Canada Centre For Remote Sensing Research Articles

Landfast ice properties over the Beaufort Sea region in 2000–2019 from MODIS and Canadian Ice Service data

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.

Minimum Snow/Ice Extent over the Northern Circumpolar Landmass in 2000–19: How Much Snow Survives the Summer Melt?

AbstractA 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.

Clear-Sky Composites over Canada from Visible Infrared Imaging Radiometer Suite: Continuing MODIS Time Series into the Future

The Visible Infrared Imaging Radiometer Suite (VIIRS) represents a new generation of satellite imagers for global operational observations. In many aspects, it is comparable to the Moderate Resolution Imaging Spectroradiometer (MODIS) operated since 2000, i.e. almost for two decades. The Canada Centre for Remote Sensing has developed a unique MODIS processing chain to produce a long-term time series of clear-sky composites and some terrestrial products at 250 m spatial resolution over a 5700 km × 4800 km region centered on Canada. The paper describes an extension of the MODIS time series at the top of the atmosphere level using VIIRS data. The VIIRS clear-sky composites are produced on a 250-m spatial grid for I-bands and a 500-m grid for M-bands. Nominal products are generated as 10-day composites, while snow mask and normalized difference vegetation index are generated as daily products. Preliminary assessment of VIIRS versus MODIS composites has been conducted through comparison of value-added warm season snow/ice probability maps and minimum snow/ice extent. The results demonstrate a high level of consistency (with the average different difference around 0.12%), which indicates that the developed VIIRS processing technology produces results that can potentially be used to extend MODIS time series into the future.

Variations of Climate, Surface Energy Budget, and Minimum Snow/Ice Extent over Canadian Arctic Landmass for 2000–16

Abstract 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).

Circa 2010 Land Cover of Canada: Local Optimization Methodology and Product Development

Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as the first of a planned series of maps to be updated every five years, or more frequently. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2010 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions under a collaboration called the North American Land Change Monitoring System (NALCMS). This paper describes the mapping approach used for generating this land cover dataset for Canada from Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) Landsat sensor observations. The innovative part of the mapping approach is the local optimization of the land cover classifier, which has resulted in increased spatial consistency and accuracy. Training and classifying with locally confined reference samples over a large number of partially overlapping areas (i.e., moving windows) ensures the optimization of the classifier to a local land cover distribution, and decreases the negative effect of signature extension. A weighted combination of labels, which is determined by the classifier in overlapping windows, defines the final label for each pixel. Since the approach requires extensive computation, it has been developed and deployed using the Government of Canada’s High-Performance Computing Center (HPC). An accuracy assessment based on 2811 randomly distributed samples shows that land cover data produced with this new approach has achieved 76.60% accuracy with no marked spatial disparities.

Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2–3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests’ features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

Solar Eclipse as a Source of Satellite Image Contamination in Multiscene Clear-Sky Composites

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.

VALIDATION OF SPACEBORNE RADAR SURFACE WATER MAPPING WITH OPTICAL sUAS IMAGES

Abstract. The Canada Centre for Remote Sensing (CCRS) has over 40 years of experience with airborne and spaceborne sensors and is now starting to use small Unmanned Aerial Systems (sUAS) to validate products from large coverage area sensors and create new methodologies for very high resolution products. Wetlands have several functions including water storage and retention which can reduce flooding and provide continuous flow for hydroelectric generation and irrigation for agriculture. Synthetic Aperture Radar is well suited as a tool for monitoring surface water by supplying acquisitions irrespective of cloud cover or time of day. Wetlands can be subdivided into three classes: open water, flooded vegetation and upland which can vary seasonally with time and water level changes. RADARSAT‐2 data from the Wide-Ultra Fine, Spotlight and Fine Quad-Pol modes has been used to map the open water in the Peace‐Athabasca Delta, Alberta using intensity thresholding. We also use spotlight modes for higher resolution and the fully polarimetric mode (FQ) for polarimetric decomposition. Validation of these products will be done using a low altitude flying sUAS to generate optical georeferenced images. This project provides methodologies which could be used for flood mapping as well as ecological monitoring.

