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Two decades (2000–2019) of the landfast ice properties in the Beaufort Sea region in the Canadian Arctic were analyzed at 250 m spatial resolution from two sources: (1) monthly maps derived at the Canada Centre for Remote Sensing from the Moderate Resolution Imaging Spectroradiometer clear-sky satellite image composites; and (2) Canadian Ice Service charts. Detailed comparisons have been conducted for the landfast ice spatial extent, the water depth at, and the distance to the outer seaward edge from the coast in four sub-regions: (1) Alaska coast; (2) Barter Island to Herschel Island; (3) Mackenzie Bay; and (4) Richards Island to Cape Bathurst. The results from both sources demonstrate good agreement. The average spatial extent for the entire region over the April–June period is 48.5 (±5.0) × 103 km2 from Canadian Ice Service data versus 45.1 (±6.1) × 103 km2 from satellite data used in this study (7.0% difference). The correlation coefficient for April–June is 0.73 (p = 2.91 × 10−4). The long-term linear trends of the April–June spatial extent since 2000 demonstrated statistically significant decline: −4.45 (±1.69) × 103 km2/decade and −4.73 (±2.17) × 103 km2/decade from Canadian Ice Service and satellite data, respectively. The landfast ice in the Beaufort Sea region showed the general tendency for an earlier break-up, later onset, and longer ice-free period. The break-up date has decreased by 7.6 days/decade in the Mackenzie Bay region. The western part of the study area did not demonstrate statistically significant changes since 2000.

A novel satellite image processing technique developed at the Canada Centre for Remote Sensing has been utilized to produce annual time series of the minimum snow/ice (MSI) extent over the northern circumpolar landmass area (9,000 km × 9,000 km) for 2000–19. The information has been derived from the Moderate Resolution Imaging Spectroradiometer 10-day clear-sky composites generated at 250-m spatial resolution over the April–September period. Derived interannual variations agree very well with the warm-season average surface air temperatures from the European reanalysis (ERA5). The region-average correlation coefficient is −0.78. The total MSI extent demonstrated a statistically significant declining trend equal to −1,477 km2 yr−1. Results have been compared with data from the Randolph Glacier Inventory (RGI 6.0). The comparison points to a significant contribution of minimum seasonal snow cover relative to RGI glacierized areas. Quantitative estimates obtained for the first time showed that the region-average snow extent that survives the summer melt and resides outside of RGI area can be as high as 15% (or 53 × 103 km2) while in the northern Canadian Arctic it can reach 41% (or 43 × 103 km2). The derived MSI time series data can be recommended to the glacier and land-cover scientific community as a source of validation data and annual updates of snow and ice maps over the northern circumpolar landmass.

Landfast ice (LFI) is a prominent climatological feature in the Canadian Arctic. LFI is generally defined as immobile near-shore ice that remains fast along the coast and forms seaward from the lan...

Continuous observation of polar regions from space remains an important unsolved technical challenge of great interest for the international meteorological community. This capacity would allow achieving global continuous coverage once combined with the geostationary (GEO) satellite network. From a practical point of view, continuous coverage of polar regions with a small number of spacecraft can be obtained from a constellation of satellites either in highly elliptical orbits (HEO) or in medium Earth orbits (MEO). The study compares HEO and MEO satellite constellations for their capacity to provide continuous imaging of polar regions as function of the viewing zenith angle (VZA) and evaluates the corresponding latitude limits that ensure sufficient overlap with GEO imagery. Earlier studies assumed the latitude boundary of 60° and the VZA range 70°–85° depending on the space mission focus: meteorological purposes or communications. From the detailed analysis of meteorological retrieval requirements, this study suggests that the overlap of the GEO and polar observing systems (HEO or MEO) should occur down to the latitude band 45°–50° with a maximum VZA ranging between 60° and 64°. This coverage requirement can be met with two sets of three-satellite HEO constellations (one for each polar area) or a six-satellite MEO constellation. The 12-h Molniya and 14-, 15-, and 16-h HEO systems have been analyzed and determined to meet these revised requirements. The study demonstrates that the six-satellite 24-h MEO system can provide a suitable solution, which is also beneficial from the point of view of ionizing radiation and image acquisition geometry. Among the HEO systems, the 16-h HEO has some advantages relative to other HEO systems from the point of view of spatial coverage and space radiation.

AIM-North is a proposed satellite mission that would provide observations of unprecedented frequency and density for monitoring northern greenhouse gases (GHGs), air quality (AQ) and vegeta...

The study reports results of analysis related to minimization of the total ionizing dose (TID) for the Multiple Apogee Highly Elliptical Orbit with periods 14 h, 15 h and 16 h introduced earlier for continuous observation of the Earth’s polar regions. The modeling of space environment has been conducted with use of the European Space Agency’s SPENVIS tool based on the AE8/AP8 radiation models. Originally, the set of orbital parameters has been derived through the optimization process that included among other factors criteria for the apogee height limit and minimization of the radiation dose caused by trapped protons. By relaxing the apogee altitude limit, this study found the total ionizing dose TID can be significantly reduced for 15-h and 16-h orbits, while the originally proposed 14-h orbit is already at the minimum of radiation dose. For 15-h and 16-h orbits this converts into reduction of the thickness of aluminum shielding by factor 1.24–1.28 or an equivalent increase in the mission lifetime by up to 8.1 years. For example, an increase in apogee altitude to 49,620 km for 16-h orbit (eccentricity e = 0.74) in comparison to the originally proposed 16-h orbit (altitude equal to 43,500 km, e = 0.55) reduces the TID so that the shielding thickness decreases to 3.53 mm, instead of 4.35 mm of aluminum slab for the same 15-year duration of mission. Decrease of the TID is achieved due to significant reduction of ionizing radiation from the trapped electrons through the better placing of the orbit trajectory in the slot area, but at the expense of slight increase of ionizing radiation from the trapped protons and increase in apogee altitude to 46,640 km and 49,620 km for 15-h and 16-h orbit, correspondingly.

