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In 1998, NASA launches EOS-AM1, the first of a series of the Earth Observing System (EOS) satellites. EOS will monitor the evolution of the state of the earth for 18 years, starting with the morning observations of EOS-AM1 (10:30 a.m. equatorial crossing time). An integrated view of the earth, as planned by EOS, is needed to study the interchange of energy, moisture, and carbon between the lands, oceans, and atmosphere. The launch of EOS-AM1 and other international satellites marks a new phase of climate and global change research. Both natural and anthropogenic climate change have been studied for more than a century. It is now recognized that processes that vary rapidly in time and space - e.g, aerosol, clouds, land use, and exchanges of energy and moisture - must be considered to adequately explain the temperature record and predict future climate change. Frequent measurements with adequate resolution, as only possible from spacecraft, are key tools in such an effort. The versatile and highly accurate EOS-AM1 data, together with previous satellite records, as well as data from the Advanced Earth Observing System (ADEOS) (I and II), Tropical Rainfall Measuring Mission (TRMM), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Along Track Scanning Radiometer (ATSR), Medium Resolution Imaging Spectrometer (MERIS), Environmental Satellite (ENVISAT), EOS-PM1, Land Remote-Sensing Satellite (Landsat), and ground-based networks is expected to revolutionize the way scientists look at climate change. This article introduces the EOS-AM1 mission and the special issue devoted to it. Following a brief historical perspective for an insight into the purpose and objectives of the mission, we shall summarize the characteristics of the five instruments onboard EOS-AM1. Specifically, we concentrate on the innovative elements of these five instruments and provide examples of the science issues that require this type of data. These examples show the importance of collecting data simultaneously from each of the five EOS-AM1 sensors for studying rapidly varying processes and parameters. Index Terms - Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), aerosol, anthropogenic forcing, atmospheric chemistry, biomass burning, carbon cycle, carbon dioxide, climate, climate change, clouds, Clouds and Earth's Radiant Energy System (CERES), Earth Observing System (EOS)-AM1, earth science, global change, global warming, greenhouse gas, land use, Measurements Of Pollution In The Troposphere (MOPITT), Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), phytoplankton, predictive computer models, radiation budget, remote sensing, sea surface temperature, troposphere, vegetation.

The results of mapping standing biomass for a northern forest in Maine, using NASA/JPL AIRSAR polarimetric radar data, is presented. By examining the dependence of backscattering on standing biomass using backscatter modeling and aircraft data, it was determined, in agreement with other recent reports, that the cross-polarized (HV) data from longer wavelengths (L, P-band) were the best radar channels for mapping total above-ground forest biomass. The radar signal appeared to lose sensitivity to changes in biomass for dry biomass levels beyond about 15 kg/[m.sup.2] (150 Mton/Ha). The ratio of HV backscattering from two bands, a longer wavelength P (wavelength = 68 cm) or L band (24 cm) to a shorter wavelength C band (6 cm), enhanced the correlation of the image signature to standing biomass ([r.sup.2] = 0.83 for P/C and [r.sup.2] = 0.79 for L/C) and showed increased sensitivity to dry biomass beyond 15 kg/[m.sup.2].

An understanding of the interactions of C-band microwave energy and vegetation canopies is important in order to effectively use air- and space-borne radar sensors for monitoring ecosystems. An experiment to examine the C-band backscattering characteristics of conifer trees was conducted using a truck-mounted scatterometer. Small (1 m tall) balsam fir (Abies balsamea) were arranged at various equidistant spacings on a platform to present canopies of varying density to the radar. C-band backscattering measurements of a range of canopy densities were acquired under different polarizations and incidence angles. In addition, physical measurements of the trees were made including leaf area index, biomass, leaf and branch angle distributions, and dielectric constant. The measured backscattering coefficient from the tree canopies increased with increasing biomass, but approached a maximum at a LAI of 2.5 and fresh biomass of 3.3 kg/m squared. A backscatter model was implemented using measured canopy attributes and showed close agreement with scatterometer measurements over the range of canopy densities. Model results indicated that branches were the prime scatterers of the radar while needles were found to only slightly attenuate the radar signal.

