Special Issue "Remotely Sensed Albedo"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (25 February 2019).

Special Issue Editors

Dr. Jean-Louis Roujean
E-Mail Website
Guest Editor
CESBIO, France
Interests: albedo, BRDF, agriculture, radiation forcing, modeling
Prof. Shunlin Liang
E-Mail Website
Guest Editor
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Interests: quantitative land remote sensing, Earth’s energy budget, global environmental change
Special Issues and Collections in MDPI journals
Prof. Tao He
E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, People’s Republic of China
Interests: radiation budget; retrieval of biophysical properties; data fusion; satellite product development

Special Issue Information

Dear Colleagues,

A regular and timely monitoring of surface albedo from local to global scales is vital for determining the radiation exchanges in the continuum soil-vegetation-atmosphere in the context of a changing climate. The surface albedo is a quantity of particular interest that has been identified as a primary essential climate variable. An accurate assessment of surface albedo is relevant for vast domains, such as climate, agriculture, hydrology, meteorology, glaciology, urbanism, and geology.

Land surface albedo has become a standard deliverable of most space missions. Remote sensing measurements have proven a high potential to provide valuable information regarding the mapping of land surface albedo at various spatial and temporal scales. The role of radiation forcing versus atmosphere forcing requires a thorough knowledge of the surface albedo.

With this Special Issue we will compile state-of-the-art research that addresses various aspects of land surface albedo: mapping from patch, landscape to continental scales, impact of directional sampling, surface radiation modelling, spectral albedo conversion, satellite data merging, environmental monitoring, criteria for quality and uncertainty assessment, link with land cover and land use classification, data assimilation, thematic applications, satellite missions, field campaigns, ground observation networks, and validation. Review contributions are welcome, as well as papers describing new measurement concepts/sensors.

Dr. Jean-Louis Roujean
Prof. Shunlin Liang
Prof. Tao He
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • BRDF and land surface albedo
  • Radiation forcing
  • Environmental factors
  • Radiative transfer modeling and inversion
  • Remote sensing data assimilation
  • Agriculture and forestry
  • Glaciology
  • Urban
  • Carbon and water cycles
  • In situ measurements and validation

Published Papers (13 papers)

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Editorial

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Open AccessEditorial
Editorial for Special Issue: “Remotely Sensed Albedo”
Remote Sens. 2019, 11(16), 1941; https://doi.org/10.3390/rs11161941 - 20 Aug 2019
Abstract
Land surface (bare soil, vegetation, and snow) albedo is an essential climate variable that affects the Earth’s radiation budget, and therefore, is of vital interest for a broad number of applications: Thematic (urban, cryosphere, land cover, and bare soil), climate (Long Term Data [...] Read more.
Land surface (bare soil, vegetation, and snow) albedo is an essential climate variable that affects the Earth’s radiation budget, and therefore, is of vital interest for a broad number of applications: Thematic (urban, cryosphere, land cover, and bare soil), climate (Long Term Data Record), processing technics (gap filling, data merging), and products validation (cal/val) [...] Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)