Snow and ice over land are important hydrological resources and sensitive indicators of climate change. The Moderate Resolution Imaging Spectroradiometer (MODIS) dataset at 250-m spatial resolution generated at the Canada Centre for Remote Sensing (CCRS) is used to derive the annual minimum snow and ice (MSI) extent over the Canadian Arctic landmass over a 17-yr time span (2000–16). The smallest MSI extent (1.53 × 105 km2) was observed in 2012, the largest (2.09 × 105 km2) was observed in 2013; the average value was 1.70 × 105 km2. Several reanalyses and observational datasets are assessed to explain the derived MSI variations: the ERA-Interim reanalysis, North American Regional Reanalysis (NARR), Clouds and the Earth’s Radiant Energy System (CERES) radiative fluxes, and European Space Agency’s GlobSnow dataset. Comparison with the Randolph Glacier Inventory (RGI) showed two important facts: 1) the semipermanent snowpack in the Canadian Arctic that persists through the entire melting season is a significant component relative to the ice caps and glacier-covered areas (up to 36% or 5.58 × 104 km2), and 2) the MSI variations are related to variations in the local climate dynamics such as warm season average temperature, energy fluxes, and snow cover. The correlation coefficients (absolute values) can be as high as 0.77. The reanalysis-based MSI estimates agree with satellite MSI results (average bias of 2.2 × 103 km2 or 1.3% of the mean value).

The solar eclipse is a relatively rare event, but it could cause significant impact on satellite products if not accounted for properly. The solar eclipse effect on daily and 10-day clear-sky composites is described here. It is shown that the standard scene identification and compositing algorithm developed at the Canada Centre for Remote Sensing (CCRS) for Moderate Resolution Imaging Spectroradiometer (MODIS) imagery fails to identify pixels affected by a solar eclipse. As a result, the solar-eclipse-affected imagery is almost entirely included in the composite product. A similar effect is observed in the standard NASA MOD09/MYD09 daily composite reflectance products. We describe here the details of the eclipse-affected imagery, the reason why clear-sky compositing criteria failed, and propose some modifications to the standard algorithm, such as reflectance normalization and scene identification procedures, to overcome the solar eclipse contamination problem in multiscene image composites.

Snow and ice are important hydrological resources. Their minimum spatial extent over land, here referred to as annual minimum snow/ice (MSI) cover, plays a very important role as an indicator of long-term changes and baseline capacity for surface water storage. Data from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite for the period of 2000–2014 were utilized in this study. The level-2 MODIS swath imagery for bands B1 to B7 was employed and the 500-m bands B3–B7 were spatially downscaled to a 250-m swath grid. The imagery is available daily with multiple overpasses. This allows for more accurate identification of annual minimum in comparison to high-resolution imagery (e.g., Landsat, ASTER, etc.) available at much coarser temporal rates. Atmospherically corrected 10-day clear-sky composites converted into normalized surface reflectance over the warm season (April 1 to September 20) were employed to identify persistent snow and ice presence. Results were compared with our...

A novel type of multiple-apogee highly elliptical orbits termed as MAP HEO with a period of rotation between 14 h and 15 h is introduced. These orbits are designed to achieve continuous geostationary (GEO)-like imaging of the polar regions in an optimum way. The combination of GEO and HEO satellites would then offer continuous monitoring of weather from space at any point of the globe. This capacity would represent a breakthrough for short- and long-term weather forecasting and narrowing uncertainties in the knowledge of the Earth’s climate through better sampling and more accurate characterization of the diurnal cycle. MAP HEO systems can be launched at critical inclination and are characterized by a local minimum of ionizing radiation. These features simplify the process of orbit maintenance, reduce radiation shielding requirements, and favor a longer lifetime of the mission. Unlike previously considered HEO systems implemented for communications, such as 12-h Molniya and 24-h Sirius radio systems, a MAP HEO constellation achieves a uniform geometrical sampling, which reduces view angle dependent biases. These observational conditions with complete coverage of the diurnal cycle, diverse range of solar illumination, and viewing observational conditions are beneficial for high-latitude meteorological and climate applications, such as the retrieval of Essential Climate Variables (ECV).

Applying the Multidimensional Small Baseline Subset interferometric synthetic aperture radar algorithm to about 1500 Envisat and RADARSAT-2 interferograms spanning 2003–2013, we computed time series of ground deformation over Naples Bay Area in Italy. Two active volcanoes, Vesuvius and Campi Flegrei, are located in this area in close proximity to the densely populated city of Naples. For the first time, and with remarkable clarity, we observed decade-long elevation-dependent seasonal oscillations of the vertical displacement component with a peak-to-peak amplitude of up to 3.0 cm, substantially larger than the long-term deformation rate (

There is a well-recognized spatiotemporal meteorological observation gap at latitudes higher than 55°, especially in the region 55°–70°. A possible solution to address this issue is a constellation of four satellites in a highly elliptical orbit (HEO), that is, two satellites for each polar region. An important satellite product to support weather prediction is atmospheric motion wind vectors (AMVs). This study uses observing system simulation experiments (OSSEs) to evaluate the benefit to forecasts resulting from the assimilation of HEO AMVs covering one or both polar regions. The OSSE employs the operational global data assimilation system of the Canadian Meteorological Center. HEO AMVs are assimilated north of 50°N and south of 50°S. From 2-month assimilation cycles, the study examines the following three issues: 1) the impact of AMV assimilation in the real system, and how this compares to the impact seen in the simulated system, 2) the added value of HEO AMVs in the Arctic on top of what is currently available, and 3) the relative impact of HEO AMVs in the Arctic and Antarctic in comparison with no AMVs. Although the simulated impact of currently available AMVs is somewhat higher than the real impact, a firm conclusion is that the added value of Arctic HEO AMVs is substantial, improving predictability at days 3–5 by a few hours in terms of 500-hPa geopotential height. The impact of HEO AMVs is relatively stronger in the Southern Hemisphere. Forecast validation of atmospheric profiles against the simulated “true” state and against analyses generated within the assimilation cycles yields very similar results beyond 48 h.

