Concentration Retrieval Research Articles

Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations

Abstract. Understanding the evolution of the ice phase within mixed-phase clouds (MPCs) is necessary to reduce uncertainties related to the cloud radiative feedback in climate projections and precipitation initiation. Both primary ice formation via ice-nucleating particles (INPs) and secondary ice production (SIP) within MPCs are unconstrained, not least because of the lack of atmospheric observations. In the past decades, advanced remote sensing methods have emerged which provide high-resolution data of aerosol and cloud properties and could be key in understanding microphysical processes on a global scale. In this study, we retrieved INP concentrations and ice multiplication factors (IMFs) in wintertime orographic clouds using active remote sensing and in situ observations obtained during the RACLETS campaign in the Swiss Alps. INP concentrations in air masses dominated by Saharan dust and continental aerosol were retrieved from a polarization Raman lidar and validated with aerosol and INP in situ observations on a mountaintop. A calibration factor of 0.0204 for the global INP parameterization by DeMott et al. (2010) is derived by comparing in situ aerosol and INP measurements, improving the INP concentration retrieval for continental aerosols. Based on combined lidar and radar measurements, the ice crystal number concentration and ice water content were retrieved and validated with balloon-borne in situ observations, which agreed with the balloon-borne in situ observations within an order of magnitude. For seven cloud cases the ice multiplication factors (IMFs), defined as the quotient of the ice crystal number concentration to the INP concentration, were calculated. The median IMF was around 80, and SIP was active (defined as IMFs > 1) nearly 85 % of the time. SIP was found to be active at all observed temperatures (−30 to −5 ∘C), with the highest IMFs between −20 and −5 ∘C. The introduced methodology could be extended to larger datasets to better understand the impact of SIP not only over the Alps but also at other locations and for other cloud types.

Improvement and Assessment of Ocean Color Algorithms in the Northwest Pacific Fishing Ground Using Himawari-8, MODIS-Aqua, and VIIRS-SNPP

Chlorophyll-a (Chl-a) is an important marine indicator, and the improvement in Chl-a concentration retrieval for ocean color remote sensing is always a major challenge. This study focuses on the northwest Pacific fishing ground (NPFG) to evaluate and improve the Chl-a products of three mainstream remote sensing satellites, Himawari-8, MODIS-Aqua, and VIIRS-SNPP. We analyzed in situ data and found that an in situ Chl-a concentration of 0.3 mg m−3 could be used as a threshold to distinguish the systematic deviation of remote sensing Chl-a data in the NPFG. Based on this threshold, we optimized the Chl-a algorithms of the three satellites by data grouping, and integrated multisource satellite Chl-a data by weighted averaging to acquire high-coverage merged data. The merged data were thoroughly verified by Argo Chl-a data. The Chl-a front of merged Chl-a data could be represented accurately and completely and had a good correlation with the distribution of the NPFG. The most important marine factors for Chl-a are nutrients and temperature, which are affected by mesoscale eddies and variations in the Kuroshio extension. The variation trend of merged Chl-a data is consistent with mesoscale eddies and Kuroshio extension and has more sensitive responses to the marine climatic conditions of ENSO.

Comparison of phycocyanin concentrations in Chaohu Lake, China, retrieved using MODIS and OLCI images

Phycocyanin (PC) concentration is used as an indicator to characterize cyanobacteria biomass while monitoring eutrophication in inland water. Remote sensing provides useful methods for quantifying PC concentration; however, there is a shortage of datasets for the long-term monitoring of PC concentration when only a single remote sensing data is used. Therefore, PC concentrations obtained from multisource remote sensing images should be compared before integrating them for long-term monitoring. In this study, machine learning (ML) regression algorithms are used to develop PC concentration retrieval models suitable for Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-3 Ocean and Land Colour Instrument (OLCI) images, and their accuracies are compared. The two optimal retrieval models are applied to satellite images acquired on the same days to compare the spatial consistency of the two PC concentration retrieval results. The results show that the sensitive spectral range of PC concentration is 560–680 nm. Among the ML regression algorithms, gradient boosted tree (GBT) regression exhibits the highest PC retrieval accuracy for both the MODIS images (R2 = 0.82, RMSE = 61.9 μg/L) and OLCI images (R2 = 0.86, RMSE = 45.44 μg/L). The PC concentrations retrieved from the MODIS and OLCI images acquired in bloom and no-bloom periods have a high spatial consistency in most areas of Chaohu Lake. Their correlation coefficient also exceeds 0.7, and the average relative error reaches 0.293 μg/L. However, a large difference exists in areas with high PC concentrations, which may cause by the poor applicability of atmospheric correction algorithms and PC retrieval models in these areas. The proposed PC concentration retrieval models developed using GBT regression in this paper can expend the idea for the quantitative retrieval of other inland water quality parameters in inland water, and the conclusions should enable the effective integration of MODIS and OLCI images for the time series monitoring of PC concentrations in reservoirs and lakes.

Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China

The Chlorophyll-a (Chl-a) concentration is an important indicator of water environmental conditions; thus, the simultaneous monitoring of large-area water bodies can be realized through the remote sensing-based retrieval of the Chl-a concentrations. The back propagation (BP) neural network learning method has been widely used for the remote sensing retrieval of water quality in first and second-class water bodies. However, many Chl-a concentration measurements must be used as learning samples with this method, which is constrained by the number of samples, due to the limited time and resources available for simultaneous measurements. In this paper, we conduct correlation analysis between the Chl-a concentration data measured at Dianshan Lake in 2020 and 2021 and synchronized Landat-8 data. Through analysis and study of the radiative transfer model and the retrieval method, a BP neural network retrieval model based on multi-phase Chl-a concentration data is proposed, which allows for the realization of remote sensing-based Chl-a monitoring in third-class water bodies. An analysis of spatiotemporal distribution characteristics was performed, and the method was compared with other constructed models. The research results indicate that the retrieval performance of the proposed BP neural network model is better than that of models constructed using multiple regression analysis and curve estimation analysis approaches, with a coefficient of determination of 0.86 and an average relative error of 19.48%. The spatial and temporal Chl-a distribution over Dianshan Lake was uneven, with high concentrations close to human production and low concentrations in the open areas of the lake. During the period from 2020 to 2021, the Chl-a concentration showed a significant upward trend. These research findings provide reference for monitoring the water environment in Dianshan Lake.

Spatiotemporal Variations in Summertime Ground-Level Ozone around Gasoline Stations in Shenzhen between 2014 and 2020

Ground-level ozone has become the primary air pollutant in many urban areas of China. Oil vapor pollution from gasoline stations accelerates the generation of ground-level ozone, especially in densely populated urban areas with high demands for transportation. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) around gasoline stations is urgently needed. However, urban GOCs vary sharply over short distances, increasing the need for GOCs at a high-spatial resolution. Thus, a high-spatial resolution (i.e., 1 km) concentration retrieval model based on the GLM and BME method was developed to obtain the daily spatiotemporal characteristics of GOCs. The hourly ozone records provided by the national air quality monitoring stations and multiple geospatial datasets were used as input data. The model exhibited satisfactory performance (R2 = 0.75, RMSE = 10.86 µg/m3). The derived GOCs show that the ozone levels at gasoline stations and their adjacent areas (1~3 km away from the gasoline stations) were significantly higher than the citywide average level, and this phenomenon gradually eased with the increasing distance from the gasoline stations. The findings indicate that special attention should be given to the prevention and control of ground-level ozone exposure risks in human settlements and activity areas near gasoline stations.

Evaluation of aerosol number concentrations from CALIPSO with ATom airborne in situ measurements

