In 1985, the authors reported the development of a semiempirical
dielectric model for soils, covering the frequency range between 1.4 and
18 GHz. The model provides expressions for the real and imaginary parts
of the relative dielectric constant of a soil medium in terms of the
soil's textural composition (sand, silt, and clay fractions), the bulk
density and volumetric moisture content of the soil, and the dielectric
constant of water at the specified microwave frequency and physical
temperature. This communication provides similar expressions for the
0.3-1.3-GHz range. Upon comparing experimental results measured in this
study with predictions based on the semiempirical model, it was found
that the model underpredicts the real part of the dielectric constant
for high-moisture cases and underestimates the imaginary part for all
soils and moisture conditions. A small linear adjustment has been
introduced to correct the expression for the real part and a new
equation was generated for the effective conductivity to correct the
expression for the imaginary part. In addition, dielectric measurements
were made to evaluate the dependence of the dielectric constant on clay
type. The results show significant variations for the real part and
large variations for the imaginary part among soils with the same clay
fractions but with clays of different specific surface areas
The CHEM spectrometer on the CCE spacecraft is designed to measure the mass and charge-state compositions as well as the energy spectra and pitch-angle distributions of all major ions from H through Fe with energies from 0.3 to 300 keV/charge and a time resolution of less than 1 min in the Earth's magnetosphere and magnetosheath. It has the sensitivity and resolution to detect artificially injected Li ions. Complementing the hot-plasma composition experiment and the medium-energy particle analyzer, this experiment will provide essential information on outstanding problems related to dynamical processes of space plasmas and of suprathermal ions. The instrument uses a combination of electrostatic deflection, post acceleration, and time of flight versus energy measurements to determine the ionization state Q, mass M, and energy E of the ambient-ion population. Pitch angle and anisotropy measurements are made utilizing the spinning motion of the CCE spacecraft. Isotopes of hydrogen and helium are resolved as are individual elements up to neon and dominant elements up to iron. Because of the intrinsically low instrument background achieved by using fast coincidence techniques combined with electrostatic deflection, the instrument has a large dynamic range and can identify rare elements and ions even in the presence of high-intensity radiation background. To increase significantly the information returned from the experiment within the allocated telemetry, an intelligent on-board data system which is part of the CHEM instrument performs fast M versus M/Q classifications.
Advanced Land Imager (ALI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Hyperion imaging spectrometer data covering an area in the Central Andes between Volcan Socompa and Salar de Llullaillaco were used to map hydrothermally altered rocks associated with several young volcanic systems. Six ALI channels in the visible and near-infrared wavelength range (0.4-1.0 μm) were useful for discriminating between ferric-iron alteration minerals based on the spectral shapes of electronic absorption features seen in continuum-removed spectra. Six ASTER channels in the short wavelength infrared (1.0-2.5 μm) enabled distinctions between clay and sulfate mineral types based on the positions of band minima related to Al-OH vibrational absorption features. Hyperion imagery embedded in the broader image coverage of ALI and ASTER provided essential leverage for calibrating and improving the mapping accuracy of the multispectral data. This capability is especially valuable in remote areas of the earth where available geologic and other ground truth information is limited.
Over the past two decades, a key indicator of climate change has
been the long time series of global maps of the normalized difference
vegetation index (NDVI), derived from remotely sensed data acquired with
a series of NOAA advanced very high resolution radiometer (AVHRR)
instruments from space. These NDVI values are calculated from relatively
broad AVHRR channels in the red and near-infrared regions. Continuation
of this long term data set is extremely valuable for climate-related
research, However, sometime in the coming decade, the AVHRR time series
measurements will no longer be continued. Instead, the measurements will
be made using newer generation satellite instruments having narrower
channels and improved spatial resolution. For example, the moderate
resolution imaging spectroradiometer (MODIS) onboard the Terra
spacecraft has several narrow channels in the 0.4-1.0 spectral range.
The NDVI values derived from the MODIS red channel and near-IR channel
will be biased compared to those derived from the broader AVHRR channels
because of differences in channel positions and widths for the two
instruments. The narrow MODIS near-IR channel is only slightly affected
by atmospheric water vapor absorption, while the broad AVHRR near-IR
channel is strongly affected by water vapor absorption. As a result, the
largest bias comes from the near-IR channels on the two instruments. To
a lesser extent, the bias also comes from the differences between the
red channel positions and the widths of MODIS and AVHRR instruments. In
this paper, the authors describe a practical method for simulating AVHRR
NDVI values using several narrower MODIS channels in the 0.4-1.0 μm
spectral range, including the MODIS green channel and the water vapor
In this paper, we introduce a method to retrieve the optical thickness of tropical cirrus clouds using the isolated visible cirrus reflectance (without atmospheric and surface effects). The isolated cirrus reflectance is inferred from level 1b calibrated 0.66- and 1.375-μm Moderate Resolution Imaging Spectroradiometer (MODIS) data. We created an optical properties database and optical thickness lookup library using previously calculated single-scattering data in conjunction with the discrete ordinates radiative transfer (DISORT) code. An algorithm was constructed based on this lookup library to infer the optical thickness of tropical cirrus clouds for each pixel in a MODIS image. We demonstrate the applicability of this algorithm using several independent MODIS images from the Terra satellite. The present method is complimentary to the MODIS operational cloud retrieval algorithm for the case of cirrus clouds.
