Supernova Spectra

The determination of cirrus properties over large spatial and temporal scales will, in most instances, require the use of satellite data. Global coverage at resolutions as fine as several meters are attainable with instruments on Landsat, and temporal coverage at 1-min intervals is now available with the latest Geostationary Operational Environmental Satellite (GOES) imagers. Extracting information about cirrus clouds from these satellite data sets is often difficult because of variations in background, similarities to other cloud types, and the frequently semitransparent nature of cirrus clouds. From the surface, cirrus clouds are readily discerned by the human observer via the patterns of scattered visible radiation from the sun, moon, and stars. The relatively uniform background presented by the sky facilitates cloud detection and the familiar textures, structures, and apparent altitude of cirrus distinguish it from other cloud types. From satellites, cirrus can also be detected from scattered visible radiation, but the demands of accurate identification for different surface backgrounds over the entire diurnal cycle and quantification of the cirrus properties require the analysis of radiances scattered or emitted over a wide range of the electromagnetic spectrum. Many of these spectra and high-resolution satellite data can be used to understand certain aspects of cirrus clouds in particular situations. Intensive study of well-measured cases can yield a wealth of information about cirrus properties on fine scales (e.g., Minnis et al. 1990; Westphal et al. 1996). Production of a global climatology of cirrus clouds, however, requires compromises in spatial, temporal, and spectral coverage (e.g., Schiffer and Rossow 1983). This chapter summarizes both the state of the art and the potential for future passive remote sensing systems to aid the understanding of cirrus processes and to acquire sufficient statistics for constraining and refining weather and climate models. Theoretically, many different aspects of cirrus can be determined from passive sensing systems. A limited number of quantities are the focus of most efforts to describe cirrus clouds. These include the areal coverage, top and base altitude or pressure, thickness, top and base temperatures, optical depth, effective particle size and shape, vertical ice water path, and size, shape and spacing of the cloud cells.

Remote monitoring of giant kelp biomass and physiological condition: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) mission

The temperature distribution of the emitting plasma is a crucial constraint when studying the heating of solar flare footpoints. However, determining this for impulsive phase footpoints has been difficult in the past due to insufficient spatial resolution to resolve the footpoints from the loop structures, and a lack of spectral and temporal coverage. We use the capabilities of Hinode/EIS to obtain the first emission measure distributions (EMDs) from impulsive phase footpoints in six flares. Observations with good spectral coverage were analysed using a regularized inversion method to recover the EMDs. We find that the EMDs all share a peak temperature of around 8 MK, with lines formed around this temperature having emission measures peaking between 10^28 and 10^29 cm^-5, indicating a substantial presence of plasma at very high temperatures within the footpoints. An EMD gradient of EM(T) ~ T is found in all events. Previous theoretical work on emission measure gradients shows this to be consistent with a scenario in which the deposited flare energy directly heats only the top layer of the flare chromosphere, while deeper layers are heated by conduction.

A NASA Earth mission concept has been developed that focuses on a set of science objectives related to the measurement of plant physiology and functional type for terrestrial and aquatic ecosystems. The NRC Decadal Survey specifically calls for the HyspIRI mission to measure terrestrial and aquatic ecosystems. A review of the literature in conjunction with analysis of ongoing ecosystem research established imaging spectroscopy in the solar reflected portion of the spectrum as the appropriate approach to address these objectives. For these topics a detailed requirement analysis was performed that specified the measurement objectives, measurement requirements, instrument requirement and other requirements. These were distilled into a single set of spectral, radiometric, spatial, uniformity and temporal requirement. Key among these are: spectral coverage from 380 to 2500 nm at 10 nm sampling, radiometric resolution and precision giving high signal-to-noise ratios for dark aquatic targets, spatial sampling of 60 m, spectral and spectral IFOV uniformity > 95%, and temporal coverage with a 19 day repeat at the equator.

