Concentration Retrieval Research Articles

Annual dynamic remote sensing monitoring of phycocyanin concentration in Lake Chaohu based on Sentinel-3 OLCI images

藻蓝素（PC）是水体蓝藻的指示性色素，其浓度反映了蓝藻生物量，利用卫星遥感监测藻蓝素浓度年内动态对蓝藻水华的有效防控有着重要意义.根据不同季节的巢湖藻蓝素浓度实测数据与同期Sentinel-3 OLCI影像，构建机器学习回归反演模型，应用于巢湖2019年OLCI影像集上，对巢湖藻蓝素浓度的空间分布、年内变化进行遥感监测.研究表明：在MUMM和C2RCC水体大气校正方法中，C2RCC的大气校正结果更接近实测光谱反射率；在机器学习回归算法中，基于梯度提升回归的藻蓝素浓度反演模型精度最高，其R²、RMSE和rRMSE分别达到0.84、49.76 μg/L和34.1%.水体藻蓝素浓度在1—4月及12月较低，在5—11月浓度较高且波动频繁，日均气温是水体藻蓝素浓度年内变化的主要原因，而藻蓝素短期剧烈波动主要是受到日降水量和日照时数的影响；在夏、秋季节，西湖区藻蓝素浓度明显高于中、东湖区，主要与入湖河流中氮磷等物质的高输入有关.Sentinel-3 OLCI影像为湖库水体藻蓝素浓度动态监测提供了重要数据源，梯度提升回归算法在富营养化水体藻蓝素浓度反演中具有较大的应用潜力.;Phycocyanin (PC) is an indicator pigment of cyanobacteria in water, and its concentration reflects the biomass of cyanobacteria. Monitoring the annual dynamics of PC concentration by satellite remote sensing is of great significance for the effective prevention and control of cyanobacteria bloom. Based on the measured data of phycocyanin concentration in Lake Chaohu in different seasons and the Sentinel-3 OLCI images of the same period, a machine learning regression retrieval model was constructed and applied to the 2019 OLCI image set of Lake Chaohu to monitor the spatial distribution and annual variation of PC concentration. The results show that among MUMM and C2RCC atmospheric correction methods, the atmospheric correction result of C2RCC is closer to the measured spectral reflectance. Among the machine learning regression algorithms, the retrieval model of PC concentration based on gradient boosting regression has the highest accuracy, its R², RMSE and rRMSE reach 0.84, 49.76 μg/L and 34.1%, respectively. The concentration of PC was low from January to April and December, but high from May to November and fluctuated frequently. The average daily temperature was the main reason for the annual variation of PC concentration, and the short-term fluctuation of PC was mainly affected by the daily precipitation and sunshine duration. In summer and autumn, the PC concentration in the West Lake area was significantly higher than that in the middle and east lake area, which was mainly related to the high input of nitrogen and phosphorus into the lake. Sentinel-3 OLCI images provide important data sources for the dynamic monitoring of PC concentration in lake and reservoir waters, and the gradient boosting regression algorithm has great application potential in the retrieval of PC concentration in eutrophication waters.

Near-infrared methane sensor with neural network filtering

A novel methane sensor based on neural network filter (NNF) assisted direct absorption spectroscopy (DAS) technique was proposed and experimentally demonstrated. The developed detection device adds the benefits of a digital filter based on the neural network, thereby compensating the shortcomings of traditional DAS. We overcame the scarce data problem by using the simulated absorption spectra that are highly consistent with practical experimental conditions to construct and train the NNF. The proposed NNF showed the best performance compared with several widely used filtering algorithms. We performed a detailed assessment of the NNF-improved detection system. The proposed sensor shows more accurate concentration retrieval and better stability in a real-time measurement. The minimum detection limit of 2.93 ppm∙m (1σ) was obtained, which is a significant improvement compared to previous reports of near-infrared methane detection with the DAS technique. Finally, we systematically discuss the frequency principle underlying the NNF to explicitly interpret the mechanism of the generalized filtering. The improved methane sensor proves the feasibility of enhancing the performance of DAS technique with the neural network algorithm and broad applicability of this approach to the high-sensitivity measurements of methane and other trace gases.

Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms

Some essential water conservation areas in China have continuously suffered from various serious problems such as water pollution and water quality deterioration in recent decades and thus called for real-time water pollution monitoring system underwater resources management. On the basis of the remote sensing data and ground monitoring data, this study firstly constructed a more accurate retrieval model for total phosphorus (TP) concentration by comparing 12 machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), Bayesian ridge regression (BRR), lasso regression (Lasso), elastic net (EN), linear regression (LR), decision tree regressor (DTR), K neighbor regressor (KNR), random forest regressor (RFR), extra trees regressor (ETR), AdaBoost regressor (ABR) and gradient boosting regressor (GBR). Then, this study applied the constructed retrieval model to explore the spatial-temporal evolution of the Miyun Reservoir and finally assessed the water quality. The results showed that the model of TP concentration built by the ETR algorithm had the best accuracy, with the coefficient R2 reaching over 85% and the mean absolute error lower than 0.000433. The TP concentration in Miyun Reservoir was between 0.0380 and 0.1298 mg/L, and there was relatively significant spatial and temporal heterogeneity. It changed remarkably during the periods of the flood season, winter tillage, planting, and regreening, and it was lower in summer than in other seasons. Moreover, the TP in the southwest part of the reservoir was generally lower than in the northeast, as there was less human activities interference. According to the Environmental Quality Standard for the surface water environment, the water quality of Miyun Reservoir was overall safe, except only for an over-standard case occurrence in the spring and September. These conclusions can provide a significant scientific reference for water quality monitoring and management in Miyun Reservoir.

Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY-3B/MWRI data

Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY-3B/MWRI data

Retrieval of Chlorophyll-a concentration and associated product uncertainty in optically diverse lakes and reservoirs

Satellite product uncertainty estimates are critical for the further development and evaluation of remote sensing algorithms, as well as for the user community (e.g., modelers, climate scientists, and decision-makers). Optical remote sensing of water quality is affected by significant uncertainties stemming from correction for atmospheric effects as well as a lack of algorithms that can be universally applied to waterbodies spanning several orders of magnitude in non-covarying substance concentrations. We developed a method to produce estimates of Chlorophyll-a (Chla) satellite product uncertainty on a pixel-by-pixel basis within an Optical Water Type (OWT) classification scheme. This scheme helps to dynamically select the most appropriate algorithms for each satellite pixel, whereas the associated uncertainty informs downstream use of the data (e.g., for trend detection or modeling) as well as the future direction of algorithm research. Observations of Chla were related to 13 previously established OWT classes based on their corresponding water-leaving reflectance (Rw), each class corresponding to specific bio-optical characteristics. Uncertainty models corresponding to specific algorithm - OWT combinations for Chla were then expressed as a function of OWT class membership score. Embedding these uncertainty models into a fuzzy OWT classification approach for satellite imagery allows Chla and associated product uncertainty to be estimated without a priori knowledge of the biogeochemical characteristics of a water body. Following blending of Chla algorithm results according to per-pixel fuzzy OWT membership, Chla retrieval shows a generally robust response over a wide range of class memberships, indicating a wide application range (ranging from 0.01 to 362.5 mg/m3). Low OWT membership scores and high product uncertainty identify conditions where optical water types need further exploration, and where biogeochemical satellite retrieval algorithms require further improvement. The procedure is demonstrated here for the Medium Resolution Imaging Spectrometer (MERIS) but could be repeated for other sensors, atmospheric correction methods and optical water quality variables.

A Fiber-Integrated CRDS Sensor for In-Situ Measurement of Dissolved Carbon Dioxide in Seawater.

