Multi-Mission Remote Sensing of Suspended Particulate Matter and Diffuse Attenuation Coefficient in the Yangtze Estuarine and Coastal Waters

NASA Astrophysics Data System (ADS)

Yu, X.; Salama, S.; Shen, F.

2016-08-01

During the Dragon-3 project (ID: 10555) period, we developed and improved the atmospheric correction algorithms (AC) and retrieval models of suspended sediment concentration ( ) and diffuse attenuation coefficient ( ) for the Yangtze estuarine and coastal waters. The developed models were validated by measurements with consistently stable and fairly accurate estimations, reproducing reasonable distribution maps of and over the study area. Spatial-temporal variations of were presented and the mechanisms of the sediment transport were discussed. We further examined the compatibility of the developed AC algorithms and retrieval model and the consistency of satellite products for multi-sensor such as MODIS/Terra/Aqua, MERIS/Envisat, MERSI/ FY-3 and GOCI. The inter-comparison of multi- sensor suggested that different satellite products can be combined to increase revisit frequency and complement a temporal gap of time series satellites that may exist between on-orbit and off- orbit, facilitating a better monitor on the spatial- temporal dynamics of .

Toward a Coherent Detailed Evaluation of Aerosol Data Products from Multiple Satellite Sensors

NASA Technical Reports Server (NTRS)

Ichoku, Charles; Petrenko, Maksym; Leptoukh, Gregory

2011-01-01

Atmospheric aerosols represent one of the greatest uncertainties in climate research. Although satellite-based aerosol retrieval has practically become routine, especially during the last decade, there is often disagreement between similar aerosol parameters retrieved from different sensors, leaving users confused as to which sensors to trust for answering important science questions about the distribution, properties, and impacts of aerosols. As long as there is no consensus and the inconsistencies are not well characterized and understood, there will be no way of developing reliable climate data records from satellite aerosol measurements. Fortunately, the most globally representative well-calibrated ground-based aerosol measurements corresponding to the satellite-retrieved products are available from the Aerosol Robotic Network (AERONET). To adequately utilize the advantages offered by this vital resource, an online Multi-sensor Aerosol Products Sampling System (MAPSS) was recently developed. The aim of MAPSS is to facilitate detailed comparative analysis of satellite aerosol measurements from different sensors (Terra-MODIS, Aqua-MODIS, TerraMISR, Aura-OMI, Parasol-POLDER, and Calipso-CALIOP) based on the collocation of these data products over AERONET stations. In this presentation, we will describe the strategy of the MASS system, its potential advantages for the aerosol community, and the preliminary results of an integrated comparative uncertainly analysis of aerosol products from multiple satellite sensors.

Validating Microwave-Based Satellite Rain Rate Retrievals Over TRMM Ground Validation Sites

NASA Astrophysics Data System (ADS)

Fisher, B. L.; Wolff, D. B.

2008-12-01

Multi-channel, passive microwave instruments are commonly used today to probe the structure of rain systems and to estimate surface rainfall from space. Until the advent of meteorological satellites and the development of remote sensing techniques for measuring precipitation from space, there was no observational system capable of providing accurate estimates of surface precipitation on global scales. Since the early 1970s, microwave measurements from satellites have provided quantitative estimates of surface rainfall by observing the emission and scattering processes due to the existence of clouds and precipitation in the atmosphere. This study assesses the relative performance of microwave precipitation estimates from seven polar-orbiting satellites and the TRMM TMI using four years (2003-2006) of instantaneous radar rain estimates obtained from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The seven polar orbiters include three different sensor types: SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), and AMSR-E. The TMI aboard the TRMM satellite flies in a sun asynchronous orbit between 35 S and 35 N latitudes. The rain information from these satellites are combined and used to generate several multi-satellite rain products, namely the Goddard TRMM Multi-satellite Precipitation Analysis (TMPA), NOAA's CPC Morphing Technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Instantaneous rain rates derived from each sensor were matched to the GV estimates in time and space at a resolution of 0.25 degrees. The study evaluates the measurement and error characteristics of the various satellite estimates through inter-comparisons with GV radar estimates. The GV rain observations provided an empirical ground-based reference for assessing the relative performance of each sensor and sensor class. Because the relative performance of the rain algorithms depends on the underlying surface terrain, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Relative to GV, AMSR-E and the TMI exhibited the highest correlation and skill over the full dynamic range of observed rain rates at both validation sites. The AMSU sensors, on the other hand, exhibited the lowest correlation and skill, though all sensors performed reasonably well compared to GV. The general tendency was for the microwave sensors to overestimate rain rates below 1 mm/hr where the sampling was highest and to underestimate the high rain rates above 10 mm/hr where the sampling was lowest. Underestimation of the low rain rate regime is attributed to difficulties of detecting and measuring low rain rates, while overestimation over the oceans was attributed largely to saturation of the brightness temperatures at high rain rates. Overall biases depended on the relative differences in the total rainfall at the extremes and the performance of each sensor at the nominal rain rates.

A Novel Multi-Aperture Based Sun Sensor Based on a Fast Multi-Point MEANSHIFT (FMMS) Algorithm

PubMed Central

You, Zheng; Sun, Jian; Xing, Fei; Zhang, Gao-Fei

2011-01-01

With the current increased widespread interest in the development and applications of micro/nanosatellites, it was found that we needed to design a small high accuracy satellite attitude determination system, because the star trackers widely used in large satellites are large and heavy, and therefore not suitable for installation on micro/nanosatellites. A Sun sensor + magnetometer is proven to be a better alternative, but the conventional sun sensor has low accuracy, and cannot meet the requirements of the attitude determination systems of micro/nanosatellites, so the development of a small high accuracy sun sensor with high reliability is very significant. This paper presents a multi-aperture based sun sensor, which is composed of a micro-electro-mechanical system (MEMS) mask with 36 apertures and an active pixels sensor (APS) CMOS placed below the mask at a certain distance. A novel fast multi-point MEANSHIFT (FMMS) algorithm is proposed to improve the accuracy and reliability, the two key performance features, of an APS sun sensor. When the sunlight illuminates the sensor, a sun spot array image is formed on the APS detector. Then the sun angles can be derived by analyzing the aperture image location on the detector via the FMMS algorithm. With this system, the centroid accuracy of the sun image can reach 0.01 pixels, without increasing the weight and power consumption, even when some missing apertures and bad pixels appear on the detector due to aging of the devices and operation in a harsh space environment, while the pointing accuracy of the single-aperture sun sensor using the conventional correlation algorithm is only 0.05 pixels. PMID:22163770

Techniques for Sea Ice Characteristics Extraction and Sea Ice Monitoring Using Multi-Sensor Satellite Data in the Bohai Sea-Dragon 3 Programme Final Report (2012-2016)

NASA Astrophysics Data System (ADS)

Zhang, Xi; Zhang, Jie; Meng, Junmin

2016-08-01

The objectives of Dragon-3 programme (ID: 10501) are to develop methods for classification sea ice types and retrieving ice thickness based on multi-sensor data. In this final results paper, we give a briefly introduction for our research work and mainly results. Key words: the Bohai Sea ice, Sea ice, optical and

Satellite Imagery Analysis for Automated Global Food Security Forecasting

NASA Astrophysics Data System (ADS)

Moody, D.; Brumby, S. P.; Chartrand, R.; Keisler, R.; Mathis, M.; Beneke, C. M.; Nicholaeff, D.; Skillman, S.; Warren, M. S.; Poehnelt, J.

2017-12-01

The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. Cloud computing and storage, combined with recent advances in machine learning, are enabling understanding of the world at a scale and at a level of detail never before feasible. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and that can scale with the high-rate and dimensionality of imagery being collected. We focus on the problem of monitoring food crop productivity across the Middle East and North Africa, and show how an analysis-ready, multi-sensor data platform enables quick prototyping of satellite imagery analysis algorithms, from land use/land cover classification and natural resource mapping, to yearly and monthly vegetative health change trends at the structural field level.

Comparability of Red/Near-Infrared Reflectance and NDVI Based on the Spectral Response Function between MODIS and 30 Other Satellite Sensors Using Rice Canopy Spectra

PubMed Central

Huang, Weijiao; Huang, Jingfeng; Wang, Xiuzhen; Wang, Fumin; Shi, Jingjing

2013-01-01

Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from −12.67% to 36.30% for the red reflectance, −8.52% to −0.23% for the NIR reflectance, and −9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors. PMID:24287529

Comparability of red/near-infrared reflectance and NDVI based on the spectral response function between MODIS and 30 other satellite sensors using rice canopy spectra.

PubMed

Huang, Weijiao; Huang, Jingfeng; Wang, Xiuzhen; Wang, Fumin; Shi, Jingjing

2013-11-26

Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from -12.67% to 36.30% for the red reflectance, -8.52% to -0.23% for the NIR reflectance, and -9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors.

Considerations for blending data from various sensors

USGS Publications Warehouse

Bauer, Brian P.; Barringer, Anthony R.

1980-01-01

A project is being proposed at the EROS Data Center to blend the information from sensors aboard various satellites. The problems of, and considerations for, blending data from several satellite-borne sensors are discussed. System descriptions of the sensors aboard the HCMM, TIROS-N, GOES-D, Landsat 3, Landsat D, Seasat, SPOT, Stereosat, and NOSS satellites, and the quantity, quality, image dimensions, and availability of these data are summaries to define attributes of a multi-sensor satellite data base. Unique configurations of equipment, storage, media, and specialized hardware to meet the data system requirement are described as well as archival media and improved sensors that will be on-line within the next 5 years. Definitions and rigor required for blending various sensor data are given. Problems of merging data from the same sensor (intrasensor comparison) and from different sensors (intersensor comparison), the characteristics and advantages of cross-calibration of data, and integration of data into a product matrix field are addressed. Data processing considerations as affected by formation, resolution, and problems of merging large data sets, and organization of data bases for blending data are presented. Examples utilizing GOES and Landsat data are presented to demonstrate techniques of data blending, and recommendations for future implementation of a set of standard scenes and their characteristics necessary for optimal data blending are discussed.

A novel space-based observation strategy for GEO objects based on daily pointing adjustment of multi-sensors

NASA Astrophysics Data System (ADS)

Hu, Yun-peng; Li, Ke-bo; Xu, Wei; Chen, Lei; Huang, Jian-yu

2016-08-01

Space-based visible (SBV) program has been proved to be with a large advantage to observe geosynchronous earth orbit (GEO) objects. With the development of SBV observation started from 1996, many strategies have come out for the purpose of observing GEO objects more efficiently. However it is a big challenge to visit all the GEO objects in a relatively short time because of the distribution characteristics of GEO belt and limited field of view (FOV) of sensor. And it's also difficult to keep a high coverage of the GEO belt every day in a whole year. In this paper, a space-based observation strategy for GEO objects is designed based on the characteristics of the GEO belt. The mathematical formula of GEO belt is deduced and the evolvement of GEO objects is illustrated. There are basically two kinds of orientation strategies for most observation satellites, i.e., earth-oriented and inertia-directional. Influences of both strategies to their own observation regions are analyzed and compared with each other. A passive optical instrument with daily attitude-adjusting strategies is proposed to increase the daily coverage rate of GEO objects in a whole year. Furthermore, in order to observe more GEO objects in a relatively short time, the strategy of a satellite with multi-sensors is proposed. The installation parameters between different sensors are optimized, more than 98% of GEO satellites can be observed every day and almost all the GEO satellites can be observed every two days with 3 sensors (FOV: 6° × 6°) on the satellite under the strategy of daily pointing adjustment in a whole year.

Multi sensor satellite imagers for commercial remote sensing

NASA Astrophysics Data System (ADS)

Cronje, T.; Burger, H.; Du Plessis, J.; Du Toit, J. F.; Marais, L.; Strumpfer, F.

2005-10-01

This paper will discuss and compare recent refractive and catodioptric imager designs developed and manufactured at SunSpace for Multi Sensor Satellite Imagers with Panchromatic, Multi-spectral, Area and Hyperspectral sensors on a single Focal Plane Array (FPA). These satellite optical systems were designed with applications to monitor food supplies, crop yield and disaster monitoring in mind. The aim of these imagers is to achieve medium to high resolution (2.5m to 15m) spatial sampling, wide swaths (up to 45km) and noise equivalent reflectance (NER) values of less than 0.5%. State-of-the-art FPA designs are discussed and address the choice of detectors to achieve these performances. Special attention is given to thermal robustness and compactness, the use of folding prisms to place multiple detectors in a large FPA and a specially developed process to customize the spectral selection with the need to minimize mass, power and cost. A refractive imager with up to 6 spectral bands (6.25m GSD) and a catodioptric imager with panchromatic (2.7m GSD), multi-spectral (6 bands, 4.6m GSD), hyperspectral (400nm to 2.35μm, 200 bands, 15m GSD) sensors on the same FPA will be discussed. Both of these imagers are also equipped with real time video view finding capabilities. The electronic units could be subdivided into the Front-End Electronics and Control Electronics with analogue and digital signal processing. A dedicated Analogue Front-End is used for Correlated Double Sampling (CDS), black level correction, variable gain and up to 12-bit digitizing and high speed LVDS data link to a mass memory unit.

The GEOS-5 Neural Network Retrieval for AOD

NASA Astrophysics Data System (ADS)

Castellanos, P.; da Silva, A. M., Jr.

2017-12-01

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from MODIS observations on board the Terra and Aqua satellites. In this talk, I will present the implementation of and recent updates to the GEOS-5 NNR for MODIS collection 6 data.

The GEOS-5 Neural Network Retrieval (NNR) for AOD

NASA Technical Reports Server (NTRS)

Castellanos, Patricia; Da Silva, Arlindo

2017-01-01

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from MODIS observations on board the Terra and Aqua satellites. In this talk, I will present the implementation of and recent updates to the GEOS-5 NNR for MODIS collection 6 data.

MOBY, A Radiometric Buoy for Performance Monitoring and Vicarious Calibration of Satellite Ocean Color Sensors: Measurement and Data Analysis Protocols. Chapter 2

NASA Technical Reports Server (NTRS)

Clark, Dennis K.; Yarbrough, Mark A.; Feinholz, Mike; Flora, Stephanie; Broenkow, William; Kim, Yong Sung; Johnson, B. Carol; Brown, Steven W.; Yuen, Marilyn; Mueller, James L.

2003-01-01

The Marine Optical Buoy (MOBY) is the centerpiece of the primary ocean measurement site for calibration of satellite ocean color sensors based on independent in situ measurements. Since late 1996, the time series of normalized water-leaving radiances L(sub WN)(lambda) determined from the array of radiometric sensors attached to MOBY are the primary basis for the on-orbit calibrations of the USA Sea-viewing Wide Field-of-view Sensor (SeaWiFS), the Japanese Ocean Color and Temperature Sensor (OCTS), the French Polarization Detection Environmental Radiometer (POLDER), the German Modular Optoelectronic Scanner on the Indian Research Satellite (IRS1-MOS), and the USA Moderate Resolution Imaging Spectrometer (MODIS). The MOBY vicarious calibration L(sub WN)(lambda) reference is an essential element in the international effort to develop a global, multi-year time series of consistently calibrated ocean color products using data from a wide variety of independent satellite sensors. A longstanding goal of the SeaWiFS and MODIS (Ocean) Science Teams is to determine satellite-derived L(sub WN)(labda) with a relative combined standard uncertainty of 5 %. Other satellite ocean color projects and the Sensor Intercomparison for Marine Biology and Interdisciplinary Oceanic Studies (SIMBIOS) project have also adopted this goal, at least implicitly. Because water-leaving radiance contributes at most 10 % of the total radiance measured by a satellite sensor above the atmosphere, a 5 % uncertainty in L(sub WN)(lambda) implies a 0.5 % uncertainty in the above-atmosphere radiance measurements. This level of uncertainty can only be approached using vicarious-calibration approaches as described below. In practice, this means that the satellite radiance responsivity is adjusted to achieve the best agreement, in a least-squares sense, for the L(sub WN)(lambda) results determined using the satellite and the independent optical sensors (e.g. MOBY). The end result of this approach is to implicitly absorb unquantified, but systematic, errors in the atmospheric correction, incident solar flux, and satellite sensor calibration into a single correction factor to produce consistency with the in situ data.

New approaches to merging multi-sensor satellite measurements of volcanic SO2 emissions

NASA Astrophysics Data System (ADS)

Carn, S. A.; Telling, J. W.; Krotkov, N. A.

2015-12-01

As part of the NASA MEaSUREs program, we are developing a unique long-term database of volcanic sulfur dioxide (SO2) emissions for use by the scientific community, using observations from multiple satellite instruments collected since 1978. Challenges to creating such a database include assessing data continuity between multiple satellite missions and SO2 retrieval algorithms and estimating measurement uncertainties. Here, we describe the approaches that we are using to merge multi-decadal SO2 measurements from the ultraviolet (UV) Total Ozone Mapping Spectrometer (TOMS), Ozone Monitoring Instrument (OMI) and Ozone Monitoring and Profiler Suite (OMPS) sensors. A particular challenge has involved accounting for the OMI row anomaly (ORA), a data gap in OMI measurements since 2008 that partially or wholly obscures some volcanic eruption clouds, whilst still profiting from the high OMI spatial resolution and data quality, and prior OMI SO2 validation. We present a new method to substitute missing SO2 information in the ORA with near-coincident SO2 data from OMPS, providing improved estimates of eruptive volcanic SO2 emissions. The technique can also be used to assess consistency between different satellite instruments and SO2 retrieval algorithms, investigate the impact of variable sensor spatial resolution, and estimate measurement uncertainties. It is particularly effective for larger eruptions producing extensive SO2 clouds where the ORA obscures the volcanic plume in multiple contiguous orbits. Application of the technique is demonstrated using recent volcanic eruptions including the 2015 eruption of Calbuco, Chile. We also provide an update on the status of the multi-satellite long-term volcanic SO2 database (MSVOLSO2L4).

Daily monitoring of vegetation conditions and evapotranspiration at field scale by fusing multi-satellite images

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires frequent remote sensing observations. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for vegetation monitoring. The medium spatial resolution (10-100m) sensors are su...

OSOAA: A Vector Radiative Transfer Model of Coupled Atmosphere-Ocean System for a Rough Sea Surface Application to the Estimates of the Directional Variations of the Water Leaving Reflectance to Better Process Multi-angular Satellite Sensors Data Over the Ocean

NASA Technical Reports Server (NTRS)

Chami, Malik; LaFrance, Bruno; Fougnie, Bertrand; Chowdhary, Jacek; Harmel, Tristan; Waquet, Fabien

2015-01-01

In this study, we present a radiative transfer model, so-called OSOAA, that is able to predict the radiance and degree of polarization within the coupled atmosphere-ocean system in the presence of a rough sea surface. The OSOAA model solves the radiative transfer equation using the successive orders of scattering method. Comparisons with another operational radiative transfer model showed a satisfactory agreement within 0.8%. The OSOAA model has been designed with a graphical user interface to make it user friendly for the community. The radiance and degree of polarization are provided at any level, from the top of atmosphere to the ocean bottom. An application of the OSOAA model is carried out to quantify the directional variations of the water leaving reflectance and degree of polarization for phytoplankton and mineral-like dominated waters. The difference between the water leaving reflectance at a given geometry and that obtained for the nadir direction could reach 40%, thus questioning the Lambertian assumption of the sea surface that is used by inverse satellite algorithms dedicated to multi-angular sensors. It is shown as well that the directional features of the water leaving reflectance are weakly dependent on wind speed. The quantification of the directional variations of the water leaving reflectance obtained in this study should help to correctly exploit the satellite data that will be acquired by the current or forthcoming multi-angular satellite sensors.

Precipitation Estimation Using Combined Radar/Radiometer Measurements Within the GPM Framework

NASA Technical Reports Server (NTRS)

Hou, Arthur

2012-01-01

The Global Precipitation Measurement (GPM) Mission is an international satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. The GPM mission centers upon the deployment of a Core Observatory in a 65o non-Sun-synchronous orbit to serve as a physics observatory and a transfer standard for intersatellite calibration of constellation radiometers. The GPM Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The DPR will be the first dual-frequency radar in space to provide not only measurements of 3-D precipitation structures but also quantitative information on microphysical properties of precipitating particles needed for improving precipitation retrievals from microwave sensors. The DPR and GMI measurements will together provide a database that relates vertical hydrometeor profiles to multi-frequency microwave radiances over a variety of environmental conditions across the globe. This combined database will be used as a common transfer standard for improving the accuracy and consistency of precipitation retrievals from all constellation radiometers. For global coverage, GPM relies on existing satellite programs and new mission opportunities from a consortium of partners through bilateral agreements with either NASA or JAXA. Each constellation member may have its unique scientific or operational objectives but contributes microwave observations to GPM for the generation and dissemination of unified global precipitation data products. In addition to the DPR and GMI on the Core Observatory, the baseline GPM constellation consists of the following sensors: (1) Special Sensor Microwave Imager/Sounder (SSMIS) instruments on the U.S. Defense Meteorological Satellite Program (DMSP) satellites, (2) the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on the GCOM-W1 satellite of JAXA, (3) the Multi-Frequency Microwave Scanning Radiometer (MADRAS) and the multi-channel microwave humidity sounder (SAPHIR) on the French-Indian Megha- Tropiques satellite, (4) the Microwave Humidity Sounder (MHS) on the National Oceanic and Atmospheric Administration (NOAA)-19, (5) MHS instruments on MetOp satellites launched by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), (6) the Advanced Technology Microwave Sounder (ATMS) on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), and (7) ATMS instruments on the NOAA-NASA Joint Polar Satellite System (JPSS) satellites. Data from Chinese and Russian microwave radiometers may also become available through international collaboration under the auspices of the Committee on Earth Observation Satellites (CEOS) and Group on Earth Observations (GEO). The current generation of global rainfall products combines observations from a network of uncoordinated satellite missions using a variety of merging techniques. GPM will provide next-generation precipitation products characterized by: (1) more accurate instantaneous precipitation estimate (especially for light rain and cold-season solid precipitation), (2) intercalibrated microwave brightness temperatures from constellation radiometers within a consistent framework, and (3) unified precipitation retrievals from constellation radiometers using a common a priori hydrometeor database constrained by combined radar/radiometer measurements provided by the GPM Core Observatory.

Forest height Mapping using the fusion of Lidar and MULTI-ANGLE spectral data

NASA Astrophysics Data System (ADS)

Pang, Y.; Li, Z.

2016-12-01

Characterizing the complexity of forest ecosystem over large area is highly complex. Light detection and Ranging (LIDAR) approaches have demonstrated a high capacity to accurately estimate forest structural parameters. A number of satellite mission concepts have been proposed to fuse LiDAR with other optical imagery allowing Multi-angle spectral observations to be captured using the Bidirectional Reflectance Distribution Function (BRDF) characteristics of forests. China is developing the concept of Chinese Terrestrial Carbon Mapping Satellite. A multi-beam waveform Lidar is the main sensor. A multi-angle imagery system is considered as the spatial mapping sensor. In this study, we explore the fusion potential of Lidar and multi-angle spectral data to estimate forest height across different scales. We flew intensive airborne Lidar and Multi-angle hyperspectral data in Genhe Forest Ecological Research Station, Northeast China. Then extended the spatial scale with some long transect flights to cover more forest structures. Forest height data derived from airborne lidar data was used as reference data and the multi-angle hyperspectral data was used as model inputs. Our results demonstrate that the multi-angle spectral data can be used to estimate forest height with the RMSE of 1.1 m with an R2 approximately 0.8.

Addendum to Site Assessment and Feasibility of a New Operations Base on the Greenland Ice Sheet: Addendum to Preliminary Report

DTIC Science & Technology

2015-11-01

National Guard PLR Division of Polar Programs SMM /I Special Sensor Microwave/Imager SMMR Scanning Multi-channel Microwave Radiometer ERDC/CRREL...and the Special Sensor Microwave/Imager ( SMM /I). The satellite-based technique uses a difference in the passive microwave brightness temperatures

Evaluating the capacity of GF-4 satellite data for estimating fractional vegetation cover

NASA Astrophysics Data System (ADS)

Zhang, C.; Qin, Q.; Ren, H.; Zhang, T.; Sun, Y.

2016-12-01

Fractional vegetation cover (FVC) is a crucial parameter for many agricultural, environmental, meteorological and ecological applications, which is of great importance for studies on ecosystem structure and function. The Chinese GaoFen-4 (GF-4) geostationary satellite designed for the purpose of environmental and ecological observation was launched in December 29, 2015, and official use has been started by Chinese Government on June 13, 2016. Multi-spectral images with spatial resolution of 50 m and high temporal resolution, could be acquired by the sensor on GF-4 satellite on the 36000 km-altitude orbit. To take full advantage of the outstanding performance of GF-4 satellite, this study evaluated the capacity of GF-4 satellite data for monitoring FVC. To the best of our knowledge, this is the first research about estimating FVC from GF-4 satellite images. First, we developed a procedure for preprocessing GF-4 satellite data, including radiometric calibration and atmospheric correction, to acquire surface reflectance. Then single image and multi-temporal images were used for extracting the endmembers of vegetation and soil, respectively. After that, dimidiate pixel model and square model based on vegetation indices were used for estimating FVC. Finally, the estimation results were comparatively analyzed with FVC estimated by other existing sensors. The experimental results showed that satisfying accuracy of FVC estimation could be achieved from GF-4 satellite images using dimidiate pixel model and square model based on vegetation indices. What's more, the multi-temporal images increased the probability to find pure vegetation and soil endmembers, thus the characteristic of high temporal resolution of GF-4 satellite images improved the accuracy of FVC estimation. This study demonstrated the capacity of GF-4 satellite data for monitoring FVC. The conclusions reached by this study are significant for improving the accuracy and spatial-temporal resolution of existing FVC products, which provides a basis for the studies on ecosystem structure and function using remote sensing data acquired by GF-4 satellite.

Simulation of olive grove gross primary production by the combination of ground and multi-sensor satellite data

NASA Astrophysics Data System (ADS)

Brilli, L.; Chiesi, M.; Maselli, F.; Moriondo, M.; Gioli, B.; Toscano, P.; Zaldei, A.; Bindi, M.

2013-08-01

We developed and tested a methodology to estimate olive (Olea europaea L.) gross primary production (GPP) combining ground and multi-sensor satellite data. An eddy-covariance station placed in an olive grove in central Italy provided carbon and water fluxes over two years (2010-2011), which were used as reference to evaluate the performance of a GPP estimation methodology based on a Monteith type model (modified C-Fix) and driven by meteorological and satellite (NDVI) data. A major issue was related to the consideration of the two main olive grove components, i.e. olive trees and inter-tree ground vegetation: this issue was addressed by the separate simulation of carbon fluxes within the two ecosystem layers, followed by their recombination. In this way the eddy covariance GPP measurements were successfully reproduced, with the exception of two periods that followed tillage operations. For these periods measured GPP could be approximated by considering synthetic NDVI values which simulated the expected response of inter-tree ground vegetation to tillages.

Absolute Radiometric Calibration of the GÖKTÜRK-2 Satellite Sensor Using Tuz GÖLÜ (landnet Site) from Ndvi Perspective

NASA Astrophysics Data System (ADS)

Sakarya, Ufuk; Hakkı Demirhan, İsmail; Seda Deveci, Hüsne; Teke, Mustafa; Demirkesen, Can; Küpçü, Ramazan; Feray Öztoprak, A.; Efendioğlu, Mehmet; Fehmi Şimşek, F.; Berke, Erdinç; Zübeyde Gürbüz, Sevgi

2016-06-01

TÜBİTAK UZAY has conducted a research study on the use of space-based satellite resources for several aspects of agriculture. Especially, there are two precision agriculture related projects: HASSAS (Widespread application of sustainable precision agriculture practices in Southeastern Anatolia Project Region (GAP) Project) and AKTAR (Smart Agriculture Feasibility Project). The HASSAS project aims to study development of precision agriculture practice in GAP region. Multi-spectral satellite imagery and aerial hyperspectral data along with ground measurements was collected to analyze data in an information system. AKTAR aims to develop models for irrigation, fertilization and spectral signatures of crops in Inner Anatolia. By the end of the project precision agriculture practices to control irrigation, fertilization, pesticide and estimation of crop yield will be developed. Analyzing the phenology of crops using NDVI is critical for the projects. For this reason, absolute radiometric calibration of the Red and NIR bands in space-based satellite sensors is an important issue. The Göktürk-2 satellite is an earth observation satellite which was designed and built in Turkey and was launched in 2012. The Göktürk-2 satellite sensor has a resolution 2.5 meters in panchromatic and 5 meters in R/G/B/NIR bands. The absolute radiometric calibration of the Göktürk-2 satellite sensor was performed via the ground-based measurements - spectra-radiometer, sun photometer, and meteorological station- in Tuz Gölü cal/val site in 2015. In this paper, the first ground-based absolute radiometric calibration results of the Göktürk-2 satellite sensor using Tuz Gölü is demonstrated. The absolute radiometric calibration results of this paper are compared with the published cross-calibration results of the Göktürk-2 satellite sensor utilizing Landsat 8 imagery. According to the experimental comparison results, the Göktürk-2 satellite sensor coefficients for red and NIR bands estimated in this work sustained to agree within 2% of calibration coefficients estimated in the cross-calibration results.

