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The Arctic region is predicted to experience considerable climatic and environmental changes as the global atmospheric CO2 increases. Growing awareness of the role of tundra and taiga ecosystems and their transition zone in the climate change process has resulted in a recent increase in remote sensing studies focusing on the Arctic latitudes. Remote sensing of biophysical properties of the canopy layer in the forested part of the region is often, however, challenged by the dominating role of the understory in the spectral signal. In this paper, we examine the influence of understory vegetation on forest reflectance in the Arctic region of Finland during no-snow conditions. The study is based on SPOT HRVIR images, field goniospectrometry, 300 ground reference plots and a physically-based forest reflectance model (PARAS). The results indicate that lichen-dominated forest site types can be distinguished from sites dominated by dwarf shrubs. The paper also contains results from applying an analytical method for calculating photon recollision probability from canopy transmittance data for forest stands, and then using it to simulate the reflectance of the same stands. [Copyright &y& Elsevier]

Many passive microwave remote sensing applications such as land surface temperature, snow water equivalent and soil moisture retrievals need to take into account a soil parameterization to the overall surface signal emission. Soil emission modeling presents large uncertainties when the soil is frozen. In this paper, an empirical retrieval method is presented, specifically for rough frozen soil permittivity estimates at 10.7, 19 and 37 GHz. The method was tested and validated using in-situ passive microwave measurements at incidence angles from 0 to 60° of sub-arctic frozen organic soils in Northeastern Canada. The retrieved permittivity values give an overall RMSE between the measured and simulated brightness temperatures of 4.6 K for all frequencies combined. A sensitivity analysis was conducted on the different soil parameters optimized in this study. This analysis suggests that the accuracy of the retrieved parameters, using the method given here, is of ± 1.00 for the permittivity and ± 0.12 cm for surface roughness. Also, a comparison was conducted between the parameterization used in this study and the one of Wegmüller and Mätzler (1999) to estimate the soil contribution to the emitted brightness temperature of snowpacks. An improvement of 66% of the RMSE between the modeled and measured snow brightness temperatures was observed when using the approach of this study compared to the previous work. The method shows great potential to improve the estimation of the frozen soil contribution to the measured passive microwave brightness temperature. [ABSTRACT FROM AUTHOR]

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ∼5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. We evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems. • We developed an approach to map the composition of Arctic PFTs with AVIRIS-NG. • We used UAS imagery over an area of ∼600 ha to train our AVIRIS-NG models. • Our models produced robust FCover (RMSE <0.22) and PFT maps (accuracy >84%). • Our multi-scale method outperformed traditional spectral unmixing methods. [ABSTRACT FROM AUTHOR]

The accelerated warming of the Arctic climate may alter the local and regional surface energy balances, for which changing land surface temperatures (LSTs) are a key indicator. Modeling current and anticipated changes in the surface energy balance requires an understanding of the spatio-temporal interactions between LSTs and land cover, both of which can be monitored globally by measurements from space. This paper investigates the accuracy of the MODIS LST/Emissivity Daily L3 Global 1 km V005 product and its spatio-temporal sensitivity to land surface properties in a Canadian High Arctic permafrost landscape. The land cover ranged from fully vegetated wet sedge tundra to barren rock. MODIS LSTs were compared with in situ radiometer measurements from wet tundra areas collected over a 2-year period from July 2008 to July 2010 including both summer and winter conditions. The accuracy of the MODIS LSTs was − 1.1 °C with a root mean square error of 3.9 °C over the entire observation period. Agreement was lowest during the freeze-back periods where MODIS LST showed a cold bias likely due to the overrepresentation of clear-sky conditions. A multi-year analysis of LST spatial anomalies, i.e., the difference between MODIS LSTs and the MODIS LST regional mean, revealed a robust spatio-temporal pattern. Highest variability in LST anomalies was found during freeze-up and thaw periods as well as for open water surface in early summer due to the presence or absence of snow or ice. The summer anomaly pattern was similar for all three years despite strong differences in precipitation, air temperature and net radiation. Summer periods with regional mean LSTs above 5.0 °C showed the greatest spatial diversity with four distinct 2.0 °C classes. Summer anomalies ranged from − 4.5 °C to 2.6 °C with an average standard deviation of 1.8 °C. Dry ridge areas heated up the most, while wetland areas and dry areas of sparsely vegetated bedrock with a high albedo remained coolest. The observed summer LST anomalies can be used as a baseline against which to evaluate both past and future changes in land surface properties that relate to the surface energy balance. Summer anomaly classes mainly reflected a combination of albedo and surface wetness. The potential to use this tool to monitor surface drying and wetting in the Arctic should therefore be further explored. A multi-sensor approach combining thermal satellite measurements with optical and radar imagery promises to be an effective tool for a dynamic, process-based ecosystem monitoring scheme. [ABSTRACT FROM AUTHOR]

