A 3D cloud-construction algorithm for the EarthCARE satellite mission

This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active–passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed. The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds. Domain-average 1D broadband radiative fluxes at the top of theatmosphere(TOA)for (21 km)2 domains constructed from MODIS, CloudSat andCloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth’s Radiant Energy System (CERES) radiances: differences betweenmodelled and measured reflected shortwave fluxes are within±10Wm−2 for∼35% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation∼65% are within ±10Wm−2

Influence of snow properties on directional surface reflectance in Antarctica

The significance of the polar regions for the Earth’s climate system and their observed amplified response to climate change indicate the necessity for high temporal and spatial coverage for the monitoring of the reflective properties of snow surfaces and their influencing factors. Therefore, the specific surface area (SSA, as a proxy for snow grain size) and the hemispherical directional reflectance factor (HDRF) of snow were measured for a 2-month period in central Antarctica (Kohnen research station) during austral summer 2013/14. The SSA data were retrieved on the basis of ground-based spectral surface albedo measurements collected by the COmpact RAdiation measurement System (CORAS) and airborne observations with the Spectral Modular Airborne Radiation measurement sysTem (SMART). The snow grain size and pollution amount (SGSP) algorithm, originally developed to analyze spaceborne reflectance measurements by the MODerate Resolution Imaging Spectroradiometer (MODIS), was modified in order to reduce the impact of the solar zenith angle on the retrieval results and to cover measurements in overcast conditions. Spectral ratios of surface albedo at 1280 and 1100 nm wavelength were used to reduce the retrieval uncertainty. The retrieval was applied to the ground-based and airborne observations and validated against optical in situ observations of SSA utilizing an IceCube device. The SSA retrieved from CORAS observations varied between 29 and 96 m2 kg-1. Snowfall events caused distinct relative maxima of the SSA which were followed by a gradual decrease in SSA due to snow metamorphism and wind-induced transport of freshly fallen ice crystals. The ability of the modified algorithm to include measurements in overcast conditions improved the data coverage, in particular at times when precipitation events occurred and the SSA changed quickly. SSA retrieved from measurements with CORAS and MODIS agree with the in situ observations within the ranges given by the measurement uncertainties. However, SSA retrieved from the airborne SMART data underestimated the ground-based results. The spatial variability of SSA in Dronning Maud Land ranged in the same order of magnitude as the temporal variability revealing differences between coastal areas and regions in interior Antarctica. The validation presented in this study provided an unique test bed for retrievals of SSA under Antarctic conditions where in situ data are scarce and can be used for testing prognostic snowpack models in Antarctic conditions. The HDRF of snow was derived from airborne measurements of a digital 180° fish-eye camera for a variety of conditions with different surface roughness, snow grain size, and solar zenith angle. The camera provides radiance measurements with high angular resolution utilizing detailed radiometric and geometric calibrations. The comparison between smooth and rough surfaces (sastrugi) showed significant differences in the HDRF of snow, which are superimposed on the diurnal cycle. By inverting a semi-empirical kernel-driven model for the bidirectional reflectance distribution function (BRDF), the snow HDRF was parameterized with respect to surface roughness, snow grain size, and solar zenith angle. This allows a direct comparison of the HDRF measurements with BRDF products from satellite remote sensing

Improved Estimation of PM2.5 Using Lagrangian Satellite-Measured Aerosol Optical Depth

