Aerosol retrieval over land from AVHRR data-application foratmospheric correction

Correction of Advanced Very High Resolution Radiometer (AVHRR) imagery for the aerosol effect requires retrieval of the aerosol loading from the images. Two retrieval algorithms that were previously developed for Landsat are modified for the AVHRR. The methods determine the aerosol optical thickness over land surfaces from AVHRR band one data independently of ancillary information. The first method retrieves aerosols based on the atmospheric effect on the path radiance. This method requires the surface reflectance to be 0.02±0.01, which is found over forests in the red channel. Two techniques are used to screen an AVHRR scene for pixels that have this low reflectance. The qualifying requirements for these techniques are discussed, and the method is demonstrated to retrieve aerosol optical thicknesses to ~±0.1. The second method uses the change in contrast for several scenes to determine the change in the optical thickness between the scenes. A reference scene allows absolute determination. The method has an rms error of ~0.1     

Atmospheric correction against algorithm for NOAA-AVHRR products:theory and application

Investigation of the effect of atmospheric constituents on NOAA Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared data is presented. The general remote sensing equation, including scattering, absorption, and bidirectional reflectance effects for the AVHRR solar bands, is described. The magnitude of the atmospheric effects for AVHRR solar bands with respect to their impact on the normalized difference vegetation index (NDVI) and the surface bidirection reflectance is examined. Possible approaches for acquiring atmospheric information are discussed, and examples of atmospheric correction of surface reflectance and NDVI are given. Invariant effects (ozone absorption and molecular scattering) and variant effects (water vapor absorption and aerosol scattering) are shown to dominate the atmospheric effects in the AVHRR solar bands     

Impact of the atmospheric transmittance and total water vaporcontent in the algorithms for estimating satellite sea surfacetemperatures

Sea surface temperature (SST) algorithms for NOAA AVHRR data can determine SST with rms values of 0.7 K on a global basis. However, this figure is not compatible with the high accuracy of 0.3 K required by climate studies. Biases in the SST product, arising when the factors that increase the optical path-length (absorbents concentration in the atmosphere or viewing angles) are large, cause problems in the use of the split-window formulation for climate monitoring. The reason is that the split-window coefficients currently used are not adequate to cover for all the atmospheric variability. To show this, simulations of channels 4 and 5 of AVHRR/2 of NOAA-11 using a radiative transfer model have been made. The range of atmospheric conditions and surface temperatures introduced in the simulation covers the variability of these parameters on a worldwide scale. From these data, the authors present new split-window coefficients that take into account the atmospheric variability through the ratio of the channel transmittances, or else through the total water vapor content along the path. They also show, using simulated and actual data, that the proposed split-window algorithm has a real global character and represents an improvement over the conventional algorithms     

Atmospheric corrections in the thermal infrared: global and watervapor dependent split-window algorithms-applications to ATSR and AVHRRdata

The split-window method is an appropriate way to perform atmospheric corrections of satellite brightness temperatures in order to retrieve the surface temperature. A climatological data set of 1761 different radio soundings, the TIGR database, has been used to develop two different split-window methods. A global quadratic (QUAD) method, with global coefficients to be applied on a worldwide scale, and a water vapor dependent (WVD) algorithm. The first method includes a quadratic term in the split-window equation that roughly accounts for the water vapor amount. The other method explicitly includes the water vapor amount in each split-window coefficient. When applied to the 1761 radio soundings, the latter method gives better results than the global one, especially when the surface emissivity is far from unity (0.95 or less) and when the water vapor reaches great values. Both algorithms have been tested on ATSR/ERSI and AVHRR/NOAA data over sea pixels. The QUAD algorithm gives correct results for simulations (the standard error is 0.2 K) and experimental data (the bias ranges from -0.1 to 0.4 K). The WVD algorithm appears to be more accurate for both simulations (the standard error is less than 0.1 K) and AVHRR experimental data when climatological water vapor contents are used (the bias ranges from -0.2 to 0.1 K)     

Normalized differential spectral attenuation (NDSA): a novel approach to estimate atmospheric water vapor along a LEO-LEO satellite link in the ku/K bands