Abstract. The present study aims to evaluate the available aerosol number concentration (ANC) retrieval algorithms for spaceborne lidar CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite with the airborne in situ measurements from the ATom (Atmospheric Tomography Mission) campaign. We used HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory model) to match both the measurements in space and identified 53 cases that were suitable for comparison. Since the ATom data include the dry aerosol extinction coefficient, we used kappa parameterization to adjust the ambient measurements from CALIOP to dry conditions. As both the datasets have a different vertical resolution, we regrid them to uniform height bins of 240 m from the surface to a height of 5 km. On comparing the dry extinction coefficients, we found a reasonable agreement between the CALIOP and ATom measurements with Spearman's correlation coefficient of 0.715. Disagreement was found mostly for retrievals above 3 km altitude. Thus, to compare the ANC, which may vary by orders of magnitude in space and time, we further limit the datasets and only select those height bins for which the CALIOP-derived dry extinction coefficient is within ±50 % of the ATom measurements. This additional filter further increases the probability of comparing the same air parcel. The altitude bins which qualify the extinction coefficient constraint are used to estimate ANC with a dry radius >50 nm (n50,dry) and >250 nm (n250,dry). The POLIPHON (Polarization Lidar Photometer Networking) and OMCAM (Optical Modelling of CALIPSO Aerosol Microphysics) algorithms were used to estimate the n50,dry and n250,dry. The POLIPHON estimates of n50,dry and n250,dry were found to be in good agreement with the in situ measurements, with a correlation coefficient of 0.829 and 0.47, root mean square error (RMSE) of 234 and 13 cm−3, and bias of −97 and 4 cm−3, respectively. The OMCAM estimates of n50,dry and n250,dry were also in reasonable agreement with the in situ measurements, with a correlation coefficient of 0.823 and 0.463, RMSE of 247 and 13 cm−3, and bias of 44 and 4 cm−3, respectively. However, we found that the OMCAM-estimated n50,dry were about an order of magnitude less than the in situ measurements for marine-dominated cases. We propose a modification to the OMCAM algorithm by using an AERONET-based marine model. With the updated OMCAM algorithm, the n50,dry agrees well with the ATom measurements. Such concurrence between the satellite-derived ANC and the independent ATom in situ measurements emboldens the use of CALIOP in studying the aerosol–cloud interactions.

Calibration and field test of mobile lidar for remote sensing of atmospheric methane

Subject of study. Methane concentration in near-the-surface tropospheric sensing paths was investigated under background conditions using a mobile differential absorption lidar. Aim of study. A mobile differential absorption lidar for remote sensing of atmospheric methane in the mid-infrared spectral range was designed, calibrated, and tested in natural field experiments. Method. The designed lidar enables investigation in the atmosphere using the differential absorption method. This method is based on the effect of resonant absorption of laser emission by gases. The emission source of the lidar has two operating wavelengths, one (on-line) in the center of the methane absorption line and the other (off-line) on the wing of the absorption line. The lidar signals obtained at on- and off-line wavelengths enable retrieval of the methane concentration under background conditions. Main results. The designed mobile IR differential absorption lidar for investigation of atmospheric methane is described. The mobile IR emission source of the differential absorption lidar was calibrated in the informative range of methane sensing near 3400 nm. The results of a natural field test of the mobile IR lidar for detection of atmospheric response at the calibrated sensing wavelengths and retrieval of methane background concentrations of approximately 2.0 ppm in horizontal surface atmospheric sensing paths are presented. Practical significance. The technical solutions for the design of the mobile lidar for remote methane sensing proposed in this paper enable formulation of the requirements on its further improvement aiming to increase the measurement range, design a vertical configuration for remote sensing from an aircraft, and use it at arctic latitudes.

Consecutive retrieval of aerosol optical depth and total suspended solid concentration in turbid coastal water of Eastern China

An algorithm is proposed for consecutive retrieval of aerosol optical depth (AOD) and total suspended solid (TSS) concentration in turbid coastal water using a shortwave infrared band and a visible band. MODIS band 7 (∼2.1 µm) was used to retrieve the AOD in turbid coastal water, as the top-of-atmosphere reflectance in this band is more sensitive than in other bands and water-leaving reflectance (ρw) can be ignored. The aerosol model is determined through iteration by predetermining the proportion range of four basic aerosol modes (dust, water-soluble, ocean and soot). The retrieved AOD is used for atmospheric correction. Based on the Pearson correlation coefficient between TSS and ρw for the different bands, MODIS band 4 was used to obtain ρw. A logarithmic model associated with TSS retrieval was established between ρw and TSS products of GOCI. For the MOD09GA product, the median, moving, adaptive, and Fourier-transform filters were employed to eliminate strip noise with respect to TSS retrieval. The retrieved AOD was strongly correlated with AOD observed at the Huaniao Island site (R = 0.94; RMSE = 0.0437; mean absolute percentage error (MAPE) = 10.9%). The new AOD retrievals are more accurate than MODIS ocean retrievals and can provide missing AOD products in turbid coastal areas. Fourier-filtered TSS has the best retrieval results (RMSE = 1.3538; MAPE = 32.03%) in comparisons between retrieved and in situ TSS.