To assess the utility of laser altimetry for studies in dynamical
oceanography, the authors present a comparison of the Shuttle Laser
Altimeter (SLA)-01 and the TOPEX/POSEIDON (T/P) radar altimeter on
global and regional scales. They compare all ~1.1 million SLA-01 range
measurements over the oceans to the CSRMSS95 gridded mean sea surface
model and find the overall root mean square (RMS) difference to be 2.33
m. The misfit was improved significantly by removing the mean and trend
from individual SLA-01 profiles, often resulting in RMS differences less
than 1 m. They also show that in regions where sea surface height varies
dramatically over relatively short horizontal scales such as across
major subduction zones, SLA-01 as capable of resolving rapid changes in
sea surface height. Finally, they examine a number of coastal zones and
illustrate SLA-01's ability to track continuously across the land-sea
interface, even in regions of dramatic coastal topography. The dominant
sources of radial error in the SLA-01 data are time-interval unit (TIU)
error (±0.75 m) and radial orbit error (1 m RMS). Therefore,
limitations of the SLA-01 data set are caused primarily by system
constraints as opposed to instrumentation error. Their results indicate
that future laser altimeters will provide valuable information regarding
ocean topography. In particular, laser altimetry data can be used to
supplement orbiting microwave ranging systems in coastal areas where
such sensors are incapable of maintaining lock across the
Lyzenga's paper ("Unconstrained inversion of waveheight spectra from SAR images" IEEE Trans. Geosci. Remote Sens., vol. 40, no. 2, pp. 261-270, Feb. 2002) after being corrected (ibid., vol. 40, no. 3, p. 729, Mar. 2002) has still a few remaining errors. This paper presents the additional corrections.
The IEEE Geoscience and Remote Sensing Society's Awards were presented at the IGARSS'03 banquet on Thursday, July 24 in the Hotel Dieu in Toulouse France. The GRS-S President Charles Luther, assisted by the Awards Committee Chairman Werner Wiesbeck, presented the awards. To promote excellence in research and service, each year the Geoscience and Remote Sensing Society of IEEE recognizes individuals among its members by bestowing IEEE certificates and awards.
The Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode offers an opportunity for global soil moisture (SM) monitoring at much finer spatial resolution than that provided by the currently operational Advanced Microwave Scanning Radiometer for the Earth Observing System and future planned missions such as Soil Moisture and Ocean Salinity and Soil Moisture Active Passive. Considering the difficulties in modeling the complex soil-vegetation scattering mechanisms and the great need of ancillary data for microwave backscatter SM inversion, algorithms based on temporal change are currently the best method to examine SM variability. This paper evaluates the spatial sensitivity of the ASAR GM surface SM product derived using the temporal change detection methodology developed by the Vienna University of Technology. This evaluation is made for an area in southeastern Australia using data from the National Airborne Field Experiment 2005. The spatial evaluation is made using three different types of SM data (station, field, and airborne) across several different scales (1-25 km). Results confirmed the expected better agreement when using point ( R <sub>station</sub> = 0.75) data as compared to spatial ( R <sub>PLMR, 1 km</sub> = 0.4) data. While the aircraft-ASAR GM correlation values at 1-km resolution were low, they significantly improved when averaged to 5 km ( R <sub>PLMR, 5 km</sub> = 0.67) or coarser. Consequently, this assessment shows the ASAR GM potential for monitoring SM when averaged to a spatial resolution of at least 5 km.
The 38 papers in this special issue were originally presented at the 9th Specialist Meeting on Microwave Radiometry and Remote Sensing Applications (MicroRad '06). These papers are organized into topical areas and applications, which are in the general order of the MicroRad technical sessions: Radiometer Calibration and RFI Mitigation (4); Synthetic Aperture Radiometry (3); Land and Vegetation (6); Ocean Salinity (5); Ocean Wind (4); Atmosphere (3); Temperature and Humidity Sounding (8); and Precipitation (5).
The 14 papers in this special issue are organized into topical areas and applications, which are in the general order of the MicroRad technical sessions: Radiometer Techniques (4), Vegetation (2), Ocean (2), Atmosphere and Precipitation (4), and Snow and Sea Ice (2).
The nine papers in this special issue were originally presented at the 2009 International Geoscience and Remote Sensing Symposium (IGARSS '09), held at the University of Cape Town, Rondebosch, South Africa, from July 12 to 17.
