Two of the biggest uncertainties in understanding and predicting climate change are the effects of aerosols and clouds. NASA's satellite mission, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, will provide vertical, curtain-like images of the atmosphere on a global scale and assist scientists in better determining how aerosols and clouds affect the Earth's radiation budget. The data from a previous space shuttle mission, LITE (Lidar In-space Technology Experiment, launched in Sept., 1994), have been employed to develop algorithms (e.g., spaceborne lidar system calibration and aerosol retrievals) in support of CALIPSO. In this work, a new calibration approach for 1064 nm lidar channel has been developed via comparisons of the 532 nm and 1064 nm backscatter signals from cirrus clouds. Some modeling analyses and simulations have also been implemented for CALIPSO's narrow bandwidth receiver filter to quantitatively distinguish Cabannes scattering from the full bandwidth Rayleigh scattering and correct the calibration of 532 nm channel. LITE data were also employed in some analyses with the aim of recovering the estimates of the backscatter ratio, R, of clean air regions. The uncertainties in aerosol retrieval due to different error sources, especially the bias and random errors of the extinction-to-backscatter ratio, Sa, have been investigated. A revised Sa table look-up approach is incorporated with two notable revisions for improved S a selection, which, as a consequence enable more bounded aerosol retrievals. Approximate but quantitatively useful multiple-scattering corrections are reported using a modeled multiple scattering factor, eta, which approximates the reduced attenuation caused by multiple scattering. Assessment of multiple scattering effects for a reasonable range of eta values is included for a combination of retrieval approaches.

Three frequency-domain electromagnetic subsurface profiling systems have been developed which use frequencies from 30Hz to 30kHz, 1kHz to 1MHz, and from 30kHz to 30MHz respectively. The systems operate in the near-field and measure the ellipticity of the magnetic field. A grounded wire or a vertical magnetic dipole is used as the transmitter antenna. The receiving antennas consist of three mutually orthogonal antennas which are placed on the ground in an arbitrary orientation. Instead of performing rotations in three-dimensional complex space, a simple two-dimensional rotation operating in the complex plane is used to find ellipticity and relative tilt angle in three dimensions. Cross-talk between the receiver coils and corrections for coil misalignment are corrected using fixed coefficients. By employing cross-talk and coil misalignment corrections, coil-orientation invariance is achieved. Algorithms using one-dimensional computer modeling are developed to determine the expected minimum and maximum depths of penetration as a function of system noise and anomaly amplitude. Optimum target depth is computed from three-layer one-dimensional computer modeling and compares well with the magneto-telluric depth in the far-field. A large 100,000 gallon concrete-lined basin has been designed and constructed to perform full-scale physical modeling of the system response to various objects. The basin has been filled with water to simulate a conductive medium and a variety of targets have been submerged in the basin to simulate targets. Initial results indicate data can be collected from surveys over the basin to train neural networks. Trained neural networks can then perform real-time modeling during routine surveys.

Forest fire policies are changing. Managers now face conflicting imperatives to re-establish pre-suppression fire regimes, while simultaneously preventing resource destruction. They must, therefore, understand the spatial patterns of fires. Geographers can facilitate this understanding by developing new techniques for mapping fire behavior. This dissertation develops such techniques for mapping recent fires and using these maps to calibrate models of potential fire hazards. In so doing, it features techniques that strive to address the inherent complexity of modeling the combinations of variables found in most ecological systems. Image processing techniques were used to stratify the elements of terrain, slope, elevation, and aspect. These stratification images were used to assure sample placement considered the role of terrain in fire behavior. Examination of multiple stratification images indicated samples were placed representatively across a controlled range of scales. The incorporation of terrain data also improved preliminary fire hazard classification accuracy by 40%, compared with remotely sensed data alone. A Kauth-Thomas transformation (KT) of pre-fire and post-fire Thematic Mapper (TM) remotely sensed data produced brightness, greenness, and wetness images. Image subtraction indicated fire induced change in brightness, greenness, and wetness. Field data guided a fuzzy classification of these change images. Because fuzzy classification can characterize a continuum of a phenomena where discrete classification may produce artificial borders, fuzzy classification was found to offer a range of fire severity information unavailable with discrete classification. These mapped fire patterns were used to calibrate a model of fire hazards for the entire mountain range. Pre-fire TM, and a digital elevation model produced a set of co-registered images. Training statistics were developed from 30 polygons associated with the previously mapped fire severity. Fuzzy classifications of potential burn patterns were produced from these images. Observed field data values were displayed over the hazard imagery to indicate the effectiveness of the model. Areas that burned without suppression during maximum fire severity are predicted best. Areas with widely spaced trees and grassy understory appear to be misrepresented, perhaps as a consequence of inaccuracies in the initial fire mapping.

