Level-2 Products Research Articles

A Deep Learning Lidar Denoising Approach for Improving Atmospheric Feature Detection

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model’s performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~7.3×10−4 km−1sr−1, which is lower than the CALIOP 1064 nm night MDB value of 8.6×10−4 km−1sr−1. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products.

Suppressing stripe noise in GRACE/GFO level-2 products with dual low-pass filtering

Suppressing stripe noise in GRACE/GFO level-2 products with dual low-pass filtering

The Extraction of Terrestrial Water Storage Anomaly from GRACE in the Region with Medium Scale and Adjacent Weak Signal Area: A Case for the Dnieper River Basin

The accuracy of estimating changes in terrestrial water storage (TWS) using Gravity Recovery and Climate Experiment (GRACE) level-2 products is limited by the leakage effect resulting from post-processing and the weak signal magnitude in adjacent areas. The TWS anomaly from 2003 to 2016 in the Dnieper River basin, with characteristics of medium scale and an adjacent weak TWS anomaly area, are estimated in this work. Two categories of leakage error repair approaches (including forward modeling, data-driven, single, and multiple scaling factor approaches) are employed. Root mean square error (RMSE) and Nash–Sutcliffe Efficiency (NSE) are used to evaluate the efficiency of approaches. The TWS anomaly inverted by the forward modeling approach (FM) is more accurate in terms of RMSE 3.04 and NSE 0.796. We compared single and multiple scaling approaches for the TWS anomaly and found that leakage signals mostly come from semi-annual terms. From the recovered results demonstrated in the spatial domain, the South of Dnieper River basin is more sensitive to the leakage effect because of it is adjacent to a weak hydrological signal region near the Black Sea. Further, comprehensive climate insights and physical mechanisms behind the TWS anomaly were confirmed. The temperate continental climate of this river basin is shown according to the variation in TWS anomaly in the spatial domain. Snowmelt plays a significant role in the TWS anomaly of the Dnieper River basin, following the precipitation record and the 14-year temperature spatial distribution for February. We compared single and multiple scaling approaches for the TWS anomaly and found that leakage signals mostly come from semi-annual terms.

Geostationary Environment Monitoring Spectrometer (GEMS) polarization characteristics and correction algorithm

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is the first geostationary earth orbit (GEO) environmental instrument, onboard the Geostationary Korea Multi-Purpose Satellite–2B (GEO-KOMPSAT-2B) launched on 19 February 2020, and is measuring reflected radiance from the earth's surface and atmosphere system in the range of 300–500 nm in the ultraviolet–visible (UV–Vis) region. The radiometric response of a satellite sensor that measures the UV–Vis wavelength region can depend on the polarization states of the incoming light. To reduce the sensitivity due to polarization, many current low earth orbit (LEO) satellites are equipped with a scrambler to depolarize the signals or a polarization measurement device (PMD) that simultaneously measures the polarization state of the atmosphere, then utilizes it for a polarization correction. However, a novel polarization correction algorithm is required since GEMS does not have a scrambler or a PMD. Therefore, this study aims to improve the radiometric accuracy of GEMS by developing a polarization correction algorithm optimized for GEMS that simultaneously considers the atmosphere's polarization state and the instrument's polarization sensitivity characteristics. The polarization factor and axis were derived by the preflight test on the ground as a function of wavelengths, showing a polarization sensitivity of more than 2 % at some specific wavelengths. The polarization states of the atmosphere are configured as a look-up table (LUT) using the Vector Linearized Discrete Ordinate Radiative-Transfer model (VLIDORT). Depending on the observation geometry and atmospheric conditions, the observed radiance spectrum can include a polarization error of 2 %. The performance of the proposed GEMS polarization algorithm was assessed using synthetic data, and the errors due to polarization were found to be larger in clear regions than in cloudy regions. After the polarization correction, polarization errors were reduced close to zero for almost all wavelengths, including the wavelength regions with high peaks and curvatures in the GEMS polarization factor, which sufficiently demonstrates the effectiveness of the proposed polarization correction algorithm. From the actual observation data after the launch of GEMS, the diurnal variation for the spatial distribution of polarization error was confirmed to be minimum at noon and maximum at sunrise/sunset. This can be used to improve the quality of GEMS measurements, the first geostationary environmental satellite, and then contribute to the retrieved accuracy of various Level-2 products, such as trace gases and aerosols in the atmosphere.