Research on carbon dioxide (CO2) geological and biogeochemical cycles in the ocean is important to support the geoscience study. Continuous in-situ measurement of dissolved CO2 is critically needed. However, the time and spatial resolution are being restricted due to the challenges of very high submarine pressure and quite low efficiency in water-gas separation, which, therefore, are emerging the main barriers to deep sea investigation. We develop a fiber-integrated sensor based on cavity ring-down spectroscopy for in-situ CO2 measurement. Furthermore, a fast concentration retrieval model using exponential fit is proposed at non-equilibrium condition. The in-situ dissolved CO2 measurement achieves 10 times faster than conventional methods, where an equilibrium condition is needed. As a proof of principle, near-coast in-situ CO2 measurement was implemented in Sanya City, Haina, China, obtaining an effective dissolved CO2 concentration of ~950 ppm. The experimental results prove the feasibly for fast dissolved gas measurement, which would benefit the ocean investigation with more detailed scientific data.

Deep Learning for Chlorophyll-a Concentration Retrieval: A Case Study for the Pearl River Estuary

The abundance of phytoplankton is generally estimated by measuring the chlorophyll-a concentration (Cchla), which is an important factor in photosynthesis and can be used to analyze the density and biomass of phytoplankton in the ecosystem. The band-ratio-based empirical or semi-analytical algorithms are operationally applied to retrieve Cchla in global oceans, which generally experience difficulties from the diversity of optical properties and the complexity of the radiative transfer equations in analytical analyses, respectively. With an attempt to develop an accurate Cchla retrieval model for the optically complex coastal and estuarine waters, this study aimed to explore the deep learning (DL) methods in satellite retrieval of Cchla. A two-stage convolutional neural network (CNN), named Cchla-Net, was proposed, which utilized the spectral information of remote sensing reflectances at MODIS/Aqua’s visible bands. In the first-stage phase, the Cchla-Net was pretrained by a set of remote sensing patches, in which the Cchla was generated from an existing model (OC3M). The pretrained results were than used as the initial values to refine the network with the synthetic oversampled in-situ dataset in the second-stage training phase. Using in-situ samples for training with the new initial values has a higher probability to reach the global optimum. The quantitative analyses showed that the two-stage training was more likely to achieve a global optimum in the optimization than the one-stage training. Matchups of the in-situ Cchla measurements were used to evaluate the retrieval models. Results showed that the proposed Cchla-Net produced obvious better performance than the empirical and semi-analytical algorithms, implying the DL method was more effective for optically complex waters with extremely high Cchla. This study provided an applicable method for remote sensing retrieval of Cchla, which should be helpful for studying the spatial distribution and temporal variability in the productive Pearl River estuary (PRE) waters.

Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery

Retrieval of the phycocyanin concentration (PC), a characteristic pigment of, and proxy for, cyanobacteria biomass, from hyperspectral satellite remote sensing measurements is challenging due to uncertainties in the remote sensing reflectance (∆Rrs) resulting from atmospheric correction and instrument radiometric noise. Although several individual algorithms have been proven to capture local variations in cyanobacteria biomass in specific regions, their performance has not been assessed on hyperspectral images from satellite sensors. Our work leverages a machine-learning model, Mixture Density Networks (MDNs), trained on a large (N = 939) dataset of collocated in situ chlorophyll-a concentrations (Chla), PCs, and remote sensing reflectance (Rrs) measurements to estimate PC from all relevant spectral bands. The performance of the developed model is demonstrated via PC maps produced from select images of the Hyperspectral Imager for the Coastal Ocean (HICO) and Italian Space Agency's PRecursore IperSpettrale della Missione Applicativa (PRISMA) using a matchup dataset. As input to the MDN, we incorporate a combination of widely used band ratios (BRs) and line heights (LHs) taken from existing multispectral algorithms, that have been proven for both Chla and PC estimation, as well as novel BRs and LHs to increase the overall cyanobacteria biomass estimation accuracy and reduce the sensitivity to ∆Rrs. When trained on a random half of the dataset, the MDN achieves uncertainties of 44.3%, which is less than half of the uncertainties of all viable optimized multispectral PC algorithms. The MDN is notably better than multispectral algorithms at preventing overestimation on low (<10 mg m−3) PC. Visibly, HICO and PRISMA PC maps show the wider dynamic range that can be represented by the MDN. The available in situ and satellite-derived Rrs matchups and measured in situ PC demonstrate the robustness of the MDN for estimating low (<10 mg m−3) PC and the reduced impact of ∆Rrs on medium-to-high in situ PC (>10 mg m−3). According to our extensive assessments, the developed model is anticipated to enable practical PC products from PRISMA and HICO, therefore the model is promising for planned hyperspectral missions, such as the Plankton Aerosol and Cloud Ecosystem (PACE). This advancement will enhance the complementary roles of hyperspectral radiometry from satellite and low-altitude platforms for quantifying and monitoring cyanobacteria harmful algal blooms at both large and local spatial scales.