We introduce here the normalized differential spectral attenuation (NDSA) approach, which is a novel differential measurement way for estimating the total content of water vapor integrated water vapor (IWV) along a tropospheric propagation path between two Low Earth Orbit (LEO) satellites. The NDSA approach requires a transmitter onboard the first LEO satellite and a receiver onboard the second one. It is based on the simultaneous measurement of the total attenuation at two relatively close frequencies in the Ku/K bands, and on the estimate of a "spectral sensitivity parameter" that can be directly converted into IWV. NDSA is potentially able to emphasize the water vapor contribution, to cancel out all spectrally flat unwanted contributions and to limit the impairments due to tropospheric scintillation. The objective of the paper is to analyze the level of correlation between the spectral sensitivity parameter and the IWV at a given altitude from ground of the LEO-LEO link (tangent altitude), in order to single out the best performing frequencies. Simulation results are based on microwave propagation models and on radiosonde data. The results shows the potential of the NDSA approach to provide direct estimates of IWV along LEO-LEO tropospheric propagation paths in the 15-25 GHz frequency range, under different atmospheric conditions.     

Improvements in the split-window technique for land surfacetemperature determination

Land surface temperature (LST) retrievals obtained from NOAA Advanced Very High Resolution Radiometer (AVHRR) are of considerable importance for climatic research. However, the accurate evaluation of LST from space has been severely limited because of the difficulty in separating atmospheric from surface effects as the surface cannot be modeled as a black-body radiator. With this goal in mind, a novel extension of the split-window technique is presented in which the atmospheric contribution to the radiance measured by the satellite is investigated by the ratioing of covariance and variance of the brightness temperatures measured in channels 4 and 5 of AVHRR/2. Furthermore, the contribution of emissivity is evaluated from coefficients that depend on the spectral emissivities in both thermal channels. Using a wide range of simulations from an atmospheric radiative transfer model it is shown that the proposed algorithm provides an estimate of LST, to within 0.4 K if the spectral surface emissivity is known, which is better than that given by the currently used split-window algorithms for LST determination. Also the limitations on algorithm accuracy are discussed considering different values of noise equivalent temperature. Finally the authors present the preliminary results obtained using the proposed method from AVHRR data over a semi-arid region-of Northwestern Victoria in Australia provided by CSIRO, and a mountainous region of Northeast of France acquired in the frame of Regio Klimat Projekt     

A generalized split-window algorithm for retrieving land-surfacetemperature from space

Proposes a generalized split-window method for retrieving land-surface temperature (LST) from AVHRR and MODIS data. Accurate radiative transfer simulations show that the coefficients in the split-window algorithm for LST must vary with the viewing angle, if the authors are to achieve a LST accuracy of about 1 K for the whole scan swath range (±55° from nadir) and for the ranges of surface temperature and atmospheric conditions over land, which are much wider than those over oceans. The authors obtain these coefficients from regression analysis of radiative transfer simulations, and they analyze sensitivity and error over wide ranges of surface temperature and emissivity and atmospheric water vapor abundance and temperature. Simulations show that when atmospheric water vapor increases and viewing angle is larger than 45°, it is necessary to optimize the split-window method by separating the ranges of the atmospheric water vapor, lower boundary temperature, and the surface temperature into tractable subranges. The atmospheric lower boundary temperature and (vertical) column water vapor values retrieved from HIRS/2 or MODIS atmospheric sounding channels can be used to determine the range for the optimum coefficients of the split-window method. This new algorithm not only retrieves land-surface temperature more accurately, but is also less sensitive to uncertainty in emissivity and to instrument quantization error     

Normalized Differential Spectral Attenuation (NDSA) Measurements Between Two LEO Satellites: Performance Analysis in the Ku/K-Bands

Normalized differential spectral attenuation (NDSA) is a novel differential measurement method to estimate the total content of water vapor [integrated water vapor (IWV)] along a tropospheric propagation path between two low Earth orbit (LEO) satellites. A transmitter onboard the first LEO satellite and a receiver onboard the second one are required. The NDSA approach is based on the simultaneous estimates of the total attenuation at two relatively close frequencies in the Ku/K-bands and of a “spectral sensitivity parameter” that can be directly converted into IWV. The spectral sensitivity has the potential to determine the water vapor contribution, to cancel out all spectrally flat unwanted contributions, and to limit the impairments due to tropospheric scintillation. In this paper, we focus on the measurement accuracy of the spectral sensitivity parameter. Specifically, we examine this accuracy at three different frequencies and for two models of atmospheric structure. We first provide an approximate expression of the accuracy and then validate this expression through Monte Carlo simulations based on microwave propagation models.      

