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The Advanced Very High Resolution Radiometer (AVHRR) provides a global, long-term, consistent time series of radiance data in several wavebands which are used for the retrieval of surface spectral reflectance, albedo and surface temperature. Long-term time series of such data products are necessary for studies addressing climate change, sea ice distribution and movement, and ice sheet coastal configuration. AVHRR radiances have also been used to retrieve aerosol properties over ocean and land surfaces. However, the retrieval of aerosol over land is challenging because of the limited information content in the data which renders the inversion problem ill defined. Solving the radiative transfer equations requires additional information to reduce the number of unknowns. In this contribution we utilise an empirical linear relationship between the surface reflectances in the AVHRR channels at wavelengths of 3.75 μm and 2.1 μm, which has been identified in the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Following the MODIS dark target approach, this relationship is used to obtain the surface reflectance at 0.64 μm. The comparison of the estimated surface reflectances with MODIS reflectance products (MOD09) shows a strong correlation. Once this was established, the MODIS "dark-target" aerosol retrieval method was adapted to AVHRR data. A simplified look-up table (LUT) method, adopted from the Bremen AErosol Retrieval (BAER) algorithm, was used in the retrieval. The aerosol optical depth (AOD) values retrieved from AVHRR with this method compare favourably with ground-based measurements, with 71.8% of the points located within ±(0.1 + 0.15τ) (τ is the AOD) of the identity line. This method can be easily applied to other satellite instruments which do not have a 2.1 μm channel, such as those currently planned to be used on geostationary satellites.

Measurements by the hyperspectral spectrometers GOME, SCIAMACHY and GOME-2 are used to determine the rate of linear change (and trends) in cloud top height (CTH) in the period between June 1996 and May 2012. The retrievals are obtained from Top-Of-Atmosphere (TOA) backscattered solar light in the oxygen A-band using the Semi-Analytical CloUd Retrieval Algorithm SACURA. The physical framework relies on the asymptotic equations of radiative transfer, valid for optically thick clouds. Using linear least-squares techniques, a global trend of −1.78 ± 2.14 m yr−1 in deseasonalized CTH has been found, in the latitude belt within ±60°, with diverging tendencies over land (+0.27 ± 3.2 m yr−1) and ocean (−2.51 ± 2.8 m yr−1). The El Niño–Southern Oscillation (ENSO), strongly coupled to CTH, forces clouds to lower altitudes. The global ENSO-corrected trend in CTH amounts to −0.49 ± 2.22 m yr−1. At a global scale, no explicit regional pattern of statistically significant trends (at 95% confidence level, estimated with bootstrap technique) have been found, which would be representative of typical natural synoptical features. One exception is North Africa, which exhibits the strongest upward trend in CTH sustained by an increasing trend in water vapour.

The influence of horizontal and vertical inhomogeneity of snow surfaces on solar light reflectance is studied using the radiative transfer theory (RTT). We compared 1-D RTT and 2-D RTT and found that large errors are produced if the 1-D RTT is used for the calculation of the snow reflection function (and, therefore, also in the retrievals of the snow grain radii) in 2-D measurement geometries. Such 2-D geometries are common in the procedures for the determination of the effective snow grain radii using near-infrared photography and spectroscopy of vertical snow walls. In particular, we have considered three cases for the numerical calculations: (1) the case with no black film; (2) the case with a black film at the pit's bottom; (3) the case with a black film at the pit's bottom and also at one of the vertical snow walls.

This study compares different methods to retrieve the specific surface area (SSA) of snow from satellite radiance measurements in mountainous terrain. It aims at addressing the effect on the retrieval of topographic corrections of reflectance, namely slope and aspect of terrain, multiple reflections on neighbouring slopes and accounting (or not) for the anisotropy of snow reflectance. Using MODerate resolution Imaging Spectrometer (MODIS) data for six different clear sky scenes spanning a wide range of snow conditions during the winter season 2008–2009 over a domain of 46 × 50 km in the French Alps, we compared SSA retrievals with and without topographic correction, with a spherical or non-spherical snow reflectance model and, in spherical case, with or without anisotropy corrections. The retrieved SSA values were compared to field measurements and to the results of the detailed snowpack model Crocus, fed by driving data from the SAFRAN meteorological analysis. It was found that the difference in terms of surface SSA between retrieved values and SAFRAN-Crocus output was minimal when the topographic correction was taken into account, when using a retrieval method assuming disconnected spherical snow grains. In this case, the root mean square deviation was 9.4 m2 kg−1 and the mean difference was 0.1 m2 kg−1, based on 3170 pairs of observation and simulated values. The added-value of the anisotropy correction was not significant in our case, which may be explained by the presence of mixed pixels and surface roughness. MODIS retrieved data show SSA variations with elevation and aspect which are physically consistent and in good agreement with SAFRAN-Crocus outputs. The variability of the MODIS retrieved SSA within the topographic classes of the model was found to be relatively small (3.9 m2 kg−1). This indicates that semi-distributed snowpack simulations in mountainous terrain with a sufficiently large number of classes provides a representation of the snowpack variability consistent with the scale of MODIS 500 m pixels.

