Assessment of Satellite-Derived Surface Reflectances by NASA’s CAR Airborne Radiometer over Railroad Valley Playa

CAR (Cloud Absorption Radiometer) is a multi-angular and multi-spectral airborne radiometer instrument, whose radiometric and geometric characteristics are well calibrated and adjusted before and after each flight campaign. CAR was built by NASA (National Aeronautics and Space Administration) in 1984. On 16 May 2008, a CAR flight campaign took place over the well-known calibration and validation site of Railroad Valley in Nevada, USA (38.504°N, 115.692°W). The campaign coincided with the overpasses of several key EO (Earth Observation) satellites such as Landsat-7, Envisat and Terra. Thus, there are nearly simultaneous measurements from these satellites and the CAR airborne sensor over the same calibration site. The CAR spectral bands are close to those of most EO satellites. CAR has the ability to cover the whole range of azimuth view angles and a variety of zenith angles depending on altitude and, as a consequence, the biases seen between satellite and CAR measurements due to both unmatched spectral bands and unmatched angles can be significantly reduced. A comparison is presented here between CAR’s land surface reflectance (BRF or Bidirectional Reflectance Factor) with those derived from Terra/MODIS (MOD09 and MAIAC), Terra/MISR, Envisat/MERIS and Landsat-7. In this study, we utilized CAR data from low altitude flights (approx. 180 m above the surface) in order to minimize the effects of the atmosphere on these measurements and then obtain a valuable ground-truth data set of surface reflectance. Furthermore, this study shows that differences between measurements caused by surface heterogeneity can be tolerated, thanks to the high homogeneity of the study site on the one hand, and on the other hand, to the spatial sampling and the large number of CAR samples. These results demonstrate that satellite BRF measurements over this site are in good agreement with CAR with variable biases across different spectral bands. This is most likely due to residual aerosol effects in the EO derived reflectance

Use of In Situ and Airborne Multiangle Data to Assess MODIS- and Landsat-based Estimates of Surface Albedo

The quantification of uncertainty of global surface albedo data and products is a critical part of producing complete, physically consistent, and decadal land property data records for studying ecosystem change. A current challenge in validating satellite retrievals of surface albedo is the ability to overcome the spatial scaling errors that can contribute on the order of 20% disagreement between satellite and field-measured values. Here, we present the results from an uncertain ty analysis of MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat albedo retrievals, based on collocated comparisons with tower and airborne multi-angular measurements collected at the Atmospheric Radiation Measurement Program s (ARM) Cloud and Radiation Testbed (CART) site during the 2007 Cloud and Land Surface Interaction Campaign (CLAS33 IC 07). Using standard error propagation techniques, airborne measurements obtained by NASA s Cloud Absorption Radiometer (CAR) were used to quantify the uncertainties associated with MODIS and Landsat albedos across a broad range of mixed vegetation and structural types. Initial focus was on evaluating inter-sensor consistency through assessments of temporal stability, as well as examining the overall performance of satellite-derived albedos obtained at all diurnal solar zenith angles. In general, the accuracy of the MODIS and Landsat albedos remained under a 10% margin of error in the SW(0.3 - 5.0 m) domain. However, results reveal a high degree of variability in the RMSE (root mean square error) and bias of albedos in both the visible (0.3 - 0.7 m) and near-infrared (0.3 - 5.0 m) broadband channels; where, in some cases, retrieval uncertainties were found to be in excess of 20%. For the period of CLASIC 07, the primary factors that contributed to uncertainties in the satellite-derived albedo values include: (1) the assumption of temporal stability in the retrieval of 500 m MODIS BRDF values over extended periods of cloud-contaminated observations; and (2) the assumption of spatial 45 and structural uniformity at the Landsat (30 m) pixel scale

Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect

The launch of ADEOS in August 1996 with POLDER, TOMS, and OCTS instruments on board and the future launch of EOS-AM 1 in mid-1998 with MODIS and MISR instruments on board start a new era in remote sensing of aerosol as part of a new remote sensing of the whole Earth system (see a list of the acronyms in the Notation section of the paper). These platforms will be followed by other international platforms with unique aerosol sensing capability, some still in this century (e.g., ENVISAT in 1999). These international spaceborne multispectral, multiangular, and polarization measurements, combined for the first time with international automatic, routine monitoring of aerosol from the ground, are expected to form a quantum leap in our ability to observe the highly variable global aerosol. This new capability is contrasted with present single-channel techniques for AVHRR, Meteosat, and GOES that although poorly calibrated and poorly characterized already generated important aerosol global maps and regional transport assessments. The new data will improve significantly atmospheric corrections for the aerosol effect on remote sensing of the oceans and be used to generate first real-time atmospheric corrections over the land. This special issue summarizes the science behind this change in remote sensing, and the sensitivity studies and applications of the new algorithms to data from present satellite and aircraft instruments. Background information and a summary of a critical discussion that took place in a workshop devoted to this topic is given in this introductory paper. In the discussion it was concluded that the anticipated remote sensing of aerosol simultaneously from several space platforms with different observation strategies, together with continuous validations around the world, is expected to be of significant importance to test remote sensing approaches to characterize the complex and highly variable aerosol field. So far, we have only partial understanding of the information content and accuracy of the radiative transfer inversion of aerosol information from the satellite data, due to lack of sufficient theoretical analysis and applications to proper field data. This limitation will make the anticipated new data even more interesting and challenging. A main concern is the present inadequate ability to sense aerosol absorption, from space or from the ground. Absorption is a critical parameter for climate studies and atmospheric corrections. Over oceans, main concerns are the effects of white caps and dust on the correction scheme. Future improvement in aerosol retrieval and atmospheric corrections will require better climatology of the aerosol properties and understanding of the effects of mixed composition and shape of the particles. The main ingredient missing in the planned remote sensing of aerosol are spaceborne and ground-based lidar observations of the aerosol profiles

