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The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth’s surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods. [ABSTRACT FROM AUTHOR]

Identifying and mapping tropical trees at the species level from space can support an improved assessment of forest composition, forest carbon uptake, tree species distribution and preferred habitat as well as a better understanding of the response of forests to climate change. In this study, the development of a validated data and image-processing schema demonstrated the capability of current metre-scale satellite technology (WorldView-3) to identify specific tree species within an unmanaged tropical forest. The experimental site, La Selva Biological Station in Costa Rica, provided access for field validation and spectral data acquisition of individual tree canopies from established canopy towers. It is also a representative biome of diverse lowland Atlantic tropical forests in Central America. The process defined in this paper calibrated and corrected field-acquired ASD field spectra for ten tree species and corrected WorldView-3 image data for viewing and illumination geometry. In addition, assessments of three current atmospheric compensation methods for correcting recent WorldView-3 satellite imagery established the most accurate compensation process for a tropical forest setting. Corrected reflectance in the satellite data matched the spectrometer data to ±0.25% for visible bands and ±0.5% for near-infrared bands. This study shows that spectral data from the satellite and field spectrometer data are nearly equivalent when applying the appropriate atmospheric compensation, band response emulation, and viewing correction processes established in this study. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) was launched on July 15, 2004 on the National Aeronautics and Space Administration's Earth Observing System Aura satellite. OMI is an ultraviolet-visible imaging spectrograph providing daily global coverage with high spatial resolution. This paper discusses the ground data processing software used for Level 0 to Level lb processing of OMI data. In addition, the OMI operations scenario is described together with the data processing concept. This paper is intended to serve as a reference guide for users of OMI (Level 1b) data. [ABSTRACT FROM AUTHOR]

The Multispectral Thermal Imager (MTI) satellite is a research and development project sponsored by the U.S. Department of Energy. The primary mission is to demonstrate advanced multispectral and thermal imaging from a satellite, Including new technologies, data processing, and analysis techniques and validation by reference to ground truth. The MTI builds on the efforts of a number of earlier efforts, Including Landsat, National Aeronautics and Space Administration remote sensing missions, and others, but the MTI incorporates a unique combination of attributes designed to advance the state of the art. The MTI satellite was launched on March 12,2000 into a 580 km × 610 km, sun-syn-chronous orbit with nominal 1 A.M. and 1 P.M. equatorial crossing times. The Air Force Space Test Program provided the Orbital Sciences Taurus launch. The satellite-based sensors obtain radiance data that are subsequently processed into measurements of atmospheric and surface properties such as column water vapor, atmospheric aerosol loading, surface temperatures, material composition, and others. This paper provides an overview of the MTI research objectives, design, operations, data products, and data processing and analysis. Several other papers provide greater detail on selected topics. [ABSTRACT FROM AUTHOR]

Abstract: 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 1km 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 10day 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. [Copyright &y& Elsevier]

A comprehensive system that is used to invert the geophysical products from microwave measurements has recently been developed. This system, known as the Microwave Integrated Retrieval System (MiRS), ensures that the final solution is consistent with the measurements and, when used as input to the forward operator, fits them to within the instrument noise levels. In the presence of precipitation, this variational algorithm retrieves a set of hydrometeor products consisting of cloud liquid water, ice water, and rain water content profiles. This paper presents the development and assessment of the MiRS rainfall rate that is derived based on a predetermined relationship of the rainfall with these hydrometeor products. Since this relationship relies on the geophysical products retrieved by the MiRS as inputs and not on sensor-dependent parameters, the technique is suitable for all microwave sensors to which the MiRS is applied. This precipitation technique has been designed to facilitate its transition from research to operations when applied to current and future satellite-based sensors. Currently, the MiRS rainfall rate technique has been implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) for the NOAA-18, NOAA-19, Metop-A Advanced Microwave Sounding Unit, and Microwave Humidity Sensor, as well as for the Defense Meteorological Satellite Program (DMSP)-F16 and DMSP-F18 Special Sensor Microwave Imager/Sounder microwave satellite sensors. For the validation of the MiRS rainfall rate technique, extensive comparisons with state-of-the-art precipitation products derived from rain gauge, ground-based radar, and satellite-based microwave observations are presented for different regions and seasons, and over land and ocean. The MiRS rainfall rate technique is shown to estimate precipitation, with a skill comparable to other satellite-based microwave precipitation algorithms, including the MSPPS, 3B40RT, and MWCOMB, while showing no discontinuities at coasts. This is a relevant result, considering that the MiRS is a system not merely designed to retrieve the rainfall rate but to consistently estimate a comprehensive set of atmospheric and surface parameters from microwave measurements. [ABSTRACT FROM AUTHOR]