Detection of large amounts of dust in high redshift galaxies suggests that core collapse supernovae (CCSNe) may play a critical role in the dust budget of galaxies in the early universe, when galaxies are only a few hundred million years old. At an age of only 1Gyr, asymptotic giant branch (AGB) stars may not have had the time to become significant dust contributors, leaving CCSNe as an alternative explanation since they quickly evolve and return their material to the surrounding interstellar medium. For the past three years, I have been observing the CCSNe 2007it and 2007od with Gemini, Hubble Space Telescope, and Spitzer Space Telescope in the optical and infrared to look for indicators of dust formation, which appear within the first few years after explosion. The data sets contain large temporal and wavelength coverage, and have led to some unusual and interesting results. In both cases there is evidence for interaction with surrounding circumstellar material (CSM), although neither was classified as a Type IIn. SN 2007it was found to be oxygen rich with a 56Ni mass quite large for a Type IIP, while SN 2007od is oxygen poor with a very low, over two orders of magnitude less, 56Ni mass. Scattered light echoes also seem to be present in both SNe. An estimated 10-4 solar masses of new dust has formed in each SN, consistent with other CCSNe, but still significantly less than needed to account for the amount of dust seen at high redshift. I will discuss these results and their implications for SNe as major dust contributors in the universe.

The main meteorological parameters which influencing the rainfall can be distilled from the MODIS satellite cloud imagery and the artificial neural network (ANN) model constructed by these meteorological parameters and can be applied on distributed rainfall estimation. Because it is difficult to decide the structure of back propagation neural network (BPNN) and to solve the problem of local convergence, an appropriate training and modeling method of ANN such as the real code genetic algorithm (RGA) is vital to the accuracy of rainfall estimation. The data of the simulation tests show that the Mean Relative Error (MRE) of BPA model is 23.6%, while the MRE of RGA model is 20.7%, Compared with the ANN trained by BPA, the estimation error of the ANN trained by RGA is cut down by 2.9%, and the Root Mean Squared Error (RMSE) is cut down by 2.5% at the same time, hence, the results prove that the ANN model trained using RGA will significantly outperform the back propagation algorithm (BPA) trained ANN model and improve the precision of rainfall estimation. Keywords: remote sensing; EOS/MODIS; artificial neural network (ANN); back propagation algorithm (BPA); genetic algorithm (GA); distributed rainfall estimation 1. INTRODUCTION Rainfall precipitation is an important but highly variable atmospheric parameter, and in a large river basin, different area has different weather condition, conventional methods of retrieved meteorological parameters are pretty difficult to satisfy the hydrological need. While the technology of remote sensing can obtain the distributed meteorological parameters in each unit area of the river basin, therefore, remote sensing is more effective and convenient than conventional methods in relevant surveys and studies. Moreover, the existing rainfall station network cannot provide the temporal and spatial coverage which are necessary for sufficient monitoring, so their application for accurate precipitation estimation with good temporal and spatial coverage is hampered by the existing technical limitation problems. Compared with the existing rainfall station network, the satellite measurements have the advantage of providing spatially and temporally homogeneous observations over a large area, such as GMS, TM, AVHRR and MODIS satellite images. In these satellite sensors, the moderate resolution imaging spectroradiometer (MODIS) has the wide spectral range and spatial coverage of 36 spectral bands sampling the electromagnetic spectrum from 0.4 to 14 um with a spatial resolution ranging from 250 to 1,000 meters

The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the five Earth-observing instruments on-board the National Aeronautics and Space Administration (NASA) Earth-Observing System (EOS) Terra spacecraft, launched in December 1999. It has 36 spectral bands with wavelengths ranging from 0.41 to 14.4 μm and collects data at three nadir spatial resolutions: 0.25 km for 2 bands with 40 detectors each, 0.5 km for 5 bands with 20 detectors each and 1 km for the remaining 29 bands with 10 detectors each. MODIS bands are located on four separate focal plane assemblies (FPAs) according to their spectral wavelengths and aligned in the cross-track direction. Detectors of each spectral band are aligned in the along-track direction. MODIS makes observations using a two-sided paddle-wheel scan mirror. Its on-board calibrators (OBCs) for the reflective solar bands (RSBs) include a solar diffuser (SD), a solar diffuser stability monitor (SDSM) and a spectral-radiometric calibration assembly (SRCA). Calibration is performed for each band, detector, sub-sample (for sub-kilometre resolution bands) and mirror side. In this study, a ratio approach is applied to MODIS observed Earth scene reflectances to track the detector-to-detector and mirror side differences. Simultaneous observed reflectances from the Multi-angle Imaging Spectroradiometer (MISR), also onboard the Terra spacecraft, are used with MODIS observed reflectances in this ratio approach for four closely matched spectral bands. Results show that the detector-to-detector difference between two adjacent detectors within each spectral band is typically less than 0.2% and, depending on the wavelengths, the maximum difference among all detectors varies from 0.5% to 0.8%. The mirror side differences are found to be very small for all bands except for band 3 at 0.44 μm. This is the band with the shortest wavelength among the selected matching bands, showing a time-dependent increase for the mirror side difference. This study is part of the effort by the MODIS Characterization Support Team (MCST) in order to track the RSB on-orbit performance for MODIS collection 5 data products. To support MCST efforts for future data re-processing, this analysis will be extended to include more spectral bands and temporal coverage.