Time series of the Normalized Difference Vegetation Index (NDVI) derived from satellite observations provide important information on the state of terrestrial vegetation over a wide range of spatiotemporal scales. For understanding long-term changes in terrestrial ecosystems (post-1981), data collected by the Advanced Very High Resolution Radiometer (AVHRR) on board the satellites of National Oceanic and Atmospheric Administration (NOAA) series is a unique source of information. In this paper, we describe a new processing methodology for a comprehensive AVHRR data set at 1 km spatial resolution acquired over Canada, the northern United States and Greenland post-1981. The methodology incorporates a pre-processing algorithm, Canadian AVHRR Processing System (CAPS), recently developed by the Canada Centre of Remote Sensing (CCRS), which enables highly accurate geolocation and ortho-rectification at efficiency rates of > 90%. Once image navigation is completed, our approach consists of five key steps: first, two clear-sky composites for each 10 day interval are generated from the forward or backward scattering hemisphere; second, AVHRR Channel 1 and 2 reflectances are normalized to the AVHRR/3 on board NOAA-17 to account for differences in the spectral response function among the AVHRR sensors; third, atmospheric correction is performed using the Simplified Method for Atmospheric correction (SMAC) algorithm, using standard meteorological data sets (water vapor, surface level air pressure, ozone); fourth, NDVI is calculated based on atmospherically corrected Channel 1 and 2 reflectances; and finally, the NDVI is adjusted for directional effects based on the Ross-Thick Li-Sparse Bidirectional Reflectance Distribution Function (BRDF) model. The processed NDVI data are compared to an equivalent spatially and temporally overlapping MODIS NDVI data set from 2001 to 2005 for validation. Results at continental scale indicate that time series of MODIS and AVHRR were similar for a wide range of biomes and generalized ecoregions. Analysis stratified by land cover indicated that the correlation was strongest for homogeneous land cover types, such as cropland, when compared to structurally more diverse classes, such as deciduous broadleaf forests. The comparison of the NDVI at the local scale at seven sites of the Fluxnet Canada Research Network resulted in the correlation coefficient r = 0.95. Given confidence in the processing approach, this NDVI data set can be a valuable source of information for climate and vegetation-related studies over Canada and the northern United States.

Recent reports on Arctic climate change sparked an interest in the development of a new observing system for this region, specifically a satellite system making use of a highly elliptical orbit (HEO), an idea supported by the World Meteorological Organization. This paper attempts to quantify the advantages of such a system relative to a traditional constellation of Lower Earth Orbit (LEO) polar satellites. A realistic two-satellite HEO system is compared with currently operating two-, four-, and seven-satellite LEO constellations in terms of spatio-temporal coverage and capability to provide sequences of single, dual, and triplet images at a required temporal resolution. This is important to properly monitor dynamic events, such as volcanic ash transport, frontal systems, smoke from wild fires, and cloud motion from which wind vectors are derived. A two-satellite HEO system is 5–10 times more efficient for monitoring dynamic events from image pairs above 70°N than a standard four-satellite morning-afterno...

Droughts are among the world's most costly natural disasters and collectively affect more people than any other form of natural disaster. The Canadian Prairies are very susceptible to drought and have experienced this phenomenon many times. However, the recent 1999–2005 Prairie drought was one of the worst meteorological, agricultural and hydrologic droughts over the instrumental record. It also had major socio-economic consequences, adding up to losses in the billions of dollars. This recent drought was the focus of the Drought Research Initiative (DRI), the first integrated network focusing on drought in Canada. This article addresses some of the key objectives of DRI by providing a collective summary, understanding and synthesis of the 1999–2005 drought. Bringing together the many datasets used in this study was in itself a major accomplishment. This drought exhibited many important, and sometimes surprising, features. This includes, for example, (1) a non-steady large-scale atmospheric circulation (an...

A highly elliptical orbit (HEO) with a 16-h period is proposed for continuous meteorological imaging of polar regions from a two-satellite constellation. This orbit is characterized by three apogees (TAP) separated by 120°. The two satellites are 8 h apart, with repeatable ground track in the course of 2 days. Advantages are highlighted in comparison to the Molniya 12-h orbit described in detail in a previous study (Trishchenko and Garand). Orbital parameters (period, eccentricity, and inclination) are obtained as a result of an optimization process. The principles of orbit optimization are based on the following four key requirements: spatial resolution (apogee height), the altitude of crossing the trapped proton region at the equator (minimization of radiation doze caused by trapped protons), imaging time over the polar regions, and the stability of the orbit, which is mostly defined by the rotation of perigee. The interplay between these requirements points to a 16-h period with an eccentricity of 0.55 as the optimum solution. The practical range of orbit inclinations that could be maintained during the spacecraft lifetime can vary from a critical value of 63.435° to 70° (subject to the amount of propellant available for orbital maneuvers). In comparison to Molniya, this type of orbit reduces the radiation exposure to high-energy protons by factor of 103–104. On the other hand, the main advantage of 16 h versus longer orbital periods up to 24 h is better spatial resolution as a result of a lower apogee height. A two-satellite TAP constellation with an orbital inclination of 66° provides 100% temporal coverage above 60°N, >95% above 55°N, >85% above 50°N, and >75% above 45°N.