Most infrared-based techniques of satellite rainfall estimation contain substantial uncertainties due to the indirect relationship between precipitation particles and space-borne infrared observations of clouds. Generally, these uncertainties include (1) IR temperature threshold defining cold clouds; (2) inclusion of no-rain clouds; (3) exclusion of warm rain clouds; and (4) the coefficients between rain rate and cloud-top properties. To address these uncertainties, a methodology, Cloud Patch Analysis, was developed to estimate rainfall by removing large portion of no-rain clouds from IR cloud imagery. Seven cloud features, including physical, geometric and textural, were defined, and ID3, an inductive decision tree, was used to identify no-rain clouds. Particularly, textural characteristics were extended from square images to irregular cloud patches to extract cloud features related to rainfall. In addition, the method adopted a mechanism to adjust IR temperature threshold according to locations and seasons, and this adjustment can be made by the combination of microwave observations by polar-orbiting satellites with infrared observations by geostationary satellites. The application of the adjusted IR threshold to GPI algorithm showed significant improvement for monthly rainfall estimation. The method was applied to the Japanese Islands and surrounding oceanic regions in June and July/August 1989 and to the Florida region in June and August 1996. The monthly rainfall estimates by the proposed method showed significant and consistent improvements over those by GPI.

The broad objective of this research was to develop a spatial model which provides both timely and quantitative regional maize yield estimates for real-time Early Warning Systems (EWS) by integrating satellite data with ground-based ancillary data. The Food and Agriculture Organization (FAO) Crop Specific Water Balance (CSWB) model was modified by using the real-time spatial data that include: dekad (ten-day) estimated rainfall (RFE) and Normalized Difference Vegetation Index (NDVI) composites derived from the METEOSAT and NOAA-AVHRR satellites, respectively; ground-based dekad potential evapo-transpiration (PET) data and seasonal estimated area-planted data provided by the Government of Kenya (GoK). A Geographical Information System (GIS) software was utilized to: drive the crop yield model; manage the spatial and temporal variability of the satellite images; interpolate between ground-based potential evapo-transpiration and rainfall measurements; and import ancillary data such as soil maps, administrative boundaries, etc. In addition, agro-ecological zones, length of growing season, and crop production functions, as defined by the FAO, were utilized to estimate quantitative maize yields. The GIS-based CSWB model was developed for three different resolutions: agro-ecological zone (AEZ) polygons; 7.6-kilometer pixels; and 1.1-kilometer pixels. The model was validated by comparing model production estimates from archived satellite and agro-meteorological data to historical district maize production reports from two Kenya government agencies, the Ministry of Agriculture (MoA) and the Department of Resource Surveys and Remote Sensing (DRSRS). For the AEZ analysis, comparison of model district maize production results and district maize production estimates from the MoA (1989-1997) and the DRSRS (1989-1993) revealed correlation coefficients of 0.94 and 0.93, respectively. The comparison for the 7.6-kilometer analysis showed correlation coefficients of 0.95 and 0.94, respectively. Comparison of results from the 1.1-kilometer model with district maize production data from the MoA (1993-1997) gave a correlation coefficient of 0.94. These results indicate the 7.6-kilometer pixel-by-pixel analysis is the most favorable method. Recommendations to improve the model are finer resolution images for area planted, soil moisture storage, and RFE maps; and measuring the actual length of growing season from a satellite-derived Growing Degree Day product.