The purpose of data merger activities undertaken by the National Aeronautic and Space Administration's (NASA) Sensor Intercomparison and Merger for Biological and Interdisciplinary Studies (SIMBIOS) Project is to create scientific quality ocean color data encompassing measurements from multiple satellite missions. The fusion of data from multiple satellites will improve the quality of ocean color products over single-mission data sets by expanding spatial and temporal coverage of the world's oceans and increasing statistical confidence in generated parameters. The merger will also support a variety of new applications by taking advantage of sensor-varying calibration, spectral, spatial, temporal, and ground coverage characteristics. Leading to the data merger goals, the SIMBIOS Project has established a thorough ocean color validation program and has been cross-comparing and cross-calibrating sensor data with in situ measurements and data among the missions. The SIMBIOS Science Team has been studying data merger algorithms based on spectral data assimilation and spatial interpolation. The SIMBIOS Project Office has implemented statistical objective analysis and regression techniques based on artificial neural networks and support vector machines. The accuracy of the merger methods will be evaluated using in situ data, statistical analyses, and simple chlorophyll means -- the method already implemented within the SIMBIOS Project. This paper defines challenges and suggests solutions for data merger based on the example of daily chlorophyll concentration products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS).© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Soils have critical relevance to global issues, such as food and water security, climate regulation, sustainable energy, desertification and biodiversity protec-tion. As a consequence, soil is becoming one of the top priorities for the global environmental policy agenda. Conventional soil maps suffer from large limita-tions, i.e. most of them are static and often obsolete, are often generated at coarse scale, and can be uneasy to handle. Digital Soil Mapping has been developed as a solution to generate high-resolution maps of soil properties over large areas. Two projects, GlobalSoilMap and SoilGrids, presently aim at delivering the first generation of global, high-resolution soil property fine grids. In this paper, we briefly describe the GlobalSoilMap history, its present status and present achievements, and illustrate some of these with (mainly) French examples. At given moment there is still an enormous potential for forthcoming research and for delivering products more helpful for end users. Key here is the continuous progress in available covariates, in their spatial, spectral and temporal coverage and resolution through remote sensing products. All over the world, there is still a very large amount of point soil data still to be rescued and this effort should be pursued and encouraged. Statistically advances are expected by exploring and implementing new models. Especially relevant are spatial-temporal models and contemporary Artificial Intelligence for handling the complex big data. Advances should be made and research efforts are needed on estimating the uncertainties, and even on estimating uncertainties on uncertainties. Attempts to merge different model strategies and products (for instance deriving from different covariates, spatial extents, soil data sources, and models) should be made in order to get the most useful information from each of these predictions, and to identify how controlling factors may change depending on scales.

We have conducted a variability study of several radio/millimeter sources that are possible counterparts of high-energy gamma-ray sources detected by EGRET. Some sources were possibly variable during this period. The radio source with the highest spectral and temporal coverage, PMN J0850-1213, behaves in a manner consistent with the shocked-jet models for extragalactic radio sources proposed in the literature. In addition, GB 105536.5+564424 is an X-ray-selected BL Lac object, and, if the identification with the EGRET source is correct, it would be the most distant such object detected at energies >100 MeV (z = 0.41). A future detection in the TeV range, as well, could provide an important constraint on absorption of very high energy gamma rays by the intergalactic infrared photon field. We also discuss the possibility that this object is a gravitationally microlensed active galactic nucleus, with the foreground lensing object at z = 0.144.

