Assessing change in the Earth's land surface albedo with moderate resolution satellite imagery

Land surface albedo describes the proportion of incident solar radiant flux that is reflected from the Earth's surface and therefore is a crucial parameter in modeling and monitoring attempts to capture the current climate, hydrological, and biogeochemical cycles and predict future scenarios. Due to the temporal variability and spatial heterogeneity of land surface albedo, remote sensing offers the only realistic method of monitoring albedo on a global scale. While the distribution of bright, highly reflective surfaces (clouds, snow, deserts) govern the vast majority of the fluctuation, variations in the intrinsic surface albedo due to natural and human disturbances such as urban development, fire, pests, harvesting, grazing, flooding, and erosion, as well as the natural seasonal rhythm of vegetation phenology, play a significant role as well. The development of times series of global snow-free and cloud-free albedo from remotely sensed observations over the past decade and a half offers a unique opportunity to monitor and assess the impact of these alterations to the Earth's land surface. By utilizing multiple satellite records from the MODerate-resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging Spectroradiometer (MISR) and the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, and developing innovative spectral conversion coefficients and temporal gap-filling strategies, it has been possible to utilize the strengths of the various sensors to improve the spatial and temporal coverage of global land surface albedo retrievals. The availability of these products is particularly important in tropical regions where cloud cover obscures the forest for significant periods. In the Amazon, field ecologists have noted that some areas of the forest ecosystem respond rapidly with foliage growth at the beginning of the dry season, when sunlight can finally penetrate fully to the surface and have suggested this phenomenon can continue until reductions in water availability (particularly in times of drought) impact the growth cycle. While it has been difficult to capture this variability from individual optical satellite sensors, the temporally gap-filled albedo products developed during this research are used in a case study to monitor the Amazon during the dry season and identify the extent of these regions of foliage growth

Quantifying the effects of freeze-thaw transitions and snowpack melt on land surface albedo and energy exchange over Alaska and Western Canada

Variations in land surface albedo and snow-cover strongly impact the global biosphere, particularly through the snow-albedo feedback on climate. The seasonal freeze-thaw (FT) transition is coupled with snowpack melt dynamics and strongly impacts surface water mobility and the energy budget in the northern (≥45°N) arctic and boreal region (ABR). However, understanding of the regional variation in snowmelt and its effect on the surface energy budget are limited due to sparse in situ measurements of these processes and environmental constraints on effective monitoring within the ABR. In this study, we combined synergistic observations from overlapping satellite optical-infrared and microwave sensor records to quantify the regional patterns and seasonal progression in wet snow conditions during the spring snowmelt and autumn snow accumulation periods across Alaska and western Canada. The integrated satellite record included daily landscape FT status from AMSR microwave brightness temperature retrievals; and snow-cover extent, black sky albedo and net shortwave solar radiation (R snet) derived from MODIS and AVHRR observations. The integrated satellite records were analyzed with in situ surface air temperature and humidity observations from regional weather stations over a two-year study period (2015–2016) overlapping with the NASA ABoVE (Arctic Boreal Vulnerability Experiment). Our results show a large (79%) mean decline in land surface albedo between dry snow and snow-free conditions during the spring (March–June) and autumn (August–November) transition periods. Onset of diurnal thawing and refreezing of the surface snow layer and associated wet snow conditions in spring contributed to an approximate 25% decrease in snow cover albedo that extended over a seven to 21 week snowpack depletion period. The lower wet snow albedo enhances R snet by approximately 74% (9–10 MJ m−2 d−1) relative to dry snow conditions, reinforcing snowmelt and surface warming, and contributing to growing season onset and activation of biological and hydrological processes in the ABR. These results contribute to better understanding of snow albedo feedbacks to Arctic amplification, and the representation of these processes in global Earth system models

Assessing Snow Extent Data Sets over North America to Inform and Improve Trace Gas Retrievals from Solar Backscatter

Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability and thus snow-covered scenes are often omitted from retrieval data sets; however, the high reflectance of snow is potentially advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (>50%) of the TEMPO field of regard can be snow covered in January, and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered. We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy of 93%, precision of 87%, recall of 83%). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS-observed radiances had high precision (90% for Aqua and Terra), but underestimated the presence of snow (recall of 74% for Aqua, 75% for Terra). MAIAC generally outperforms the standard MODIS products (precision of 51%, recall of 43% for Aqua; precision of 69%, recall of 45% for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83%) but missed a significant number of snow-covered pixels (recall of 45%). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy of 91%, precision of 79%, recall of 82%). We use the F score, which balances precision and recall, to determine overall product performance (F = 85%, 82(82)%, 81%, 58%, 46(54)% for IMS, MAIAC Aqua(Terra), CMC, NISE, MODIS Aqua(Terra) respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. We find that using IMS to identify snow cover and enable inclusion of snow-covered scenes in clear-sky conditions across North America in January can increase both the number of observations by a factor of 2.1 and the average sensitivity to the tropospheric NO2 column by a factor of 2.7

Technical note: A view from space on global flux towers by MODIS and Landsat: The FluxnetEO dataset

Funding Information: Acknowledgements. We thank the team at the ICOS Carbon Portal for their support in publishing the FluxnetEO data sets, with great thanks in particular to Ute Karstens and Zois Zogopoulos. SW acknowledges funding from an ESA Living Planet Fellowship in the project Vad3e mecum. Alexey Vasilevich Panov acknowledges funding from the Max Planck Society (Germany), Russian Foundation for Basic Re- search, Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science, project no. 20-45-242908. Frederik Schrader and Christian Brümmer acknowledge funds from the German Federal Ministry of Food and Agriculture (BMEL) received through Thünen Institute of Climate-Smart Agriculture. Simon Besnard acknowledges funding from the European Union through the BIOMAS-CAT (project code: 4000115192/18/I/NB) (https://eo4society.esa. int/projects/biomascat/, last access: 3 May 2022) and VERIFY (project code: BO-55-101-006) (https://cordis.europa.eu/project/id/ 776810, last access: 3 May 2022) projects. Funding Information: Financial support. This research has been supported by the Euro- Publisher Copyright: © 2022 Sophia Walther et al.The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, AmeriFlux, and Terrestrial Ecosystem Research Network (TERN)/OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the benchmarking of terrestrial biosphere models. However, insufficient quality and frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices, and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40g off-nadir. Finally, we offer the community living data sets of pre-processed Earth observation data, where version 1.0 features the MCD43A4/A2 and MxD11A1 MODIS products and Landsat Collection 1 Tier 1 and Tier 2 products in a radius of 2 km around 338 flux sites. The data sets we provide can widely facilitate the integration of activities in the eddy-covariance, remote sensing, and modelling fields.publishersversionpublishe

A mathematical transformation of multi-angular remote sensing data for the study of vegetation change

Vegetation change is an important factor affecting the global carbon cycle, land-atmosphere interaction, and terrestrial ecology. The study of vegetation change on a global scale can be used to evaluate the impact of global climate change on terrestrial ecosystems. Satellite remote sensing can monitor vegetation change at the global scale, providing continuous samples of radiation reflected by vegetated surfaces with a temporal resolution of days. The MISR instrument offers the potential to sample the specular anisotropy of the Earth from up to 9 angles. Characterization of the specular anisotropy of vegetated surfaces on a global scale will provide information on the physical characteristics of vegetation affecting anisotropy not available from nadir-view only remote sensing. The objective of this study is to develop a Principal Components Analysis (PCA) transformation of multi-angular measurements of the Earth’s surface acquired by the MISR instrument, to examine the feasibility of quantifying the structural characteristics of different vegetation communities at a global scale. This transformation will be applied to a time-series analysis of the Kalmioposis Wilderness in the Siskiyou National Forest in Southwestern Oregon to better understand the changes in spectral and angular reflectance of a forest stand during re-growth after a stand-replacing disturbance. A sample encompassing a full phenologic cycle, of the red bands only from MISR cameras Ca – Cf, at scaled surface reflectance, provided the template on which PCA was performed. The sample of MISR data was created using imagery collected from 2001 – 2005 to provide a wide variety of vegetation and soils reflectance over a phenologic cycle. Sample data was rotated to the principal components as the new axes using the coefficients of rotation from an un-standardized PCA. Samples were evaluated at various latitudes, differing topography, and varying vegetation density and land cover to determine the properties of the scene controlling the range and magnitude of the principal components. Principal component 1 was found to have high negative correlation to NDVI. Principal component 2 was found to have high positive correlation to both the solar zenith angle and the relative azimuth angle between the MISR sensor and the path of incident radiation. Principal component 3 could not be correlated to any available metric, although evidence suggests that component 3 may carry useful information. The PCA transformation proved useful at relating the changes in vegetation after a fire at the Biscuit Complex. The changes in the BRDF as sampled by MISR were expressed through the principal components, but these changes could not be directly related to changing structural characteristics of the vegetation. The goal of assessing structural characteristics of vegetation through the PCA transformation to a single metric of vegetation structure was unsuccessful. The PCA transformation of the MISR sample successfully yielded a transformation where different classes of vegetation occupied distinct and unique regions of PCA space. The first two principal components were successfully correlated to measurable and definable metrics of vegetation and solar illumination. The third principal component, for which a correlation could not be found, was suggestive of carrying unique information and merits further investigation. The transformation of multi-angular red band reflectance as presented in this study may prove to be a valuable method of estimating biomass at a global scale. With principal components correlated to measures of biomass in NDVI and to the shadowing of the ground through the angle of solar illumination, the PCA relates characteristics of vegetated scenes in a minimum of bands