The methodology used to store a number of the Moderate Resolution
Imaging Spectroradiometer (MODIS) land products is described. The
approach has several scientific and data processing advantages over
conventional approaches used to store remotely sensed data sets and may
be applied to any remote-sensing data set in which the observations are
geolocated to subpixel accuracy. The methodology will enable new
algorithms to be more accurately developed because important information
about the intersection between the sensor observations and the output
grid cells are preserved. The methodology will satisfy the very
different needs of the MODIS land product generation algorithms, allow
sophisticated users to develop their own application-specific MODIS land
data sets, and enable efficient processing and reprocessing of MODIS
land products. A generic MODIS land gridding and compositing algorithm
that takes advantage of the data storage structure and enables the
exploitation of multiple observations of the surface more fully than
conventional approaches is described. The algorithms are illustrated
with simulated MODIS data, and the practical considerations of increased
data storage are discussed
Identification of clouds over Earth's polar regions is difficult from satellite radiometric measurements in the visible and infrared (IR) atmospheric window regions because of the high albedos of snow- and ice-covered surfaces in the visible and the low-temperature contrast between the surface and the troposphere in the IR. The Moderate Resolution Imaging SpectroRadiometer (MODIS) on the Terra Spacecraft has a near-IR channel located within the strong water vapor absorption regions close to 1.38 μm. This channel was originally designed for remote sensing of high-altitude clouds in the tropical and mid-latitude regions. In this paper, we report that this channel is also quite useful for detecting high clouds in polar regions during the daytime. Comparisons with IR emission techniques for polar cloud detections are also presented.
The Moderate Resolution Imaging Spectro-Radiometer (MODIS) on the Terra spacecraft has a channel near 1.38 μm for remote sensing of high clouds from space. The implementation of this channel on MODIS was primarily based on previous analysis of hyperspectral imaging data collected with the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). We describe an algorithm to retrieve cirrus bidirectional reflectance using channels near 0.66 and 1.38 μm. It is shown that the apparent reflectance of the 1.38-μm channel is essentially the bidirectional reflectance of cirrus clouds attenuated by the absorption of water vapor above cirrus clouds. A practical algorithm based on the scatterplot of 1.38-μm channel apparent reflectance versus 0.66-μm channel apparent reflectance has been developed to scale the effect of water vapor absorption so that the true cirrus reflectance in the visible spectral region can be obtained. To illustrate the applicability of the present algorithm, results for cirrus reflectance retrievals from AVIRIS and MODIS data are shown. The derived cirrus reflectance in the spectral region of 0.4-1 μm can be used to remove cirrus contamination in a satellite image obtained at a visible channel. An example of such an application is shown. The spatially averaged cirrus reflectances derived from MODIS data can be used to establish global cirrus climatology, as is demonstrated by a sample global cirrus reflectance image.
Techniques for retrieving cloud optical properties, i.e., the optical depths and particle size distributions, using atmospheric "window" channels in the visible and near-infrared spectral regions are well established. For partially transparent thin cirrus clouds, these "window" channels receive solar radiances scattered by the surface and lower level water clouds. Accurate retrieval of optical properties of thin cirrus clouds requires proper modeling of the effects from the surface and the lower level water clouds. In this paper, we describe a new concept using two strong water vapor absorption channels near 1.38 and 1.88 μm, together with one window channel, for remote sensing of cirrus optical properties. Both the 1.38- and 1.88-μm channels are highly sensitive in detecting the upper level cirrus clouds. Both channels receive little scattered solar radiances from the surface and lower level water clouds because of the strong water vapor absorption below cirrus. The 1.88-μm channel is quite sensitive to changes in ice particle size distributions, while the 1.38-μm channel is less sensitive. These properties allow for simultaneous retrievals of optical depths and particle size distributions of cirrus clouds with minimal contaminations from the surface and lower level water clouds. Preliminary tests of this new concept are made using hyperspectral imaging data collected with the Airborne Visible Infrared Imaging Spectrometer. The addition of a channel near 1.88 μm to future multichannel meteorological satellite sensors would improve our ability in global remote sensing of cirrus optical properties.
This paper presents a novel direct RF sampling receiver
architecture that will greatly facilitate the implementation of higher
spatial resolution satellite radiometers for improved near-term climate
forecasting. Direct-sampling is especially suitable for integration onto
the distributed, multiple element platform used in L-band synthetic
thinned array radiometry (STAR). To evaluate the direct-sampling
concept, the authors have developed a statistical model that predicts
the worst case radiometric sensitivity for a 1.4 GHz digital receiver.