ASSESSMENT OF RADARSAT-2 HR STEREO DATA OVER CANADIAN NORTHERN AND ARCTIC STUDY SITES

Abstract. Digital surface models (DSMs) extracted from high-resolution Radarsat-2 (R2) stereo images using a new hybrid radargrammetric modeling developed at the Canada Centre for Remote Sensing are evaluated over two Canadian northern and arctic study sites. Because the new hybrid model uses the full metadata of R2, it does not require any ground control point. The first study site in the north of Quebec is used for the scientific validation where accurate checked data (dGPS, lidas) is available. The second study site in the Arctic (steep relief and glaciated surfaces) is challenging for the operational evaluation of topographic mapping capabilities of R2. For the first study site, the bias and elevation linear errors with 68 percent confidence level (LE68) of R2 stero-extracted DSM compared to lidar data were computed over bare surfaces: LE90 of 3.9 m and no bias were achieved. For the second study site the comparison was performed between the R2 DEM and ICESat data. A negative 18-m bias was computed and certainly results suggests a bias in the stereo-model of R2 and thus in the metadata used in the model computation because there is few temporal variation in the data acquisition (R2 and ICESat)/ LE68 of 28 m was obtained. However, the differential melting and thinning depending of the glaciers elevations and planimetric surging of glacier tongues with less accumulation of debris and moraines, a lower LE68 of around 20 m could be expected. In addition to evaluate the potential of R2 over ice bodies, which generally have low slope relief and because the errors are strongly correlated with slopes, other statistical results of elevation differences were also computed: LE68 of 15 m was obtained over ice fields with 0–5° slopes while a little more than 20-m over less than 30° slopes was achieved.

3D PHOTOGRAMMETRIC PROCESSING OF WORLDVIEW-2 DATA WITHOUT GCP

Abstract. A hybrid model based on 3D deterministic Toutin's model developed at the Canada Centre for Remote Sensing and taking full advantages of the image metadata was used for the 3D photogrammetric processing of WorldView-1 stereo images without in situ ground control points (GCP) collection. The accuracy of the modelling results in monoscopy and stereoscopy was around 0.7-1.8 m in planimetry and 2.7 m in altimetry computed over 40 dGPs checked points. Elevations were thus extracted and compared to 0.2 m accurate lidar elevation data. Elevations linear error with 68 percent confidence level (LE68) computed over bare surfaces was 2.6 m, with small biases. These results were compared with the solution using 8 dGPS GCPs and demonstrated there is no significant difference in auccuracy. Consequently, the new hybrid model will offer a strong advantage of no-control collection in operational conditions, mainly in remote and harsh environments or when cartographic or ground control data do not exist.

Impact of no GCP on elevation extraction from WorldView stereo data

A hybrid model based on 3D deterministic Toutin’s model developed at the Canada Centre for Remote Sensing and taking full advantages of the image metadata was used to geometrically process WorldView-1 and -2 stereo images without in situ ground control points (GCPs) collection. Elevations were thus extracted and compared to 0.2-m accurate lidar elevation data to see the impact of no-GCP. Elevations linear errors with 68% confidence level (LE68) computed over bare surfaces were 2.6m and 2.1m for WorldView-1 and -2, respectively with small biases. The not-significant difference in LE68 (around 10–20cm), when compared to the solution using accurate GCPs, will offer strong advantages of no-control collection in operational conditions, mainly in remote and harsh environments or when cartographic or ground control data do not exist.

Radarsat-2 DSM Generation With New Hybrid, Deterministic, and Empirical Geometric Modeling Without GCP

Digital surface models (DSMs) extracted from high-resolution Radarsat-2 stereo-images using different geometric modeling (deterministic, new hybrid, and empirical) are evaluated. The 3-D deterministic models are Toutin's and hybrid Toutin's models (TM and HTM) developed at the Canada Centre for Remote Sensing, and the empirical model is the rational function model (RFM). TM is computed with one and eight ground control points (GCPs), HTM without GCP and RFM supplied by MacDonald, Dettwiler and Associates Ltd. is postprocessed with 3-9 GCPs depending of degrees of 2-D polynomial functions. The DSMs are then generated and compared to 0.2-m accurate lidar elevation data. Because DSMs included the height of land covers, elevation linear errors with 68% and 90% confidence level (LE68 and LE90) are computed and compared over bare surfaces only. LE90 results are: TM with eight GCPs achieves the best results (6.3 m), then HTM with no GCP (7 m), TM with one GCP (8.6 m), and finally RFM the worst (9.7 m) whatever the polynomial degree and GCP number. HTM is the only modeling not using any GCP, which offers a strong advantage in operational environments.

