Hsiao-hua K. Burke

Effects of Varying Soil Moisture Contents And Vegetation Canopies on Microwave Emissions

This paper presents an analysis of radiometric data taken at 21, 2.8, and 1.67 cm during a NASA sponsored flight over agricultural fields in Phoenix, AZ. The objective of the mission was to provide comprehensive information concerning microwave responses due to a broad range of soil moisture contents. Generally, data taken over bare fields agree well with theoretical estimates from a combined multilayer radiative transfer model with simple roughness correction. With the surface moisture content ranging between <5 and >35 percent, the emissivity ranges between >0.9 and ~0.7. The response to soil moisture content at 21 cm is more senstive than that at either 2.8 or 1.67 cm. The vegetation model takes into account both the effect of dielectric coefficient and the volume scattering characteristics of the vegetation layer. At the longer wavelengths (e.g., 21 cm) radiation from soil penetrates through vegetation layers of wheat and alfalfa and provides surface moisture information. However, short wavelength radiation from soil cannot penetrate through vegetation canopies; the volume scattering characteristics of vegetation controls the overall microwave signatures.

Cloud cover detection algorithm for EO-1 Hyperion imagery

A cloud cover detection algorithm was developed for application to EO-1 Hyperion hyperspectral data. The algorithm uses only bands in the reflected solar spectral regions to discriminate clouds from surface features and was designed to be used on-board the EO-1 satellite as part of the EO-1 Extended Mission Phase of the EO-1 Science Program. The cloud cover algorithm uses only 6 bands to discriminate clouds from other bright surface features such as snow, ice, and desert sand. The code was developed using 20 Hyperion scenes with varying cloud amount, cloud type, underlying surface characteristics and seasonal conditions. Results from the application of the algorithm to these test scenes is given with a discussion on the accuracy of the procedure used in the cloud cover discrimination. Compared to subjective estimates of the scene cloud cover, the algorithm was typically within a few percent of the estimated total cloud cover.

Characterization and delineation of plumes, clouds, and fires in hyperspectral images

Two approaches, one for discriminating features in a set of AVIRIS scenes dominated by areas of smoke, plumes, clouds and burning grassland as well as scarred (burned) areas and another for identifying those features are presented here. A semiautomated feature extraction approach using principal components analysis was used to separate the scenes into feature classes. Typically, only 3 component images were used to classify the image. A physics-based approach which utilized the spectral diversity of the features in the image was used to identify the nature of the classes produced in the component analysis. The results from this study show how the two approaches can be used in unison to fully characterize a smoke or cloud-filled scene.

A procedure for embedding effluent plumes into LWIR imagery

Longwave Infrared (LWIR) data sets collected from airborne platforms provide opportunities for study of atmospheric and surface features in the emissive spectral regime. The transfer of radiation for LWIR scenes can be formulated in a manner that allows recovery of the surface-leaving radiance (a result of atmospheric compensation). Using a forward radiative transfer model, a number of modifications to the atmospheric component of the scene can be made and applied to the surface-leaving radiance to predict sensor radiance that reflects a desired scenario. One such modification is the inclusion of a layer of effluent, the structure of which can be simulated by a plume model. Additionally, a different set of atmospheric conditions can be modeled and used to replace the conditions present in the scene. The resultant scene radiance field can be used to test algorithms for effluent characterization since the composition of the effluent layer and the intervening atmosphere is known. This approach allows for the embedding of a plume layer containing any combination of effluents from a set of over 400 gas spectra, the dispersion of which can be simulated using various plume models. Examples of simulated plume scenes are given, one of which contains an existing plume which is replicated using known emission information. Comparison of the real and simulated plume brightness temperatures yielded differences on the order of 0.2 K.

Polarimetric bio-aerosol detection: numerical simulation

This paper examines the use of bi-static lidar to remotely detect the release of aerosolized biological agent. The detection scheme exploits bio-aerosol induced changes in the Stokes parameters of scattered radiation in comparison to scattered radiation from ambient background aerosols alone. A polarization distance metric is introduced to discriminate between changes caused by the two types of aerosols. Scattering code computations are the information source. Three application scenarios are considered: outdoor arena, indoor auditorium, and building heating-ventilation-air-conditioning (HVAC) system. Numerical simulations are employed to determine sensitivity of detection to laser wavelength and to particle physical properties. Results of the study are described and details are given for the specific example of a 1.50 μm lidar system operating outdoors over a 1000-m range.