We present a global and regional multi-annual (June 1996–May 2003) analysis of cloud properties (spherical cloud albedo – CA, cloud optical thickness – COT and cloud top height – CTH) of optically thick (COT > 5) clouds, derived using measurements from the GOME instrument on board the ESA ERS-2 space platform. We focus on cloud top height, which is obtained from top-of-atmosphere backscattered solar light measurements in the O2 A-band using the Semi-Analytical CloUd Retrieval Algorithm SACURA. The physical framework relies on the asymptotic equations of radiative transfer. The dataset has been validated against independent ground- and satellite-based retrievals and is aimed to support trace-gases retrievals as well as to create a robust long-term climatology together with SCIAMACHY and GOME-2 ensuing retrievals. We observed the El Niño-Southern Oscillation anomaly in the 1997–1998 record through CTH values over the Pacific Ocean. The global average CTH as derived from GOME is 5.6 ± 3.2 km, for a corresponding average COT of 19.1 ± 13.9.

Regular aerosol observations based on well-calibrated instruments have led to a better understanding of the aerosol radiative budget on Earth. In recent years, these instruments have played an important role in the determination of the increase of anthropogenic aerosols by means of long-term studies. Only few investigations regarding long-term trends of aerosol optical characteristics (e.g. aerosol optical thickness (AOT) and Ångström exponent (ÅE)) have been derived from ground-based observations. This paper aims to derive and discuss linear trends of AOT (440, 675, 870, and 1020 nm) and ÅE (440–870 nm) using AErosol RObotic NETwork (AERONET) level 2.0 spectral observations. Additionally, temporal trends of coarse- and fine-mode dominant AOTs (CdAOT and FdAOT) have been estimated by applying an aerosol classification based on accurate ÅE and Ångström exponent difference (ÅED). In order to take into account the fact that cloud disturbance is having a significant influence on the trend analysis of aerosols, we introduce a weighted least squares regression depending on two weights: (1) monthly standard deviation (σt) and (2) number of observations per month (nt). Temporal increase of FdAOTs (440 nm) prevails over newly industrializing countries in East Asia (weighted trends; +6.23% yr−1 at Beijing) and active agricultural burning regions in South Africa (+1.89% yr−1 at Mongu). On the other hand, insignificant or negative trends for FdAOTs are detected over Western Europe (+0.25% yr−1 at Avignon and −2.29% yr−1 at Ispra) and North America (−0.52% yr−1 for GSFC and −0.01% yr−1 at MD_Science_Center). Over desert regions, both increase and decrease of CdAOTs (+3.37% yr−1 at Solar_Village and −1.18% yr−1 at Ouagadougou) are observed depending on meteorological conditions.

Infrared (IR) retrievals of aerosol optical thickness (AOT) are challenging because of the low reflectance of aerosol layer at longer wavelengths. In this paper we present a closer analysis of this problem, performed with radiative transfer (RT) simulations for coarse and accumulation mode of four main aerosol components. It shows the strong angular dependence of aerosol IR reflectance at low solar elevations resulting from the significant asymmetry of aerosol phase function at these wavelengths. This results in detectable values of aerosol IR reflectance at certain non-nadir observation angles providing the advantage of multiangle remote sensing instruments for a retrieval of AOT at longer wavelengths. Such retrievals can be of importance e.g. in case of a very strong effect of the surface on the top of atmosphere (TOA) reflectance in the visible spectral range. In the current work, a new method to retrieve AOT of the coarse and accumulation mode particles over snow has been developed using the measurements of Advanced Along Track Scanning Radiometer (AATSR) on board the ENVISAT satellite. The algorithm uses AATSR channel at 3.7 μm and utilizes its dual-viewing observation technique, implying the forward view with an observation zenith angle of around 55 degrees and the nadir view. It includes cloud/snow discrimination, extraction of the atmospheric reflectance out of measured brightness temperature (BT) at 3.7 μm, and interpolation of look-up tables (LUTs) for a given aerosol reflectance. The algorithm uses LUTs, separately simulated with RT forward calculations. The resulting AOT at 500 nm is estimated from the value at 3.7 μm using a fixed Angström parameter. The presented method has been validated against ground-based Aerosol Robotic Network (AERONET) data for 4 high Arctic stations and shows good agreement. A case study has been performed at W-Greenland on 5 July 2008. The day before was characterized by a noticeable dust event. The retrieved AOT maps of the region show a clear increase of AOT in the Kangerlussuaq area. The area of increased AOT was detected on 5 July on the ice sheet east of Kangelussuaq, opposite to the observed north easterly wind at ground level. This position can be explained by a small scale atmospheric circulation transporting the mobilized mineral dust upslope, after its intrusion into the upper branch of the circulation. The performed study of atmospheric reflectance at 3.7 μm also shows possibilities for the detection and retrievals of cloud properties over snow surfaces.