The next 40 years of remote sensing in Canada

While the impetus for the development of sensors for Earth observation (EO) originated primarily in the world wars and although the first weather and cloud observing satellite was launched in 1960, the modern field dates its beginning to the launch of Landsat in July, 1972. The Earth Resources Technology Satellite (ERTS) was the first Earth-observing satellite to be launched with the express intent to study and monitor our planet's landmasses. The lift-off was preceded by several years of experimentation and technology development in the United States (http://landsat.gsfc.nasa.gov/about/history.html) and in Canada by program planning and organizational developments. Between 1962 and 1970, Lawrence Whitaker (Larry) Morley of Energy, Mines, and Resources (now Natural Resources Canada) and Ensley Allen (Lee) Godby of the National Research Council promoted the establishment of the Canada Centre for Remote Sensing (CCRS), (http://www.oars.on.ca/honorary/morley.php). These efforts came to fruition on 16 February 1971 when the establishment of CCRS was approved by the Government of Canada.Since its establishment, CCRS and its staff have created a remarkable record of accomplishments in technology, science, applications, industrial development, international cooperation, and in numerous other areas (Bancroft and Thormodsgard, 2011). Thus, the last 40 years may be considered a success that far exceeded the expectations the people involved had at the beginning. The growth of EO in Canada – in the government (establishment of the Canadian Space Agency, increased use of remote sensing in various departments of the federal and provincial governments), industry (establishment and (or) growth of companies providing hardware, software, data, and services), and academia – can to various degrees be traced to the pioneering and “missionary” work carried out by CCRS.The 40th anniversary of CCRS was celebrated during 2011 through several events. While looking back can be immensely satisfying, a hallmark of the EO community – and an important reason for the successes to date – has been the desire to look forward, to plan for the future, and to anticipate as well as respond to challenges in technology and applications that may arise. Such perspectives have stimulated and guided CCRS, its staff, and other Canadians involved in the EO field. It is with these considerations in mind that a special session, a “fireside chat”, was organized for the 32nd Canadian Symposium on Remote Sensing which took place in Sherbrooke, Quebec, in conjunction with the 14th Congress of L'Association québécoise de télédétection. The session was titled “Les prochain 40 ans de télédétection / The next 40 years of Remote Sensing in Canada”, and was organized as a panel with five participants representing various components of the EO community:Douglas Bancroft, Director General of CCRS (2010–present);Edryd Shaw, CCRS Director General (1995–2001);Savi Sachdev, Director General Space Utilization, Canadian Space Agency;Pierre Vincent, Executive Vice-President, VIASAT GeoTechnologies; andPham Van Cu, Director of International Centre for Advanced Research on Global Change, Hanoi, Vietnam.It is difficult to imagine that the original expectations for EO in Canada could have anticipated the eventual achievements in detail and specificity. This is in part because these depended on the concurrent developments in not only technologies regarding sensors, computers, and communications, but also on the growing need for objective and timely information regarding the status and changes of the Earth whose fragility and vulnerability we have increasingly become aware of. Yet, thinking about and planning for the future is an indispensable component of a field that relies on complex technologies with sometimes long life cycles. It can also be intellectually stimulating and yield useful insights.To help focus the discussion, questions were framed in advance for panel members to prepare their remarks. This note contains a synthesis of their views and contributions regarding the identified topics.

Leaf area index mapping in northern Canada

Leaf area index (LAI) is an important structural vegetation parameter that is commonly derived from remotely sensed data. It has been used as a reliable indicator for vegetation's cover, status, health and productivity. In the past two decades, various Canada-wide LAI maps have been generated by the Canada Centre for Remote Sensing (CCRS). These products have been produced using a variety of very coarse satellite data such as those from SPOT VGT and NOAA AVHRR satellite data. However, in these LAI products, the mapping of the Canadian northern vegetation has not been performed with field LAI measurements due in large part to scarce in situ measurements over northern biomes. The coarse resolution maps have been extensively used in Canada, but finer resolution LAI maps are needed over the northern Canadian ecozones, in particular for studying caribou habitats and feeding grounds. In this study, a new LAI algorithm was developed with particular emphasis over northern Canada using a much finer resolution of remotely sensed data and in situ measurements collected over a wide range of northern arctic vegetation. A statistical relationship was developed between the in situ LAI measurements collected over vegetation plots in northern Canada and their corresponding pixel spectral information from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Furthermore, all Landsat TM and ETM+ data have been pre-normalized to NOAA AVHRR and SPOT VGT data from the growing season of 2005 to reduce any seasonal or temporal variations. Various spectral vegetation indices developed from the Landsat TM and ETM + data were analysed in this study. The reduced simple ratio index (RSR) was found to be the most robust and an accurate estimator of LAI for northern arctic vegetation. An exponential relationship developed using the Theil–Sen regression technique showed an R 2 of 0.51 between field LAI measurement and the RSR. The developed statistical relationship was applied to a pre-existing Landsat TM 250 m resolution mosaic for northern Canada to produce the final LAI map for northern Canada ecological zones. Furthermore, the 250 m resolution LAI estimates, per ecological zone, were almost generally lower than those of the CCRS Canada-wide VGT LAI maps for the same ecozones. Validation of the map with LAI field data from the 2008 season, not used in the derivation of the algorithm, shows strong agreement between the in situ LAI measurement values and the map-estimated LAI values.