Early Spring Post-Fire Snow Albedo Dynamics in High Latitude Boreal Forests Using Landsat-8 OLI Data

Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (less than 100 m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high-burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500 m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems

Effect of the aerosol type selection for the retrieval of shortwave ground net radiation: case study using landsat 8 data

This paper discusses the aerosol radiative effects involved in the accuracy of shortwave net radiation, Rn.sw, with sw 2 (400–900) nm, retrieved by the Operational Land Imager (OLI), the new generation sensor of the Landsat mission. Net radiation is a key parameter for the energy exchange between the land and atmosphere; thus, Rn.sw retrieval from space is under investigation by exploiting the increased spatial resolution of the visible and near-infrared OLI data. We adopted the latest version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmospheric radiative transfer model implemented in the atmospheric correction algorithm (OLI Atmospherically-Corrected Reflectance Imagery (OLI@CRI)) developed specifically for OLI data. The values of Rn.sw were obtained by varying the microphysical properties of the aerosol during the OLI@CRI retrieval of both the OLI surface reflectance, roli pxl , and the incoming solar irradiance at the surface. The analysis of the aerosol effects on the Rn.sw was carried out on a spectrally-homogeneous desert area located in the southwestern Nile Delta. The results reveal that the Rn.sw available for energy exchange between the land and atmosphere reduces the accuracy (NRMSE ' 14%) when the local aerosol microphysical properties are not considered during the processing of space data. Consequently, these findings suggest that the aerosol type should be considered for variables retrieved by satellite observations concerning the energy exchange in the natural ecosystems, such as Photosynthetically-Active Radiation (PAR). This will also improve the accuracy of land monitoring and of solar energy for power generation when space data are used

Sensor capability and atmospheric correction in ocean colour remote sensing

© 2015 by the authors; licensee MDPI, Basel, Switzerland. Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges

Intercomparison of Surface Albedo Retrievals from MISR, MODIS, CGLS Using Tower and Upscaled Tower Measurements

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

Boreal forest albedo and its spatial and temporal variation

Surface albedo refers to the fraction of solar irradiance that is reflected by a surface. Accurate characterisation of the albedo of various land cover types is required for evaluating the energy exchange between the Earth s surface and the atmosphere. The optical and structural properties of a surface determine its albedo. Boreal forest albedo can vary due to factors such as tree species composition, forest structure, understorey vegetation composition, and seasonal changes in vegetation and snow cover. The aim of this study was to characterise typical albedos of Finnish forests dominated by different tree species, evaluate the seasonal variation in forest albedo, and to estimate the effects of structural forest variables and understorey composition on forest albedo or spectral reflectance. To achieve these aims, forest albedo was measured in-situ using pyranometers, estimated from satellite data and calculated using a forest albedo model. Unmixing methods were used to estimate forest albedo from coarse spatial resolution MODIS albedo retrievals and understorey spectral reflectance from Landsat observations. Mature or middle aged pine, spruce and broadleaved deciduous (mainly birch) forests had distinctly different albedos in both summer and winter. Coniferous forest albedo was lower and showed less seasonal variation than albedo in open areas or broadleaved deciduous forests. Albedo of pine was somewhat higher than that of spruce. Snow cover on the ground and canopy increased forest albedo. Young stands with an assumedly high proportion of deciduous species in the under- and overstorey were characterised by a higher albedo than the mature coniferous forests. The high albedo at early succession rapidly decreased as the forest matured. The forest floor was typically covered by green understorey vegetation with rather low albedo, which decreased the influence of a changing canopy cover or leaf area index (LAI) on forest albedo. The spectral reflectances of the understorey varied with site fertility and forest age.Metsän albedoksi kutsutaan sitä osuutta metsään saapuvasta auringonsäteilyn energiasta, joka ei sitoudu metsään vaan heijastuu takaisin taivaalle. Metsän albedon tiedetään vaihtelevan muun muassa puulajin, metsän rakenteen ja lumipeitteen mukaan, mutta näiden vaihtelujen suuruutta ei ole Suomessa tarkemmin arvioitu. Tietoa albedosta tarvitaan kun arvioidaan metsien energiatasetta sekä metsien vaikutusta ilmastoon. Tämän tutkimuksen tavoitteena oli arvioida kuinka suomalaisten metsien albedo vaihtelee puulajin, metsän rakenteen, aluskasvillisuuden ja vuodenaikojen vaihtelun mukaan. Aineistona käytettiin maastossa tehtyjä albedomittauksia, satelliittimittauksista laskettuja arvoja sekä mallinnusta. Maastossa tehdyistä mittauksista saadut tulokset ovat vain suuntaa-antavia pienen koealamäärän takia. Satelliittikuva-aineistojen tulkinnassa käytettiin apuna malleja, joilla voitiin arvioida heijastusarvoja kuvanalkioita suuremmassa mittakaavassa. Kasvukauden aikainen vaihtelu havumetsien albedossa oli melko pientä, mutta lehtimetsissä albedo oli keväällä ja syksyllä lumettomana aikana hieman matalampi kuin keskikesällä. Lumipeite kasvatti albedoa sekä havu- että lehtimetsissä. Albedo oli kaikkina vuodenaikoina matalin kuusimetsissä, hieman korkeampi mäntymetsissä ja korkein lehtimetsissä. Poikkeuksen muodostivat jaksot, jolloin havumetsien latvus oli lumen peitossa keskitalvella. Saman puulajin keski-ikäisissä tai varttuneissa metsissä lehtiala tai latvuspeittävyys vaikutti lumettoman ajan albedoon vain vähän, mikä saattoi osittain johtua melko matalasta aluskasvillisuuden albedosta. Nuorissa havumetsissä albedo oli suurempi kuin varttuneissa, mikä todennäköisesti johtui nuorten metsien pienemmästä lehtialasta sekä aluskasvillisuuden suuremmasta näkyvyydestä. Aluskasvillisuuden aallonpituudesta riippuva heijastus muuttui metsikön varttuessa ja riippui metsätyypistä

