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The presence of thick clouds in single optical images shows the contamination of interesting objects. Besides, the difficulty of thick cloud removal is mainly the restoration of the weak boundary information from cloud-contaminated areas. Recently, many deep-learning-based frameworks were applied for cloud removal by obtain the related semantic information from the weak boundary information. However, the large-size cloud-contaminated areas lead to the artificial textures in the resulting images. Thus, obtaining the optimal semantic information from finite boundary information is the key to solve this problem. In this work, we design a deep-learning framework for cloud removal, especially large-size clouds removal (i.e., more than 30% coverage of the whole image). First, we design a cloud location model (CLM), which adopted the fully convolutional network to locate the cloud. Second, desired by theory of the coarse-to-fine restoration, we build a dense-attention network (termed as DANet) for restoring cloud contaminated areas. In the DANet, we design a dense block into the coarse network for training the features of restoring directions of each pixel from the weak boundary information. Furthermore, a contextual attention module is built into refinement network for restoring contaminated areas relying on the semantic relationship between the background and foreground information. Compared with the state-of-the-art methods, the proposed DANet achieved greater removal performances and reconstruct more natural image textures.

Microwave radiometer (MRM) is one of the important payloads on the Chang’e-2 (CE-2) Lunar satellite. In the Chang’e satellite’s observation of the microwave radiation brightness temperature (TB) on the lunar surface, there are some “cold spots” of microwave thermal radiation at night containing the Jackson crater. In order to compare the diurnal radiation TB differences of “cold spots” on the lunar surface, two typical craters at similar latitudes on the northern hemisphere on the lunar farside were selected: Jackson, which represents the new craters with a large number of discrete rocks on their surfaces; and Morse, which no longer has a large number of rocks after long-term meteorite impact and lunar evolution. In this paper, the diurnal variation of CE-2 MRM data in the two craters is presented, and a comparative analysis is made with the (FeO + TiO2) abundance (FTA) obtained by Clementine UV-VIS data and the rock abundance (RA) data of LRO Diviner. We find that the variation of the "cold spots" of lunar surface thermal radiation is closely related to the RA distribution in the newly formed craters on the lunar surface, and also has a certain correlation with the FTA.

In support of the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission, this study evaluates the performance of the Remote sensing of Trace gas and Aerosol Product (RemoTAP) algorithm based on synthetic orbit measurements of realistic atmospheric and geophysical scenes over land. To make use of the added value of the multi-angle polarimeter (MAP) aboard the CO2M mission, the RemoTAP algorithm is developed to perform simultaneous retrieval of trace gas and aerosol properties from both MAP and CO2 imager (CO2I) measurements. At the same time, it has the capability to perform the retrieval of trace gas from only CO2I measurements. To set up the baseline tests, we apply a simple filter based on non-scattering retrievals in different CO2I bands which is able to filter out 80% of the cirrus-contaminated pixels, and after posterior filtering based on goodness of fit, 95% of the cirrus-contaminated cases are screened out. The MAP-CO2I retrieval method is able to reduce the aerosol-induced retrieval error in column-averaged dry-air mole fraction of CO2 (XCO2) in terms of RMSE and bias by more than a factor of 2, compared to CO2I-only retrievals on the filtered pixels. A strong correlation between XCO2 error and surface albedo in CO2I-only retrievals is significantly reduced for MAP-CO2I retrievals. Moreover, XCO2 biases in CO2I-only retrievals exhibit a significant spatiotemporal variability caused by a strong dependence on aerosol load. The biases can be up to 2 ppm over some regions, which are much larger than for the global case. It shows that only by the inclusion of MAP measurements, the large aerosol-induced biases can be mitigated, resulting in the retrievals that meet the mission requirement (precision

The Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission presents new opportunities and new challenges in applying observations of two complementary multi-angle polarimeters for the space-based retrieval of global aerosol properties. Aerosol remote sensing from multi-angle radiometric-only observations enables aerosol characterization to a greater degree than single-view radiometers, as demonstrated by nearly two decades of heritage instruments. Adding polarimetry to the multi-angle observations allows for the retrieval of aerosol optical depth, Angstrom exponent, parameters of size distribution, measures of aerosol absorption, complex refractive index and degree of non-sphericity of the particles, as demonstrated by two independent retrieval algorithms applied to the heritage POLarization and Directionality of the Earth's Reflectance (POLDER) instrument. The reason why this detailed particle characterization is possible is because a multi-angle polarimeter measurement contains twice the number of Degrees of Freedom of Signal (DFS) compared to an observation from a single-view radiometer. The challenges of making use of this information content involve separating surface signal from atmospheric signal, especially when the surface is optically complex and especially in the ultraviolet portion of the spectrum where we show the necessity of polarization in making that separation. The path forward is likely to involve joint retrievals that will simultaneously retrieve aerosol and surface properties, although advances will be required in radiative transfer modeling and in representing optically complex constituents in those models. Another challenge is in having the processing capability that can keep pace with the output of these instruments in an operational environment. Yet, preliminary algorithms applied to airborne multi-angle polarimeter observations offer encouraging results that demonstrate the advantages of these instruments to retrieve aerosol layer height, particle single scattering albedo, size distribution and spectral optical depth, and also show the necessity of polarization measurements, not just multi-angle radiometric measurements, to achieve these results.

The TROPOspheric Monitoring Instrument (TROPOMI) due to its wide swath performs observations over the ocean in every orbit, enhancing the monitoring capabilities of methane from space. In the short-wave infrarred (SWIR) spectral band ocean surfaces are dark except for the specific sun-glint geometry, for which the specular reflectance detected by the satellite provides a signal that is high enough to retrieve methane with high accuracy and precision. In this study, we build upon the RemoTeC full-physics retrieval algorithm for land measurements and we retrieve four years of methane concentrations over the ocean from TROPOMI. We fully assess the quality of the dataset by performing a validation using ground-based measurements of the Total Carbon Column Observing Network (TCCON) from near-ocean sites. The validation results in an agreement of -0.5 ± 0.3% (-8.4 ± 6.3 ppb) for the mean bias and station-to-station variability, which show that glint measurements comply with the mission requirement of precision and accuracy below 1 %. Comparison to ocean measurements from the Greenhouse gases Observing SATellite (GOSAT) satellite results in a bias of -0.2 ± 0.9% (-4.4 ± 15.7 ppb), equivalent to the comparison of measurements over land. The full-physics algorithm simultaneously retrieves the amount of atmospheric methane and the physical scattering properties of the atmosphere from measurements in the near-infrarred (NIR) and SWIR spectral bands. Based on the backscattering properties of the atmosphere and ocean surface reflection we further validate retrievals over the ocean. Using the "upper-edge" method, we identify a set of ocean glint observations where scattering by aerosols and clouds can be ignored in the measurement simulation to investigate other possible error sources such as instrumental errors, radiometric inaccuracies or uncertainties related to spectroscopic absorption cross-sections. With this ensemble we evaluate the RemoTeC forward model via the validation of the total atmospheric oxygen (O2) column retrieved from the O2-A band, as well as the consistency of XCH4 retrievals using sub-bands from the SWIR band, which show a consistency within 1%. We discard any instrumental and radiometric errors by a calibration of the O2 absorption line strengths as suggested in the literature. [ABSTRACT FROM AUTHOR]