PROSPECT-PMP+: Simultaneous Retrievals of Chlorophyll a and b, Carotenoids and Anthocyanins in the Leaf Optical Properties Model.

The PROSPECT leaf optical radiative transfer models, including PROSPECT-MP, have addressed the contributions of multiple photosynthetic pigments (chlorophyll a and b, and carotenoids) to leaf optical properties, but photo-protective pigment (anthocyanins), another important indicator of vegetation physiological and ecological functions, has not been simultaneously combined within a leaf optical model. Here, we present a new calibration and validation of PROSPECT-MP+ that separates the contributions of multiple photosynthetic and photo-protective pigments to leaf spectrum in the 400–800 nm range using a new empirical dataset that contains multiple photosynthetic and photo-protective pigments (LOPEX_ZJU dataset). We first provide multiple distinct in vivo individual photosynthetic and photo-protective pigment absorption coefficients and leaf average refractive index of the leaf interior using the LOPEX_ZJU dataset. Then, we evaluate the capabilities of PROSPECT-MP+ for forward modelling of leaf directional hemispherical reflectance and transmittance spectra and for retrieval of pigment concentrations by model inversion. The main result of this study is that the absorption coefficients of chlorophyll a and b, carotenoids, and anthocyanins display the physical principles of absorption spectra. Moreover, the validation result of this study demonstrates the potential of PROSPECT-MP+ for improving capabilities in remote sensing of leaf photosynthetic pigments (chlorophyll a and b, and carotenoids) and photo-protective pigment (anthocyanins).

Retrieval of Chlorophyll-a Concentrations of Class II Water Bodies of Inland Lakes and Reservoirs Based on ZY1-02D Satellite Hyperspectral Data

Chlorophyll-a is an important parameter that characterizes the eutrophication of water bodies. The advantage of ZY1-02D hyperspectral satellite subdivision in the visible light and near-infrared bands is that it highlights the unique characteristics of water bodies in the spectral dimension, and it helps to assess the Class II water bodies of inland lakes and reservoirs, making it an important tool for refined remote sensing detection of the environment. In this study, the Baiyangdian Nature Reserve in northern China, which contains a typical inland lake and wetland, was chosen as the study area. Using ZY1-02D hyperspectral synchronization transit images and in situ measured chlorophyll-a concentration as the data source, remote sensing of the chlorophyll-a concentration of inland lakes was conducted. By analyzing the correlation between the spectral reflectance of the ZY1-02D hyperspectral image and the chlorophyll-a concentration and using algorithms such as the single band, band ratio, and three bands to compare and filter characteristic wavelengths, a quantitative hyperspectral model of the chlorophyll-a concentration was established to determine the chlorophyll-a concentration of Baiyangdian Lake. The dynamic monitoring of the water body and the assessment of the nutritional status of the water body were determined. The results revealed that the estimation of the chlorophyll-a concentration of Baiyangdian Lake based on the hyperspectral Fluorescence Line Height (FLH) model was ideal, with an R2 value of 0.78. The FLH model not only comprehensively considers the effects of suspended solids, yellow substances, and backscattering of the water body on the estimation of the chlorophyll-a concentration, but also considers the influence of the elastic scattering efficiency of the chlorophyll. Based on the ZY1-02D hyperspectral data, a spatial distribution map of the chlorophyll-a concentration of Baiyangdian Lake was created to provide new ideas and technical support for monitoring inland water environments.