Attention is given to various areas of satellite applications to monitoring the earth's environment. These trends primarily concern spectral, areal, and temporal coverage. Various environmental monitors are discussed in terms of derived economic benefits. Several types of remote sensors for earth applications are described, noting spectral channels, resolution cell size, swath width, and data rate. A sample environmental monitoring system is presented which includes five geostationary satellites, and three or four low earth orbit spacecraft

The remarkable progress in nonlinear materials and laser pump sources continues to advance optical parametric oscillators (OPOs) to new frontiers in spectral and temporal coverage and performance capability, with no end in sight. The advent of a new class of mid-infrared (mid-IR) birefringent nonlinear materials such as CdSiP2 (CSP) and quasi-phase-matched (QPM) crystals such as orientation-patterned GaP (OP-GaP) has finally paved the way for the practical development of OPOs into the deep-IR at wavelengths beyond 4 μm, where multiphonon absorption in oxide-based QPM materials such as MgO:PPLN has proved a persistent barrier to direct wavelength generation for more than two decades. By exploiting the newly developed nonlinear crystals in combination with well-established and widely available Nd-based solid-state and Yb-doped fiber lasers near ∼1 μm, new OPO sources in different time-scales from pulsed nanosecond to ultrafast picosecond and few-cycle femtosecond domain have been realized, accessing spectral regions across 5–12 μm in deep-IR, and offering unprecedented performance capabilities. On the other hand, the exploitation of cascaded pumping techniques in combination with CSP has made possible the generation of femtosecond pulses out to ∼8 μm in the deep-IR using the Kerr-lens-mode-locked Ti:sapphire laser as the primary pump source (Fig. 1). At the same time, to enable the exploitation of alternative materials for deep-IR generation, recent efforts have led to the development of practical high-power near-degenerate Yb-fiber-laser-based picosecond OPOs at ∼2.1 μm (Fig. 2) as alternative pump sources for birefringent crystals such as ZnGeP2 and QpM crystal such as OP-GaAs.

This paper focuses on the fundamental system engineering tradeoff driving almost all remote sensing design efforts, affecting complexity, cost, performance, schedule, and risk: image quality vs. sensitivity. This single trade encompasses every aspect of performance, including radiometric accuracy, dynamic range and precision, as well as spatial, spectral, and temporal coverage and resolution. This single trade also encompasses every aspect of design, including mass, dimensions, power, orbit selection, spacecraft interface, sensor and spacecraft functional trades, pointing or scanning architecture, sensor architecture (e.g., field-of-view, optical form, aperture, f/#, material properties), electronics, mechanical and thermal properties. The relationship between image quality and sensitivity is introduced based on the concepts of modulation transfer function (MTF) and signal-to-noise ratio (SNR) with examples to illustrate the balance to be achieved by the system architect to optimize cost, complexity, performance and risk relative to end-user requirements.

This paper provides an overview on the application of satellite synthetic aperture radar (SAR) technology in archaeology. The growing developments of space SAR technologies in terms of observational capabilities (spatial, spectral, radiometric, and temporal coverage) had made the use of these technologies very attractive for archaeological investigations. Although several achievements have been made in recent years on the basis of pioneering efforts addressed to the assessment of the potentiality of the L-, C-, and X-band SAR in archaeology, the full capability of these technologies for archaeological site detection is still incompletely evaluated until now. Moreover, significant advances are expected from the most recent satellite data available at 25 cm in X-band (TerraSAR) and at 1 m in multipolarized L-band (PALSAR). These enhanced characteristics, in terms of spatial resolution and radiometric quality, take the most recent SAR technologies to a new level for archaeological applications, addressed to object detection and target recognition.

We report on a near-infrared non-collinear optical parametric amplifier (NOPA) based on periodically poled stoichiometric lithium tantalate. The NOPA generates muJ-energy pulses with spectrum spanning the 1-1.7 microm wavelength range, which are compressed to nearly transformlimited 8.5 fs duration by a deformable mirror. By synchronizing this source with a sub-10-fs visible NOPA, we demonstrate an unprecedented combination of temporal resolution and spectral coverage in two-colour pump-probe spectroscopy.