There has been a significant increase of interest in the building of a comprehensive Arctic observing system in recent years to properly and timely track the environmental and climate processes in this vast region. In this regard, a satellite observing system on highly elliptical orbit (HEO) with 12-h period (Molniya type) is of particular interest, because it enables continuous coverage of the entire Arctic region (58°–90°N) from a constellation of two satellites. Canada is currently proposing to operate such a constellation by 2017. Extending the pioneering study of S. Q. Kidder and T. H. Vonder Haar, this paper presents in-depth analysis of spatiotemporal sampling properties of the imagery from this system. This paper also discusses challenges and advantages of this orbit for various applications that require high temporal resolution and angular sampling.

Precise geolocation is one of the fundamental requirements for satellite imagery to be suitable for climate applications. The Global Climate Observing System and the Committee on Earth Observing Satellites identified the requirement for the accuracy of geolocation of satellite data for climate applications as 1/3 field of view (FOV). This requirement for the series of the Advanced Very High Resolution Radiometer (AVHRR) on the National Oceanic and Atmospheric Administration platforms cannot be met without implementing the ground control point (GCP) correction, particularly for historical data, because of limited accuracy of orbit modeling and knowledge of satellite attitude angles. This paper presents a new method for precise georeferencing of the AVHRR imagery developed as part of the new Canadian AVHRR processing system (CAPS) designed for generating high-quality AVHRR satellite climate data record at 1-km spatial resolution. The method works in swath projection and uses the following: 1) the reference monthly images from Moderate Resolution Imaging Spectroradiometer at 250-m resolution; 2) orthorectification to correct for surface elevation; and 3) a novel image matching technique in swath projection to achieve the subpixel resolution. The method is designed for processing daytime data as it intensively employs observations from optical solar bands, the near-infrared channel in particular. The application of the developed processing system showed that the algorithm achieved better than 1/3 FOV geolocation accuracy for AVHRR 1-km scenes. It has very high efficiency rate (> 97%) due to the dense and uniform GCP coverage of the study area (5700 × 4800 km2 ), covering the entire Canada, the Northern U.S., Alaska, Greenland, and surrounding oceans.

A number of remote sensing studies have evaluated the temporal trends of the normalized difference vegetation index (NDVI or vegetation greenness) in the North American boreal forest during the last two decades, often getting quite different results. To examine the effect that the use of different datasets might be having on the estimated trends, we compared the temporal trends of recently burned and unburned sites of boreal forest in central Canada calculated from two datasets: the Global Inventory, Monitoring, and Modeling Studies (GIMMS), which is the most commonly used 8 km dataset, and a new 1 km dataset developed by the Canadian Centre for Remote Sensing (CCRS). We compared the NDVI trends of both datasets along a fire severity gradient in order to evaluate the variance in regeneration rates. Temporal trends were calculated using the seasonal Mann-Kendall trend test, a rank-based, nonparametric test, which is robust against seasonality, nonnormality, heteroscedasticity, missing values, and serial dependence. The results showed contrasting NDVI trends between the CCRS and the GIMMS datasets. The CCRS dataset showed NDVI increases in all recently burned sites and in 50% of the unburned sites. Surprisingly, the GIMMS dataset did not capture the NDVI recovery in most burned sites and even showed NDVI declines in some burned sites one decade after fire. Between 50% and 75% of GIMMS pixels showed NDVI decreases in the unburned forest compared with

Topography and accuracy of image geometric registration significantly affect the quality of satellite data, since pixels are displaced depending on surface elevation and viewing geometry. This effect should be corrected for through the process of accurate image navigation and orthorectification in order to meet the geolocation accuracy for systematic observations specified by the Global Climate Observing System (GCOS) requirements for satellite climate data records. We investigated the impact of orthorectification on the accuracy of maximum Normalized Difference Vegetation Index (NDVI) composite data for a mountain region in north-western Canada at various spatial resolutions (1 km, 4 km, 5 km, and 8 km). Data from AVHRR on board NOAA-11 (1989 and 1990) and NOAA-16 (2001, 2002, and 2003) processed using a system called CAPS (Canadian AVHRR Processing System) for the month of August were considered. Results demonstrate the significant impact of orthorectification on the quality of composite NDVI data in mountainous terrain. Differences between orthorectified and non-orthorectified NDVI composites (ΔNDVI) adopted both large positive and negative values, with the 1% and 99% percentiles of ΔNDVI at 1 km resolution spanning values between − 0.16

Stomatal conductance ( g ) is a key parameter in controlling energy and water exchanges between canopy and the atmosphere. Stomatal conductance models proposed by Ball, Woodrow and Berry (BWB) and Leuning have been increasingly used in land surface schemes. In a recent study, a new diagnostic index was developed by Wang et al. to examine the response of g to humidity and new models were proposed to resolve problems identified in the BWB and Leuning models. This approach is theoretically sound, but relies on canopy latent heat and CO 2 fluxes and environmental variables at the leaf surface which are not available at most eddy correlation (EC) observation sites. In this study, we tested the diagnostic index by empirically correcting EC measurements to canopy-level fluxes and by replacing leaf surface variables by their corresponding ambient air variables, and re-examined the stomatal conductance models of BWB, Leuning, and Wang et al. We found that the impact of the above modifications on the evaluation of g –humidity relationships is very small. This study provides a practical approach to investigate the stomatal response to humidity using routine EC measurements.