Theoretical results show that only 2-3 bits of converter resolution are
needed to approach the performance of an ideal analog radiometer and
that sampling jitter will not significantly degrade the performance of
A measurement procedure has been developed and tested to determine
horizontal and vertical polarization radiative transfer properties,
i,e., single scattering albedo (ω) and optical depth (τ), of
vegetation under field conditions. The procedure was applied to a wheat
crop for a series of biomass densities. The measurements were done using
two different radiometers (1.4 and 5 GHz) and for different view angles.
The measurements and calculations indicated that the ratios of
horizontal and vertical polarization radiative transfer properties
β=ωh/ω<sub>ν</sub>) are slightly dependent on view
angle. However, no significant dependence on biomass density could be
The L-band (1.4 GHz) two-dimensional microwave interferometric radiometer, the payload of the Soil Moisture and Ocean Salinity (SMOS) mission, will observe elements of the Earth's surface simultaneously at multiple angles. Compared to single-angle observations, this multiangle observation technology is expected to significantly improve the capability of passive microwave remote sensing to retrieve soil moisture and vegetation properties from space. Although multiangle retrieval algorithms have been developed and successfully evaluated for homogeneous surfaces on the basis of simulation studies, the inherently large footprint of microwave observations from space has serious consequences for parameter retrieval from "real-world" inhomogeneous surfaces. At the spatial scale of SMOS (∼30 km for nadir observations), the Earth's surface is inhomogeneous almost by default. This aspect has not been fully accounted for yet. This study gives some insight into the consequences of vegetation spatial heterogeneity for the retrieval of "effective" surface parameters (soil moisture, canopy microwave transmissivity, and effective surface temperature) from inhomogeneous surfaces without prior knowledge of the within-pixel canopy heterogeneity.
We investigate anisotropy in 1.4-GHz brightness induced by a field corn vegetation canopy. We find that both polarizations of brightness are isotropic in azimuth during most of the growing season. When the canopy is senescent, the brightness is a strong function of row direction. On the other hand, the 1.4-GHz brightness is anisotropic in elevation: an isotropic zero-order radiative transfer model could not reproduce the observed change in brightness with incidence angle. Significant scatter darkening was found. The consequence of unanticipated scatter darkening would be a wet bias in soil moisture retrievals through a combination of underestimation of soil brightness (at H-pol) and underestimation of vegetation biomass (at V-pol). A new zero-order parameterization was formulated by allowing the volume scattering coefficient to be a function of incidence angle and polarization. The small magnitude of the scattering coefficients allows the zero-order model to retain its limited physical significance.
Physically based land surface process/radiobrightness (LSP/R) models may characterize well the relationship between radiometric signatures and surface parameters. They can be used to develop and improve the means of sensing surface parameters by microwave radiometry. However, due to a lack in the skill to properly understand the behavior of the data, a statistical approach is often adopted. In this paper, we present the retrieval of wheat plant water content (PWC) and soil moisture content (SMC) profiles from the measured H-polarized and V-polarized brightness temperatures at 1.4 (L-band), and 10.65 (X-band) GHz by an error propagation learning back propagation (EPLBP) neural network. The PWC is defined as the total water content in the vegetation. The brightness temperatures were taken by the PORTOS radiometer over wheat fields through three month growth cycles in 1993 (PORTOS-93) and 1996 (PORTOS-96). Note that, through the neural network, there is no requirement of ancillary information on the complex surface parameters such as vegetation biomass, surface temperature, and surface roughness, etc. During both field campaigns, the L-band radiometer was used to measure brightness temperatures at incident angles from 0 to 50° at L-band and at an incident angle of 50° at X-band. The SMC profiles were measured to the depths of 10 cm in 1993 and 5 cm in 1996. The wheat was sampled approximately once a week in 1993 and 1996 to obtain its dry and wet biomass (i.e., PWC). The EPLBP neural network was trained with observations randomly chosen from the PORTOS-93 data, and evaluated by the remaining data from the same set. The trained neural network is further evaluated with the PORTOS-96 data.
Terrestrial microwave emission is sensitive to soil moisture. Soil moisture is an important yet unobserved reservoir of the hydrologic cycle linked to precipitation variability. Remote sensing satellites that observe terrestrial microwave emission have the potential to map the spatial and temporal variabilities of soil moisture on a global basis. Unfortunately, terrestrial microwave emission is also sensitive to water within the vegetation canopy, and the effect of free water residing on vegetation, either as intercepted precipitation or dew, is not clear. Current microwave emission models neglect the effect of free water. We found that the precipitation intercepted by a maize (corn) canopy increased its brightness temperature at 1.4 GHz. This effect is opposite that of dew: dew decreases the brightness temperature of maize at 1.4 GHz. The increase in brightness temperature due to the intercepted precipitation was only about 1 K for vertically polarized brightness temperature and about 3 K for horizontally polarized (H-pol) brightness temperature. It may be acceptable to neglect the effect of free water in microwave emission models. A more serious concern, however, is the underestimation, by current microwave emission models, of the sensitivity of the H-pol brightness temperature to soil moisture through maize. Understanding the physics associated with the effect of free water in vegetation on the emission, scattering, and attenuation of microwave radiation will lead to improved emission models, and potentially, models that correctly reproduce the sensitivity of the 1.4-GHz brightness temperature to soil moisture at high levels of biomass when vegetation effects are greatest.
