth, on the basis of the relationship between SOC density data from soil profiles of different depths. We used three different machine learning methods to evaluate their robustness in estimating wetland SOC storage and changes in China. The results indicated that random forest model achieved accurate wetland SOC estimation with R2 being .65. The results showed that average SOC density of top 1 m in China's wetlands was 25.03 ± 3.11 kg C m-2 in 2000 and 26.57 ± 3.73 kg C m-2 in 2020, an increase of 6.15%. SOC storage change from 4.73 ± 0.58 Pg in 2000 to 4.35 ± 0.61 Pg in 2020, a decrease of 8.03%, due to 13.6% decreased in wetland area from 189.12 × 103 to 162.8 × 103 km2 in 2020, despite the increase in SOC density during the same time period. The carbon accumulation rate was 107.5 ± 12.4 g C m-2 year-1 since 2000 in wetlands with no area changes. Climate change caused variations in wetland SOC density, and a future warming and drying climate would lead to decreases in wetland SOC storage. Estimates under Shared Socioeconomic Pathway 1-2.6 (low-carbon emissions) suggested that wetland SOC storage in China would not change significantly by 2100, but under Shared Socioeconomic Pathway 5-8.5 (high-carbon emissions), it would decrease significantly by approximately 5.77%. In this study, estimates of wetland SOC storage were optimized from three aspects, including sample database, wetland extent, and estimation method. Our study indicates the importance of using consistent SOC density and extent data in estimating and projecting wetland SOC storage.

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.

Estimation of the lake trophic state index (TSI) using hyperspectral remote sensing in Northeast China.

The Trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Estimating TSI by remote sensing is still a challenge due to the multivariate complexity of the eutrophication process. A comprehensive in situ spectral-biogeochemical dataset for 7 lakes in Northeast China was collected in October 2020. The dataset covers trophic states from oligotrophic to eutrophic, with a wide range of total phosphorus (TP, 0.07-0.2 mg L-1), Secchi disk depth (SDD, 0.1-0.78 m), and chlorophyll a (Chla, 0.11-20.41 µg L-1). Here, we propose an empirical method to estimate TSI from remote sensing data. First, TP, SDD, and Chla were estimated by band ratio/band combination models. Then TSI was estimated using the Carlson model with a high R2 (0.88), a low RMSE (3.87), and a low MRE (6.83%). Synergistic effects between TP, SDD, and Chla dominated the trophic state, changed the distribution of light in the water column, affected the spectral characteristics. Furthermore, the contribution of each parameter for eutrophication were different among the studied lakes from ternary plot. High Chla concentration was the main reason for eutrophication in HMT Lake with 45.4% of contribution more than the other two parameters, However, in XXK Lake, high TP concentrations were the main reason for eutrophication with 66.8% of contribution rather than Chla and SDD. Overall, the trophic state was dominated by TP, and SDD accounted for 85.6% of contribution in all sampled lakes. Additionally, we found using one-parameter index to evaluate the lake trophic state will lead to a great deviation, even with two levels of difference. Therefore, multi-parameter TSI is strongly recommended for the lake trophic state assessment. Summarily, our findings provide a theoretical and methodological basis for future large-scale estimations of lake TSI using satellite image data, help with water quality monitoring and management.

Global divergent trends of algal blooms detected by satellite during 1982-2018.

Algal blooms (ABs) in inland lakes have caused adverse ecological effects, and health impairment of animals and humans. We used archived Landsat images to examine ABs in lakes (>1 km2 ) around the globe over a 37-year time span (1982-2018). Out of the 176032 lakes with area >1 km2 detected globally, 863 were impacted by ABs, 708 had sufficiently long records to define a trend, and 66% exhibited increasing trends in frequency ratio (FRQR, ratio of the number of ABs events observed in a year in a given lake to the number of available Landsat images for that lake) or area ratio (AR, ratio of annual maximum area covered by ABs observed in a lake to the surface area of that lake), while 34% showed a decreasing trend. Across North America, an intensification of ABs severity was observed for FRQR (p < .01) and AR (p < .01) before 1999, followed by a decrease in ABs FRQR (p < .01) and AR (p < .05) after the 2000s. The strongest intensification of ABs was observed in Asia, followed by South America, Africa, and Europe. No clear trend was detected for the Oceania. Across climatic zones, the contributions of anthropogenic factors to ABs intensification (16.5% for fertilizer, 19.4% for gross domestic product, and 18.7% for population) were slightly stronger than climatic drivers (10.1% for temperature, 11.7% for wind speed, 16.8% for pressure, and for 11.6% for rainfall). Collectively, these divergent trends indicate that consideration of anthropogenic factors as well as climate change should be at the forefront of management policies aimed at reducing the severity and frequency of ABs in inland waters.