Research

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Open AccessArticle
Combining MODIS and National Land Resource Products to Model Land Cover-Dependent Surface Albedo for Norway
Remote Sens. 2019, 11(7), 871; https://doi.org/10.3390/rs11070871 - 10 Apr 2019
Cited by 1
Abstract
Surface albedo is an important physical attribute of the climate system and satellite retrievals are useful for understanding how it varies in time and space. Surface albedo is sensitive to land cover and structure, which can vary considerably within the area comprising the [...] Read more.
Surface albedo is an important physical attribute of the climate system and satellite retrievals are useful for understanding how it varies in time and space. Surface albedo is sensitive to land cover and structure, which can vary considerably within the area comprising the effective spatial resolution of the satellite-based retrieval. This is particularly true for MODIS products and for topographically complex regions, such as Norway, which makes it difficult to separate the environmental drivers (e.g., temperature and snow) from those related to land cover and vegetation structure. In the present study, we employ high resolution datasets of Norwegian land cover and structure to spectrally unmix MODIS surface albedo retrievals (MCD43A3 v6) to study how surface albedo varies with land cover and structure. Such insights are useful for constraining land cover-dependent albedo parameterizations in models employed for regional climate or hydrological research and for developing new empirical models. At the scale of individual land cover types, we found that the monthly surface albedo can be predicted at a high accuracy when given additional information about forest structure, snow cover, and near surface air temperature. Such predictions can provide useful empirical benchmarks for climate model predictions made at the land cover level, which is critical for instilling greater confidence in the albedo-related climate impacts of anthropogenic land use/land cover change (LULCC). Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle
Time Series High-Resolution Land Surface Albedo Estimation Based on the Ensemble Kalman Filter Algorithm
Remote Sens. 2019, 11(7), 753; https://doi.org/10.3390/rs11070753 - 28 Mar 2019
Cited by 2
Abstract
Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of [...] Read more.
Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an “amalgamation albedo” approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model. The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085–0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle
Mapping Climatological Bare Soil Albedos over the Contiguous United States Using MODIS Data
Remote Sens. 2019, 11(6), 666; https://doi.org/10.3390/rs11060666 - 19 Mar 2019
Cited by 1
Abstract
Surface bare soil albedo is an important variable in climate modeling studies and satellite-based retrievals of land-surface properties. In this study, we used multiyear 500 m albedo products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive the bare soil albedo for seven [...] Read more.
Surface bare soil albedo is an important variable in climate modeling studies and satellite-based retrievals of land-surface properties. In this study, we used multiyear 500 m albedo products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive the bare soil albedo for seven spectral bands and three broadbands over the contiguous United States (CONUS). The soil line based on red and green spectral signatures derived from MODIS data was used as the basis to detect and extract bare soil albedo. A comparison against bare soil albedo derived from 30 m Landsat data has been made, showing that the MODIS bare soil albedo had a bias of 0.003 and a root-mean-square-error (RMSE) of 0.036. We found that the bare soil albedo was negatively correlated with soil moisture from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), with a relatively stable exponential relationship reflecting the darkening effect that moisture has on most soils. However, quantification of the relationship between bare soil albedo and soil moisture still needs to be improved through simultaneous and instantaneous measurements at a finer spatial resolution. Statistics of the multiyear climatological bare soil albedos calculated using soil types and the International Geosphere-Biosphere Programme (IGBP) land cover types suggest that: Land cover type is a better indicator for determining the magnitude of bare soil albedos for the vegetated areas, as the vegetation density is correlated with soil moisture; and soil type is a better indicator for determining the slope of soil lines over sparsely vegetated areas, as it contains information of the soil texture, roughness, and composition. The generated bare soil albedo can be applied to improve the parameterization of surface energy budget in climate and remote sensing models as well as the retrieval accuracy of some satellite products. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle
Intercomparison of Surface Albedo Retrievals from MISR, MODIS, CGLS Using Tower and Upscaled Tower Measurements
Remote Sens. 2019, 11(6), 644; https://doi.org/10.3390/rs11060644 - 16 Mar 2019
Cited by 2
Abstract