For the determination of aerosol optical thickness (AOT) Bremen AErosol Retrieval (BAER) has been developed. Method and main features on the aerosol retrieval are described together with validation and results. The retrieval separates the spectral aerosol reflectance from surface and Rayleigh path reflectance for the shortwave range of the measured spectrum of top-of-atmosphere reflectance for wavelength less than 0.670 μm. The advantage of MERIS (Medium Resolution Imaging Spectrometer on the Environmental Satellite – ENVISAT – of the European Space Agency – ESA) and SeaWiFS (Sea viewing Wide Field Sensor on OrbView-2 spacecraft) observations is the availability of several spectral channels in the blue and visible range enabling the spectral determination of AOT in 7 (or 6) channels (0.412–0.670 μm) and additionally channels in the NIR, which can be used to characterize the surface properties. A dynamical spectral surface reflectance model for different surface types is used to obtain the spectral surface reflectance for this separation. The normalized differential vegetation index (NDVI), taken from the satellite observations, is the model input. Further surface bi-directional reflectance distribution function (BRDF) is considered by the Raman-Pinty-Verstraete (RPV) model. Spectral AOT is obtained from aerosol reflectance using look-up-tables, obtained from radiative transfer calculations with given aerosol phase functions and single scattering albedos either from aerosol models, given by model package "optical properties of aerosol components" (OPAC) or from experimental campaigns. Validations of the obtained AOT retrieval results with data of Aerosol Robotic Network (AERONET) over Europe gave a preference for experimental phase functions derived from almucantar measurements. Finally long-term observations of SeaWiFS have been investigated for 11 year trends in AOT. Western European regions have negative trends with decreasing AOT with time. For the investigated Asian region increasing AOT have been found.

Since clouds play an essential role in the Earth's climate system, it is important to understand the cloud characteristics as well as their distribution on a global scale using satellite observations. The main scientific objective of SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) onboard the ENVISAT satellite is the retrieval of vertical columns of trace gases. On the one hand, SCIAMACHY has to be sensitive to low variations in trace gas concentrations which means the ground pixel size has to be large enough. On the other hand, such a large pixel size leads to the problem that SCIAMACHY spectra are often contaminated by clouds. SCIAMACHY spectral measurements are not well suitable to derive a reliable sub-pixel cloud fraction that can be used as input parameter for subsequent retrievals of cloud properties or vertical trace gas columns. Therefore, we use MERIS/ENVISAT spectral measurements with its high spatial resolution as sub-pixel information for the determination of MerIs Cloud fRation fOr Sciamachy (MICROS). Since MERIS covers an even broader swath width than SCIAMACHY, no problems in spatial and temporal collocation of measurements occur. This enables the derivation of a SCIAMACHY cloud fraction with an accuracy much higher as compared with other current cloud fractions that are based on SCIAMACHY's PMD (Polarization Measurement Device) data. We present our new developed MICROS algorithm, based on the threshold approach, as well as a qualitative validation of our results with MERIS satellite images for different locations, especially with respect to bright surfaces such as snow/ice and sands. In addition, the SCIAMACHY cloud fractions derived from MICROS are intercompared with other current SCIAMACHY cloud fractions based on different approaches demonstrating a considerable improvement regarding geometric cloud fraction determination using the MICROS algorithm.