Clouds and the Earth's Radiant Energy System (CERES) Algorithm Theoretical Basis Document

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 3 details the advanced CERES methods for performing scene identification and inverting each CERES scanner radiance to a top-of-the-atmosphere (TOA) flux. CERES determines cloud fraction, height, phase, effective particle size, layering, and thickness from high-resolution, multispectral imager data. CERES derives cloud properties for each pixel of the Tropical Rainfall Measuring Mission (TRMM) visible and infrared scanner and the Earth Observing System (EOS) moderate-resolution imaging spectroradiometer. Cloud properties for each imager pixel are convolved with the CERES footprint point spread function to produce average cloud properties for each CERES scanner radiance. The mean cloud properties are used to determine an angular distribution model (ADM) to convert each CERES radiance to a TOA flux. The TOA fluxes are used in simple parameterization to derive surface radiative fluxes. This state-of-the-art cloud-radiation product will be used to substantially improve our understanding of the complex relationship between clouds and the radiation budget of the Earth-atmosphere system

Procedures for the analysis and use of multiple view angle image data

It is recognised that the majority of vegetative cover types have anisotropic reflectance characteristics that are largely a function of their canopy geometry. Several studies have made attempts at formulating methods for the use of data remotely sensed from off-nadir directions. The best of these methods attempt to utilise the "extra" information implicitly contained in off-nadir image datasets. In this study, an attempt is made to extract information concerning agro-physical parameters of a number of vegetative cover types using imagery acquired by an airborne sensor, the Daedalus Airborne Thematic Mapper (ATM). It is also recognised in the literature that the nature of spatial variance in images is related to the size and distribution of the objects in the scene and the sampling characteristics of the sensor. In previous work this relationship has been explored by examining scenes using images of varying spatial resolutions, using a number of measurements of spatial variance. The underlying trend of these measurements is then used to interpret the nature of the objects in the scene. No previous work exists which attempts to utilise the change in variance of images acquired at different off-nadir view angles. In this study, the understanding of this relationship is developed by examining the change in variance of a number of vegetative cover types from multiple view angle image datasets. The geometry of the ATM sensor is derived to allow an understanding of the sampling characteristics of the instrument. Two important geometric factors are established: first, the area of the ground resolution element increases with view angle, which effectively reduces spatial resolution at off-nadir angles; and second, overlap between adjacent ground resolution elements increases with view angle, increasing the spatial auto-correlation between these samples. The effects of illumination, atmosphere and topography can all influence variance in an image. A parametric procedure for normalising multiple view angle (and therefore multitemporal) datasets for these factors is developed, based upon the production of reflectance images using a sky radiance model of the spectral and spatial distributions of irradiance, ground measurements of irradiance, and a digital terrain model of the study site. Finally, it is shown that image variance is likely to decrease at off-nadir view angles, the magnitude of this decrease being related to the sensor geometry and (more importantly) the geometry of the canopy. By a simple statistical analytical procedure it is possible to construct broad classes within which the nature of the canopy can be classified

Nitrogen dioxide and formaldehyde measurements from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator over Houston, Texas

The GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) was developed in support of NASA's decadal survey GEO-CAPE geostationary satellite mission. GCAS is an airborne push-broom remote-sensing instrument, consisting of two channels which make hyperspectral measurements in the ultraviolet/visible (optimized for air quality observations) and the visible–near infrared (optimized for ocean color observations). The GCAS instrument participated in its first intensive field campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign in Texas in September 2013. During this campaign, the instrument flew on a King Air B-200 aircraft during 21 flights on 11 days to make air quality observations over Houston, Texas. We present GCAS trace gas retrievals of nitrogen dioxide (NO2) and formaldehyde (CH2O), and compare these results with trace gas columns derived from coincident in situ profile measurements of NO2 and CH2O made by instruments on a P-3B aircraft, and with NO2 observations from ground-based Pandora spectrometers operating in direct-sun and scattered light modes. GCAS tropospheric column measurements correlate well spatially and temporally with columns estimated from the P-3B measurements for both NO2 (r2 = 0.89) and CH2O (r2 = 0.54) and with Pandora direct-sun (r2 = 0.85) and scattered light (r2 = 0.94) observed NO2 columns. Coincident GCAS columns agree in magnitude with NO2 and CH2O P-3B-observed columns to within 10&thinsp;% but are larger than scattered light Pandora tropospheric NO2 columns by 33&thinsp;% and direct-sun Pandora NO2 columns by 50&thinsp;%.</p

Empirical approach to satellite snow detection

Lumipeitteellä on huomattava vaikutus säähän, ilmastoon, luontoon ja yhteiskuntaan. Pelkästään sääasemilla tehtävät lumihavainnot (lumen syvyys ja maanpinnan laatu) eivät anna kattavaa kuvaa lumen peittävyydestä tai muista lumipeitteen ominaisuuksista. Sääasemien tuottamia havaintoja voidaan täydentää satelliiteista tehtävillä havainnoilla. Geostationaariset sääsatelliitit tuottavat havaintoja tihein välein, mutta havaintoresoluutio on heikko monilla alueilla, joilla esiintyy kausittaista lunta. Polaariradoilla sääsatelliittien havaintoresoluutio on napa-alueiden läheisyydessä huomattavasti parempi, mutta silloinkaan satelliitit eivät tuota jatkuvaa havaintopeittoa. Tiheimmän havaintoresoluution tuottavat sääsatelliittiradiometrit, jotka toimivat optisilla aallonpituuksilla (näkyvä valo ja infrapuna). Lumipeitteen kaukokartoitusta satelliiteista vaikeuttavat lumipeitteen oman vaihtelun lisäksi pinnan ominaisuuksien vaihtelu (kasvillisuus, vesistöt, topografia) ja valaistusolojen vaihtelu. Epävarma ja osittain puutteellinen tieto pinnan ja kasvipeitteen ominaisuuksista vaikeuttaa luotettavan automaattisen analyyttisen lumentunnistusmenetelmän kehittämistä ja siksi empiirinen lähestymistapa saattaa olla toimivin vaihtoehto automaattista lumentunnistusmenetelmää kehitettäessä. Tässä työssä esitellään kaksi EUMETSATin osittain rahoittamassa H SAFissa kehitettyä lumituotetta ja niissä käytetyt empiiristä lähestymistapaa soveltaen kehitetyt algoritmit. Geostationaarinen MSG/SEVIRI H31 lumituote on saatavilla vuodesta 2008 alkaen ja polaarituote Metop/AVHRR H32 vuodesta 2015 alkaen. Lisäksi esitellään pintahavaintoihin perustuvat validointitulokset, jotka osoittavat tuotteiden saavuttavan määritellyt tavoitteet.Snow cover plays a significant role in the weather and climate system, ecosystems and many human activities, such as traffic. Weather station snow observations (snow depth and state of the ground) do not provide highresolution continental or global snow coverage data. The satellite observations complement in situ observations from weather stations. Geostationary weather satellites provide observations at high temporal resolution, but the spatial resolution is low, especially in polar regions. Polarorbiting weather satellites provide better spatial resolution in polar regions with limited temporal resolution. The best detection resolution is provided by optical and infra-red radiometers onboard weather satellites. Snow cover in itself is highly variable. Also, the variability of the surface properties (such as vegetation, water bodies, topography) and changing light conditions make satellite snow detection challenging. Much of this variability is in subpixel scales, and this uncertainty creates additional challenges for the development of snow detection methods. Thus, an empirical approach may be the most practical option when developing algorithms for automatic snow detection. In this work, which is a part of the EUMETSAT-funded H SAF project, two new empirically developed snow extent products for the EUMETSAT weather satellites are presented. The geostationary MSG/SEVIRI H32 snow product has been in operational production since 2008. The polar product Metop/AVHRR H32 is available since 2015. In addition, validation results based on weather station snow observations between 2015 and 2019 are presented. The results show that both products achieve the requirements set by the H SAF