Accurate Atmospheric Correction Research Articles

Ocean Colour Atmospheric Correction for Optically Complex Waters under High Solar Zenith Angles: Facilitating Frequent Diurnal Monitoring and Management

Accurate atmospheric correction (AC) is one fundamental and essential step for successful ocean colour remote-sensing applications. Currently, most ACs and the associated ocean colour remote-sensing applications are restricted to solar zenith angles (SZAs) lower than 70°. The ACs under high SZAs present degraded accuracy or even failure problems, rendering the satellite retrievals of water quality parameters more challenging. Additionally, the complexity of the bio-optical properties of the coastal waters and the presence of complex aerosols add to the difficulty of AC. To address this challenge, this study proposed an AC algorithm based on extreme gradient boosting (XGBoost) for optically complex waters under high SZAs. The algorithm presented in this research has been developed using pairs of Geostationary Ocean Colour Imager (GOCI) high-quality noontime remote-sensing reflectance (Rrs) and the Rayleigh-corrected reflectance (ρrc) derived from the Ocean Colour–Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) in the morning (08:55 LT) and at dusk (15:55 LT). The algorithm was further examined using the daily GOCI images acquired in the morning and at dusk, and the hourly (total suspended sediment) TSS concentration was also obtained based on the atmospherically corrected GOCI data. The results showed that: (i) the model produced an accurate fitting performance (R2 ≥ 0.90, RMSD ≤ 0.0034 sr−1); (ii) the model had a high validation accuracy with an independent dataset (R2 = 0.92–0.97, MAPD = 8.2–26.81% and quality assurance (QA) score = 0.9–1); and (iii) the model successfully retrieved more valid Rrs for GOCI images under high SZAs and enhanced the accuracy and coverage of TSS mapping. This algorithm has great potential to be applied to AC for optically complex waters under high SZAs, thus increasing the frequency of available observations in a day.

Atmospheric correction algorithm based on deep learning with spatial-spectral feature constraints for broadband optical satellites: Examples from the HY-1C Coastal Zone Imager

Broadband optical satellites have been widely used for fine monitoring of coastal waters and inland lakes for their high spatial resolution. Atmospheric correction (AC) is one of the essential data processing steps for remote sensing of water environments with high spatial resolution satellites. Most broadband optical satellites, such as HY-1C Coastal Zone Imager (CZI) with a spatial resolution of 50 m, lack of near-infrared and shortwave infrared bands needed for atmospheric correction, making accurate AC difficult. Auxiliary aerosol data is often needed for accurate AC of broadband optical satellite data, which suffers from the differences in spatial resolution and imaging time between auxiliary data and satellite data. Furthermore, existing AC algorithms always perform AC pixel by pixel, which ignores the spatial relationship between adjacent pixels. Taking HY-1C CZI as an example, this paper proposes a novel atmospheric correction algorithm based on deep learning (SSACNet). Considering the inherent spatial-spectral features of satellite images, the SSACNet combines 2D and 3D convolution. The 3D convolution was used to mine the spatial and spectral features of the image and 2D convolution was explored to perform spatial information compensation. The SSACNet was trained and evaluated using the spatio-temporally synchronized dataset of HY-1C CZI and Landsat8 Operational Land Imager (OLI). The evaluation results by in-situ data show that the SSACNet has good performance, with the average correlation coefficient of 0.89, and the absolute percentage deviation (APD) of four bands ranging from 21.53 % to 35.41 %. Compared with the quasi-synchronous Landsat8 OLI remote sensing reflectance (Rrs), the APD is less than 10 %. Compared with traditional atmospheric correction algorithms, SSACNet has significantly improved the spatial and spectral information fidelity. In addition, SSACNet also shows good applicability, as it can be used in both clear ocean waters and turbid coastal waters. This study lays a foundation for the quantitative remote sensing of water environment by broadband optical satellites.

Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery.

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors' retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon () estimates. This study assessed atmospheric correction's impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction.