First mapping of polarization-dependent vegetation optical depth and soil moisture from SMAP L-band radiometry

Soil moisture (SM) and vegetation optical depth (VOD) estimates using passive microwave remote sensing at L-band (1.4 GHz) are essential for attaining a better understanding of water exchanges at the land-atmosphere interface. However, current retrieval algorithms often ignore the polarization dependence of vegetation effects. This study proposed a parameter self-calibrating framework for the multi-channel collaborative algorithm (MCCA) and presented a new SM and the first polarization-dependent VOD product based on the dual-polarized L-band observations at a fixed angle (40°) from the NASA Soil Moisture Active Passive (SMAP) mission. The parameter self-calibrating framework utilizes an information theory-based approach to obtain surface roughness and effective scattering albedo globally. Furthermore, the MCCA does not require auxiliary data for vegetation or soil moisture to constrain the retrieval process. Comparison with other SM and VOD products, such as MT-DCA version 5, DCA, SCA-H, SCA-V from SMAP Level-3 products version 8, and SMAP-IB, demonstrate analogous spatial patterns. The MCCA-derived SM exhibits the lowest unbiased root mean square deviation (ubRMSD, about 0.055 m3/m3), followed by SMAP-IB and DCA (0.061 m3/m3), with an overall Pearson's correlation coefficient of 0.744 (SMAP-IB performed best with R = 0.764) when evaluated against in-situ observations from 18 dense soil moisture networks. The MCCA generates VOD values for both vertical and horizontal polarization, demonstrating a slight polarization difference of vegetation effect at the satellite scale. Both VODs exhibit a strong linear relationship with above-ground biomass and canopy height. The polarization difference of VODs is primarily observed in densely vegetated and arid areas.

Retrieval and evaluation of surface soil moisture from CYGNSS using blended microwave soil moisture products

The Global Navigation Satellite System-Reflectometry (GNSS-R) can estimate land surface soil moisture (SSM) as a viable and promising approach. However, it has some large uncertainty in retrieving SSM. In this study, the SSM is retrieved from different Cyclone GNSS (CYGNSS) SSM retrieval models formed with different SSM reference data products, including two blended microwave SSM products from the European Space Agency's Climate Change Initiative (CCI) and the National Oceanic and Atmospheric Administration's Soil Moisture Operational Product System (SMOPS), and a single microwave sensor-derived Soil Moisture Active Passive (SMAP) Level-3 product. The performance of the developed retrieval models, characterized by spatial resolutions of 36 km × 36 km and 0.25° × 0.25°, is evaluated using K-fold cross-validation. Furthermore, the accuracy of these models is validated against ground measurements acquired from Chinese automated soil moisture observation network. In order to alleviate the impact of spatial mismatching between the predicted gridded SSM and the point-scale in-situ measurements, a cumulative distribution function (CDF) rescaling strategy is applied. The results indicated that all models are effective at capturing spatial variations in SSM, and the SMOPS-based model achieves the highest correlation coefficient (0.930) and the lowest root mean square error (RMSD, 0.028 cm3/cm3), followed by the CCI-based model (0.906 and 0.042 cm3/cm3). The SMAP-based model performs poorly in the comparison. The suboptimal performance of models evaluated with Chinese automated soil moisture measurements is largely attributed to the insufficient calibration of the original reference data in the region.

