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NASA NTRS2007

SIMBOL-X: A Formation Flying Mission on HEO for Exploring the Universe

SIMBOL-X is a high energy new generation telescope covering by a single instrument a continuous energy range starting at classical X-rays and extending to hard X-rays, i.e. from 0.5 to 80 keV. It is using in this field a focalizing payload which until now was used for energy below 10 keV only, via the construction of a telescope distributed on two satellites flying in formation. SIMBOL-X permits a gain of two orders of magnitude in sensibility and spatial resolution in comparison to state of the art hard X-rays instruments. The mirror satellite will be in free flight on a high elliptical orbit and will target the object to observe very precisely, thus focusing the hard X-ray emission thanks to this mirror module. At the focal point area which is situated 20 meters behind the mirror satellite, the detector satellite maintains its position on a forced orbit thanks to a radio link with the mirror satellite and a lateral displacement sensor using a beam emitted onboard the mirror satellite. This configuration is said "formation flying". The location of the detector satellite shall be very finely tuned as it carries the focal plane of this distributed telescope. To provide science measurements, the Simbol-X orbit has been chosen High elliptic (HEO), which means elliptical orbit with a high perigee altitude. Preliminary studies where made with an orbit with an altitude of the perigee of 44000km and altitude of the apogee of 253000km. The orbit was seven days ground track repeated in order to maintain a perigee pass over the Malindi ground station to download scientific telemetry. But as studies went on, difficulties in mass budget, link budget, perigee maintenance and formation flying maintenance were raised. This was mainly due to the vicinity of the Moon and its disturbing effect on the satellites orbits. Alternative orbits have been proposed in order to demonstrate the feasibility of the mission. The problematic of bringing the two satellites from their injection orbit to their operational orbit 20 m apart from each other and then maintain this configuration is very challenging. It requires theoretical development of the relative motion between two satellites in high eccentric orbit with large differential disturbance on the two bodies. This paper will present the mission analysis for the Simbol-X satellites with the complex problematic of doing formation flying in high elliptic orbit.

Gamet, Philippe↗

NASA NTRS2007

The Use of the Airborne Thermal/Visible Land Application Sensor (ATLAS) to Determine the Thermal Response Numbers for Urban Areas

Although satellite data are very useful for analysis of the urban heat island effect at a coarse scale, they do not lend themselves to developing a better understanding of which surfaces across the city contribute or drive the development of the urban heat island effect. Analysis of thermal energy responses for specific or discrete surfaces typical of the urban landscape (e.g., asphalt, building rooftops, vegetation) requires measurements at a very fine spatial scale (i.e., < 15m) to adequately resolve these surfaces and their attendant thermal energy regimes. Additionally, very fine scale spatial resolution thermal infrared data, such as that obtained from aircraft, are very useful for demonstrating to planning officials, policy makers, and the general populace the benefits of the urban forest. These benefits include mitigating the urban heat island effect, making cities more aesthetically pleasing and more habitable environments, and aid in overall cooling of the community. High spatial resolution thermal data are required to quantify how artificial surfaces within the city contribute to an increase in urban heating and the benefit of cool surfaces (e.g., surface coatings that reflect much of the incoming solar radiation as opposed to absorbing it thereby lowering urban temperatures). The TRN (thermal response number)(Luvall and Holbo 1989) is a technique using aircraft remotely sensed surface temperatures to quantify the thermal response of urban surfaces. The TRN was used to quantify the thermal response of various urban surface types ranging from completely vegetated surfaces to asphalt and concrete parking lots for several cities in the United States.

Luvall, Jeffrey C.↗

NASA NTRS2016

On-Orbit Calibration and Performance of S-NPP VIIRS DNB

The S-NPP VIIRS instrument has successfully operated since its launch in October 2011. The VIIRS Day-Night Band (DNB) is a panchromatic channel covering wavelengths from 0.5 to 0.9 m that is capable of observing Earth scenes during both day and nighttime orbits at a spatial resolution of 750 m. To cover the large dynamic range, the DNB operates at low, mid, or high gain stages, and it uses an onboard solar diffuser (SD) for its low gain stage calibration. The SD observations also provide a means to compute gain ratios of low-to-mid and mid-to-high gain stages. This paper describes the DNB on-orbit calibration methodologies used by the VIIRS Characterization Support Team (VCST) in supporting the NASA earth science community with consistent VIIRS sensor data records (SDRs) made available by the Land Science Investigator-led Processing Systems (SIPS). It provides an assessment and update of DNB on-orbit performance, including the SD degradation in the DNB spectral range, detector gain and gain ratio trending, stray light contamination and its correction. Also presented in this paper are performance validations based on earth scenes and lunar observations.