Remote sensing techniques that utilize light detection and ranging (lidar) provide unique data on canopy geometry and subcanopy topography. This type of information will lead to improved understanding of important structures and processes of Earth's vegetation cover. To understand the relation between canopy structure and the lidar return waveform, a three-dimensional (3-D) model was developed and implemented. Detailed field measurements and forest growth model simulations of forest stands were used to parameterize this vegetation lidar waveform model. In the model, the crown shape of trees determines the vertical distribution of plant material and the corresponding lidar waveforms. Preliminary comparisons of averaged waveforms from an airborne lidar and model simulations shows that the shape of the measured waveform was more similar to simulations using an ellipsoid or hemi-ellipsoid shape. The observed slower decay of the airborne lidar waveforms than the simulated waveforms from indicate the existence of the understories and may also suggest that higher order scattering from the upper canopy may contribute to the lidar signals. The lidar waveforms from stands simulated from a forest growth model show the dependence of the waveform on stand structure. Index Terms--Forest growth model, lidar waveform, three-dimensional (3-D) model.

A 3-D coherent radar backscatter model for forest canopies was developed and used to improve the understanding of synthetic aperture radar (SAR) interferometric data. The model was based on a realistic 3-D spatial structure of a forest stand, in which every scatterer has its deterministic location. A backscattering signal from a scatterer was mapped into a pixel according to its range or signal time delay. The range or the time delay also determines the phase of the scattered field. All scattering matrices within a pixel were coherently added to yield the total backscattering field of the pixel. The coherent radar backscatter model takes into account not only the scattering contribution from the scatterers in the forest canopy but also the direct backscattering of the ground surface. Forest stands with three different spatial structures were simulated using L-system and field measurements. The number and sizes of trees in these forest stands were identical, but the 2-D arrangements of the trees were different. The interferometric SAR (InSAR) signals of these scenes were simulated using the 3-D coherent SAR model, and the heights of scattering phase centers were estimated from the simulated InSAR data. The results reported in this paper show that the spatial structures of vegetation play an important role in the location of the scattering phase center. The height of scattering phase center depends on canopy height, attenuation of canopy, and the gaps within the canopy. This paper shows that the spatial structure needs to be considered when the InSAR data are used for the estimation of forest structural parameters. [ABSTRACT FROM AUTHOR]

Fires are a common occurrence in the Siberian boreal forest. The thermal anomalies product of the Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) product suite is designed to detect thermal anomalies (i.e., hotspots or fires) on the Earth's surface. Many, but not all, of the hot spots detected by MODIS in Siberia are wild fires. Agricultural burning and industrial activities also contribute. Using MODIS data from the years 2001, 2002, and 2003 along with a geographical information system (GIS), the type, extent, and duration of hot spots were examined. In addition, high correlations were found between the number of fires and proximity to human activities. Different types of land-cover thermal anomalies were found to have a strong, positive correlation with some anthropogenic features, such as roads, human settlements, and mineral industry locations. The agricultural (r2 = 0.95) and the forest (r2=0.81) thermal anomalies had the highest positive correlation with proximity to roads. The correlation was stronger between burned forests and roads (r2 2001 = 0.81, r2 2002 = 0.90, r2 2003 = 0.88) than between any forested land and roads (r 2 = 0.52). The results indicate that forest fires tend to occur near agricultural fires (r2 2001 = 0.93, r 2 2002 = 0.87, r 2 2003 = 0.94). Anthropogenic feature and land-cover thermal anomaly (LCTA) relationships tend to be stronger in a high fire year (2003) than in a low fire year (2001). This alone does imply causation, but might be an indicator of natural and anthropogenic factors acting together to shape where and when fires are burning. These findings have important implications for carbon and climate modelers wishing to use MODIS products to quantify and predict carbon storage and climate change. [ABSTRACT FROM AUTHOR]

Ground tie points have been used to improve the accuracy of a DEM derived from interferometric SAR (InSAR) data. The number of tie pointsrequired for ensuring certain accuracy of the InSAR-derived DEM was investigated in this study in two ways. One is the least-squares estimate of the corrections to a DEM using tie points, and the other is using an optimization procedure to estimate the uncertainties in estimations of InSAR parameters, mainly the baseline length, tilt angle, and reference phase using tie points, and resultant DEM error. It was found that, in terms of required tie points, both give comparable resuits. The latter method, though, can be applied in more realistic cases to design the tie point acquisition strategy for a mission. [ABSTRACT FROM AUTHOR]