Retrieval of water quality parameters from hyperspectral images using a hybrid feedback deep factorization machine model

Environmental protection of water resources is of critical importance to daily life of human beings. In recent years, monitoring the variation of water quality using remote sensing techniques has become prevalent. Unmanned aerial vehicle (UAV) based remote sensing techniques have been applied to quantitative retrieval of concentrations of water quality parameters including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a (Chl-a), successfully and efficiently. In this study, a novel method with deep factorization machine, spatial distribution pattern analysis, and probabilistic analysis engaged, named hybrid feedback deep factorization machine (HF-DFM), has been developed to quantitatively estimate concentrations of water quality parameters based on hyperspectral reflectance data on large scale effectively. Our proposed method is a unified model for quantifying concentrations of water quality parameters with an end to end structure, which integrates UAV based optical remote sensing techniques and deep learning to estimate concentrations of water quality parameters. Furthermore, our proposed model was applied to real-time quantitative monitoring the variation of water quality of Mazhou River, Shenzhen, Guangdong, China. Finally, we evaluate the performance of proposed model on a real-world dataset in terms of root of mean squared error (RMSE), mean absolute percent error (MAPE), and coefficient of determination (R2). The experimental results show that our proposed model outperforms other state-of-the-art models with respect to RMSE, MAPE, and R2, where resulting MAPEs for quantifying all water quality parameters range from 8.78% to 12.36%, and resulting R2s range from 0.81 to 0.93. It can serve as a useful tool for decision makers in effectively monitoring water quality of urban rivers.

Label‐Free Quantitative Analysis of Coacervates via 3D Phase Imaging

AbstractCoacervation is considered as a ubiquitous mechanism for assembling biomolecular materials outside cells and organizing membraneless organelles inside cells. Despite the importance of mapping binodals to understand the driving forces and thermodynamics of coacervate, quantifying protein concentration within a droplet is significantly challenging owing to its dynamic and viscous nature. A direct imaging‐based method is presented to quantify coacervate using real‐time 3D quantitative phase imaging. The proposed method utilizes the measurements of the refractive index tomograms of individual coacervates and retrieves the protein concentration and volume of individual protein droplets exploiting light‐scattering analysis. The retrievals of accurate protein concentrations are demonstrated in droplets, whereas conventional fluorescence‐based techniques present underestimations. With its simple, direct, real‐time, and quantitative analysis capability, the present method can be utilized in various protein analyses and quantifications for the study of coacervation both in vitro and in vivo.

Uncertainty of atmospheric correction algorithms for chlorophyll α concentration retrieval in lakes from Sentinel-2 data

One of the largest uncertainties in remote sensing data comes from atmospheric influence. This research aims to explain the uncertainties emanating from atmospheric correction (AC) product selection and how they influence chlorophyll α concentration retrieval in lakes in eastern Lithuania. We tested seven products from six AC processors (Acolite, Acolite Rayleigh, iCOR, Sen2Cor, C2RCC, C2X, and POLYMER) and 10 chlorophyll α retrieval algorithms with different architectures. The uncertainty of AC products transferred to chlorophyll α concentrations, and large differences in the chlorophyll α concentrations retrieved using different AC products were observed. The match-up analysis showed that chlorophyll α algorithms based on band difference performed best in terms of a high coefficient of determination and the lowest median bias when used with image-based, Sen2Cor, and TOA data. The results of this study highlight the uncertainties of AC products as well as how the selection of the chlorophyll α retrieval algorithm can mitigate the influence of AC selection.