Development of total suspended matter prediction in waters using fractional-order derivative spectra.

More and more hyper-spectral satellites will be used to estimate total suspended matter (TSM) in waters instead of multi-spectral satellites, such as China's Gaofen-5 and Zhuhai-1. Although they have not been widely used because of the consistency of sampling and image time. Hence, the study based on measured hyper-spectroscopy is important for applying to hyper-spectral satellites. Fractional-order derivatives (FODs) considers more detailed spectral information, and it is a better spectral preprocessing method than conventional integer-order derivatives. The application and analysis of FODs for spectra in waters is rare. If FOD is successfully applied to estimate TSM, the TSM mapping with FOD using hyper-spectral satellites will be meaningful. Based on these points, this study aimed to apply FOD to predict TSM and to prove the prediction feasibility of FOD in waters. Different prediction models and eight FOD transformation processes with increment of 0.25 per step for 392 spectral reflectance data from China were used and compared. The prediction models include the optimum models of the single wavelength, ratio index, difference index and TSM index at each FOD order, and the random forest (RF) model with all wavelengths was also used. Discrete wavelet transform (DWT) was used to reduce noise and improve the model accuracy after using FOD. Our results achieved the followings First, FOD enhanced spectral characteristics at 500-600 nm and 800 nm that were affected by TSM. Second, the correlation between TSM and FOD spectra was enhanced (e.g., the correlation coefficients of 19 wavelengths (789-807 nm) of 0.75-order were higher than 0.8 but the original spectra were not). Third, FOD improved the performance of different prediction models, and the RF model from 0.5-order to 1.25-order derivative spectra all led good results (). Fourth, DWT can reduce the noise and improve the performance, and FOD-DWT model of 1.25-order led the R2 of 0.84, RMSE of 16.30 and MAPE of 78.62 in validation. Overall, our results suggest that FOD can improve the prediction performance for most models, and the optimum order of some models is not integer. Our results also provide a reference for predicting other water quality parameters and mapping these parameters using hyper-spectral satellites. The accurate estimation of TSM is helpful for protecting ecological and social environments.

Climatic versus Anthropogenic Controls of Decadal Trends (1983-2017) in Algal Blooms in Lakes and Reservoirs across China.

The proliferation of algal blooms (ABs) in lakes and reservoirs (L&Rs) poses a threat to water quality and the ecological health of aquatic communities. With global climate change, there is a concern that the frequency and geographical expansion of ABs in L&Rs could increase. China has experienced rapid economic growth and major land-use changes over the last several decades and therefore provides an excellent context for such an analysis. About 289,600 Landsat images were used to examine the spatiotemporal distribution of ABs in L&Rs (>1 km2) across China (1983-2017). Results showed significant changes in the temporal slope of the sum of normalized area (0.26), frequency (2.28), duration (6.14), and early outbreak (-3.48) of AB events in L&Rs across China. Specifically, AB-impacted water bodies expanded longitudinally, and the time range of AB observation has expanded starting in the 2000s. Spearman correlation and random forest regression analyses further indicated that, among climatic factors, wind speed and temperature contributed the most to AB expansion. Overall, anthropogenic forces have overridden the imprints of climatic factors on the temporal evolution of ABs in China's L&Rs and therefore could inform policy decisions for the management of these resources.

Songhua River basin's improving water quality since 2005 based on Landsat observation of water clarity.

Water clarity, denoted by the Secchi disk depth (SDD), is one of the most important indicators for monitoring water quality. In the Songhua River basin (SHRB), few studies have used Landsat to monitor long-term (3-4 decades) changes in lake SDD and explore the impact of natural and human factors on SDD interannual variation at the watershed scale. Lakes in the SHRB are of great significance to local populations. Understanding the spatiotemporal dynamics of SDD could help policymakers manage, protect, and predict lake water quality. We utilized the Landsat red/blue band ratio in the Google Earth Engine to estimate the SDD of 77 lakes and generated annual mean SDD maps from 1990 to 2018. The results of the SDD interannual changes showed that the water quality in the SHRB has improved since 2005. Specifically, the SDD in the SHRB displayed a significant increasing trend (p < 0.05) from 0.29 m in 2005 to 0.37 m in 2018. Moreover, the number of lakes displaying a significant increasing trend for SDD increased from 18 between 1990 and 2005 to 31 between 2005 and 2018. We also found that use of chemical fertilizer significantly impacted lakes, followed by wastewater discharge and normalized difference vegetation index. Improvements in the quantity and ability of wastewater discharge treatment and increased vegetation cover have alleviated water pollution; however, the non-point pollution of agriculture still poses a threat to some lakes in the SHRB. Therefore, more efforts should be made to further improve the aquatic ecological environment of SHRBs.