Data merging with interpolation is a method of combining near-coincident satellite observations to provide complete global or regional maps for comparison with models and ground station observations. We investigate various methods and limitations of data merging (or data fusion), with and without interpolation, as a first step toward merging data sets archived in the National Aeronautics and Space Administration Goddard Earth Sciences Data and Information Services Center and made public through the Goddard Interactive Online Visualization and ANalysis Infrastructure (Giovanni) data portals. As a prototype for the data-merging algorithm, this paper uses daily global observations of aerosol optical thickness (AOT), as measured by the MODerate resolution Imaging Spectroradiometer onboard the Terra and Aqua satellites. The goal is to develop a very fast and accurate online method of data merging for implementation into Giovanni. We demonstrate three different methods for pure merging (without interpolation): simple arithmetic averaging (SAA), maximum likelihood estimate (MLE), and weighting by pixel counts. All three methods are roughly comparable, with the MLE (SAA) being slightly preferable when validating with respect to the AOT standard deviations (AOT means). To evaluate the merged product, we introduce two confidence functions, which characterize the percentage of the merged AOT pixels as a function of the relative deviation of the merged AOT from the initial Terra and Aqua AOTs. Eight combinations of merging–interpolation are applied to scenes with regular and irregular data gap patterns. Our results show that the merging–interpolation procedure can produce complete spatial fields with acceptable errors. [ABSTRACT FROM AUTHOR]

Abstract: MODIS is a major instrument for NASA’s Earth Observing System (EOS) missions. It is currently operating on-board the EOS Terra and Aqua spacecraft, launched in December 1999 and May 2002, respectively. The MODIS instrument was developed with improvements over heritage sensors in terms of its spectral, spatial, and temporal resolutions, and with more stringent calibration requirements. It makes observations in 36 spectral bands covering wavelengths from 0.41 to 14.4μm and at three nadir spatial resolutions: 250m, 500m, and 1km. Together Terra and Aqua MODIS have produced more than 10 years of global data sets that have significantly helped scientists worldwide to better understand the Earth as an interacting system and the impacts on this system due to human related activities. In order to maintain on-orbit calibration and data product quality, MODIS was built with a complete set of on-board calibrators (OBCs), including a solar diffuser (SD), a solar diffuser stability monitor (SDSM), a blackbody (BB), a deep space view (SV) port, and a spectro-radiometric calibration assembly (SRCA). In this paper, we provide a brief description of both Terra and Aqua MODIS on-orbit operation and calibration activities and present results of on-orbit radiometric, spatial, and spectral characterization. Examples of short-term stability and long-term response changes are illustrated using observations made with the on-board calibrators. On-orbit performance parameters, including detectors noise characterization, are also compared to pre-launch design specifications. [Copyright &y& Elsevier]