A novel quantitative analysis for diurnal dynamics of Ulva prolifera patch in the Yellow Sea from Geostationary Ocean Color Imager observation

IntroductionIn the last decade, the outbreak of large-scale green tides caused by Ulva prolifera has continuously occurred in the Yellow Sea. Satellite remote sensing techniques have been widely used to monitor the distribution area and duration of green tides due to their advantages of their large-area synchronous observation. Ulva prolifera in the Yellow Sea is mainly distributed in bands or large patches during its flourishing stage. Previous studies have rarely reported the quantitative analysis of a single Ulva prolifera patch and its changes in the short term.MethodsConsidering the high temporal resolution of the Geostationary Ocean Color Imager (GOCI) sensor and the patchy distribution of Ulva prolifera floating on the sea surface, we developed a feasible method for monitoringUlva prolifera by performing clustering analysis with density-based spatial clustering of applications with noise (DBSCAN) to capture the diurnal variation characteristics of a single Ulva prolifera patch.ResultsThis new approach was used to extract informationfrom a single Ulva prolifera patch in the Yellow Sea in 2012 and 2017. The results showed that during the time of GOCI imaging, the tidal current was the main factor driving the drift of Ulva prolifera, and the drifting direction of Ulva prolifera was consistent with the direction of the local tidal current, with a coefficient of determination of 0.94.DiscussionBy changing the normalized difference vegetation index (NDVI) threshold, further more accurate atmospheric correction (AC) of GOCI data during the twilight periods was indirectly achieved. By comparing the areal change in the single patch before and after AC, we speculated that the daily change in signal intensity received by the GOCI sensor may be the main reason for the diurnal variation in the Ulva proliferacoverage area. The results showed the details of the diurnal variation in Ulvaprolifera patches in the dynamic marine environment, and the main reason that may cause this variation was speculated.

A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification

Low- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resolution, insufficient water sampling, and higher uncertainty in retrieval accuracy exist. In this paper, a hybrid Chl-a estimation method based on spectral characteristics (i.e., remote sensing reflectance (Rrs)) classification was developed for oligotrophic and mesotrophic waters using high-resolution satellite Sentinel-2 (A and B) data. First, 99 samples and quasi-synchronous Sentinel-2 satellite data were collected from four small- and medium-sized reservoirs in central China, and the usability of the Sentinel-2 Rrs data in inland oligotrophic and mesotrophic waters was verified by accurate atmospheric correction. Second, a new optical classification method was constructed based on different water characteristics to classify waters into clear water, phytoplankton-dominated water, and water dominated by phytoplankton and suspended matter together using the thresholds of Rrs490/Rrs560 and Rrs665/Rrs560. The proposed method has a higher classification accuracy compared to other classification methods, and the band-ratio algorithm is simpler and more effective for satellite sensors without NIR bands. Third, given the sensitivity of the empirical method to water variability and the ease of development and implementation, a nonlinear least squares fitted one-dimensional nonlinear function was established based on the selection of the best-fitting spectral indices for different optical water types (OWTs) and compared with other Chl-a estimation algorithms. The validation results showed that the hybrid two-band method had the highest accuracy with squared correlation coefficient, root mean squared difference, mean absolute percentage error, and bias of 0.85, 2.93, 32.42%, and −0.75 mg/m3, respectively, and the results of the residual values further validated the applicability and reliability of the model. Finally, the performance of the classification and estimation algorithms on the four reservoirs was evaluated to obtain images mapping the Chl-a in the reservoirs. In conclusion, this study improves the accuracy of Chl-a estimation for oligotrophic and mesotrophic waters by combining a new classification algorithm with a two-band hybrid model, which is an important contribution to solving the problem of low resolution and high uncertainty in the retrieval of Chl-a in oligotrophic and mesotrophic waters in small- and medium-sized reservoirs and has the potential to be applied to other optically similar oligotrophic and mesotrophic lakes and reservoirs using similar spectrally satellite sensors.

HY-1C/D CZI Image Atmospheric Correction and Quantifying Suspended Particulate Matter