We present here the aerosol retrieval technique FAR that uses radiative transfer computations in the process of retrieval rather than look-up tables (LUT). This approach provides operational satellite data processing due to the use of the accurate and extremely fast radiative transfer code RAY previously developed by authors along with approximate analytical solutions of the radiative transfer theory. The model of the stratified atmosphere is taken as two coupled layers. Both layers include aerosol scattering and absorption, molecular scattering and gas absorption. The atmosphere parameters are assumed to change from pixel to pixel in the lower atmosphere layer, but the upper stratified layer of the atmosphere over 2–3 km is supposed to be horizontally homogenous for the frame under retrieval. The model of the land spectral albedo is taken as a weighted sum of two a priory chosen basic spectra. The aerosol optical thickness (AOT), Angström exponent and the weight in the land spectral albedo are optimized in the iteration process using the least-squares technique with fast computations of the derivatives of radiative characteristics with respect to retrieved values. The aerosol model and, hence, the aerosol phase function and single scattering albedo, is predefined and does not change in the iteration process. The presented version of FAR is adjusted to process the MERIS data. But it is important that the developed technique can be adapted for processing data of various satellite instruments (including any spectral multi-angle polarization-sensitive sensors). The use of approximate analytical radiative transfer solutions considerably speeds up data processing but may lead to about 15–20% increase of AOT retrieval errors. This approach is advantageous when just the satellite data processing time rather than high accuracy of the AOT retrieval is crucial. A good example is monitoring the trans-boundary transfer of aerosol impurities, particularly in the case of emergencies such as volcano eruptions, or various industrial disasters. Beside, two important problems that determine the accuracy of the AOT retrieval are considered. The first one is the effect of the preliminary choice of the aerosol model, particularly for the retrieval from satellite instruments providing only spectral data (MERIS, MODIS). The second problem is the influence of clouds in adjacent pixels. As for our knowledge, this problem has not been given the required attention up to now and it should be properly accounted for in the AOT retrieval algorithms.

A new method to detect cloud-free snow-covered areas has been developed using the measurements by the Advanced Along Track Scanning Radiometer (AATSR) on board the ENVISAT satellite in order to discriminate clear snow fields for the retrieval of aerosol optical thickness or snow properties. The algorithm uses seven AATSR channels from visible (VIS) to thermal infrared (TIR) and analyses the spectral behaviour of each pixel in order to recognize the spectral signature of snow. The algorithm includes a set of relative thresholds and combines all seven channels into one flexible criterion, which allows us to filter out all the pixels with spectral behaviour similar to that of snow. The algorithm does not use any kind of morphological criteria and does not require the studied surface to have any special structure. The snow spectral shape criterion was determined by a comprehensive theoretical study, which included radiative transfer simulations for various atmospheric conditions as well as studying existing models and measurements of optical and physical properties of snow in different spectral bands. The method has been optimized to detect cloud-free snow-covered areas, and does not produce cloud/land/ocean/snow mask. However, the algorithm can be extended and able to discriminate various kinds of surfaces. The presented method has been validated against Micro Pulse Lidar data and compared to Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask over snow-covered areas, showing quite good correspondence to each other. Comparison of both MODIS cloud mask and presented snow mask to AATSR operational cloud mask showed that in some cases of snow surface the accuracy of AATSR operational cloud mask is questionable.

Radiative transfer models (RTMs) are of utmost importance for quantitative remote sensing, especially for compensating atmospheric perturbation. A persistent trade-off exists between approaches that prefer accuracy at the cost of computational complexity, versus those favouring simplicity at the cost of reduced accuracy. We propose an approach in the latter category, using analytical equations, parameterizations and a correction factor to efficiently estimate the effect of molecular multiple scattering. We discuss the approximations together with an analysis of the resulting performance and accuracy. The proposed Simple Model for Atmospheric Radiative Transfer (SMART) decreases the calculation time by a factor of more than 25 in comparison to the benchmark RTM 6S on the same infrastructure. The relative difference between SMART and 6S is about 5% for spaceborne and about 10% for airborne computations of the atmospheric reflectance function. The combination of a large solar zenith angle (SZA) with high aerosol optical depth (AOD) at low wavelengths lead to relative differences of up to 15%. SMART can be used to simulate the hemispherical conical reflectance factor (HCRF) for spaceborne and airborne sensors, as well as for the retrieval of columnar AOD.

Remote sensing of aerosol from space is a challenging and typically underdetermined retrieval task, requiring many assumptions to be made with respect to the aerosol and surface models. Therefore, the quality of a priori information plays a central role in any retrieval process (apart from the cloud screening procedure and the forward radiative transfer model, which to be most accurate should include the treatment of light polarization and molecular-aerosol coupling). In this paper the performance of various algorithms with respect to the of spectral aerosol optical thickness determination from optical spaceborne measurements is studied. The algorithms are based on various types of measurements (spectral, angular, polarization, or some combination of these). It is confirmed that multiangular spectropolarimetric measurements provide more powerful constraints compared to spectral intensity measurements alone, particularly those acquired at a single view angle and which rely on a priori assumptions regarding the particle phase function in the retrieval process.

A new technique to retrieve the particulate matter vertical columns from spaceborne observations is described. The method is based on the measurements of the spectral aerosol optical thickness (AOT). The spectral slope of the derived aerosol optical thickness is used to infer the size of particles, which is needed (along with the absolute value of AOT) to determine corresponding vertical columns. The technique is applied to the case of a cloudless atmosphere over Germany and results are compared with ground-based observations. Several assumptions are made in the retrieval process such as the prescribed phase function, single scattering albedo, the refractive index of aerosol, and the half-width of the size distribution.