Evaluating the Global Precipitation Measurement mission with NOAA/NSSL Multi-Radar Multisensor: current status and future directions.

NASA Astrophysics Data System (ADS)

Kirstetter, P. E.; Petersen, W. A.; Gourley, J. J.; Kummerow, C.; Huffman, G. J.; Turk, J.; Tanelli, S.; Maggioni, V.; Anagnostou, E. N.; Hong, Y.; Schwaller, M.

2017-12-01

Accurate characterization of uncertainties in space-borne precipitation estimates is critical for many applications including water budget studies or prediction of natural hazards at the global scale. The GPM precipitation Level II (active and passive) and Level III (IMERG) estimates are compared to the high quality and high resolution NEXRAD-based precipitation estimates derived from the NOAA/NSSL's Multi-Radar, Multi-Sensor (MRMS) platform. A surface reference is derived from the MRMS suite of products to be accurate with known uncertainty bounds and measured at a resolution below the pixel sizes of any GPM estimate, providing great flexibility in matching to grid scales or footprints. It provides an independent and consistent reference research framework for directly evaluating GPM precipitation products across a large number of meteorological regimes as a function of resolution, accuracy and sample size. The consistency of the ground and space-based sensors in term of precipitation detection, typology and quantification are systematically evaluated. Satellite precipitation retrievals are further investigated in terms of precipitation distributions, systematic biases and random errors, influence of precipitation sub-pixel variability and comparison between satellite products. Prognostic analysis directly provides feedback to algorithm developers on how to improve the satellite estimates. Specific factors for passive (e.g. surface conditions for GMI) and active (e.g. non uniform beam filling for DPR) sensors are investigated. This cross products characterization acts as a bridge to intercalibrate microwave measurements from the GPM constellation satellites and propagate to the combined and global precipitation estimates. Precipitation features previously used to analyze Level II satellite estimates under various precipitation processes are now intoduced for Level III to test several assumptions in the IMERG algorithm. Specifically, the contribution of Level II is explicitly characterized and a rigorous characterization is performed to migrate across scales fully understanding the propagation of errors from Level II to Level III. Perpectives are presented to advance the use of uncertainty as an integral part of QPE for ground-based and space-borne sensors

The tip/tilt tracking sensor based on multi-anode photo-multiplier tube

NASA Astrophysics Data System (ADS)

Ma, Xiao-yu; Rao, Chang-hui; Tian, Yu; Wei, Kai

2013-09-01

Based on the demands of high sensitivity, precision and frame rate of tip/tilt tracking sensors in acquisition, tracking and pointing (ATP) systems for satellite-ground optical communications, this paper proposes to employ the multiple-anode photo-multiplier tubes (MAPMTs) in tip/tilt tracking sensors. Meanwhile, an array-type photon-counting system was designed to meet the requirements of the tip/tilt tracking sensors. The experiment results show that the tip/tilt tracking sensors based on MAPMTs can achieve photon sensitivity and high frame rate as well as low noise.

Multi-sensor satellite and in situ monitoring of phytoplankton development in a eutrophic-mesotrophic lake.

PubMed

Dörnhöfer, Katja; Klinger, Philip; Heege, Thomas; Oppelt, Natascha

2018-01-15

Phytoplankton indicated by its photosynthetic pigment chlorophyll-a is an important pointer on lake ecology and a regularly monitored parameter within the European Water Framework Directive. Along with eutrophication and global warming cyanobacteria gain increasing importance concerning human health aspects. Optical remote sensing may support both the monitoring of horizontal distribution of phytoplankton and cyanobacteria at the lake surface and the reduction of spatial uncertainties associated with limited water sample analyses. Temporal and spatial resolution of using only one satellite sensor, however, may constrain its information value. To discuss the advantages of a multi-sensor approach the sensor-independent, physically based model MIP (Modular Inversion and Processing System) was applied at Lake Kummerow, Germany, and lake surface chlorophyll-a was derived from 33 images of five different sensors (MODIS-Terra, MODIS-Aqua, Landsat 8, Landsat 7 and Sentinel-2A). Remotely sensed lake average chlorophyll-a concentration showed a reasonable development and varied between 2.3±0.4 and 35.8±2.0mg·m -3 from July to October 2015. Match-ups between in situ and satellite chlorophyll-a revealed varying performances of Landsat 8 (RMSE: 3.6 and 19.7mg·m -3 ), Landsat 7 (RMSE: 6.2mg·m -3 ), Sentinel-2A (RMSE: 5.1mg·m -3 ) and MODIS (RMSE: 12.8mg·m -3 ), whereas an in situ data uncertainty of 48% needs to be respected. The temporal development of an index on harmful algal blooms corresponded well with the cyanobacteria biomass development during summer months. Satellite chlorophyll-a maps allowed to follow spatial patterns of chlorophyll-a distribution during a phytoplankton bloom event. Wind conditions mainly explained spatial patterns. Integrating satellite chlorophyll-a into trophic state assessment resulted in different trophic classes. Our study endorsed a combined use of satellite and in situ chlorophyll-a data to alleviate weaknesses of both approaches and to better characterise and understand phytoplankton development in lakes. Copyright © 2017 Elsevier B.V. All rights reserved.

Statistically Optimized Inversion Algorithm for Enhanced Retrieval of Aerosol Properties from Spectral Multi-Angle Polarimetric Satellite Observations

NASA Technical Reports Server (NTRS)

Dubovik, O; Herman, M.; Holdak, A.; Lapyonok, T.; Taure, D.; Deuze, J. L.; Ducos, F.; Sinyuk, A.

2011-01-01

The proposed development is an attempt to enhance aerosol retrieval by emphasizing statistical optimization in inversion of advanced satellite observations. This optimization concept improves retrieval accuracy relying on the knowledge of measurement error distribution. Efficient application of such optimization requires pronounced data redundancy (excess of the measurements number over number of unknowns) that is not common in satellite observations. The POLDER imager on board the PARASOL microsatellite registers spectral polarimetric characteristics of the reflected atmospheric radiation at up to 16 viewing directions over each observed pixel. The completeness of such observations is notably higher than for most currently operating passive satellite aerosol sensors. This provides an opportunity for profound utilization of statistical optimization principles in satellite data inversion. The proposed retrieval scheme is designed as statistically optimized multi-variable fitting of all available angular observations obtained by the POLDER sensor in the window spectral channels where absorption by gas is minimal. The total number of such observations by PARASOL always exceeds a hundred over each pixel and the statistical optimization concept promises to be efficient even if the algorithm retrieves several tens of aerosol parameters. Based on this idea, the proposed algorithm uses a large number of unknowns and is aimed at retrieval of extended set of parameters affecting measured radiation.

Downscaling of Remotely Sensed Land Surface Temperature with multi-sensor based products

NASA Astrophysics Data System (ADS)

Jeong, J.; Baik, J.; Choi, M.

2016-12-01

Remotely sensed satellite data provides a bird's eye view, which allows us to understand spatiotemporal behavior of hydrologic variables at global scale. Especially, geostationary satellite continuously observing specific regions is useful to monitor the fluctuations of hydrologic variables as well as meteorological factors. However, there are still problems regarding spatial resolution whether the fine scale land cover can be represented with the spatial resolution of the satellite sensor, especially in the area of complex topography. To solve these problems, many researchers have been trying to establish the relationship among various hydrological factors and combine images from multi-sensor to downscale land surface products. One of geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS), has Meteorological Imager (MI) and Geostationary Ocean Color Imager (GOCI). MI performing the meteorological mission produce Rainfall Intensity (RI), Land Surface Temperature (LST), and many others every 15 minutes. Even though it has high temporal resolution, low spatial resolution of MI data is treated as major research problem in many studies. This study suggests a methodology to downscale 4 km LST datasets derived from MI in finer resolution (500m) by using GOCI datasets in Northeast Asia. Normalized Difference Vegetation Index (NDVI) recognized as variable which has significant relationship with LST are chosen to estimate LST in finer resolution. Each pixels of NDVI and LST are separated according to land cover provided from MODerate resolution Imaging Spectroradiometer (MODIS) to achieve more accurate relationship. Downscaled LST are compared with LST observed from Automated Synoptic Observing System (ASOS) for assessing its accuracy. The downscaled LST results of this study, coupled with advantage of geostationary satellite, can be applied to observe hydrologic process efficiently.

Satellite detection of wastewater diversion plumes in Southern California

NASA Astrophysics Data System (ADS)

Gierach, Michelle M.; Holt, Benjamin; Trinh, Rebecca; Jack Pan, B.; Rains, Christine

2017-02-01

Multi-sensor satellite observations proved useful in detecting surfacing wastewater plumes during the 2006 Hyperion Treatment Plant (HTP) and 2012 Orange County Sanitation District (OCSD) wastewater diversion events in Southern California. Satellite sensors were capable of detecting biophysical signatures associated with the wastewater, compared to ambient ocean waters, enabling monitoring of environmental impacts over a greater spatial extent than in situ sampling alone. Thermal satellite sensors measured decreased sea surface temperatures (SSTs) associated with the surfacing plumes. Ocean color satellite sensors did not measure a distinguishable biological response in terms of chlorophyll-a (chl-a) concentrations during the short lived, three-day long, 2006 HTP diversion. A period of decreased chl-a concentration was observed during the three-week long 2012 OCSD diversion, likely in association with enhanced chlorination of the discharged wastewater that suppressed the phytoplankton response and/or significant uptake by heterotrophic bacteria. Synthetic aperture radar (SAR) satellite data were able to identify and track the 2006 HTP wastewater plume through changes in surface roughness related to the oily components of the treated surfacing wastewater. Overall, it was found that chl-a and SST values must have differences of at least 1 mg m-3 and 0.5 °C, respectively, in comparison with adjacent waters for wastewater plumes and their biophysical impact to be detectable from satellite. For a wastewater plume to be identifiable in SAR imagery, wind speeds must range between ∼3 and 8 m s-1. The findings of this study illustrate the benefit of utilizing multiple satellite sensors to monitor the rapidly changing environmental response to surfacing wastewater plumes, and can help inform future wastewater diversions in coastal areas.

Connecting Satellite-Based Precipitation Estimates to Users

NASA Technical Reports Server (NTRS)

Huffman, George J.; Bolvin, David T.; Nelkin, Eric

2018-01-01

Beginning in 1997, the Merged Precipitation Group at NASA Goddard has distributed gridded global precipitation products built by combining satellite and surface gauge data. This started with the Global Precipitation Climatology Project (GPCP), then the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), and recently the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG). This 20+-year (and on-going) activity has yielded an important set of insights and lessons learned for making state-of-the-art precipitation data accessible to the diverse communities of users. Merged-data products critically depend on the input sensors and the retrieval algorithms providing accurate, reliable estimates, but it is also important to provide ancillary information that helps users determine suitability for their application. We typically provide fields of estimated random error, and recently reintroduced the quality index concept at user request. Also at user request we have added a (diagnostic) field of estimated precipitation phase. Over time, increasingly more ancillary fields have been introduced for intermediate products that give expert users insight into the detailed performance of the combination algorithm, such as individual merged microwave and microwave-calibrated infrared estimates, the contributing microwave sensor types, and the relative influence of the infrared estimate.

Improving long-term global precipitation dataset using multi-sensor surface soil moisture retrievals and the soil moisture analysis rainfall tool (SMART)

USDA-ARS?s Scientific Manuscript database

Using multiple historical satellite surface soil moisture products, the Kalman Filtering-based Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve the accuracy of a multi-decadal global daily rainfall product that has been bias-corrected to match the monthly totals of available rain g...

A low cost, high precision extreme/harsh cold environment, autonomous sensor data gathering and transmission platform.

NASA Astrophysics Data System (ADS)

Chetty, S.; Field, L. A.

2014-12-01

SWIMS III, is a low cost, autonomous sensor data gathering platform developed specifically for extreme/harsh cold environments. Arctic ocean's continuing decrease of summer-time ice is related to rapidly diminishing multi-year ice due to the effects of climate change. Ice911 Research aims to develop environmentally inert materials that when deployed will increase the albedo, enabling the formation and/preservation of multi-year ice. SWIMS III's sophisticated autonomous sensors are designed to measure the albedo, weather, water temperature and other environmental parameters. This platform uses low cost, high accuracy/precision sensors, extreme environment command and data handling computer system using satellite and terrestrial wireless solution. The system also incorporates tilt sensors and sonar based ice thickness sensors. The system is light weight and can be deployed by hand by a single person. This presentation covers the technical, and design challenges in developing and deploying these platforms.

Extended and refined multi sensor reanalysis of total ozone for the period 1970-2012

NASA Astrophysics Data System (ADS)

van der A, R. J.; Allaart, M. A. F.; Eskes, H. J.

2015-07-01

The ozone multi-sensor reanalysis (MSR) is a multi-decadal ozone column data record constructed using all available ozone column satellite data sets, surface Brewer and Dobson observations and a data assimilation technique with detailed error modelling. The result is a high-resolution time series of 6-hourly global ozone column fields and forecast error fields that may be used for ozone trend analyses as well as detailed case studies. The ozone MSR is produced in two steps. First, the latest reprocessed versions of all available ozone column satellite data sets are collected and then are corrected for biases as a function of solar zenith angle (SZA), viewing zenith angle (VZA), time (trend), and stratospheric temperature using surface observations of the ozone column from Brewer and Dobson spectrophotometers from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Subsequently the de-biased satellite observations are assimilated within the ozone chemistry and data assimilation model TMDAM. The MSR2 (MSR version 2) reanalysis upgrade described in this paper consists of an ozone record for the 43-year period 1970-2012. The chemistry transport model and data assimilation system have been adapted to improve the resolution, error modelling and processing speed. Backscatter ultraviolet (BUV) satellite observations have been included for the period 1970-1977. The total record is extended by 13 years compared to the first version of the ozone multi sensor reanalysis, the MSR1. The latest total ozone retrievals of 15 satellite instruments are used: BUV-Nimbus4, TOMS-Nimbus7, TOMS-EP, SBUV-7, -9, -11, -14, -16, -17, -18, -19, GOME, SCIAMACHY, OMI and GOME-2. The resolution of the model runs, assimilation and output is increased from 2° × 3° to 1° × 1°. The analysis is driven by 3-hourly meteorology from the ERA-Interim reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) starting from 1979, and ERA-40 before that date. The chemistry parameterization has been updated. The performance of the MSR2 analysis is studied with the help of observation-minus-forecast (OmF) departures from the data assimilation, by comparisons with the individual station observations and with ozone sondes. The OmF statistics show that the mean bias of the MSR2 analyses is less than 1 % with respect to de-biased satellite observations after 1979.

Multi-sensor data processing method for improved satellite retrievals

NASA Astrophysics Data System (ADS)

Fan, Xingwang

2017-04-01

Satellite remote sensing has provided massive data that improve the overall accuracy and extend the time series of environmental studies. In reflective solar bands, satellite data are related to land surface properties via radiative transfer (RT) equations. These equations generally include sensor-related (calibration coefficients), atmosphere-related (aerosol optical thickness) and surface-related (surface reflectance) parameters. It is an ill-posed problem to solve three parameters with only one RT equation. Even if there are two RT equations (dual-sensor data), the problem is still unsolvable. However, a robust solution can be obtained when any two parameters are known. If surface and atmosphere are known, sensor intercalibration can be performed. For example, the Advanced Very High Resolution Radiometer (AVHRR) was calibrated to the MODerate-resolution Imaging Spectroradiometer (MODIS) in Fan and Liu (2014) [Fan, X., and Liu, Y. (2014). Quantifying the relationship between intersensor images in solar reflective bands: Implications for intercalibration. IEEE Transactions on Geoscience and Remote Sensing, 52(12), 7727-7737.]. If sensor and surface are known, atmospheric data can be retrieved. For example, aerosol data were retrieved using tandem TERRA and AQUA MODIS images in Fan and Liu (2016a) [Fan, X., and Liu, Y. (2016a). Exploiting TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. Remote Sensing, 8(12), 996.]. If sensor and atmosphere are known, data consistency can be obtained. For example, Normalized Difference Vegetation Index (NDVI) data were intercalibrated among coarse-resolution sensors in Fan and Liu (2016b) [Fan, X., and Liu, Y. (2016b). A global study of NDVI difference among moderate-resolution satellite sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 177-191.], and among fine-resolution sensors in Fan and Liu (2017) [Fan, X., and Liu, Y. (2017). A generalized model for intersensor NDVI calibration and its comparison with regression approaches. IEEE Transactions on Geoscience and Remote Sensing, 55(3), doi: 10.1109/TGRS.2016.2635802.]. These studies demonstrate the success of multi-sensor data and novel methods in the research domain of geoscience. These data will benefit remote sensing of terrestrial parameters in decadal timescales, such as soil salinity content in Fan et al. (2016) [Fan, X., Weng, Y., and Tao, J. (2016). Towards decadal soil salinity mapping using Landsat time series data. International Journal of Applied Earth Observation and Geoinformation, 52, 32-41.].

An Overview of the CBERS-2 Satellite and Comparison of the CBERS-2 CCD Data with the L5 TM Data

NASA Technical Reports Server (NTRS)

Chandler, Gyanesh

2007-01-01

CBERS satellite carries on-board a multi sensor payload with different spatial resolutions and collection frequencies. HRCCD (High Resolution CCD Camera), IRMSS (Infrared Multispectral Scanner), and WFI (Wide-Field Imager). The CCD and the WFI camera operate in the VNIR regions, while the IRMSS operates in SWIR and thermal region. In addition to the imaging payload, the satellite carries a Data Collection System (DCS) and Space Environment Monitor (SEM).

Evaluation on the impact of IMU grades on BDS + GPS PPP/INS tightly coupled integration

NASA Astrophysics Data System (ADS)

Gao, Zhouzheng; Ge, Maorong; Shen, Wenbin; Li, You; Chen, Qijin; Zhang, Hongping; Niu, Xiaoji

2017-09-01

The unexpected observing environments in dynamic applications may lead to partial and/or complete satellite signal outages frequently, which can definitely impact on the positioning performance of the Precise Point Positioning (PPP) in terms of decreasing available satellite numbers, breaking the continuity of observations, and degrading PPP's positioning accuracy. Generally, both the Inertial Navigation System (INS) and the multi-constellation Global Navigation Satellite System (GNSS) can be used to enhance the performance of PPP. This paper introduces the mathematical models of the multi-GNSS PPP/INS Tightly Coupled Integration (TCI), and investigates its performance from several aspects. Specifically, it covers (1) the use of the BDS/GPS PPP, PPP/INS, and their combination; (2) three positioning modes including PPP, PPP/INS TCI, and PPP/INS Loosely Coupled Integration (LCI); (3) the use of four various INS systems named navigation grade, tactical grade, auto grade, and Micro-Electro-Mechanical-Sensors (MEMS) one; (4) three PPP observation scenarios including PPP available, partially available, and fully outage. According to the statistics results, (1) the positioning performance of the PPP/INS (either TCI or LCI) mode is insignificantly depended on the grade of inertial sensor, when there are enough available satellites; (2) after the complete GNSS outages, the TCI mode expresses both higher convergence speed and more accurate positioning solutions than the LCI mode. Furthermore, in the TCI mode, using a higher grade inertial sensor is beneficial for the PPP convergence; (3) under the partial GNSS outage situations, the PPP/INS TCI mode position divergence speed is also restrained significantly; and (4) the attitude determination accuracy of the PPP/INS integration is highly correlated with the grade of inertial sensor.

Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

NASA Astrophysics Data System (ADS)

Kit, Oleksandr; Lüdeke, Matthias

2013-09-01

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

On the exploitation of optical and thermal band for river discharge estimation: synergy with radar altimetry

NASA Astrophysics Data System (ADS)

Tarpanelli, Angelica; Filippucci, Paolo; Brocca, Luca

2017-04-01

River discharge is recognized as a fundamental physical variable and it is included among the Essential Climate Variables by GCOS (Global Climate Observing System). Notwithstanding river discharge is one of the most measured components of the hydrological cycle, its monitoring is still an open issue. Collection, archiving and distribution of river discharge data globally is limited, and the currently operating network is inadequate in many parts of the Earth and is still declining. Remote sensing, especially satellite sensors, have great potential in offering new ways to monitor river discharge. Remote sensing guarantees regular, uniform and global measurements for long period thanks to the large number of satellites launched during the last twenty years. Because of its nature, river discharge cannot be measured directly and both satellite and traditional monitoring are referred to measurements of other hydraulic variables, e.g. water level, flow velocity, water extent and slope. In this study, we illustrate the potential of different satellite sensors for river discharge estimation. The recent advances in radar altimetry technology offered important information for water levels monitoring of rivers even if the spatio-temporal sampling is still a limitation. The multi-mission approach, i.e. interpolating different altimetry tracks, has potential to cope with the spatial and temporal resolution, but so far few studies were dedicated to deal with this issue. Alternatively, optical sensors, thanks to their frequent revisit time and large spatial coverage, could give a better support for the evaluation of river discharge variations. In this study, we focus on the optical (Near InfraRed) and thermal bands of different satellite sensors (MODIS, MERIS, AATSR, Landsat, Sentinel-2) and particularly, on the derived products such as reflectance, emissivity and land surface temperature. The performances are compared with respect to the well-known altimetry (Envisat/Ra-2, Jason-2/Poseidon-3 and Saral/Altika) for estimating the river discharge variation in Nigeria and Italy. For optical and thermal bands, results are more affected by the temporal resolution than the spatial resolution. Indeed, even if affected by cloud cover that limits the number of available images, thermal bands from MODIS (spatial resolution of 1 km) can be conveniently used for the estimation of the variation in the river discharge, whereas optical sensors as Landsat or Sentinel-2, characterized by 10 - 30 m of spatial resolution, fail in the estimation of extreme events, missing most of the peak values, because of the long revisit time ( 14-16 days). The best performances are obtained with the Near InfraRed bands from MODIS and MERIS that give similar results in river discharge estimation, even though with some underestimation of the flood peak values. Moreover, the multi-mission approach applied to radar altimetry data is found to be the most reliable tool to estimate river discharge in large rivers but its success is constrained both spatially (number of satellite tracks) and temporally (revisit time of the satellites). Therefore, it is expected that the multi-mission approach, merging also sensors of different characteristics (radar altimetry, and optical/thermal sensors), could improve the performances, if a consistent and comparable methodology is used for reducing the inter-satellite biases.

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter

PubMed Central

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Gu, Chengfan

2018-01-01

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation. PMID:29415509

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

PubMed

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

2018-02-06

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

An Evolutionary Algorithm for Fast Intensity Based Image Matching Between Optical and SAR Satellite Imagery

NASA Astrophysics Data System (ADS)

Fischer, Peter; Schuegraf, Philipp; Merkle, Nina; Storch, Tobias

2018-04-01

This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

Automated Geo/Co-Registration of Multi-Temporal Very-High-Resolution Imagery.

PubMed

Han, Youkyung; Oh, Jaehong

2018-05-17

For time-series analysis using very-high-resolution (VHR) multi-temporal satellite images, both accurate georegistration to the map coordinates and subpixel-level co-registration among the images should be conducted. However, applying well-known matching methods, such as scale-invariant feature transform and speeded up robust features for VHR multi-temporal images, has limitations. First, they cannot be used for matching an optical image to heterogeneous non-optical data for georegistration. Second, they produce a local misalignment induced by differences in acquisition conditions, such as acquisition platform stability, the sensor's off-nadir angle, and relief displacement of the considered scene. Therefore, this study addresses the problem by proposing an automated geo/co-registration framework for full-scene multi-temporal images acquired from a VHR optical satellite sensor. The proposed method comprises two primary steps: (1) a global georegistration process, followed by (2) a fine co-registration process. During the first step, two-dimensional multi-temporal satellite images are matched to three-dimensional topographic maps to assign the map coordinates. During the second step, a local analysis of registration noise pixels extracted between the multi-temporal images that have been mapped to the map coordinates is conducted to extract a large number of well-distributed corresponding points (CPs). The CPs are finally used to construct a non-rigid transformation function that enables minimization of the local misalignment existing among the images. Experiments conducted on five Kompsat-3 full scenes confirmed the effectiveness of the proposed framework, showing that the georegistration performance resulted in an approximately pixel-level accuracy for most of the scenes, and the co-registration performance further improved the results among all combinations of the georegistered Kompsat-3 image pairs by increasing the calculated cross-correlation values.

Towards a consistent framework to oversample multi-sensors, multi-species satellite data into a common grid

NASA Astrophysics Data System (ADS)

Sun, K.; Zhu, L.; Gonzalez Abad, G.; Nowlan, C. R.; Miller, C. E.; Huang, G.; Liu, X.; Chance, K.; Yang, K.

2017-12-01

It has been well demonstrated that regridding Level 2 products (satellite observations from individual footprints, or pixels) from multiple sensors/species onto regular spatial and temporal grids makes the data more accessible for scientific studies and can even lead to additional discoveries. However, synergizing multiple species retrieved from multiple satellite sensors faces many challenges, including differences in spatial coverage, viewing geometry, and data filtering criteria. These differences will lead to errors and biases if not treated carefully. Operational gridded products are often at 0.25°×0.25° resolution with a global scale, which is too coarse for local heterogeneous emission sources (e.g., urban areas), and at fixed temporal intervals (e.g., daily or monthly). We propose a consistent framework to fully use and properly weight the information of all possible individual satellite observations. A key aspect of this work is an accurate knowledge of the spatial response function (SRF) of the satellite Level 2 pixels. We found that the conventional overlap-area-weighting method (tessellation) is accurate only when the SRF is homogeneous within the parameterized pixel boundary and zero outside the boundary. There will be a tessellation error if the SRF is a smooth distribution, and if this distribution is not properly considered. On the other hand, discretizing the SRF at the destination grid will also induce errors. By balancing these error sources, we found that the SRF should be used in gridding OMI data to 0.2° for fine resolutions. Case studies by merging multiple species and wind data into 0.01° grid will be shown in the presentation.

Asynchronous Processing of a Constellation of Geostationary and Polar-Orbiting Satellites for Fire Detection and Smoke Estimation

NASA Astrophysics Data System (ADS)

Hyer, E. J.; Peterson, D. A.; Curtis, C. A.; Schmidt, C. C.; Hoffman, J.; Prins, E. M.

2014-12-01

The Fire Locating and Monitoring of Burning Emissions (FLAMBE) system converts satellite observations of thermally anomalous pixels into spatially and temporally continuous estimates of smoke release from open biomass burning. This system currently processes data from a constellation of 5 geostationary and 2 polar-orbiting sensors. Additional sensors, including NPP VIIRS and the imager on the Korea COMS-1 geostationary satellite, will soon be added. This constellation experiences schedule changes and outages of various durations, making the set of available scenes for fire detection highly variable on an hourly and daily basis. Adding to the complexity, the latency of the satellite data is variable between and within sensors. FLAMBE shares with many fire detection systems the goal of detecting as many fires as possible as early as possible, but the FLAMBE system must also produce a consistent estimate of smoke production with minimal artifacts from the changing constellation. To achieve this, NRL has developed a system of asynchronous processing and cross-calibration that permits satellite data to be used as it arrives, while preserving the consistency of the smoke emission estimates. This talk describes the asynchronous data ingest methodology, including latency statistics for the constellation. We also provide an overview and show results from the system we have developed to normalize multi-sensor fire detection for consistency.

Towards Simpler Custom and OpenSearch Services for Voluminous NEWS Merged A-Train Data (Invited)

NASA Astrophysics Data System (ADS)

Hua, H.; Fetzer, E.; Braverman, A. J.; Lewis, S.; Henderson, M. L.; Guillaume, A.; Lee, S.; de La Torre Juarez, M.; Dang, H. T.

2010-12-01

To simplify access to large and complex satellite data sets for climate analysis and model verification, we developed web services that is used to study long-term and global-scale trends in climate, water and energy cycle, and weather variability. A related NASA Energy and Water Cycle Study (NEWS) task has created a merged NEWS Level 2 data from multiple instruments in NASA’s A-Train constellation of satellites. We used this data to enable creation of climatologies that include correlation between observed temperature, water vapor and cloud properties from the A-Train sensors. Instead of imposing on the user an often rigid and limiting web-based analysis environment, we recognize the need for simple and well-designed services so that users can perform analysis in their own familiar computing environments. Custom on-demand services were developed to improve data accessibility of voluminous multi-sensor data. Services enabling geospatial, geographical, and multi-sensor parameter subsets of the data, as well a custom time-averaged Level 3 service will be presented. We will also show how a Level 3Q data reduction approach can be used to help “browse” the voluminous multi-sensor Level 2 data. An OpenSearch capability with full text + space + time search of data products will also be presented as an approach to facilitated interoperability with other data systems. We will present our experiences for improving user usability as well as strategies for facilitating interoperability with other data systems.

Earth Observation Satellites and Chinese Applications

NASA Astrophysics Data System (ADS)

Li, D.