Crop yield forecasting on the Canadian Prairies using MODIS NDVI data

Although Normalised Difference Vegetation Index (NDVI) data derived from the advanced very high resolution radiometer (AVHRR) sensor have been extensively used to assess crop condition and yield on the Canadian Prairies and elsewhere, NDVI data derived from the new moderate resolution imaging spectroradiometer (MODIS) sensor have so far not been used for crop yield prediction on the Canadian Prairies. Therefore, the objective of this study was to evaluate the possibility of using MODIS-NDVI to forecast crop yield on the Canadian Prairies and also to identify the best time for making a reliable crop yield forecast. Growing season (May–August) MODIS 10-day composite NDVI data for the years 2000–2006 were obtained from the Canada Centre for Remote Sensing (CCRS). Crop yield data (i.e., barley, canola, field peas and spring wheat) for each Census Agricultural Region (CAR) were obtained from Statistics Canada. Correlation and regression analyses were performed using 10-day composite NDVI and running average NDVI for 2, 3 and 4 dekads with the highest correlation coefficients ( r) as the independent variables and crop grain yield as the dependent variable. To test the robustness and the ability of the generated regression models to forecast crops grain yield, one year at a time was removed and new regression models were developed, which were then used to predict the grain yield for the missing year. Results showed that MODIS-NDVI data can be used effectively to predict crop yield on the Canadian Prairies. Depending on the agro-climatic zone, the power function models developed for each crop accounted for 48 to 90%, 32 to 82%, 53 to 89% and 47 to 80% of the grain yield variability for barley, canola, field peas and spring wheat, respectively, with the best prediction in the semi-arid zone. Overall (54 out of 84), the % difference of the predicted from the actual grain yield was within ±10%. On the whole, RMSE values ranged from 150 to 654, 108 to 475, 204 to 677 and 104 to 714 kg ha −1 for barley, canola, field peas and spring wheat, respectively. When expressed as percentages of actual yield, the RMSE values ranged from 8 to 25% for barley, 10 to 58% for canola, 10 to 38% for field peas and 6 to 34% for spring wheat. The MAE values followed a similar trend but were slightly lower than the RMSE values. For all the crops, the best time for making grain yield predictions was found to be from the third dekad of June through the third dekad of July in the sub-humid zone and from the first dekad of July through the first dekad of August in both the semi-arid and arid zones. This means that accurate crop grain yield forecasts using the developed regression models can be made one to two months before harvest.

Comparison of UV-B Broadband Brewer Measurements with Irradiances from Surface-Based and Satellite-Based Models

UV-B irradiance can be estimated from surface meteorological data or from satellite measurements. This paper compares irradiance estimates from the Davies surface-based radiation model and the Canada Centre for Remote Sensing (CCRS) satellite model with Brewer spectrophotometer measurements for all sky conditions at six Canadian stations (Edmonton, Regina, Winnipeg, Montreal, Halifax and Toronto). The Davies model is applied with both the discrete ordinate radiative transfer (DISORT) and the delta-Eddington algorithms to solve the radiative transfer equation. Both models’ estimates are compared with instantaneous Brewer measurements. Both perform similarly with mean bias errors within 6% of the mean measured irradiance for the measurement period and root mean square errors between 25% and 30%.