Remote sensing instruments are key players to map land surface temperature (LST) at large temporal and spatial scales. In this paper, we present how we combine passive microwave and thermal infrared data to estimate LST during summer snow-free periods over northern high latitudes. The methodology is based on the SSM/I–SSMIS 37 GHz measurements at both vertical and horizontal polarizations on a 25 km × 25 km grid size. LST is retrieved from brightness temperatures introducing an empirical linear relationship between emissivities at both polarizations as described in Royer and Poirier (2010). This relationship is calibrated at pixel scale, using cloud-free independent LST data from MODIS instruments. The SSM/I–SSMIS and MODIS data are synchronized by fitting a diurnal cycle model built on skin temperature reanalysis provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The resulting temperature dataset is provided at 25 km scale and at an hourly time step during the ten-year analysis period (2000–2011). This new product was locally evaluated at five experimental sites of the EU-PAGE21 project against air temperature measurements and meteorological model reanalysis, and compared to the MODIS LST product at both local and circumpolar scale. The results giving a mean RMSE of the order of 2.2 K demonstrate the usefulness of the microwave product, which is unaffected by clouds as opposed to thermal infrared products and offers a better resolution compared to model reanalysis. The dataset can be downloaded from the PANGAEA website: http://doi.pangaea.de/10.1594/PANGAEA.833409 . [ABSTRACT FROM AUTHOR]

Abstract: The Arctic is vulnerable to the long-term transport of aerosols because they affect the surface albedo when particles are deposited on snow and ice. However, aerosol observations for this area are sparse and hence there is considerable uncertainty in the knowledge on the properties of the Arctic aerosol. Atmospheric remote sensing using satellite-based instruments offers an opportunity to obtain information on aerosol properties, in particular the aerosol optical depth (AOD) on an extended spatial scale as determined by the instrument''s swath width. However, AOD retrieval over a bright surface is a difficult task because it is difficult to separate and explicitly describe the contribution of the surface and that due to back-scattering by aerosols to the radiance observed by a satellite at the top of the atmosphere (TOA), especially at large Solar Zenith Angles (SZA). In this paper, an approach to achieve this is presented based on a synergetic approach using data from both Moderate Resolution Imaging Spectroradiometer (MODIS) instruments flying on the TERRA and AQUA satellites. The approached also uses prior knowledge for aerosol properties retrieval over snow as well as a Snow Bidirectional Reflectance Distribution Function (BRDF) model. The detailed analysis of the model results demonstrates that the Aerosol Properties Retrieval over Snow (APRS) algorithm is suitable for Arctic region Aerosol Optical Depth (AOD) retrieval. The study periods include April 2010 and April 2011, when the Arctic haze mostly occurs. Six AERONET stations at high latitude (Andenes, Barrow, Ittoqqortoormiit, OPAL, Thule, and PEARL) were used for comparison. The correlation coefficient between retrieved AODs and AERONET AODs was 0.8 and the relative error is between 10% and 20%, demonstrating the potential of the APRS method to retrieve AOD over the Arctic, with highly reflective snow/ice surfaces and large solar zenith angles. [Copyright &y& Elsevier]

Abstract: The lidar and radar profiling capabilities of the CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) satellites provide opportunities to improve the characterization of cloud properties. An Arctic cloud climatology based on their observations may be fundamentally different from earlier Arctic cloud climatologies based on passive satellite observations, which have limited contrast between the cloud and underlying surface. Specifically, the Radar–Lidar Geometrical Profile product (RL-GEOPROF) provides cloud vertical profiles from the combination of active lidar and radar. Based on this data product for the period July 2006 to March 2011, this paper presents a new cloud macrophysical property characteristic analysis for the Arctic, including cloud occurrence fraction (COF), vertical distributions, and probability density functions (PDF) of cloud base and top heights. Seasonal mean COF shows maximum values in autumn, minimum values in winter, and moderate values in spring and summer; this seasonality is more prominent over the Arctic Ocean on the Pacific side. The mean ratios of multi-layer cloud to total cloud over the ocean and land are between 24% and 28%. Low-level COFs are higher over ocean than over land. The ratio of low-level cloud to total cloud is also higher over ocean. Middle-level and high-level COFs are smaller over ocean than over land except in summer, and the ratios of middle-level and high-level clouds to total cloud are also smaller over ocean. Over the central Arctic Ocean, PDFs of cloud top height and cloud bottom height show (1) two cloud top height PDF peaks, one for cloud top heights lower than 1200m and another between 7 and 9km; and (2) high frequency for cloud base below 1000m with the majority of cloud base heights lower than 2000m. [Copyright &y& Elsevier]