Theory and experiments have shown that passive microwave
radiometers can be used to measure soil moisture. However, the presence
of a vegetative cover alters the measurement that might be obtained
under bare conditions. Two significant obstacles to the practical use of
this approach are deterministically accounting for the effect of
vegetation; and developing algorithms for extracting soil moisture from
observations of a vegetation-soil complex. The presence of a vegetation
canopy reduces the sensitivity of passive microwave instruments to soil
moisture variations. Data collected using truck-mounted microwave
radiometers were used to examine the specific effects of corn and
This paper reports on the retrieval of soil moisture from dual-polarized L-band (1.6 GHz) radar observations acquired at view angles of 15deg, 35deg, and 55deg, which were collected during a field campaign covering a corn growth cycle in 2002. The applied soil moisture retrieval algorithm includes a surface roughness and vegetation correction and could potentially be implemented as an operational global soil moisture retrieval algorithm. The surface roughness parameterization is obtained through inversion of the Integral Equation Method (IEM) from dual-polarized (HH and VV) radar observations acquired under nearly bare soil conditions. The vegetation correction is based on the relationship found between the ratio of modeled bare soil scattering contribution and observed backscatter coefficient (sigma<sup>soil</sup>/sigma<sup>obs</sup>) and vegetation water content (W). Validation of the retrieval algorithm against ground measurements shows that the top 5-cm soil moisture can be estimated with an accuracy between 0.033 and 0.064 cm<sup>3</sup> ldr cm<sup>-3</sup>, depending on the view angle and polarization.
Aided by the U.S. Department of Agriculture, the NASA Johnson Space Center made an observational study of the radar-backscattering properties of corn and soybeans in commercial fields in a test site in Webster County, Iowa. Aircraft-based radar scatterometers measured the backscattering coefficient of the crops at three frequencies, 1.6 GHz (L-band), 4.75 GHz (C-band), and 13.3 GHz (Ku-band), at 10 sensor look-angles (5 to 50 degrees from the nadir in steps of 5 degrees), and with several polarization combinations. They produced 50 combinations of frequency, look-angle, and polarization for analysis for about 80 fields on two flight dates.
Polar sea ice plays a critical role in regulating the global climate. Seasonal variation in sea ice extent, however, coupled with the difficulties associated with in situ observations of polar sea ice, makes remote sensing the only practical way to estimate this important climatic variable on the space and time scales required. Unfortunately, accurate retrieval of sea ice extent from satellite data is a difficult task. Sea ice and high cold clouds have similar visible reflectance, but some other types of clouds can appear darker than sea ice. Moreover, strong atmospheric inversions and isothermal structures, both common in winter at some polar locations, further complicate the classification. This paper uses a combination of feed-forward neural networks and 1.6-μm data from the new Chinese Fengyun-1C satellite to mitigate these difficulties. The 1.6-μm data are especially useful for detecting illuminated water clouds in polar regions because 1) at 1.6 μm, the reflectance of water droplets is significantly higher than that of snow or ice and 2) 1.6-μm data are unaffected by atmospheric inversions. Validation data confirm the accuracy of the new classification technique. Application to other sensors with 1.6-μm capabilities also is discussed.
In the framework of the Soil Moisture and Ocean Salinity mission, a two-year (1987-1988) global simulation of brightness temperatures (TB) at L-band was performed using a simple model [L-band microwave emission of the biosphere, (L-MEB)] based on radiative transfer equations. However, the lack of alternative L-band spaceborne measurements corresponding to real-world data prevented from assessing the realism of the simulated global-scale TB fields. In this study, using a similar modeling approach, TB simulations were performed at C-band and X-band. These simulations required the development of C-MEB and X-MEB models, corresponding to the equivalent of L-MEB at C-band and X-band, respectively. These simulations were compared with Scanning Multichannel Microwave Radiometer (SMMR) measurements during the period January to August 1987 (corresponding to the end of life of the SMMR mission). A sensitivity study was also carried out to assess, at a global scale, the relative contributions of the main MEB parameters (particularly the roughness and vegetation model parameters). Regional differences between simulated and measured TBs were analyzed, discriminating possible issues either linked to the radiative transfer model (C-MEB and X-MEB) or due to land surface simulations. A global agreement between observations and simulations was discussed and allowed to evaluate regions where soil moisture retrievals would give best results. This comparison step made at C-band and X-band allowed to better assess how realistic and/or accurate the L-band simulations could be
Atmospheric studies often require the generation of high-resolution maps of ozone distribution across space and time. The high natural variability of ozone concentrations and the different levels of accuracy of the algorithms used to generate data from remote sensing instruments introduce major sources of uncertainty in ozone modeling and mapping. These aspects of atmospheric ozone distribution cannot be confronted satisfactorily by means of conventional interpolation and statistical data analysis. We suggest that the techniques of Modern Spatiotemporal Geostatistics (MSG) can be used efficiently to integrate salient (although of varying uncertainty) physical knowledge bases about atmospheric ozone in order to generate and update realistic pictures of ozone distribution across space and time. The MSG techniques rely on a powerful scientific methodology that does not make the restrictive modeling assumptions of previous techniques. A numerical study is discussed involving datasets generated by measuring instruments onboard the Nimbus 7 satellite. In addition to exact (hard) ozone data, the MSG techniques process uncertain measurements and secondary (soft) information in terms of total ozone-tropopause pressure empirical relationships. Nonlinear estimators are used, in general, and non-Gaussian probability laws are automatically incorporated. The proposed total ozone analysis can take into consideration major sources of error in the Total Ozone Mapping Spectrometer solar backscatter ultraviolet tropospheric ozone residual (related to data sampling, etc.) and produce high spatial resolution maps that are more accurate and informative than those obtained by conventional interpolation techniques.
