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.
While existing satellite Earth Observing (EO) systems provide many baseline observations, they are lacking an important combination of capabilities in terms of angular sampling, spectral coverage, and spatial resolution. This has an impact on the accuracy of retrievals and the capability to provide accurate regular operational monitoring of surface and atmospheric properties on a large scale (global or continental).
The concept of Advanced Multiangular MEdium Resolution System (AMMERS), that addresses the above issues and provides unique capability presently unavailable from other space observing systems, is proposed here. The mission's unique feature is a combination of medium resolution (400m), multi-spectral observations (13 spectral bands in visible, NIR, SWIR, IR, SW and LW), and multi-angular capabilities (7 angles). AMMERS's multiangular features and swath width allow bi-directional angular sampling close to the solar principal plane and in the perpendicular plane. These capabilities are critically important for accurate estimation of surface albedo, vegetation structure, and forest parameters. AMMERS will also be superior to many other missions in retrieving SST, aerosol, clouds parameters, snow, and wild fire mapping.
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.
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.
Since the satellites provide frequent and global observations of atmospheric and terrestrial environment, attempts have been made to use satellite data for long-term monitoring of land reflectances, vegetation indices and clouds properties. Although the construction and characteristics of spaceborne instruments may be quite similar, they are not identical among all missions, even for the same type of instrument like AVHRR. Consequently, the effect of varying spectral response may create an artificial noise imposed upon a subtle natural variability. We report the results of a study on the sensitivity of Normalized Difference Vegetation Index (NDVI), surface and cloud reflectance to differences in instrument spectral response functions (SRF) for various satellite sensors. They include AVHRR radiometers onboard NOAA satellites NOAA-6 - NOAA-16, the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). We also analyzed the SRF effects for several geostationary satellites used for cloud studies, such as GOES-8 - 12, METEOSAT-2 - 7, GMS -1 - 5. The results obtained here demonstrate that the effect of instrument spectral response function cannot be ignored in long-term monitoring studies that employ space observations from different sensors. The SRF effect introduces differences in observed reflectances and retrieved quantities that may be comparable or exceed the range of natural variability and possible systematic trends, the contribution from the calibration, atmospheric and other corrections. Some modeling results were validated against real satellite observations with good agreement.
Data from the ScaRaB radiometer flown on board the Meteor-3/7 satellite were first employed for validating and correcting a TOA Earth radiation budget product generated from GOES-7 and the latter was then combined with ground radiation measurements for addressing the effect of clouds on atmospheric absorption of solar radiation. By virtue of comparison between coincident and collocated radiative quantities derived from ScaRaB and GOES sensors, it was found that GOES calibrations for both visible and infrared window channels appear to be adequate, but narrow to broad-band conversion of short-wave measurements suffers systematic errors. After correcting this problem, the cloud radiative forcing at the top of the atmosphere (TOA) and at the surface were derived from space- and ground-based measurements made during the US Atmospheric Radiation Measurement (ARM). The ratio of the two forcing terms is in excellent agreement with that determined by radiative transfer models, in contradiction to the recent claim of cloud absorption anomaly.
This work is mainly focused on the application of the line-by-line (LBL) radiative parameters databases in processing satellite low-resolution measurements of outgoing solar and IR radiation associated with two space instruments such as the scanner of radiation budget (ScaRab on Meteor-3/7 experiment) and ISTOK-1 (PRIRODA-MIR mission). Details of the efficient computer techniques for routine but accurate calculations of solar and IR radiative parameters in order to develop the benchmark databases are briefly described. Some practical results are presented that involve the utilization of line-by-line benchmarks in the ScaRaB measurement validation. The narrow-band model designed for the ISTOK-1 measurement processing and developed on the basis of the LBL precalculations is presented.
KEYWORDS: Satellites, Error analysis, Data modeling, Solar processes, Motion models, Atmospheric modeling, Space operations, Satellite navigation systems, Data processing, Radiometry
The results of orbit parameters calculations for satellites METEOR-2/20, 2/21 (h equals 950 km) and METEOR-3/5, 3/7 (h equals 1200 km) are analyzed. The accuracy of orbit approximation and extrapolation (prediction) for different initial data assimilation techniques is evaluated using analytical navigation model. Mean 14 day orbit prediction error during 1991-1994 amounted to 9.5 km for METEOR-2/20 and 5.6 km for METEOR-3/5. The same value for METEOR-2/21 (start 31 Aug 1993) was 2.9 km, for METEOR-3/7 (start 25 Jan 1994) was 1.3 km. The accuracy of satellite orbit prediction as well as orbit approximation is changing for the worse under high solar activity conditions. The estimates of real pixel location and estimates of deflections of axes positions for IR- radiometers << Klimat >> (10.5-12.0 mkm, 12 X 12 km2) of METEOR-3/5 and 3/7 and ScaRaBlaunched on METEOR-3/7 for Earth radiation budget measurements have been obtained. No essential systematic axes deflections and pixel location errors were detected except systematic roll angle bias of -29' for << Klimat >> on METEOR-3/5. Estimates of ground control points positions using MWDB-II database demonstrate pixel location accuracy of about 0.5 pixel size.
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