In this talk existing and future Earth observation satellites are briefly described These satellites include meteorological satellites ocean satellites land resources satellites cartographic satellites and gravimetric satellites The Chinese government has paid and will pay more attention to and put more effort into enhancing Chinese earth observation satellite programs in the next fifteen years The utilization of these satellites will effectively help human beings to solve problems it faces in areas such as population natural resources and environment and natural hazards The author will emphasize the originality of the scientific and application aspects of the Chinese program in the field of Earth observations The main applications include early warning and prevention of forest fires flooding and drought disaster water and ocean ice disasters monitoring of landslides and urban subsidence investigation of land cover change and urban expansion as well as urban and rural planning The author introduces the most up-to-date technology used by Chinese scientists including fusion and integration of multi-sensor multi-platform optical and SAR data of remote sensing Most applications in China have obtained much support from related international organizations and universities around the world These applications in China are helpful for economic construction and the efficient improvement of living quality

Coherent Uncertainty Analysis of Aerosol Measurements from Multiple Satellite Sensors

NASA Technical Reports Server (NTRS)

Petrenko, M.; Ichoku, C.

2013-01-01

Aerosol retrievals from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS altogether, a total of 11 different aerosol products were comparatively analyzed using data collocated with ground-based aerosol observations from the Aerosol Robotic Network (AERONET) stations within the Multi-sensor Aerosol Products Sampling System (MAPSS, http://giovanni.gsfc.nasa.gov/mapss/ and http://giovanni.gsfc.nasa.gov/aerostat/). The analysis was performed by comparing quality-screened satellite aerosol optical depth or thickness (AOD or AOT) retrievals during 2006-2010 to available collocated AERONET measurements globally, regionally, and seasonally, and deriving a number of statistical measures of accuracy. We used a robust statistical approach to detect and remove possible outliers in the collocated data that can bias the results of the analysis. Overall, the proportion of outliers in each of the quality-screened AOD products was within 12%. Squared correlation coefficient (R2) values of the satellite AOD retrievals relative to AERONET exceeded 0.6, with R2 for most of the products exceeding 0.7 over land and 0.8 over ocean. Root mean square error (RMSE) values for most of the AOD products were within 0.15 over land and 0.09 over ocean. We have been able to generate global maps showing regions where the different products present advantages over the others, as well as the relative performance of each product over different landcover types. It was observed that while MODIS, MISR, and SeaWiFS provide accurate retrievals over most of the landcover types, multi-angle capabilities make MISR the only sensor to retrieve reliable AOD over barren and snow / ice surfaces. Likewise, active sensing enables CALIOP to retrieve aerosol properties over bright-surface shrublands more accurately than the other sensors, while POLDER, which is the only one of the sensors capable of measuring polarized aerosols, outperforms other sensors in certain smoke-dominated regions, including broadleaf evergreens in Brazil and South-East Asia.

Developing Information Services and Tools to Access and Evaluate Data Quality in Global Satellite-based Precipitation Products

NASA Astrophysics Data System (ADS)

Liu, Z.; Shie, C. L.; Meyer, D. J.

2017-12-01

Global satellite-based precipitation products have been widely used in research and applications around the world. Compared to ground-based observations, satellite-based measurements provide precipitation data on a global scale, especially in remote continents and over oceans. Over the years, satellite-based precipitation products have evolved from single sensor and single algorithm to multi-sensors and multi-algorithms. As a result, many satellite-based precipitation products have been enhanced such as spatial and temporal coverages. With inclusion of ground-based measurements, biases of satellite-based precipitation products have been significantly reduced. However, data quality issues still exist and can be caused by many factors such as observations, satellite platform anomaly, algorithms, production, calibration, validation, data services, etc. The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) is home to NASA global precipitation product archives including the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM), as well as other global and regional precipitation products. Precipitation is one of the top downloaded and accessed parameters in the GES DISC data archive. Meanwhile, users want to easily locate and obtain data quality information at regional and global scales to better understand how precipitation products perform and how reliable they are. As data service providers, it is necessary to provide an easy access to data quality information, however, such information normally is not available, and when it is available, it is not in one place and difficult to locate. In this presentation, we will present challenges and activities at the GES DISC to address precipitation data quality issues.

Smoothing-Based Relative Navigation and Coded Aperture Imaging

NASA Technical Reports Server (NTRS)

Saenz-Otero, Alvar; Liebe, Carl Christian; Hunter, Roger C.; Baker, Christopher

2017-01-01

This project will develop an efficient smoothing software for incremental estimation of the relative poses and velocities between multiple, small spacecraft in a formation, and a small, long range depth sensor based on coded aperture imaging that is capable of identifying other spacecraft in the formation. The smoothing algorithm will obtain the maximum a posteriori estimate of the relative poses between the spacecraft by using all available sensor information in the spacecraft formation.This algorithm will be portable between different satellite platforms that possess different sensor suites and computational capabilities, and will be adaptable in the case that one or more satellites in the formation become inoperable. It will obtain a solution that will approach an exact solution, as opposed to one with linearization approximation that is typical of filtering algorithms. Thus, the algorithms developed and demonstrated as part of this program will enhance the applicability of small spacecraft to multi-platform operations, such as precisely aligned constellations and fractionated satellite systems.

Microdot - A Four-Bit Microcontroller Designed for Distributed Low-End Computing in Satellites

NASA Astrophysics Data System (ADS)

2002-03-01

Many satellites are an integrated collection of sensors and actuators that require dedicated real-time control. For single processor systems, additional sensors require an increase in computing power and speed to provide the multi-tasking capability needed to service each sensor. Faster processors cost more and consume more power, which taxes a satellite's power resources and may lead to shorter satellite lifetimes. An alternative design approach is a distributed network of small and low power microcontrollers designed for space that handle the computing requirements of each individual sensor and actuator. The design of microdot, a four-bit microcontroller for distributed low-end computing, is presented. The design is based on previous research completed at the Space Electronics Branch, Air Force Research Laboratory (AFRL/VSSE) at Kirtland AFB, NM, and the Air Force Institute of Technology at Wright-Patterson AFB, OH. The Microdot has 29 instructions and a 1K x 4 instruction memory. The distributed computing architecture is based on the Philips Semiconductor I2C Serial Bus Protocol. A prototype was implemented and tested using an Altera Field Programmable Gate Array (FPGA). The prototype was operable to 9.1 MHz. The design was targeted for fabrication in a radiation-hardened-by-design gate-array cell library for the TSMC 0.35 micrometer CMOS process.

Push-Broom-Type Very High-Resolution Satellite Sensor Data Correction Using Combined Wavelet-Fourier and Multiscale Non-Local Means Filtering.

PubMed

Kang, Wonseok; Yu, Soohwan; Seo, Doochun; Jeong, Jaeheon; Paik, Joonki

2015-09-10

In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments.

Push-Broom-Type Very High-Resolution Satellite Sensor Data Correction Using Combined Wavelet-Fourier and Multiscale Non-Local Means Filtering

PubMed Central

Kang, Wonseok; Yu, Soohwan; Seo, Doochun; Jeong, Jaeheon; Paik, Joonki

2015-01-01

In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments. PMID:26378532

Design study for multi-channel tape recorder system, volume 1

NASA Technical Reports Server (NTRS)

1972-01-01

The means of storing multispectral, high resolution sensor data on an Earth observing satellite are studied. It is concluded that this is best done digitally on a multi-track, longitudinal, magnetic tape recorder. The machine proposed will store 8 X 10 to the 10th power bits of data on 1040 m of 51 mm-wide magnetic tape mounted on two co-planar reels.

Monitoring volcanic thermal activity by Robust Satellite Techniques: achievements and perspectives

NASA Astrophysics Data System (ADS)

Tramutoli, V.; Marchese, F.; Mazzeo, G.; Pergola, N.

2009-12-01

Satellite data have been increasingly used in last decades to study active volcanoes and to monitor thermal activity variation in space-time domain. Several satellite techniques and original methods have been developed and tested, devoted to hotspot detection and thermal monitoring. Among them, a multi-temporal approach, named RST (Robust Satellite Techniques), has shown high performances in detecting hotspots, with a low false positive rate under different observational and atmospheric conditions, providing also a potential toward low-level thermal anomalies which may announce incoming eruptions. As the RST scheme is intrinsically exportable on different geographic areas and satellite sensors, it has been applied and tested on a number of volcanoes and in different environmental conditions. This work presents major results and outcomes of studies carried out on Etna and Stromboli (Italy), Merapi (Java Indonesia), Asamayama (Japan), Jebel Al Tair (Yemen) by using different satellite systems and sensors (e.g. NOAA-AVHRR, EOS-MODIS, MSG-SEVIRI). Performances on hotspot detection, early warning and real-time monitoring, together with capabilities in possible thermal precursor identification, will be presented and discussed.

Global Precipitation Measurement (GPM) Mission: Overview and Status

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2012-01-01

The Global Precipitation Measurement (GPM) Mission is an international satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. NASA and JAXA will deploy a Core Observatory in 2014 to serve as a reference satellite to unify precipitation measurements from the constellation of sensors. The GPM Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The DPR will be the first dual-frequency radar in space to provide not only measurements of 3-D precipitation structures but also quantitative information on microphysical properties of precipitating particles. The DPR and GMI measurements will together provide a database that relates vertical hydrometeor profiles to multi-frequency microwave radiances over a variety of environmental conditions across the globe. This combined database will be used as a common transfer standard for improving the accuracy and consistency of precipitation retrievals from all constellation radiometers. For global coverage, GPM relies on existing satellite programs and new mission opportunities from a consortium of partners through bilateral agreements with either NASA or JAXA. Each constellation member may have its unique scientific or operational objectives but contributes microwave observations to GPM for the generation and dissemination of unified global precipitation data products. In addition to the DPR and GMI on the Core Observatory, the baseline GPM constellation consists of the following sensors: (1) Special Sensor Microwave Imager/Sounder (SSMIS) instruments on the U.S. Defense Meteorological Satellite Program (DMSP) satellites, (2) the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on the GCOM-W1 satellite of JAXA, (3) the Multi-Frequency Microwave Scanning Radiometer (MADRAS) and the multi-channel microwave humidity sounder (SAPHIR) on the French-Indian MeghaTropiques satellite, (4) the Microwave Humidity Sounder (MHS) on the National Oceanic and Atmospheric Administration (NOAA)-19, (5) MHS instruments on MetOp satellites launched by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), (6) the Advanced Technology Microwave Sounder (ATMS) on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), and (7) ATMS instruments on the NOAA-NASA Joint Polar Satellite System (JPSS) satellites. Data from Chinese and Russian microwave radiometers may also become available through international collaboration under the auspices of the Committee on Earth Observation Satellites (CEOS) and Group on Earth Observations (GEO). The current generation of global rainfall products combines observations from a network of uncoordinated satellite missions using a variety of merging techniques. GPM will provide "next-generation" precipitation products characterized by: (1) more accurate instantaneous precipitation estimate (especially for light rain and cold-season solid precipitation), (2) intercalibrated microwave brightness temperatures from constellation radiometers within a consistent framework, and (3) unified precipitation retrievals from constellation radiometers using a common a priori hydrometeor database constrained by combined radar/radiometer measurements provided by the GPM Core Observatory. GPM is a science mission with integrated applications goals. GPM will provide a key measurement to improve understanding of global water cycle variability and freshwater availability in a changing climate. The DPR and GMI measurements will offer insights into 3-dimensional structures of hurricanes and midlatitude storms, microphysical properties of precipitating particles, and latent heat associated with precipitation processes. The GPM mission will also make data available in near realtime (within 3 hours of observations) forocietal applications ranging from position fixes of storm centers, numerical weather prediction, flood forecasting, freshwater management, landslide warning, crop prediction, to tracking of water-borne diseases. An overview of the GPM mission design, retrieval strategy, ground validation activities, and international science collaboration will be presented.

Integrated approach using multi-platform sensors for enhanced high-resolution daily ice cover product

NASA Astrophysics Data System (ADS)

Bonev, George; Gladkova, Irina; Grossberg, Michael; Romanov, Peter; Helfrich, Sean

2016-09-01

The ultimate objective of this work is to improve characterization of the ice cover distribution in the polar areas, to improve sea ice mapping and to develop a new automated real-time high spatial resolution multi-sensor ice extent and ice edge product for use in operational applications. Despite a large number of currently available automated satellite-based sea ice extent datasets, analysts at the National Ice Center tend to rely on original satellite imagery (provided by satellite optical, passive microwave and active microwave sensors) mainly because the automated products derived from satellite optical data have gaps in the area coverage due to clouds and darkness, passive microwave products have poor spatial resolution, automated ice identifications based on radar data are not quite reliable due to a considerable difficulty in discriminating between the ice cover and rough ice-free ocean surface due to winds. We have developed a multisensor algorithm that first extracts maximum information on the sea ice cover from imaging instruments VIIRS and MODIS, including regions covered by thin, semitransparent clouds, then supplements the output by the microwave measurements and finally aggregates the results into a cloud gap free daily product. This ability to identify ice cover underneath thin clouds, which is usually masked out by traditional cloud detection algorithms, allows for expansion of the effective coverage of the sea ice maps and thus more accurate and detailed delineation of the ice edge. We have also developed a web-based monitoring system that allows comparison of our daily ice extent product with the several other independent operational daily products.

Design and testing of a multi-sensor pedestrian location and navigation platform.

PubMed

Morrison, Aiden; Renaudin, Valérie; Bancroft, Jared B; Lachapelle, Gérard

2012-01-01

Navigation and location technologies are continually advancing, allowing ever higher accuracies and operation under ever more challenging conditions. The development of such technologies requires the rapid evaluation of a large number of sensors and related utilization strategies. The integration of Global Navigation Satellite Systems (GNSSs) such as the Global Positioning System (GPS) with accelerometers, gyros, barometers, magnetometers and other sensors is allowing for novel applications, but is hindered by the difficulties to test and compare integrated solutions using multiple sensor sets. In order to achieve compatibility and flexibility in terms of multiple sensors, an advanced adaptable platform is required. This paper describes the design and testing of the NavCube, a multi-sensor navigation, location and timing platform. The system provides a research tool for pedestrian navigation, location and body motion analysis in an unobtrusive form factor that enables in situ data collections with minimal gait and posture impact. Testing and examples of applications of the NavCube are provided.

Evaluation and cross-comparison of vegetation indices for crop monitoring from sentinel-2 and worldview-2 images

NASA Astrophysics Data System (ADS)

Psomiadis, Emmanouil; Dercas, Nicholas; Dalezios, Nicolas R.; Spyropoulos, Nikolaos V.

2017-10-01

Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing applications are designed to provide farmers with timely crop monitoring and production information. Such information can be used to identify crop vigor problems. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation. However, due to the various sensor characteristics, there are differences among VIs derived from multiple sensors for the same target. Therefore, multi-sensor VI capability and effectiveness are critical but complicated issues in the application of multi-sensor vegetation observations. Various factors such as the atmospheric conditions during acquisition, sensor and geometric characteristics, such as viewing angle, field of view, and sun elevation influence direct comparability of vegetation indicators among different sensors. In the present study, two experimental areas were used which are located near the villages Nea Lefki and Melia of Larissa Prefecture in Thessaly Plain area, containing a wheat and a cotton crop, respectively. Two satellite systems with different spatial resolution, WorldView-2 (W2) and Sentinel-2 (S2) with 2 and 10 meters pixel size, were used. Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) were calculated and a statistical comparison of the VIs was made to designate their correlation and dependency. Finally, several other innovative indices were calculated and compared to evaluate their effectiveness in the detection of problematic plant growth areas.

Online Tools for Uncovering Data Quality (DQ) Issues in Satellite-Based Global Precipitation Products

NASA Technical Reports Server (NTRS)

Liu, Zhong; Heo, Gil

2015-01-01

Data quality (DQ) has many attributes or facets (i.e., errors, biases, systematic differences, uncertainties, benchmark, false trends, false alarm ratio, etc.)Sources can be complicated (measurements, environmental conditions, surface types, algorithms, etc.) and difficult to be identified especially for multi-sensor and multi-satellite products with bias correction (TMPA, IMERG, etc.) How to obtain DQ info fast and easily, especially quantified info in ROI Existing parameters (random error), literature, DIY, etc.How to apply the knowledge in research and applications.Here, we focus on online systems for integration of products and parameters, visualization and analysis as well as investigation and extraction of DQ information.

The Role of Combination Techniques in Maximizing the Utility of Precipitation Estimates from Several Multi-Purpose Remote-Sensing Systems

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.; Curtis, Scott; Einaudi, Franco (Technical Monitor)

2001-01-01

Multi-purpose remote-sensing products from various satellites have proved crucial in developing global estimates of precipitation. Examples of these products include low-earth-orbit and geosynchronous-orbit infrared (leo- and geo-IR), Outgoing Longwave Radiation (OLR), Television Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) data, and passive microwave data such as that from the Special Sensor Microwave/ Imager (SSM/I). Each of these datasets has served as the basis for at least one useful quasi-global precipitation estimation algorithm; however, the quality of estimates varies tremendously among the algorithms for the different climatic regions around the globe.

A multi-sensor data-driven methodology for all-sky passive microwave inundation retrieval

NASA Astrophysics Data System (ADS)

Takbiri, Zeinab; Ebtehaj, Ardeshir M.; Foufoula-Georgiou, Efi

2017-06-01

We present a multi-sensor Bayesian passive microwave retrieval algorithm for flood inundation mapping at high spatial and temporal resolutions. The algorithm takes advantage of observations from multiple sensors in optical, short-infrared, and microwave bands, thereby allowing for detection and mapping of the sub-pixel fraction of inundated areas under almost all-sky conditions. The method relies on a nearest-neighbor search and a modern sparsity-promoting inversion method that make use of an a priori dataset in the form of two joint dictionaries. These dictionaries contain almost overlapping observations by the Special Sensor Microwave Imager and Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F17 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua and Terra satellites. Evaluation of the retrieval algorithm over the Mekong Delta shows that it is capable of capturing to a good degree the inundation diurnal variability due to localized convective precipitation. At longer timescales, the results demonstrate consistency with the ground-based water level observations, denoting that the method is properly capturing inundation seasonal patterns in response to regional monsoonal rain. The calculated Euclidean distance, rank-correlation, and also copula quantile analysis demonstrate a good agreement between the outputs of the algorithm and the observed water levels at monthly and daily timescales. The current inundation products are at a resolution of 12.5 km and taken twice per day, but a higher resolution (order of 5 km and every 3 h) can be achieved using the same algorithm with the dictionary populated by the Global Precipitation Mission (GPM) Microwave Imager (GMI) products.

Multi-platform assessment of turbidity plumes during dredging operations in a major estuarine system

NASA Astrophysics Data System (ADS)

Caballero, Isabel; Navarro, Gabriel; Ruiz, Javier

2018-06-01

Dredging activities in estuaries frequently cause deleterious environmental effects on the water quality which can impact flora, fauna, and hydrodynamics, among others. A medium- and high-resolution satellite-based procedure is used in this study to monitor turbidity plumes generated during the dredging operations in the Guadalquivir estuary, a major estuarine system providing important ecosystem services in southwest Europe. A multi-sensor scheme is evaluated using a combination of five public and commercial medium- and high-resolution satellites, including Landsat-8, Sentinel-2A, WorldView-2, WorldView-3, and GeoEye-1, with pixel sizes ranging from 30 m to 0.3 m. Applying a multi-conditional algorithm after the atmospheric correction of the optical imagery with ACOLITE, Sen2Cor and QUAC processors, it is demonstrated the feasibility to monitoring suspended solids during dredging operations at a spatial resolution unachievable with traditional satellite-based ocean color sensors (>300 m). The frame work can be used to map on-going, post and pre-dredging activities and asses Total Suspended Solids (TSS) anomalies caused by natural and anthropogenic processes in coastal and inland waters. These promising results are suitable to effectively improve the assessment of features relevant to environmental policies for the challenging coastal management and might serve as a notable contribution to the Earth Observation Program.

Evaluation of Long-Term Cloud-Resolving Model Simulations Using Satellite Radiance Observations and Multi-Frequency Satellite Simulators

NASA Technical Reports Server (NTRS)

Matsui, Toshihisa; Zeng, Xiping; Tao, Wei-Kuo; Masunaga, Hirohiko; Olson, William S.; Lang, Stephen

2008-01-01

This paper proposes a methodology known as the Tropical Rainfall Measuring Mission (TRMM) Triple-Sensor Three-step Evaluation Framework (T3EF) for the systematic evaluation of precipitating cloud types and microphysics in a cloud-resolving model (CRM). T3EF utilizes multi-frequency satellite simulators and novel statistics of multi-frequency radiance and backscattering signals observed from the TRMM satellite. Specifically, T3EF compares CRM and satellite observations in the form of combined probability distributions of precipitation radar (PR) reflectivity, polarization-corrected microwave brightness temperature (Tb), and infrared Tb to evaluate the candidate CRM. T3EF is used to evaluate the Goddard Cumulus Ensemble (GCE) model for cases involving the South China Sea Monsoon Experiment (SCSMEX) and Kwajalein Experiment (KWAJEX). This evaluation reveals that the GCE properly captures the satellite-measured frequencies of different precipitating cloud types in the SCSMEX case but underestimates the frequencies of deep convective and deep stratiform types in the KWAJEX case. Moreover, the GCE tends to simulate excessively large and abundant frozen condensates in deep convective clouds as inferred from the overestimated GCE-simulated radar reflectivities and microwave Tb depressions. Unveiling the detailed errors in the GCE s performance provides the best direction for model improvements.

Cruise Missile Penaid Nonproliferation: Hindering the Spread of Countermeasures Against Cruise Missile Defenses

DTIC Science & Technology

2014-01-01

this report treats cruise missile penaids and UAV penaids, sometimes called “self-protection” (see La Franchi , 2004), interchangeably. 8 Cruise...Penaid Export Controls 41 2. Anti-Jam Equipment MTCR Item 11.A.3.b.3 (Avionics): Current text: “Receiving equipment for Global Navigation Satellite...subsystems beyond those for global navigation satellite systems to all sensor, navigation, and communications systems, and add “including multi-mode

Characterization of precipitation features over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2013-12-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, surface observations, and models to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets of TMPA 3B42, CMORPH, and PERSIANN. The satellite based QPEs are compared over the concurrent period with the NCEP Stage IV product, which is a near real time product providing precipitation data at the hourly temporal scale gridded at a nominal 4-km spatial resolution. In addition, remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model), which provides gridded precipitation estimates that are used as a baseline for multi-sensor QPE products comparison. The comparisons are performed at the annual, seasonal, monthly, and daily scales with focus on selected river basins (Southeastern US, Pacific Northwest, Great Plains). While, unconditional annual rain rates present a satisfying agreement between all products, results suggest that satellite QPE datasets exhibit important biases in particular at higher rain rates (≥4 mm/day). Conversely, on seasonal scales differences between remotely sensed data and ground surface observations can be greater than 50% and up to 90% for low daily accumulation (≤1 mm/day) such as in the Western US (summer) and Central US (winter). The conditional analysis performed using different daily rainfall accumulation thresholds (from low rainfall intensity to intense precipitation) shows that while intense events measured at the ground are infrequent (around 2% for daily accumulation above 2 inches/day), remotely sensed products displayed differences from 20-50% and up to 90-100%. A discussion on the impact of differing spatial and temporal resolutions with respect to the datasets ability to capture extreme precipitation events is also provided. Furthermore, this work is part of a broader effort to evaluate long-term multi-sensor QPEs in the perspective of developing Climate Data Records (CDRs) for precipitation.

A multi-scale automatic observatory of soil moisture and temperature served for satellite product validation in Tibetan Plateau

NASA Astrophysics Data System (ADS)

Tang, S.; Dong, L.; Lu, P.; Zhou, K.; Wang, F.; Han, S.; Min, M.; Chen, L.; Xu, N.; Chen, J.; Zhao, P.; Li, B.; Wang, Y.

2016-12-01

Due to the lack of observing data which match the satellite pixel size, the inversion accuracy of satellite products in Tibetan Plateau(TP) is difficult to be evaluated. Hence, the in situ observations are necessary to support the calibration and validation activities. Under the support of the Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III) projec a multi-scale automatic observatory of soil moisture and temperature served for satellite product validation (TIPEX-III-SMTN) were established in Tibetan Plateau. The observatory consists of two regional scale networks, including the Naqu network and the Geji network. The Naqu network is located in the north of TP, and characterized by alpine grasslands. The Geji network is located in the west of TP, and characterized by marshes. Naqu network includes 33 stations, which are deployed in a 75KM*75KM region according to a pre-designed pattern. At Each station, soil moisture and temperature are measured by five sensors at five soil depths. One sensor is vertically inserted into 0 2 cm depth to measure the averaged near-surface soil moisture and temperature. The other four sensors are horizontally inserted at 5, 10, 20, and 30 cm depths, respectively. The data are recorded every 10 minutes. A wireless transmission system is applied to transmit the data in real time, and a dual power supply system is adopted to keep the continuity of the observation. The construction of Naqu network has been accomplished in August, 2015, and Geji network will be established before Oct., 2016. Observations acquired from TIPEX-III-SMTN can be used to validate satellite products with different spatial resolution, and TIPEX-III-SMTN can also be used as a complementary of the existing similar networks in this area, such as CTP-SMTMN (the multiscale Soil Moistureand Temperature Monitoring Network on the central TP) . Keywords: multi-scale soil moisture soil temperature, Tibetan Plateau Acknowledgments: This work was jointly supported by CMA Special Fund for Scientific Research in the Public Interest (Grant No. GYHY201406001, GYHY201206008-01), and Climate change special fund (QHBH2014)'

Performance Analysis of Satellite Missions for Multi-Temporal SAR Interferometry

PubMed Central

Belmonte, Antonella; Nutricato, Raffaele; Nitti, Davide O.; Chiaradia, Maria T.

2018-01-01

Multi-temporal InSAR (MTI) applications pose challenges related to the availability of coherent scattering from the ground surface, the complexity of the ground deformations, atmospheric artifacts, and visibility problems related to ground elevation. Nowadays, several satellite missions are available providing interferometric SAR data at different wavelengths, spatial resolutions, and revisit time. A new and interesting opportunity is provided by Sentinel-1, which has a spatial resolution comparable to that of previous ESA C-band sensors, and revisit times improved by up to 6 days. According to these different SAR space-borne missions, the present work discusses current and future opportunities of MTI applications in terms of ground instability monitoring. Issues related to coherent target detection, mean velocity precision, and product geo-location are addressed through a simple theoretical model assuming backscattering mechanisms related to point scatterers. The paper also presents an example of a multi-sensor ground instability investigation over Lesina Marina, a village in Southern Italy lying over a gypsum diapir, where a hydration process, involving the underlying anhydride, causes a smooth uplift and the formation of scattered sinkholes. More than 20 years of MTI SAR data have been processed, coming from both legacy ERS and ENVISAT missions, and latest-generation RADARSAT-2, COSMO-SkyMed, and Sentinel-1A sensors. Results confirm the presence of a rather steady uplift process, with limited to null variations throughout the whole monitored time-period. PMID:29702588

Performance Analysis of Satellite Missions for Multi-Temporal SAR Interferometry.

PubMed

Bovenga, Fabio; Belmonte, Antonella; Refice, Alberto; Pasquariello, Guido; Nutricato, Raffaele; Nitti, Davide O; Chiaradia, Maria T

2018-04-27

Multi-temporal InSAR (MTI) applications pose challenges related to the availability of coherent scattering from the ground surface, the complexity of the ground deformations, atmospheric artifacts, and visibility problems related to ground elevation. Nowadays, several satellite missions are available providing interferometric SAR data at different wavelengths, spatial resolutions, and revisit time. A new and interesting opportunity is provided by Sentinel-1, which has a spatial resolution comparable to that of previous ESA C-band sensors, and revisit times improved by up to 6 days. According to these different SAR space-borne missions, the present work discusses current and future opportunities of MTI applications in terms of ground instability monitoring. Issues related to coherent target detection, mean velocity precision, and product geo-location are addressed through a simple theoretical model assuming backscattering mechanisms related to point scatterers. The paper also presents an example of a multi-sensor ground instability investigation over Lesina Marina, a village in Southern Italy lying over a gypsum diapir, where a hydration process, involving the underlying anhydride, causes a smooth uplift and the formation of scattered sinkholes. More than 20 years of MTI SAR data have been processed, coming from both legacy ERS and ENVISAT missions, and latest-generation RADARSAT-2, COSMO-SkyMed, and Sentinel-1A sensors. Results confirm the presence of a rather steady uplift process, with limited to null variations throughout the whole monitored time-period.

Promise and Capability of NASA's Earth Observing System to Monitor Human-Induced Climate Variations

NASA Technical Reports Server (NTRS)

King, M. D.

2003-01-01

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. The Moderate Resolution Imaging Spectroradiometer (MODIS), developed as part of the Earth Observing System (EOS) and launched on Terra in December 1999 and Aqua in May 2002, is designed to meet the scientific needs for satellite remote sensing of clouds, aerosols, water vapor, and land and ocean surface properties. This sensor and multi-platform observing system is especially well suited to observing detailed interdisciplinary components of the Earth s surface and atmosphere in and around urban environments, including aerosol optical properties, cloud optical and microphysical properties of both liquid water and ice clouds, land surface reflectance, fire occurrence, and many other properties that influence the urban environment and are influenced by them. In this presentation I will summarize the current capabilities of MODIS and other EOS sensors currently in orbit to study human-induced climate variations.