The impact of COVID-19 lockdown on the variation of sea surface chlorophyll-a in Bohai Sea, China

To prevent the pandemic of COVID-19, Chinese government implemented restrict city lockdown policy, which directly changed human daily habitats and customs, and further influence environment. Using the GOCI-derived sea surface chlorophyll-a Level-2 products, the impacts of city lockdown policy on sea surface chlorophyll-a in the coastal regions of Bohai Sea were evaluated by difference-in-differences (DiD) model. To improve the spatiotemporal coverage of the remote sensing data, data interpolating empirical orthogonal functions were employed to reconstruct the sea surface chlorophyll-a data. The results of DiD model showed that the city lockdown policy will contribute to a significant increment (with the range 1.12–1.14 mg/m3) of sea surface chlorophyll-a within 15 km from the coastline; meanwhile, the impact strength weakens with the distance to coastline increases. Applying the DiD model for the cities with human development index larger than 0.77, the inference is more prominent and significant with the increment (1.15–1.29 mg/m3) of sea surface chlorophyll-a; while non-significant increment of sea surface chlorophyll-a was detected for the cities with a human development index smaller than 0.77. The detected 15 km delimiter can be regarded as a guidance for environmental manager to make sampling strategies.

Improving mass change estimation in Panama with the GRACE/GFO gravity field using the variational mode decomposition

In this study, we aim to estimate the mass changes in Panama using the Gravity Recovery and Climate Experiment level-2 products, which are formed as spherical harmonic coefficients and limited by stripe noise. The empirical de-striping method and the temporal filter achieved by empirical mode decomposition can be used to reveal the signals but are still limited in signal reservation and noise reduction. To this end, we put forward a novel efficient strategy that uses the variational mode decomposition algorithm to filter the time series of each SHC separately. Based on the two reliable mascon products and in situ short-term groundwater observations, various comparisons in spatial, spectral, and temporal domains are implemented. In addition, the SNR (signal-to-noise ratio) index and the three-cornered hat method are adopted for assessment. The main results and conclusions are as follows: 1) Our filter outperforms the two previous methods with the best SNR (2.14) and the lowest Panama regional uncertainty (70 mm) for all available months. 2) Our estimate of the basin groundwater storage is closest to one of the groundwater observations with the maximum correlation coefficient (0.72, p<0.05). This result suggests that our method seems to detect small-scale mass signals that are undetectable in the two mascon products. Our work provides a reference for studying the mass change of small-scale basins in low latitudes.

A novel GRACE reconstructive filter to extract the mass changes in Madagascar

SUMMARYThe precise estimation of the mass changes in Madagascar is a challenge by using the Gravity Recovery and Climate Experiment (GRACE) mission Level-2 products since they are contaminated by noise. Although this issue can be alleviated by the empirical destriping method or spatial filtering, they result in potential signal distortion or signal leakage. To improve this, we propose a reconstructive filter, whose parameters are optimized by the signal-to-noise ratio. Subsequently, our optimal filter corresponding to the best signal-to-noise ratio (5.63) is used to estimate the mass changes (2002–2017) in Madagascar. Eventually, our results are compared with two reliable GRACE mascon products and other independent observations. Correspondingly, here are our major conclusions: (1) Compared with groundwater storage from the mascon products, our estimates have the highest Pearson correlation (0.5) with in situ observation and can detect the rapid increase of groundwater storage during the rainy season. (2) The Fourier spectrum analysis detects a ∼3.8-yr periodic signal in the terrestrial water storage changes in Madagascar, which is contributed from the interannual precipitation driven by climate factor (Indian Ocean Dipole) and the aliasing error for imperfect GRACE pre-process. Our work introduces an effective filter for processing GRACE Level-2 data and presents novel insights into mass changes in Madagascar.

Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity

Abstract. While the use and data assimilation (DA) of operational Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol data is commonplace, MODIS is scheduled to sunset in the next year. For data continuity, focus has turned to the development of next-generation aerosol products and sensors such as those associated with the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPOESS Preparation Project (S-NPP) and NOAA-20. Like MODIS algorithms, products from these sensors require their own set of extensive error characterization and correction exercises. This is particularly true in the context of monitoring significant aerosol events that tax an algorithm's ability to separate cloud from aerosol and account for multiple scattering related errors exacerbated by uncertainties in aerosol optical properties. To investigate the performance of polar-orbiting satellite algorithms to monitor and characterize significant events, a level 3 (L3) product has been developed using a consistent aggregation methodology for 4 years of observations (2016–2019) that is referred to as the SSEC/NRL L3 product. Included in this product are the AErosol RObotic NETwork (AERONET), MODIS Dark Target, Deep Blue, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms. These MODIS “baseline algorithms” are compared to NASA's recently released NASA Deep Blue algorithm for use with VIIRS. Using this new dataset, the relative performance of the algorithms for both land and ocean were investigated with a focus on the relative skill of detecting severe events and accuracy of the retrievals using AERONET. Maps of higher-percentile aerosol optical depth (AOD) regions of the world by product identified those with the highest measured AODs and determined what is high by local standards. While patterns in AOD match across products and median to moderate AOD values match well, there are regionally correlated biases between products based on sampling, algorithm differences, and AOD range – in particular for higher AOD events. Most notable are differences in boreal biomass burning and Saharan dust. Significant percentile biases must be accounted for when data are used in trend studies, data assimilation, or inverse modeling. These biases vary by aerosol regime and are likely due to retrieval assumptions in lower boundary conditions and aerosol optical models.

Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products of atmospheric trace gas columns

Abstract. We introduce the new Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 product of total column ozone (O3), total and tropospheric column nitrogen dioxide (NO2), total column water vapour, total column bromine oxide (BrO), total column formaldehyde (HCHO), and total column sulfur dioxide (SO2) (daily products https://doi.org/10.15770/EUM_SAF_AC_0048, AC SAF, 2023a; monthly products https://doi.org/10.15770/EUM_SAF_AC_0049, AC SAF, 2023b). The GOME-2 level-3 products aim to provide easily translatable and user-friendly data sets to the scientific community for scientific progress as well as to satisfy public interest. The purpose of this paper is to present the theoretical basis as well as the verification and validation of the GOME-2 daily and monthly level-3 products. The GOME-2 level-3 products are produced using the overlapping area-weighting method. Details of the gridding algorithm are presented. The spatial resolution of the GOME-2 level-3 products is selected based on the sensitivity study. The consistency of the resulting level-3 products among three GOME-2 sensors is investigated through time series of global averages, zonal averages, and bias. The accuracy of the products is validated by comparison to ground-based observations. The verification and validation results show that the GOME-2 level-3 products are consistent with the level-2 data. Small discrepancies are found among three GOME-2 sensors, which are mainly caused by the differences in the instrument characteristic and level-2 processor. The comparison of GOME-2 level-3 products to ground-based observations in general shows very good agreement, indicating that the products are consistent and fulfil the requirements to serve the scientific community and general public.

Validation of NASA Sea Surface Temperature Satellite Products Using Saildrone Data

Sea Surface Temperature (SST) is at the core of many processes in the oceans. Various remote sensing platforms have been used to obtain SST products of different scales, but their validation remains a topic of ongoing research. One promising platform is an uncrewed surface vehicle called Saildrone. We use the data from eight Saildrone deployments of the USA West Coast 2019 campaign to validate MODIS level-2 and Multi-scale Ultra-high Resolution (MUR) level-4 satellite SST products at 1 km spatial resolution and to assess the robustness of the quality levels of MODIS level-2 products over the California Coast. Pixel-based SST comparisons between Saildrone and the satellite products were performed, as well as thermal gradient comparisons computed both at the pixel-base level and using kriging interpolation. The results generally showed better accuracies for the MUR products. The characterization of the MODIS quality level proved to be valid in areas covered by bad-quality MODIS pixels but less valid in areas covered by lower-quality pixels. The latter implies possible errors in the MODIS quality level characterization and MUR interpolation processes. We have demonstrated the ability of the Saildrones to accurately validate near-shore satellite SST products and provide important information for the quality assessment of satellite products.