In support of the Ice, Cloud, and land Elevation Satellite (ICESat)-II mission, this paper studies the bias in surface-elevation measurements caused by undetected thin clouds. The ICESat-II satellite may only have a 1064-nm single-channel lidar onboard. Less sensitive to clouds than the 532-nm channel, the 1064-nm channel tends to miss thin clouds. Previous studies have demonstrated that scattering by cloud particles increases the photon-path length, thus resulting in biases in ice-sheet-elevation measurements from spaceborne lidars. This effect is referred to as atmospheric path delay. This paper complements previous studies in the following ways: First, atmospheric path delay is estimated over the ice sheets based on cloud statistics from the Geoscience Laser Altimeter System onboard ICESat and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua. Second, the effect of cloud particle size and shape is studied with the state-of-the-art phase functions developed for MODIS cirrus-cloud microphysical model. Third, the contribution of various orders of scattering events to the path delay is studied, and an analytical model of the first-order scattering contribution is developed. This paper focuses on the path delay as a function of telescope field of view (FOV). The results show that reducing telescope FOV can significantly reduce the expected path delay. As an example, the average path delays for FOV = 167 ??rad (a 100-m-diameter circle on the surface) caused by thin undetected clouds by the 1064-nm channel over Greenland and East Antarctica are illustrated.
The detection of partially contaminated pixels over land is
necessary for quantitative applications of satellite optical
measurements to estimate surface biophysical parameters such as leaf
area index or vegetation composition. Threshold-based algorithms suffer
from the heterogeneity of land cover and the seasonal variability of the
radiation reflected and emitted by the land surface. As an alternative,
a method based on a Fourier series approximation to the seasonal
trajectory of the normalized difference vegetation index (NDVI) had been
previously developed (Cihlar 1996). In this paper, we introduce
modifications to the basic algorithm to more closely represent NDVI
seasonal trends for different land cover types, as well as a simplified
way to determine the time- and pixel-specific contamination thresholds.
Based on the tests with 1993-1996 Advanced Very High Resolution
Radiometer (AVHRR) data over Canada, the modified procedure effectively
detects contaminated pixels for boreal ecosystems after the growing
season of interest. The modifications also improved its performance
while the growing season is in progress; in this case, at least one
complete previous growing season coverage is required to provide the
temporal series needed to establish the thresholds. The modified
procedure also yields a contamination parameter that may be used to
estimate the most likely value for NDVI or other variables for each
pixel. It is concluded that the procedure would perform effectively in
other areas, provided that the NDVI temporal trajectories of the cover
types of interest can he represented by a mathematical model
In this paper, we investigate the influence of radar sonde eccentricity on a direct wave between a transmitting and a receiving antenna in a single-hole borehole radar measurement. We analyze the direct wave using an analytical method with the approximated solution of branch cut integrals and that of residues of poles. According to our calculation, at high frequencies above 200 MHz, guided waves, which are caused by the poles, play a vital role in the direct wave. We found that the most important pole is the HE<sub>11</sub> mode one, which is excited only when the antenna is eccentered in the borehole. We show that this causes artificial noise in the moving average subtraction, which is a common signal processing method used to remove the direct wave. In a laboratory experiment with a ground plane, we confirmed the excitation of the guided waves when the antenna was eccentered. In field experiments in granite, we conducted a special experiment, in which the location and rotation of the radar sonde were controlled mechanically. We found the excitation of the HE<sub>11</sub> mode at high frequencies when the sonde was noncentered by 1.7 cm in a borehole. These effects are also predicted in a theoretical analysis.