Multi-functional Extreme Environment Surfaces: Nanotribology for Air and Space

DTIC Science & Technology

2010-09-14

TRIBOLOGY ( QCM ) (STM) Fundamental Challenges and Unsolved Issues How do adsorbed and tribo-generated films impact friction and wear? How is heat dissipated...InfraRed sensor mechanisms Jet engine bearings 2 mm NCD MCD 300 mm Thrust II: Cryotribology and Nanocrystalline Diamond for Space Applications...Satellite bearings, InfraRed sensor mechanisms Jet engine bearings 2 mm NCD MCD 300 mm Five Years ago: Three publications in the area of vacuum

Estimating Precipitation Susceptibility in Warm Marine Clouds Using Multi-sensor Aerosol and Cloud Products from A-Train Satellites

NASA Astrophysics Data System (ADS)

Bai, H.; Gong, C.; Wang, M.; Zhang, Z.

2017-12-01

Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol-cloud interactions and constraining aerosol indirect effects. However, large discrepancies exist in the previous satellite estimates of precipitation susceptibility. In this paper, multi-sensor aerosol and cloud products, including those from CALIPSO, CloudSat, MODIS, and AMSR-E from June 2006 to April 2011 are analyzed to estimate precipitation susceptibility (including precipitation frequency susceptibility SPOP, precipitation intensity susceptibility SI, and precipitation rate susceptibility SR) in warm marine clouds. Our results show that SPOP demonstrates relatively robust features throughout independent LWP products and diverse rain products. In contrast, the behaviors of SI are more subject to LWP or rain products. Our results further show that SPOP strongly depends on atmospherics stability, with larger value under more stable environment. Precipitation susceptibility calculated with respect to cloud droplet number concentration (CDNC) is generally much larger than that estimated with respect to aerosol index (AI), which results from the weak dependency of CDNC on AI.

The Global Precipitation Measurement (GPM) Mission: Overview and U.S. Science Status

NASA Astrophysics Data System (ADS)

Hou, Arthur Y.; Skofronick-Jackson, Gail; Stocker, Erich F.

2013-04-01

The Global Precipitation Measurement (GPM) Mission is a satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors provided by a consortium of international partners. NASA and JAXA will deploy a Core Observatory in 2014 to serve as a reference satellite for precipitation measurements by the constellation sensors. The GPM Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The DPR, the first dual-frequency radar in space, will provide not only measurements of 3-D precipitation structures but also quantitative information on microphysical properties of precipitating particles. The DPR and GMI measurements will together provide a database that relates vertical hydrometeor profiles to multi-frequency microwave radiances over a variety of environmental conditions across the globe. This combined database will serve as a common transfer standard for improving the accuracy and consistency of precipitation retrievals from all constellation radiometers. In addition to the Core Observatory, the GPM constellation consists of (1) Special Sensor Microwave Imager/Sounder (SSMIS) instruments on the U.S. Defense Meteorological Satellite Program (DMSP) satellites, (2) the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on the GCOM-W1 satellite of JAXA, (3) the Multi-Frequency Microwave Scanning Radiometer (MADRAS) and the multi-channel microwave humidity sounder (SAPHIR) on the French-Indian Megha-Tropiques satellite, (4) the Microwave Humidity Sounder (MHS) on the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites (POES), (5) MHS instruments on MetOp satellites launched by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), (6) the Advanced Technology Microwave Sounder (ATMS) on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), and (7) ATMS instruments on the NOAA-NASA Joint Polar Satellite System (JPSS) satellites. Each constellation member may have its unique scientific or operational objectives but contributes microwave observations to GPM for the generation and dissemination of unified global precipitation data products. Currently global rainfall products combine observations from a network of uncoordinated satellite missions using a variety of merging techniques. GPM is designed to provide the next-generation of precipitation products characterized by: (1) more accurate instantaneous precipitation estimate (especially for light rain and cold-season solid precipitation), (2) intercalibrated microwave brightness temperatures from constellation radiometers within a consistent framework, and (3) unified precipitation retrievals from constellation radiometers using a common a priori hydrometeor database consistent with combined radar/radiometer measurements by the GPM Core Observatory. As a science mission with integrated applications goals, GPM will advance the understanding of global water cycle variability in a changing climate by offering insights into 3-dimensional structures of hurricanes and midlatitude storms, microphysical properties of precipitating particles, and latent heat associated with precipitation processes. The GPM Mission will also make data available in near realtime (within 3 hours of observations) for societal applications ranging from position fixes of storm centers, numerical weather prediction, flood forecasting, freshwater management, landslide warning, crop prediction, to tracking of water-borne diseases. This presentation will give an overview of the GPM mission and its development status approximately one-year prior to launch.

Spatial Distribution of Accuracy of Aerosol Retrievals from Multiple Satellite Sensors

NASA Technical Reports Server (NTRS)

Petrenko, Maksym; Ichoku, Charles

2012-01-01

Remote sensing of aerosols from space has been a subject of extensive research, with multiple sensors retrieving aerosol properties globally on a daily or weekly basis. The diverse algorithms used for these retrievals operate on different types of reflected signals based on different assumptions about the underlying physical phenomena. Depending on the actual retrieval conditions and especially on the geographical location of the sensed aerosol parcels, the combination of these factors might be advantageous for one or more of the sensors and unfavorable for others, resulting in disagreements between similar aerosol parameters retrieved from different sensors. In this presentation, we will demonstrate the use of the Multi-sensor Aerosol Products Sampling System (MAPSS) to analyze and intercompare aerosol retrievals from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS. Based on this intercomparison, we are determining geographical locations where these products provide the greatest accuracy of the retrievals and identifying the products that are the most suitable for retrieval at these locations. The analyses are performed by comparing quality-screened satellite aerosol products to available collocated ground-based aerosol observations from the Aerosol Robotic Network (AERONET) stations, during the period of 2006-2010 when all the satellite sensors were operating concurrently. Furthermore, we will discuss results of a statistical approach that is applied to the collocated data to detect and remove potential data outliers that can bias the results of the analysis.

Korea Earth Observation Satellite Program

NASA Astrophysics Data System (ADS)

Baek, Myung-Jin; Kim, Zeen-Chul

via Korea Aerospace Research Institute (KARI) as the prime contractor in the area of Korea earth observation satellite program to enhance Korea's space program development capability. In this paper, Korea's on-going and future earth observation satellite programs are introduced: KOMPSAT- 1 (Korea Multi Purpose Satellite-1), KOMPSAT-2 and Communication, Broadcasting and Meteorological Satellite (CBMS) program. KOMPSAT-1 satellite successfully launched in December 1999 with Taurus launch vehicle. Since launch, KOMPSAT-1 is downlinking images of Korea Peninsular every day. Until now, KOMPSAT-1 has been operated more than 2 and half years without any major hardware malfunction for the mission operation. KOMPSAT-1 payload has 6.6m panchromatic spatial resolution at 685 km on-orbit and the spacecraft bus had NASA TOMS-EP (Total Ozone Mapping Spectrometer-Earth Probe) spacecraft bus heritage designed and built by TRW, U.S.A.KOMPSAT-1 program was international co-development program between KARI and TRW funded by Korean Government. be launched in 2004. Main mission objective is to provide geo-information products based on the multi-spectral high resolution sensor called Multi-Spectral Camera (MSC) which will provide 1m panchromatic and 4m multi-spectral high resolution images. ELOP of Israel is the prime contractor of the MSC payload system and KARI is the total system prime contractor including spacecraft bus development and ground segment. KARI also has the contract with Astrium of Europe for the purpose of technical consultation and hardware procurement. Based on the experience throughout KOMPSAT-1 and KOMPSAT-2 space system development, Korea is expecting to establish the infrastructure of developing satellite system. Currently, KOMPSAT-2 program is in the critical design stage. are scheduled to launch in 2008 and in 2014, respectively. The mission of CBMS consists of two areas. One is of space technology test for the communications mission, and the other is of a real- time environmental observation for meteorological mission on the geosynchronous orbit for public services. The CBMS is expected to weigh about 2 ~ 2.5 tons, and 6 channels of Ka-band and S- band transponder are equipped for communications service and observation payloads such as meteorological and ocean sensors. To increase the reliability of the first CBMS, a cooperative development with advanced foreign companies of the space business is being considered.

Lessons learned and way forward from 6 years of Aerosol_cci

NASA Astrophysics Data System (ADS)

Popp, Thomas; de Leeuw, Gerrit; Pinnock, Simon

2017-04-01

Within the ESA Climate Change Initiative (CCI) Aerosol_cci (2010 - 2017) conducts intensive work to improve and qualify algorithms for the retrieval of aerosol information from European sensors. Meanwhile, several validated (multi-) decadal time series of different aerosol parameters from complementary sensors are available: Aerosol Optical Depth (AOD), stratospheric extinction profiles, a qualitative Absorbing Aerosol Index (AAI), fine mode AOD, mineral dust AOD; absorption information and aerosol layer height are in an evaluation phase and the multi-pixel GRASP algorithm for the POLDER instrument is used for selected regions. Validation (vs. AERONET, MAN) and inter-comparison to other satellite datasets (MODIS, MISR, SeaWIFS) proved the high quality of the available datasets comparable to other satellite retrievals and revealed needs for algorithm improvement (for example for higher AOD values) which were taken into account in an iterative evolution cycle. The datasets contain pixel level uncertainty estimates which were also validated and improved in the reprocessing. The use of an ensemble method was tested, where several algorithms are applied to the same sensor. The presentation will summarize and discuss the lessons learned from the 6 years of intensive collaboration and highlight major achievements (significantly improved AOD quality, fine mode AOD, dust AOD, pixel level uncertainties, ensemble approach); also limitations and remaining deficits shall be discussed. An outlook will discuss the way forward for the continuous algorithm improvement and re-processing together with opportunities for time series extension with successor instruments of the Sentinel family and the complementarity of the different satellite aerosol products.

Merging Satellite Optical Sensors and Radar Altimetry for Daily River Discharge Estimation

NASA Astrophysics Data System (ADS)

Tarpanelli, A.; Santi, E. S.; Tourian, M. J.; Filippucci, P.; Amarnath, G.; Brocca, L.; Benveniste, J.

2017-12-01

River discharge is a fundamental physical variable of the hydrological cycle and notwithstanding its importance the monitoring of the flow in many parts of the Earth is still an open issue. Satellite sensors have great potential in offering new ways to monitor river discharge, because they guarantees regular, uniform and global measurements for long period thanks to the large number of satellites launched during the last twenty-five years. The multi-mission approach has been becoming a useful tool to integrate measurements and intensify the number of samples in space and time. In this study, we investigated the possibility to merge data from optical, i.e. Near InfraRed bands (from MODIS, MERIS, Landsat, and OLCI) and altimetry data (from Topex-Poseidon, Envisat/RA-2, Jason-2, SARAL/AltiKa and CryoSat-2) for estimating daily river discharge in Nigeria and Italy. The merging procedure is carried out by using artificial neural networks. Regarding the optical sensors, results are more affected by the temporal resolution than the spatial resolution. Landsat fails in the estimation of extreme events missing most of the peak values because of the long revisit time (14-16 days). Better performances are obtained with the Near InfraRed bands from MODIS and MERIS that give similar results in river discharge estimation. Finally, the multi-mission approach involving also radar altimetry data is found to be the most reliable tool to estimate river discharge in medium to large rivers.

Modeling UV-B Effects on Primary Production Throughout the Southern Ocean Using Multi-Sensor Satellite Data

NASA Technical Reports Server (NTRS)

Lubin, Dan

2001-01-01

This study has used a combination of ocean color, backscattered ultraviolet, and passive microwave satellite data to investigate the impact of the springtime Antarctic ozone depletion on the base of the Antarctic marine food web - primary production by phytoplankton. Spectral ultraviolet (UV) radiation fields derived from the satellite data are propagated into the water column where they force physiologically-based numerical models of phytoplankton growth. This large-scale study has been divided into two components: (1) the use of Total Ozone Mapping Spectrometer (TOMS) and Special Sensor Microwave Imager (SSM/I) data in conjunction with radiative transfer theory to derive the surface spectral UV irradiance throughout the Southern Ocean; and (2) the merging of these UV irradiances with the climatology of chlorophyll derived from SeaWiFS data to specify the input data for the physiological models.

Land use change detection based on multi-date imagery from different satellite sensor systems

NASA Technical Reports Server (NTRS)

Stow, Douglas A.; Collins, Doretta; Mckinsey, David

1990-01-01

An empirical study is conducted to assess the accuracy of land use change detection using satellite image data acquired ten years apart by sensors with differing spatial resolutions. The primary goals of the investigation were to (1) compare standard change detection methods applied to image data of varying spatial resolution, (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice versa in the registration process, (3) determine if Landsat/Thermatic Mapper or SPOT/High Resolution Visible multispectral data provide more accurate detection of land use changes when registered to historical Landsat/MSS data. It is concluded that image ratioing of multisensor, multidate satellite data produced higher change detection accuracies than did principal components analysis, and that it is useful as a land use change enhancement method.

Improving the RST Approach for Earthquake Prone Areas Monitoring: Results of Correlation Analysis among Significant Sequences of TIR Anomalies and Earthquakes (M>4) occurred in Italy during 2004-2014

NASA Astrophysics Data System (ADS)

Tramutoli, V.; Coviello, I.; Filizzola, C.; Genzano, N.; Lisi, M.; Paciello, R.; Pergola, N.

2015-12-01

Looking toward the assessment of a multi-parametric system for dynamically updating seismic hazard estimates and earthquake short term (from days to weeks) forecast, a preliminary step is to identify those parameters (chemical, physical, biological, etc.) whose anomalous variations can be, to some extent, associated to the complex process of preparation of a big earthquake. Among the different parameters, the fluctuations of Earth's thermally emitted radiation, as measured by sensors on board of satellite system operating in the Thermal Infra-Red (TIR) spectral range, have been proposed since long time as potential earthquake precursors. Since 2001, a general approach called Robust Satellite Techniques (RST) has been used to discriminate anomalous thermal signals, possibly associated to seismic activity from normal fluctuations of Earth's thermal emission related to other causes (e.g. meteorological) independent on the earthquake occurrence. Thanks to its full exportability on different satellite packages, RST has been implemented on TIR images acquired by polar (e.g. NOAA-AVHRR, EOS-MODIS) and geostationary (e.g. MSG-SEVIRI, NOAA-GOES/W, GMS-5/VISSR) satellite sensors, in order to verify the presence (or absence) of TIR anomalies in presence (absence) of earthquakes (with M>4) in different seismogenic areas around the world (e.g. Italy, Turkey, Greece, California, Taiwan, etc.).In this paper, a refined RST (Robust Satellite Techniques) data analysis approach and RETIRA (Robust Estimator of TIR Anomalies) index were used to identify Significant Sequences of TIR Anomalies (SSTAs) during eleven years (from May 2004 to December 2014) of TIR satellite records, collected over Italy by the geostationary satellite sensor MSG-SEVIRI. On the basis of specific validation rules (mainly based on physical models and results obtained by applying RST approach to several earthquakes all around the world) the level of space-time correlation among SSTAs and earthquakes (with M≥4) occurrence has been evaluated. Achieved results will be discussed, also in the framework of a multi-parametric approach to time-Dependent Assessment of Seismic Hazard (t-DASH).

Planetary-scale surface water detection from space

NASA Astrophysics Data System (ADS)

Donchyts, G.; Baart, F.; Winsemius, H.; Gorelick, N.

2017-12-01

Accurate, efficient and high-resolution methods of surface water detection are needed for a better water management. Datasets on surface water extent and dynamics are crucial for a better understanding of natural and human-made processes, and as an input data for hydrological and hydraulic models. In spite of considerable progress in the harmonization of freely available satellite data, producing accurate and efficient higher-level surface water data products remains very challenging. This presentation will provide an overview of existing methods for surface water extent and change detection from multitemporal and multi-sensor satellite imagery. An algorithm to detect surface water changes from multi-temporal satellite imagery will be demonstrated as well as its open-source implementation (http://aqua-monitor.deltares.nl). This algorithm was used to estimate global surface water changes at high spatial resolution. These changes include climate change, land reclamation, reservoir construction/decommissioning, erosion/accretion, and many other. This presentation will demonstrate how open satellite data and open platforms such as Google Earth Engine have helped with this research.

A scale space feature based registration technique for fusion of satellite imagery

NASA Technical Reports Server (NTRS)

Raghavan, Srini; Cromp, Robert F.; Campbell, William C.

1997-01-01

Feature based registration is one of the most reliable methods to register multi-sensor images (both active and passive imagery) since features are often more reliable than intensity or radiometric values. The only situation where a feature based approach will fail is when the scene is completely homogenous or densely textural in which case a combination of feature and intensity based methods may yield better results. In this paper, we present some preliminary results of testing our scale space feature based registration technique, a modified version of feature based method developed earlier for classification of multi-sensor imagery. The proposed approach removes the sensitivity in parameter selection experienced in the earlier version as explained later.

Tier-scalable reconnaissance: the challenge of sensor optimization, sensor deployment, sensor fusion, and sensor interoperability

NASA Astrophysics Data System (ADS)

Fink, Wolfgang; George, Thomas; Tarbell, Mark A.

2007-04-01

Robotic reconnaissance operations are called for in extreme environments, not only those such as space, including planetary atmospheres, surfaces, and subsurfaces, but also in potentially hazardous or inaccessible operational areas on Earth, such as mine fields, battlefield environments, enemy occupied territories, terrorist infiltrated environments, or areas that have been exposed to biochemical agents or radiation. Real time reconnaissance enables the identification and characterization of transient events. A fundamentally new mission concept for tier-scalable reconnaissance of operational areas, originated by Fink et al., is aimed at replacing the engineering and safety constrained mission designs of the past. The tier-scalable paradigm integrates multi-tier (orbit atmosphere surface/subsurface) and multi-agent (satellite UAV/blimp surface/subsurface sensing platforms) hierarchical mission architectures, introducing not only mission redundancy and safety, but also enabling and optimizing intelligent, less constrained, and distributed reconnaissance in real time. Given the mass, size, and power constraints faced by such a multi-platform approach, this is an ideal application scenario for a diverse set of MEMS sensors. To support such mission architectures, a high degree of operational autonomy is required. Essential elements of such operational autonomy are: (1) automatic mapping of an operational area from different vantage points (including vehicle health monitoring); (2) automatic feature extraction and target/region-of-interest identification within the mapped operational area; and (3) automatic target prioritization for close-up examination. These requirements imply the optimal deployment of MEMS sensors and sensor platforms, sensor fusion, and sensor interoperability.

Incorporating Satellite Time-Series Data into Modeling

NASA Technical Reports Server (NTRS)

Gregg, Watson

2008-01-01

In situ time series observations have provided a multi-decadal view of long-term changes in ocean biology. These observations are sufficiently reliable to enable discernment of even relatively small changes, and provide continuous information on a host of variables. Their key drawback is their limited domain. Satellite observations from ocean color sensors do not suffer the drawback of domain, and simultaneously view the global oceans. This attribute lends credence to their use in global and regional model validation and data assimilation. We focus on these applications using the NASA Ocean Biogeochemical Model. The enhancement of the satellite data using data assimilation is featured and the limitation of tongterm satellite data sets is also discussed.

The Day-1 GPM Combined Precipitation Algorithm: IMERG

NASA Astrophysics Data System (ADS)

Huffman, G. J.; Bolvin, D. T.; Braithwaite, D.; Hsu, K.; Joyce, R.; Kidd, C.; Sorooshian, S.; Xie, P.

2012-12-01

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) algorithm will provide the at-launch combined-sensor precipitation dataset being produced by the U.S. GPM Science Team. IMERG is being developed as a unified U.S. algorithm that takes advantage of strengths in three current U.S. algorithms: - the TRMM Multi-satellite Precipitation Analysis (TMPA), which addresses inter-satellite calibration of precipitation estimates and monthly scale combination of satellite and gauge analyses; - the CPC Morphing algorithm with Kalman Filtering (KF-CMORPH), which provides quality-weighted time interpolation of precipitation patterns following storm motion; and - the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS), which provides a neural-network-based scheme for generating microwave-calibrated precipitation estimates from geosynchronous infrared brightness temperatures, and filters out some non-raining cold clouds. The goal is to provide a long-term, fine-scale record of global precipitation from the entire constellation of precipitation-relevant satellite sensors, with input from surface precipitation gauges. The record will begin January 1998 at the start of the Tropical Rainfall Measuring Mission (TRMM) and extend as GPM records additional data. Although homogeneity is considered desirable, the use of diverse and evolving data sources works against the strict long-term homogeneity that characterizes a Climate Data Record (CDR). This talk will briefly review the design requirements for IMERG, including multiple runs at different latencies (most likely around 4 hours, 12 hours, and 2 months after observation time), various intermediate data fields as part of the IMERG data file, and the plans to bring up IMERG with calibration by TRMM initially, transitioning to GPM when its individual-sensor precipitation algorithms are fully functional. Then we will present some early examples of IMERG data products and compare them with existing products to illustrate how the design of IMERG affects the overall performance of the algorithm.

In-depth Analysis of Land Surface Emissivity using Microwave Polarization Difference Index to Improve Satellite QPE

NASA Astrophysics Data System (ADS)

Zheng, Y.; Kirstetter, P. E.; Hong, Y.; Wen, Y.; Turk, J.; Gourley, J. J.

2015-12-01

One of primary uncertainties in satellite overland quantitative precipitation estimates (QPE) from passive sensors such as radiometers is the impact on the brightness temperatures by the surface land emissivity. The complexity of surface land emissivity is linked to its temporal variations (diurnal and seasonal) and spatial variations (subsurface vertical profiles of soil moisture, vegetation structure and surface temperature) translating into sub-pixel heterogeneity within the satellite field of view (FOV). To better extract the useful signal from hydrometeors, surface land emissivity needs to be determined and filtered from the satellite-measured brightness temperatures. Based on the dielectric properties of surface land cover constitutes, Microwave Polarization Differential index (MPDI) is expected to carry the composite effect of surface land properties on land surface emissivity, with a higher MPDI indicating a lower emissivity. This study analyses the dependence of MPDI to soil moisture, vegetation and surface skin temperature over 9 different land surface types. Such analysis is performed using the normalized difference vegetation index (NDVI) from MODIS, the near surface air temperature from the RAP model and ante-precedent precipitation accumulation from the Multi-Radar Multi-Sensor as surrogates for the vegetation, surface skin temperature and shallow layer soil moisture, respectively. This paper provides 1) evaluations of brightness temperature-based MPDI from the TRMM and GPM Microwave Imagers in both raining and non-raining conditions to test the dependence of MPDI to precipitation; 2) comparisons of MPDI categorized into instantly before, during and immediately after selected precipitation events to examine the impact of modest-to-heavy precipitation on the spatial pattern of MPDI; 3) inspections of relationship between MPDI versus rain fraction and rain rate within the satellite sensors FOV to investigate the behaviors of MPDI in varying precipitation conditions; 4) analysis of discrepancies of MPDI over 10.65, 19.35, 37 and 85.8 GHz to identify the sensitivity of MPDS to microwave wavelengths.

Determination of Spring Onset and Growing Season Duration using Satellite Measurements

NASA Technical Reports Server (NTRS)

Min, Q.; Lin, Bing

2006-01-01

An integrated approach to retrieve microwave emissivity difference vegetation index (EDVI) over land regions has been developed from combined multi-platform/multi-sensor satellite measurements, including SSM/I measurements. A possible relationship of the remotely sensed EDVI and the leaf physiology of canopy is exploited at the Harvard Forest site for two growing seasons. This study finds that the EDVI is sensitive to leaf development through vegetation water content of the crown layer of the forest canopy, and has demonstrated that the spring onset and growing season duration can be determined accurately from the time series of satellite estimated EDVI within uncertainties about 3 and 7 days for spring onsets and growing season duration, respectively, compared to in-situ observations. The leaf growing stage may also be quantitatively monitored by a normalized EDVI. Since EDVI retrievals from satellite are generally possible during both daytime and nighttime under non-rain conditions, the EDVI technique studied here may provide higher temporal resolution observations for monitoring the onset of spring and the duration of growing season compared to currently operational satellite methods.

THE IDEA IS TO USEMODIS IN CONJUNCTION WITH THE CURRENT LIMITED LANDSAT CAPABILITY, COMMERCIAL SATELLITES, ANDUNMANNED AERIAL VEHICLES (UAV), IN A MULTI-STAGE APPROACH TO MEET EPA INFORMATION NEEDS.REMOTE SENSING OVERVIEW: EPA CAPABILITIES, PRIORITY AGENCY APPLICATIONS, SENSOR/AIRCRAFT CAPABILITIES, COST CONSIDERATIONS, SPECTRAL AND SPATIAL RESOLUTIONS, AND TEMPORAL CONSIDERATIONS

EPA Science Inventory

EPA remote sensing capabilities include applied research for priority applications and technology support for operational assistance to clients across the Agency. The idea is to use MODIS in conjunction with the current limited Landsat capability, commercial satellites, and Unma...

Polder 2 in-flight results and parasol perspectives

NASA Astrophysics Data System (ADS)

Bermudo, F.; Fougnie, B.; Bret-Dibat, T.

2017-11-01

This paper presents a global approach of the POLDER 2 mission: from instrument design, pre-flight and inflight calibrations till the first in-flight results. The POLDER 2 sensor has been developed by the Centre National d'Etudes Spatiales, the French space agency. It is part of the payload of the ADEOS II satellite developed by JAXA and launched in December 2002. POLDER 2 collected global data from April 2003, end of ADEOS II system check out phase, till the loss of the satellite on October 2003 due to a failure of the satellite power supply system. The POLDER 2 sensor is designed to collect global and repetitive observations of the solar radiation reflected by the Earth-Atmosphere system for climate research. The sensor is a wide field-of-view (2400 Km swath), low resolution (6x7 Km² at nadir) multi-spectral imaging radiometer / polarimeter. The instrument concept is based on a telecentric optics, a rotating wheel carrying 15 spectral filters and polarizers, and a bidimensionnal CCD detector array. The multidisciplinary scientific objectives of POLDER 2 lead to severe radiometric and geometrical requirements, as well as a very accurate calibration of the sensor. These requirements are achieved through a stable instrument design, exhaustive pre-flight and original in-flight calibrations. A derived model of POLDER 2 instrument will be flown on the payload of the CNES PARASOL micro satellite, the launch of which is planned end 2004. The PARASOL mission is part of the "Aqua train" i.e. the formation flying of 3 satellites following EOS-PM, so called "Aqua".

Radiometric and geometric assessment of data from the RapidEye constellation of satellites

USGS Publications Warehouse

Chander, Gyanesh; Haque, Md. Obaidul; Sampath, Aparajithan; Brunn, A.; Trosset, G.; Hoffmann, D.; Roloff, S.; Thiele, M.; Anderson, C.

2013-01-01

To monitor land surface processes over a wide range of temporal and spatial scales, it is critical to have coordinated observations of the Earth's surface using imagery acquired from multiple spaceborne imaging sensors. The RapidEye (RE) satellite constellation acquires high-resolution satellite images covering the entire globe within a very short period of time by sensors identical in construction and cross-calibrated to each other. To evaluate the RE high-resolution Multi-spectral Imager (MSI) sensor capabilities, a cross-comparison between the RE constellation of sensors was performed first using image statistics based on large common areas observed over pseudo-invariant calibration sites (PICS) by the sensors and, second, by comparing the on-orbit radiometric calibration temporal trending over a large number of calibration sites. For any spectral band, the individual responses measured by the five satellites of the RE constellation were found to differ <2–3% from the average constellation response depending on the method used for evaluation. Geometric assessment was also performed to study the positional accuracy and relative band-to-band (B2B) alignment of the image data sets. The position accuracy was assessed by comparing the RE imagery against high-resolution aerial imagery, while the B2B characterization was performed by registering each band against every other band to ensure that the proper band alignment is provided for an image product. The B2B results indicate that the internal alignments of these five RE bands are in agreement, with bands typically registered to within 0.25 pixels of each other or better.

The combined use of the RST-FIRES algorithm and geostationary satellite data to timely detect fires

NASA Astrophysics Data System (ADS)

Filizzola, Carolina; Corrado, Rosita; Marchese, Francesco; Mazzeo, Giuseppe; Paciello, Rossana; Pergola, Nicola; Tramutoli, Valerio

2017-04-01

Timely detection of fires may enable a rapid contrast action before they become uncontrolled and wipe out entire forests. Remote sensing, especially based on geostationary satellite data, can be successfully used to this aim. Differently from sensors onboard polar orbiting platforms, instruments on geostationary satellites guarantee a very high temporal resolution (from 30 to 2,5 minutes) which may be usefully employed to carry out a "continuous" monitoring over large areas as well as to timely detect fires at their early stages. Together with adequate satellite data, an appropriate fire detection algorithm should be used. Over the last years, many fire detection algorithms have been just adapted from polar to geostationary sensors and, consequently, the very high temporal resolution of geostationary sensors is not exploited at all in tests for fire identification. In addition, even when specifically designed for geostationary satellite sensors, fire detection algorithms are frequently based on fixed thresholds tests which are generally set up in the most conservative way to avoid false alarm proliferation. The result is a low algorithm sensitivity which generally means that only large and/or extremely intense events are detected. This work describes the Robust Satellite Techniques for FIRES detection and monitoring (RST-FIRES) which is a multi-temporal change-detection technique trying to overcome the above mentioned issues. Its performance in terms of reliability and sensitivity was verified using data acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the Meteosat Second Generation (MSG) geostationary platform. More than 20,000 SEVIRI images, collected during a four-year-collaboration with the Regional Civil Protection Departments and Local Authorities of two Italian regions, were used. About 950 near real-time ground and aerial checks of the RST-FIRES detections were performed. This study also demonstrates the added value of the RST-FIRES technique to detect starting/small fires and its sensitivity from 3 to 70 times higher than any other similar SEVIRI-based products.