Chen, H.↗

NASA NTRS2018

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihood–based classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrence–based texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms.

Pasolli, Edoardo↗

NASA NTRS2020

Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.

snow cover extent↗

NASA NTRSDate not supplied

Modeling Methods for 3D Lightning Mapping from Space

Global lightning detection has advanced greatly over the past few decades both on ground and from orbit. Ground-based systems like the Lightning Mapping Array (LMA) excel at high-precision 3D reconstruction of local flashes, while spaceborne lightning sensors have much larger potential coverage but have been limited by coarser resolution and often rely on data from other instruments to determine flash location. The primary focus of this study is to examine the level of flash detail that can be expected from one or more low-Earth orbiting small satellites that combine VHF and optical measurements of lightning, which is a new mission concept called CubeSpark. The goal of CubeSpark is to map the 3D charge structure of thunderstorms at 1-2 km spatial resolution on a global scale, enabling a host of new atmospheric and space electricity studies. In order to maximize the potential data quality and minimize potential cost, flashes were simulated beneath single- and multi-satellite configurations. In the multi-satellite approach, RF detectors on each of the six satellites would measure the arrival times of impulsive VHF sources to collectively pinpoint their locations in 3D and reconstruct flashes with higher resolution than has been achieved from space. A single-station approach for 3D observation of lightning would follow the method of determining altitudes of strong VHF sources that produce trans-ionospheric pulse pairs (TIPPs) while relying on an on-board high-resolution optical day/night lightning mapper for approximate horizontal flash locations. Here we present preliminary results comparing the expected data fidelity and accuracy between these methods to inform potential satellite missions like CubeSpark.

lightning↗

NASA NTRS2022

Spatial Characterization of PACE OCI ETU Using Time-Delay Mode

The OCI (Ocean Color Instrument) is the main sensor on the upcoming PACE (Plankton Aerosol Cloud ocean Ecosystem) mission. OCI has two hyperspectral CCD sensors covering 340nm to 885nm and 9 SWIR (Short Wave IR) bands from 940nm to 2260nm. SWIR bands have nominal 1km ground pixel size and CCD bands have native 1/8 km ground pixel size in diagnostic mode that will be aggregated into 1km pixels to improve SNR and meet the data rate constraints. OCI has a rotating telescope that is synchronized to the readout of the CCD and SWIR detectors. Full pre-launch system level testing for the OCI ETU (Engineering Test Unit) was completed in June 2021.With time-delayed scan mode, a sub-pixel level time-delay step is applied to the detector readout. This sub-pixel level time-delay step causes a sub-pixel level shift in the start of the data collection. After collecting time-delay step scans with different step sizes, a scan profile with sub-pixel resolution can be constructed. 1/8 and 1/4 of CCD pixel resolutions were achieved using this mode. In this paper, the OCI time-delayed scan mode will be described as well as how it was used to calculate OCI’s high spatial resolution PSF (Point Spread Function), IFOV (instantaneous Field of View), MTF (Modulation Transfer Function), and BBR (Band to Band Registration).

PACE↗

DOE OSTI2019

Hammerhead, an ultrahigh resolution ePix camera for wavelength-dispersive spectrometers

Wavelength-dispersive spectrometers (WDS) are often used in synchrotron and FEL applications where high energy resolution (in the order of eV) is important. Increasing WDS energy resolution requires increasing spatial resolution of the detectors in the dispersion direction. The common approaches are not ideal: using strip detectors loses the 2D position sensitivity (important for, e.g., background estimation) and the high counting rate; using pixel detectors with a small pitch results in complex charge sharing behaviour and typically have a small size. Developing pixel detectors with high aspect ratio and a small pitch in the wavelength dispersive direction would be ideal, however, it would require a substantial ASIC development. We present a new approach, with a novel sensor design using rectangular pixels with a high aspect ratio (between strips and pixels, further called “strixels”), and strixel redistribution to match the square pixel arrays of typical ASICs. This results in a sensor area of 17.4 mm × 77 mm, with a fine pitch of 25 µm in the horizontal direction resulting in 3072 columns and 176 rows. The sensors use ePix100 readout ASICs, leveraging their low noise (43 e - , or 180 eV rms). We present results obtained with a Hammerhead ePix100 camera, showing that the small pitch (25 µm) in the dispersion direction maximizes performance for both high and low photon occupancies, resulting in optimal WDS energy resolution. The low noise level at high photon occupancy allows precise photon counting, while at low occupancy, both the energy and the subpixel position can be reconstructed for every photon, allowing an ultrahigh resolution (in the order of 1 µm) in the dispersion direction and rejection of scattered beam and harmonics. Using strixel sensors with redistribution and flip-chip bonding to standard ePix readout ASICs results in ultrahigh position resolution (~1 µm) and low noise in WDS applications, leveraging the advantages of hybrid pixel detectors (high production yield, good availability, relatively inexpensive) while minimizing development complexity through sharing the ASIC, hardware, software and DAQ development with other versions of ePix cameras.