As the field of remote sensing continues to grow with the launches of many new and complex satellite sensors in the next year, the ability to provide absolute calibration of these sensors becomes paramount for the many environmental studies proposed. In particular, temporal studies that monitor global changes in atmospheric constituents, ocean and terrestrial temperatures, and vegetation require that changes in the sensor itself, over the period of the study, be understood so that the data may be corrected. Numerous studies have established that satellite sensors change in orbit with respect to preflight calibration, in some cases, up to 20% or more over periods of three years. This research describes the development of an improved cross-calibration method of on-orbit satellite sensor radiometric calibration. The objective of the cross-calibration method is to transfer one sensor's calibration to another sensor which is typically difficult or expensive to calibrate with other methods. The cross-calibration method is relatively inexpensive to apply, and therefore there was a strong incentive to improve the application of the method and the understanding of the uncertainties associated with the method. The primary effort in this work has been the development of a cross-calibration software program which provides the means to easily perform end-to-end cross-calibrations. The program allows for a multiplicity of sites to be run, provides a search mechanism in order to identify calibration sites with particular characteristics, and contains an extensive error analysis capability. As part of this work, a search for acceptable cross-calibration sites was also performed which would allow a reduction in uncertainties of the method. Calibrations of five different sensor band pairs using System Pour l'Observation de la Terre (SPOT) 3, Landsat Thematic Mapper, and Advanced Very High Resolution Radiometer (AVHRR) sensors are performed. Very good results are obtained when the results are compared with other more expensive calibration methods, and the calibrations yielded uncertainties lower than reported in previous work.

The land surface skin temperature diurnal cycle (LSTD) is very important for the understanding of surface climate and for evaluating climate models. This variable, however, cannot be obtained globally from polar-orbiting satellites because the satellites usually pass a given area twice per day and because their infrared channels cannot observe the surface when the sky is cloudy. In order to more optimally use the satellite data, this research is designed, for the first time, to solve the above two problems by advance use of remote sensing techniques and climate modeling. Specifically, this work is divided into two parts. Part one deals with obtaining the skin temperature diurnal cycle for cloud-free cases. We have developed a "cloud-free algorithm" to combine model results with satellite and surface-based observations, thus interpolating satellite twice-daily observations to the diurnal cycle. Part two studies the cloudy cases. The "cloudy-pixel treatment" presented here is a hybrid technique of "neighboring-pixel" and "surface air temperature" approaches. The whole algorithm has been tested against field experiments and climate model CCM3/BATS in global and single column mode simulations. It shows that this proposed algorithm can obtain skin temperature diurnal cycles with an accuracy of 1-2 K at the monthly pixel level.

Recent efforts in Earth remote sensing have focused on accurately measuring top-of-atmosphere and surface leaving radiances. One factor that must be accounted for in the radiometric calibration of an Earth remote sensing satellite is the polarization of the radiance. This dissertation provides a comprehensive analysis of how polarization has an impact on the radiometric calibration of visible through long wave infrared Earth remote sensing satellites (0.4 through ∼15 μm). The first part of this dissertation concentrates on reviewing the current status of calibration and of polarization measurements in Earth remote sensing. It provides a comprehensive review of polarization in Earth scenes, calibration targets, and the sensitivity of instruments. The second part examines how polarization affects calibration during the application of the calibration coefficients. One must account for the differences in polarization between the calibration target, used to determine the calibration coefficients, and the scene itself. This dissertation derives the impact of polarization on the radiometric calibration coefficients using both the Stokes vector and the Jones vector formalisms and accounts for the instrument polarization sensitivity, calibration target polarization, and scene polarization through normalization. Using these derived results, the impacts of polarization on radiometric uncertainty are calculated for the family of theoretical cases and for cases based on literature data. The third part of this dissertation examines how the polarization response of an instrument can affect the calibration by creating a variation in the response vs. scan angle (RVS). It derives the mathematical relationship between the polarization response of an instrument and its response vs. scan angle. It examines the correlation between the two using MODIS pre-launch system level polarization and RVS measurement data and derives the sensitivity of the RVS to aft optics polarization. This establishes when scan mirror data is sufficient to characterize RVS and when a system level measurement is required. This dissertation then examines potential ways to determine the instrument's polarization response and response vs. scan angle post-launch. Finally, this dissertation identifies sensitivity thresholds in both cases and summarizes when polarization should be accounted for in radiometric calibration. Potential areas for future advancement of the field are discussed.