Surface albedo is of crucial interest in land–climate interaction studies, since it is a key parameter that affects the Earth’s radiation budget. The temporal and spatial variation of surface albedo can be retrieved from conventional satellite observations after a series of processes, including [...] Read more.
Surface albedo is of crucial interest in land–climate interaction studies, since it is a key parameter that affects the Earth’s radiation budget. The temporal and spatial variation of surface albedo can be retrieved from conventional satellite observations after a series of processes, including atmospheric correction to surface spectral bi-directional reflectance factor (BRF), bi-directional reflectance distribution function (BRDF) modelling using these BRFs, and, where required, narrow-to-broadband albedo conversions. This processing chain introduces errors that can be accumulated and then affect the accuracy of the retrieved albedo products. In this study, the albedo products derived from the multi-angle imaging spectroradiometer (MISR), moderate resolution imaging spectroradiometer (MODIS) and the Copernicus Global Land Service (CGLS), based on the VEGETATION and now the PROBA-V sensors, are compared with albedometer and upscaled in situ measurements from 19 tower sites from the FLUXNET network, surface radiation budget network (SURFRAD) and Baseline Surface Radiation Network (BSRN) networks. The MISR sensor onboard the Terra satellite has 9 cameras at different view angles, which allows a near-simultaneous retrieval of surface albedo. Using a 16-day retrieval algorithm, the MODIS generates the daily albedo products (MCD43A) at a 500-m resolution. The CGLS albedo products are derived from the VEGETATION and PROBA-V, and updated every 10 days using a weighted 30-day window. We describe a newly developed method to derive the two types of albedo, which are directional hemispherical reflectance (DHR) and bi-hemispherical reflectance (BHR), directly from three tower-measured variables of shortwave radiation: downwelling, upwelling and diffuse shortwave radiation. In the validation process, the MISR, MODIS and CGLS-derived albedos (DHR and BHR) are first compared with tower measured albedos, using pixel-to-point analysis, between 2012 to 2016. The tower measured point albedos are then upscaled to coarse-resolution albedos, based on atmospherically corrected BRFs from high-resolution Earth observation (HR-EO) data, alongside MODIS BRDF climatology from a larger area. Then a pixel-to-pixel comparison is performed between DHR and BHR retrieved from coarse-resolution satellite observations and DHR and BHR upscaled from accurate tower measurements. The experimental results are presented on exploring the parameter space associated with land cover type, heterogeneous vs. homogeneous and instantaneous vs. time composite retrievals of surface albedo. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessEditor’s ChoiceArticle
A Method for Landsat and Sentinel 2 (HLS) BRDF Normalization
Remote Sens. 2019, 11(6), 632; https://doi.org/10.3390/rs11060632 - 15 Mar 2019
Cited by 4
Abstract
The Harmonized Landsat/Sentinel-2 (HLS) project aims to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2 remote sensing satellites. These satellites’ sampling characteristics provide nearly constant observation geometry and low illumination variation through the scene. However, the [...] Read more.
The Harmonized Landsat/Sentinel-2 (HLS) project aims to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2 remote sensing satellites. These satellites’ sampling characteristics provide nearly constant observation geometry and low illumination variation through the scene. However, the illumination variation throughout the year impacts the surface reflectance by producing higher values for low solar zenith angles and lower reflectance for large zenith angles. In this work, we present a model to derive the bidirectional reflectance distribution function (BRDF) normalization and apply it to the HLS product at 30 m spatial resolution. It is based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (M{O,Y}D09) at 1 km spatial resolution using the VJB method (Vermote et al., 2009). Unsupervised classification (segmentation) of HLS images is used to disaggregate the BRDF parameters to the HLS spatial resolution and to build a BRDF parameters database at HLS scale. We first test the proposed BRDF normalization for different solar zenith angles over two homogeneous sites, in particular one desert and one Peruvian Amazon forest. The proposed method reduces both the correlation with the solar zenith angle and the coefficient of variation (CV) of the reflectance time series in the red and near infrared bands to 4% in forest and keeps a low CV of 3% to 4% for the deserts. Additionally, we assess the impact of the view zenith angle (VZA) in an area of the Brazilian Amazon forest close to the equator, where impact of the angular variation is stronger because it occurs in the principal plane. The directional reflectance shows a strong dependency with the VZA. The current HLS BRDF correction reduces this dependency but still shows an under-correction, especially in the near infrared, while the proposed method shows no dependency with the view angles. We also evaluate the BRDF parameters using field surface albedo measurements as a reference over seven different sites of the US surface radiation budget observing network (SURFRAD) and five sites of the Australian OzFlux network. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle
Evaluating the Spatial Representativeness of the MODerate Resolution Image Spectroradiometer Albedo Product (MCD43) at AmeriFlux Sites
Remote Sens. 2019, 11(5), 547; https://doi.org/10.3390/rs11050547 - 06 Mar 2019
Cited by 1
Abstract
Land surface albedo is a key parameter in regulating surface radiation budgets. The gridded remote sensing albedo product often represents information concerning an area larger than the nominal spatial resolution because of the large viewing angles of the observations. It is essential to [...] Read more.
Land surface albedo is a key parameter in regulating surface radiation budgets. The gridded remote sensing albedo product often represents information concerning an area larger than the nominal spatial resolution because of the large viewing angles of the observations. It is essential to quantify the spatial representativeness of remote sensing products to better guide the sampling strategy in field experiments and match products from different sources. This study quantifies the spatial representativeness of the MODerate Resolution Image Spectroradiometer (MODIS) (collection V006) 500 m daily albedo product (MCD43A3) using the high-resolution product as intermediate data for different land cover types. A total of 1820 paired high-resolution Landsat Thematic Mapper (TM) and coarse-resolution (MODIS) albedo data from five land cover types were used. The TM albedo data was used as the spatial-complete high resolution data to evaluate the spatial representativeness of the MODIS albedo product. Semivarioagrams were estimated from 30 m Landsat data at different spatial scales. Surface heterogeneity was evaluated with sill value and relative coefficient of variation. The 30 m Landsat albedo data was aggregated to 450 m–1800 m using two different methods and compared with MODIS albedo product. The spatial representativeness of MODIS albedo product was determined according to the surface heterogeneity and the consistency of MODIS data and the aggregated TM value. Results indicated that for evergreen broadleaf forests, deciduous broadleaf forests, open shrub lands, woody savannas and grasslands, the MODIS 500 m daily albedo product represents a spatial scale of approximately 630 m. For mixed forests and croplands, the representative spatial scale was approximately 690 m. The difference obtained was primarily because of the complexity of the landscape structure. For mixed forests and croplands, the structure of the landscape was relatively complex due to the presence of different forest and plant types in the pixel area, whereas the other landscape structures were considerably simpler. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle
Improving the AVHRR Long Term Data Record BRDF Correction
Remote Sens. 2019, 11(5), 502; https://doi.org/10.3390/rs11050502 - 01 Mar 2019
Cited by 2
Abstract
The Long Term Data Record (LTDR) project has the goal of developing a quality and consistent surface reflectance product from coarse resolution optical sensors. This paper focuses on the Advanced Very High Resolution Radiometer (AVHRR) part of the record, using the Moderate Resolution [...] Read more.
The Long Term Data Record (LTDR) project has the goal of developing a quality and consistent surface reflectance product from coarse resolution optical sensors. This paper focuses on the Advanced Very High Resolution Radiometer (AVHRR) part of the record, using the Moderate Resolution Imaging Spectrometer (MODIS) instrument as a reference. When a surface reflectance time series is acquired from satellites with variable observation geometry, the directional variation generates an apparent noise which can be corrected by modeling the bidirectional reflectance distribution function (BRDF). The VJB (Vermote, Justice and Bréon, 2009) method estimates a target’s BRDF shape using 5 years of observation and corrects for directional effects maintaining the high temporal resolution of the measurement using the instantaneous Normalized Difference Vegetation Index (NDVI). The method was originally established on MODIS data but its viability and optimization for AVHRR data have not been fully explored. In this study we analyze different approaches to find the most robust way of applying the VJB correction to AVHRR data, considering that high noise in the red band (B1) caused by atmospheric effect makes the VJB method unstable. Firstly, our results show that for coarse spatial resolution, where the vegetation dynamics of the target don’t change significantly, deriving BRDF parameters from 15+ years of observations reduces the average noise by up to 7% in the Near Infrared (NIR) band and 6% in the NDVI, in comparison to using 3-year windows. Secondly, we find that the VJB method can be modified for AVHRR data to improve the robustness of the correction parameters and decrease the noise by an extra 8% and 9% in the red and NIR bands with respect to using the classical VJB inversion. We do this by using the Stable method, which obtains the volumetric BRDF parameter (V) based on its NDVI dependency, and then obtains the geometric BRDF parameter (R) through the inversion of just one parameter. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle
The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation
Remote Sens. 2018, 10(11), 1826; https://doi.org/10.3390/rs10111826 - 17 Nov 2018
Cited by 1
Abstract
Ice albedo feedback amplifies climate change signals and thus affects the global climate. Global long-term records on sea-ice albedo are important to characterize the regional or global energy budget. As the successor of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer [...] Read more.
Ice albedo feedback amplifies climate change signals and thus affects the global climate. Global long-term records on sea-ice albedo are important to characterize the regional or global energy budget. As the successor of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite) started its observation from October 2011 on S-NPP (Suomi National Polar-orbiting Partnership). It has improved upon the capabilities of the operational Advanced Very High Resolution Radiometer (AVHRR) and provides observation continuity with MODIS. We used a direct estimation algorithm to produce a VIIRS sea-ice albedo (VSIA) product, which will be operational in the National Oceanic and Atmospheric Administration’s (NOAA) S-NPP Data Exploration (NDE) version of the VIIRS albedo product. The algorithm is developed from the angular bin regression method to simulate the sea-ice surface bidirectional reflectance distribution function (BRDF) from physical models, which can represent different sea-ice types and vary mixing fractions among snow, ice, and seawater. We compared the VSIA with six years of ground measurements at 30 automatic weather stations from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-NET) as a proxy for sea-ice albedo. The results show that the VSIA product highly agreed with the station measurements with low bias (about 0.03) and low root mean square error (RMSE) (about 0.07) considering the Joint Polar Satellite System (JPSS) requirement is 0.05 and 0.08 at 4 km scale, respectively. We also evaluated the VSIA using two datasets of field measured sea-ice albedo from previous field campaigns. The comparisons suggest that VSIA generally matches the magnitude of the ground measurements, with a bias of 0.09 between the instantaneous albedos in the central Arctic and a bias of 0.077 between the daily mean albedos near Alaska. The discrepancy is mainly due to the scale difference at both spatial and temporal dimensions and the limited sample size. The VSIA data will serve for weather prediction applications and climate model calibrations. Combined with the historical observations from MODIS, current S-NPP VIIRS, and NOAA-20 VIIRS observations, VSIA will dramatically contribute to providing high-accuracy routine sea-ice albedo products and irreplaceable records for monitoring the long-term sea-ice albedo for climate research. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle
Measuring Landscape Albedo Using Unmanned Aerial Vehicles
Remote Sens. 2018, 10(11), 1812; https://doi.org/10.3390/rs10111812 - 15 Nov 2018
Cited by 3
Abstract
Surface albedo is a critical parameter in surface energy balance, and albedo change is an important driver of changes in local climate. In this study, we developed a workflow for landscape albedo estimation using images acquired with a consumer-grade camera on board unmanned [...] Read more.
Surface albedo is a critical parameter in surface energy balance, and albedo change is an important driver of changes in local climate. In this study, we developed a workflow for landscape albedo estimation using images acquired with a consumer-grade camera on board unmanned aerial vehicles (UAVs). Flight experiments were conducted at two sites in Connecticut, USA and the UAV-derived albedo was compared with the albedo obtained from a Landsat image acquired at about the same time as the UAV experiments. We find that the UAV estimate of the visibleband albedo of an urban playground (0.037 ± 0.063, mean ± standard deviation of pixel values) under clear sky conditions agrees reasonably well with the estimates based on the Landsat image (0.047 ± 0.012). However, because the cameras could only measure reflectance in three visible bands (blue, green, and red), the agreement is poor for shortwave albedo. We suggest that the deployment of a camera that is capable of detecting reflectance at a near-infrared waveband should improve the accuracy of the shortwave albedo estimation. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle
The Role of Climate and Land Use in the Changes in Surface Albedo Prior to Snow Melt and the Timing of Melt Season of Seasonal Snow in Northern Land Areas of 40°N–80°N during 1982–2015
Remote Sens. 2018, 10(10), 1619; https://doi.org/10.3390/rs10101619 - 11 Oct 2018
Cited by 4
Abstract
The rapid warming of the Northern Hemisphere high latitudes and the observed changes in boreal forest areas affect the global surface albedo and climate. This study looks at the trends in the timing of the snow melt season as well as the albedo [...] Read more.