HY-1C/D both carry a coastal zone imager (CZI) with a spatial resolution of 50 m and a swath width of 950 km, two observations can be achieved in three days when two satellites operating in a network. Accurate atmospheric correction is the basis for quantitative inversion of ocean color parameters using CZI However, atmospheric correction in estuarine and coastal waters with complex optical properties is a challenge due to the band setting of CZI. This paper proposed a novel atmospheric correction algorithm for CZI images applicable to turbid waters in estuarine and coastal zone. The Rayleigh scattering reflectance of CZI was calculated based on a vector radiative transfer model. Next, a semi-empirical radiative transfer model with suspended particle concentration as the parameter is used to model the water-atmosphere coupling. Finally, the parameters of the coupling model are solved by combining a global optimization method based on a genetic algorithm. The results indicate that the CZI-derived remote-sensing reflectance (Rrs) are in good agreement with the quasi-synchronous Landsat-8/9 operational land imager (OLI) derived Rrs in the green and red bands (R2 > 0.96). Validation using in situ data revealed that the RMSE of the CZI-derived Rrs in the green and red bands was 0.0036 sr−1 and 0.0035 sr−1. More importantly, the values and spatial distributions of suspended particulate matter (SPM) estimated by CZI and those estimated by OLI in the Subei Shoal and the Yangtze River Estuary are basically consistent, and the validation using in situ data revealed that the inversion of SPM concentration by CZI was effective (R2 = 0.86, RMSE = 0.0362 g/L), indicating that CZI has great potential and broad application prospects for monitoring the spatial and temporal dynamics of SPM in estuarine and coastal waters. The study results will lay the foundation for further estimating suspended sediment fluxes and carbon fluxes, thus providing data support and scientific basis for promoting resource development, utilization and conservation strategies in estuarine and coastal areas.

Atmospheric correction for HY-1C CZI images using neural network in western Pacific region

ABSTRACT With a spatial resolution of 50 m, a revisit time of three days, and a swath of 950 km, the coastal zone imager (CZI) offers great potential in monitoring coastal zone dynamics. Accurate atmospheric correction (AC) is needed to exploit the potential of quantitative ocean color inversion. However, due to the band setting of CZI, the AC over coastal waters in the western Pacific region with complex optical properties cannot be realized easily. This research introduces a novel neural network (NN) AC algorithm for CZI data over coastal waters. Total 100,000 match-ups of HY-1 C CZI-observed reflectance at the top-of-atmosphere and Operational Land Imager (OLI)-retrieved high-quality remote sensing reflectance (R rs) at the CZI bands are built to train the NN model. These reflectance data are obtained from the standard AC algorithm in the SeaDAS. Results indicate that the distributions of the CZI retrieved R rs were consistent with the quasi-synchronous OLI data, but the spatial information from the CZI is more detailed. Then, the accuracy of the CZI data for AC is evaluated using the multi-source in-situ data. Results further show that the NN-AC can successfully retrieve R rs for CZI and the coefficients of determination in the blue, green, red, and near-infrared bands were 0.70, 0.77, 0.76, and 0.67, respectively. The NN algorithm does not depend on shortwave-infrared bands and runs very fast once properly trained.

An ensemble neural network atmospheric correction for Sentinel-3 OLCI over coastal waters providing inherent model uncertainty estimation and sensor noise propagation

Accurate atmospheric correction (AC) is a prerequisite for quantitative ocean colour remote sensing and remains a challenge in particular over coastal waters. Commonly AC algorithms are validated by establishing a mean retrieval error from match-up analysis, which compares the satellite-derived surface reflectance with concurrent ground radiometric observations. Pixel-based reflectance uncertainties however, are rarely provided by AC algorithms and those for the operational Ocean and Land Colour Instrument (OLCI) marine reflectance product are not yet recommended for use. AC retrieval errors and uncertainties directly determine the quality with which ocean colour products can be estimated from the marine surface reflectance. Increasingly there is also the need for reflectance uncertainty products to be used as data assimilation inputs into biogeochemical models.This paper describes the development of a new coastal AC algorithm for Sentinel-3 OLCI that provides pixel-based estimation of the inherent model inversion uncertainty and sensor noise propagation. The algorithm is a full-spectral model-based inversion of radiative transfer (RT) simulations in a coupled atmosphere–ocean system using an ensemble of artificial neural networks (ANN) that were initialized differently during the training process, but composed of the same network architecture. The algorithm has been validated against in-situ radiometric observations across a wide range of optical water types, and has been compared with the latest EUMETSAT operational Level 2 processor IPF-OL-2 v7.01.In this analysis we found that the ensemble ANN showed improved performance over the operational Level 2 processor with a band-averaged (412–708 nm) mean absolute percentage error (MAPE) of 16% compared to 37% and a four-times lower band-averaged bias of -0.00045 sr-1.In the ensemble inversion process we account for three uncertainty components: (1) the total model variance that describes the variance of the data from the different ANNs, (2) the prediction variance of the mean, which is based on calculations of the RT simulations and (3) the instrument noise variance of the mean by propagating the OLCI spectral signal-to-noise ratios (SNR). To study algorithm performance and to quantify the contribution of the different uncertainty components to the total uncertainty, we applied the algorithm to an optically complex full resolution (FR) test scene covering coastal waters of the Great Barrier Reef, Australia. The uncertainties associated with the instrument noise variance were found to be two orders of magnitude lower than the uncertainty components of the prediction and total model variances. The overall largest uncertainty component in our uncertainty framework is attributed to the total model inversion error from averaging the responses of the slightly different adapted networks in the ensemble. The algorithm is made publicly available as a Python/C plugin for the Sentinel Application Platform (SNAP).