Suspended particulate matter (aerosols) with aerodynamic diameters less than 2.5 μm (PM2.5) has negative effects on human health, plays an important role in climate change and also causes the corrosion of structures by acid deposition. Accurate estimates of PM2.5 concentrations are thus relevant in air quality, epidemiology, cloud microphysics and climate forcing studies. Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument has been used as an empirical predictor to estimate ground-level concentrations of PM2.5. These estimates usually have large uncertainties and errors. The main objective of this work is to assess the value of using upwind (Lagrangian) MODIS-AOD as predictors in empirical models of PM2.5. The upwind locations of the Lagrangian AOD were estimated using modeled backward air trajectories. Since the specification of an arrival elevation is somewhat arbitrary, trajectories were calculated to arrive at four different elevations at ten measurement sites within the continental United States. A systematic examination revealed trajectory model calculations to be sensitive to starting elevation. With a 500 m difference in starting elevation, the 48-hr mean horizontal separation of trajectory endpoints was 326 km. When the difference in starting elevation was doubled and tripled to 1000 m and 1500m, the mean horizontal separation of trajectory endpoints approximately doubled and tripled to 627 km and 886 km, respectively. A seasonal dependence of this sensitivity was also found: the smallest mean horizontal separation of trajectory endpoints was exhibited during the summer and the largest separations during the winter. A daily average AOD product was generated and coupled to the trajectory model in order to determine AOD values upwind of the measurement sites during the period 2003-2007. Empirical models that included in situ AOD and upwind AOD as predictors of PM2.5 were generated by multivariate linear regressions using the least squares method. The multivariate models showed improved performance over the single variable regression (PM2.5 and in situ AOD) models. The statistical significance of the improvement of the multivariate models over the single variable regression models was tested using the extra sum of squares principle. In many cases, even when the R-squared was high for the multivariate models, the improvement over the single models was not statistically significant. The R-squared of these multivariate models varied with respect to seasons, with the best performance occurring during the summer months. A set of seasonal categorical variables was included in the regressions to exploit this variability. The multivariate regression models that included these categorical seasonal variables performed better than the models that didn\u27t account for seasonal variability. Furthermore, 71% of these regressions exhibited improvement over the single variable models that was statistically significant at a 95% confidence level

Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth’s surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) method has been proposed and delivered good performance in filling large-area gaps of single-date Landsat images. However, it can be less practical for a time series longer than one year due to the lack of mechanics that exclude dissimilar data in time series (e.g., different phenology or changes in land cover). To solve this problem, this study proposes a new gap-filling method, Spectral Temporal Information for Missing Data Reconstruction (STIMDR), and examines its performance in Landsat reflectance time series. Two groups of experiments, including 2000 × 2000 pixel Landsat single-date images and Landsat time series acquired from four sites (Kenya, Finland, Germany, and China), were performed to test the new method. We simulated artificial gaps to evaluate predicted pixel values with real observations. Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. In addition, the proposed method was also able to fill gaps contaminated by extreme cloud cover for a period (e.g., winter in high-latitude areas). A down-stream task of random forest supervised classification through both gap-filled simulated datasets and the original valid datasets verified that STIMDR-generated products are relevant to the user community for land cover applications

Towards an operational model for estimating day and night instantaneous near-surface air temperature for urban heat island studies: outline and assessment

Near-surface air temperature (NSAT) is key for assessing urban heat islands, human health, and well-being. However, a widely recognized and cost- and time-effective replicable approach for estimating hourly NSAT is still urgent. In this study, we outline and validate an easy-to-replicate, yet effective, operational model, for automating the estimation of high-resolution day and night instantaneous NSAT. The model is tested on a heat wave event and for a large geographical area. The model combines remotely sensed land surface temperature and digital elevation model, with air temperature from local fixed weather station networks. Achieved NSAT has daily and hourly frequency consistent with MODIS revisiting time. A geographically weighted regression method is employed, with exponential weighting found to be highly accurate for our purpose. A robust assessment of different methods, at different time slots, both day- and night-time, and during a heatwave event, is provided based on a cross-validation protocol. Four-time periods are modelled and tested, for two consecutive days, i.e. 31st of July 2020 at 10:40 and 21:50, and 1st of August 2020 at 02:00 and 13:10 local time. High R2 was found for all time slots, ranging from 0.82 to 0.88, with a bias close to 0, RMSE ranging from 1.45 °C to 1.77 °C, and MAE from 1.15 °C to 1.36 °C. Normalized RMSE and MAE are roughly 0.05 to 0.08. Overall, if compared to other recognized regression models, higher effectiveness is allowed also in terms of spatial autocorrelation of residuals, as well as in terms of model sensitivity

On the reconstruction of three-dimensional cloud fields by synergistic use of different remote sensing data