Snow on the ground is a complex multiphase photochemical reactor that dramatically modifies the chemical composition of the overlying atmosphere. A quantitative description of the emissions of reactive gases by snow requires knowledge of snow physical properties. This overview details our current understanding of how those physical properties relevant to snow photochemistry vary during snow metamorphism. Properties discussed are density, specific surface area, thermal conductivity, permeability, gas diffusivity and optical properties. Inasmuch as possible, equations to parameterize these properties as functions of climatic variables are proposed, based on field measurements, laboratory experiments and theory. The potential of remote sensing methods to obtain information on some snow physical variables such as grain size, liquid water content and snow depth are discussed. The possibilities for and difficulties of building a snow photochemistry model by adapting current snow physics models are explored. Elaborate snow physics models already exist, and including variables of particular interest to snow photochemistry such as light fluxes and specific surface area appears possible. On the other hand, understanding the nature and location of reactive molecules in snow seems to be the greatest difficulty modelers will have to face for lack of experimental data, and progress on this aspect will require the detailed study of natural snow samples.

The authors propose a new method for the assignment of rainfall confidences on a pixel basis using cloud properties derived from optical satellite data during daytime. This approach is based on the concept model that the probability for precipitation is a function of the liquid water path, which in turn can be computed using the satellite-retrieved cloud optical thickness and the cloud effective droplet radius. In order to evaluate the principal potential of this idea, scenes from the Terra-MODIS sensor during the severe European summer floods in 2002 have been analysed in order to derive a corresponding regression function that interlinks the liquid water path with the rainfall probability or better with the confidence that a pixel which is classified as raining does actually rain. A first evaluation against ground-based radar data during March 2004 shows good skill of this new method.

Aerosol remote sensing is very much dependent on the accurate knowledge of the top-of-atmosphere (TOA) reflectance measured by a particular instrument. The status of the calibration of such an instrument is reflected in the quality of the aerosol retrieval. Current data of the SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) instrument (operated with the data processor version 5 and earlier) give too small values of the TOA reflectance, compared e.g. to data from MERIS (Medium Resolution Imaging Spectrometer), both operating on ENVISAT (ENVIronmental SATellite). This effect causes retrievals of wrong aerosol optical thickness and disables the processing of aerosol parameters. From an inter-comparison of MERIS and SCIAMACHY TOA reflectance, for collocated scenes correction factors are derived to improve the insufficient SCIAMACHY L1 data calibration for data obtained with the processor 5 for the purpose of aerosol remote sensing. The corrected reflectance has been used for tests of remote sensing of the aerosol optical thickness by the BAER (Bremen AErosol Retrieval) approach using SCIAMACHY data.

We propose a new method for the delineation of precipitation using cloud properties derived from optical satellite data. This approach is not only sufficient for the detection of mainly convective precipitation by means of the commonly used connection between infrared cloud top temperature and rainfall probability but enables the detection of stratiform precipitation (e.g., in connection with mid-latitude frontal systems). The scheme presented is based on the concept model, that precipitating clouds must have both a sufficient vertical extent and large enough droplets. Therefore, we have analysed MODIS scenes during the severe European summer floods in 2002 and retrieved functions for the computation of an auto-adaptive threshold value of the effective cloud droplet radius with respect to the corresponding optical thickness which links these cloud properties with rainfall areas on a pixel basis.

The authors introduce a radiative transfer model CLOUD for reflection, transmission, and absorption characteristics of terrestrial clouds and discuss the accuracy of the approximations used within the model. A Fortran implementation of CLOUD is available for download. This model is fast, accurate, and capable of calculating multiple radiative characteristics of cloudy media including the spherical and plane albedo, reflection and transmission functions, absorptance as well as global and diffuse transmittance. The approximations are based on the asymptotic solutions of the radiative transfer equations valid at cloud optical thicknesses larger than 5. While the analytic part of the solutions is treated in the code in an approximate way, the correspondent reflection function (RF) of a semi-infinite water cloud R∞ is calculated using numerical solutions of the radiative transfer equation in the assumption of Deirmendjian's cloud C1 model. In the case of ice clouds, the fractal ice crystal model is used. The resulting values of R∞ with respect to the viewing geometry are stored in a look-up table (LUT). The results obtained are of importance for quick estimations of main radiative characteristics of clouds and also for the solution of inverse problems.