Recent Improvements in Retrieving Near-Surface Air Temperature and Humidity Using Microwave Remote Sensing

NASA Technical Reports Server (NTRS)

Roberts, J. Brent

2010-01-01

Detailed studies of the energy and water cycles require accurate estimation of the turbulent fluxes of moisture and heat across the atmosphere-ocean interface at regional to basin scale. Providing estimates of these latent and sensible heat fluxes over the global ocean necessitates the use of satellite or reanalysis-based estimates of near surface variables. Recent studies have shown that errors in the surface (10 meter)estimates of humidity and temperature are currently the largest sources of uncertainty in the production of turbulent fluxes from satellite observations. Therefore, emphasis has been placed on reducing the systematic errors in the retrieval of these parameters from microwave radiometers. This study discusses recent improvements in the retrieval of air temperature and humidity through improvements in the choice of algorithms (linear vs. nonlinear) and the choice of microwave sensors. Particular focus is placed on improvements using a neural network approach with a single sensor (Special Sensor Microwave/Imager) and the use of combined sensors from the NASA AQUA satellite platform. The latter algorithm utilizes the unique sampling available on AQUA from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A). Current estimates of uncertainty in the near-surface humidity and temperature from single and multi-sensor approaches are discussed and used to estimate errors in the turbulent fluxes.

Physical retrieval of precipitation water contents from Special Sensor Microwave/Imager (SSM/I) data. Part 1: A cloud ensemble/radiative parameterization for sensor response (report version)

NASA Technical Reports Server (NTRS)

Olson, William S.; Raymond, William H.

1990-01-01

The physical retrieval of geophysical parameters based upon remotely sensed data requires a sensor response model which relates the upwelling radiances that the sensor observes to the parameters to be retrieved. In the retrieval of precipitation water contents from satellite passive microwave observations, the sensor response model has two basic components. First, a description of the radiative transfer of microwaves through a precipitating atmosphere must be considered, because it is necessary to establish the physical relationship between precipitation water content and upwelling microwave brightness temperature. Also the spatial response of the satellite microwave sensor (or antenna pattern) must be included in the description of sensor response, since precipitation and the associated brightness temperature field can vary over a typical microwave sensor resolution footprint. A 'population' of convective cells, as well as stratiform clouds, are simulated using a computationally-efficient multi-cylinder cloud model. Ensembles of clouds selected at random from the population, distributed over a 25 km x 25 km model domain, serve as the basis for radiative transfer calculations of upwelling brightness temperatures at the SSM/I frequencies. Sensor spatial response is treated explicitly by convolving the upwelling brightness temperature by the domain-integrated SSM/I antenna patterns. The sensor response model is utilized in precipitation water content retrievals.

A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans

NASA Astrophysics Data System (ADS)

Blondeau-Patissier, David; Gower, James F. R.; Dekker, Arnold G.; Phinn, Stuart R.; Brando, Vittorio E.

2014-04-01

The need for more effective environmental monitoring of the open and coastal ocean has recently led to notable advances in satellite ocean color technology and algorithm research. Satellite ocean color sensors' data are widely used for the detection, mapping and monitoring of phytoplankton blooms because earth observation provides a synoptic view of the ocean, both spatially and temporally. Algal blooms are indicators of marine ecosystem health; thus, their monitoring is a key component of effective management of coastal and oceanic resources. Since the late 1970s, a wide variety of operational ocean color satellite sensors and algorithms have been developed. The comprehensive review presented in this article captures the details of the progress and discusses the advantages and limitations of the algorithms used with the multi-spectral ocean color sensors CZCS, SeaWiFS, MODIS and MERIS. Present challenges include overcoming the severe limitation of these algorithms in coastal waters and refining detection limits in various oceanic and coastal environments. To understand the spatio-temporal patterns of algal blooms and their triggering factors, it is essential to consider the possible effects of environmental parameters, such as water temperature, turbidity, solar radiation and bathymetry. Hence, this review will also discuss the use of statistical techniques and additional datasets derived from ecosystem models or other satellite sensors to characterize further the factors triggering or limiting the development of algal blooms in coastal and open ocean waters.

a Semi-Empirical Topographic Correction Model for Multi-Source Satellite Images

NASA Astrophysics Data System (ADS)

Xiao, Sa; Tian, Xinpeng; Liu, Qiang; Wen, Jianguang; Ma, Yushuang; Song, Zhenwei

2018-04-01

Topographic correction of surface reflectance in rugged terrain areas is the prerequisite for the quantitative application of remote sensing in mountainous areas. Physics-based radiative transfer model can be applied to correct the topographic effect and accurately retrieve the reflectance of the slope surface from high quality satellite image such as Landsat8 OLI. However, as more and more images data available from various of sensors, some times we can not get the accurate sensor calibration parameters and atmosphere conditions which are needed in the physics-based topographic correction model. This paper proposed a semi-empirical atmosphere and topographic corrction model for muti-source satellite images without accurate calibration parameters.Based on this model we can get the topographic corrected surface reflectance from DN data, and we tested and verified this model with image data from Chinese satellite HJ and GF. The result shows that the correlation factor was reduced almost 85 % for near infrared bands and the classification overall accuracy of classification increased 14 % after correction for HJ. The reflectance difference of slope face the sun and face away the sun have reduced after correction.

Research on Scheduling Algorithm for Multi-satellite and Point Target Task on Swinging Mode

NASA Astrophysics Data System (ADS)

Wang, M.; Dai, G.; Peng, L.; Song, Z.; Chen, G.

2012-12-01

Nowadays, using satellite in space to observe ground is an important and major method to obtain ground information. With the development of the scientific technology in the field of space, many fields such as military and economic and other areas have more and more requirement of space technology because of the benefits of the satellite's widespread, timeliness and unlimited of area and country. And at the same time, because of the wide use of all kinds of satellites, sensors, repeater satellites and ground receiving stations, ground control system are now facing great challenge. Therefore, how to make the best value of satellite resources so as to make full use of them becomes an important problem of ground control system. Satellite scheduling is to distribute the resource to all tasks without conflict to obtain the scheduling result so as to complete as many tasks as possible to meet user's requirement under considering the condition of the requirement of satellites, sensors and ground receiving stations. Considering the size of the task, we can divide tasks into point task and area task. This paper only considers point targets. In this paper, a description of satellite scheduling problem and a chief introduction of the theory of satellite scheduling are firstly made. We also analyze the restriction of resource and task in scheduling satellites. The input and output flow of scheduling process are also chiefly described in the paper. On the basis of these analyses, we put forward a scheduling model named as multi-variable optimization model for multi-satellite and point target task on swinging mode. In the multi-variable optimization model, the scheduling problem is transformed the parametric optimization problem. The parameter we wish to optimize is the swinging angle of every time-window. In the view of the efficiency and accuracy, some important problems relating the satellite scheduling such as the angle relation between satellites and ground targets, positive and negative swinging angle and the computation of time window are analyzed and discussed. And many strategies to improve the efficiency of this model are also put forward. In order to solve the model, we bring forward the conception of activity sequence map. By using the activity sequence map, the activity choice and the start time of the activity can be divided. We also bring forward three neighborhood operators to search the result space. The front movement remaining time and the back movement remaining time are used to analyze the feasibility to generate solution from neighborhood operators. Lastly, the algorithm to solve the problem and model is put forward based genetic algorithm. Population initialization, crossover operator, mutation operator, individual evaluation, collision decrease operator, select operator and collision elimination operator is designed in the paper. Finally, the scheduling result and the simulation for a practical example on 5 satellites and 100 point targets with swinging mode is given, and the scheduling performances are also analyzed while the swinging angle in 0, 5, 10, 15, 25. It can be shown by the result that the model and the algorithm are more effective than those ones without swinging mode.

SED16 autonomous star tracker night sky testing

NASA Astrophysics Data System (ADS)

Foisneau, Thierry; Piriou, Véronique; Perrimon, Nicolas; Jacob, Philippe; Blarre, Ludovic; Vilaire, Didier

2017-11-01

The SED16 is an autonomous multi-missions star tracker which delivers three axis satellite attitude in an inertial reference frame and the satellite angular velocity with no prior information. The qualification process of this star sensor includes five validation steps using optical star simulator, digitized image simulator and a night sky tests setup. The night sky testing was the final step of the qualification process during which all the functions of the star tracker were used in almost nominal conditions : Autonomous Acquisition of the attitude, Autonomous Tracking of ten stars. These tests were performed in Calern in the premises of the OCA (Observatoire de la Cote d'Azur). The test set-up and the test results are described after a brief review of the sensor main characteristics and qualification process.

Automatic Registration of GF4 Pms: a High Resolution Multi-Spectral Sensor on Board a Satellite on Geostationary Orbit

NASA Astrophysics Data System (ADS)

Gao, M.; Li, J.

2018-04-01

Geometric correction is an important preprocessing process in the application of GF4 PMS image. The method of geometric correction that is based on the manual selection of geometric control points is time-consuming and laborious. The more common method, based on a reference image, is automatic image registration. This method involves several steps and parameters. For the multi-spectral sensor GF4 PMS, it is necessary for us to identify the best combination of parameters and steps. This study mainly focuses on the following issues: necessity of Rational Polynomial Coefficients (RPC) correction before automatic registration, base band in the automatic registration and configuration of GF4 PMS spatial resolution.

Evaluating the Performance of the Goddard Multi-Scale Modeling Framework against GPM, TRMM and CloudSat/CALIPSO Products

NASA Astrophysics Data System (ADS)

Chern, J. D.; Tao, W. K.; Lang, S. E.; Matsui, T.; Mohr, K. I.

2014-12-01

Four six-month (March-August 2014) experiments with the Goddard Multi-scale Modeling Framework (MMF) were performed to study the impacts of different Goddard one-moment bulk microphysical schemes and large-scale forcings on the performance of the MMF. Recently a new Goddard one-moment bulk microphysics with four-ice classes (cloud ice, snow, graupel, and frozen drops/hail) has been developed based on cloud-resolving model simulations with large-scale forcings from field campaign observations. The new scheme has been successfully implemented to the MMF and two MMF experiments were carried out with this new scheme and the old three-ice classes (cloud ice, snow graupel) scheme. The MMF has global coverage and can rigorously evaluate microphysics performance for different cloud regimes. The results show MMF with the new scheme outperformed the old one. The MMF simulations are also strongly affected by the interaction between large-scale and cloud-scale processes. Two MMF sensitivity experiments with and without nudging large-scale forcings to those of ERA-Interim reanalysis were carried out to study the impacts of large-scale forcings. The model simulated mean and variability of surface precipitation, cloud types, cloud properties such as cloud amount, hydrometeors vertical profiles, and cloud water contents, etc. in different geographic locations and climate regimes are evaluated against GPM, TRMM, CloudSat/CALIPSO satellite observations. The Goddard MMF has also been coupled with the Goddard Satellite Data Simulation Unit (G-SDSU), a system with multi-satellite, multi-sensor, and multi-spectrum satellite simulators. The statistics of MMF simulated radiances and backscattering can be directly compared with satellite observations to assess the strengths and/or deficiencies of MMF simulations and provide guidance on how to improve the MMF and microphysics.

Mapping snow cover using multi-source satellite data on big data platforms

NASA Astrophysics Data System (ADS)

Lhermitte, Stef

2017-04-01

Snowmelt is an important and dynamically changing water resource in mountainous regions around the world. In this framework, remote sensing data of snow cover data provides an essential input for hydrological models to model the water contribution from remote mountain areas and to understand how this water resource might alter as a result of climate change. Traditionally, however, many of these remote sensing products show a trade-off between spatial and temporal resolution (e.g., 16-day Landsat at 30m vs. daily MODIS at 500m resolution). With the advent of Sentinel-1 and 2 and the PROBA-V 100m products this trade-off can partially be tackled by having data that corresponds more closely to the spatial and temporal variations in snow cover typically observed over complex mountain areas. This study provides first a quantitative analysis of the trade-offs between the state-of-the-art snow cover mapping methodologies for Landsat, MODIS, PROBA-V, Sentinel-1 and 2 and applies them on big data platforms such as Google Earth Engine (GEE), RSS (ESA Research Service & Support) CloudToolbox, and the PROBA-V Mission Exploitation Platform (MEP). Second, it combines the different sensor data-cubes in one multi-sensor classification approach using newly developed spatio-temporal probability classifiers within the big data platform environments. Analysis of the spatio-temporal differences in derived snow cover areas from the different sensors reveals the importance of understanding the spatial and temporal scales at which variations occur. Moreover, it shows the importance of i) temporal resolution when monitoring highly dynamical properties such as snow cover and of ii) differences in satellite viewing angles over complex mountain areas. Finally, it highlights the potential and drawbacks of big data platforms for combining multi-source satellite data for monitoring dynamical processes such as snow cover.

Evaluation of Integrated Multi-satellitE Retrievals for GPM with All Weather Gauge Observations over CONUS

NASA Astrophysics Data System (ADS)

Chen, S.; Qi, Y.; Hu, B.; Hu, J.; Hong, Y.

2015-12-01

The Global Precipitation Measurement (GPM) mission is composed of an international network of satellites that provide the next-generation global observations of rain and snow. Integrated Multi-satellitE Retrievals for GPM (IMERG) is the state-of-art precipitation products with high spatio-temporal resolution of 0.1°/30min. IMERG unifies precipitation measurements from a constellation of research and operational satellites with the core sensors dual-frequency precipitation radar (DPR) and microwave imager (GMI) on board a "Core" satellite. Additionally, IMERG blends the advantages of currently most popular satellite-based quantitative precipitation estimates (QPE) algorithms, i.e. TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). The real-time and post real-time IMERG products are now available online at https://stormpps.gsfc.nasa.gov/storm. In this study, the final run post real-time IMERG is evaluated with all-weather manual gauge observations over CONUS from June 2014 through May 2015. Relative Bias (RB), Root-Mean-Squared Error (RMSE), Correlation Coefficient (CC), Probability Of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) are used to quantify the performance of IMERG. The performance of IMERG in estimating snowfall precipitation is highlighted in the study. This timely evaluation with all-weather gauge observations is expected to offer insights into performance of IMERG and thus provide useful feedback to the algorithm developers as well as the GPM data users.

Robust Satellite Techniques to support the short-term assessment of the seismic hazard in Japan: an analysis on 11 years (2005-2015) of MTSAT TIR observations

NASA Astrophysics Data System (ADS)

Genzano, Nicola; Filizzola, Carolina; Hattori, Katsumi; Lisi, Mariano; Paciello, Rossana; Pergola, Nicola; Tramutoli, Valerio

2017-04-01

In order to increase reliability and precision of short-term seismic hazard assessment (but also a possible earthquakes forecast), the integration of different kinds of observations (chemical, physical, biological, etc.) in a multi-parametric approach could be a useful strategy to be undertaken. Among the different observational methodologies, the fluctuations of Earth's thermally emitted radiation, measured by satellite sensors operating in the thermal infrared (TIR) spectral range, have been proposed since eighties as a potential earthquake precursor. Since 2001, the general change detention approach Robust Satellite Techniques (RST), used in combination with RETIRA (Robust Estimator of TIR Anomalies) index, showed good ability to discriminate anomalous TIR signals possibly associated to seismic activity, from the normal variability of TIR signal due to other causes (e.g. meteorological). In this paper, the RST data analysis approach has been implemented on TIR satellite records collected over Japan by the geostationary satellite sensor MTSAT (Multifunctional Transport SATellites) in the period June 2005 - December 2015 in order to evaluate its possible contribute to an improved multi parametric system for a time-Dependent Assessment of Seismic Hazard (t-DASH). For the first time, thermal anomalies have been identified comparing the daily TIR radiation of each location of the considered satellite portions, with its historical expected value and variation range (i.e. RST reference fields) computed using a a 30 days moving window (i.e. 15 days before and 15 days after the considered day of the year) instead than fixed monthly window. Preliminary results of correlation analysis among the appearance of Significant Sequences of TIR Anomalies (SSTAs) and time, location and magnitude of earthquakes (M≥5), performed by applying predefined space-temporal and magnitude constraints, show that 80% of SSTAs were in an apparent space-time relations with earthquakes with a false alarm rate of 20%.

Detection of Multi-Layer and Vertically-Extended Clouds Using A-Train Sensors

NASA Technical Reports Server (NTRS)

Joiner, J.; Vasilkov, A. P.; Bhartia, P. K.; Wind, G.; Platnick, S.; Menzel, W. P.

2010-01-01

The detection of mUltiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (l2kmx24km at nadir) and at the 5kmx5km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (approx.20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.

The effects of mineral aerosol deposits on the BRDF (bidirectional reflectance distribution function) of sea ice for the calibration of satellite remote sensing products: an experimental and modelling study.

NASA Astrophysics Data System (ADS)

Lamare, Maxim; Hedley, John; King, Martin

2016-04-01

Knowledge of the albedo in the cryosphere is essential to monitor a range of climatic processes that have an impact on a global scale. Optical Earth Observation satellites are ideal for the synoptic observation of expansive and inaccessible areas, providing large datasets used to derive essential products, such as albedo. The application of remote sensing to investigate climate processes requires the combination of data from different sensors. However, although there is significant value in the analysis of data from individual sensors, global observing systems require accurate knowledge of sensor-to-sensor biases. Therefore, the inter-calibration of sensors used for climate studies is essential to avoid inconsistencies, which may mask climate effects. CEOS (Committee on Earth Observing Satellites) has established a number of natural Earth targets to serve as international reference standards, amongst which sea ice has great potential. The reflectance of natural surfaces is not isotropic and reflectance varies with the illumination and viewing geometries, consequently impacting satellite observations. Furthermore, variations in the physical properties (sea ice type, thickness) and the light absorbing impurities deposited in the sea ice have a strong impact on reflectance. Thus, the characterisation of the bi-directional reflectance distribution function (BRDF) of sea ice is a fundamental step toward the inter-calibration of optical satellite sensors. This study provides a characterisation of the effects of mineral aerosol and black carbon deposits on the BRDF of three different sea ice types. BRDF measurements were performed on bare sea ice grown in an experimental ice tank, using a state-of-the-art laboratory goniometer. The sea ice was "poisoned" with concentrations of mineral dust and black carbon varying between 100 and 5 000 ng g-1 deposited uniformly in a 5 cm surface layer. Using measurements from the experimental facility, novel information about sea ice BRDF as a function of sea ice type, thickness and light-absorbing impurities was derived using a radiative-transfer model (PlanarRad). This extensive characterisation of the multi angular reflectance of sea ice reveals the importance of BRDF for the validation and calibration of Earth Observation satellite sensor data.

Geospatial Information from Satellite Imagery for Geovisualisation of Smart Cities in India

NASA Astrophysics Data System (ADS)

Mohan, M.

2016-06-01

In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.

Landsat Time-Series Analysis Opens New Approaches for Regional Glacier Mapping

NASA Astrophysics Data System (ADS)

Winsvold, S. H.; Kääb, A.; Nuth, C.; Altena, B.

2016-12-01

The archive of Landsat satellite scenes is important for mapping of glaciers, especially as it represents the longest running and continuous satellite record of sufficient resolution to track glacier changes over time. Contributing optical sensors newly launched (Landsat 8 and Sentinel-2A) or upcoming in the near future (Sentinel-2B), will promote very high temporal resolution of optical satellite images especially in high-latitude regions. Because of the potential that lies within such near-future dense time series, methods for mapping glaciers from space should be revisited. We present application scenarios that utilize and explore dense time series of optical data for automatic mapping of glacier outlines and glacier facies. Throughout the season, glaciers display a temporal sequence of properties in optical reflection as the seasonal snow melts away, and glacier ice appears in the ablation area and firn in the accumulation area. In one application scenario presented we simulated potential future seasonal resolution using several years of Landsat 5TM/7ETM+ data, and found a sinusoidal evolution of the spectral reflectance for on-glacier pixels throughout a year. We believe this is because of the short wave infrared band and its sensitivity to snow grain size. The parameters retrieved from the fitting sinus curve can be used for glacier mapping purposes, thus we also found similar results using e.g. the mean of summer band ratio images. In individual optical mapping scenes, conditions will vary (e.g., snow, ice, and clouds) and will not be equally optimal over the entire scene. Using robust statistics on stacked pixels reveals a potential for synthesizing optimal mapping scenes from a temporal stack, as we present in a further application scenario. The dense time series available from satellite imagery will also promote multi-temporal and multi-sensor based analyses. The seasonal pattern of snow and ice on a glacier seen in the optical time series can in the summer season also be observed using radar backscatter series. Optical sensors reveal the reflective properties at the surface, while radar sensors may penetrate the surface revealing properties from a certain volume.In an outlook to this contribution we have explored how we can combine information from SAR and optical sensor systems for different purposes.

Long-Term Monitoring of Water Dynamics in the Sahel Region Using the Multi-Sar

NASA Astrophysics Data System (ADS)

Bertram, A.; Wendleder, A.; Schmitt, A.; Huber, M.

2016-06-01

Fresh water is a scarce resource in the West-African Sahel region, seasonally influenced by droughts and floods. Particularly in terms of climate change, the importance of wetlands increases for flora, fauna, human population, agriculture, livestock and fishery. Hence, access to open water is a key factor. Long-term monitoring of water dynamics is of great importance, especially with regard to the spatio-temporal extend of wetlands and drylands. It can predict future trends and facilitate the development of adequate management strategies. Lake Tabalak, a Ramsar wetland of international importance, is one of the most significant ponds in Niger and a refuge for waterbirds. Nevertheless, human population growth increased the pressure on this ecosystem, which is now degrading for all uses. The main objective of the study is a long-term monitoring of the Lake Tabalak's water dynamics to delineate permanent and seasonal water bodies, using weather- and daytime-independent multi-sensor and multi-temporal Synthetic Aperture Radar (SAR) data available for the study area. Data of the following sensors from 1993 until 2016 are used: Sentinel-1A, TerraSARX, ALOS PALSAR-1/2, Envisat ASAR, RADARSAT-1/2, and ERS-1/2. All SAR data are processed with the Multi-SAR-System, unifying the different characteristics of all above mentioned sensors in terms of geometric, radiometric and polarimetric resolution to a consistent format. The polarimetric representation in Kennaugh elements allows fusing single-polarized data acquired by older sensors with multi-polarized data acquired by current sensors. The TANH-normalization guarantees a consistent and therefore comparable description in a closed data range in terms of radiometry. The geometric aspect is solved by projecting all images to an earth-fixed coordinate system correcting the brightness by the help of the incidence angle. The elevation model used in the geocoding step is the novel global model produced by the TanDEM-X satellite mission. The advantage of the Multi-SAR-System is that it comprises ortho-rectification, radiometric enhancement, normalization and Kennaugh decomposition, independent from sensors, modes, polarizations or acquisition date of SAR data. In addition, optical satellite data can be included as well, to fill gaps where SAR data are missing due to the special normalization scheme. This kind of pre-processing is exclusively implemented at the Earth Observation Center of the German Aerospace Center in Oberpfaffenhofen, Germany. Therefore, the dynamic change of the open water of the Lake Tabalak could be classified over dry and rainy seasons and years, using different SAR data. The study provides a unique database and contributes to a better understanding of wetland systems in the Sahel region influenced by human pressure and climate change.

A Space-Based Perspective of the 2017 Hurricane Season from the Global Precipitation Measurement (GPM) Mission

NASA Astrophysics Data System (ADS)

Skofronick Jackson, G.; Petersen, W. A.; Huffman, G. J.; Kirschbaum, D.; Wolff, D. B.; Tan, J.; Zavodsky, B.

2017-12-01

The Global Precipitation Measurement (GPM) mission collected unique, near real time 3-D satellite-based views of hurricanes in 2017 together with estimated precipitation accumulation using merged satellite data for scientific studies and societal applications. Central to GPM is the NASA-JAXA GPM Core Observatory (CO). The GPM-CO carries an advanced dual-frequency precipitation radar (DPR) and a well-calibrated, multi-frequency passive microwave radiometer that together serve as an on orbit reference for precipitation measurements made by the international GPM satellite constellation. GPM-CO overpasses of major Hurricanes such as Harvey, Irma, Maria, and Ophelia revealed intense convective structures in DPR radar reflectivity together with deep ice-phase microphysics in both the eyewalls and outer rain bands. Of considerable scientific interest, and yet to be determined, will be DPR-diagnosed characteristics of the rain drop size distribution as a function of convective structure, intensity and microphysics. The GPM-CO active/passive suite also provided important decision support information. For example, the National Hurricane Center used GPM-CO observations as a tool to inform track and intensity estimates in their forecast briefings. Near-real-time rainfall accumulation from the Integrated Multi-satellitE Retrievals for GPM (IMERG) was also provided via the NASA SPoRT team to Puerto Rico following Hurricane Maria when ground-based radar systems on the island failed. Comparisons between IMERG, NOAA Multi-Radar Multi-Sensor data, and rain gauge rainfall accumulations near Houston, Texas during Hurricane Harvey revealed spatial biases between ground and IMERG satellite estimates, and a general underestimation of IMERG rain accumulations associated with infrared observations, collectively illustrating the difficulty of measuring rainfall in hurricanes.GPM data continue to advance scientific research on tropical cyclone intensification and structure, and contribute to societal and operational applications for improving storm forecasting. Precipitation accumulations from the multi-satellite product IMERG also contribute to a better understanding of rainfall accumulation, inland flooding, and landslide susceptibility during the passage of these major events.

Global multi-sensor satellite monitoring of volcanic SO2 and ash emissions in support to aviation control

NASA Astrophysics Data System (ADS)

Brenot, H.; Theys, N.; van Gent, J.; Van Roozendael, M.; van der A, R.; Clarisse, L.; Hurtmans, D.; Ngadi, Y.; Coheur, P.-F.; Clerbaux, C.

2012-04-01

The "Support to Aviation Control Service" (SACS; http://sacs.aeronomie.be) is an ESA-funded project hosted by the Belgian Institute for Space Aeronomy. The service provides near real-time (NRT) global SO2 and volcanic ash data, as well as alerts in case of volcanic eruptions. The SACS service is primarily designed to support the Volcanic Ash Advisory Centers (VAACs) in their mandate to gather information on volcanic clouds and give advice to airline and air traffic control organisations. SACS also serves other users that subscribe to the service, in particular local volcano observatories and research scientists. SACS is based on the combined use of UV-visible (SCIAMACHY, OMI, GOME-2) and infrared (AIRS, IASI) satellite instruments. When a volcanic eruption is detected, SACS issues an alert that takes the form of a notification sent by e-mail to users. This notification points to a dedicated web page where all relevant information is available and can be visualized with user-friendly tools. The strength of a multi-sensor approach relies in the use of satellite data with different overpasses times, minimizing the time-lag for detection and enhancing the reliability of such alerts. This paper will give a general presentation of the SACS service, different techniques used to detect volcanic plumes. It will also highlight the strengths and limitations of the service and measurements.

Spatial resolution and frequency of satellite data acquisition for multi-temporal analysis of environment

NASA Astrophysics Data System (ADS)

Tanaka, S.; Sugimura, T.; Kameda, K.

1992-07-01

The environmental monitoring capacity by satellite depends upon the spatial resolution and the acquisition frequency it provides. The information on environmental change obtained by Landsat, the first earth observation satellite, was a rectangular reclamation area on Tokyo Bay meaning only a few square kilometers. However, multi-temporal SPOT/HRV data enables newly built small buildings meaning just ten square meters or so to be detected. Environmental changes of the global dimensions are today attracting world attention. In Japan, the major environmental problems are decaying cedar forests due to acid rain, decaying pine forests due to the pine beetle, landslides due to left-cut forests and problem resulting from agricultural chemicals on golf courses. All of these pose a national problem, but each is a phenomenon which covers an area of a few meters square at the largest. The existing earth observation satellites are unable to monitor these seemingly small sized environmental changes. For this, satellites with a spatial resolution of a few meters only or less than a meter are required. This situation becomes apparent when specific cases are examined, and it is expected considering the speed of past sensor development satellite observation systems providing this capacity will most probably be developed by the year 2020.

How will we ensure the long-term sea ice data record continues?

NASA Astrophysics Data System (ADS)

Stroeve, J. C.; Kaleschke, L.