The objective of this study was to assess if new cloud datasets, namely horizontal fields of integrated cloud parameters and transects of cloud profiles becoming available from current and future satellites like MODIS and CloudSAT as well as EarthCARE will allow for the reconstruction of three-dimensional cloud fields. Because three-dimensional measured cloud fields do not exist, surrogate cloud fields were used to develop and test reconstruction techniques. In order to answer the question if surrogate cloud fields may represent real cloud fields and to evaluate potential constraints for cloud field reconstruction, statistics of surrogate cloud fields have been compared to statistics of various remote sensing retrievals. It has turned out that except for cloud droplet effective radius, which is too low, other cloud parameters are in line with parameters derived from measurements. The reconstruction approach is divided into two parts. The first one deals with the reconstruction of the cloud fields. Three techniques with varying complexity are presented constraining the reconstruction by measurements to various degrees. Whereas the first one applies only information of a satellite radiometer, the other two constrain the retrieval also by profile information measured within the domain. Comparing the reconstruction quality of the approaches, there is no superior algorithm performing better for all cloud fields. This might be ascribed to liquid water content profiles of the surrogate cloud fields close to their adiabatic reference. Consequently, the assumption of adiabatic liquid water content profiles of the first scheme yields adequate estimates and additional information from profiles does not improve the reconstruction. The second part of the reconstruction approach addresses the reconstruction quality by comparing parameters of radiative transfer describing photon path statistics as well as reflectances. Therefore three-dimensional radiative transfer simulations with a Monte Carlo code were carried out for the surrogate cloud fields as well as for the reconstructed cloud fields. It was assumed that deviations of the parameter simulated for the reconstructed cloud and the surrogate cloud field are smaller when reconstruction is more accurate. For parameter describing photon pathes it has been found that only deviations of geometrical pathlength statistics reflect the reconstruction quality to a certain degree. Deviations of other parameters like photon penetration depth do not allow for either assessing local differences in reconstruction quality by an individual reconstruction scheme or to infer the most appropriate reconstruction scheme. The differences in reflectances do also not enable to evaluate reconstruction quality. They prevent from gaining insight in local accuracy of reconstruction due to effects like horizontal photon transport weakening the relations between microphysical as well as optical properties and reflectances of the column. In order to address these effects, grids of various complexity, derived by applying photon path properties, were used to weight deviations of cloud properties when analyzing the relationships. Unfortunately, there is no increase of explained variance due to the application of the weighting grids. Additionally, the sensitivity of the results to the model set-up, namely the spatial resolution of the cloud fields as well as the simplification and neglection of ancillary parameters, were analyzed. Though one would assume a strengthening of relationships between deviations of cloud parameters and deviations of reflectances due to more reliable sampling and reduced inter-column transport of photons when column size increases, there is no indication for resolutions where an assessment of the reconstruction quality by means of reflectance deviations becomes feasible. It also has been shown that inappropriate treatment of aerosols in the radiative transfer simulation impose an error comparable in magnitude to differences in reflectances due to inaccurate cloud field reconstruction. This is especially the case when clouds are located in the boundary layer of the aerosol model. Consequently, appropriate aerosol models should be applied in the analysis. May be due to the low surface reflection and the high cloud optical depths, the representation of the surface reflection function seems to be of minor importance. Summarizing the results, differences in radiative transfer do not allow for the assessment of cloud field reconstruction quality. In order to accomplish the task of cloud field reconstruction, the reconstruction part could be constrained employing information from additional measurements. Observational geometries enabling to use tomographic methods and the application of additional wavelengths for validation might help, too.Ziel der Arbeit war die Evaluierung inwieweit Datensätze von Wolkenparametern, horizontale Felder integraler Wolkenparameter und Schnitte vertikal aufgelöster Parameter, zur Rekonstruktion dreidimensionaler Wolkenfelder genutzt werden können. Entsprechende Datensätze sind durch MODIS und CloudSAT erstmals vorhanden