The SemiAnalytical CloUd Retrieval Algorithm (SACURA) is applied to the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) data. In particular, we derive simultaneously cloud optical thickness (COT) and cloud top height (CTH), using SCIAMACHY measurements in the visible (442 nm, COT) and in the oxygen A-band (755–775 nm, CTH). Some of the results obtained are compared with those derived from the Medium Resolution Imaging Spectrometer (MERIS), which has better spatial resolution and observes almost the same scene as SCIAMACHY. The same cloud algorithm is applied to both MERIS and SCIAMACHY data. In addition, we perform the vicarious calibration of SCIAMACHY at the wavelength 442 nm, using MERIS measurements at the same wavelength. Differences in the retrieved COT for the same cloud field obtained using MERIS and SCIAMACHY measurements are discussed.

Modifications of existing clouds by the exhaust of ships are well-known but inadequately quantified impacts, which could contribute to climate change. The perturbation of a cloud layer by ship-generated aerosol changes the cloud reflectivity and is identified by long curves in satellite images, known as ship tracks. As ship tracks indicate a pollution of a very clean marine environment and also affect the radiation budget below and above the cloud, it is important to investigate their radiative and climatic effects. Satellite-data from MODIS on Terra are used to examine a scene from 10 February 2003 where ship tracks were detected close to the North American West-Coast. The cloud optical and microphysical properties are derived using a semi-analytical retrieval technique combined with a look-up-table approach. An algorithm is presented to distinguish ship-track-pixels from the unperturbed cloud pixels in the scene and from this the optical properties of the former are compared to those of the latter. Within the ship tracks a significant change in the droplet number concentration, the effective radius and the optical thickness are found compared to the unaffected cloud. The resulting cloud properties are used to calculate the radiation budget below and above the cloud. Assuming a mean solar zenith angle of 63° for the selected scene, the mean solar surface radiation below the ship track is decreased by 43.2 Wm−2 and the mean reflectance at top of atmosphere (TOA) is increased by 40.8 Wm−2. For the entire analyzed scene the ship emission decreases the solar radiation at the surface by 2.1 Wm−2 and increases the backscattered solar radiation at TOA by 2.0 Wm−2, whereas no significant effect on thermal radiation was detected.

A recently developed cloud retrieval algorithm for the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) is briefly presented and validated using independent and well tested cloud retrieval techniques based on the look-up-table approach for MODeration resolutIon Spectrometer (MODIS) data. The results of the cloud top height retrievals using measurements in the oxygen A-band by an airborne crossed Czerny-Turner spectrograph and the Global Ozone Monitoring Experiment (GOME) instrument are compared with those obtained from airborne dual photography and retrievals using data from Along Track Scanning Radiometer (ATSR-2), respectively.

Abstract PRISMA is a hyperspectral satellite mission launched by the Italian Space Agency (ASI) in April 2019. The mission is designed to collect data at global scale for a variety of applications, including those related to the cryosphere. This study presents an evaluation of PRISMA Level 1 (L1) and Level 2 (L2D) products for different snow conditions. To the aim, PRISMA data were collected at three sites: two in the Western European Alps (Torgnon and Plateau Rosa) and one in East Antarctica (Nansen Ice Shelf). PRISMA data were acquired contemporary to both field measurements and Sentinel‐2 data. Simulated Top of the Atmosphere (TOA) radiance data were then compared to L1 PRISMA and Sentinel‐2 TOA radiance. Bottom Of Atmosphere (BOA) reflectance from PRISMA L2D and Sentinel‐2 L2A data were then evaluated by direct comparison with field data. Both TOA radiance and BOA reflectance PRISMA products were generally in good agreement with field data, showing a Mean Absolute Difference (MAD) lower than 5%. L1 PRISMA TOA radiance products resulted in higher MAD for the site of Torgnon, which features the highest topographic complexity within the investigated areas. In Plateau Rosa we obtained the best comparison between PRISMA L2D reflectance data and in situ measurements, with MAD values lower than 5% for the 400–900 nm range. The Nansen Ice Shelf instead resulted in MAD values

The two-LAyered snow Radiative Transfer (LART) model has been proposed for snow remote sensing applications. It is based on analytical approximations of the radiative transfer theory. The geometrical optics approximation has been used to derive the local snow optical parameters, such as the probability of photon absorption by ice grains and the average cosine of single light scattering in a given direction in a snowpack. The application of the model to the selected area in Antarctica has shown that the technique is capable of retrieving the snow grain size both in the upper and lower snow layers, with grains larger in the lower snow layer as one might expect due to the metamorphism processes. Such a conclusion is confirmed by ground measurements of the vertical snow grain size variability in Antarctica.