Several single-platform satellite missions have been designed during the past decades in order to retrieve the atmospheric concentrations of anthropogenic greenhouse gases (GHG), initiating worldwide efforts towards better monitoring of their sources and sinks. To set up a future operational system for anthropogenic GHG emission monitoring, both revisit frequency and spatial resolution need to be improved. The Space CARBon Observatory (SCARBO) project aims at significantly increasing the revisit frequency of spaceborne GHG measurements, while reaching state-of-the-art precision requirements, by implementing a concept of small satellite constellation. It would accommodate a miniaturized GHG sensor named NanoCarb coupled with an aerosol instrument, the multi-angle polarimeter SPEXone. More specifically, the NanoCarb sensor is a static Fabry-Perot imaging interferometer with a 2.3 x 2.3 km² spatial resolution and 200 km swath. It samples a truncated interferogram at optical path differences (OPDs) optimally sensitive to all the geophysical parameters necessary to retrieve column-averaged dry-air mole fractions of CO2 and CH4 (hereafter XCO2 and XCH4). In this work, we present the Level 2 performance assessment of the concept proposed in the SCARBO project. We perform inverse radiative transfer to retrieve XCO2 and XCH4 directly from synthetic NanoCarb truncated interferograms, and provide their systematic and random errors, column vertical sensitivities and degrees of freedom as a function of five scattering error-critical atmospheric and observational parameters. We show that NanoCarb XCO2 and XCH4 systematic retrieval errors can be greatly reduced with SPEXone posterior outputs used as improved prior aerosol constraints. For two thirds of the soundings, located at the centre of the 200 km NanoCarb swath, XCO2 and XCH4 random errors span 0.5 – 1 ppm and 4 – 6 ppb, respectively, compliant with their respective 1-ppm and 6-ppb precision objectives. Finally, these Level 2 performance results are parameterized as a function of the explored scattering error-critical atmospheric and observational parameters in order to time-efficiently compute extensive L2 error maps for future CO2 and CH4 flux estimation performance studies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic resulted in reduced anthropogenic carbon dioxide (CO2) emissions during 2020 in large parts of the world. We report results from a first attempt to determine whether a regional-scale reduction of anthropogenic CO2 emissions during the COVID-19 pandemic can be detected using space-based observations of atmospheric CO2. For this purpose, we have analysed a small ensemble of satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2. We focus on East China because COVID-19 related CO2 emission reductions are expected to be largest there early in the pandemic. We analysed four XCO2 data products from the satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse gases Observing SATellite (GOSAT). We use a data-driven approach that does not rely on a priori information about CO2 sources and sinks and ignores atmospheric transport. Our approach utilises the computation of XCO2 anomalies, ?XCO2, from the satellite Level 2 data products using a method called DAM (Daily Anomalies via (latitude band) Medians). DAM removes large-scale, daily XCO2 background variations, yielding XCO2 anomalies that correlate with the location of major CO2 source regions such as East China. We analysed satellite data between January 2015 and May 2020 and compared monthly XCO2 anomalies in 2020 with corresponding monthly XCO2 anomalies of previous years. In order to link the XCO2 anomalies to East China fossil fuel (FF) emissions, we used XCO2 and corresponding FF emissions from NOAA's (National Oceanic and Atmospheric Administration) CarbonTracker version CT2019 from 2015 to 2018. Using this CT2019 data set, we found that the relationship between target region ?XCO2 and the FF emissions of the target region is approximately linear and we quantified slope and offset via a linear fit. We use the empirically obtained linear equation to compute ?XCO2FF, an estimate of the target region FF emissions, from the satellite-derived XCO2 anomalies, ?XCO2. We focus on October to May periods to minimize contributions from biospheric carbon fluxes and quantified the error of our FF estimation method for this period by applying it to CT2019. We found that the difference of the retrieved FF emissions and the CT2019 FF emissions in terms of the root-mean-square-error (RMSE) is 0.39 GtCO2/year (4 %). We applied our method to NASA's (National Aeronautics and Space Administration) OCO-2 XCO2 data product (version 10r) and to three GOSAT products. We focus on estimates of the relative change of East China monthly emissions in 2020 relative to previous months. Our results show considerable month-to-month variability (especially for the GOSAT products) and significant differences across the ensemble of satellite data products analysed. The ensemble mean indicates emission reductions by approximately 8 % ± 10 % in March 2020 and 10 % ± 10 % in April 2020 (uncertainties are 1-sigma) and somewhat lower reductions for the other months in 2020. Using only the OCO-2 data product, we obtain smaller reductions of 1-2 % (depending on month) with an uncertainty of ± 2 %. The large uncertainty and the differences of the results obtained for the individual ensemble members indicates that it is challenging to reliably detect and to accurately quantify the emission reduction. There are several reasons for this including the weak signal (the expected regional XCO2 reduction is only on the order of 0.1-0.2 ppm), the sparseness of the satellite data, remaining biases and limitations of our relatively simple data-driven analysis approach. Inferring COVID-19 related information on regional-scale CO2 emissions using current satellite XCO2 retrievals likely requires, if at all possible, a more sophisticated analysis method including detailed transport modelling and considering a priori information on anthropogenic and natural CO2 surface fluxes. [ABSTRACT FROM AUTHOR]

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term Climate Data Records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003-2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT, TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory-2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the “ensemble median algorithm” (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5ox5o data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consists of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets): single observation random error (1-sigma): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm), spatio-temporal bias or “relative accuracy” (1-sigma): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are: single observation random error (1-sigma): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb), spatio-temporal bias (1-sigma): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from December 2019 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/). [ABSTRACT FROM AUTHOR]