und werden zusätzlich mit dem Start von EarthCARE zur Verfügung stehen. Da dreidimensionale Wolkenfelder aus Messungen nicht existieren, wurden zur Entwicklung der Rekonstruktionsmethoden surrogate Wolkenfelder genutzt. Um die Qualität der surrogaten Wolkenfelder abzuschätzen und um mögliche Randbedingungen zur Rekonstruktion aufzuzeigen, wurden Statistiken der surrogaten Wolkenfelder mit denen unterschiedlicher Fernerkundungsprodukte verglichen. Dabei zeigte sich, dass, abgesehen von den gegenüber Messungen zu geringen Effektivradien der Wolkentropfen in den surrogaten Wolkenfeldern, die übrigen Wolkenparameter gut übereinstimmen. Der Rekonstruktionsansatz gliedert sich in zwei Teile. Der erste Teil beinhaltet die Rekonstruktion der Wolkenfelder. Dazu werden drei Techniken unterschiedlicher Komplexität genutzt, wobei die Komplexität durch den Grad der eingebundenen Messungen bestimmt wird. Während die einfachste Technik lediglich Informationen, wie sie aus Messungen mit einem Satellitenradiometer gewonnen werden können, nutzt, binden die anderen Techniken zusätzlich Profilinformationen aus dem beobachteten Gebiet ein. Analysen zeigten, dass keine der Methoden für alle untersuchten Wolkenfelder den anderen Methoden überlegen ist. Dies mag daran liegen, dass die Flüssigwasserprofile der surrogaten Wolkenfelder nur geringfügig von den in der ersten Rekonstruktionsmethode angenommenen adiabatischen Flüssigwasserprofilen abweichen, so dass die Nutzung der Profile kaum zusätzliche Information für die Rekonstruktion liefert. Im zweiten Teil des Rekonstruktionsansatzes wird die Qualität der rekonstruierten Wolkenfelder durch den Vergleich von Parametern des Strahlungstransfers, wie Photonenpfad-Statistiken und Strahlungsgrößen, evaluiert. Dazu wurden sowohl für die surrogaten Wolkenfelder als auch für die rekonstruierten Wolkenfelder dreidimensionale Strahlungstransfersimulationen mit einem Monte-Carlo-Modell durchgeführt. Angenommen wurde hierbei, dass eine bessere Rekonstruktionsqualität durch geringere Abweichungen der betrachteten Strahlungsparameter aus Simulationen mit rekonstruierten und surrogaten Wolkenfeldern gekennzeichnet ist. Bei den Parametern, die die Photonenwege beschreiben, unterstützen lediglich die Abweichungen der geometrischen Photonenweglängen diese These. Weder erlauben die Abweichungen der übrigen Parameter, zum Beispiel der Eindringtiefen, Rückschlüsse auf die lokale Rekonstruktionsqualität der einzelnen Methoden zu ziehen, noch ermöglichen sie die beste Rekonstruktionsmethode zu identifizieren. Auch die Unterschiede der simulierten Reflektanzen können nicht zur Bestimmung der Rekonstruktionsqualität herangezogen werden. Durch Effekte wie horizontale Photonentransporte werden die Zusammenhänge zwischen mikrophysikalischen und optischen Eigenschaften und Reflektanzen der jeweiligen Gittersäule aufgeweicht, und folglich sind keine Rückschlüsse auf die lokale Rekonstruktionsqualität möglich. Um auf entsprechende Effekte einzugehen, wurden für die Analyse Wichtungsfelder unterschiedlicher Komplexität aus Photonenwegeigenschaften generiert, um diese zur Wichtung der Abweichungen der Wolkeneigenschaften zu nutzen. Der Anteil der erklärten Varianz konnte jedoch durch die Nutzung der entsprechenden Wichtungsfelder nicht erhöht werden. Zusätzlich wurden Sensitivitätsstudien hinsichtlich einzelner Vorgaben der Untersuchung durchgeführt. Dazu wurden sowohl der Einfluss der räumlichen Auflösung der Wolkenfelder als auch die Vereinfachung oder Nichtbetrachtung einzelner Modellparameter analysiert. Eine Reduzierung der Auflösung einhergehend mit einem zuverlässigeren Sampling und reduzierten Photonentransport zwischen den Gittersäulen führte zu keinem direkteren Zusammenhang zwischen den Abweichungen der Reflektanzen und den Abweichungen der mikrophysikalischen Eigenschaften. Folglich existiert keine Auflösung, die die Anwendung des Verfahrens ermöglichen würde. Ebenso wurde gezeigt, dass die unzureichende Einbeziehung von Aerosolen bei den Strahlungstransfersimulationen einen Fehler verursachen kann, der in der Größe dem Unterschied der Reflektanzen unzureichender Wolkenfeldrekonstruktionen gleichkommt. Dies ist insbesondere der Fall, wenn die Wolken sich innerhalb der Grenzschicht des Aerosolmodells befinden. Entspechend sollte in solchen Situationen dem verwendeten Aerosolmodell besondere Beachtung geschenkt werden. Hingegen ist der Einfluss des Ansatzes, wie die Bodenreflektion beschrieben wird, eher gering. Dies mag an dem verwendeten Modell mit einer geringen Albedo in Kombination mit optisch dicken Wolken liegen. Zusammenfassend kann festgestellt werden, dass die Unterschiede im Strahlungstransfer nicht zur Abschätzung der Rekonstruktionsqualität der Wolkenfelder herangezogen werden können. Um dem Ziel einer dreidimensionalen Wolkenfeldrekonstruktion näher zu kommen, könnten beim Rekonstruktionsteil Informationen aus zusätzlichen Messungen als Vorgaben genutzt werden. Ebenso könnten Beobachtungsgeometrien, welche die Anwendung tomographischer Methoden erlauben, sowie zusätzliche Wellenlängen zur Validierung der Rekonstruktionsergebnisse verwendet werden

