Yi Luo

Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies

Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies Rasim Latifovic, Alexander P. Trishchenko, Ji Chen, William B. Park, Konstantin V. Khlopenkov, Richard Fernandes, Darren Pouliot, Calin Ungureanu, Yi Luo, Shusen Wang, Andrew Davidson, and Josef Cihlar Pages 324-346 Abstract. Satellite data are an important component of the global climate observing system (GCOS). To serve the purpose of climate change monitoring, these data should satisfy certain criteria in terms of the length of observations and the continuity and consistency between different missions and instruments. Despite the great potential and obvious advantages of satellite observations, such as frequent repeat cycles and global coverage, their use in climate studies is hindered by substantial difficulties arising from large data volumes, complicated processing, and significant computer resources required for archiving and analysis. Successful examples of satellite earth observation (EO) data in climate studies include, among others, analyses of the earths radiation budget (Earth Radiation Budget Experiment (ERBE), Scanner for Radiation Budget (ScaRaB), and Cloud and the Earths Radiant Energy System (CERES)), cloudiness (International Satellite Cloud Climatology Project (ISCCP)), vegetation research (Global Inventory Modeling and Mapping Studies (GIMMS)), and the National Oceanic and Atmospheric Administration – National Aeronautics and Space Administration (NOAA–NASA) Pathfinder Program. Despite several attempts, the great potential of the advanced very high resolution radiometer (AVHRR) 1 km satellite data for climate research remains substantially underutilized. To address this issue, the generation of a comprehensive satellite data archive of AVHRR data and products at 1 km spatial resolution over Canada for 1981–2004 (24 years) has been initiated, and a new system for processing at level 1B has been developed. This processing system was employed to generate baseline 1 day and 10 day year-round clear-sky composites for a 5700 km × 4800 km area of North America. This region is centred over Canada but also includes the northern United States, Alaska, Greenland, and surrounding ocean regions. The baseline products include top-of-atmosphere (TOA) visible and near-infrared reflectance, TOA band 4 and band 5 brightness temperature, a cloud – clear – shadow – snow and ice mask, and viewing geometry. Details of the data processing system are presented in the paper. An evaluation of the system characteristics and comparison with previous results demonstrate important improvements in the quality and efficiency of the data processing. The system can process data in a highly automated manner, both for snow-covered and snow-free scenes, and for daytime and nighttime orbits, with high georeferencing accuracy and good radiometric consistency for all sensors from AVHRR NOAA-6 to AVHRR NOAA-17. Other processing improvements include the implementation of advanced algorithms for clear sky – cloud – shadow – snow and ice scene identification, as well as atmospheric correction and compositing. At the time of writing, the assembled dataset is the most comprehensive AVHRR archive at 1 km spatial resolution over Canada that includes all available observations from AVHRR between 1981 and 2004. The archive and the processing system are valuable assets for studying different aspects of land, oceans, and atmosphere related to climate variability and climate change.

A method for downscaling MODIS land channels to 250-m spatial resolution using adaptive regression and normalization

A method is proposed to derive spatially enhanced imagery for all seven Moderate Imaging Spectroradiometer (MODIS) land spectral bands at 250 m spatial resolution. Originally, only bands B1 and B2 [visible (VIS) at 0.65 μm, and near-infrared (NIR) at 0.85 μm] are available from MODIS at 250 m spatial resolution. The remaining five land channels (bands B3 to B7) are observed at 500 m resolution. The adaptive regression is constructed for each individual MODIS L1B granule of 500 m spatial resolution by splitting the area into smaller blocks and generating nonlinear regression between bands B3 to B7 and B1, B2 and NDVI. Once a set of regression coefficients is generated based on 500 m image, it is then applied to 250 m data containing only channels B1 and B2 to produce five intermediate synthetic channels (B3 to B7) at 250 m spatial resolution. The final step involves normalizing the generated 250 m images to original 500 m images to preserve radiometric consistency. It is achieved in two stages and ensures that downscaled results are unbiased relative to original observations. The developed method was applied to generate Canada-wide clear-sky composites containing all seven MODIS land spectral channels at 250 m spatial resolution over the area of North America 5700 km by 4800 km.

A Method to Derive the Multispectral Surface Albedo Consistent with MODIS from Historical AVHRR and VGT Satellite Data

Abstract 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 int...