Satellite microwave radiometers capable of accurately retrieving sea surface temperature (SST) have provided great advances in oceanographic research. A number of future satellite missions are planned to carry microwave radiometers of various designs and orbits. While it is well known that the 11 GHz SST retrievals are less accurate than the 7 GHz retrievals, particularly in colder waters, it has not been demonstrated using existing microwave data. The Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) provides the means to examine the accuracies of SST retrievals using these channels in a systematic manner. In this paper, the accuracies of SSTs at 7 and 11 GHz are determined using two approaches: modeled and empirical. The modeled accuracies are calculated using an emissivity model and climatology SSTs, while empirical accuracies are estimated through validation of AMSR-E 7 and 11 GHz SST retrievals using over six years of data. It was found that the 7 GHz SST retrievals have less errors due to radiometer noise and geophysical errors than the 11 GHz retrievals at all latitudes. Additionally, while averaging the 11 GHz retrievals will diminish error due to uncorrelated radiometer noise, the geophysical error is still higher than for the 7 GHz retrievals, particularly at the higher latitudes.
To investigate the possibilities of using dual-frequency, multipolarization synthetic aperture radar (SAR) data to monitor sea ice, we derived the relationship between various polarization characteristics and the physical parameters of sea ice. We discuss the frequency and polarization characteristics of the backscattering coefficients of sea ice and then characterize its thickness by comparing the corresponding backscattering coefficient for each polarization with the physical parameters of the ice. We first propose a methodology for classifying sea-ice types by using a polarimetric decomposition technique, before comparing an estimation of the sea-ice thickness with the corresponding dual-frequency, multipolarization SAR data. We utilized the backscattering ratio to estimate the thickness of the sea ice. This ratio canceled the effect of roughness on the backscattering. The method was validated using Pi-SAR (polarimetric and interferometric airborne SAR) observation data obtained at ground-truth sites.
This paper reports on the measured electromagnetic properties of
dry and water saturated basalt rock over a frequency range of 1-110 GHz.
Tests were conducted in standard waveguides below 12 GHz and with
free-space systems above 12 GHz. The basalt used in this investigation
was found to be nonmagnetic. Measured relative permittivities for the
dry basalt varied from approximately (8.19,-0.71) at 1.12 GHz to
(6.80,-0.49) at 110 GHz. The porosity of the basalt was determined to be
roughly 10%. Properties of water saturated basalt were also measured at
several frequencies and compared to theoretical predictions based on a
simple Maxwell-Garnett mixing theory. Finally, the potential of using
microwave tomography on basalt rock was examined based on the resulting
This paper describes the optics design and field-of-view (FOV) calibration for five radiometers covering 114-660 GHz which share a common antenna in the Microwave Limb Sounder instrument on the National Aeronautics and Space Administration's Aura satellite. Details of near-field pattern measurements are presented. Estimated systematic scaling uncertainties (3σ) on calibrated limb emissions, due to FOV calibration uncertainties, are below 0.4%. 3σ uncertainties in beamwidth and relative pointing of radiometer boresights are 0.006° and 0.003°, respectively. The uncertainty in modeled instrument response, due to the scan dependence of FOV patterns, is less than ±0.24 K equivalent black-body temperature. Refinements to the calibration using in-flight data are presented.
The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Aircraft Sounder Testbed-Microwave (NAST-M) includes spectrometers operating near the oxygen lines at 50-57, 118.75, and 424.76 GHz, and a spectrometer centered on the water vapor absorption line at 183.31 GHz. All four of the spectrometers' antenna horns are collocated, have 3-dB (full-width at half-maximum) beamwidths of 7.5°, and are directed at a single mirror that scans cross-track beneath the aircraft with a swath up to 100-km wide. The first part of the paper describes the instrumentation and calibration for the newly installed spectrometers at 183.31 and 424.76 GHz. The second part demonstrates the potential performance of NAST-M, by presenting radiance images and precipitation rate and cell-top retrievals obtained during overflights of isolated convective storm cells, and by comparing these results with coincident visible images. NAST-M radiances are also compared with visible, infrared, and radar images. The nonlinear retrieval method was trained with a simple precipitation model. The data were obtained during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment (CRYSTAL-FACE 2002) and the Pacific THORpex (THe Observing-system Research and predictability experiment) Observing System Test (PTOST 2003).
Linear statistical temperature profile retrievals from nadiral
passive 118-GHz O<sub>2</sub> spectra are demonstrated using time- and
space-coincident microwave observations and radiosonde profiles.