2017-12-01

The multi-channel satellite passive microwave record has been of enormous benefit to the science community and society at large since the late 1970s. Starting with the launch of the Nimbus-7 Scanning Multi-Channel Microwave Radiometer (SMMR) in October 1978, and continuing with the launch of a series of Special Sensor Microwave Imagers (SSM/Is) in June 1987 by the Defense Meteorological Satellite Program (DMSP), places previously difficult to monitor year-round, such as the polar regions, came to light. Together these sensors have provided nearly 4 decades of climate data records on the state of sea ice cover over the ocean and snow on land. This data has also been used to map melt extent on the large ice sheets, timing of snow melt onset over land and sea ice. Application also extend well beyond the polar regions, mapping important climate variables, such as soil moisture content, oceanic wind speed, rainfall, water vapor, cloud liquid water and total precipitable water. Today the current SSMIS operational satellite (F18) is 7 years old and there is no follow-on mission planned by the DMSP. With the end of the SSMI family of Sensors, will the polar regions once again be in the dark? Other sensors that may contribute to the long-term data record include the JAXA AMSR2 (5 years old as of May 2017), the Chinese Fen-Yung-3 and the Russian Meteor-N2. Scatterometry and L-band radiometry from SMOS and NASA's SMOS may also provide some potential means of extending the sea ice extent data record, as well as future sensors by the DoD, JAXA and ESA. However, this will require considerable effort to intercalibrate the different sensors to ensure consistency in the long-term data record. Differences in measurement approach, frequency and spatial resolution make this a non-trivial matter. The passive microwave sea ice extent data record is one of the longest and most consistent climate data records available. It provides daily monitoring of one of the most striking changes in our climate system, the loss of the Arctic sea ice cover. A series of replacement sensors is urgently needed, preferably at higher spatial resolution to better delineate the ice edge for marine applications such as ship routing.

Aerosol Climate Time Series in ESA Aerosol_cci

NASA Astrophysics Data System (ADS)

Popp, Thomas; de Leeuw, Gerrit; Pinnock, Simon

2016-04-01

Within the ESA Climate Change Initiative (CCI) Aerosol_cci (2010 - 2017) conducts intensive work to improve algorithms for the retrieval of aerosol information from European sensors. Meanwhile, full mission time series of 2 GCOS-required aerosol parameters are completely validated and released: Aerosol Optical Depth (AOD) from dual view ATSR-2 / AATSR radiometers (3 algorithms, 1995 - 2012), and stratospheric extinction profiles from star occultation GOMOS spectrometer (2002 - 2012). Additionally, a 35-year multi-sensor time series of the qualitative Absorbing Aerosol Index (AAI) together with sensitivity information and an AAI model simulator is available. Complementary aerosol properties requested by GCOS are in a "round robin" phase, where various algorithms are inter-compared: fine mode AOD, mineral dust AOD (from the thermal IASI spectrometer, but also from ATSR instruments and the POLDER sensor), absorption information and aerosol layer height. As a quasi-reference for validation in few selected regions with sparse ground-based observations the multi-pixel GRASP algorithm for the POLDER instrument is used. Validation of first dataset versions (vs. AERONET, MAN) and inter-comparison to other satellite datasets (MODIS, MISR, SeaWIFS) proved the high quality of the available datasets comparable to other satellite retrievals and revealed needs for algorithm improvement (for example for higher AOD values) which were taken into account for a reprocessing. The datasets contain pixel level uncertainty estimates which were also validated and improved in the reprocessing. For the three ATSR algorithms the use of an ensemble method was tested. The paper will summarize and discuss the status of dataset reprocessing and validation. The focus will be on the ATSR, GOMOS and IASI datasets. Pixel level uncertainties validation will be summarized and discussed including unknown components and their potential usefulness and limitations. Opportunities for time series extension with successor instruments of the Sentinel family will be described and the complementarity of the different satellite aerosol products (e.g. dust vs. total AOD, ensembles from different algorithms for the same sensor) will be discussed.

Earth Surface Monitoring with COSI-Corr, Techniques and Applications

NASA Astrophysics Data System (ADS)

Leprince, S.; Ayoub, F.; Avouac, J.

2009-12-01

Co-registration of Optically Sensed Images and Correlation (COSI-Corr) is a software package developed at the California Institute of Technology (USA) for accurate geometrical processing of optical satellite and aerial imagery. Initially developed for the measurement of co-seismic ground deformation using optical imagery, COSI-Corr is now used for a wide range of applications in Earth Sciences, which take advantage of the software capability to co-register, with very high accuracy, images taken from different sensors and acquired at different times. As long as a sensor is supported in COSI-Corr, all images between the supported sensors can be accurately orthorectified and co-registered. For example, it is possible to co-register a series of SPOT images, a series of aerial photographs, as well as to register a series of aerial photographs with a series of SPOT images, etc... Currently supported sensors include the SPOT 1-5, Quickbird, Worldview 1 and Formosat 2 satellites, the ASTER instrument, and frame camera acquisitions from e.g., aerial survey or declassified satellite imagery. Potential applications include accurate change detection between multi-temporal and multi-spectral images, and the calibration of pushbroom cameras. In particular, COSI-Corr provides a powerful correlation tool, which allows for accurate estimation of surface displacement. The accuracy depends on many factors (e.g., cloud, snow, and vegetation cover, shadows, temporal changes in general, steadiness of the imaging platform, defects of the imaging system, etc...) but in practice, the standard deviation of the measurements obtained from the correlation of mutli-temporal images is typically around 1/20 to 1/10 of the pixel size. The software package also includes post-processing tools such as denoising, destriping, and stacking tools to facilitate data interpretation. Examples drawn from current research in, e.g., seismotectonics, glaciology, and geomorphology will be presented. COSI-Corr is developed in IDL (Interactive Data Language), integrated under the user friendly interface ENVI (Environment for Visualizing Images), and is distributed free of charge for academic research purposes.

1984-1995 Evolution of Stratospheric Aerosol Size, Surface Area, and Volume Derived by Combining SAGE II and CLAES Extinction Measurements

NASA Technical Reports Server (NTRS)

Russell, Philip B.; Bauman, Jill J.

2000-01-01

This SAGE II Science Team task focuses on the development of a multi-wavelength, multi- sensor Look-Up-Table (LUT) algorithm for retrieving information about stratospheric aerosols from global satellite-based observations of particulate extinction. The LUT algorithm combines the 4-wavelength SAGE II extinction measurements (0.385 <= lambda <= 1.02 microns) with the 7.96 micron and 12.82 micron extinction measurements from the Cryogenic Limb Array Etalon Spectrometer (CLAES) instrument, thus increasing the information content available from either sensor alone. The algorithm uses the SAGE II/CLAES composite spectra in month-latitude-altitude bins to retrieve values and uncertainties of particle effective radius R(sub eff), surface area S, volume V and size distribution width sigma(sub g).

Towards a Near Real-Time Satellite-Based Flux Monitoring System for the MENA Region

NASA Astrophysics Data System (ADS)

Ershadi, A.; Houborg, R.; McCabe, M. F.; Anderson, M. C.; Hain, C.

2013-12-01

Satellite remote sensing has the potential to offer spatially and temporally distributed information on land surface characteristics, which may be used as inputs and constraints for estimating land surface fluxes of carbon, water and energy. Enhanced satellite-based monitoring systems for aiding local water resource assessments and agricultural management activities are particularly needed for the Middle East and North Africa (MENA) region. The MENA region is an area characterized by limited fresh water resources, an often inefficient use of these, and relatively poor in-situ monitoring as a result of sparse meteorological observations. To address these issues, an integrated modeling approach for near real-time monitoring of land surface states and fluxes at fine spatio-temporal scales over the MENA region is presented. This approach is based on synergistic application of multiple sensors and wavebands in the visible to shortwave infrared and thermal infrared (TIR) domain. The multi-scale flux mapping and monitoring system uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI), and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in conjunction with model reanalysis data and multi-sensor remotely sensed data from polar orbiting (e.g. Landsat and MODerate resolution Imaging Spectroradiometer (MODIS)) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate time-continuous (i.e. daily) estimates of field-scale water, energy and carbon fluxes. Within this modeling system, TIR satellite data provide information about the sub-surface moisture status and plant stress, obviating the need for precipitation input and a detailed soil surface characterization (i.e. for prognostic modeling of soil transport processes). The STARFM fusion methodology blends aspects of high frequency (spatially coarse) and spatially fine resolution sensors and is applied directly to flux output fields to facilitate daily mapping of fluxes at sub-field scales. A complete processing infrastructure to automatically ingest and pre-process all required input data and to execute the integrated modeling system for near real-time agricultural monitoring purposes over targeted MENA sites is being developed, and initial results from this concerted effort will be discussed.

Rainfall Estimation over the Nile Basin using Multi-Spectral, Multi- Instrument Satellite Techniques

NASA Astrophysics Data System (ADS)

Habib, E.; Kuligowski, R.; Sazib, N.; Elshamy, M.; Amin, D.; Ahmed, M.

2012-04-01

Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite- derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared (IR) algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). In this study, the authors report on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self- Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application by NFC over the Nile Basin. The algorithm uses a set of rainfall predictors that come from multi-spectral Infrared cloud top observations and self-calibrate them to a set of predictands that come from the more accurate, but less frequent, Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels that have become recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as the Special Sensor Microwave/Imager (SSM/I), the Special Sensor Microwave Imager and Sounder (SSMIS), the Advanced Microwave Sounding Unit (AMSU), the Advanced Microwave Scanning Radiometer on EOS (AMSR-E), and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real- time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static coefficients that are derived from IR-MW data from past observations. We also compare the SCaMPR algorithm to other global-scale satellite rainfall algorithms (e.g., 'Tropical Rainfall Measuring Mission (TRMM) and other sources' (TRMM-3B42) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) CMORPH product. The algorithm has several potential future applications such as: improving the performance accuracy of hydrologic forecasting models over the Nile Basin, and utilizing the enhanced rainfall datasets and better-calibrated hydrologic models to assess the impacts of climate change on the region's water availability using global circulation models and regional climate models.

Spectral and spatial resolution analysis of multi sensor satellite data for coral reef mapping: Tioman Island, Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet; Kabiri, Keivan

2012-07-01

This paper describes an assessment of coral reef mapping using multi sensor satellite images such as Landsat ETM, SPOT and IKONOS images for Tioman Island, Malaysia. The study area is known to be one of the best Islands in South East Asia for its unique collection of diversified coral reefs and serves host to thousands of tourists every year. For the coral reef identification, classification and analysis, Landsat ETM, SPOT and IKONOS images were collected processed and classified using hierarchical classification schemes. At first, Decision tree classification method was implemented to separate three main land cover classes i.e. water, rural and vegetation and then maximum likelihood supervised classification method was used to classify these main classes. The accuracy of the classification result is evaluated by a separated test sample set, which is selected based on the fieldwork survey and view interpretation from IKONOS image. Few types of ancillary data in used are: (a) DGPS ground control points; (b) Water quality parameters measured by Hydrolab DS4a; (c) Sea-bed substrates spectrum measured by Unispec and; (d) Landcover observation photos along Tioman island coastal area. The overall accuracy of the final classification result obtained was 92.25% with the kappa coefficient is 0.8940. Key words: Coral reef, Multi-spectral Segmentation, Pixel-Based Classification, Decision Tree, Tioman Island

Improving PERSIANN-CCS rain estimation using probabilistic approach and multi-sensors information

NASA Astrophysics Data System (ADS)

Karbalaee, N.; Hsu, K. L.; Sorooshian, S.; Kirstetter, P.; Hong, Y.

2016-12-01

This presentation discusses the recent implemented approaches to improve the rainfall estimation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System (PERSIANN-CCS). PERSIANN-CCS is an infrared (IR) based algorithm being integrated in the IMERG (Integrated Multi-Satellite Retrievals for the Global Precipitation Mission GPM) to create a precipitation product in 0.1x0.1degree resolution over the chosen domain 50N to 50S every 30 minutes. Although PERSIANN-CCS has a high spatial and temporal resolution, it overestimates or underestimates due to some limitations.PERSIANN-CCS can estimate rainfall based on the extracted information from IR channels at three different temperature threshold levels (220, 235, and 253k). This algorithm relies only on infrared data to estimate rainfall indirectly from this channel which cause missing the rainfall from warm clouds and false estimation for no precipitating cold clouds. In this research the effectiveness of using other channels of GOES satellites such as visible and water vapors has been investigated. By using multi-sensors the precipitation can be estimated based on the extracted information from multiple channels. Also, instead of using the exponential function for estimating rainfall from cloud top temperature, the probabilistic method has been used. Using probability distributions of precipitation rates instead of deterministic values has improved the rainfall estimation for different type of clouds.

Comparison of Cloud Properties from CALIPSO-CloudSat and Geostationary Satellite Data

NASA Technical Reports Server (NTRS)

Nguyen, L.; Minnis, P.; Chang, F.; Winker, D.; Sun-Mack, S.; Spangenberg, D.; Austin, R.

2007-01-01

Cloud properties are being derived in near-real time from geostationary satellite imager data for a variety of weather and climate applications and research. Assessment of the uncertainties in each of the derived cloud parameters is essential for confident use of the products. Determination of cloud amount, cloud top height, and cloud layering is especially important for using these real -time products for applications such as aircraft icing condition diagnosis and numerical weather prediction model assimilation. Furthermore, the distribution of clouds as a function of altitude has become a central component of efforts to evaluate climate model cloud simulations. Validation of those parameters has been difficult except over limited areas where ground-based active sensors, such as cloud radars or lidars, have been available on a regular basis. Retrievals of cloud properties are sensitive to the surface background, time of day, and the clouds themselves. Thus, it is essential to assess the geostationary satellite retrievals over a variety of locations. The availability of cloud radar data from CloudSat and lidar data from CALIPSO make it possible to perform those assessments over each geostationary domain at 0130 and 1330 LT. In this paper, CloudSat and CALIPSO data are matched with contemporaneous Geostationary Operational Environmental Satellite (GOES), Multi-functional Transport Satellite (MTSAT), and Meteosat-8 data. Unlike comparisons with cloud products derived from A-Train imagers, this study considers comparisons of nadir active sensor data with off-nadir retrievals. These matched data are used to determine the uncertainties in cloud-top heights and cloud amounts derived from the geostationary satellite data using the Clouds and the Earth s Radiant Energy System (CERES) cloud retrieval algorithms. The CERES multi-layer cloud detection method is also evaluated to determine its accuracy and limitations in the off-nadir mode. The results will be useful for constraining the use of the passive retrieval data in models and for improving the accuracy of the retrievals.

Architectures Toward Reusable Science Data Systems

NASA Astrophysics Data System (ADS)

Moses, J. F.

2014-12-01

Science Data Systems (SDS) comprise an important class of data processing systems that support product generation from remote sensors and in-situ observations. These systems enable research into new science data products, replication of experiments and verification of results. NASA has been building ground systems for satellite data processing since the first Earth observing satellites launched and is continuing development of systems to support NASA science research, NOAA's weather satellites and USGS's Earth observing satellite operations. The basic data processing workflows and scenarios continue to be valid for remote sensor observations research as well as for the complex multi-instrument operational satellite data systems being built today. System functions such as ingest, product generation and distribution need to be configured and performed in a consistent and repeatable way with an emphasis on scalability. This paper will examine the key architectural elements of several NASA satellite data processing systems currently in operation and under development that make them suitable for scaling and reuse. Examples of architectural elements that have become attractive include virtual machine environments, standard data product formats, metadata content and file naming, workflow and job management frameworks, data acquisition, search, and distribution protocols. By highlighting key elements and implementation experience the goal is to recognize architectures that will outlast their original application and be readily adaptable for new applications. Concepts and principles are explored that lead to sound guidance for SDS developers and strategists.

Atmospheric correction for hyperspectral ocean color sensors

NASA Astrophysics Data System (ADS)

Ibrahim, A.; Ahmad, Z.; Franz, B. A.; Knobelspiesse, K. D.

2017-12-01

NASA's heritage Atmospheric Correction (AC) algorithm for multi-spectral ocean color sensors is inadequate for the new generation of spaceborne hyperspectral sensors, such as NASA's first hyperspectral Ocean Color Instrument (OCI) onboard the anticipated Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission. The AC process must estimate and remove the atmospheric path radiance contribution due to the Rayleigh scattering by air molecules and by aerosols from the measured top-of-atmosphere (TOA) radiance. Further, it must also compensate for the absorption by atmospheric gases and correct for reflection and refraction of the air-sea interface. We present and evaluate an improved AC for hyperspectral sensors beyond the heritage approach by utilizing the additional spectral information of the hyperspectral sensor. The study encompasses a theoretical radiative transfer sensitivity analysis as well as a practical application of the Hyperspectral Imager for the Coastal Ocean (HICO) and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensors.

New Directions: Emerging Satellite Observations of Above-cloud Aerosols and Direct Radiative Forcing

NASA Technical Reports Server (NTRS)

Yu, Hongbin; Zhang, Zhibo

2013-01-01

Spaceborne lidar and passive sensors with multi-wavelength and polarization capabilities onboard the A-Train provide unprecedented opportunities of observing above-cloud aerosols and direct radiative forcing. Significant progress has been made in recent years in exploring these new aerosol remote sensing capabilities and generating unique datasets. The emerging observations will advance the understanding of aerosol climate forcing.

Monitoring water use and crop condition in California vineyards at multiple scales using multi-sensor satellite data fusion

USDA-ARS?s Scientific Manuscript database

Recent weather patterns have left California’s agricultural areas in severe drought. Given the reduced water availability in much of California it is critical to be able to measure water use and crop condition over large areas, but also in fine detail at scales of individual fields to support water...

Multi-Sensor Remote Sensing of Forest Dynamics in Central Siberia

NASA Technical Reports Server (NTRS)

Ransom, K. J.; Sun, G.; Kharuk, V. I.; Howl, J.

2011-01-01

The forested regions of Siberia, Russia are vast and contain about a quarter of the world's forests that have not experienced harvesting. However, many Siberian forests are facing twin pressures of rapidly changing climate and increasing timber harvest activity. Monitoring the dynamics and mapping the structural parameters of the forest is important for understanding the causes and consequences of changes observed in these areas. Because of the inaccessibility and large extent of this forest, remote sensing data can play an important role for observing forest state and change. In Central Siberia, multi-sensor remote sensing data have been used to monitor forest disturbances and to map above-ground biomass from the Sayan Mountains in the south to the taiga-tundra boundaries in the north. Radar images from the Shuttle Imaging Radar-C (SIR-C)/XSAR mission were used for forest biomass estimation in the Sayan Mountains. Radar images from the Japanese Earth Resources Satellite-1 (JERS-1), European Remote Sensing Satellite-1 (ERS-1) and Canada's RADARSAT-1, and data from ETM+ on-board Landsat-7 were used to characterize forest disturbances from logging, fire, and insect damage in Boguchany and Priangare areas.

New Approach to Monitor Transboundary Particulate Pollution over Northeast Asia

NASA Technical Reports Server (NTRS)

Park, M. E.; Song, C. H.; Park, R. S.; Lee, Jaehwa; Kim, J.; Lee, S.; Woo, J. H.; Carmichael, G. R.; Eck, Thomas F.; Holben, Brent N.;

New approach to monitor transboundary particulate pollution over Northeast Asia

NASA Astrophysics Data System (ADS)

Park, M. E.; Song, C. H.; Park, R. S.; Lee, J.; Kim, J.; Lee, S.; Woo, J.-H.; Carmichael, G. R.; Eck, T. F.; Holben, B. N.; Lee, S.-S.; Song, C. K.; Hong, Y. D.

2014-01-01

A new approach to more accurately monitor and evaluate transboundary particulate matter (PM) pollution is introduced based on aerosol optical products from Korea's Geostationary Ocean Color Imager (GOCI). The area studied is Northeast Asia (including eastern parts of China, the Korean peninsula and Japan), where GOCI has been monitoring since June 2010. The hourly multi-spectral aerosol optical data that were retrieved from GOCI sensor onboard geostationary satellite COMS (Communication, Ocean, and Meteorology Satellite) through the Yonsei aerosol retrieval algorithm were first presented and used in this study. The GOCI-retrieved aerosol optical data are integrated with estimated aerosol distributions from US EPA Models-3/CMAQ (Community Multi-scale Air Quality) v4.5.1 model simulations via data assimilation technique, thereby making the aerosol data spatially continuous and available even for cloud contamination cells. The assimilated aerosol optical data are utilized to provide quantitative estimates of transboundary PM pollution from China to the Korean peninsula and Japan. For the period of 1 April to 31 May, 2011 this analysis yields estimates that AOD as a proxy for PM2.5 or PM10 during long-range transport events increased by 117-265% compared to background average AOD (aerosol optical depth) at the four AERONET sites in Korea, and average AOD increases of 121% were found when averaged over the entire Korean peninsula. This paper demonstrates that the use of multi-spectral AOD retrievals from geostationary satellites can improve estimates of transboundary PM pollution. Such data will become more widely available later this decade when new sensors such as the GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI-2 are scheduled to be launched.

Land cover characterization and mapping of continental southeast Asia using multi-resolution satellite sensor data

USGS Publications Warehouse

Giri, Chandra; Defourny, Pierre; Shrestha, Surendra

2003-01-01

Land use/land cover change, particularly that of tropical deforestation and forest degradation, has been occurring at an unprecedented rate and scale in Southeast Asia. The rapid rate of economic development, demographics and poverty are believed to be the underlying forces responsible for the change. Accurate and up-to-date information to support the above statement is, however, not available. The available data, if any, are outdated and are not comparable for various technical reasons. Time series analysis of land cover change and the identification of the driving forces responsible for these changes are needed for the sustainable management of natural resources and also for projecting future land cover trajectories. We analysed the multi-temporal and multi-seasonal NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data of 1985/86 and 1992 to (1) prepare historical land cover maps and (2) to identify areas undergoing major land cover transformations (called ‘hot spots’). The identified ‘hot spot’ areas were investigated in detail using high-resolution satellite sensor data such as Landsat and SPOT supplemented by intensive field surveys. Shifting cultivation, intensification of agricultural activities and change of cropping patterns, and conversion of forest to agricultural land were found to be the principal reasons for land use/land cover change in the Oudomxay province of Lao PDR, the Mekong Delta of Vietnam and the Loei province of Thailand, respectively. Moreover, typical land use/land cover change patterns of the ‘hot spot’ areas were also examined. In addition, we developed an operational methodology for land use/land cover change analysis at the national level with the help of national remote sensing institutions.

Micro-satellites thermal control—concepts and components

NASA Astrophysics Data System (ADS)

Baturkin, Volodymyr

2005-01-01

The main idea of this paper is to present the survey of current tendencies in micro-satellites thermal control concepts that can be rational and useful for posterior missions due to intensive expansion of satellites of such type. For this purpose, the available references and lessons learned by the National Technical University of Ukraine during the elaboration of thermal control hardware for micro-satellites Magion 4, 5, BIRD and autonomous thermal control systems for interplanetary missions VEGA, PHOBOS have been used. The main parameters taken into consideration for analysis are the satellite sizes, mass, power consumption, orbit parameters, altitude control peculiarities and thermal control description. It was defined that passive thermal control concepts are widely used, excepting autonomous temperature regulation for sensitive components such as batteries, high-precision optics, and some types of sensors. The practical means for realization of passive thermal control design as multi-layer insulation, optical coatings, heat conductive elements, gaskets are briefly described.

Robust Satellite Techniques for monitoring earth emitted radiation in the Japanese seismic area by using MTSAT observations in the TIR spectral range

NASA Astrophysics Data System (ADS)

Genzano, Nicola; Filizzola, Carolina; Hattori, Katsumi; Lisi, Mariano; Paciello, Rossana; Pergola, Nicola; Tramutoli, Valerio

2016-04-01

Since eighties, the fluctuations of Earth's thermally emitted radiation, measured by satellite sensors operating in the thermal infrared (TIR) spectral range, have been associated with the complex process of preparation for major earthquakes. But, like other claimed earthquake precursors (seismological, physical, chemical, biological, etc.) they have been for long-time considered with some caution by scientific community. The lack of a rigorous definition of anomalous TIR signal fluctuations and the scarce attention paid to the possibility that other causes (e.g. meteorological) different from seismic activity could be responsible for the observed TIR variations were the main causes of such skepticism. Compared with previously proposed approaches the general change detection approach, named Robust Satellite Techniques (RST), showed good ability to discriminate anomalous TIR signals possibly associated to seismic activity, from the normal variability of TIR signal due to other causes. Thanks to its full exportability on different satellite packages, since 2001 RST has been implemented on TIR images acquired by polar (e.g. NOAA-AVHRR, EOS -MODIS) and geostationary (e.g. MSG-SEVIRI, NOAA-GOES/W, GMS-5/VISSR) satellite sensors, in order to verify the presence (or absence) of TIR anomalies in presence (absence) of earthquakes (with M>4) in different seismogenic areas around the world (e.g. Italy, Greece, Turkey, India, Taiwan, etc.). In this paper, the RST data analysis approach has been implemented on TIR satellite records collected over Japan by the geostationary satellite sensor MTSAT (Multifunctional Transport SATellites) and RETIRA (Robust Estimator of TIR Anomalies) index was used to identify Significant Sequences of TIR Anomalies (SSTAs) in a possible space-time relations with seismic events. Achieved results will be discussed in the perspective of a multi-parametric approach for a time-Dependent Assessment of Seismic Hazard (t-DASH).

Weather Satellite Enterprise Information Chain

NASA Astrophysics Data System (ADS)

Jamilkowski, M. L.; Grant, K. D.; Miller, S. W.; Cochran, S.

2015-12-01

NOAA & NASA are acquiring the next-generation civilian operational weather satellite: Joint Polar Satellite System (JPSS). Contributing the afternoon orbit & ground system (GS) to replace current NOAA POES Satellites, its sensors will collect meteorological, oceanographic & climatological data. The JPSS Common Ground System (CGS), consisting of C3 and IDP segments, is developed by Raytheon. It now flies the Suomi National Polar-orbiting Partnership (S-NPP) satellite, transferring data between ground facilities, processing them into environmental products for NOAA weather centers, and expanding to support JPSS-1 in 2017. As a multi-mission system, CGS provides combinations of C3, data processing, and product delivery for numerous NASA, NOAA, DoD and international missions.The CGS provides a wide range of support to a number of missions: Command and control and mission management for the S-NPP mission today, expanding this support to the JPSS-1 satellite mission in 2017 Data acquisition for S-NPP, the JAXA's Global Change Observation Mission - Water (GCOM-W1), POES, and the Defense Meteorological Satellite Program (DMSP) and Coriolis/WindSat for the DoD Data routing over a global fiber network for S-NPP, JPSS-1, GCOM-W1, POES, DMSP, Coriolis/WindSat, NASA EOS missions, MetOp for EUMETSAT and the National Science Foundation Environmental data processing and distribution for S-NPP, GCOM-W1 and JPSS-1 The CGS plays a key role in facilitating the movement and value-added enhancement of data all the way from satellite-based sensor data to delivery to the consumers who generate forecasts and produce watches and warnings. This presentation will discuss the information flow from sensors, through data routing and processing, and finally to product delivery. It will highlight how advances in architecture developed through lessons learned from S-NPP and implemented for JPSS-1 will increase data availability and reduce latency for end user applications.

Multi-temporal RADARSAT-1 and ERS backscattering signatures of coastal wetlands in southeastern Louisiana

USGS Publications Warehouse

Kwoun, Oh-Ig; Lu, Z.

2009-01-01

Using multi-temporal European Remote-sensing Satellites (ERS-1/-2) and Canadian Radar Satellite (RADARSAT-1) synthetic aperture radar (SAR) data over the Louisiana coastal zone, we characterize seasonal variations of radar backscat-tering according to vegetation type. Our main findings are as follows. First, ERS-1/-2 and RADARSAT-1 require careful radiometric calibration to perform multi-temporal backscattering analysis for wetland mapping. We use SAR backscattering signals from cities for the relative calibration. Second, using seasonally averaged backscattering coefficients from ERS-1/-2 and RADARSAT-1, we can differentiate most forests (bottomland and swamp forests) and marshes (freshwater, intermediate, brackish, and saline marshes) in coastal wetlands. The student t-test results support the usefulness of season-averaged backscatter data for classification. Third, combining SAR backscattering coefficients and an optical-sensor-based normalized difference vegetation index can provide further insight into vegetation type and enhance the separation between forests and marshes. Our study demonstrates that SAR can provide necessary information to characterize coastal wetlands and monitor their changes.

Merging climate and multi-sensor time-series data in real-time drought monitoring across the U.S.A.

USGS Publications Warehouse

Brown, Jesslyn F.; Miura, T.; Wardlow, B.; Gu, Yingxin

2011-01-01

Droughts occur repeatedly in the United States resulting in billions of dollars of damage. Monitoring and reporting on drought conditions is a necessary function of government agencies at multiple levels. A team of Federal and university partners developed a drought decision- support tool with higher spatial resolution relative to traditional climate-based drought maps. The Vegetation Drought Response Index (VegDRI) indicates general canopy vegetation condition assimilation of climate, satellite, and biophysical data via geospatial modeling. In VegDRI, complementary drought-related data are merged to provide a comprehensive, detailed representation of drought stress on vegetation. Time-series data from daily polar-orbiting earth observing systems [Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS)] providing global measurements of land surface conditions are ingested into VegDRI. Inter-sensor compatibility is required to extend multi-sensor data records; thus, translations were developed using overlapping observations to create consistent, long-term data time series. 