How robust are in-situ observations for validating satellite-derived albedo over the dark zone of the Greenland Ice Sheet?

Calibration and validation of satellite‐derived ice sheet albedo data require high‐quality, in situ measurements commonly acquired by up and down facing pyranometers mounted on automated weather stations (AWS). However, direct comparison between ground and satellite‐derived albedo can only be justified when the measured surface is homogeneous at the length‐scale of both satellite pixel and in situ footprint. Here we use digital imagery acquired by an unmanned aerial vehicle to evaluate point‐to‐pixel albedo comparisons across the western, ablating margin of the Greenland Ice Sheet. Our results reveal that in situ measurements overestimate albedo by up to 0.10 at the end of the melt season because the ground footprints of AWS‐mounted pyranometers are insufficient to capture the spatial heterogeneity of the ice surface as it progressively ablates and darkens. Statistical analysis of 21 AWS across the entire Greenland Ice Sheet reveals that almost half suffer from this bias, including some AWS located within the wet snow zone

Uncertainty assessment of surface net radiation derived from Landsat images

The net radiation flux available at the Earth's surface drives evapotranspiration, photosynthesis and other physical and biological processes. The only cost-effective way to capture its spatial and temporal variability at regional and global scales is remote sensing. However, the accuracy of net radiation derived from remote sensing data has been evaluated up to now over a limited number of in situ measurements and ecosystems. This study aims at evaluating estimates and uncertainties on net radiation derived from Landsat-7 images depending on reliability of the input surface variables albedo, emissivity and surface temperature. The later includes the reliability of remote sensing information (spectral reflectances and top of canopy brightness temperature) and shortwave and longwave incoming radiations. Primary information describing the surface is derived from remote sensing observations. Surface albedo is estimated from spectral reflectances using a narrow-to-broadband conversion method. Land surface temperature is retrieved from top of canopy brightness temperature by accounting for land surface emissivity and reflection of atmospheric radiation; and emissivity is estimated using a relationship with a vegetation index and a spectral database of soil and plant canopy properties in the study area. The net radiation uncertainty is assessed using comparison with ground measurements over the Crau–Camargue and lower Rhone valley regions in France. We found Root Mean Square Errors between retrievals and field measurements of 0.25–0.33 (14–19%) for albedo, ~ 1.7 K for surface temperature and ~ 20 W·m− 2 (5%) for net radiation. Results show a substantial underestimation of Landsat-7 albedo (up to 0.024), particularly for estimates retrieved using the middle infrared, which could be due to different sources: the calibration of field sensors, the correction of radiometric signals from Landsat-7 or the differences in spectral bands with the sensors for which the models where originally derived, or the atmospheric corrections. We report a global uncertainty in net radiation of 40–100 W·m− 2 equally distributed over the shortwave and longwave radiation, which varies spatially and temporally depending on the land use and the time of year. In situ measurements of incoming shortwave and longwave radiation contribute the most to uncertainty in net radiation (10–40 W·m− 2 and 20–30 W·m− 2, respectively), followed by uncertainties in albedo (< 25 W·m− 2) and surface temperature (~ 8 W·m− 2). For the latter, the main factors were the uncertainties in top of canopy reflectances (< 10 W·m− 2) and brightness temperature (5–7 W·m− 2). The generalization of these results to other sensors and study regions could be considered, except for the emissivity if prior knowledge on its characterization is not available