Development and Validation of an End-to-End Simulator and Gas Concentration Retrieval Processor Applied to the MERLIN Lidar Mission

In the context of MERLIN (MEthane Remote LIdar missioN), a French–German spatial lidar mission dedicated to monitoring the atmospheric methane content, two software programs have been developed: LIDSIM (LIDar SIMulator) and PROLID (PROcessor LIDar). The objectives are to assess whether the instrument design meets the performance requirements and to study the sensitivity of this performance to geophysical parameters. LIDSIM is an end-to-end mission simulator and PROLID is a retrieval processor that provides mole fractions of methane in dry air, averaged over an atmospheric column. These two tools are described in this paper. Results of the validation tests and the first full orbit simulations are reported. Merlin target performance does not seem to be reachable but breakthrough performance is reached.

Open‐Path Dual‐Comb Spectroscopy for Multispecies Trace Gas Detection in the 4.5–5 µm Spectral Region

AbstractOpen‐path dual‐comb spectroscopy provides multispecies atmospheric gas concentration measurements with high precision. Here, open‐path dual‐comb spectroscopy is extended to the mid‐infrared 5 µm atmospheric window, enabling atmospheric concentration retrievals of the primary greenhouse gases, N2O, CO2, and H2O, as well as the criteria air pollutants O3 and CO across 600 m and 2 km round‐trip paths. Measurements are demonstrated over a five‐day period at 2 min temporal resolution with 80% uptime. The achieved precision is sufficient to resolve the atmospheric concentration variations of the multiple gas species; retrieved dry mixing ratios of CO and N2O are in good agreement with a colocated point sensor. In addition, the retrieved ratio of excess CO versus CO2 agrees with similar urban studies but disagrees with the US National Emission Inventory by a factor of 3. The retrieved ratio of excess N2O versus CO2 exhibits a plume‐dependent value, indicating the variability of sources of the greenhouse gas N2O.

Estimation of Ultrahigh Resolution PM2.5 Mass Concentrations Based on Mie Scattering Theory by Using Landsat8 OLI Images over Pearl River Delta

The aerosol optical depth (AOD), retrieved by satellites, has been widely used to estimate ground-level PM2.5 mass concentrations, due to its advantage of large-scale spatial continuity. However, it is difficult to obtain urban-scale pollution patterns from the coarse resolution retrieval results (e.g., 1 km, 3 km, or 10 km) at present, and little research has been conducted on PM2.5 mass concentration retrieval from high resolution remote sensing data. In this study, a physical model is proposed based on Mie scattering theory to evaluate the PM2.5 mass concentrations by using Landsat8 Operational Land Imager (OLI) images. First, the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model (which can simulate the transmission process of solar radiation in the Earth-atmosphere system and calculate the radiance at the top of the atmosphere) is used to build a lookup table to retrieve the AOD of the coast and blue bands based on the improved deep blue (DB) method. Then, the Angstrom formula is used to obtain the AOD of the green and red bands. Second, the dry near-surface AOD of four bands (coast, blue, green, red) is obtained through vertical correction and humidity correction. Third, aerosol particles are divided into four types based on the standard radiation atmosphere (SRA) model, and the optical properties of different aerosol types are analyzed to derive the volume distribution of aerosol particles. Finally, the relationship between the dry near-surface AOD of each band and the volume distribution of four aerosol particles is correlated, based on Mie scattering theory, and a physical model is established between the AOD and PM2.5 mass concentrations. Then, the distribution of PM2.5 mass concentrations is obtained. The retrieval results show that the distribution of AOD and PM2.5 at the urban scale in detail. The AOD results show that a reasonable relationship with a correlation coefficient (R2) of 0.66 and root mean square error (RMSE) of 0.1037 between Landsat8 OLI AOD and MODO4 DB AOD at 550 nm. The PM2.5 retrieval results are compared with the PM2.5 values measured by ground monitoring stations. The RMSEs for a certain day in different years, including 2017, 2018, 2019, and 2020, are 11.9470 μg/m³, 11.9787 μg/m³, 7.4217 μg/m³, and 5.4723 μg/m³, respectively. The total RMSE is 10.0224 μg/m³. The ultrahigh resolution PM2.5 results can provide pollution details at the urban scale and support better decisions on urban atmospheric environmental governance.