Perennial snow and ice variations (2000–2008) in the Arctic circumpolar land area from satellite observations

Perennial snow and ice (PSI) extent is an important parameter of mountain environments with regard to its involvement in the hydrological cycle and the surface energy budget. We investigated interannual variations of PSI in nine mountain regions of interest (ROI) between 2000 and 2008. For that purpose, a novel MODIS data set processed at the Canada Centre for Remote Sensing at 250 m spatial resolution was utilized. The extent of PSI exhibited significant interannual variations, with coefficients of variation ranging from 5% to 81% depending on the ROI. A strong negative relationship was found between PSI and positive degree‐days (threshold 0°C) during the summer months in most ROIs, with linear correlation coefficients (r) being as low as r = −0.90. In the European Alps and Scandinavia, PSI extent was significantly correlated with annual net glacier mass balances, with r = 0.91 and r = 0.85, respectively, suggesting that MODIS‐derived PSI extent may be used as an indicator of net glacier mass balances. Validation of PSI extent in two land surface classifications for the years 2000 and 2005, GLC‐2000 and Globcover, revealed significant discrepancies of up to 129% for both classifications. With regard to the importance of such classifications for land surface parameterizations in climate and land surface process models, this is a potential source of error to be investigated in future studies. The results presented here provide an interesting insight into variations of PSI in several ROIs and are instrumental for our understanding of sensitive mountain regions in the context of global climate change assessment.

Comparison and evaluation of Medium Resolution Imaging Spectrometer leaf area index products across a range of land use

Leaf area index (LAI) is a commonly required parameter when modelling land surface fluxes. Satellite based imagers, such as the 300 m full resolution (FR) Medium Spectral Resolution Imaging Spectrometer (MERIS), offer the potential for timely LAI mapping. The availability of multiple MERIS LAI algorithms prompts the need for an evaluation of their performance, especially over a range of land use conditions. Four current methods for deriving LAI from MERIS FR data were compared to estimates from in-situ measurements over a 3 km × 3 km region near Ottawa, Canada. The LAI of deciduous dominant forest stands and corn, soybean and pasture fields was measured in-situ using digital hemispherical photography and processed using the CANEYE software. MERIS LAI estimates were derived using the MERIS Top of Atmosphere (TOA) algorithm, MERIS Top of Canopy (TOC) algorithm, the Canada Centre for Remote Sensing (CCRS) Empirical algorithm and the University of Toronto (UofT) GLOBCARBON algorithm. Results show that TOA and TOC LAI estimates were nearly identical ( R 2 > 0.98) with underestimation of LAI when it is larger than 4 and overestimation when smaller than 2 over the study region. The UofT and CCRS LAI estimates had root mean square errors over 1.4 units with large (∼ 25%) relative residuals over forests and consistent underestimates over corn fields. Both algorithms were correlated ( R 2 > 0.8) possibly due to their use of the same spectral bands derived vegetation index for retrieving LAI. LAI time series from TOA, TOC and CCRS algorithms showed smooth growth trajectories however similar errors were found when the values were compared with the in-situ LAI. In summary, none of the MERIS LAI algorithms currently meet performance requirements from the Global Climate Observing System.

A comparative analysis of three different MODIS NDVI datasets for Alaska and adjacent Canada

Moderate Resolution Imaging Spectroradiometer (MODIS) data offer great potential for monitoring vegetation dynamics in Alaska. However, certain MODIS image quality issues, such as geometric distortion, have been analyzed and documented in high latitudes and regions distant from the Greenwich Meridian. To improve MODIS data usability, the Canada Centre for Remote Sensing (CCRS) developed a seven-band reflectance dataset (10 day composite) at 250 m resolution for Canada and North America. More recently, the US Geological Survey Earth Resources Observation and Science Center produced an eMODIS dataset that includes a 7 day composite of normalized difference vegetation index (NDVI) and surface reflectance data at 250 m, 500 m, and 1 km resolutions for the conterminous United States and Alaska. Although these two datasets are based on the same MODIS level 1B data as those of the standard MODIS products, they are processed to improve their use in high-latitude regions. In this study, we conducted a comparative analysis of the standard MODIS, CCRS MODIS, and eMODIS Alaska 250 m NDVI products using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images as a reference source. CCRS MODIS and eMODIS Alaska NDVI images have significantly improved geometric features over those of the standard MODIS product. Pixel-by-pixel comparisons of the MODIS datasets indicated that all retained the original MODIS radiometric characteristics, but considerable mismatches at the pixel level were found due to geometric distortions caused by resampling. All three MODIS datasets agreed well as images were degraded to 5 or 10 km resolution.

Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America

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.