An efficient and simple method has been developed to improve quality and accuracy of satellite-based VIS/IR images through cloud-top relief spatial displacements adjustment. The products of this algorithm, including cloud-top temperatures and heights, atmospheric temperature profiles for cloudy sky, and displacement-adjusted cloud images, can be useful for weather/climate and atmospheric studies, particularly for high-resolution hydrologic applications such as developing IR satellite-based rainfall estimates, which are urgently needed by mesoscale atmospheric modeling and studies, severe weather monitoring, and heavy precipitation and flash flood forecasting. Cloud-top height and displacement are estimated by applying stereoscopic analysis to a pair of corresponding scan-synchronous infrared images from geostationary satellites (GOES-east and GOES-west). A piecewise linear approximation relationship between cloud-top height and temperature, with a few (6 and 8) parameters is developed to simplify and speed-up the retrieval process. Optimal parameters are estimated using the Shuffled Complex Evolution (SCE-UA) algorithm to minimize the discrepancies between the brightness temperatures of the same location as registered by two satellites. The combination of the linear approximation and the fast optimization algorithm simplifies stereoscopic analysis and allows for its implementation on standard desktop computers. When compared to the standard isotherm matching approaches the proposed method yields higher correlation between simultaneous GOES-8 and GOES-9 images after parallax adjustment. The validity of the linear approximation was also tested against temperature profiles obtained from ground sounding measurements of the TRMM-TEFLUN experiments. This comparison demonstrated good fit between the optimized relationship and atmospheric sounding profile. The accuracy of cloud pixel geo-location was demonstrated through a spatial comparison between correlation of ground-based radar rainfall rate and corresponding both adjusted and original satellite IR images. Higher correlation was represented using displacement-adjusted IR images from both geostationary satellites (GOES) with high altitudes and low altitude satellite (TRMM). Higher correlation and lower RMSE between ground-based NEXRAD observations and estimated rainfall rates from spatial adjusted IR images, using an artificial neural networks algorithm (PERSIANN), present the rainfall retrieval improvement. The ability to differentiate ground surface particularly snow-covered areas from clouds in near-real-time is another useful application of estimated cloud-top height.

A set of error/uncertainty analyses were performed on several "improved" vegetation indices (VIs) planned for operational use in the Moderate Resolution Imaging Spectroradiometer (MODIS) VI products onboard the Terra (EOS AM-1) and Aqua (EOS PM-1) satellite platforms. The objective was to investigate the performance and accuracy of the satellite-derived VI products under improved sensor characteristics and algorithms. These include the "atmospheric resistant" VIs that incorporate the "blue" band for normalization of aerosol effects and the most widely-used, normalized difference vegetation index (NDVI). The analyses were conducted to evaluate specifically: (1) the impact of sensor calibration uncertainties on VI accuracies, (2) the capabilities of the atmospheric resistant VIs and various middle-infrared (MIR) derived VIs to minimize smoke aerosol contamination, and (3) the performances of the atmospheric resistant VIs under "residual" aerosol effects resulting from the assumptions in the MODIS aerosol correction algorithm. The results of these studies showed both the advantages and disadvantages of using the atmospheric resistant VIs for operational vegetation monitoring. The atmospheric resistant VIs successfully minimized optically thin aerosol smoke contamination (aerosol optical thickness (AOT) at 0.67 μm < 1.0) but not optically thick smoke (AOT at 0.67 μm > 1.0). On the other hand, their resistances to "residual" aerosol effects were greater when the effects resulted from the correction of optically-thick aerosol atmosphere. The atmospheric resistant VIs did not successfully minimize the residual aerosol effects from optically-thin aerosol atmosphere (AOT at 0.67 μm ≤ ∼0.15), which was caused mainly by the possible wrong choice of aerosol model used for the AOT estimation and correction. The resultant uncertainties of the atmospheric resistant Vls associated with calibration, which were twice as large as that of the NDVI, increased with increasing AOT. These results suggest that the atmospheric resistant VIs be computed from partially (Rayleigh/O₃) corrected reflectances under normal atmospheric conditions (e.g., visibility > 10 km). Aerosol corrections should only be performed when biomass burning, urban/industrial pollution, and dust storms (larger AOT) are detected.