47 OTHER INSTRUMENTATION↗

NASA NTRS1984

Coupled orbiting inertial reference systems and Geopotential Research Mission (GRM) geodesy

Two spacecraft, one following the other with a separation of a few hundred kilometers and orbiting at an altitude of 160 km in a 90-degree polar orbit, are used to detect the minute variations in the earth's gravitational field. Housed in a capacitive cavity located at the centroid of the spacecraft is a free-floating metallic spherical proof-mass, which is the sensor controlling the thrusters that nullify all nongravitational forces on the spacecraft. The proof-mass in each spacecraft is the inertial-guidance reference for its host spacecraft, and the proof-masses are coupled together by a millimeter-wave link. This coupled system detects minute changes in the spacecraft's orbital positions and thus measures the anomalies present in the gravitational field of the earth. To derive a global geodetic model of the gravitational field with an amplitude resolution of 1 milligal and a spatial resolution of 100 km requires that the relative velocity between the proof-mass references be determined to within 1 micron/s.

Keating, T.↗

NASA NTRS2003

The Land Use and Land Cover Dichotomy: A Comparison of Two Land Classification Systems in Support of Urban Earth Science Applications

One is likely to read the terms 'land use' and 'land cover' in the same sentence, yet these concepts have different origins and different applications. Land cover is typically analyzed by earth scientists working with remotely sensed images. Land use is typically studied by urban planners who must prescribe solutions that could prevent future problems. This apparent dichotomy has led to different classification systems for land-based data. The works of earth scientists and urban planning practitioners are beginning to come together in the field of spatial analysis and in their common use of new spatial analysis technology. In this context, the technology can stimulate a common 'language' that allows a broader sharing of ideas. The increasing amount of land use and land cover change challenges the various efforts to classify in ways that are efficient, effective, and agreeable to all groups of users. If land cover and land uses can be identified by remote methods using aerial photography and satellites, then these ways are more efficient than field surveys of the same area. New technology, such as high-resolution satellite sensors, and new methods, such as more refined algorithms for image interpretation, are providing refined data to better identify the actual cover and apparent use of land, thus effectiveness is improved. However, the closer together and the more vertical the land uses are, the more difficult the task of identification is, and the greater is the need to supplement remotely sensed data with field study (in situ). Thus, a number of land classification methods were developed in order to organize the greatly expanding volume of data on land characteristics in ways useful to different groups. This paper distinguishes two land based classification systems, one developed primarily for remotely sensed data, and the other, a more comprehensive system requiring in situ collection methods. The intent is to look at how the two systems developed and how they can work together so that land based information can be shared among different users and compared over time.

McAllister, William K.↗

NASA NTRS2003

The Bermuda BioOptics Project (BBOP) Years 9-11

The Bermuda BioOptics Project (BBOP) is a collaborative effort between the Institute for Computational Earth System Science (ICESS) at the University of California at Santa Barbara (UCSB) and the Bermuda Biological Station for Research (BBSR). This research program is designed to characterize light availability and utilization in the Sargasso Sea, and to provide an optical link by which biogeochemical observations may be used to evaluate bio-optical models for pigment concentration, primary production, and sinking particle fluxes from satellite-based ocean color sensors. The BBOP time-series was initiated in 1992, and is carried out in conjunction with the U.S. JGOFS Bermuda Atlantic Time-series Study (BATS) at the Bermuda Biological Station for Research. The BATS program itself has been observing biogeochemical processes (primary productivity, particle flux and elemental cycles) in the mesotrophic waters of the Sargasso Sea since 1988. Closely affiliated with BBOP and BATS is a separate NASA-funded study of the spatial variability of biogeochemical processes in the Sargasso Sea using high-resolution AVHRR and SeaWiFS data collected at Bermuda. The collaboration between BATS and BBOP measurements has resulted in a unique data set that addresses not only the SIMBIOS goals but also the broader issues of important factors controlling the carbon cycle. This final report addresses specific research activities, research results, and lists of presentations and papers submitted for publication.