Maritime Aerosol Network optical depth measurements and comparison with satellite retrievals from various different sensors

NASA Astrophysics Data System (ADS)

Smirnov, Alexander; Petrenko, Maksym; Ichoku, Charles; Holben, Brent N.

2017-10-01

The paper reports on the current status of the Maritime Aerosol Network (MAN) which is a component of the Aerosol Robotic Network (AERONET). A public domain web-based data archive dedicated to MAN activity can be found at https://aeronet.gsfc.nasa.gov/new_web/maritime_aerosol_network.html . Since 2006 over 450 cruises were completed and the data archive consists of more than 6000 measurement days. In this work, we present MAN observations collocated with MODIS Terra, MODIS Aqua, MISR, POLDER, SeaWIFS, OMI, and CALIOP spaceborne aerosol products using a modified version of the Multi-Sensor Aerosol Products Sampling System (MAPSS) framework. Because of different spatio-temporal characteristics of the analyzed products, the number of MAN data points collocated with spaceborne retrievals varied between 1500 matchups for MODIS to 39 for CALIOP (as of August 2016). Despite these unavoidable sampling biases, latitudinal dependencies of AOD differences for all satellite sensors, except for SeaWIFS and POLDER, showed positive biases against ground truth (i.e. MAN) in the southern latitudes (<50° S), and substantial scatter in the Northern Atlantic "dust belt" (5°-15° N). Our analysis did not intend to determine whether satellite retrievals are within claimed uncertainty boundaries, but rather show where bias exists and corrections are needed.

Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes.

PubMed

Prasad, Dilip K; Agarwal, Krishna

2016-03-22

We propose a method for classifying radiometric oceanic color data measured by hyperspectral satellite sensors into known spectral classes, irrespective of the downwelling irradiance of the particular day, i.e., the illumination conditions. The focus is not on retrieving the inherent optical properties but to classify the pixels according to the known spectral classes of the reflectances from the ocean. The method compensates for the unknown downwelling irradiance by white balancing the radiometric data at the ocean pixels using the radiometric data of bright pixels (typically from clouds). The white-balanced data is compared with the entries in a pre-calibrated lookup table in which each entry represents the spectral properties of one class. The proposed approach is tested on two datasets of in situ measurements and 26 different daylight illumination spectra for medium resolution imaging spectrometer (MERIS), moderate-resolution imaging spectroradiometer (MODIS), sea-viewing wide field-of-view sensor (SeaWiFS), coastal zone color scanner (CZCS), ocean and land colour instrument (OLCI), and visible infrared imaging radiometer suite (VIIRS) sensors. Results are also shown for CIMEL's SeaPRISM sun photometer sensor used on-board field trips. Accuracy of more than 92% is observed on the validation dataset and more than 86% is observed on the other dataset for all satellite sensors. The potential of applying the algorithms to non-satellite and non-multi-spectral sensors mountable on airborne systems is demonstrated by showing classification results for two consumer cameras. Classification on actual MERIS data is also shown. Additional results comparing the spectra of remote sensing reflectance with level 2 MERIS data and chlorophyll concentration estimates of the data are included.

Multi-Sensory Aerosol Data and the NRL NAAPS model for Regulatory Exceptional Event Analysis

NASA Astrophysics Data System (ADS)

Husar, R. B.; Hoijarvi, K.; Westphal, D. L.; Haynes, J.; Omar, A. H.; Frank, N. H.

2013-12-01

Beyond scientific exploration and analysis, multi-sensory observations along with models are finding increasing applications for operational air quality management. EPA's Exceptional Event (EE) Rule allows the exclusion of data strongly influenced by impacts from "exceptional events," such as smoke from wildfires or dust from abnormally high winds. The EE Rule encourages the use of satellite observations and other non-standard data along with models as evidence for formal documentation of EE samples for exclusion. Thus, the implementation of the EE Rule is uniquely suited for the direct application of integrated multi-sensory observations and indirectly through the assimilation into an aerosol simulation model. Here we report the results of a project: NASA and NAAPS Products for Air Quality Decision Making. The project uses of observations from multiple satellite sensors, surface-based aerosol measurements and the NRL Aerosol Analysis and Prediction System (NAAPS) model that assimilates key satellite observations. The satellite sensor data for detecting and documenting smoke and dust events include: MODIS AOD and Images; OMI Aerosol Index, Tropospheric NO2; AIRS, CO. The surface observations include the EPA regulatory PM2.5 network; the IMPROVE/STN aerosol chemical network; AIRNOW PM2.5 mass network, and surface met. data. Within this application, crucial role is assigned to the NAAPS model for estimating the surface concentration of windblown dust and biomass smoke. The operational model assimilates quality-assured daily MODIS data and 2DVAR to adjust the model concentrations and CALIOP-based climatology to adjust the vertical profiles at 6-hour intervals. The assimilation of satellite data from multiple satellites significantly contributes to the usefulness of NAAPS for EE analysis. The NAAPS smoke and dust simulations were evaluated using the IMPROVE/STN chemical data. The multi-sensory observations along with the model simulations are integrated into a web-based Exceptional Event Decision System (EE DSS) application program, designed to support air quality analysts at the Federal and Regional EPA offices and the EE-affected States. EE DSS screening tool automatically identifies the EPA PM2.5 mass samples that are candidates for EE flagging, based mainly on the NAAPS-simulated surface concentration of dust and smoke. The AQ analysts at the States and the EPA can also use the EE DSS to gather further evidence from the examination of spatio-temporal pattern, Absorbing Aerosol Index, CO and NO2 concentration, backward and forward airmass trajectories and other signatures. Since early 2013, the DSS has been used for the identification and analysis of dozens of events. Hence, integration of multi-sensory observations and modeling with data assimilation is maturing to support real-world operational AQ management applications. The remaining challenges can be resolved by seeking ';closure' of the system components; i.e. the systematic adjustments to reconcile the satellite and surface observations, the emissions and their integration through a suitable AQ model.

Development of a multi-sensor based urban discharge forecasting system using remotely sensed data: A case study of extreme rainfall in South Korea

NASA Astrophysics Data System (ADS)

Yoon, Sunkwon; Jang, Sangmin; Park, Kyungwon

2017-04-01

Extreme weather due to changing climate is a main source of water-related disasters such as flooding and inundation and its damage will be accelerated somewhere in world wide. To prevent the water-related disasters and mitigate their damage in urban areas in future, we developed a multi-sensor based real-time discharge forecasting system using remotely sensed data such as radar and satellite. We used Communication, Ocean and Meteorological Satellite (COMS) and Korea Meteorological Agency (KMA) weather radar for quantitative precipitation estimation. The Automatic Weather System (AWS) and McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) were used for verification of rainfall accuracy. The optimal Z-R relation was applied the Tropical Z-R relationship (Z=32R1.65), it has been confirmed that the accuracy is improved in the extreme rainfall events. In addition, the performance of blended multi-sensor combining rainfall was improved in 60mm/h rainfall and more strong heavy rainfall events. Moreover, we adjusted to forecast the urban discharge using Storm Water Management Model (SWMM). Several statistical methods have been used for assessment of model simulation between observed and simulated discharge. In terms of the correlation coefficient and r-squared discharge between observed and forecasted were highly correlated. Based on this study, we captured a possibility of real-time urban discharge forecasting system using remotely sensed data and its utilization for real-time flood warning. Acknowledgement This research was supported by a grant (13AWMP-B066744-01) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korean government.

Surface Albedo/BRDF Parameters (Terra/Aqua MODIS)

DOE Data Explorer

Trishchenko, Alexander

2008-01-15

Spatially and temporally complete surface spectral albedo/BRDF products over the ARM SGP area were generated using data from two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on Terra and Aqua satellites. A landcover-based fitting (LBF) algorithm is developed to derive the BRDF model parameters and albedo product (Luo et al., 2004a). The approach employs a landcover map and multi-day clearsky composites of directional surface reflectance. The landcover map is derived from the Landsat TM 30-meter data set (Trishchenko et al., 2004a), and the surface reflectances are from MODIS 500m-resolution 8-day composite products (MOD09/MYD09). The MOD09/MYD09 data are re-arranged into 10-day intervals for compatibility with other satellite products, such as those from the NOVA/AVHRR and SPOT/VGT sensors. The LBF method increases the success rate of the BRDF fitting process and enables more accurate monitoring of surface temporal changes during periods of rapid spring vegetation green-up and autumn leaf-fall, as well as changes due to agricultural practices and snowcover variations (Luo et al., 2004b, Trishchenko et al., 2004b). Albedo/BRDF products for MODIS on Terra and MODIS on Aqua, as well as for Terra/Aqua combined dataset, are generated at 500m spatial resolution and every 10-day since March 2000 (Terra) and July 2002 (Aqua and combined), respectively. The purpose for the latter product is to obtain a more comprehensive dataset that takes advantages of multi-sensor observations (Trishchenko et al., 2002). To fill data gaps due to cloud presence, various interpolation procedures are applied based on a multi-year observation database and referring to results from other locations with similar landcover property. Special seasonal smoothing procedure is also applied to further remove outliers and artifacts in data series.

Payload Configurations for Efficient Image Acquisition - Indian Perspective

NASA Astrophysics Data System (ADS)

Samudraiah, D. R. M.; Saxena, M.; Paul, S.; Narayanababu, P.; Kuriakose, S.; Kiran Kumar, A. S.

2014-11-01

The world is increasingly depending on remotely sensed data. The data is regularly used for monitoring the earth resources and also for solving problems of the world like disasters, climate degradation, etc. Remotely sensed data has changed our perspective of understanding of other planets. With innovative approaches in data utilization, the demands of remote sensing data are ever increasing. More and more research and developments are taken up for data utilization. The satellite resources are scarce and each launch costs heavily. Each launch is also associated with large effort for developing the hardware prior to launch. It is also associated with large number of software elements and mathematical algorithms post-launch. The proliferation of low-earth and geostationary satellites has led to increased scarcity in the available orbital slots for the newer satellites. Indian Space Research Organization has always tried to maximize the utility of satellites. Multiple sensors are flown on each satellite. In each of the satellites, sensors are designed to cater to various spectral bands/frequencies, spatial and temporal resolutions. Bhaskara-1, the first experimental satellite started with 2 bands in electro-optical spectrum and 3 bands in microwave spectrum. The recent Resourcesat-2 incorporates very efficient image acquisition approach with multi-resolution (3 types of spatial resolution) multi-band (4 spectral bands) electro-optical sensors (LISS-4, LISS-3* and AWiFS). The system has been designed to provide data globally with various data reception stations and onboard data storage capabilities. Oceansat-2 satellite has unique sensor combination with 8 band electro-optical high sensitive ocean colour monitor (catering to ocean and land) along with Ku band scatterometer to acquire information on ocean winds. INSAT- 3D launched recently provides high resolution 6 band image data in visible, short-wave, mid-wave and long-wave infrared spectrum. It also has 19 band sounder for providing vertical profile of water vapour, temperature, etc. The same system has data relay transponders for acquiring data from weather stations. The payload configurations have gone through significant changes over the years to increase data rate per kilogram of payload. Future Indian remote sensing systems are planned with very high efficient ways of image acquisition. This paper analyses the strides taken by ISRO (Indian Space research Organisation) in achieving high efficiency in remote sensing image data acquisition. Parameters related to efficiency of image data acquisition are defined and a methodology is worked out to compute the same. Some of the Indian payloads are analysed with respect to some of the system/ subsystem parameters that decide the configuration of payload. Based on the analysis, possible configuration approaches that can provide high efficiency are identified. A case study is carried out with improved configuration and the results of efficiency improvements are reported. This methodology may be used for assessing other electro-optical payloads or missions and can be extended to other types of payloads and missions.

Intensive time series data exploitation: the Multi-sensor Evolution Analysis (MEA) platform

NASA Astrophysics Data System (ADS)

Mantovani, Simone; Natali, Stefano; Folegani, Marco; Scremin, Alessandro

2014-05-01

The monitoring of the temporal evolution of natural phenomena must be performed in order to ensure their correct description and to allow improvements in modelling and forecast capabilities. This assumption, that is obvious for ground-based measurements, has not always been true for data collected through space-based platforms: except for geostationary satellites and sensors, that allow providing a very effective monitoring of phenomena with geometric scale from regional to global; smaller phenomena (with characteristic dimension lower than few kilometres) have been monitored with instruments that could collect data only with a time interval in the order of several days; bi-temporal techniques have been the most used ones for years, in order to characterise temporal changes and try identifying specific phenomena. The more the number of flying sensor has grown and their performance improved, the more their capability of monitoring natural phenomena at a smaller geographic scale has grown: we can now count on tenth of years of remotely sensed data, collected by hundreds of sensors that are now accessible from a wide users' community, and the techniques for data processing have to be adapted to move toward a data intensive exploitation. Starting from 2008, the European Space Agency has initiated the development of the Multi-sensor Evolution Analysis (MEA) platform (https://mea.eo.esa.int), whose first aim was to permit the access and exploitation of long term remotely sensed satellite data from different platforms: 15 years of global (A)ATSR data together with 5 years of regional AVNIR-2 data were loaded into the system and were used, through a web-based graphic user interface, for land cover change analysis. The MEA data availability has grown during years integrating multi-disciplinary data that feature spatial and temporal dimensions: so far tenths of Terabytes of data in the land and atmosphere domains are available and can be visualized and exploited, keeping the time dimension as the most relevant one (https://mea.eo.esa.int/data_availability.html). MEA is also used as Climate Data gateway in the framework of the FP7 EarthServer Project. In the present work, principles of the MEA platform are presented, emphasizing the general concept and the methods that have been implemented for data access (including OGC standard data access) and exploitation. In order to show its effectiveness, use cases focused on multi-field and multi-temporal data analysis are shown.

Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

NASA Astrophysics Data System (ADS)

Alonso, Carmelo; Tarquis, Ana M.; Zúñiga, Ignacio; Benito, Rosa M.

2017-03-01

Several studies have shown that vegetation indexes can be used to estimate root zone soil moisture. Earth surface images, obtained by high-resolution satellites, presently give a lot of information on these indexes, based on the data of several wavelengths. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends the possible data archives from the present time to several decades back. Because of this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. In this work, four band images have been considered, which are involved in these vegetation indexes, and were taken by satellites Ikonos-2 and Landsat-7 of the same geographic location, to study the effect of both spatial (pixel size) and radiometric (number of bits coding the image) resolution on these wavelength bands as well as two vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). In order to do so, a multi-fractal analysis of these multi-spectral images was applied in each of these bands and the two indexes derived. The results showed that spatial resolution has a similar scaling effect in the four bands, but radiometric resolution has a larger influence in blue and green bands than in red and near-infrared bands. The NDVI showed a higher sensitivity to the radiometric resolution than EVI. Both were equally affected by the spatial resolution. From both factors, the spatial resolution has a major impact in the multi-fractal spectrum for all the bands and the vegetation indexes. This information should be taken in to account when vegetation indexes based on different satellite sensors are obtained.

Space based observations: A state of the art solution for spatial monitoring tropical forested watershed productivity at regional scale in developing countries

NASA Astrophysics Data System (ADS)

Mahmud, M. R.

2014-02-01

This paper presents the simplified and operational approach of mapping the water yield in tropical watershed using space-based multi sensor remote sensing data. Two main critical hydrological rainfall variables namely rainfall and evapotranspiration are being estimated by satellite measurement and reinforce the famous Thornthwaite & Mather water balance model. The satellite rainfall and ET estimates were able to represent the actual value on the ground with accuracy under considerable conditions. The satellite derived water yield had good agreement and relation with actual streamflow. A high bias measurement may result due to; i) influence of satellite rainfall estimates during heavy storm, and ii) large uncertainties and standard deviation of MODIS temperature data product. The output of this study managed to improve the regional scale of hydrology assessment in Peninsular Malaysia.

Retrieval Algorithm for Broadband Albedo at the Top of the Atmosphere

NASA Astrophysics Data System (ADS)

Lee, Sang-Ho; Lee, Kyu-Tae; Kim, Bu-Yo; Zo, ll-Sung; Jung, Hyun-Seok; Rim, Se-Hun

2018-05-01

The objective of this study is to develop an algorithm that retrieves the broadband albedo at the top of the atmosphere (TOA albedo) for radiation budget and climate analysis of Earth's atmosphere using Geostationary Korea Multi-Purse Satellite/Advanced Meteorological Imager (GK-2A/AMI) data. Because the GK-2A satellite will launch in 2018, we used data from the Japanese weather satellite Himawari-8 and onboard sensor Advanced Himawari Imager (AHI), which has similar sensor properties and observation area to those of GK-2A. TOA albedo was retrieved based on reflectance and regression coefficients of shortwave channels 1 to 6 of AHI. The regression coefficient was calculated using the results of the radiative transfer model (SBDART) and ridge regression. The SBDART used simulations of the correlation between TOA albedo and reflectance of each channel according to each atmospheric conditions (solar zenith angle, viewing zenith angle, relative azimuth angle, surface type, and absence/presence of clouds). The TOA albedo from Himawari-8/AHI were compared to that from the National Aeronautics and Space Administration (NASA) satellite Terra with onboard sensor Clouds and the Earth's Radiant Energy System (CERES). The correlation coefficients between the two datasets from the week containing the first day of every month between 1st August 2015 and 1st July 2016 were high, ranging between 0.934 and 0.955, with the root mean square error in the 0.053-0.068 range.

Building Change Detection in Very High Resolution Satellite Stereo Image Time Series

NASA Astrophysics Data System (ADS)

Tian, J.; Qin, R.; Cerra, D.; Reinartz, P.

2016-06-01

There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.

Sensors and OBIA synergy for operational monitoring of surface water

NASA Astrophysics Data System (ADS)

Masson, Eric; Thenard, Lucas

2010-05-01

This contribution will focus on combining Object Based Image Analysis (i.e. OBIA with e-Cognition 8) and recent sensors (i.e. Spot 5 XS, Pan and ALOS Prism, Avnir2, Palsar) to address the technical feasibility for an operational monitoring of surface water. Three cases of river meandering (India), flood mapping (Nepal) and dam's seasonal water level monitoring (Morocco) using recent sensors will present various application of surface water monitoring. The operational aspect will be demonstrated either by sensor properties (i.e. spatial resolution and bandwidth), data acquisition properties (i.e. multi sensor, return period and near real-time acquisition) but also with OBIA algorithms (i.e. fusion of multi sensors / multi resolution data and batch processes). In the first case of river meandering (India) we will address multi sensor and multi date satellite acquisition to monitor the river bed mobility within a floodplain using an ALOS dataset. It will demonstrate the possibility of an operational monitoring system that helps the geomorphologist in the analysis of fluvial dynamic and sediment budget for high energy rivers. In the second case of flood mapping (Nepal) we will address near real time Palsar data acquisition at high spatial resolution to monitor and to map a flood extension. This ALOS sensor takes benefit both from SAR and L band properties (i.e. atmospheric transparency, day/night acquisition, low sensibility to surface wind). It's a real achievement compared to optical imagery or even other high resolution SAR properties (i.e. acquisition swath, bandwidth and data price). These advantages meet the operational needs set by crisis management of hydrological disasters but also for the implementation of flood risk management plans. The last case of dam surface water monitoring (Morocco) will address an important issue of water resource management in countries affected by water scarcity. In such countries water users have to cope with over exploitation, frequent drought period and now with foreseen climate change impacts. This third case will demonstrate the efficiency of SPOT 5 programming in synergy with OBIA methodology to assess the evolution of dam surface water within a complete water cycle (i.e. 2008-09). In all those three cases image segmentation and classification algorithms developed with e-Cognition 8 software allow an easy to use implementation of simple to highly sophisticate OBIA rulsets fully operational in batch processes. Finally this contribution foresees the new opportunity of integration of Worldview 2 multispectral imagery (i.e. 8 bands) including its "coastal" band that will also find an application in continental surface water bathymetry. Worldview 2 is a recently launch satellite (e.g. October 2009) that starts to collect earth observation data since January 2010. It is therefore a promising new remote sensing tool to develop operational hydrology in combination high resolution SAR imagery and OBIA methodology. This contribution will conclude on the strong potential for operationalisation in hydrology and water resources management that recent and future sensors and image analysis methodologies are offering to water management and decision makers.

Physical assessment of coastal vulnerability under enhanced land subsidence in Semarang, Indonesia, using multi-sensor satellite data

NASA Astrophysics Data System (ADS)

Husnayaen; Rimba, A. Besse; Osawa, Takahiro; Parwata, I. Nyoman Sudi; As-syakur, Abd. Rahman; Kasim, Faizal; Astarini, Ida Ayu

2018-04-01

Research has been conducted in Semarang, Indonesia, to assess coastal vulnerability under enhanced land subsidence using multi-sensor satellite data, including the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR), Landsat TM, IKONOS, and TOPEX/Poseidon. A coastal vulnerability index (CVI) was constructed to estimate the level of vulnerability of a coastline approximately 48.68 km in length using seven physical variables, namely, land subsidence, relative sea level change, coastal geomorphology, coastal slope, shoreline change, mean tidal range, and significant wave height. A comparison was also performed between a CVI calculated using seven parameters and a CVI using six parameters, the latter of which excludes the land subsidence parameter, to determine the effects of land subsidence during the coastal vulnerability assessment. This study showed that the accuracy of coastal vulnerability was increased 40% by adding the land subsidence factor (i.e., CVI 6 parameters = 53%, CVI 7 parameters = 93%). Moreover, Kappa coefficient indicated very good agreement (0.90) for CVI 7 parameters and fair agreement (0.3) for CVI 6 parameters. The results indicate that the area of very high vulnerability increased by 7% when land subsidence was added. Hence, using the CVI calculation including land subsidence parameters, the very high vulnerability area is determined to be 20% of the total coastline or 9.7 km of the total 48.7 km of coastline. This study proved that land subsidence has significant influence on coastal vulnerability in Semarang.

Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping

NASA Astrophysics Data System (ADS)

Shelestov, Andrii; Lavreniuk, Mykola; Kussul, Nataliia; Novikov, Alexei; Skakun, Sergii

2017-02-01

Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory ( 28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

MISST: The Multi-Sensor Improved Sea Surface Temperature Project

DTIC Science & Technology

2009-06-01

climate change studies, fisheries management, and a wide range of other applications. Measurements are taken by several satellites carrying infrared and...TEMPERATURE PROJECT ABSTRACT. Sea surface temperature (SST) measurements are vital to global weather prediction, climate change studies, fisheries management...important variables related to the global ocean-atmosphere system. It is a key indicator of climate change , is widely applied to studies of upper

Effects of Data Quality on the Characterization of Aerosol Properties from Multiple Sensors

NASA Technical Reports Server (NTRS)

Petrenko, Maksym; Ichoku, Charles; Leptoukh, Gregory

2011-01-01

Cross-comparison of aerosol properties between ground-based and spaceborne measurements is an important validation technique that helps to investigate the uncertainties of aerosol products acquired using spaceborne sensors. However, it has been shown that even minor differences in the cross-characterization procedure may significantly impact the results of such validation. Of particular consideration is the quality assurance I quality control (QA/QC) information - an auxiliary data indicating a "confidence" level (e.g., Bad, Fair, Good, Excellent, etc.) conferred by the retrieval algorithms on the produced data. Depending on the treatment of available QA/QC information, a cross-characterization procedure has the potential of filtering out invalid data points, such as uncertain or erroneous retrievals, which tend to reduce the credibility of such comparisons. However, under certain circumstances, even high QA/QC values may not fully guarantee the quality of the data. For example, retrievals in proximity of a cloud might be particularly perplexing for an aerosol retrieval algorithm, resulting in an invalid data that, nonetheless, could be assigned a high QA/QC confidence. In this presentation, we will study the effects of several QA/QC parameters on cross-characterization of aerosol properties between the data acquired by multiple spaceborne sensors. We will utilize the Multi-sensor Aerosol Products Sampling System (MAPSS) that provides a consistent platform for multi-sensor comparison, including collocation with measurements acquired by the ground-based Aerosol Robotic Network (AERONET), The multi-sensor spaceborne data analyzed include those acquired by the Terra-MODIS, Aqua-MODIS, Terra-MISR, Aura-OMI, Parasol-POLDER, and CalipsoCALIOP satellite instruments.

Joint Polar Satellite System (JPSS) Common Ground System (CGS) Current Technical Performance Measures

NASA Astrophysics Data System (ADS)

Cochran, S.; Panas, M.; Jamilkowski, M. L.; Miller, S. W.

2015-12-01

ABSTRACT The National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) are jointly acquiring the next-generation civilian weather and environmental satellite system: the Joint Polar Satellite System (JPSS). The Joint Polar Satellite System will replace the afternoon orbit component and ground processing system of the current Polar-orbiting Operational Environmental Satellites (POES) managed by NOAA. The JPSS satellites will carry a suite of sensors designed to collect meteorological, oceanographic, climatological and geophysical observations of the Earth. The ground processing system for JPSS is known as the JPSS Common Ground System (JPSS CGS). Developed and maintained by Raytheon Intelligence, Information and Services (IIS), the CGS is a multi-mission enterprise system serving NOAA, NASA and their national and international partners. The CGS has demonstrated its scalability and flexibility to incorporate multiple missions efficiently and with minimal cost, schedule and risk, while strengthening global partnerships in weather and environmental monitoring. The CGS architecture is being upgraded to Block 2.0 in 2015 to "operationalize" S-NPP, leverage lessons learned to date in multi-mission support, take advantage of more reliable and efficient technologies, and satisfy new requirements and constraints in the continually evolving budgetary environment. To ensure the CGS meets these needs, we have developed 49 Technical Performance Measures (TPMs) across 10 categories, such as data latency, operational availability and scalability. This paper will provide an overview of the CGS Block 2.0 architecture, with particular focus on the 10 TPM categories listed above. We will provide updates on how we ensure the deployed architecture meets these TPMs to satisfy our multi-mission objectives with the deployment of Block 2.0.

An overview of in-orbit radiometric calibration of typical satellite sensors

NASA Astrophysics Data System (ADS)

Zhou, G. Q.; Li, C. Y.; Yue, T.; Jiang, L. J.; Liu, N.; Sun, Y.; Li, M. Y.

2015-06-01

This paper reviews the development of in-orbit radiometric calibration methods in the past 40 years. It summarizes the development of in-orbit radiometric calibration technology of typical satellite sensors in the visible/near-infrared bands and the thermal infrared band. Focuses on the visible/near-infrared bands radiometric calibration method including: Lamp calibration and solar radiationbased calibration. Summarizes the calibration technology of Landsat series satellite sensors including MSS, TM, ETM+, OLI, TIRS; SPOT series satellite sensors including HRV, HRS. In addition to the above sensors, there are also summarizing ALI which was equipped on EO-1, IRMSS which was equipped on CBERS series satellite. Comparing the in-orbit radiometric calibration technology of different periods but the same type satellite sensors analyzes the similarities and differences of calibration technology. Meanwhile summarizes the in-orbit radiometric calibration technology in the same periods but different country satellite sensors advantages and disadvantages of calibration technology.

Architectures Toward Reusable Science Data Systems

NASA Technical Reports Server (NTRS)

Moses, John

2015-01-01

Science Data Systems (SDS) comprise an important class of data processing systems that support product generation from remote sensors and in-situ observations. These systems enable research into new science data products, replication of experiments and verification of results. NASA has been building systems for satellite data processing since the first Earth observing satellites launched and is continuing development of systems to support NASA science research and NOAAs Earth observing satellite operations. The basic data processing workflows and scenarios continue to be valid for remote sensor observations research as well as for the complex multi-instrument operational satellite data systems being built today. System functions such as ingest, product generation and distribution need to be configured and performed in a consistent and repeatable way with an emphasis on scalability. This paper will examine the key architectural elements of several NASA satellite data processing systems currently in operation and under development that make them suitable for scaling and reuse. Examples of architectural elements that have become attractive include virtual machine environments, standard data product formats, metadata content and file naming, workflow and job management frameworks, data acquisition, search, and distribution protocols. By highlighting key elements and implementation experience we expect to find architectures that will outlast their original application and be readily adaptable for new applications. Concepts and principles are explored that lead to sound guidance for SDS developers and strategists.

Data Analysis of GPM Constellation Satellites-IMERG and ERA-Interim precipitation products over West of Iran

NASA Astrophysics Data System (ADS)

Sharifi, Ehsan; Steinacker, Reinhold; Saghafian, Bahram

2016-04-01

Precipitation is a critical component of the Earth's hydrological cycle. The primary requirement in precipitation measurement is to know where and how much precipitation is falling at any given time. Especially in data sparse regions with insufficient radar coverage, satellite information can provide a spatial and temporal context. Nonetheless, evaluation of satellite precipitation is essential prior to operational use. This is why many previous studies are devoted to the validation of satellite estimation. Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards. In situ observations over mountainous areas are mostly limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for meteorological and hydrological applications. One of the newest and blended methods that use multi-satellites and multi-sensors has been developed for estimating global precipitation. The considered data set known as Integrated Multi-satellitE Retrievals (IMERG) for GPM (Global Precipitation Measurement) is routinely produced by the GPM constellation satellites. Moreover, recent efforts have been put into the improvement of the precipitation products derived from reanalysis systems, which has led to significant progress. One of the best and a worldwide used model is developed by the European Centre for Medium Range Weather Forecasts (ECMWF). They have produced global reanalysis daily precipitation, known as ERA-Interim. This study has evaluated one year of precipitation data from the GPM-IMERG and ERA-Interim reanalysis daily time series over West of Iran. IMERG and ERA-Interim yield underestimate the observed values while IMERG underestimated slightly and performed better when precipitation is greater than 10mm. Furthermore, with respect to evaluation of probability of detection (POD), threat score (TS), false alarm ratio (FAR) and probability of false detection (POFD) IMERG yields a better value of POD, TS, FAR and POFD in comparison to era-Interim. Overall, ERA-Interim product produced fewer robust results when compared to IMERG.

