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We provide a comprehensive analysis of the effect of spectral albedo on photovoltaic (PV) module measurements and system model predictions. We demonstrate how to account for albedo in indoor bifacial device measurements by adjusting the applied irradiance using the scaled rear irradiance method, exemplified on fabricated silicon heterojunction (SHJ) modules. System model performance is studied using a detailed 3D finite‐element model, DUET, for fixed‐tilt and horizontal single‐axis tracked (SAT) arrays between 15 and 75°N. Spectral effects cause variations in measured SHJ module short‐circuit current up to 2% and efficiency variation up to 0.3% abs. We further demonstrate that rear‐side spectral mismatch factors (SMMs) resulting from including or omitting spectral albedo in PV system modeling vary between ±13%, while total (front+rear) SMMs vary up to 3%, depending on the deployment configuration and latitude. SAT array SMMs are weakly correlated with latitude, while fixed‐tilt array SMMs increase with latitude, driven by an increasing proportion of ground‐reflected light on the front‐side of modules. Ground‐reflections can constitute between 2% and 32% of total incident module irradiance, with notably high (>10%) contributions for fixed‐tilt arrays at high latitude. Effects of spectral albedo are most significant for: (1) fixed‐tilt deployments at high latitudes, (2) wide bandgap technologies such as perovskite and cadmium telluride cells, (3) albedos which vary steeply over the technology's absorption range, and (4) high albedo ground covers. Overall, we demonstrate that omitting spectral albedo effects can result in PV measurement and system‐level modeling uncertainties on the order of several percent in these cases. [ABSTRACT FROM AUTHOR]

The validated influence of urban biophysical structure on environmental processes within urban areas has heightened the emphasis on studies examining morphological patterns to determine precise locations and underlying causes of urban climate conditions. The present study aims to characterise morphological patterns describing the distribution of Land Surface Temperature (LST) based on a prior classification of biophysical variables, including urban density (building intensity and average height), surface characteristics, shortwave solar radiation (broadband albedo), and seasonal variations in vegetation cover (high, medium, and low levels), retrieved from multisource datasets. To describe the distribution of LST, the variables were calculated, classified, and subsequently, analysed individually and collectively concerning winter and summer LST values applied in an urban neighbourhood in Madrid, Spain. The results from the analytical approaches (observation, correlations, and multiple regressions) were compared to define the morphological patterns. The selection of areas resulting from the morphological patterns with the most unfavourable LST values showed agreement of up to 89% in summer and up to 70% for winter, demonstrating the feasibility of the methods applied to identify priority areas for intervention by season. Notably, low and high vegetation levels emerged as pivotal biophysical characteristics influencing LST distribution compared to the other characteristics, emphasising the significance of integrating detailed seasonal vegetation variations in urban analyses. [ABSTRACT FROM AUTHOR]