Nelson, Norm↗

NASA NTRS2003

Bermuda Bio Optics Project

The Bermuda BioOptics Project (BBOP) is a collaborative effort between the Institute for Computational Earth System Science (ICESS) at the University of California at Santa Barbara (UCSB) and the Bermuda Biological Station for Research (BBSR). This research program is designed to characterize light availability and utilization in the Sargasso Sea, and to provide an optical link by which biogeochemical observations may be used to evaluate bio-optical models for pigment concentration, primary production, and sinking particle fluxes from satellite-based ocean color sensors. The BBOP time-series was initiated in 1992, and is carried out in conjunction with the U.S. JGOFS Bermuda Atlantic Time-series Study (BATS) at the Bermuda Biological Station for Research. The BATS program itself has been observing biogeochemical processes (primary productivity, particle flux and elemental cycles) in the mesotrophic waters of the Sargasso Sea since 1988. Closely affiliated with BBOP and BATS is a separate NASA-funded study of the spatial variability of biogeochemical processes in the Sargasso Sea using high-resolution AVHRR and SeaWiFS data collected at Bermuda (N. Nelson, P.I.). The collaboration between BATS and BBOP measurements has resulted in a unique data set that addresses not only the SIMBIOS goals but also the broader issues of important factors controlling the carbon cycle.

Nelson, Norm↗

NASA NTRS2004

The Bermuda BioOptics Project (BBOP) Years 9-11

Maritorena, S.↗

NASA NTRSDate not supplied

CERES BiDirectional Scans (BDS) data in HDF (CER_BDS_Aqua-FM3_Edition2)

Each BiDirectional Scans (BDS) data product contains twenty-four hours of Level-1b data for each CERES scanner instrument mounted on each spacecraft. The BDS includes samples taken in normal and short Earth scan elevation profiles in both fixed and rotating azimuth scan modes (including space, internal calibration, and solar calibration views). The BDS contains Level-0 raw (unconverted) science and instrument data as well as the geolocated converted science and instrument data. The BDS contains additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data, converted digital status data, and parameters used in the radiance count conversion equations. The following CERES BDS data sets are currently available: CER_BDS_TRMM-PFM_Edition1 CER_BDS_Terra-FM1_Edition1 CER_BDS_Terra-FM2_Edition1 CER_BDS_Terra-FM1_Edition2 CER_BDS_Terra-FM2_Edition2 CER_BDS_Aqua-FM3_Edition1 CER_BDS_Aqua-FM4_Edition1 CER_BDS_Aqua-FM3_Edition2 CER_BDS_Aqua-FM4_Edition2 CER_BDS_Aqua-FM3_Edition1-CV CER_BDS_Aqua-FM4_Edition1-CV CER_BDS_Terra-FM1_Edition1-CV CER_BDS_Terra-FM2_Edition1-CV. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1997-12-27; Stop_Date=2006-01-01] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 day; Temporal_Resolution_Range=Daily - < Weekly].

SHORTWAVE FILTERED RADIANCE UPWARDS↗

NASA NTRS2014

Recent Advances in Registration, Integration and Fusion of Remotely Sensed Data: Redundant Representations and Frames

In recent years, sophisticated mathematical techniques have been successfully applied to the field of remote sensing to produce significant advances in applications such as registration, integration and fusion of remotely sensed data. Registration, integration and fusion of multiple source imagery are the most important issues when dealing with Earth Science remote sensing data where information from multiple sensors, exhibiting various resolutions, must be integrated. Issues ranging from different sensor geometries, different spectral responses, differing illumination conditions, different seasons, and various amounts of noise need to be dealt with when designing an image registration, integration or fusion method. This tutorial will first define the problems and challenges associated with these applications and then will review some mathematical techniques that have been successfully utilized to solve them. In particular, we will cover topics on geometric multiscale representations, redundant representations and fusion frames, graph operators, diffusion wavelets, as well as spatial-spectral and operator-based data fusion. All the algorithms will be illustrated using remotely sensed data, with an emphasis on current and operational instruments.