Retrieval of aerosol optical thickness over snow and ice surfaces in the Arctic using Advanced Along Track Scanning Radiometer

Aerosols in the Arctic cause radiative forcing and a variety of climatic feedbacks, which affect climate of both local and global scales. In order to assess the state of the Arctic climate, information on the aerosol type and amount is needed. Harsh conditions and remoteness of the Arctic region result in very few ground based measurements of aerosol optical thickness. Remote sensing has the potential to provide the necessary temporal and spatial coverage. A non-trivial task of aerosol retrieval over a very bright surface is being solved within the thesis; the developed retrieval consists of cloud screening over snow and two types of aerosol retrieval over snow - in the visible and infrared spectral regions. A number of validation and case studies has been performed to assess the quality of the retrieval. The developed algorithm applies to the data of Advanced Along Track Scanning Radiometer and produces maps of aerosol optical thickness over snow and ice

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Sources of Atmospheric Fine Particles and Adsorbed Polycyclic Aromatic Hydrocarbons in Syracuse, New York

Land surface temperature (LST) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor have been widely utilized across scientific disciplines for a variety of purposes. The goal of this dissertation was to utilize MODIS LST for three spatial modeling applications within the conterminous United States (CONUS). These topics broadly encompassed agriculture and human health. The first manuscript compared the performance of all methods previously used to interpolate missing values in 8-day MODIS LST images. At low cloud cover (\u3c30%), the Spline spatial method outperformed all of the temporal and spatiotemporal methods by a wide margin, with median absolute errors (MAEs) ranging from 0.2°C-0.6°C. However, the Weiss spatiotemporal method generally performed best at greater cloud cover, with MAEs ranging from 0.3°C-1.2°C. Considering the distribution of cloud contamination and difficulty of implementing Weiss, using Spline under all conditions for simplicity would be sufficient. The second manuscript compared the corn yield predictive capability across the US Corn Belt of a novel killing degree day metric (LST KDD), computed with daily MODIS LST, and a traditional air temperature-based metric (Tair KDD). LST KDD was capable of predicting annual corn yield with considerably less error than Tair KDD (R2 /RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). The superior performance can be attributed to LST’s ability to better reflect evaporative cooling and water stress. Moreover, these findings suggest that long-term yield projections based on Tair and precipitation alone will contain error, especially for years of extreme drought. Finally, the third manuscript assessed the extent to which daily maximum heat index (HI) across the CONUS can be estimated by MODIS multispectral imagery in conjunction with land cover, topographic, and locational factors. The derived model was capable of estimating HI in 2012 with an acceptable level of error (R 2 = 0.83, RMSE = 4.4°F). LST and water vapor (WV) were, by far, the most important variables for estimation. Expanding this analytical framework to a more extensive study area (both temporally and spatially) would further validate these findings. Moreover, identifying an appropriate interpolation and downscaling approach for daily MODIS imagery would substantially increase the utility of the corn yield and HI models