Differences in MOPITT surface level CO retrievals and trends from Level 2 and Level 3 products in coastal grid boxes

Abstract. Users of MOPITT (Measurement of Pollution in the Troposphere) data are advised to discard retrievals performed over water from analyses. This is because MOPITT retrievals are more sensitive to near-surface CO when performed over land than water, meaning that they have a greater measurement component and are less tied to the a priori CO concentrations (which are taken from a model climatology) that are necessarily used in their retrieval. MOPITT Level 3 (L3) products are a 1∘ × 1∘ gridded average of finer-resolution (∼ 22 × 22 km) Level 2 (L2) retrievals. In the case of coastal L3 grid boxes, L2 retrievals performed over both land and water may be averaged together to create the L3 product, with L2 retrievals over land not contributing to the average at all in certain situations. This conflicts with data usage recommendations. The aim of this paper is to highlight the consequences that this has on surface level retrievals and their temporal trends in “as-downloaded” L3 data (L3O), by comparing them to those obtained if only the L2 retrievals performed over land are averaged to create the L3 product (L3L), for all identified coastal L3 MOPITT grid boxes. First, the difference between surface level retrievals in L3L and the corresponding L2 retrievals performed over water (L3W) is established for days when they are averaged together to create the L3O product for coastal grid boxes (yielding an L3O surface index of “mixed”, L3OM). Mean retrieved volume mixing ratios (VMRs) in L3L differ by over 10 ppbv from those in L3W, and temporal trends detected in L3L are between 0.28 and 0.43 ppbv yr−1 stronger than in L3W, on average. These L3L − L3W differences are clearly linked to retrieval sensitivity differences, with L3W being more heavily tied to the a priori CO profiles used in the retrieval, which are a model-derived monthly mean climatology that, by definition, has no trend year to year. VMRs in the resulting L3OM are significantly different to L3L for 45 % of all coastal grid boxes, corresponding to 75 % of grid boxes where the L3L − L3W difference is also significant. Just under half of the grid boxes that featured a significant L3L − L3W trend difference also see trends differing significantly between L3L and L3OM. Factors that determine whether L3OM and L3L differ significantly include the proportion of the surface covered by land/water and the magnitude of land–water contrast in retrieval sensitivity. Comparing the full L3O dataset to L3L, it is shown that if L3O is filtered so that only retrievals over land (L3OL) are analysed – as recommended – there is a huge loss of days with data for coastal grid boxes. This is because L2 retrievals over land are routinely discarded during the L3O creation process for these grid boxes. There is less data loss if L3OM retrievals are also retained, but the resulting L3O “land or mixed” (L3OLM) subset still has fewer data days than L3L for 61 % of coastal grid boxes. As shown, these additional days with data feature some influence from retrievals made over water, demonstrably affecting mean VMRs and their trends. Coastal L3 grid boxes contain 33 of the 100 largest coastal cities in the world, by population. Focusing on the L3 grid boxes containing these cities, it is shown that mean VMRs in L3OL and L3L differ significantly for 11 of the 27 grid boxes that can be compared (there are no L3OL data for 6 of the grid boxes studied), with 9 of the 18 grid boxes where temporal trend analysis can be performed in L3OL featuring a trend that is significantly different to that in L3L. These differences are a direct result of the data loss in L3OL – data that are available in L2 data (and are incorporated into the L3L product created for this study). The L3L − L3OLM mean VMR difference exceeds 10 (22) ppbv for 11 (3) of these 33 grid boxes, significant in 13 cases, with significant temporal trend differences in 5 cases. It is concluded that an L3 product based only on L2 retrievals over land – the L3L product analysed in this paper, available for public download – could be of benefit to MOPITT data users.

Benefits of fully focused SAR altimetry to coastal wave height estimates: A case study in the North Sea