In light of the proposed space segment of Europe's future CO2 monitoring system, we investigate the spectral resolution of the CO2 spectrometer, which measures Earthshine radiance in the three relevant spectral bands at 0.76, 1.61 and 2.06 μm. The Orbiting Carbon Observatory-2 (OCO-2) mission covers these bands with fine spectral resolution but limited spatial coverage, which hampers the monitoring of localized anthropogenic CO2 emission. The future European CO2 monitoring constellation, currently undergoing feasibility studies at the European Space Agency (ESA), is targeting a moderate spectral resolution of 0.1, 0.3 and 0.3–0.55 nm in the three spectral bands with high signal-to-noise (SNR) ratio. This spectral and radiometric sizing is deemed to be favorable for large-swath imaging of point-sources of CO2 emission. To assess this choice, we use real and synthetic OCO-2 satellite observations, which we spectrally degrade to the envisaged lower spectral resolution. We evaluate the corresponding CO2 retrieval accuracy by taking the Total Carbon Column Observing Network (TCCON) observations as reference. Here, a lower spectral resolution enhances the scatter error of the retrieved CO2 column mixing ratio (XCO2) but has little effect on the station-to-station variation of the biases. We show that the scatter error gradually increases when decreasing spectral resolution. Part of the scatter error increase can be attributed to the retrieval noise error which can be compensated by a future instrument with improved SNR. Moreover, we consider the effect of the reduced spectral resolution on the capability to capture regional XCO2 variations and XCO2 plumes from selected OCO-2 orbits. The investigation using measurements from the Greenhouse gases Observing SATellite (GOSAT) and synthetic measurements confirms our finding and indicates that one major source of uncertainties regarding CO2 retrieval is the insufficient information on aerosol properties that can be inferred from the observations. We hence recommend the implementation of simultaneous, co-located measurements that have larger information content on aerosols with an auxiliary instrument in the future European observing system. [ABSTRACT FROM AUTHOR]

Passive remote sensing of atmospheric carbon dioxide uses spectroscopic measurements of sunlight back-scattered by the Earth's surface and atmosphere. The current state-of-the-art retrieval methods use three different spectral bands, the oxygen A band at 0.76 μm and the weak and strong CO2 absorption bands at 1.61 and 2.06 μm, respectively, to infer information on light scattering and the carbon dioxide column-averaged dry-air mole fraction XCO2. In this study, we propose a one-band XCO2 retrieval technique which uses only the 2.06 μm band measurements from the OCO-2 satellite. We examine the data quality by comparing the OCO-2 XCO2 with collocated ground based measurements from the Total Carbon Column Observing Network (TCCON). Over land and ocean the OCO-2 one-band retrieval shows differences to TCCON observations with a standard deviation of ~ 1.30 ppm and a station-to-station variability of ~ 0.50 ppm. Moreover, we compare one-band and three-band retrievals over Europe,the Middle East and Africa region and see high correlation between the two retrievals with a SD of 0.93 ppm. Compared to the three-band retrievals, using only the 2.06 μm band similar XCO2 retrieval accuracy and precision can be obtained while retaining a similar data yield. [ABSTRACT FROM AUTHOR]

In this study we present the retrieval of the column averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) satellite observations using the RemoTeC algorithm, previously successfully applied to retrieval of greenhouse gas concentration from the Greenhouse Gases Observing Satellite (GOSAT). The XCO2 product has been validated with collocated ground based measurements from the Total Carbon Column Observing Network (TCCON) for almost 2 years of OCO-2 data from September 2014 to July 2016. We found that fitting an additive radiometric offset in all three spectral bands of OCO-2 significantly improved the retrieval. Based on a small correlation of the XCO2 error over land with fit residuals, we applied an a posteriori bias correction to our OCO-2 retrievals. In daily averaged results, XCO2 retrievals have a standard deviation ~ 1.30 ppm and a station-to-station variability of ~ 0.40 ppm among collocated TCCON sites. The seasonal relative accuracy (SRA) has a value of 0.52 ppm. The validation shows relatively larger difference with TCCON over high latitude areas and some specific regions like Japan. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm for the retrieval of aerosol and land surface properties from airborne spectropolarimetric measurements - combining neural networks and an iterative scheme based on Phillips-Tikhonov regularization - is described. The algorithm - which is an extension of a scheme previously designed for ground-based retrievals - is applied to measurements from the Research Scanning Polarimeter (RSP) on board the NASA ER-2 aircraft. A neural network, trained on a large data set of synthetic measurements, is applied to perform aerosol retrievals from real RSP data, and the neural network retrievals are subsequently used as a first guess for the Phillips-Tikhonov retrieval. The resulting algorithm appears capable of accurately retrieving aerosol optical thickness, fine-mode effective radius and aerosol layer height from RSP data. Among the advantages of using a neural network as initial guess for an iterative algorithm are a decrease in processing time and an increase in the number of converging retrievals. [ABSTRACT FROM AUTHOR]