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.
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 mission's 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.
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.
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 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.
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.
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