Estimating leaf area index from satellite data

A method for estimating leaf area index from visible and near infrared measurements of vegetation above a soil background is applied to a Landsat Thematic Mapper data set. Some constants required for the procedure are inferred from the scattergram of data values. The resulting image illustrates variability of leaf area index over an agricultural area. The mixed-pixel case, corresponding to low-resolution data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) is also discussed, and a vegetation index is suggested for both high- and low-resolution data. Consideration of the two types of data leads to the suggestion that a sampled high spatial resolution sensor (50-100 m) be added to the AVHRR in order to permit accurate inference of vegetation conditions over agricultural areas     

Comparison of Ground-Based Millimeter-Wave Observations and Simulations in the Arctic Winter

During the Radiative Heating in Underexplored Bands Campaign (RHUBC), held in February–March 2007, three millimeter-wave radiometers were operated at the Atmospheric Radiation Measurement Program's site in Barrow, Alaska. These radiometers contain several channels located around the strong 183.31-GHz water vapor line, which is crucial for ground-based water-vapor measurements in very dry conditions, typical of the Arctic. Simultaneous radiosonde observations were carried out during conditions with very low integrated-water-vapor (IWV) content ( $≪ 2 hbox{mm}$). Observations from the three instruments are compared, accounting for their different design characteristics. The overall agreement during RHUBC among the three instruments and between instruments and forward model is discussed quantitatively. In general, the instrument cross-validation performed for sets of channel pairs showed agreement within the total expected uncertainty. The consistency between instruments allows the determination of the IWV to within around 2% for these dry conditions. Comparisons between these data sets and forward-model simulations using radiosondes as input show spectral features in the brightness-temperature residuals, indicating some degree of inconsistency between the instruments and the forward model. The most likely cause of forward-model error is systematic errors in the radiosonde humidity profiles.      

Higher resolution Earth surface features from repeat moderateresolution satellite imagery

This paper demonstrates that a high-resolution reflectivity model used in conjunction with an instrument pointspread function (PSF) can both determine georegistration parameters of coarse resolution sensors and improve the spatial resolution by compositing noncoincident repeat satellite data. To demonstrate this ability, an ideal location is selected and several first principle assumptions are made to simplify the reflectivity model. Twenty-three 1-km advanced very high-resolution radiometer (AVHRR) images are composited by using a Bayesian statistical sampling technique to yield estimates of a simple terrain-based reflectivity model with 180-m resolution. The terrain values are determined from a 90-m resolution digital elevation model (DEM). The Bayesian technique uses the AVHRR data to iteratively determine the most likely values for the model spectral albedos contained within an AVHRR field of view. Model predicted radiances for the repeat AVHRR footprints are computed by integrating model albedo values over the AVHRR PSF. As a first-order verification, simulated AVHRR reflectivities are shown to reconstruct well a smoothly varying prescribed albedo field. Comparisons of the composited real AVHRR image result with Landsat Multi Spectral Scanner (MSS) data show that the model reconstruction resolves surface features, which are not resolved in a single AVHRR image     

Early Warning and Crop Condition Assessment Research

The Early Warning Crop Condition Assessment Project of AgRISTARS was a multiagency and multidisciplinary effort. Its mission and objectives were centered around development and testing of remote-sensing techniques that enhance operational methodologies for global crop-condition assessments. The project developed crop stress indicator models that provide data filter and alert capabilities for monitoring global agricultural conditions. The project developed a technique for using NOAA-n satellite advanced very-high-resolution radiometer (AVHRR) data for operational crop-condition assessments. This technology was transferred to the Foreign Agricultural Service of the USDA. The project developed a U. S. Great Plains data base that contains various meteorological parameters and vegetative index numbers (VIN) derived from AVHRR satellite data. It developed cloud screening techniques and scan angle correction models for AVHRR data. It also developed technology for using remotely acquired thermal data for crop water stress indicator modeling. The project provided basic technology including spectral characteristics of soils, water, stressed and nonstressed crop and range vegetation, solar zenith angle, and atmospheric and canopy structure effects.     