Humidity of air is a key environmental variable in controlling the stomatal conductance (g) of plant leaves. The stomatal conductance–humidity relationships employed in the Ball–Woodrow–Berry (BWB) model and the Leuning model have been widely used in the last decade. Results of independent evaluations of the two models vary greatly. In this study, the authors develop a new diagnostic parameter that is based on canopy water vapor and CO2 fluxes to assess the response of canopy g to humidity. Using eddy-covariance flux measurements at three boreal forest sites in Canada, they critically examine the performance of the BWB and the Leuning models. The results show that the BWB model, which employs a linear relationship between g and relative humidity (hs), leads to large underestimates of g when the air is wet. The Leuning model, which employs a nonlinear function of water vapor pressure deficit (Ds), reduced this bias, but it still could not adequately capture the significant increase of g under the wet conditions. New models are proposed to improve the prediction of canopy g to humidity. The best performance was obtained by the model that employs a power function of Ds, followed by the model that employs a power function of relative humidity deficit (1 − hs). The results also indicate that models based on water vapor pressure deficit generally performed better than those based on relative humidity. This is consistent with the hypothesis that the stomatal aperture responds to leaf water loss because water vapor pressure deficit rather than relative humidity directly affects the transpiration rate of canopy leaves.

The first spatially enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky mosaic for the Arctic circumpolar zone (9000 km×9000 km) is presented, as a contribution to the Canadian component of the International Polar Year (IPY) Programme. The imagery was obtained by fusion of MODIS bands B1-B2 observed at 250 m spatial resolution with bands B3-B7 observed at 500 m spatial resolution to satisfy the Global Climate Observing System (GCOS) requirement for a spatial resolution of 250 m for satellite-based products for climate. The fusion method used adaptive regression and normalization to preserve the image radiometric properties. A new cloud and cloud shadow detection method and a clear-sky compositing scheme were used for the 250 m multispectral data. By the end of the IPY in 2009, a decade-long (2000-2009) time series of these data documenting the state and variability of the Arctic region at fine spatial (250 m) and temporal (10-day) resolution will be produced if MODIS continues to operate until the end of this period. The product is generated in the Lambert Azimuthal Equal-Area (LAEA) projection centred over the North Pole. The major intended application of the new data is mapping the surface albedo at 250 m spatial resolution. This product in turn can be used as an input for generating several other Essential Climate Variables (ECVs) as defined by the GCOS.

This work extends the previous study of Trishchenko et al. [Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 81 (1), 1–18] that analyzed the spectral response function (SRF) effect for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA-16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The developed approach is now applied to cover three new AVHRR sensors launched in recent years on NOAA-17, 18, and METOP-A platforms. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. The differences in reflectance among these three radiometers relative to the AVHRR NOAA-9 are similar to each other and range from − 0.015 to 0.015 (− 20% to + 2% relative) for visible (red) channel, and from − 0.03 to 0.02 (− 5% to 5%) for the near infrared (NIR) channel. The absolute change in the Normalized Difference Vegetation Index (NDVI) ranged from − 0.03 to + 0.06. Due to systematic biases of the visible channels toward smaller values and the NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The polynomial approximations are provided for the bulk spectral correction with respect to the AVHRR NOAA-9 for consistency with previous study. Analysis was also conducted for the SRF effect only among the AVHRR-3 type of radiometer on NOAA-15, 16, 17, 18 and METOP-A using AVHRR NOAA-18 as a reference. The results show more consistency between sensors with typical correction being under 5% (or 0.01 in absolute values). The AVHRR METOP-A reveals the most different behavior among the AVHRR-3 group with generally positive bias for visible channel (up to + 5%, relative), slightly negative bias for the NIR channel (1%–2% relative), and negative NDVI bias (− 0.02 to + 0.005). Polynomial corrections are also suggested for normalization of AVHRR on NOAA-15, 16, 17 and METOP-A to AVHRR NOAA-18.

A new technology was developed at the Canada Centre for Remote Sensing (CCRS) for generating Canada-wide and North America continental scale clear-sky composites at 250 m spatial resolution for all seven MODIS land spectral bands (B1–B7). The MODIS Level 1B (MOD02) swath level data are used as input to circumvent the problems with image distortion in the mid latitude and polar regions inherent to the global sinusoidal (SIN) projection utilized for the standard MODIS data products. The MODIS 500 m land bands B3 to B7 are first downscaled to 250 m resolution using an adaptive regression and normalization scheme for compatibility with the 250 m bands B1 and B2. A new method has been developed to produce the mask of clear-sky, cloud and cloud shadow at 250 m resolution. It shows substantial advantages in comparison with the MODIS 250 m standard cloud masks. The testing of new cloud mask showed that it is in reasonable agreement with the MODIS 1-km standard product once it is aggregated to 1-km scale, while the cloud shadow detection looks more reliable with the new methodology. Nevertheless, more quantitative analyses of the presented scene identification technique are required to understand its performance over the range of input scenes in various seasons. The new clear-sky compositing scheme employs a scene-dependent decision matrix. It is demonstrated that this new scheme provides better results than any others based on a single compositing criterion, such as maximum NDVI or minimum visible reflectance. To account for surface bi-directional properties, two clear-sky composites for the same time period are produced by separating backward scattering and forward scattering geometries, which separate pixels with the sun-satellite relative azimuth angles within 90°–270° and outside of this range. Comparison with Landsat imagery and with MODIS standard composite products demonstrated the advantage of the new technique for screening cloud and cloud shadow, and generating high spatial resolution MODIS clear-sky composites. The new data products are mapped in the Lambert Conformal Conic (LCC) projection for Canada and the Lambert Azimuthal Equal-Area (LAEA) projection for North America. Presently this activity is limited to MODIS/TERRA due to known problems with band-to-band registration and noisy SWIR channels on MODIS/AQUA.