Satellite remote sensing has been widely used for the evaluation of wildfire burn severity in various ecosystems. While a variety of remote sensing-based burn severity indices have been developed, the Landsat-based differenced Normalized Burn Ratio (dNBR) presents the most widely-used approach to burn severity assessment for fire research and management. Although dNBR-based approaches have been continuously updated, including the development of the relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), one key obstacle for the reliable applications of the burn severity indices in the high northern latitudes has not been adequately addressed. Specifically, optimal Landsat image pairs are very rarely available for the calculation of the burn severity indices in certain regions of the high northern latitudes (HNL), resulting in the burn severity index outputs calculated for a large number of wildfires being considerably affected by non-fire-related factors. The suboptimal selection of image pairs may have partially contributed to the lack of consistency in the performance of the burn severity indices in the HNL regions. In this paper, we systematically evaluated the impacts of suboptimal image pairs on signal stability through two sets of analyses conducted at different spatial scales. First, at the regional scale, we examined the burn severity indices calculated for a selection of wildfires in Alaska. Here, we demonstrate the inconsistent performance of the same index calculated based on Landsat data that are typically considered as "optimal". In addition, we show that the calculated indices may be more prone to the negative impacts of these inconsistencies, thus leading to reduced reliability, when there is limited Landsat data availability. Second, at the continental scale, we show that certain areas in the HNL, especially Alaska during the pre-2000 era, are subject to the potentially strong negative impact associated with the limited data availability. Through systematically analyzing this issue, we hope to not only divert more attention to it but also to provide potential solutions, based on which further improvements that may be of particular importance for research on Arctic wildfires could be made. • Examines a limitation of burn severity indices that is specific to the Arctic region • Takes advantage both local and cloud-based platforms for more robust analyses • Utilizes full Landsat archive over North America in the spatio-temporal analysis • Offers potential solutions to address the revealed limitation [ABSTRACT FROM AUTHOR]

Climate change is amplified in the Arctic region (north of 60°N) relative to elsewhere. By analyzing climate model simulations, it has been found that the largest factor in Arctic Amplification comes from enhanced temperature feedbacks, caused by the different vertical atmospheric warming profiles and a larger temperature change for longwave emission per unit of warming at high- compared to low-latitudes. Radiometers on polar-orbiting satellites offer the best mechanism to derive sea surface temperature (SST) in the Arctic, but given that the retrieval algorithms in the infrared (IR) are designed to compensate for the effects of the atmosphere, mainly water vapor (WV), IR satellite-derived SSTs have larger inaccuracies at high latitudes because the atmosphere can be very dry and cold. So, the motivation of this study is to improve the algorithms to obtain more accurate SSTs which can be used to research the feedback mechanisms. To undertake the study, the 5-year matchup database (MUDB) for MODIS (Moderate resolution Imaging Spectroradiometer) on Aqua has been analyzed to characterize the differences between collocated and simultaneous satellite retrieved skin SSTs (SST skin) using the R2014 dataset and in situ buoy temperatures, and to identify the main causes of the discrepancies. According to the radiative transfer simulations, the sea surface emissivity is proven to be significant in satellite SST skin retrievals at high latitudes due to the low atmospheric WV content, especially during winter. An Emissivity-introduced Brightness Temperature Difference (E ∆ BT) is developed to correct this emissivity effect in the algorithm. Furthermore, the reference temperature as a weighting factor for differences in brightness temperature (BT) measured in MODIS bands 31 and 32 has also been adjusted. The satellite SST skin mean bias and standard deviation are reduced by 0.35 K and 0.085 K after the corrections. The approaches presented in this paper are capable to make more appropriate atmospheric corrections for MODIS SST skin retrieval algorithm, leading to regional optimization of the SST retrievals. We report on the progress towards improving the satellite-derived SST skin with the expectation that the near two-decadal time series of MODIS SST skin fields will contribute to studying climate change in the Arctic. • Understand the characteristics of satellite retrieved SST errors at high latitudes. • Improving Aqua MODIS SSTs in the Arctic regions (north of 60°N). • Emissivity effect is proven to be significant in MODIS SST retrievals in Arctic. • MODIS SST accuracies are improved after the corrections. [ABSTRACT FROM AUTHOR]