Mapping of Surface Albedo over Mackenzie River Basin from Satellite Observations

This chapter presents the approach and results of mapping surface albedo and bi-directional reflectance distribution function (BRDF) properties over the Mackenzie River Basin (MRB). Satellite observations from three types of sensors were used: (1) the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard the NOAA platforms, (2) the VEGETATION (VGT) sensor onboard the SPOT platforms, and (3) the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the TERRA platform. The data collected using these sensors span the period of 1985 to 2004. Seasonal and interannual variability of spectral and broadband albedo over the MRB was analyzed. Broadband albedo averaged over the region changed from 0.11±0.03 in summer to 0.4-0.55 in winter. No substantial long-term systematic trends in surface albedo could be detected over the study period, mainly due to large interannual variability, uncertainties in sensors properties, atmospheric correction, and retrieval procedure.

Retrieval of BRDF for pure landcover types from MODIS and MISR using an angular unmixing approach

Information about the surface bi-directional reflectance distribution function (BRDF) and albedo is required as a boundary condition for radiative transfer modeling, aerosol retrievals, cloud retrievals, and atmospheric modeling. The typical spatial resolution provided by MODIS and MISR standard surface products (~1km) is insufficient to measure the BRDF of the pure surface types, because most pixels at this scale correspond to mixed classes. We present an approach for the retrieval of the basic surface BRDFs from the observations of MODIS/Terra and MISR using an angular unmixing method. Our analysis is focused on the Atmospheric Radiation Measurement (ARM) Program area in the Southern Great Planes (SGP) region, which is a predominantly agricultural area with a few major crop types. Pure surface classes were identified using high-resolution (30m) Landsat imagery and results of a ground survey. Assuming that the reflectance for each coarse pixel is a linear superposition of reflectances of basic surface types, it is possible to estimate the original BRDF parameters for each landcover type. In our case, three dominant classes were selected: wheat, grass, and baresoil. In the case of wheat and grass, the dispersion of the results is smaller than in the case of soil. This can be explained by the relatively low fractional coverage of the soil class within large pixels and by the significant variability of soil reflectance depending on wetness, soil type (sand, clay, etc.), and other factors. The correlation between the BRDF shape factors and the normalized difference vegetation index (NDVI) has also been analyzed. There is a high degree of correlation between the NDVI and BRDF isotropic factor (r0 in the case of MISR), while the correlation with other BRDF parameters was found to be smaller. In general, the NDVI can be used as a crude proxy for the BRDF shape.

BRDF/Albedo retrievals from the Terra and Aqua MODIS systems at 500-m spatial resolution and 10-day intervals

Surface bi-directional reflectance distribution function (BRDF) and albedo properties are retrieved over the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) area. A landcover-based fitting approach is employed by using a newly developed landcover classification map and the MODIS 10-day surface reflectance product (MOD09). The surface albedo derived by this method is validated against other satellite systems (e.g. Landsat-7 and MISR) and ground measurements made by an ASD spectroradiometer. Our results show good agreements between the datasets in general. The advantages of this method include the ability to capture rapid changes in surface properties and an improved performance over other methods under a frequent presence of clouds. Results indicate that the developed landcover-based fitting methodology is valuable for generating spatially and temporally complete surface albedo and BRDF maps using MODIS observations.

Fusion of MODIS land channels to produce regional time series of multispectral surface albedo at 250m and 10-day intervals for climate change and terrestrial monitoring appplications

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a unique source of reach spectral information useful for many applications. It provides observations in 36 spectral bands ranging in wavelengths from 0.4μm to 14.4μm with a spatial resolution from 250m to 1km. The standard MODIS data processing system and products cover the basic operational needs for a number of products and applications. Implemented globally they, however, cannot always make the best use of MODIS 250m and 500m land channels required for terrestrial monitoring and climate change applications. To address the need of regional users in enhanced MODIS data, especially in terms of spatial resolution, an independent technology for processing MODIS imagery has been developed. It uses MODIS level 1B top of the atmosphere swath data as input. The system includes the following steps: 1) fusion (downscaling) of MODIS 500m land channels B3-B7 with 250m bands B1-B2 to obtain consistent 250m imagery for all seven bands B1-B7; 2) re-projection of 250m bands into standard geographic projection; 3) scene identification at 250m spatial resolution to obtain mask of clear-sky, cloud and cloud shadows; 4) compositing clear-sky pixels over 10-day intervals; 5) atmospheric correction; 6) landcover-based BRDF fitting procedure. The fusion technique is designed to work with MODIS/TERRA data due to known problems with band-to-band registration accuracy on MODIS/AQUA. The developed method is applied to generate MODIS clear-sky land products in the Lambert Conformal Conical (LCC) projection for Canada and the Lambert Azimuthal Equal-Area (LAEA) projection for the North America and the Arctic circumpolar zone. The novel clear-sky compositing approach is proposed that significantly reduces impact of BRDF effect on raw composites by separation of pixels into two ranges of relative azimuth angle within 90°-270° and outside of this interval.