This paper presents details regarding implementation of a novel algorithm for reprojection of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B imagery. The method is based on a simultaneous 2-D search in latitude and longitude geolocation fields by using their local gradients. Due to the segmented structure of MODIS imagery caused by the instrument whiskbroom electrooptical design, the gradient search is realized in the following two steps: intersegment and intrasegment search. This approach resolves the discontinuity of the latitude/longitude geolocation fields caused by overlap between consecutively scanned MODIS multidetector image segments. The structure of the algorithm allows equal efficiency with nearest neighbor and bilinear interpolation. A special procedure that combines analytical and numerical schemes is designed for reprojecting imagery near the polar region, where the standard gradient search may become unstable. The performance of the method was validated by comparison of reprojected MODIS/Terra and MODIS/Aqua images with georectified Landsat-7 Enhanced Thematic Mapper Plus imagery over Canada. It was found that the proposed method preserves the absolute geolocation accuracy of MODIS pixels determined by the MODIS geolocation team. The method was implemented to reproject MODIS Level 1B imagery over Canada, North America, and Arctic circumpolar zone in the following four popular geographic projections: Plate Care (cylindrical equidistant), Lambert Conic Conformal, Universal Transverse Mercator, and Lambert Azimuthal Equal-Area. It was also found to be efficient for reprojection of Advanced Very High Resolution Radiometer and Medium Resolution Imaging Spectrometer satellite images and general-type meteorological fields, such as the North American Regional Reanalysis data sets.

A time series of burned land areas was generated for a 23 year period (1984–2006) using 10-day composites of AVHRR data. The study area covers 1.6 million km2 of boreal forest in western Canada. The algorithm was intended to be consistent throughout the study period and region, and to avoid commission errors, so as to obtain a reliable sample of temporal trends in burned area in the region. The algorithm relies on temporal comparisons of several spectral indices (GEMI, BAI), as well as near infrared reflectance. It emphasizes the stability of the post-fire signal, to avoid false detections associated with cloud, cloud shadows, missed data and radiometric or geometric calibration between AVHRR sensors. Final results show a very consistent temporal adjustment to official statistics and fire perimeters, with very low commission error (

Multispectral surface albedo and bidirectional properties are required for accurate determination of the surface and atmosphere solar radiation budget. A method is developed here to obtain time series of these surface characteristics consistent with the Moderate Resolution Imaging Spectroradiometer (MODIS) using historical satellite observations with limited spectral coverage available from NOAA Advanced Very High Resolution Radiometer (AVHRR) and VEGETATION/Satellite pour l’Observation de la Terre (SPOT). A nonlinear regression model was developed that relates retrievals from four spectral channels of VEGETATION/SPOT or three spectral channels of NOAA AVHRR with retrieval from each of the seven MODIS channels designed for land applications. The model also takes into account the surface land cover type, the normalized difference vegetation index, and the seasonal cycle. It was applied to generate surface albedo and bidirectional parameters of the seven MODIS-like spectral channels at a 10-day interval for the 1995–2004 period over the U.S. southern Great Plains. The relative retrieval accuracy for the MODIS channels replicated from AVHRR or VEGETATION/SPOT data was typically better than 5%. Correlation coefficients between replicated and original data varied from 0.92 to 0.98 for all channels except MODIS channel 5, where it was lower (0.77–0.84). The developed method provides valuable information for parameterization of spectral albedo in global climate models and can be extended to generate global multispectral data compatible with MODIS from historical AVHRR and VEGETATION/SPOT observations for the pre-MODIS era.

A new canopy radiation transfer and surface albedo scheme is developed as part of the land surface model EALCO (Ecological Assimilation of Land and Climate Observations). The model uses a gap probability-based successive orders of scattering approach that explicitly includes the heterogeneities of stands and crown elements and the radiation multiple scattering. The model uses the optical parameters of ecosystem elements and physically represents ecosystem processes in surface albedo dynamics. Model tests using measurements from a boreal deciduous forest ecosystem show that the model well reproduced the observed diurnal and seasonal albedo dynamics under different weather and ecosystem conditions. The annual mean absolute errors between modeled and measured daily albedo and reflected radiation are 0.01 and 1.33 W m−2, respectively. The model results provide a quantitative assessment of the impacts of plant shading and sky conditions on surface albedo observed in high-latitude ecosystems. The contribution of ground snow to surface albedo in winter was found to be less than 0.1 even though the canopy is leafless during this time. The interception of snow by the leafless canopy can increase the surface albedo by 0.1–0.15. The model results show that the spectral properties of albedo have large seasonal variations. In summer, the near infrared component is substantially larger than visible, and surface albedo is less sensitive to sky conditions. In winter, the visible band component is markedly increased and can exceed the near infrared proportion under cloudy conditions or when snow exists on the canopy. The spectral properties of albedo are also found to have large diurnal variations under the clear-sky conditions in winter.