The rapid warming of the Northern Hemisphere high latitudes and the observed changes in boreal forest areas affect the global surface albedo and climate. This study looks at the trends in the timing of the snow melt season as well as the albedo levels before and after the melt season in Northern Hemisphere land areas between 40°N and 80°N over the years 1982 to 2015. The analysis is based on optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). The results show that the changes in surface albedo already begin before the start of the melt season. These albedo changes are significant (the mean of absolute change is 4.4 albedo percentage units per 34 years). The largest absolute changes in pre-melt-season albedo are concentrated in areas of the boreal forest, while the pre-melt albedo of tundra remains unchanged. Trends in melt season timing are consistent over large areas. The mean of absolute change of start date of melt season is 11.2 days per 34 years, 10.6 days for end date of melt season and 14.8 days for length of melt season. The changes result in longer and shorter melt seasons, as well as changed timing of the melt, depending on the area. The albedo levels preceding the onset of melt and start of the melt season correlate with climatic parameters (air temperature, precipitation, wind speed). The changes in albedo are more closely linked to changes in vegetation, whereas the changes in melt season timing are linked to changes in climate. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle
Novel Measurements of Fine-Scale Albedo: Using a Commercial Quadcopter to Measure Radiation Fluxes
Remote Sens. 2018, 10(8), 1303; https://doi.org/10.3390/rs10081303 - 18 Aug 2018
Cited by 5
Abstract
Remote sensing of radiative indices must balance spatially and temporally coarse satellite measurements with finer-scale, but geographically limited, in-situ surface measurements. Instruments mounted upon an Unmanned Aerial Vehicle (UAV) can provide small-scale, mobile remote measurements that fill this resolution gap. Here we present [...] Read more.
Remote sensing of radiative indices must balance spatially and temporally coarse satellite measurements with finer-scale, but geographically limited, in-situ surface measurements. Instruments mounted upon an Unmanned Aerial Vehicle (UAV) can provide small-scale, mobile remote measurements that fill this resolution gap. Here we present and validate a novel method of obtaining albedo values using an unmodified quadcopter at a deciduous northern hardwood forest. We validate this method by comparing simultaneous albedo estimates by UAV and a fixed tower at the same site. We found that UAV provided stable albedo measurements across multiple flights, with results that were well within the range of tower-estimated albedo at similar forested sites. Our results indicate that in-situ albedo measurements (tower and UAV) capture more site-to-site variation in albedo than satellite measurements. Overall, we show that UAVs produce reliable, consistent albedo measurements that can capture crucial surface heterogeneity, clearly distinguishing between different land uses. Future application of this approach can provide detailed measurements of albedo and potentially other vegetation indices to enhance global research and modeling efforts. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle
Assessing the Impacts of Urbanization on Albedo in Jing-Jin-Ji Region of China
Remote Sens. 2018, 10(7), 1096; https://doi.org/10.3390/rs10071096 - 10 Jul 2018
Cited by 1
Abstract
As an indicative parameter that represents the ability of the Earth’s surface to reflect solar radiation, albedo determines the allocation of solar energy between the Earth’s surface and the atmosphere, which plays an important role in both global and local climate change. Urbanization [...] Read more.
As an indicative parameter that represents the ability of the Earth’s surface to reflect solar radiation, albedo determines the allocation of solar energy between the Earth’s surface and the atmosphere, which plays an important role in both global and local climate change. Urbanization is a complicated progress that greatly affects urban albedo via land cover change, human heat, aerosol, and other human activities. Although many studies have been conducted to identify the effects of these various factors on albedo separately, there are few studies that have quantitatively determined the combined effects of urbanization on albedo. In this study, based on a partial derivative method, vegetation index data and nighttime light data were used to quantitatively calculate the natural climate change and human activities’ contributions to albedo variations in the Jing-Jin-Ji region, during its highest population growth period from 2001 to 2011. The results show that (1) 2005 is the year when urbanization starts accelerating in the Jing-Jin-Ji region; (2) albedo trends are equal to 0.0065 year−1 before urbanization and 0.0012 year−1 after urbanization, which is a reduction of 4/5; and (3) the contribution rate of urbanization increases from 15% to 48.4%, which leads to a decrease in albedo of approximately 0.05. Understanding the contribution of urbanization to variations in urban albedo is significant for future studies on urban climate change via energy balance and can provide scientific data for energy conservation policymaking. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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