Estimating the three geophysical variables significant wave height (SWH), sea surface height, and wind speed from satellite altimetry continues to be challenging in the coastal zone because the received radar echoes exhibit significant interference from strongly reflective targets such as sandbanks, sheltered bays, ships etc. Fully focused SAR (FF-SAR) processing exhibits a theoretical along-track resolution of up to less than half a metre. This suggests that the application of FF-SAR altimetry might give potential gains over unfocused SAR (UF-SAR) altimetry to resolve and mitigate small-scale interferers in the along-track direction to improve the accuracy and precision of the geophysical estimates.The objective of this study is to assess the applicability of FF-SAR-processed Sentinel-6 Michael Freilich (S6-MF) coastal altimetry data to obtain SWH estimates as close as possible to the coast.We have developed a multi-mission FF-SAR processor and applied the coastal retracking algorithm CORALv2 to estimate SWH. We assess different FF-SAR and UF-SAR processing configurations, as well as the baseline Level-2 product from EUMETSAT, by comparison with the coastal, high-resolution SWAN-Kuststrook wave model from the Deltares RWsOS North Sea operational forecasting system. This includes the evaluation of the correlation, the median offset, and the percentage of cycles with high correlation as a function of distance to the nearest coastline. Moreover, we analyse the number of valid records and the L2 noise of the records. The case study comprises five coastal crossings of S6-MF that are located along the Dutch coast and the German coast along the East Frisian Islands in the North Sea.We observe that accurate and precise SWH records can be estimated in the nearshore zone within 1–3 km from the coast using satellite SAR altimetry. We find that the FF-SAR-processed dataset with a Level-1b posting rate of 140 Hz shows the greatest similarity with the wave model. We achieve a correlation of ∼0.8 at 80% of valid records and a gain in precision of up to 29% of FF-SAR vs UF-SAR for 1–3 km from the coast. FF-SAR shows, for all cycles, a high correlation of greater than or equal to 0.8 for 1–3 km from the coast. We estimate the decay of SWH from offshore at 30 km to up to 1 km from the coast to amount to 26.4% ± 3.1%.

Deep learning for daily spatiotemporally continuity of satellite surface soil Moisture over eastern China in summer

Except assimilated data of soil moisture (SM), there is always a circumstance that not every spatial point has valid value over a specific region in a day, and there are invalid values or gaps between days in a time series at a spatial point, especially the satellite and ground-based data. Thus, those data are not spatiotemporally seamless on daily time scale, and the seamless data of surface SM is fundamental for research on the land–atmosphere interactions on various time scales. Deep learning methods of multi-perceptron (MLP) and convolution (Conv) neural networks are applied for obtaining spatiotemporally seamless SM over eastern China during May-September of 2015–2020 to combine the advantages of ground-based and remote-sensed data. Firstly, taking atmospheric and land variables as inputs, MLP is trained against the satellite products, including the level-2 product of Chinese FengYun 3C (FY3C), level-3 product of the US Soil Moisture Active Passive (SMAP), and level-2 neural network product of the European Soil Moisture and Ocean Salinity (SMOS). After that, spatiotemporally seamless SM is predicted by MLP, which is stable during training and validation. Compared with the in-situ SM, MLP SM inherits the characteristics of the satellite products, though the quality is slightly improved. Secondly, Conv uses the MLP SM as inputs and is trained against in-situ SM. The unbiased errors are reduced notably that unbiased correlation increases and root mean square errors decreases. At last, from the pattern correlation between precipitation and SM anomalies, the error-reduced Conv SM can well reflect the relationships forced out by the major patterns of rainfall, but the satellite products cannot. In addition, during the evaluation, SMAP has the best quality over eastern China, and SMOS is generally better than FY3C. However, after processed by Conv, the differences of the quality among different satellites are negligible, except some differences in the mean patterns of Conv SM, which is mainly associated with the properties of the satellite products.

A preliminary assessment of Sentinel-3 ocean and land color instrument data for the estimation of chlorophyll-a concentration using bio-optical methods in Annaba Bay and El Kala’s coast (Algerian Basin)

The use of bio-optical methods and high spatial and spectral resolution satellite sensors has enabled the estimation of chlorophyll-a (Chl-a) on a large spatiotemporal scale. However, their application on a regional scale necessitates validation and, in certain cases, calibration, especially when it comes to coastal waters. This study was carried out in the eastern Algerian coastal waters to evaluate the relevance of Sentinel-3 satellite data in Annaba Bay and El Kala’s coast. Sentinel-3 OLCI (Ocean and Land Color Instrument) Level-2 products (OC4Me and Chl-a NN) and the Chl-a generated by applying the C2RCC processor on Level-1B (Chl-a C2RCC) were compared to the measured Chl-a at fixed locations. In addition, nine other Chl-a algorithms have been tested. Finally, six of those later were locally calibrated. The results demonstrate the good performance of the initially available products (Chl-a C2RCC and Chl-a NN), followed by the blue–green algorithms (OC4, OC5, OC6, MedOC4, and ADOC4), whereas the near-infrared (NIR) algorithms (2B-OLCI, 3B-OLCI, and G2B) show underwhelming performance and very poor prediction of the Chl-a concentration. Even though the OC4Me has the best correlation (r = 0.9820), it also has the highest MAE, RMSD, and BIAS (0.628, 1.264, and 0.5696, respectively). The fit of the NIR methods has been significantly refined. Nevertheless, their performance is still inadequate for the estimation of the Chl-a in our region. The performance of the calibrated OCx was improved, with the OC6-AW at the top of the list (r = 0.9881, MAE = 0.08). The latter outperforms the existing ones, which improves the estimation of Chl-a based on the Sentinel-3 OLCI data in our study area.