The identification of clear-sky and cloudy pixels is a key step in the processing of satellite observations. This is equally important for surface and cloud–atmosphere applications. In this paper, the Separation of Pixels Using Aggregated Rating over Canada (SPARC) algorithm is presented, a new method of pixel identification for image data from the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA satellites. The SPARC algorithm separates image pixels into clear-sky and cloudy categories based on a specially designed rating scheme. A mask depicting snow/ice and cloud shadows is also generated. The SPARC algorithm has been designed to work year-round (day and night) over the temperate and polar regions of North America, for current and historical AVHRR/NOAA High-Resolution Picture Transmission (HRPT) and Local Area Coverage (LAC) data with original 1-km spatial resolution. The algorithm was tested and applied to data from the AVHRR sensors flown on board NOAA-6 to NOAA-18. The method was employed in generating historical clear-sky composites for the 1982–2005 period at daily, 10-day, and monthly time scales at 1-km resolution for an area of 5700 km × 4800 km centered over Canada. This region also covers the northern part of the United States, including Alaska, as well as Greenland and the surrounding oceans. The SPARC algorithm is designed to produce an aggregated rating that accumulates the results of several tests. The magnitude of the rating serves as an indicator of the probability for a pixel to belong to the clear-sky, partly cloudy, or overcast categories. The individual tests employ the spectral properties of five AVHRR channels, as well as surface skin temperature maps from the North American Regional Reanalysis (NARR) dataset. These temperature fields are available at 32 km × 32 km spatial resolution and at 3-h time intervals. Combining all test results into one final rating for each pixel is beneficial for the generation of multiscene clear-sky composites. The selection of the best pixel to be used in the final clear-sky product is based on the magnitude of the rating. This provides much-improved results relative to other approaches or “yes/no” decision methods. The SPARC method has been compared to the results of supervised classification for a number of AVHRR scenes representing various seasons (snow-free summer, winter with snow/ice coverage, and transition seasons). The results show an overall agreement between the automated (SPARC) and the supervised classification at the level of 80% to 91%.

Satellite observations in the shortwave infrared (SWIR) part of spectrum between 3.5 and 4.0 μm deliver critically important information for many applications. The satellite signal in this spectral band consists of solar-reflected radiation and thermal radiation emitted by surface, clouds, and atmosphere. Accurate retrievals require precise knowledge of solar irradiance values within a channel's bandwidth. The magnitudes of solar irradiance for shortwave infrared channels (3.7–3.9 μm) for the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration-7 (NOAA-7) to NOAA-18 satellites and the Geostationary Operational Environmental Satellite-8 (GOES-8) to GOES-12 are considered in this paper. Four recent solar reference spectra [those of Kurucz, Gueymard, the American Society for Testing and Materials (ASTM), and Wehrli] are analyzed to determine uncertainties in the knowledge of solar irradiance values for SWIR channels of the listed sensors. Because thermal radiation is frequently converted to effective blackbody temperature for analysis, computations, and calibration purposes, it is proposed here to express band-limited solar irradiance values in terms of brightness temperature as well. It is shown that band-limited solar irradiance for AVHRR radiometers expressed in terms of blackbody equivalent brightness temperature correspond to the range 355–360 K, and vary around 345 K for the SWIR channels of the GOES imagers. The values of band-limited solar irradiance and brightness temperatures are provided for various reference solar spectra. The relative differences in band-limited solar irradiance computed for the considered reference solar spectra are between 0% and 2.5%. Differences expressed in terms of brightness temperatures may reach 0.8 K. The results for the ASTM and the Kurucz reference spectra agree within 0.1% relative difference. Parameters of linear fits relating effective brightness temperatures and spectral radiance equivalent temperatures are also determined for all sensors. They are required for precise radiance–temperature and temperature–radiance conversion through Planck's functions in the case of the finite spectral response of real sensors.

For quantitative studies of vegetation dynamics, satellite data need to be corrected for spurious effects. In this study, we have applied several changes to an earlier advanced very high resolution radiometer (AVHRR) processing methodology (ABC3; [Remote Sens. Environ. 60 (1997) 35; J. Geophys. Res.-Atmos. 102 (1997) 29625; Can. J. Remote Sens. 23 (1997) 163]), to better represent the various physical processes causing contamination of the AVHRR measurements. These included published recent estimates of the NOAA-11 and NOAA-14 AVHRR calibration trajectories for channels 1 and 2; the best available estimates for the water vapour, aerosol and ozone amounts at the time of AVHRR data acquisition; an improved bidirectional reflectance algorithm that also takes into consideration surface topography; and an improved image screening algorithm for contaminated pixels. Unlike the previous study that compared the composite images to a single-date AVHRR image, we employed coincident TM images to approximate the AVHRR pixel field of view during the data acquisition. Compared to ABC3, the modified procedure ABC3V2 was found to improve the accuracy of AVHRR pixel reflectance estimates, both in the sensitivity (slope) of the regression and in r2. The improvements were especially significant in AVHRR channel 1. In comparison with reference values derived from two full TM scenes, the corrected AVHRR surface reflectance estimates had average standard errors values of ±0.009 for AVHRR C1, ±0.019 for C2, and ±0.04 for NDVI; the corresponding r2 values were 0.55, 0.80, and 0.50, respectively. The changes in ABC3V2 were not able to completely remove interannual variability for land cover types with little or no vegetation cover, which would be expected to remain stable over time, and they increased the interannual variability of mixed forest and grassland. These results are attributed to a combination of increased sensitivity to interannual dynamics on one hand, and the inability to remove all sources of noise for barren or sparsely vegetated northern land cover types on the other.

A little more than 10 years have passed since publication of the special issue of Canadian Journal of Remote Sensing (CJRS, 2005, Vol. 31(No. 5)) commemorating the retirement of Dr. Josef Cihlar fr...

The study deals with analysis of thermal calibration of the Advanced Very High Resolution Radiometer (AVHRR) aboard National Oceanic and Atmospheric Administration (NOAA) spacecrafts. In particular, the effects caused by various types of contamination or corruption of the thermal calibration data are investigated. These phenomena lead to perturbations of the true signal, referred to here as unwanted fluctuations. They must be removed or corrected to maximum possible extent to reduce the error in the calibrated data. It is shown that methods currently employed in operational practice at NOAA and the Canada Centre for Remote Sensing (CCRS) frequently fail to remove some of the unwanted fluctuations in calibration data that may lead to biases in brightness temperature exceeding 1 K. A complex method for removing unwanted fluctuations in the thermal calibration data specifically designed for the AVHRR radiometers is proposed. The procedure is based on combining robust statistical procedures and Fourier transform filtering techniques. Application of the method is considered for various components of calibration data: temperature sensors, blackbody, and space count, as well as gain in all thermal channels. High Resolution Picture Transmission (HRPT) data and Global Area Coverage (GAC) data are analyzed. Power spectra analysis of the calibration data has been conducted to estimate impact of various frequency harmonics. The method proposed may be useful for the development of calibration techniques for similar radiometers and the future National Polar-Orbiting Operational Environmental Satellite System.