Fusion↗

NASA NTRSDate not supplied

CERES BiDirectional Scans (BDS) data in HDF (CER_BDS_TRMM-PFM_Edition1)

Each BiDirectional Scans (BDS) data product contains twenty-four hours of Level-1b data for each CERES scanner instrument mounted on each spacecraft. The BDS includes samples taken in normal and short Earth scan elevation profiles in both fixed and rotating azimuth scan modes (including space, internal calibration, and solar calibration views). The BDS contains Level-0 raw (unconverted) science and instrument data as well as the geolocated converted science and instrument data. The BDS contains additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data, converted digital status data, and parameters used in the radiance count conversion equations. The following CERES BDS data sets are currently available: CER_BDS_TRMM-PFM_Edition1 CER_BDS_Terra-FM1_Edition1 CER_BDS_Terra-FM2_Edition1 CER_BDS_Terra-FM1_Edition2 CER_BDS_Terra-FM2_Edition2 CER_BDS_Aqua-FM3_Edition1 CER_BDS_Aqua-FM4_Edition1 CER_BDS_Aqua-FM3_Edition2 CER_BDS_Aqua-FM4_Edition2 CER_BDS_Aqua-FM3_Edition1-CV CER_BDS_Aqua-FM4_Edition1-CV CER_BDS_Terra-FM1_Edition1-CV CER_BDS_Terra-FM2_Edition1-CV. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1997-12-27; Stop_Date=2000-12-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 day; Temporal_Resolution_Range=Daily - < Weekly].

SHORTWAVE FILTERED RADIANCE UPWARDS↗

NASA NTRSDate not supplied

CERES BiDirectional Scans (BDS) data in HDF (CER_BDS_Aqua-FM3_Edition1-CV)

Each BiDirectional Scans (BDS) data product contains twenty-four hours of Level-1b data for each CERES scanner instrument mounted on each spacecraft. The BDS includes samples taken in normal and short Earth scan elevation profiles in both fixed and rotating azimuth scan modes (including space, internal calibration, and solar calibration views). The BDS contains Level-0 raw (unconverted) science and instrument data as well as the geolocated converted science and instrument data. The BDS contains additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data, converted digital status data, and parameters used in the radiance count conversion equations. The following CERES BDS data sets are currently available: CER_BDS_TRMM-PFM_Edition1 CER_BDS_Terra-FM1_Edition1 CER_BDS_Terra-FM2_Edition1 CER_BDS_Terra-FM1_Edition2 CER_BDS_Terra-FM2_Edition2 CER_BDS_Aqua-FM3_Edition1 CER_BDS_Aqua-FM4_Edition1 CER_BDS_Aqua-FM3_Edition2 CER_BDS_Aqua-FM4_Edition2 CER_BDS_Aqua-FM3_Edition1-CV CER_BDS_Aqua-FM4_Edition1-CV CER_BDS_Terra-FM1_Edition1-CV CER_BDS_Terra-FM2_Edition1-CV. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1997-12-27; Stop_Date=2006-11-02] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 day; Temporal_Resolution_Range=Daily - < Weekly].

WINDOW DETECTOR OUTPUT↗

NASA NTRS1989

Senegalese land surface change analysis and biophysical parameter estimation using NOAA AVHRR spectral data

Surface biophysical estimates were derived from analysis of NOAA Advanced Very High Spectral Resolution (AVHRR) spectral data of the Senegalese area of west Africa. The parameters derived were of solar albedo, spectral visible and near-infrared band reflectance, spectral vegetative index, and ground temperature. Wet and dry linked AVHRR scenes from 1981 through 1985 in Senegal were analyzed for a semi-wet southerly site near Tambacounda and a predominantly dry northerly site near Podor. Related problems were studied to convert satellite derived radiance to biophysical estimates of the land surface. Problems studied were associated with sensor miscalibration, atmospheric and aerosol spatial variability, surface anisotropy of reflected radiation, narrow satellite band reflectance to broad solar band conversion, and ground emissivity correction. The middle-infrared reflectance was approximated with a visible AVHRR reflectance for improving solar albedo estimates. In addition, the spectral composition of solar irradiance (direct and diffuse radiation) between major spectral regions (i.e., ultraviolet, visible, near-infrared, and middle-infrared) was found to be insensitive to changes in the clear sky atmospheric optical depth in the narrow band to solar band conversion procedure. Solar albedo derived estimates for both sites were not found to change markedly with significant antecedent precipitation events or correspondingly from increases in green leaf vegetation density. The bright soil/substrate contributed to a high albedo for the dry related scenes, whereas the high internal leaf reflectance in green vegetation canopies in the near-infrared contributed to high solar albedo for the wet related scenes. The relationship between solar albedo and ground temperature was poor, indicating the solar albedo has little control of the ground temperature. The normalized difference vegetation index (NDVI) and the derived visible reflectance were more sensitive to antecedent rainfall amounts and green vegetation changes than were near-infrared changes. The information in the NDVI related to green leaf density changes primarily was from the visible reflectance. The contribution of the near-infrared reflectance to explaining green vegetation is largely reduced when there is a bright substrate.

Vukovich, Fred M.↗