Refining the Resolution of DUACS Along-Track Level-3 Sea Level Altimetry Products

This paper describes the demonstration of a regional high-resolution level-3 (L3) altimeter data unification and altimeter combination system (DUACS) developed with support from the French space agency (CNES). Deduced from full-rate (20 Hz to 40 Hz) level-2 (L2) altimeter measurements, this product provides sea level anomalies (SLA) and other essential physical variables at a spatial resolution of one sample every ~1 km over the North Atlantic Ocean. This allows us to resolve wavelengths from ~35 km to ~55 km depending on the altimeter considered. This was made possible by recent advances in radar altimeter processing for both synthetic aperture radar (SAR) and low-resolution-mode (LRM) measurements, as well as improvements made to different stages of the DUACS processing chain. Firstly, the new adaptive and low-resolution with range migration correction (LR-RMC) processing techniques were considered for Jason and Sentinel-3 (S3A), respectively. They significantly reduce errors at short wavelengths, and the adaptive processing also reduces possible land contamination near the coast. Next, up-to-date geophysical and environmental corrections were selected for this production. This includes specific corrections intended to reduce the measurement noise on LRM measurements and thus enhance the observability at short wavelengths. Compared with the 1 Hz product, the observable wavelengths reached with the demonstration high-resolution product are reduced by up to one third, or up to half in the northeast Atlantic region. The residual noises were optimally filtered from full-rate measurements, taking into consideration the different observing capabilities of the altimeters processed. A specific data recovery strategy was applied, significantly optimizing the data availability, both in the coastal and open ocean areas. This demonstration L3 product is thus better resolved than the conventional 1 Hz product, especially near the coast, where it is defined up to ~5 km against ~10 km for the 1 Hz version. Multi-mission cross-calibration processing was also optimized with an improved long-wavelength error (LWE) correction, leading to a better consistency between tracks, with a 9–15% reduction in SLA variance at cross-overs. The new L3 product improves the overall consistency with tide gauge measurements, with a reduction in SLA differences variance by 5 and 17% compared with the 1 Hz product from the S3A and Jason-3 (J3) measurements, respectively. Primarily intended for regional applications, this product can significantly contribute to improving high-resolution numerical model output via data assimilation. It also opens new perspectives for a better understanding of regional sea-surface dynamics, with an improved representation of the coastal currents and a refined spectral content revealing the unbalanced signal.

Using a Tandem Flight Configuration between Sentinel-6 and Jason-3 to Compare SAR and Conventional Altimeters in Sea Surface Signatures of Internal Solitary Waves