Atmospheric Correction of Satellite Optical Imagery over the Río de la Plata Highly Turbid Waters Using a SWIR-Based Principal Component Decomposition Technique

Estimating water reflectance accurately from satellite optical data requires implementing an accurate atmospheric correction (AC) scheme, a particularly challenging task over optically complex water bodies, where the signal that comes from the water prevents using the near-infrared (NIR) bands to separate the perturbing atmospheric signal. In the present work, we propose a new AC scheme specially designed for the Río de la Plata—a funnel-shaped estuary in the Argentine–Uruguayan border—highly scattering turbid waters. This new AC scheme uses far shortwave infrared (SWIR) bands but unlike previous algorithms relates the atmospheric signal in the SWIR to the signal in the near-infrared (NIR) and visible (VIS) bands based on the decomposition into principal components of the atmospheric signal. We describe the theoretical basis of the algorithm, analyze the spectral features of the simulated principal components, theoretically address the impact of noise on the results, and perform match-ups exercises using in situ measurements and Moderate Resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) imagery over the region. Plausible water reflectance retrievals were obtained in the NIR and VIS bands from both simulations and match-ups using field data—with better performance (i.e., lowest errors and offsets, and slopes closest to 1) compared to existing AC schemes implemented in the NASA Data Analysis Software (SeaDAS). Moreover, retrievals over images in the VIS and NIR bands showed low noise, and the correlation was low between aerosol and water reflectance spatial fields.

Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network

Atmospheric correction is an essential step in hyperspectral imaging and target detection from spectrometer remote sensing data. State-of-the-art atmospheric correction approaches either require extensive filed experiments or prior knowledge of atmospheric characteristics to improve the predicted accuracy, which are computational expensive and unsuitable for real time application. To take full advantages of remote sensing observation in quickly and reliably acquiring data for a large area, an automatic and efficient processing tool is required for atmospheric correction. In this paper, we propose a time-dependent neural network for automatic atmospheric correction and target detection using multi-scan hyperspectral data under different elevation angles. In addition to the total radiance, the collection day and time are also incorporated to improve the time-dependency of the network and represent the seasonal and diurnal characteristics of atmosphere and solar radiation. Results show that the proposed network has the capacity to accurately provide atmospheric characteristics and estimate precise reflectivity spectra with 95.72% averaged accuracy for different materials, including vegetation, sea ice, and ocean. Additional experiments are designed to investigate the network’s temporal dependency and performance on missing data. The error analysis confirms that our proposed network is capable of estimating atmospheric characteristics under both seasonally and diurnally varying environments and handling the influence of missing data. Both the predicted results and error analysis are promising and demonstrate that our network has the ability of providing accurate atmospheric correction and target detection in real time.

Diurnal variation of sun-induced chlorophyll fluorescence of agricultural crops observed from a point-based spectrometer on a UAV

Unmanned Aerial Vehicle (UAV)-based measurements allow studying sun-induced chlorophyll fluorescence (SIF) at the field scale and can potentially upscale results from ground to airborne/satellite level. The objective of this paper is to present the FluorSpec system providing SIF measurements at the field level onboard a UAV, and to evaluate the potential of this system for understanding diurnal SIF patterns for different arable crops. The core components of FluorSpec are a point spectrometer configured to measure in the O2 absorption bands at sub-nanometer resolution, bifurcated fibre optics to switch between the downwelling irradiance and upwelling radiance measurements, and a laser range finder allowing accurate atmospheric correction. The processing chain is explained and the capability of the novel Spectral Shape Assumption Fraunhofer Line Discrimination (SSA-FLD) method to retrieve SIF was tested. To test the reliability of FluorSpec diurnal SIF measurements, near-canopy diurnal SIF was monitored during the growing season over potato and sugar beet plants with a ground-based setup. The two crops exhibited a clear diurnal SIF pattern, which positively correlated with the photosynthetically active radiation (PAR). The divergence in diurnal patterns between SIF and PAR indicated that the crops might be suffering from heat stress. A significant correlation between SIF and the Photosystem II Quantum Yield was obtained. By mounting the FluorSpec on a UAV, SIF measurements were obtained over the same crops during a clear day. UAV-based SIF also exhibited a pronounced diurnal pattern similar to the ground-based measurements and it showed clear spatial variation within different crop fields. The obtained results demonstrate the ability of the FluorSpec system to reliably measure plant fluorescence at ground and field level, and the possibility of the UAV-based FluorSpec to bridge the scale gap between different levels of SIF observations.