Variations in the light absorption coefficients of phytoplankton, non-algal particles and dissolved organic matter in reservoirs across China.

Reservoirs were critical sources of drinking water for many large cities around the world, but progress in the development of large-scale monitoring protocols to obtain timely information about water quality had been hampered by the complex nature of inland waters and the various optical conditions exhibited by these aquatic ecosystems. In this study, we systematically investigated the absorption coefficient of different optically-active constituents (OACs) in 120 reservoirs of different trophic states across five eco-regions in China. The relationships were found between phytoplankton absorption coefficient at 675 nm (aph (675)) and Chlorophyll a (Chla) concentration in different regions (R2:0.60-0.82). The non-algal particle (NAP) absorption coefficient (aNAP) showed an increasing trend for reservoirs with trophic states. Significant correlation (p < 0.05) was observed between chromophoric dissolved organic matter (CDOM) absorption and water chemical parameters. The influencing factors for contributing the relative proportion of OACs absorption including the hydrological factors and water quality factors were analyzed. The non-water absorption budget from our data showed the variations of the dominant absorption types which underscored the need to develop and parameterize region-specific bio-optical models for large-scale assessment in water reservoirs.

Remote estimates of CDOM using Sentinel-2 remote sensing data in reservoirs with different trophic states across China.

Chromophoric dissolved organic matter (DOM) is called as CDOM which could affect the optical properties of surface waters, and is a useful parameter for monitoring complex inland aquatic systems. Large-scale monitoring of CDOM using remote-sensing has been a challenge due to the poor transferability of CDOM retrieval models across regions. To overcome these difficulties, a study is conducted using Sentinel-2 images, in situ reflectance spectral data, and water chemical parameters at 93 water reservoirs across China classified by trophic state. Empirical algorithms are established between CDOM absorption coefficient aCDOM(355) and reflectance band ratio (B5/B2,vegetation Red Edge/Blue) acquired in situ and via Sentinel-2 MSI sensors. Relationships are stronger (r2 > 0.7, p < 0.05) when analysis is conducted separately by trophic states. Validation models show that, by accounting for trophic state of reservoirs and using B5/B2 band ratios, it is possible to expand the geographical range of remote sensing-based models to determine CDOM. However, the accuracy of model validation decreased from oligotrophic (r2: 0.86) to eutrophic reservoirs (r2: 0.82), likely due to increased complexity of CDOM sources in nutrient-rich systems. This study provides a strategy for using local and remote-sensing data to monitor the spatial variations of CDOM in reservoirs based on different trophic states, and will contribute to water resources management.

Remote sensing of CDOM and DOC in alpine lakes across the Qinghai-Tibet Plateau using Sentinel-2A imagery data.

As important components of dissolved organic matter (DOM) in an aquatic environment, colored DOM (CDOM) and dissolved organic carbon (DOC) play an essential role in the carbon cycle of an inland aquatic system. Traditionally, CDOM and DOC in inland waters have been primarily determined using in situ observations and laboratory measurements. Most of past lake investigations on CDOM and DOC focused on easily accessible regions and covered a small fraction of lakes worldwide. To our knowledge, little is known about lakes in less accessible areas like the Qinghai-Tibet Plateau (QTP). To address this challenge, optical satellite remote sensing might be useful for capturing a synoptic view of CDOM and DOC with high frequency at large scales, complementing in situ sampling methods for inland waters. In this study, 216 samples collected from 36 lakes across the QTP (2014-2017) were examined to determine the relationships between CDOM absorption coefficient at 350 nm (a350) and Sentinel-2A Multi Spectral Instrument (MSI) imagery reflectance data. A strong positive linear correlation with a350 was observed with B4/B2 (R2 = 0.78, p < 0.01) and with B4/B3 (R2 = 0.62). A multi-step regression model was established for estimating a350 with B4/B2 and B4/B3 as input variables (R2 = 0.81, p < 0.01). A scattered CDOM-DOC relationship was revealed (R2 = 0.34, p < 0.05) using a pooled dataset. By dividing the inland waters into four separate groups in accordance with their salinity gradients, we were able to develop much stronger relationships (R2 > 0.8, p < 0.01) for CDOM-DOC. Significant differences between fresh and saline waters were demonstrated using satellite-derived CDOM and DOC, where high CDOM (0.86 ± 0.67 m-1) and low DOC (3.76 ± 4.92 mg L-1) concentrations were observed for freshwaters, while inverse trends of CDOM (0.53 ± 0.72 m-1) and DOC (15.76 ± 17.07 mg L-1) were demonstrated for saline lakes in the Tibetan Plateau. This study confirmed that satellite optical imagery can be used for the monitoring of CDOM and DOC of the lakes of the Tibetan Plateau, which are sensitive to a changing climate and are infrequently investigated due to the harsh environment and poor accessibility. Moreover, it highlighted the importance of combining salinity and remote sensing data in the process of estimating lake DOC.