Satellite altimetry has been providing a continuous record of ocean measurements with numerous applications across the entire range of ocean sciences. A reference orbit has been used since 1992 with TOPEX/Poseidon, which was repeated in the Jason missions, and in the newly launched Sentinel-6 Michael Freilich (in November 2020) to continually monitor the trends of sea level rise and other properties of the sea surface. These multidecadal missions have evolved alongside major technological advances, whose measurements are unified into a single data record owing to continuous intercalibration and validation efforts. However, the new Sentinel-6 provides synthetic aperture radar (SAR) processing, which improves the along-track resolution of conventional altimeters from a few kilometres (e.g., for Jason-3) to about 300 m. This means a major leap in sampling towards higher frequencies of the ocean spectrum, which inevitably means reconciling the assumption of a uniform Brown surface between the footprints of the larger kilometre-scale conventional altimetry and those of the finer-scale SAR altimetry. To explore this issue, this study uses the vantage point of the Sentinel-6/Jason-3 tandem phase to compare simultaneous sea surface signatures of large-scale Internal Solitary Waves (ISWs) between SAR and conventional altimetry. These waves can modulate the sea surface into arrayed sections of increased and decreased roughness with horizontal scales up to 10 km, which inflict sharp transitions between increased and decreased backscatter in the radar altimeters. It is found that Sentinel-6 can provide more detailed structures of ISWs in standard level-2 products, when compared with those from the conventional Jason-3 (similarly to previous results reported from the SAR altimeter from Sentinel-3). However, a new and striking feature is found when comparing the radar backscatter between Sentinel-6 and Jason-3, which are in opposite phases in the ISWs. These intriguing results are discussed in light of the intrinsically different acquisition geometries of SAR and conventional altimeters as well as possible implications thereof.

Himawari-8 High Temporal Resolution AOD Products Recovery: Nested Bayesian Maximum Entropy Fusion Blending GEO With SSO Satellite Observations

High temporal resolution aerosol optical depth (AOD) observations derived from new-generation geostationary (GEO) satellite possess unique advantages in analyzing aerosol fast variation processes and thereby providing more accurate assessments on their climate effects and health risks. Unfortunately, the expected advantages and values are dramatically limited by relatively large proportion of data missing in the GEO AOD products due to cloud obscuration and intrinsic retrieval algorithm. Although several data recovery algorithms have been proposed in recent years to improve the spatial coverage for GEO AOD products, yet most of them aims at filling up the data blanks rather than reconstructing the temporally continuous variation of aerosol. Accordingly, in this study, a novel framework of nested spatiotemporal fusion blending GEO with sun-synchronous orbit (SSO) satellite observations based on Bayesian maximum entropy (BME) theorem is developed for GEO Advanced Himawari-8 Imager (AHI) AOD recovery with the sufficient excavation of complementary information from GEO and SSO satellite observations, where the minute-stage and hour-stage BME fusion are jointly employed to reconcile temporal inconsistency and data discrepancies between GEO and SSO observations. The results demonstrate that the AOD spatial coverage is dramatically increased by 240.9% (from 20.5% to 70%) with ensured accuracy after Nested-BME fusion. Additionally, two case analyses, during the development and dispersion processes of haze respectively, both demonstrate that the proposed Nested-BME fusion framework could reconstruct the reliable aerosol diurnal variation trends on the basis of recovering missing data for Himawari-8 AHI AOD datasets, while the AHI official level-2 and level-3 AOD products fail to capture these key trends. Furthermore, the developed Nested-BME AOD fusion framework is also applicable for other geostationary satellites over other regions, which could substantially enhance the availability and value of high temporal resolution AOD products for better scientific applications.

Evaluation of river water quality in a tropical South Sumatra wetland during COVID-19 pandemic period

<abstract> <p>The COVID-19 outbreak affected the world badly in this 21<sup>st</sup> century leading to the closure of all types of anthropogenic activities. It is declared that there was an environmental betterment in names of water quality and air quality during the COVID-19 period. In this study, we analyzed the improvement in water quality by evaluating the suspended particulate matter (SPM) using the remote sensing technique in a tropical South Sumatra wetland i.e., Musi River in Southern Sumatra, Indonesia. The SPM values were estimated from Landsat 8 images Level-2 product. A quantitative and spatial analyses of before (20<sup>th</sup> May 2019), during (22<sup>nd</sup> May 2020), and after COVID-19 (28<sup>th</sup> May 2022) periods were also calculated. Results revealed that the mean SPM values during COVID-19 period (4.56 mg/L) were lower than that before COVID-19 period (8.33 mg/L). Surprisingly, SPM showed an increase of 54% in SPM values after COVID-19 period, compared with during COVID-19 period. The role of human activities including industrial and domestic wastes during the restriction period was the main reason for alteration of pollution loads in the river. Outputs of this study can be used to arrange policies for the sustainable management of aquatic environments and water resources.</p> </abstract>