Novel method for reconstruction of hyperspectral resolution images from multispectral data for complex coastal and inland waters

Hyperspectral resolution image products of a synthetic sensor featuring the high spatial resolution of the space-borne sensor can offer cost-effective means for enhancing our current capabilities in terms of providing an array of images in lieu of designing an expensive system for image acquisition, which can serve the expanding needs of the scientific and user communities for various critical water color applications. Despite several studies on enhancing the capability of land remote sensing sensors, full spectrum reconstruction of water color images with varying spectral bands is hampered by the lack of methods and accurate atmospheric correction procedures. In the present work, a novel method is developed for reconstruction of hyperspectral resolution images from high spatial-resolution Sentinel 2 Multispectral Instrument (MSI) data representative of many complex waters in coastal and inland zones. This method uses a deep neural network (DNN) with multiple blocks of deconvolution and dense layers. The spectral reconstruction of hyperspectral resolution images from multispectral data was based on rigorous training data from the atmospherically-corrected and validated HICO normalized water-leaving radiance products (with spectral resolution 438-868 nm sampled at 5.7 nm) of diverse water types. The generalizability and versatility of the DNN method was tested and evaluated systematically by means of various qualitative and quantitative analyses using concurrent space-borne (MSI and HICO) and in-situ measurements from different regional waters. Reconstructed hyperspectral resolution radiances obtained from the MSI images closely matched with independent HICO and MSI measurements within the desired accuracy. Successful reconstruction and validation of the hyperspectral radiances indicate that the proposed state-of-the-art method provides possible future directions for enhancing our current capabilities of space-borne sensors for various research purposes and societal applications at local, regional and global scales.

Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery

Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error (RMSE) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra (RMSE < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate (RMSE = 4.04 cm), and the model explains a large portion of the variability (R2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground.

Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data

With the longest archive of satellite remote sensing images, the Landsat series of satellites have demonstrated their great potential in aquatic environmental studies. However, although various atmospheric correction (AC) methods have been developed for Landsat observations in water color applications, a comprehensive assessment of their accuracies across different AC methods and instruments has yet to be performed. Using in situ spectral data collected by Aerosol Robotic Network-Ocean Color (AERONET-OC) sites, the performances of five types of AC methods over three different Landsat missions (i.e., Landsat 5/7/8) were evaluated. The Landsat 8 Operational Land Imager (OLI) showed more accurate AC retrievals than the other two instruments, and the results for its green and red bands appeared more reliable than those for the other wavelengths (uncertainty levels of ∼30 %). The iterative NIR algorithm with 2-bands (NIR-SWIR2) model selection embedded in SeaDAS showed the best performances for OLI in two blue bands. Moreover, larger residual errors were found for most Landsat 5/7 bands regardless of the AC methods and spectral bands employed with an uncertainty of >50 %. Interestingly, a simple aerosol subtraction method over the Rayleigh-corrected reflectance (Rrc) outperformed the exponential extrapolation (EXP) algorithms, especially for Landsat 5/7. Neither the image-based AC algorithm nor the surface reflectance (SR) products provided by the United States Geological Survey (USGS) showed acceptable performances over coastal environments. The uncertainties in the various Landsat reflectance products over water surfaces could be associated with a relatively poor signal-to-noise ratio (SNR) in addition to radiometric calibration uncertainties, imperfect aerosol removal methods. Future research is required to collect in situ data across a wider range of water optical properties (particularly more turbid inland waters) to examine the corresponding applicability of Landsat-series observations.

Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.