A simplified method for estimating the total water vapor contentover sea surfaces using NOAA-AVHRR channels 4 and 5

A simplified method for estimating the total amount of atmospheric water vapor, W, over sea surfaces using NOAA-AVHRR Channels 4 and 5 is presented. This study has been carried out using simulated AVHRR data at 11 and 12 μm (with MODTRAN 3.5 code and the TIGR database) and AVHRR, PODAAC, and AVISO databases provided by the Louis Pasteur University (Strasbourg-France), NASA-NOAA, and Meteo France, respectively. The method is named linear atmosphere-surface temperature relationship (LASTR). It is based on a linear relationship between the effective atmospheric temperature in AVHRR Channel 4 and sea surface temperature. The LASTR method was compared with the linear split-window relationship (LSWR), which is based on a linear regression between W and the difference of brightness temperature measured in the same channels (ΔT=T4-TS). The results demonstrate the advantage of the LASTR method, which is capable of estimating W from NOAA-14 afternoon passes with a bias accuracy of 0.5 g cm^-2 and a standard deviation of 0.3 g cm^-2, compared with the W obtained by the AVISO database. In turn, a global bias accuracy of 0.1 g cm^-2 and a standard deviation within 0.6 g cm^-2 have been obtained in comparison with the W included in the PODAAC database derived from the special sensor microwave/imager (SSM/I) instrument     

Atmospheric and angular effects on NDVI temporal profiles derivedfrom ADEOS-POLDER data over India

The variations of NDVI for crops, semi-evergreen forest, dry deciduous forest, and sand have been analyzed as a function of date of acquisition at 670 and 865 am using ADEOS-Polarization and Directionality of Earth Resources (POLDER) data acquired over India. After correcting the data for atmospheric effects, a semi-empirical bidirectional reflectance distribution function (BRDF) model has been fitted to the data to extract angularly normalized target reflectances. It is shown that atmospheric corrections and angular normalization are important in the quantitative analysis of NDVI and its temporal variations     

Multiple-Angle Observations of Reflectance Anisotropy from an Airborne Linear Array Sensor

An airborne pointable imaging multispectral linear array (MLA) sensor has been developed for the multidirectional observation of surface reflectance anisotropy. The sensor design permits observations up to 450 off-nadir in three spectral bands (green, red, and near-infrared). Calibration permits the conversion of sensor data to radiance units with an absolute uncertainty of 6 percent. Observations of five field plots from seven view directions are discusseed. Calibration and atmospheric corrections are used to derive hemispherical-directional reflectance factors. A three-term reflectance model is fit to the reflectance factors for each plot to represent the continuous distribution of reflectance factors with view direction. The reflectance model is integrated over all view directions to calculate bihemispherical reflectance factors. The calculated bihemispherical factors differed by 1 to 25 percent from values based on an assumption of isotropic reflectance depending on spectral band and field plot. These calculations demonstrate the technologic and scientific capabilities required for the remote characterization of surface reflectance anisotropy. Remote multidirectional observations are both feasible and needed to fully evaluate land reflectance characteristics.     

Detection of Sea Surface Temperature (SST) and Salinity Using Thermal Infrared Data of AVHRR and MODIS in the Gulf of Bohai Sea of China

This paper presents the detection of sea surface temperature (SST) and salinity in the Gulf of Bohai Sea of China using thermal infrared (TIR) data of Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). Both AVHRR and MODIS imageries are evaluated as main data sources for monitoring SST as a measure of upwelling's dynamic. The relationship between SST and salinity in the area is also discussed during 1997-2000 derived from AVHRR data and then examined using MODIS data of 2000. The obtained results indicated that both AVHRR and MODIS are useful to detect SST and salinity in the study area.     