Characterization and delineation of plumes, clouds and fires in hyperspectral images

Techniques for scene characterization can utilize individual or combinations of visible and IR spectral bands to identify specific features in an image. This paper deals primarily with the problem of characterization of a partially smoke- or cloud-filled atmosphere. Proper analysis of the scene allows further sensing of underlying surface features such as actively burning and burn scarred regions. Both a physics-based and a semi-automated feature extraction approach are used for identifying and characterizing features in a set of AVIRIS scenes dominated by areas of smoke plumes, clouds and burning grassland as well as burnt vegetation. A combination of the two approaches is used to both discriminate and classify various features in a smoke/cloud filled scene. An AVIRIS scene chosen for initial testing of the two algorithms was collected on 20 August 1992 in the foothills east of Linden, California. A typical AVIRIS scene covers a 10 km/spl times/10 km area at 20 m pixel resolution and 224 contiguous spectral bands of data over the range 400 to 2500 nm. The scene consists of a grass fire producing a thick plume of smoke extending toward the east. A cloud produced by the thermal properties of the fire overlies the smoke plume. Northwest of the main fire, two smoldering fires produce a thin veil of smoke that covers much of the upper half of the scene. The southwest portion of the scene is cloud and smoke free. This scene provides a variety of atmospheric and surface features from which to orient and characterize. A plot of the apparent reflectance of various identified features was made. The cloud is significantly brighter than the smoke over the entire spectral region.

Understanding radiative transfer in the midwave infrared: a precursor to full-spectrum atmospheric compensation

The compensation for atmospheric effects in the VNIR/SWIR has reached a mature stage of development with many algorithms available for application (ATREM, FLAASH, ACORN, etc.). Compensation of LWIR data is the focus of a number of promising algorithms. A gap in development exists in the MWIR where little or no atmospheric compensation work has been done yet an increased interest in MWIR applications is emerging. To obtain atmospheric compensation over the full spectrum (visible through LWIR), a better understanding of the radiative effects in the MWIR is needed. The MWIR is characterized by a unique combination of reduced solar irradiance and low thermal emission (for typical emitting surfaces), both providing relatively equal contributions to the daytime MWIR radiance. In the MWIR and LWIR, the compensation problem can be viewed as two interdependent processes: compensation for the effects of the atmosphere and the uncoupling of the surface temperature and emissivity. The former requires calculations of the atmospheric transmittance due to gases, aerosols, and thin clouds and the path radiance directed towards the sensor (both solar scattered and thermal emissions in the MWIR). A framework for a combined MWIR/LWIR compensation approach is presented where both scattering and absorption by atmospheric particles and gases are considered.

Examples of EO-1 data analysis

To demonstrate the utility of EO-1 data, combined analysis of panchromatic, multispectral (ALI, Advanced Land Imager) and hyperspectral (Hyperion) data was conducted. In particular, the value added by HSI with additional spectral information will be illustrated. Data sets from Coleambally Irrigation Area, Australia on 7 March 2000 and San Francisco Bay area on 17 January 2000 are employed for the analysis. Analysis examples are shown for surface characterization, anomaly detection, spectral unmixing and image sharpening.

Radiative transfer in the midwave infrared applicable to full spectrum atmospheric characterization

The compensation for atmospheric effects in the VNIR/SWIR has reached a mature stage of development with many algorithms available for application (ATREM, FLAASH, ACORN, etc.). Compensation of LWIR data is the focus of a number of promising algorithms. A gap in development exists in the MWIR where little or no atmospheric compensation work has been done yet an increased interest in MWIR applications is emerging. To obtain atmospheric compensation over the full spectrum (visible through LWIR), a better understanding of the radiative effects in the MWIR is needed. The MWIR is characterized by a unique combination of reduced solar irradiance and low thermal emission (for typical emitting surfaces), both providing relatively equal contributions to the daytime MWIR radiance. In the MWIR and LWIR, the compensation problem can be viewed as two interdependent processes: compensation for the effects of the atmosphere and the uncoupling of the surface temperature and emissivity. The former requires calculations of the atmospheric transmittance due to gases, aerosols, and thin clouds and the path radiance directed towards the sensor (both solar scattered and thermal emissions in the MWIR). A framework for a combined MWIR/LWIR compensation approach is presented where both scattering and absorption by atmospheric particles and gases are considered

Detection of Regional Air Pollution Episodes Utilizing Satellite Digital Data in the Visual Range

A comparative analysis of satellite digital data in the visual range and ground-based aerosol measurements was carried out for July 21 and 22, 1978, representing a significant pollution episode based on low surface visibility and high sulfate levels. Quantitative information on total aerosol loading derived from the satellite digitized data using an atmospheric radiative transfer model agrees well with the results obtained from the ground-based measurements. It is also shown that the extent and transport of the haze pattern can be monitored from the satellite data over the period of maximum intensity of the episode. The model simulation demonstrates the potential of utilizing satellites to monitor both the magnitude and areal extent of pollution episodes.