Aerosol optical depth (AOD) retrieval for atmospheric correction in Landsat-8 imagery using second simulation of a satellite signal in the solar spectrum-vector (6SV)

Atmospheric correction has been challenging task in digital image processing. It requires several atmospheric parameters in order to obtain accurate surface reflectance of objects within the image scene. One of the most crucial parameters required for accurate atmospheric correction is aerosol optical depth (AOD). AOD can be obtained by in-situ measurement or estimated from remote sensing observation. In this experiment, atmospheric correction was performed using second simulation of a satellite signal in the solar spectrum-vector (6SV) algorithm on Landsat-8 imagery in which AOD parameter was retrieved from surface reflectance inversion involving daily-global surface reflectance product of moderate resolution imaging spectroradiometer (MODIS). Furthermore, AOD retrieved from surface reflectance inversion was also validated using ground-based sun photometer observation data from aerosol robotic network (AERONET) station in Bandung, Indonesia. Our experiment shows the consistency between AOD from surface reflectance inversion and AOD from ground-based observation. Finally, 6SV was performed on Landsat-8 imagery to obtain the surface reflectance. We further compared surface reflectance of 6SV atmospheric correction and surface reflectance of Landsat-8 Level 2 product. The atmospherically corrected image also shared agreeable result with Landsat 8 Level-2 product.

Automated water surface temperature retrieval from Landsat 8/TIRS

Satellite remote sensing of Land and Water Surface Temperature (L/WST) has many applications in studies of terrestrial and aquatic ecology. Retrieval of L/WST requires a well calibrated radiometer and an accurate atmospheric correction. In the present study, the performance of the Thermal InfraRed Sensor (TIRS) on board Landsat 8 is evaluated for the retrieval of L/WST. libRadtran is used to retrieve atmospheric correction parameters based on atmospheric profiles of relative humidity and temperature from three global atmospheric models. Performance of single band retrievals is compared to typical MODTRAN results from the Atmospheric Correction Parameter Calculator (ACPC) and a split-window approach. A multi-temporal land masking method using imagery from the Operational Land Imager (OLI) on board Landsat 8 is demonstrated, and is used to automatically classify imagery in the matchup dataset in three classes of cloud cover. Two sources of in situ data covering the Belgian Coastal Zone (BCZ) are used for validation of the L/WST product: (1) fixed locations in the Flemish Banks measurement network and (2) underway data from regular RV Belgica campaigns. In the present study the single band methods outperformed the split-window approach, and consistent retrievals are found for the MODTRAN and libRadtran simulations. Typical single band surface temperature retrievals in quasi cloud-free conditions have Root Mean Squared Differences (RMSD) of 0.7 K and 1 K for Bands 10 and 11 with low bias, depending on the method and atmospheric profile source. For imagery with scattered clouds, RMSD values increase to 1 K and 2 K respectively with an approximately 0.5 K cold bias, likely caused by cloud proximity. The calibration efforts combined into Collection 1 allows for accurate absolute surface temperature retrievals from B10 on Landsat 8/TIRS for homogeneous targets with known emissivity, such as liquid water. The method is adapted to global processing and can be used for Land Surface Temperature retrieval with a suitable source of emissivity data.

Atmospheric Correction Algorithm for HY-1C CZI over Turbid Waters

Tong, C.; Mu, B.; Liu, R.-J.; Ding, J.; Zhang, M.W.; Xiao, Y-.F.; Liang, X.-J., and Chen, X.-Y., 2019. Atmospheric correction algorithm for HY-1C CZI over turbid waters. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 156-163. Coconut Creek (Florida), ISSN 0749-0208.China's HY-1C ocean-observing satellite was launched successfully on September 7, 2018. It carries a four-channel wide-band coastal zone imager (CZI) that has 50 m spatial resolution and 950 km swath width. To exploit the potential of quantitative ocean color inversion, accurate atmospheric correction is needed. However, because of the CZI band settings, the realization of this goal is a challenge, especially for turbid water with complex optical properties. This study investigated the atmospheric correction algorithm for the CZI over turbid water. First, using the 6SV radiative transfer model, CZI Rayleigh lookup tables (LUTs) were built to correct for atmospheric molecular Rayleigh scattering, greatly shortening the time required for operational data processing. Second, CZI aerosol scattering was removed using the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol LUTs and quasi-synchronous MODIS aerosol products. The accuracy of the CZI atmospheric correction was validated using data from the highly turbid Bohai Sea. In comparison with synchronous in situ data, the results showed the average relative error of CZI remote sensing reflectance (Rrs) in the blue, green and red bands was 32.51 %, 25.38 % and 42.10 %, respectively. Comparison of CZI Rrs with quasi-synchronous MODIS data revealed similar spatial distributions, although the spatial information from the CZI was more detailed. The results proved the validity and accuracy of the CZI atmospheric correction algorithm over turbid water, which lays a foundation for quantitative ocean color inversion with high spatial resolution in the coastal zone.