We report the results of a modeling study on the sensitivity of normalized difference vegetation index (NDVI) and surface reflectance to differences in instrument spectral response functions (SRF) for various Advanced Very High Resolution Radiometers (AVHRR) onboard the National Oceanic and Atmospheric Administration's (NOAA) satellites NOAA-6–16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Vegetation sensor (VGT), and the Global Imager (GLI). Modeling results were validated against real satellite observations employing AVHRR/NOAA-14 and -15 and MODIS, with a very good agreement. It is shown that for identical atmospheric state and similar surface spectral reflectance, the NDVI and spectral reflectances are sensitive to the sensor's SRF. Relative to a reference SRF for AVHRR/NOAA-9, the differences in reflectance among the AVHRRs range from −25% to 12% for visible channel (red) and from −2% to 4% for near-infrared (NIR) channel. Absolute change in NDVI among various AVHRRs ranged from −0.02 to 0.06. The most significant difference was observed for the AVHRR/3. Consistent results were obtained with the AVHRR sensors aboard the following afternoon satellites: NOAA-9, -11, and -12, whereas important discrepancies were found for other AVHRRs aboard NOAA-6 and -10 and especially those launched more recently (NOAA-15 and -16). Reflectance and NDVI measured by MODIS channels 1 and 2 also exhibit significant differences (up to 30–40%) relative to AVHRR. GLI and VGT have some specific features that should be taken into account when intercomparing surface or top of the atmosphere (TOA) reflectance as well as NDVI. Sensitivity of the SRF effect to variable atmospheric state (water vapor, aerosol, and ozone) was also investigated. Polynomial approximations are provided for bulk spectral correction with respect to AVHRR/NOAA-9.

A method is introduced for inferring cloud optical depth t from solar radiometric measurements made on an aircraft at altitude z. It is assessed using simulated radiometric measurements produced by a 3D Monte Carlo algorithm acting on fields of broken boundary layer clouds generated from Landsat imagery and a cloud-resolving model. The method uses upwelling flux and downwelling zenith radiance measured at two solar wavelengths where atmospheric optical properties above z are very similar but optical properties of the surface‐atmosphere system below z differ. This enables estimation of cloud reflectance into nadir for upwelling diffuse flux and, finally, t above z. An approximate one-dimensional radiative Green’s function is used to roughly account for horizontal transport of photons in all, even broken, clouds. This method is compared to its surface-based counterpart and shown to be superior. Most notably, the aircraft-based approach deals easily with inhomogeneous land surfaces, is less susceptible to poor sampling, and need not account for aerosol below z. The algorithm appears as though it will have little difficulty inferring high-resolution time series of t & 40 for most (single layer) clouds. For larger values of t, biases emerge; particularly, underestimation for the statistically infrequent interiors of cumuliform clouds as photon leakage through cloud sides is not addressed. For the cumuliform and stratiform clouds used here, mean bias errors for retrieved t are ;1 (or ;15%) and ;0.3 (or ;3%), respectively. For stratiform clouds with textured bases, performance is likely to improve slightly for flights just up from mean cloud base.

Effective use of satellite data for environmental monitoring requires consistent, high-throughput processing of large volumes of data as it is transformed from raw measurements to useful higher-level products. "GeoComp-n", the next generation of the Geocoding and Compositing System developed at the Canada Centre for Remote Sensing, Natural Resources Canada, was developed as a software solution to this challenge, for use with satellites that provide daily data for the landmass of Canada or comparably large areas. In this paper, the authors discuss the characteristics of the algorithms and methods used in the generation of GeoComp-n products. The theoretical basis and assumptions in the algorithms are described, and the quality of the products is discussed based on validation studies. Examples of a suite of products for Canada during one 10-day period illustrate the diversity and quality of observations for the terrestrial biosphere that may be derived frequently and over large areas from satellites. Issues...

We analysed the operational infrared (IR) calibration of the AVHRR radiometers onboard NOAA-12, 14 and 15 satellites. It was shown that solar blackbody contamination affects the calibration of all IR channels. This source of error in the calibration may result in uncertainties in the estimation of sea surface temperature (SST) up to 0.5 K or more. We propose an approach to correct this error using a Fourier transform filtering technique.

An angular dependence model (ADM) describes the anisotropy in the reflectance field. ADMs are a key element in determining the top-of-the-atmosphere (TOA) albedos and radiative fluxes. This study utilizes 1-yr satellite data from the Scanner for Radiation Budget (ScaRaB) for overcast scenes to examine the variation of ADMs with cloud properties. Using ScaRaB shortwave (SW) overcast radiance measurements, an SW mean overcast ADM, similar to the Earth Radiation Budget Experiment (ERBE) ADM, was generated. Differences between the ScaRaB and ERBE overcast ADMs lead to biases of ∼0.01–0.04 in mean albedos inferred from specific angular bins. The largest biases are in the backward scattering direction. Overcast ADMs for the visible (VIS) wavelength were also generated using ScaRaB VIS measurements. They are very similar to, but a little smaller at large viewing angles and a little larger at nadir, than the SW overcast ADMs. To evaluate the effect of cloud properties on ADMs, ScaRaB overcast observation...