Absorption characteristics of CDOM in treated and non-treated urban lakes in Changchun, China.

In urban settings, one may find (i) lakes that are non-treated (NT) and impacted by recurrent discharges of pollutants and nutrients, and (ii) lakes that, through restoration measures and active management, are treated (T) from external inputs. The optical properties of chromophoric dissolved organic matter (CDOM) have been used to assess the anthropogenic impact on lakes ecology, but their application in comparative assessments of urban lakes has not been attempted. For 2 years, we measured nutrients and CDOM properties in water samples collected from NT and T lakes in the city of Changchun, China. Significant differences in CDOM properties were found between the two types of lakes, and these results were supported by redundancy analysis. The NT lakes were eutrophic while the T lakes were mesotrophic, with mean trophic status index (TSI) of 74.2 and 50.3, respectively. The CDOM absorption coefficient at 350 nm, a(350), was 2-fold higher in NT than in T lakes (6.59 vs 3.21 m-1). In the NT lakes, CDOM components predominantly comprised large molecular weight (MW > 1000-Da) humus-like substances of allochthonous origin, whereas in the T lakes CDOM was dominated by low MW (<1000-Da) substances from autochthonous production. Seasonal fluctuation has a great influence on the CDOM concentration, but a little influence on its molecular composition. The CDOM concentration were higher in summer than in other seasons. Weather conditions (rainfall, temperature) and biophysical processes (biodegradation, photo-bleaching) likely contributed to these variations. We found the water quality of the treated lakes was getting better from 2016 to 2018. In summary, the study results, not only revealed seasonal effects, but most importantly documented the impact of human activities on the characteristics of CDOM in urban lakes. Most specifically, the sharp difference between the lakes in regard to a(350) (2-fold lower in T than in NT lakes) demonstrated the suitability CDOM absorption coefficient as an early indicator of the impact of treatment measures on the hydrochemistry of DOM in urban lakes.

Wetland Fire Scar Monitoring and Its Response to Changes of the Pantanal Wetland.

Fire is an important disturbance factor which results in the irreversible change of land surface ecosystems and leads to a new ecological status after the fire is extinguished. Spanning the period from August to September 2019, the Amazon Forest fires were an unprecedented event in terms of the scale and duration of burning, with a duration of 42 days in the Pantanal wetland. Based on the observation data of wildfire and two Sentinel-2A images separated by a 35-day interval, the objectives of this study are to use the Normalized Burn Ratio (NBR) to map the spatiotemporal change features of fire and then quantitatively measure the fire severity and the impact of fire on the Pantanal wetland. The overall accuracy and Kappa coefficient of the extracted results of wetland types reached 80.6% and 0.767, respectively, and the statistically analyzed results showed that wildfires did not radically change the wetland types of the Pantanal wetland, because the hydrological variation of the burned area was still the main change factor, with a dynamic ratio of ≤50%. Furthermore, the savanna wetland in the burned area was the wetland type which was most affected by the fire. Meanwhile, fire scars belonged to the moderate and low-severity burned areas, with a maximum burn area of 599 km2. The case enriches the research into the impact of wildfire as the main disturbance factor on the change of wetland types and provides a scientific reference for the restoration and sustainable development of global wetland ecosystems.

Quantifying total suspended matter (TSM) in waters using Landsat images during 1984-2018 across the Songnen Plain, Northeast China.