Retrieval of gas concentrations in optical spectroscopy with deep learning

A novel direct absorption spectroscopy gas sensing system based on end-to-end deep neural networks was proposed for measurements of gas concentration. One-dimensional convolutional neural network and deep multi-layer perceptron were explored to measure the concentrations of methane and acetylene. The accurate measurement results for both gases demonstrated that deep neural networks based direct absorption spectroscopy technique can be reliably applied to different gas molecules. The developed gas sensing system achieved more precise concentration retrieval compared with that of wavelength modulation spectroscopy, and fast computation speed as well as robustness to noisy conditions, laser aging and circuit parameter variation simultaneously. The combination of deep neural networks and direct absorption spectroscopy provides new ideas for the further research of gas absorption spectroscopy.

Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

Retrieval and Evaluation of Chlorophyll-A Spatiotemporal Variability Using GF-1 Imagery: Case Study of Qinzhou Bay, China

Chlorophyll-a (Chl-a) concentration is a measure of phytoplankton biomass, and has been used to identify ‘red tide’ events. However, nearshore waters are optically complex, making the accurate determination of the chlorophyll-a concentration challenging. Therefore, in this study, a typical area affected by the Phaeocystis ‘red tide’ bloom, Qinzhou Bay, was selected as the study area. Based on the Gaofen-1 remote sensing satellite image and water quality monitoring data, the sensitive bands and band combinations of the nearshore Chl-a concentration of Qinzhou Bay were screened, and a Qinzhou Bay Chl-a retrieval model was constructed through stepwise regression analysis. The main conclusions of this work are as follows: (1) The Chl-a concentration retrieval regression model based on 1/B4 (near-infrared band (NIR)) has the best accuracy (R2 = 0.67, root-mean-square-error = 0.70 μg/L, and mean absolute percentage error = 0.23) for the remote sensing of Chl-a concentration in Qinzhou Bay. (2) The spatiotemporal distribution of Chl-a in Qinzhou Bay is varied, with lower concentrations (0.50 μg/L) observed near the shore and higher concentrations (6.70 μg/L) observed offshore, with a gradual decreasing trend over time (−0.8).

Retrieval and Evaluation of Chlorophyll-a Concentration in Reservoirs with Main Water Supply Function in Beijing, China, Based on Landsat Satellite Images.

Remote sensing retrieval is an important technology for studying water eutrophication. In this study, Guanting Reservoir with the main water supply function of Beijing was selected as the research object. Based on the measured data in 2016, 2017, and 2019, and Landsat-8 remote sensing images, the concentration and distribution of chlorophyll-a in the Guanting Reservoir were inversed. We analyzed the changes in chlorophyll-a concentration of the reservoir in Beijing and the reasons and effects. Although the concentration of chlorophyll-a in the Guanting Reservoir decreased gradually, it may still increase. The amount and stability of water storage, chlorophyll-a concentration of the supply water, and nitrogen and phosphorus concentration change are important factors affecting the chlorophyll-a concentration of the reservoir. We also found a strong correlation between the pixel values of adjacent reservoirs in the same image, so the chlorophyll-a estimation model can be applied to each other.

Particulate matter variability in Kathmandu based on in-situ measurements, remote sensing, and reanalysis data