Monitoring Phycocyanin with Landsat 8/Operational Land Imager Orange Contra-Band

The Operational Land Imager (OLI) onboard the Landsat 8 satellite has a panchromatic band (503–676 nm) that has been used to compute a virtual spectral band known as “orange contra-band” (590–635 nm). The major application of the orange contra-band is the monitoring of cyanobacteria which is usually quantified by the measurement of the concentration of phycocyanin (PC) which has an absorption peak around 620 nm. In this study, we evaluated the use of the orange contra-band approach for estimating PC concentration from in situ proximal hyperspectral data from Eagle Creek Reservoir (ECR), in Indiana, USA. We first validated the empirical relationship for the computation of the orange contra-band by using the panchromatic, red, and green spectral bands from ECR. PC concentration retrieval using the orange contra-band were not successful when using the entire dataset (R2 < 0.1) or when using only PC concentrations higher than 50 mg/m3 (R2 < 0.24). Better results were achieved when using samples in which PC was 1.5 times higher than the chlorophyll-a concentration (R2 = 0.84). These results highlighted the need for the development of remote sensing algorithms for the accurate estimation of PC concentration from non-PC dominant waters which could be use to track and/or predict cyanobacteria blooms.

Impact of 3D cloud structures on the atmospheric trace gas products from UV–Vis sounders – Part 1: Synthetic dataset for validation of trace gas retrieval algorithms

Abstract. Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low cloud coverage. However, even fully clear pixels can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. Quantifying the error of retrieved trace gas concentrations due to cloud scattering is a difficult task. One possibility is to generate synthetic data by three-dimensional (3D) radiative transfer simulations using realistic 3D atmospheric input data, including 3D cloud structures. Retrieval algorithms may be applied on the synthetic data, and comparison to the known input trace gas concentrations yields the retrieval error due to cloud scattering. In this paper we present a comprehensive synthetic dataset which has been generated using the Monte Carlo radiative transfer model MYSTIC (Monte Carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres). The dataset includes simulated spectra in two spectral ranges (400–500 nm and the O2A-band from 755–775 nm). Moreover it includes layer air mass factors (layer-AMFs) calculated at 460 nm. All simulations are performed for a fixed background atmosphere for various sun positions, viewing directions and surface albedos. Two cloud setups are considered: the first includes simple box clouds with various geometrical and optical thicknesses. This can be used to systematically investigate the sensitivity of the retrieval error on solar zenith angle, surface albedo and cloud parameters. Corresponding 1D simulations are also provided. The second includes realistic three-dimensional clouds from an ICON large eddy simulation (LES) for a region covering Germany and parts of surrounding countries. The scene includes cloud types typical of central Europe such as shallow cumulus, convective cloud cells, cirrus and stratocumulus. This large dataset can be used to quantify the trace gas concentration retrieval error statistically. Along with the dataset, the impact of horizontal photon transport on reflectance spectra and layer-AMFs is analysed for the box-cloud scenarios. Moreover, the impact of 3D cloud scattering on the NO2 vertical column density (VCD) retrieval is presented for a specific LES case. We find that the retrieval error is largest in cloud shadow regions, where the NO2 VCD is underestimated by more than 20 %. The dataset is available for the scientific community to assess the behaviour of trace gas retrieval algorithms and cloud correction schemes in cloud conditions with 3D structure.

Retrieval of Size Distribution and Concentration of Au-Ag Alloy Nanospheroids by Spectral Extinction Method.

In order to monitor the synthesis processes or characterize nanoparticles for application, a new method that allows in situ determination of the two-dimensional size distribution and concentration of Au-Ag alloy nanospheroids, based on their extinction spectrum, is developed. Non-negative Tikhonov regularization and T-matrix method were used to solve the inverse problem. The effects of the two-dimensional size steps, wavelength range, and measurement errors of extinction spectrum on the retrieval results were analyzed to verify the feasibility and accuracy of the retrieval algorithm. Through comparative analysis, the size steps and wavelength range that make the retrieval error smaller are found. After adding 0.1% random noise to the extinction spectrum, a small variation in the retrieval error of the mean size is observed. The results showed that the error of the mean size is smaller than 2% and the error of the concentration is smaller than 3%. This method is simple, fast, cheap, nondestructive, and can be done in situ during the growth process of nanoparticles.