• Radiative transfer simulations have been created for snow containing measured concentrations of mineral dust and snow algae. • Average snow broadband albedo reduction of 7.4 ± 6.1 %, 35.3 ± 7.4 % and 40.8 ± 8.4 % has been estimated for snow algae, mineral dust and both impurities. • Average instantaneous radiative forcing 42.3 (± 36.1) W/m2, 203.7 (±45.5) W/m2, and 211.8 (±45.9) W/m2 has been estimated for snow algae, mineral dust and both impurities. • Broadband albedo reduction does not linearly sum for the combined impact of snow algae and mineral dust. • A set of spectral indices has been tested for the detection of snow algae and mineral dust concentration from multi- and hyper-spectral data. The optical properties of snow can be strongly modified by the presence of a variety of impurities including mineral dust and snow algae. We made use of measured concentration of snow algae and mineral dust to parameterize the BioSNICAR radiative transfer model. Surficial snow samples were gathered during a field campaign on 7th July 2020 at the Presena glacier (Rhaetian Alps). We collected 18 samples of surface snow containing different amount of snow algae and mineral dust. Through radiative transfer simulations we estimated an average broadband albedo reduction of 7.4 ± 6.1 % and 35.3 ± 7.4 % compared to clean snow, caused by snow algae and mineral dust presence, respectively. When we considered the combined effect of snow algae and dust, we estimated a broadband albedo reduction equal to 40.8 ± 8.4 %. We estimated an average instantaneous radiative forcing induced by snow algae, mineral dust and both impurities equals to 42.3 (± 36.1) W/m2, 203.7 (±45.5) W/m2, and 211.8 (±45.9) W/m2, respectively. Using BioSNICAR simulations, we also tested a series of narrowband spectral indices to determine the concentration of mineral dust and snow algae from multi- and hyper-spectral data. Results showed that most spectral indices used for snow algae mapping are correlated also with mineral dust concentration. We found that only an index correlates uniquely with snow algae: the scaled band integral at 680 nm. A new spectral index, namely the Green Blue Normalized Index, is therefore proposed to discriminate mineral dust from snow algae when both impurities are present. The high spectral resolution of current (e.g. PRISMA, EnMAP) and future (e.g. CHIME, SBG) hyperspectral satellite missions will be fundamental to decouple the effect of mineral dust and snow algae on the optical properties of snow. In fact, from those data it is possible to calculate all narrowband indices presented in this study. [ABSTRACT FROM AUTHOR]

The surface albedo is an essential climate variable that is considered in many applications used for predicting climate and understanding the mechanisms of climate change. In this study, surface albedo was estimated using a bidirectional reflectance distribution function model based on Communication, Ocean and Meteorological Satellite/Meteorological Imager data. Geostationary orbiting satellite data are suitable for a level 2 product like albedo, which requires a synthetic process to estimate. The authors modified established methods to consider the geometry of the solar-surface-sensor of COMS/MI. Of note, the viewing zenith angle term was removed from the kernel integration used for estimating spectral albedo. Finally, the spectral (narrow) albedo was converted into the broadband albedo with shortwave length (approximately 0.3–2.5 μm). This study determined conversion coefficients using only one spectral albedo of visible channel. The estimated albedo had a relatively high correlation with Satellite Pour l’Observation de la Terre/Vegetation and low unweighted error values specific for land types or times. The validation results show that estimated albedo has a root mean square error of 0.0134 at Jeju flux site that indicates accuracy similar to that of other satellite-based products.

For summer-accumulation-type glaciers, the glaciological literature is lacking studies on determining the snow line altitude (SLA) from optical images at the end of the summer as an indicator of the equilibrium line altitude (ELA). This paper presents a workflow for extracting the SLA from Landsat images based on the variation in the albedo with the altitude in the central line area of glaciers. The correlation of >0.8 at the 99% confidence level between the retrieved SLAs with ELAs derived from the interpolation of ground-based, mass balance measurements indicated that the workflow can be applied to derive the SLA from end-of-summer satellite data as an indicator of ELA. The ELA was under-estimated by the calculated SLA. The relationship between the end-of-summer SLA and the ELA depends on the intensity of glacier melting. Subsequently, the workflow was applied to the seven glaciers in the Eastern Tien Shan Mountains, and a time series of the SLA was obtained using 12 end-of-summer Landsat scenes from 1994 to 2016. Over the whole study period, a mean SLA of 4011.6 ± 20.7 m above sea level (a.s.l.) was derived for the seven investigated glaciers, and an increasing SLA was demonstrated. The increase in SLAs was consistent for the seven glaciers from 1994 to 2016. Concerning the spatial variability, the east–west difference was prominent, and these differences exhibited a decreasing trend. The average SLA of each glacier is more influenced by its morpho-topographic variables. The interannual variations in the average SLA are mainly driven by the increasing summer air temperature, and the high correlation with the cumulative summer solid precipitation reflects the characteristics of the summer-accumulation-type glaciers.