The FORMOSAT-3/COSMIC spacecraft constellation consisting of six LEO satellites is the world's first operational GPS Radio Occultation (RO) mission. The mission is jointly developed by Taiwan's National Space Organization (NSPO) and the United States' UCAR in collaboration with NSF, USAF, NOAA, NASA, NASA's Jet Propulsion Laboratory, and the US Naval Research Laboratory. The FORMOSAT-3/COSMIC satellites were successfully launched from Vandenberg US AFB in California at 0140 UTC 15 April 2006 into the same orbit plane of the designated 516 km altitude. The mission goal is to deploy the six satellites into six orbit planes at 800 km altitude with a 30-degree separation for evenly distributed global coverage. All six FORMOSAT-3/COSMIC satellites are currently maintaining a satisfactory good state-of-health. Five out of six satellites have reached their final mission orbit of 800 km as of November 2007. The data as received by FORMOSAT-3/COSMIC satellites constellation have been processed in near real time into 2500 good ionospheric profiles and 1800 good atmospheric profiles per day. These have outnumbered the worldwide radiosondes (∼900 mostly over land) launched from the ground per day. The processed atmospheric RO data have been assimilated into the Numerical Weather Prediction (NWP) models for real-time weather prediction and typhoon/hurricane forecasting by many major weather centers in the world. This paper describes the FORMOSAT-3/COSMIC satellite constellation system performance and the mission results that span the period from April 2006 to October 2007; and reviews the prospect of a future follow-on mission. [ABSTRACT FROM AUTHOR]

The MODerate resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) are onboard the same satellite platform NASA TERRA. Both MODIS and ASTER offer routine retrieval of land surface temperatures (LSTs), and the ASTER- and MODIS-retrieved LST products have been used worldwide. Because a large fraction of the earth surface consists of mountainous areas, variations in elevation, terrain slope and aspect angles can cause biases in the retrieved LSTs. However, terrain-induced effects are generally neglected in most satellite retrievals, which may generate discrepancy between ASTER and MODIS LSTs. In this paper, we reported the terrain effects on the LST discrepancy with a case examination over a relief area at the Loess Plateau of China. Results showed that the terrain-induced effects were not major, but nevertheless important for the total LST discrepancy. A large local slope did not necessarily lead to a large LST discrepancy. The angle of emitted radiance was more important than the angle of local slope in generating the LST discrepancy. Specifically, the conventional terrain correction may be unsuitable for densely vegetated areas. The distribution of ASTER-to-MODIS emissivity suggested that the terrain correction was included in the generalized split window (GSW) based approach used to rectify MODIS LSTs. Further study should include the classification-induced uncertainty in emissivity for reliable use of satellite-retrieved LSTs over relief areas. [ABSTRACT FROM AUTHOR]

This paper describes satellite data collected as part of the 2002/03 Cold Land Processes Experiment (CLPX). These data include multispectral and hyperspectral optical imaging, and passive and active microwave observations of the test areas. The CLPX multispectral optical data include the Advanced Very High Resolution Radiometer (AVHRR), the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Multi-angle Imaging Spectroradiometer (MISR). The spaceborne hyperspectral optical data consist of measurements acquired with the NASA Earth Observing-1 (EO-1) Hyperion imaging spectrometer. The passive microwave data include observations from the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS; AMSR-E). Observations from the Radarsat synthetic aperture radar and the SeaWinds scatterometer flown on QuikSCAT make up the active microwave data. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) was launched on July 15, 2004 on the National Aeronautics and Space Administration's Earth Observing System Aura satellite. The OMI instrument is an ultraviolet-visible imaging spectrograph that uses two-dimensional charge-coupled device detectors to register both the spectrum and the swath perpendicular to the flight direction with a 115° wide swath, which enables global daily ground coverage with high spatial resolution. This paper presents the OMI design and discusses the main performance and calibration features and results. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) flies on the National Aeronautics and Space Adminsitration's Earth Observing System Aura satellite launched in July 2004. OMI is a ultraviolet/visible (UV/VIS) nadir solar backscatter spectrometer, which provides nearly global coverage in one day with a spatial resolution of 13 km × 24 km. Trace gases measured include O3, NO2, SO2, HCHO, BrO, and OClO. In addition, OMI will measure aerosol characteristics, cloud top heights, and UV irradiance at the surface. OMI's unique capabilities for measuring important trace gases with a small footprint and daily global coverage will be a major contribution to our understanding of stratospheric and tropospheric chemistry and climate change. OMI's high spatial resolution is unprecedented and will enable detection of air pollution on urban scale resolution. In this paper, the instrument and its performance will be discussed. [ABSTRACT FROM AUTHOR]