Eastern Arctic Sea Ice Sensing: First Results from the RADARSAT Constellation Mission Data

Sea ice monitoring plays a vital role in secure navigation and offshore activities. Synthetic aperture radar (SAR) has been widely used as an effective tool for sea ice remote sensing (e.g., ice type classification, concentration and thickness retrieval) for decades because it can collect data by day and night and in almost all weather conditions. The RADARSAT Constellation Mission (RCM) is a new Canadian SAR mission providing several new services and data, with higher spatial coverage and temporal resolution than previous Radarsat missions. As a very deep convolutional neural network, Normalizer-Free ResNet (NFNet) was proposed by DeepMind in early 2021 and achieved a new state-of-the-art accuracy on the ImageNet dataset. In this paper, the RCM data are utilized for sea ice detection and classification using NFNet for the first time. HH, HV and the cross-polarization ratio are extracted from the dual-polarized RCM data with a medium resolution (50 m) for an NFNet-F0 model. Experimental results from Eastern Arctic show that destriping in the HV channel is necessary to improve the quality of sea ice classification. A two-level random forest (RF) classification model is also applied as a conventional technique for comparisons with NFNet. The sea ice concentration estimated based on the classification result from each region was validated with the corresponding polygon of the Canadian weekly regional ice chart. The overall classification accuracy confirms the superior capacity of the NFNet model over the RF model for sea ice monitoring and the sea ice sensing capacity of RCM.

Retrieving particulate matter concentrations over the contiguous United States using CALIOP observations

Using twelve years (2007–2018) of NASA Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) near-surface 532 nm aerosol extinction retrievals, multi-year mean and trends of particulate matter (PM) concentrations are derived over the contiguous United States (CONUS). Different from past studies that use column integrated aerosol optical thickness, here only near-surface CALIOP aerosol extinction is used for deriving near-surface PM with aerodynamic diameters less than 2.5 μm (PM2.5) concentrations using an innovative, bulk-mass-modeling-based method. Compared against ground based PM2.5 measurements from the U.S. Environmental Protection Agency (EPA), an encouraging relationship between CALIOP-derived PM2.5 and EPA-observed PM2.5 (Deming slope = 0.89; RMSE = 3.42 μg/m3; mean bias = −1.00 μg/m3) is found using combined daytime/nighttime CALIOP data. Also, comparable trends in PM2.5 concentrations from the EPA and daytime and nighttime CALIOP data are found for most of the eastern CONUS and imply that air quality is generally improving over this region for the study period. Over the western CONUS, a seasonal analysis reveals that PM2.5 trends are positive during the more active wildfire season (June through November) but negative for other months. This study suggests that lidar data show promise in their use for obtaining PM2.5 estimates and provides motivation to further explore aerosol extinction-based PM concentration retrievals in anticipation of future space-based lidar missions.

Retrieval of the Arctic sea ice concentration from FY-3B/MWRI using ASI algorithm

Retrieval of the Arctic sea ice concentration from FY-3B/MWRI using ASI algorithm

Unsupervised Sparse Unmixing of Atmospheric Trace Gases From Hyperspectral Satellite Data

In this letter, a new approach for the retrieval of the vertical column concentrations of trace gases from hyperspectral satellite observations, is proposed. The main idea is to perform a linear spectral unmixing by estimating the abundances of trace gases spectral signatures in each mixed pixel collected by an imaging spectrometer in the ultraviolet region. To this aim, the sparse nature of the measurements is brought to light and the compressive sensing paradigm is applied to estimate the concentrations of the gases' endemembers given by an a priori wide spectral library, including reference cross sections measured at different temperatures and pressures at the same time. The proposed approach has been experimentally assessed using both simulated and real hyperspectral dataset. Specifically, the experimental analysis relies on the retrieval of sulfur dioxide during volcanic emissions using data collected by the TROPOspheric Monitoring Instrument. To validate the procedure, we also compare the obtained results with the sulfur dioxide total column product based on the differential optical absorption spectroscopy technique and the retrieved concentrations estimated using the blind source separation.

Remote sensing retrieval of chlorophyll-a concentration in Dianchi lake based on orbita hyperspectral imagery

Remote sensing retrieval of chlorophyll-a concentration in Dianchi lake based on orbita hyperspectral imagery

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.