Observation of absorbing aerosols above clouds over the south-east Atlantic Ocean from the geostationary satellite SEVIRI – Part 1: Method description and sensitivity

Abstract. High-temporal-resolution observations from satellites have a great potential for studying the impact of biomass burning aerosols and clouds over the south-east Atlantic Ocean (SEAO). This paper presents a method developed to simultaneously retrieve aerosol and cloud properties in aerosol above-cloud conditions from the geostationary instrument Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). The above-cloud aerosol optical thickness (AOT), the cloud optical thickness (COT) and the cloud droplet effective radius (CER) are derived from the spectral contrast and the magnitude of the signal measured in three channels in the visible to shortwave infrared region. The impact of the absorption from atmospheric gases on the satellite signal is corrected by applying transmittances calculated using the water vapour profiles from a Met Office forecast model. The sensitivity analysis shows that a 10 % error on the humidity profile leads to an 18.5 % bias on the above-cloud AOT, which highlights the importance of an accurate atmospheric correction scheme. In situ measurements from the CLARIFY-2017 airborne field campaign are used to constrain the aerosol size distribution and refractive index that is assumed for the aforementioned retrieval algorithm. The sensitivities in the retrieved AOT, COT and CER to the aerosol model assumptions are assessed. Between 09:00 and 15:00 UTC, an uncertainty of 40 % is estimated on the above-cloud AOT, which is dominated by the sensitivity of the retrieval to the single-scattering albedo. The absorption AOT is less sensitive to the aerosol assumptions with an uncertainty generally lower than 17 % between 09:00 and 15:00 UTC. Outside of that time range, as the scattering angle decreases, the sensitivity of the AOT and the absorption AOT to the aerosol model increases. The retrieved cloud properties are only weakly sensitive to the aerosol model assumptions throughout the day, with biases lower than 6 % on the COT and 3 % on the CER. The stability of the retrieval over time is analysed. For observations outside of the backscattering glory region, the time series of the aerosol and cloud properties are physically consistent, which confirms the ability of the retrieval to monitor the temporal evolution of aerosol above-cloud events over the SEAO.

Inversion of multiangular polarimetric measurements over open and coastal ocean waters: a joint retrieval algorithm for aerosol and water-leaving radiance properties

Abstract. Ocean color remote sensing is a challenging task over coastal waters due to the complex optical properties of aerosols and hydrosols. In order to conduct accurate atmospheric correction, we previously implemented a joint retrieval algorithm, hereafter referred to as the Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm, to obtain the aerosol and water-leaving signal simultaneously. The MAPOL algorithm has been validated with synthetic data generated by a vector radiative transfer model, and good retrieval performance has been demonstrated in terms of both aerosol and ocean water optical properties (Gao et al., 2018). In this work we applied the algorithm to airborne polarimetric measurements from the Research Scanning Polarimeter (RSP) over both open and coastal ocean waters acquired in two field campaigns: the Ship-Aircraft Bio-Optical Research (SABOR) in 2014 and the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in 2015 and 2016. Two different yet related bio-optical models are designed for ocean water properties. One model aligns with traditional open ocean water bio-optical models that parameterize the ocean optical properties in terms of the concentration of chlorophyll a. The other is a generalized bio-optical model for coastal waters that includes seven free parameters to describe the absorption and scattering by phytoplankton, colored dissolved organic matter, and nonalgal particles. The retrieval errors of both aerosol optical depth and the water-leaving radiance are evaluated. Through the comparisons with ocean color data products from both in situ measurements and the Moderate Resolution Imaging Spectroradiometer (MODIS), and the aerosol product from both the High Spectral Resolution Lidar (HSRL) and the Aerosol Robotic Network (AERONET), the MAPOL algorithm demonstrates both flexibility and accuracy in retrieving aerosol and water-leaving radiance properties under various aerosol and ocean water conditions.