An approach for retrieval of atmospheric trace gases CO2, CH4 and CO from the future Canadian micro earth observation satellite (MEOS)

Among all trace gases, the carbon dioxide and methane provide the largest contribution to the climate radiative forcing and together with carbon monoxide also to the global atmospheric carbon budget. New Micro Earth Observation Satellite (MEOS) mission is proposed to obtain information about these gases along with some other missions objectives related to studying cloud and aerosol interactions. The miniature suit of instruments is proposed to make measurements with reduced spectral resolution (1.2nm) over wide NIR range 0.9μm to 2.45μm and with high spectral resolution (0.03nm) for three selected regions: oxygen A-band, 1.5μm-1.7μm band and 2.2μm-2.4μm band. It is also planned to supplement the spectrometer measurements with high spatial resolution imager for detailed characterization of cloud and surface albedo distribution within spectrometer field of view. The approaches for cloud/clear-sky identification and column retrievals of above trace gases are based on differential absorption technique and employ the combination of coarse and high-resolution spectral data. The combination of high and coarse resolution spectral data is beneficial for better characterization of surface spectral albedo and aerosol effects. An additional capability for retrieval of the vertical distribution amounts is obtained from the combination of nadir and limb measurements. Oxygen A-band path length will be used for normalization of trace gas retrievals.

Novel method for reprojection of MODIS level 1B images based on concurrent gradient search

A novel algorithm to address the reprojection of MODIS level 1B imagery is proposed. The method is based on the simultaneous 2D search of latitude and longitude fields using local gradients. In the case of MODIS, the gradient search is realized in two steps: inter-segment and intra-segment search, which helps to resolve the discontinuity of the latitude/longitude fields caused by overlap between consecutively scanned MODIS multi-detector image segments. It can also be applied for reprojection of imagery obtained by single-detector scanning systems, like AVHRR, or push-broom systems, like MERIS. The structure of the algorithm allows equal efficiency with either the nearest-neighbor or the bilinear interpolation modes.

Scene identification and clear-sky compositing algorithms for generating North America coverage at 250m spatial resolution from MODIS land channels

A new technology has been developed at the Canada Centre for Remote Sensing (CCRS) for generating North America continental scale clear-sky composites at 250 m spatial resolution of all seven MODIS land spectral bands (B1-B7). The MODIS Level 1B (MOD02) swath level data were used as input to circumvent the problems with image distortion in the mid-latitude and polar regions inherent to the sinusoidal (SIN) projection utilized for the standard MODIS data products. The new data products are stored in the Lambert Conformal Conical (LCC) projection for Canada and the Lambert Azimuthal Equal-Area (LAEA) projection for North America. The MODIS 500m data (B3-B7) were downscaled to 250m resolution using an adaptive regression algorithm. The clear-sky composites are generated using scene identification information produced at 250m resolution and multi-criteria selection which depends on pixel identification. Cloud shadows were also identified and removed from output product. It is demonstrated that new approach provides better results than any scheme based on a single compositing criterion, such as maximum NDVI, minimum visible reflectance, or combination of them. To account for surface bi-directional properties, two clear-sky composites for same time period are produced for the relative azimuth angles within 90°-270° and outside of this interval. Comparison with Landsat imagery and MODIS standard composite products demonstrated advantages of new technique for screening cloud and cloud shadow and providing the high spatial resolution. The final composites were produced for every 10-day intervals since March 2000. The composite products have been used for mapping albedo and vegetation properties as well as for land cover and change detections applications at 250m scale.