Bayesian-based subpixel brightness temperature estimation from multichannel infrared GOES radiometer data

In this paper, a new image reconstruction scheme is devised for estimating a high-resolution temperature map of the top of the Earth's atmosphere using the Geostationary Operational Environmental Satellite (GOES) imager infrared channels 4 and 5. By simultaneously interpolating the image while estimating temperature, the proposed algorithm achieves a more accurate estimate of the subpixel temperatures than could be obtained by performing these operations independently of one another. The proposed algorithm differs from other Bayesian-based image interpolation schemes in that it estimates brightness temperature as opposed to image intensity and incorporates a detailed optical model of the GOES multichannel imaging system. In order to test the effectiveness of the proposed technique, high-resolution estimates of cloudtop temperatures using GOES channels 4 and 5 are compared to temperature estimates obtained from the Advanced Very High Resolution Radiometer (AVHRR). This test is achieved by examining sets of infrared data taken simultaneously by the GOES and AVHRR systems over the same geographic area. The AVHRR system collects longwave infrared data with a spatial resolution of 1 km, which is higher than the 4-km spatial resolution the GOES system achieves. In some cases, The estimated temperature differences between these systems are as high as 11.5 K. It is shown in this paper that the proposed algorithm improves the consistency between the cloudtop temperatures estimated with the GOES and AVHRR systems by allowing the GOES system to achieve substantially higher spatial resolution.     

Analysis of interannual changes in northern vegetation activityobserved in AVHRR data from 1981 to 1994

This paper reports on the analysis of Pathfinder AVHRR land (PAL) data set that spans the period July 1981 to September 1994. The time series of normalized difference vegetation index (NDVI) data for land areas north of 45° N assembled by correcting the PAL data with spectral methods confirms the northerly greening trend and extension of the photosynthetically active growing season. Analysis of the channel reflectance data indicates that the interannual changes in red and near-infrared reflectances are similar to seasonal changes in the spring time period when green leaf area increases and photosynthetic activity ramps up. Model calculations and theoretical analysis of the sensitivity of NDVI to background reflectance variations confirm the hypothesis that warming driven reductions in snow cover extent and earlier onset of greening are responsible for the observed changes in spectral reflectances over vegetated land areas     

An optimum interpolation method applied to the resampling of NOAAAVHRR data

Two main problems must be solved in the geometric processing of satellite data: geometric registration and resampling. When the data must be geometrically registered over a reference map, and particularly when the output pixel size is not the same as the original pixel size, the quality of the resampling can determine the quality of the output, not only in the visual appearance of the image, but also in the numerically interpolated values when used in multitemporal or multisensor studies. The “optimum” interpolation algorithm for AVHRR data is defined over a 6×6 window in order to: consider overlapping effects among adjacent pixels. The response for each new pixel R(x, y) is determined as a linear combination of the response R _i(x_iy_i) of the surrounding pixels in the window (i=1,36). The weighting coefficients μ_i are calculated from the ground projection of the effective spatial response function for each AVHRR pixel, taking into account the particular viewing angle and geometry of the pixels on the ground. This method is intended to give an optimal interpolation of AVHRR scenes along all the scanline, in order to compensate for off-nadir radiometric alterations associated to the varying spatial resolution and the blurring introduced by the pixel overlaps. The optimum method, as mathematically defined, is highly expensive in CPU time. Then, a big effort is necessary to implement the algorithms so that they could be operationally applied. Two approaches are considered: a general numerical method and a pseudo-analytical approximation. A Landsat TM image corresponding to the same date of the AVHRR image is used to test the quality of the radiometric interpolation procedure     

Atmospheric effects on Landsat TM thermal IR data

The components of atmospherically attenuated target radiance and the path radiance emitted by the atmosphere are calculated to explain the fact that for certain meteorological conditions, properly calibrated thermal IR (infrared) data gathered from aircraft and spacecraft altitudes provide accurate temperature measurements of surface water bodies even when atmospheric corrections are not applied. Results show that although the 8-14-μm atmospheric window is far from being transparent (<50% transmission), the amount of atmospheric path radiance may be equal to the amount of attenuated target radiance. Errors in remotely sensed temperatures introduced by atmospheric effects are shown to be smaller than or of the same order of magnitude as those errors caused by sensor noise and the effects of applying a cubic convolution during the process of converting the TM (Thematic Mapper) data from A-tape to geometrically corrected P-tape data format