Dust deposited on the surface of snow and glaciers can significantly reduce the snow and ice albedo and accelerate melting. Manual observations of the dust mass concentration (DMC) on snow and glacier surfaces are routinely performed at many locations worldwide. However, snow and ice surface DMC monitoring methods based on remote sensing data still face challenges. This study presents a new retrieval scheme for estimating dust load on snow-covered surfaces from a moderate-resolution imaging spectroradiometer and visible infrared imaging radiometer suite in Northeast and Northwest China that utilizes a classical snow radiative transfer model. Our results indicate that the coefficient of variation of DMC retrieved from the in situ measurements of snow spectral reflectance is 4%, which is within a +4% difference compared with DMC observed in snow and ice samples. Estimating atmospheric dust pollutants content deposited on snow surfaces based on satellite remote sensing observations is feasible. In Northwest China, the root-mean-square error (RMSE) of the DMC values retrieved from VNP09GA data is 9.78 ppm, while that of the DMC values retrieved from MOD09GA data is 13.74 ppm. In Northeast China, the RMSE of the DMC values retrieved from VNP09GA data is 73.98 ppm, while that of the DMC values retrieved from MOD09GA data is 184.32 ppm. The research results can realize continuous monitoring of the atmospheric dust pollutants deposited on snow surfaces, which is of great practical significance to understanding and studying the pollution process of atmospheric dust on snow.

Detection of clouds and retrieval of their properties (top and bottom altitude, optical thickness) with satellite-based spectrometers needs the solution of radiative transfer in the atmosphere. Depending on the application needed for and instrument capabilities, a trade-off choice has to be made between accuracy and speed. This study aims to characterise the range of applicability of analytical asymptotic equations to real scenarios. It has been found that the errors, introduced with approximate parameterisations, are within reasonable ranges both in absolute and relative values.

We discuss a possibility of snow grain size determination using spectral reflectance measurements in the near-infrared part of the electromagnetic spectrum. Errors related to often made assumption of the sphericity of grains are studied. Also we introduce a new method for the snow albedo and snow pollution monitoring using measurements in the visible part of the electromagnetic theory. Both exact and approximate methods of the radiative transfer are used for the solution of corresponding inverse problem. It is assumed that snow grains can be presented as randomly distributed irregular fractal particles. The developed techniques are applied to both ground and satellite data.

The 5th International Electronic Conference on Remote Sensing, with a focus on “Advances in experimental and theoretical studies of the terrestrial atmosphere and underlying surface” was held on 7–21 November 2023 [...]

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The latest versions of the Ross-Li model include kernels that represent isotropic reflection of the surface, describe backward reflection of soil and vegetation systems, characterize strong forward reflection of snow, and adequately consider the hotspot effect (i.e., RossThick-LiSparseReciprocalChen-Snow, RTLSRCS), theoretically able to effectively characterize BRDF/Albedo/NBAR features for various land surface types. However, a systematic evaluation of the RTLSRCS model is still lacking for various land cover types. In this paper, we conducted a thorough assessment of the RTLSRCS and RossThick-LiSparseReciprocalChen (RTLSRC) models in characterizing BRDF/Albedo/NBAR characteristics by using the global POLDER BRDF database. The primary highlights of this paper include the following: (1) Both models demonstrate high accuracy in characterizing the BRDF characteristics across 16 IGBP types. However, the accuracy of the RTLSRC model is notably reduced for land cover types with high reflectance and strong forward reflection characteristics, such as Snow and Ice (SI), Deciduous Needleleaf Forests (DNF), and Barren or Sparsely Vegetated (BSV). In contrast, the RTLSRCS model shows a significant improvement in accuracy for these land cover types. (2) These two models exhibit highly consistent albedo inversion across various land cover types (R2 > 0.9), particularly in black-sky and blue-sky albedo, except for SI. However, significant differences in white-sky albedo inversion persist between these two models for Evergreen Needleleaf Forests (ENF), Evergreen Broadleaf Forests (EBF), Urban Areas (UA), and SI (p < 0.05). (3) The NBAR values inverted by these two models are nearly identical across the other 15 land cover types. However, the consistency of NBAR results is relatively poor for SI. The RTLSRC model tends to overestimate compared to the RTLSRCS model, with a noticeable bias of approximately 0.024. This study holds significant importance for understanding different versions of Ross-Li models and improving the accuracy of satellite BRDF/Albedo/NBAR products.

No abstract available.

In this work, we develop a software suite for studies of atmosphere–underlying SNOW-spaceborne optical receiver light TRANsmission calculations (SNOWTRAN) with applications for the solution of forward and inverse radiative transfer problems in polar regions. Assuming that the aerosol load is extremely low, the proposed theory does not require the numerical procedures for the solution of the radiative transfer equation and is based on analytical equations for the spectral nadir reflectance and simple approximations for the local optical properties of atmosphere and snow. The developed model is validated using EnMAP and PRISMA spaceborne imaging spectroscopy data close to the Concordia research station in Antarctica. A new, fast technique for the determination of the snow grain size and assessment of the snowpack vertical inhomogeneity is then proposed and further demonstrated on EnMAP imagery over the Aviator Glacier and in the vicinity of the Concordia research station in Antarctica. The results revealed a large increase in precipitable water vapor at the Concordia research station in February 2023 that was linked to a warming event and a four times larger grain size at Aviator Glacier compared with Dome C.