Understanding the spatiotemporal dynamics of total suspended matter (TSM) in waters is necessary to promote efficient water resource management. In our study, we have estimated the spatiotemporal pattern of TSM with the combination of time-series Landsat images and field survey. Among various remote sensing-derived parameters, the red/blue band turns to be robust and the most sensitive to the TSM from field measurements. In Songnen Plain, the mean annual TSM in 60.5% of the water bodies decreased from 1984 to 2018. The decreasing of TSM is likely due to the increasing of vegetation in the area. The TSM concentration in waters declined from April to July, and then increased from September onwards. We also found the TSM in water bodies in Songnen Plain has very high spatial variation. Our results indicated that the meteorological factors such as wind and precipitation may affect the variation of TSM. Our results demonstrate that long-term Landsat data are useful to examine TSM in inland waters. Our findings can support for water resource management under human activities and climate change.

Variability of chlorophyll and the influence factors during winter in seasonally ice-covered lakes.

Lake ice is an essential and integral part of the cryosphere and freshwater systems. The formation of lake ice affects the physical, hydrological, and biological conditions of ecological systems. Global warming may contribute to even shorter periods of ice cover in the lakes of the Frigid Zone, which adversely affects the growth of phytoplankton and primary productivity. This study was conducted for the purpose of evaluating the growth of phytoplankton and factors involved, in 28 ice-covered lakes across the Songnen Plain, in the Northeast of China, to understand how they take part in the whole-ecosystem functioning. A total of 1026 water samples were collected in April, September, and January during the period 2014-2018. In the frozen period, the concentration levels of dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP) were all comparable with the spring and autumn. Despite the limited light availability and low temperature, the phytoplankton survived in sub-ice waters during winter with a low concentration of chlorophyll a (Chla). Its average concentration was positively correlated with the concentration observed in the previous autumn (rp = 0.563, p < 0.01). According to the regression tree analysis, during the winter period, Chla was mainly related to the concentration of TN in sub-ice water (TNwater) and with the difference of concentration of TP between water and ice (TPcd). Furthermore, either in ice or in sub-ice water, the concentration of Chla was also significantly affected by total suspended matter (TSM) (p < 0.05). The levels of TNwater, TPcd, and TSM could explain the 77.8% of the variance in the concentration of Chla during winter with contributions in the ranges of 25.5%-35.0%, 9.2%-11.3%, and 21.5%-34.0%, respectively (p < 0.05). This research substantially contributes to comprehending how the existing conditions under-ice affect the whole ecosystem when the ice cover is reduced lakes or rivers.

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

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

Terrestrial humic-like fluorescence peak of chromophoric dissolved organic matter as a new potential indicator tracing the antibiotics in typical polluted watershed.

Natural surface waters are threatened globally by antibiotics pollution. In this study, we analyzed antibiotics and CDOM (Chromophoric dissolved organic matter) fluorescence in different water bodies using HPLC method and Excitation Emission Matrix- Parallel factor analysis, respectively. A combination of field studies in the Yinma River Watershed were conducted in rivers, reservoirs and urban rivers, and 65 CDOM and antibiotic samples were taken in April, May, July, and August 2016. EEM-PARAFAC analysis identified two components; a humic-like (C1) component and a tryptophan-like (C2) component. The redundancy analysis (RDA) demonstrated that CDOM could explain 38.2% (two axes) of the five antibiotics in reservoirs (N = 31), and 26.0% (two axes) of those in rivers and urban water (N = 30). Furthermore, the Pearson correlation coefficient between Sulfamethoxazole and C1 in reservoir water was 0.91 (t-test, 2-tailed, p < 0.01), and that between Sulfamethoxazole and C2 was 0.68 (t-test, 2-tailed, p < 0.01). This indicated that the humic-like component of CDOM PARAFAC fluorescence could detect Sulfamethoxazole contamination levels in the homogenized reservoir waters. Our results identified Sulfamethoxazole and Quinolones (Norfloxacin, 16.5 ng L-1; Enrofloxacin, 0.3 ng L-1; Ciprofloxacin, 30.9 ng L-1) at mean concentrations of 369.5 ng L-1 and 15.9 ng L-1, respectively, which were the higher levels in natural surface waters. The FTIR spectroscopy of the mixture of humic acid and sulfamethoxazole showed that the absorbance at 3415 cm-1 linked to OH stretching of OH groups and at 1386 cm-1 because of OH bending and vibration of COOH groups became weaker, indicating that COOH groups of humic acid can adsorb and react with -NH2 of sulfamethoxazole. The CDOM PARAFAC components can be adapted for online or in situ fluorescence measurements as an early warning of Sulfamethoxazole distribution and contamination in similar aquatic environments.