Transmissions from ground-based systems in C- and X-bands present a significant challenge to the use of these bands for passive microwave remote sensing from aircraft and satellites. Because future missions plan to continue to use these frequencies, it is important to characterize and understand the nature of interference in as much of the candidate spectrum as possible. This paper presents a statistical analysis of interference observed in the continental U.S. using six months of data collected from the C- and X-band channels of the WindSat microwave radiometer. Our findings are consistent with those of previous studies by Li et a!. and Njoku et aL, which are based on data obtained from the Advanced Microwave Scanning Radiometer-EOS using somewhat similar center frequencies and bandwidths. Results show significant radio-frequency interference (RFI) at C-band, including brightnesses in horizontal and vertical polarizations in excess of 330 K, while X-band RFI is less obvious through direct examination of measured linearly polarized brightnesses. Evidence of lower levels of RFI is provided through use of the spectral and polarization indexes of Li et a!., which reveal likely RFI contributions at X-band as well. Further confirmation of X-band RFI is obtained through analysis of the polarimetric channels, which are shown to provide direct evidence of RFI in contrast to the linearly polarized channels. A temporal analysis of the largest C-band RFI sources is also provided in an attempt to further understand their properties. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents three years (1998–2000) of chlorophyll a (chl a) data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) for Case 2 waters of Chesapeake Bay and the middle Atlantic bight (MAB) to describe phytoplankton dynamics on seasonal to interannual time scales. We used extensive data on inherent and apparent optical properties in conjunction with satellite retrievals to: (1) characterize the bio-optical properties of the study area relevant to processing and interpreting SeaWiFS data; (2) test the applicability of the SeaWiFS bio-optical algorithm (OC4v.4) for the estuarine and coastal waters; (3) evaluate the accuracy of the SeaWiFS remote sensing reflectance (RRS) and chl a products on regional and seasonal bases using in situ observations. The characteristically strong absorption by chromophoric dissolved organic matter (acdom) and non-pigmented particulate matter (ad) in estuarine and coastal waters contributed to overestimates of chl a using OC4v.4 applied to in situ radiances for the Bay (mean ratio 1.42±1.20) and the MAB (2.60±1.36). Values of RRS from SeaWiFS in the blue region of the spectrum were low compared to in situ RRS, suggesting that uncertainties remain in atmospheric correction. Direct comparisons of SeaWiFS retrievals of chl a with in situ chl a for the Bay showed larger biases and uncertainties (mean ratio 1.97±1.85) than for chl a estimated from OC4v.4 applied to in situ RRS. The larger biases were attributed to errors in SeaWiFS radiances and the larger uncertainties to time-space “aliasing” of satellite observations and in situ measurements. To reduce the time differences between SeaWiFS and in situ data, we compared chl a obtained from continuous underway fluorometric measurements on selected ship tracks to SeaWiFS chl a and showed that SeaWiFS captured phytoplankton dynamics in much of the Bay. The agreement of SeaWiFS chl a with in situ chl a was strongest in the mid- (regions 3, 4) to lower Bay (regions 1, 2), and deteriorated toward the upper Bay (regions 5, 6), in part due to a reduction of sensitivity and an increase of noise for SeaWiFS products in the highly absorbing, low RRS waters of the upper Bay. A three-year time-series of SeaWiFS and in situ data showed that SeaWiFS accurately and reliably captured seasonal and interannual variability of chl a associated with variations of freshwater flow. Significant short-term variability of chl a in summer that was unresolved with shipboard data was detected in the SeaWiFS time-series and the implications are discussed. The overall performance of SeaWiFS in the mid- to lower Bay and the MAB, combined with high spatial (∼1 km2) and temporal (∼100 clear scenes per year) resolution, indicate current SeaWiFS products are valuable for quantifying seasonal to interannual variability of chl a in estuarine and coastal waters. [Copyright &y& Elsevier]