The MODerate Resolution Imaging Spectroradiometer (MODIS) MCD43A products have been extensively applied in the remote sensing field, but recent researchers have demonstrated that these products still had the potential to be further improved by using the latest development of the kernel-driven model [RossThick-LiSparseReciprocal-Snow (RTLSRS)] in snow-covered areas, since the MCD43A product algorithm [RossThick-LiSparseReciprocal (RTLSR)] needed to be improved for the accurate simulation of snow bidirectional reflectance distribution function (BRDF) signatures. In this paper, we proposed a practical approach to improve the MCD43A products, which used the Polarization and Directionality of the Earth's Reflectance (POLDER) observations and random forest algorithm to establish the relationship between the BRDF parameters (MCD43A1) estimated by the RTLSR and RTLSRS models. We applied this relationship to correct the MCD43A1 product and retrieve the corresponding albedo (MCD43A3) and nadir reflectance (MCD43A4). The results obtained highlight several aspects: (a) The proposed approach can perform well in correcting BRDF parameters [root mean square error (RMSE) = ~0.04]. (b) The corrected BRDF parameters were then used to retrieve snow albedo, which matched up quite well with the results of the RTLSRS model. (c) Finally, the snow albedo retrieved by the proposed approach was assessed using ground-based albedo observations. Results indicated that the retrieved snow albedo showed a higher accuracy as compared to the station measurements (RMSE = 0.055, bias = 0.005), which was better than the results of the MODIS albedo product (RMSE = 0.064, bias = −0.018), especially at large angles. These results demonstrated that this proposed approach presented the potential to further improve the MCD43A products in snow-covered areas.

The paper presents the first retrievals of clean snow properties using spaceborne hyperspectral observations via the Environmental Mapping and Analysis Program (EnMAP). The location close to the Concordia station at the Dome C Plateau (Antarctica) was selected. At this location, the atmospheric effects (except molecular light scattering and absorption) are weak, and the simplified atmospheric correction scheme could be applied. The ice grain size, snow specific surface area, and snow spectral and broadband albedos were retrieved using single-view EnMAP measurements. In addition, we propose a technique to retrieve trace gas concentrations (e.g., water vapor and ozone) from EnMAP observations over the snow surfaces. A close correspondence of satellite and ground-measured parameters was found.

PRISMA is a hyperspectral satellite mission launched by the Italian Space Agency (ASI) in April 2019. The mission is designed to collect data at global scale for a variety of applications, including those related to the cryosphere. This study presents an evaluation of PRISMA Level 1 (L1) and Level 2 (L2D) products for different snow conditions. To the aim, PRISMA data were collected at three sites: two in the Western European Alps (Torgnon and Plateau Rosa) and one in East Antarctica (Nansen Ice Shelf). PRISMA data were acquired contemporary to both field measurements and Sentinel‐2 data. Simulated Top of the Atmosphere (TOA) radiance data were then compared to L1 PRISMA and Sentinel‐2 TOA radiance. Bottom Of Atmosphere (BOA) reflectance from PRISMA L2D and Sentinel‐2 L2A data were then evaluated by direct comparison with field data. Both TOA radiance and BOA reflectance PRISMA products were generally in good agreement with field data, showing a Mean Absolute Difference (MAD) lower than 5%. L1 PRISMA TOA radiance products resulted in higher MAD for the site of Torgnon, which features the highest topographic complexity within the investigated areas. In Plateau Rosa we obtained the best comparison between PRISMA L2D reflectance data and in situ measurements, with MAD values lower than 5% for the 400–900 nm range. The Nansen Ice Shelf instead resulted in MAD values <10% between PRISMA L2D and field data, while Sentinel‐2 BOA reflectance showed higher values than other data sources. Plain Language Summary: Satellite imaging spectroscopy data provide valuable information on Earth surface processes. In this study we evaluated data acquired from the PRISMA satellite mission in the Alps and Antarctica. Those studies are important to validate satellite observations with contemporary field measurements since they open the possibility of quantitatively use PRISMA data for cryosphere monitoring. Key Points: We present an evaluation of PRISMA Level 1 and Level 2 products for different snow conditionsContemporary to PRISMA acquisition, field spectroscopy data were collected on snow in the Alps and in AntarcticaBoth radiance and reflectance PRISMA products were generally in good agreement with field data [ABSTRACT FROM AUTHOR]