This paper proposed an algorithm for automated retrieval of snow/ice surface albedo using Landsat-8 satellite images and validation with wireless senor network data in Beas River basin, India. Cloud-free Landsat-8 images of winter season 2016-2017 have been used to estimate snow/ice broadband albedo. Albedo maps generated using satellite images have been compared and validated with in situ recorded albedo values at wireless sensor network stations. Root-mean-square error of 0.053, R-square (R2) of 0.96, bias of − 0.034 and mean absolute percentage error of ~ 3.5% have been observed. Existing global albedo model for Landsat ETM+ was also used to estimate albedo in the study area using Landsat-8 images. Algorithm proposed in this paper for Landsat-8 has shown comparatively better results. Simultaneously, albedo values for snow/ice class have also been estimated using MODIS images. High correlation has been observed between the albedo values estimated from Landsat-8 and MODIS images. Proposed algorithm appears to be the first to estimate broadband albedo of snow/ice class from narrowband reflectances using Landsat-8 images for Indian Himalaya. [ABSTRACT FROM AUTHOR]

The broadband albedo values retrieved from satellite sensors are usually compared directly to ground measurements. Some authors have noted the necessity of high spatial resolution albedo estimates to fill the gap between ground measurements and satellite retrievals. In this respect, hyperspectral airborne data with high spatial resolution is a powerful tool. Here, a new operational method for the calculation of airborne broadband apparent albedo over the spectral range of 350-2500 nm is presented. This new method uses the Hemispherical Directional Reflectance Factor (HDRF) as a proxy for the narrowband albedo, assuming a Lambertian approximation. The broadband apparent albedo obtained is compared to that estimated using theapparent albedo equation devised for the Moderate Resolution Imaging Spectroradiometer (MODIS). Airborne data were collected using the Airborne Hyperspectral Scanner (AHS). Field data were acquired at three sites: a camelina field, a green grass field, and a vineyard. The HDRF can be used to approximate the narrowband albedo for all View Zenith Angle (VZA) values for flights parallel to the solar principal plane (SPP); for flights orthogonal to the SPP, discrepancies are observed when the VZA approaches -45°. Root Mean Square Error (RMSE) values in the range 0.009-0.018 were obtained using the new method, improving upon previous results over the same area (RMSEs of 0.01-0.03). The relative error in the albedo estimation using the new method is 12% for 36.2° < VZA < 40.8° in the case of flights parallel to the SPP and less than 15% for -13° < VZA < 45° and 45% for VZA = -45° for flights orthogonal to the SPP. The good performance of the new method lies in the use of the at-surface solar irradiance and the proposed integration method. [ABSTRACT FROM AUTHOR]

For summer-accumulation-type glaciers, the glaciological literature is lacking studies on determining the snow line altitude (SLA) from optical images at the end of the summer as an indicator of the equilibrium line altitude (ELA). This paper presents a workflow for extracting the SLA from Landsat images based on the variation in the albedo with the altitude in the central line area of glaciers. The correlation of >0.8 at the 99% confidence level between the retrieved SLAs with ELAs derived from the interpolation of ground-based, mass balance measurements indicated that the workflow can be applied to derive the SLA from end-of-summer satellite data as an indicator of ELA. The ELA was under-estimated by the calculated SLA. The relationship between the end-of-summer SLA and the ELA depends on the intensity of glacier melting. Subsequently, the workflow was applied to the seven glaciers in the Eastern Tien Shan Mountains, and a time series of the SLA was obtained using 12 end-of-summer Landsat scenes from 1994 to 2016. Over the whole study period, a mean SLA of 4011.6 ± 20.7 m above sea level (a.s.l.) was derived for the seven investigated glaciers, and an increasing SLA was demonstrated. The increase in SLAs was consistent for the seven glaciers from 1994 to 2016. Concerning the spatial variability, the east–west difference was prominent, and these differences exhibited a decreasing trend. The average SLA of each glacier is more influenced by its morpho-topographic variables. The interannual variations in the average SLA are mainly driven by the increasing summer air temperature, and the high correlation with the cumulative summer solid precipitation reflects the characteristics of the summer-accumulation-type glaciers. [ABSTRACT FROM AUTHOR]