Kathmandu has one of the highest particulate matter air pollution levels in the world. However, few direct measurement data are available for long-term analyses, limiting policy interventions and public health advisories. Remote sensing-based data sets provide an alternative approach to address this issue. In this paper, we present an approach to analyze and understand the diurnal, seasonal, annual, and multi-annual variability of pollution levels based on in situ measurements of particulate matter (PM2.5), remote sensing data based on Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra Aerosol Optical Depth (AOD), and aerosol mass concentration retrievals, as well as AOD and PM2.5 data from Copernicus Atmosphere Monitoring Service (CAMS) and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data sets. The analysis of the direct measurements revealed distinct annual patterns, characterized by winter maxima and summer minima. With the exception of the summer monsoon season, public health guidelines are frequently exceeded significantly throughout the year, particularly in winter. The analysis furthermore pointed toward distinct daily patterns with primary maxima in the mornings, secondary maxima in the late evenings, and minima in the afternoons. The annual pattern of AOD derived from the MODIS data is markedly different from that. Due to the coarse spatial resolution and the fact that MODIS AOD is a column integrated property, it does not reflect the small scale phenomenon of the Kathmandu urban pollution pattern but instead shows a maximum in the spring. The same pattern was observed with the CAMS and MERRA-2 reanalysis AOD data, even though MERRA-2 captures pollution levels during the summer monsoon season very well. The CAMS reanalysis PM2.5 data are generally well-aligned with the near-surface measurement data, even though they overestimate the daily and monthly maxima and do not capture the morning maxima in the diurnal course. Nevertheless, CAMS PM2.5 data can be adjusted via linear regression to reasonably mirror the measurements. It shows that PM2.5 concentrations in Kathmandu have increased significantly in the past decades (almost 2 μg/m3 annually), mainly after the monsoon season from September to February. Our results indicate that around 85% of all winter days in Kathmandu are categorized as “unhealthy” according to the Air Quality Index (AQI).

Atmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters

The performance of different atmospheric correction algorithms for the Ocean and Land Colour Instrument (OLCI) on board of Sentinel-3 (S3) is evaluated for retrieval of water-leaving radiance reflectance, and derived parameters chlorophyll-a concentration and turbidity in turbid coastal waters in the Belgian Coastal Zone (BCZ). This is performed using in situ measurements from an autonomous pan-and-tilt hyperspectral radiometer system (PANTHYR). The PANTHYR provides validation data for any satellite band between 400 and 900 nm, with the deployment in the BCZ of particular interest due to the wide range of observed Near-InfraRed (NIR) reflectance. The Dark Spectrum Fitting (DSF) atmospheric correction algorithm is adapted for S3/OLCI processing in ACOLITE, and its performance and that of 5 other processing algorithms (L2-WFR, POLYMER, C2RCC, SeaDAS, and SeaDAS-ALT) is compared to the in situ measured reflectances. Water turbidities across the matchups in the Belgian Coastal Zone are about 20–100 FNU, and the overall performance is best for ACOLITE and L2-WFR, with the former providing lowest relative (Mean Absolute Relative Difference, MARD 7–27%) and absolute errors (Mean Average Difference, MAD -0.002, Root Mean Squared Difference, RMSD 0.01–0.016) in the bands between 442 and 681 nm. L2-WFR provides the lowest errors at longer NIR wavelengths (754–885 nm). The algorithms that assume a water reflectance model, i.e. POLYMER and C2RCC, are at present not very suitable for processing imagery over the turbid Belgian coastal waters, with especially the latter introducing problems in the 665 and 709 nm bands, and hence the chlorophyll-a and turbidity retrievals. This may be caused by their internal model and/or training dataset not being well adapted to the waters encountered in the BCZ. The 1020 nm band is used most frequently by ACOLITE/DSF for the estimation of the atmospheric path reflectance (67% of matchups), indicating its usefulness for turbid water atmospheric correction. Turbidity retrieval using a single band algorithm showed good performance for L2-WFR and ACOLITE compared to PANTHYR for e.g. the 709 nm band (MARD 15 and 17%), where their reflectances were also very close to the in situ observations (MARD 11%). For the retrieval of chlorophyll-a, all methods except C2RCC gave similar performance, due to the RedEdge band-ratio algorithm being robust to typical spectrally flat atmospheric correction errors. C2RCC does not retain the spectral relationship in the Red and RedEdge bands, and hence its chlorophyll-a concentration retrieval is not at all reliable in Belgian coastal waters. L2-WFR and ACOLITE show similar performance compared to in situ radiometry, but due to the assumption of spatially consistent aerosols, ACOLITE provides less noisy products. With the superior performance of ACOLITE in the 490–681 nm wavelength range, and smoother output products, it can be recommended for processing of S3/OLCI data in turbid waters similar to those encountered in the BCZ. The ACOLITE processor for OLCI and the in situ matchup dataset used here are made available under an open source license.