Separate retrievals are demonstrated for winter and summer midlatitude
conditions in clear air over land; observations during both day and
night are included. The retrieval operator is a linear-statistical
minimum mean-squared-error estimator. A purely statistical retrieval
operator circumvents the effects of radiative transfer model
uncertainties and biases in either the radiosonde or Millimeter-wave
Temperature Sounder data. The retrieved profile rms errors are ~0.7-1.2
K for either the winter or summer tropospheres
Based on a nearly linear relationship between Ku-band backscatter
and its derivative with respect to incidence angle over Arctic ocean sea
ice, maps of backscatter have been produced, using the NSIDC 12.5-km
pixel polar stereographic projection grid. Both three-day and one-day
maps are studied. The noise level on these maps, evaluated from the
difference between successive maps, varies from 2 to 7%, increasing as
backscatter level or number of measurements decrease. Spectral analysis
indicates the resolution of these maps to be around 40 km, that of the
25-km pixel maps around 60 km. Comparison with RADARSAT Widescan mode
scenes, north of Spitsbergen and around Novaya Zemlya, confirm
estimations of ice edge and help in interpretation of the maps, while
indicating limitations of the water/ice discrimination algorithm
The effects of snowcover on the microwave backscattering from terrain in the 8-35 GHz region are examined through the analysis of experimental data and by application of a semiempirical model. The model accounts for surface backscattering contributions by the snow-air and snow-soil interfaces, and for volume backscattering contributions by the snow layer. Through comparisons of backscattering data for different terrain surfaces measured both with and without snowcover, the masking effects of snow are evaluated as a function of snow water equivalent and liquid water content. The results indicate that with dry snowcover it is not possible to discriminate between different types of ground surface (concrete, asphalt, grass, and bare ground) if the snow water equivalent is greater than about 20 cm (or a depth greater than 60 cm for a snow density of 0.3 g Â· cm-3). For the same density, however, if the snow is wet, a depth of 10 cm is sufficient to mask the underlying surface.
An intercalibration has been conducted for near-real-time sea ice concentration products from the National Aeronautics and Space Administration Team algorithm distributed by the National Snow and Ice Data Center (NSIDC). This represents the first time that an intercalibration has been conducted with near-real-time sea ice data and allowed the NSIDC to provide updates to a parameter of high interest with only limited interruption. The intercalibration follows a similar procedure that has been used for the previous Special Sensor Microwave/Imager (SSM/I) transitions of sea ice products but develops a new method to minimize discontinuities in sea ice extent and area estimates over the transition. In addition, a full year of overlap data was used for the intercalibration, far longer than for the previous SSM/I sensor transitions. A sensitivity study indicated that the consistency of the sea ice products is dependent on the timing (season of the year) and length of the transition and that a full year provides a more consistent time series.
Thermal infrared (TIR) emissivities of soils with different textures were measured for several soil moisture (SM) contents under controlled conditions using the Box method and a high-precision multichannel TIR radiometer. The results showed a common increase of emissivity with SM at water contents lower than the field capacity. However, this dependence is negligible for higher water contents. The highest emissivity variations were observed in sandy soils, particularly in the 8-9-μm range due to water adhering to soil grains and decreasing the reflectance in the 8-9-μm quartz doublet region. Thus, in order to model the emissivity dependence on soil water content, different approaches were studied according to the a priori soil information. Soil-specific relationships were provided for each soil texture and different spectral bands between 8 and 13 μm, with determination coefficients up to 0.99, and standard estimation errors in emissivity lower than ± 0.014. When considering a general relationship for all soil types, standard estimation errors up to ±0.03 were obtained. However, if other soil properties (i.e., organic matter, quartz, and carbonate contents) were considered, along with soil water content, the general relationship predicted TIR emissivities with a standard estimation error of less than ±0.008. Furthermore, the study showed the possibility of retrieving SM from TIR emissivities with a standard estimation error of about ±0.08 m<sup>3</sup> . m<sup>-3</sup>.
Water vapor plays the key role in the global hydrologic cycle and climate change. However, the distribution and variability of water vapor in the troposphere is not understood well in the globe, particularly the high-resolution variation. In this paper, 13-year 2-h precipitable water vapors (PWV) are derived from globally distributed 155 Global Positioning System sites observations and global three-hourly surface weather data and six-hourly National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis products, which are the first used to investigate multiscale water-vapor variability on a global scale. It has been found that the distinct seasonal cycles are in summer with a maximum water vapor and in winter with a minimum water vapor. The higher amplitudes of annual PWV variations are located in midlatitudes with about 10-20 plusmn 0.5 mm, and the lower amplitudes are found in high latitudes and equatorial areas with about 5 plusmn 0.5 mm. The larger differences of mean PWV between in summer and winter are located in midlatitudes with about 10-30 mm, particularly in the Northern Hemisphere. The semiannual variation amplitudes are relatively weaker with about 0.5 plusmn 0.2 mm. In addition, significant diurnal variations of PWV are found over most International Global Navigation Satellite Systems Service stations. The diurnal (24 h) cycle has amplitude of 0.2-1.2 plusmn 0.1 mm, and the peak time is from the noon to midnight. The semidiurnal (12 h) cycle is weaker, with amplitude of less than 0.3 mm.