Diurnal variability in carbon and nitrogen pools within Chesapeake Bay and northern Gulf of Mexico: implications for future ocean color satellite sensors

NASA Astrophysics Data System (ADS)

Mannino, A.; Novak, M. G.; Tzortziou, M.; Salisbury, J.

2016-02-01

Relative to their areal extent, estuaries and coastal ocean ecosystems contribute disproportionately more to global biogeochemical cycling of carbon, nitrogen and other elements compared to the open ocean. Applying ocean color satellite data to study biological and biogeochemical processes within coastal ecosystems is challenging due to the complex mixtures of aquatic constituents derived from terrestrial, anthropogenic, and marine sources, human-impacted atmospheric properties, presence of clouds during satellite overpass, fine-scale spatial gradients, and time-varying processes on diurnal scales that cannot be resolved with current sensors. On diurnal scales, biological, photochemical, and biogeochemical processes are regulated by the variation in solar radiation. Other physical factors, such as tides, river discharge, estuarine and coastal ocean circulation, wind-driven mixing, etc., impart further variability on biological and biogeochemical processes on diurnal to multi-day time scales. Efforts to determine the temporal frequency required from a NASA GEO-CAPE ocean color satellite sensor to discern diurnal variability C and N stocks, fluxes and productivity culminated in field campaigns in the Chesapeake Bay and northern Gulf of Mexico. Near-surface drogues were released and tracked in quasi-lagrangian space to monitor hourly changes in community production, C and N stocks, and optical properties. While only small diurnal changes were observed in dissolved organic carbon (DOC) and colored dissolved organic matter (CDOM) absorption in Chesapeake Bay, substantial variation in particulate organic carbon (POC) and nitrogen (PN), chlorophyll-a, and inorganic nitrogen (DIN) were measured. Similar or greater diurnal changes in POC, PN, chlorophyll-a and DIN were found in Gulf of Mexico nearshore and offshore sites. These results suggest that satellite observations at hourly frequency are desirable to capture diurnal variability in carbon and nitrogen stocks, fluxes and productivity within coastal ecosystems.

On the Challenge of Observing Pelagic Sargassum in Coastal Oceans: A Multi-sensor Assessment

NASA Astrophysics Data System (ADS)

Hu, C.; Feng, L.; Hardy, R.; Hochberg, E. J.

2016-02-01

Remote detection of pelagic Sargassum is often hindered by its spectral similarity to other floating materials and by the inadequate spatial resolution. Using measurements from multi-spectral satellite sensors (Moderate Resolution Imaging Spectroradiometer or MODIS), Landsat, WorldView-2 (or WV-2) as well as hyperspectral sensors (Hyperspectral Imager for the Coastal Ocean or HICO, Airborne Visible-InfraRed Imaging Spectrometer or AVIRIS) and airborne digital photos, we analyze and compare their ability (in terms of spectral and spatial resolutions) to detect Sargassum and to differentiate from other floating materials such as Trichodesmium, Syringodium, Ulva, garbage, and emulsified oil. Field measurements suggest that Sargassum has a distinctive reflectance curvature around 630 nm due to its chlorophyll c pigments, which provides a unique spectral signature when combined with the reflectance ratio between brown ( 650 nm) and green ( 555 nm) wavelengths. For a 10-nm resolution sensor on the hyperspectral HyspIRI mission currently being planned by NASA, a stepwise rule to examine several indexes established from 6 bands (centered at 555, 605, 625, 645, 685, 755 nm) is shown to be effective to unambiguously differentiate Sargassum from all other floating materials Numerical simulations using spectral endmembers and noise in the satellite-derived reflectance suggest that spectral discrimination is degraded when a pixel is mixed between Sargassum and water. A minimum of 20-30% Sargassum coverage within a pixel is required to retain such ability, while the partial coverage can be as low as 1-2% when detecting floating materials without spectral discrimination. With its expected signal-to-noise ratios (SNRs 200:1), the hyperspectral HyspIRI mission may provide a compromise between spatial resolution and spatial coverage to improve our capacity to detect, discriminate, and quantify Sargassum.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

PubMed

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

2018-01-27

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

Satellite Image Classification of Building Damages Using Airborne and Satellite Image Samples in a Deep Learning Approach

NASA Astrophysics Data System (ADS)

Duarte, D.; Nex, F.; Kerle, N.; Vosselman, G.

2018-05-01

The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of building damages. However, there is an overall interest in the use of automated methods to perform this task, regardless of the used platform. Owing to its synoptic coverage and predictable availability, satellite imagery is currently used as input for the identification of building damages by the International Charter, as well as the Copernicus Emergency Management Service for the production of damage grading and reference maps. Recently proposed methods to perform image classification of building damages rely on convolutional neural networks (CNN). These are usually trained with only satellite image samples in a binary classification problem, however the number of samples derived from these images is often limited, affecting the quality of the classification results. The use of up/down-sampling image samples during the training of a CNN, has demonstrated to improve several image recognition tasks in remote sensing. However, it is currently unclear if this multi resolution information can also be captured from images with different spatial resolutions like satellite and airborne imagery (from both manned and unmanned platforms). In this paper, a CNN framework using residual connections and dilated convolutions is used considering both manned and unmanned aerial image samples to perform the satellite image classification of building damages. Three network configurations, trained with multi-resolution image samples are compared against two benchmark networks where only satellite image samples are used. Combining feature maps generated from airborne and satellite image samples, and refining these using only the satellite image samples, improved nearly 4 % the overall satellite image classification of building damages.

Acoustic Sensors for Air and Surface Navigation Applications

PubMed Central

Kapoor, Rohan; Ramasamy, Subramanian; Schyndel, Ron Van

2018-01-01

This paper presents the state-of-the-art and reviews the state-of-research of acoustic sensors used for a variety of navigation and guidance applications on air and surface vehicles. In particular, this paper focuses on echolocation, which is widely utilized in nature by certain mammals (e.g., cetaceans and bats). Although acoustic sensors have been extensively adopted in various engineering applications, their use in navigation and guidance systems is yet to be fully exploited. This technology has clear potential for applications in air and surface navigation/guidance for intelligent transport systems (ITS), especially considering air and surface operations indoors and in other environments where satellite positioning is not available. Propagation of sound in the atmosphere is discussed in detail, with all potential attenuation sources taken into account. The errors introduced in echolocation measurements due to Doppler, multipath and atmospheric effects are discussed, and an uncertainty analysis method is presented for ranging error budget prediction in acoustic navigation applications. Considering the design challenges associated with monostatic and multi-static sensor implementations and looking at the performance predictions for different possible configurations, acoustic sensors show clear promises in navigation, proximity sensing, as well as obstacle detection and tracking. The integration of acoustic sensors in multi-sensor navigation systems is also considered towards the end of the paper and a low Size, Weight and Power, and Cost (SWaP-C) sensor integration architecture is presented for possible introduction in air and surface navigation systems. PMID:29414894

Comparative analysis of recent satellite missions for multi-temporal SAR interferometry

NASA Astrophysics Data System (ADS)

Bovenga, Fabio; Refice, Alberto; Belmonte, Antonella; Pasquariello, Guido

2016-10-01

Multi-temporal InSAR (MTI) applications pose challenges related to the availability of coherent scattering from the ground surface, the complexity of the ground deformations, the atmospheric artifacts, the visibility problems related to the ground elevation. Nowadays, several satellite missions are available providing interferometric SAR data at different wavelengths, spatial resolutions, and revisit time. A new interesting opportunity is provided by Sentinel-1 mission, which has a spatial resolution comparable to previous ESA C-band missions, and revisit times reduced to up to 6 days. It is envisioned that, by offering regular, global-scale coverage, improved temporal resolution and freely available imagery, Sentinel-1 will guarantee an increasing use of MTI for ground displacement investigations. According to these different SAR space-borne missions, the present work discusses current and future opportunities of MTI applications to ground instability monitoring. Issues related to coherent target detection and mean velocity precision will be addressed through a simple theoretical model assuming backscattering mechanisms related to point scatterers. The paper also presents an example of multi-sensor ground instability investigation over the site of Marina di Lesina, Southern Italy, a village lying over a gypsum diapir, where a hydration process, involving the underlying anhydride, causes a smooth uplift pattern affecting the entire village area, and the formation of scattered sinkholes. More than 20 years of MTI SAR data have been used, coming from both legacy ERS and ENVISAT missions, and last-generation Radarsat-2, COSMO-SkyMed, and Sentinel-1A sensors.

Method of Obtaining High Resolution Intrinsic Wire Boom Damping Parameters for Multi-Body Dynamics Simulations

NASA Technical Reports Server (NTRS)

Yew, Alvin G.; Chai, Dean J.; Olney, David J.

2010-01-01

The goal of NASA's Magnetospheric MultiScale (MMS) mission is to understand magnetic reconnection with sensor measurements from four spinning satellites flown in a tight tetrahedron formation. Four of the six electric field sensors on each satellite are located at the end of 60- meter wire booms to increase measurement sensitivity in the spin plane and to minimize motion coupling from perturbations on the main body. A propulsion burn however, might induce boom oscillations that could impact science measurements if oscillations do not damp to values on the order of 0.1 degree in a timely fashion. Large damping time constants could also adversely affect flight dynamics and attitude control performance. In this paper, we will discuss the implementation of a high resolution method for calculating the boom's intrinsic damping, which was used in multi-body dynamics simulations. In summary, experimental data was obtained with a scaled-down boom, which was suspended as a pendulum in vacuum. Optical techniques were designed to accurately measure the natural decay of angular position and subsequently, data processing algorithms resulted in excellent spatial and temporal resolutions. This method was repeated in a parametric study for various lengths, root tensions and vacuum levels. For all data sets, regression models for damping were applied, including: nonlinear viscous, frequency-independent hysteretic, coulomb and some combination of them. Our data analysis and dynamics models have shown that the intrinsic damping for the baseline boom is insufficient, thereby forcing project management to explore mitigation strategies.

Incorporation of quality updates for JPSS CGS Products

NASA Astrophysics Data System (ADS)

Cochran, S.; Grant, K. D.; Ibrahim, W.; Brueske, K. F.; Smit, P.

2016-12-01

NOAA's next-generation environmental satellite, the Joint Polar Satellite System (JPSS) replaces the current Polar-orbiting Operational Environmental Satellites (POES). JPSS satellites carry sensors which collect meteorological, oceanographic, climatological, and solar-geophysical observations of the earth, atmosphere, and space. The first JPSS satellite was launched in 2011 and is currently NOAA's primary operational polar satellite. The JPSS ground system is the Common Ground System (CGS), and provides command, control, and communications (C3) and data processing (DP). A multi-mission system, CGS provides combinations of C3/DP for numerous NASA, NOAA, DoD, and international missions. In preparation for the next JPSS satellite, CGS improved its multi-mission capabilities to enhance mission operations for larger constellations of earth observing satellites with the added benefit of streamlining mission operations for other NOAA missions. This paper will discuss both the theoretical basis and the actual practices used to date to identify, test and incorporate algorithm updates into the CGS processing baseline. To provide a basis for this support, Raytheon developed a theoretical analysis framework, and the application of derived engineering processes, for the maintenance of consistency and integrity of remote sensing operational algorithm outputs. The framework is an abstraction of the operationalization of the science-grade algorithm (Sci2Ops) process used throughout the JPSS program. By combining software and systems engineering controls, manufacturing disciplines to detect and reduce defects, and a standard process to control analysis, an environment to maintain operational algorithm maturity is achieved. Results of the use of this approach to implement algorithm changes into operations will also be detailed.

Methods and Tools for Product Quality Maintenance in JPSS CGS

NASA Astrophysics Data System (ADS)

Cochran, S.; Smit, P.; Grant, K. D.; Jamilkowski, M. L.

2015-12-01

NOAA's next-generation environmental satellite, the Joint Polar Satellite System (JPSS) replaces the current Polar-orbiting Operational Environmental Satellites (POES). JPSS satellites carry sensors which collect meteorological, oceanographic, climatological, and solar-geophysical observations of the earth, atmosphere, and space. The first JPSS satellite was launched in 2011 and is currently NOAA's primary operational polar satellite. The JPSS ground system is the Common Ground System (CGS), and provides command, control, and communications (C3) and data processing (DP). A multi-mission system, CGS provides combinations of C3/DP for numerous NASA, NOAA, DoD, and international missions. In preparation for the next JPSS satellite, CGS improved its multi-mission capabilities to enhance mission operations for larger constellations of earth observing satellites with the added benefit of streamlining mission operations for other NOAA missions. This paper will discuss both the theoretical basis and the actual practices used to date to identify, test and incorporate algorithm updates into the CGS processing baseline. To provide a basis for this support, Raytheon developed a theoretical analysis framework, and the application of derived engineering processes, for the maintenance of consistency and integrity of remote sensing operational algorithm outputs. The framework is an abstraction of the operationalization of the science-grade algorithm (Sci2Ops) process used throughout the JPSS program. By combining software and systems engineering controls, manufacturing disciplines to detect and reduce defects, and a standard process to control analysis, an environment to maintain operational algorithm maturity is achieved. Results of the use of this approach to implement algorithm changes into operations will also be detailed.

Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration.

PubMed

Herbreteau, Vincent; Salem, Gérard; Souris, Marc; Hugot, Jean-Pierre; Gonzalez, Jean-Paul

2007-06-01

Remote sensing, referring to the remote study of objects, was originally developed for Earth observation, through the use of sensors on board planes or satellites. Improvements in the use and accessibility of multi-temporal satellite-derived environmental data have, for 30 years, contributed to a growing use in epidemiology. Despite the potential of remote-sensed images and processing techniques for a better knowledge of disease dynamics, an exhaustive analysis of the bibliography shows a generalized use of pre-processed spatial data and low-cost images, resulting in a limited adaptability when addressing biological questions.

Satellite/Submarine Arctic Sea Ice Remote Sensing in 2004 and 2007

NASA Astrophysics Data System (ADS)

Hughes, N. E.; Wadhams, P.; Rodrigues, J.

2007-12-01

After an interlude of 8 years the U.K. Royal Navy returned to the Arctic Ocean with an under-ice mission by the submarine shape HMS Tireless in April 2004. A full environmental monitoring programme in which U.K. civilian scientists were allowed to participate was integrated into the mission. This was subsequently followed by a second expedition, in March 2007, which allowed further measurements to be acquired. These have so far been the only opportunities for civilian scientists to utilise navy submarines in the Arctic since the demise of the U.S. SCICEX programme in 2000. This paper presents some of the data collected on these new missions and uses it for validation of sea ice information derived from coincident acquisitions by modern satellite sensors such as the ESA Envisat ASAR and NASA MODIS. In both the 2004 and 2007 expeditions shape Tireless took a track north of Greenland along the latitude 85° N. This was similar to the route used for an earlier submarine-aircraft combined survey in April 1987 with which our results shall be compared. In all three missions the submarine was equipped with a standard upward-looking echosounder and sidescan for ice observations and a full range of satellite-borne, or airborne in the case of the earlier mission, microwave and optical sensors were available for validation. In this study we concentrate on the submarine track north of Greenland from the Marginal Ice Zone (MIZ) in Fram Strait through to the Lincoln Sea around 65° W. This transect encompasses a wide range of differing sea ice conditions, from the highly mobile mixture of first year and multi year ice being transported on the trans-polar drift through to the highly deformed ice north of Greenland and Ellesmere Island. The combination of submarine measurements of ice thickness and satellite/aircraft top-side measurements gives an accurate indication of how changes in the ice regime are taking place and allows the potential development of multi-sensor data fusion algorithms for improved sea ice classification and estimation of thickness.

Atmospheric correction for remote sensing image based on multi-spectral information

NASA Astrophysics Data System (ADS)

Wang, Yu; He, Hongyan; Tan, Wei; Qi, Wenwen

2018-03-01

The light collected from remote sensors taken from space must transit through the Earth's atmosphere. All satellite images are affected at some level by lightwave scattering and absorption from aerosols, water vapor and particulates in the atmosphere. For generating high-quality scientific data, atmospheric correction is required to remove atmospheric effects and to convert digital number (DN) values to surface reflectance (SR). Every optical satellite in orbit observes the earth through the same atmosphere, but each satellite image is impacted differently because atmospheric conditions are constantly changing. A physics-based detailed radiative transfer model 6SV requires a lot of key ancillary information about the atmospheric conditions at the acquisition time. This paper investigates to achieve the simultaneous acquisition of atmospheric radiation parameters based on the multi-spectral information, in order to improve the estimates of surface reflectance through physics-based atmospheric correction. Ancillary information on the aerosol optical depth (AOD) and total water vapor (TWV) derived from the multi-spectral information based on specific spectral properties was used for the 6SV model. The experimentation was carried out on images of Sentinel-2, which carries a Multispectral Instrument (MSI), recording in 13 spectral bands, covering a wide range of wavelengths from 440 up to 2200 nm. The results suggest that per-pixel atmospheric correction through 6SV model, integrating AOD and TWV derived from multispectral information, is better suited for accurate analysis of satellite images and quantitative remote sensing application.

Determination of the Impact of Urbanization on Agricultural Lands using Multi-temporal Satellite Sensor Images

NASA Astrophysics Data System (ADS)

Kaya, S.; Alganci, U.; Sertel, E.; Ustundag, B.

2015-12-01

Throughout the history, agricultural activities have been performed close to urban areas. Main reason behind this phenomenon is the need of fast marketing of the agricultural production to urban residents and financial provision. Thus, using the areas nearby cities for agricultural activities brings out advantage of easy transportation of productions and fast marketing. For decades, heavy migration to cities has directly and negatively affected natural grasslands, forests and agricultural lands. This pressure has caused agricultural lands to be changed into urban areas. Dense urbanization causes increase in impervious surfaces, heat islands and many other problems in addition to destruction of agricultural lands. Considering the negative impacts of urbanization on agricultural lands and natural resources, a periodic monitoring of these changes becomes indisputably important. At this point, satellite images are known to be good data sources for land cover / use change monitoring with their fast data acquisition, large area coverages and temporal resolution properties. Classification of the satellite images provides thematic the land cover / use maps of the earth surface and changes can be determined with GIS based analysis multi-temporal maps. In this study, effects of heavy urbanization over agricultural lands in Istanbul, metropolitan city of Turkey, were investigated with use of multi-temporal Landsat TM satellite images acquired between 1984 and 2011. Images were geometrically registered to each other and classified using supervised maximum likelihood classification algorithm. Resulting thematic maps were exported to GIS environment and destructed agricultural lands by urbanization were determined using spatial analysis.

Multisensor satellite data for water quality analysis and water pollution risk assessment: decision making under deep uncertainty with fuzzy algorithm in framework of multimodel approach

NASA Astrophysics Data System (ADS)

Kostyuchenko, Yuriy V.; Sztoyka, Yulia; Kopachevsky, Ivan; Artemenko, Igor; Yuschenko, Maxim

2017-10-01

Multi-model approach for remote sensing data processing and interpretation is described. The problem of satellite data utilization in multi-modeling approach for socio-ecological risks assessment is formally defined. Observation, measurement and modeling data utilization method in the framework of multi-model approach is described. Methodology and models of risk assessment in framework of decision support approach are defined and described. Method of water quality assessment using satellite observation data is described. Method is based on analysis of spectral reflectance of aquifers. Spectral signatures of freshwater bodies and offshores are analyzed. Correlations between spectral reflectance, pollutions and selected water quality parameters are analyzed and quantified. Data of MODIS, MISR, AIRS and Landsat sensors received in 2002-2014 have been utilized verified by in-field spectrometry and lab measurements. Fuzzy logic based approach for decision support in field of water quality degradation risk is discussed. Decision on water quality category is making based on fuzzy algorithm using limited set of uncertain parameters. Data from satellite observations, field measurements and modeling is utilizing in the framework of the approach proposed. It is shown that this algorithm allows estimate water quality degradation rate and pollution risks. Problems of construction of spatial and temporal distribution of calculated parameters, as well as a problem of data regularization are discussed. Using proposed approach, maps of surface water pollution risk from point and diffuse sources are calculated and discussed.

Human-Computer Interaction and Information Management Research Needs

DTIC Science & Technology

2003-10-01

Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be...hand-held personal digital assistants, networked sensors and actuators, and low-power computers on satellites. 5 most complex tools that humans have...calculations using data on external media such as tapes evolved into our multi-functional 21st century systems. More ideas came as networks of computing

NASA: Assessments of Selected Large-Scale Projects

DTIC Science & Technology

2011-03-01

REPORT DATE MAR 2011 2. REPORT TYPE 3. DATES COVERED 00-00-2011 to 00-00-2011 4. TITLE AND SUBTITLE Assessments Of Selected Large-Scale Projects...Volatile EvolutioN MEP Mars Exploration Program MIB Mishap Investigation Board MMRTG Multi Mission Radioisotope Thermoelectric Generator MMS Magnetospheric...probes designed to explore the Martian surface, to satellites equipped with advanced sensors to study the earth , to telescopes intended to explore the

REMOTE SENSING IN OCEANOGRAPHY.

DTIC Science & Technology

remote sensing from satellites. Sensing of oceanographic variables from aircraft began with the photographing of waves and ice. Since then remote measurement of sea surface temperatures and wave heights have become routine. Sensors tested for oceanographic applications include multi-band color cameras, radar scatterometers, infrared spectrometers and scanners, passive microwave radiometers, and radar imagers. Remote sensing has found its greatest application in providing rapid coverage of large oceanographic areas for synoptic and analysis and

A Web-GIS Procedure Based on Satellite Multi-Spectral and Airborne LIDAR Data to Map the Road blockage Due to seismic Damages of Built-Up Urban Areas

NASA Astrophysics Data System (ADS)

Costanzo, Antonio; Montuori, Antonio; Silva, Juan Pablo; Silvestri, Malvina; Musacchio, Massimo; Buongiorno, Maria Fabrizia; Stramondo, Salvatore

2016-08-01

In this work, a web-GIS procedure to map the risk of road blockage in urban environments through the combined use of space-borne and airborne remote sensing sensors is presented. The methodology concerns (1) the provision of a geo-database through the integration of space-borne multispectral images and airborne LiDAR data products; (2) the modeling of building vulnerability, based on the corresponding 3D geometry and construction time information; (3) the GIS-based mapping of road closure due to seismic- related building collapses based on the building characteristic height and the width of the road. Experimental results, gathered for the Cosenza urban area, allow demonstrating the benefits of both the proposed approach and the GIS-based integration of multi-platforms remote sensing sensors and techniques for seismic road assessment purposes.

High Data Rate Satellite Communications for Environmental Remote Sensing

NASA Astrophysics Data System (ADS)

Jackson, J. M.; Munger, J.; Emch, P. G.; Sen, B.; Gu, D.

2014-12-01

Satellite to ground communication bandwidth limitations place constraints on current earth remote sensing instruments which limit the spatial and spectral resolution of data transmitted to the ground for processing. Instruments such as VIIRS, CrIS and OMPS on the Soumi-NPP spacecraft must aggregate data both spatially and spectrally in order to fit inside current data rate constraints limiting the optimal use of the as-built sensors. Future planned missions such as HyspIRI, SLI, PACE, and NISAR will have to trade spatial and spectral resolution if increased communication band width is not made available. A number of high-impact, environmental remote sensing disciplines such as hurricane observation, mega-city air quality, wild fire detection and monitoring, and monitoring of coastal oceans would benefit dramatically from enabling the downlinking of sensor data at higher spatial and spectral resolutions. The enabling technologies of multi-Gbps Ka-Band communication, flexible high speed on-board processing, and multi-Terabit SSRs are currently available with high technological maturity enabling high data volume mission requirements to be met with minimal mission constraints while utilizing a limited set of ground sites from NASA's Near Earth Network (NEN) or TDRSS. These enabling technologies will be described in detail with emphasis on benefits to future remote sensing missions currently under consideration by government agencies.

The absolute calibration of KOMPSAT-3 and 3A high spatial resolution satellites using radiometric tarps and MFRSR measurments

NASA Astrophysics Data System (ADS)

Yeom, J. M.

2017-12-01

Recently developed Korea Multi-Purpose Satellite-3A (KOMPSAT-3A), which is a continuation of the KOMPSAT-1, 2 and 3 earth observation satellite (EOS) programs from the Korea Aerospace Research Institute (KARI) was launched on March, 25 2015 on a Dnepr-1 launch vehicle from the Jasny Dombarovsky site in Russia. After launched, KARI performed in-orbit-test (IOT) including radiometric calibration for 6 months from 14 Apr. to 4 Sep. 2015. KOMPSAT-3A is equipped with two distinctive sensors; one is a high resolution multispectral optical sensor, namely the Advances Earth Image Sensor System-A (AEISS-A) and the other is the Scanner Infrared Imaging System (SIIS). In this study, we focused on the radiometric calibration of AEISS-A. The multispectral wavelengths of AEISS-A are covering three visible regions: blue (450 - 520 nm), green (520 - 600 nm), red (630 - 690 nm), one near infrared (760 - 900 nm) with a 2.0 m spatial resolution at nadir, whereas the panchromatic imagery (450 - 900 nm) has a 0.5 m resolution. Those are the same spectral response functions were same with KOMPSAT-3 multispectral and panchromatic bands but the spatial resolutions are improved. The main mission of KOMPSAT-3A is to develop for Geographical Information System (GIS) applications in environmental, agriculture, and oceanographic sciences, as well as natural hazard monitoring.

Lithosphere-to-Ionosphere Plug-and-Play Architecture (LION-PNP): Networking the Physical World Made Cheap and Easy

NASA Astrophysics Data System (ADS)

Darling, N. T.; Mendez, J. S.; Fritz, T. A.; Hoffman, C.

2012-12-01

The lack of rapidly reconfigurable and easily deployable instrumentation packages often results in information loss during unannounced or time-critical geophysical events such as spaceweather flare-ups, earthquakes, volcanic eruptions, and tsunamis. While increasingly powerful and sensitive sensor technologies have been created in the last years to study our planet, robust, yet simple and cost-effective, mechanical, electrical, and data interfaces between these devices and the user (scientist) have yet to be developed. Non-standardized interfaces make instrument integration and field operation cumbersome and error-prone. Indeed, the assembly and deployment of some systems can take months and incur high costs. To address this problem, we present the LIthosphere-to-IOnosphere Plug-aNd-Play architecture (LION-PNP), a complete, low cost integration protocol for space, atmospheric, and terrestrial sensor networks. Similar to the USB plug-and-play protocols created for personal computers, LION-PNP offers geophysicists and space scientists the ability to assemble and operate complex sensor packages by simply "plugging" devices (magnetometers, seismometers, GPS, spectrometers, etc) into a centralized Command and Data Handling unit (CDH). LION-PNP accomplishes this by inserting a Generic Sensor Interpreter (GSI) between the back-end of a device and the CDH. The GSI allows the CDH to automatically configure a sensor without requiring the user to manually install drivers. Mechanical integration is also accelerated by repackaging instruments according to the CubeSAT form-factor (multiples of 10 x 10 x 10 cm cubes). In the following work, we report on the development of LION-PNP. To demonstrate our initial success, we first discuss the Boston University Student-satellite for Applications and Training (BUSAT), a low-cost, modular, spaceweather satellite running LION-PNP. BUSAT is a completely student-driven project meant for magnetospheric-ionospheric research incorporating 4 scientific payloads. To further stress the broad applicability of LION-PNP we also present VolcanoNET, a ground-based, multi-sensor package that will explore charging of volcanic ash plumes and volcanic lightning.; The Boston University Student satellite for Applications and Training (BUSAT) canisterized scientific satellite concept.

United states national land cover data base development? 1992-2001 and beyond

USGS Publications Warehouse

Yang, L.

2008-01-01

An accurate, up-to-date and spatially-explicate national land cover database is required for monitoring the status and trends of the nation's terrestrial ecosystem, and for managing and conserving land resources at the national scale. With all the challenges and resources required to develop such a database, an innovative and scientifically sound planning must be in place and a partnership be formed among users from government agencies, research institutes and private sectors. In this paper, we summarize major scientific and technical issues regarding the development of the NLCD 1992 and 2001. Experiences and lessons learned from the project are documented with regard to project design, technical approaches, accuracy assessment strategy, and projecti imiplementation.Future improvements in developing next generation NLCD beyond 2001 are suggested, including: 1) enhanced satellite data preprocessing in correction of atmospheric and adjacency effect and the topographic normalization; 2) improved classification accuracy through comprehensive and consistent training data and new algorithm development; 3) multi-resolution and multi-temporal database targeting major land cover changes and land cover database updates; 4) enriched database contents by including additional biophysical parameters and/or more detailed land cover classes through synergizing multi-sensor, multi-temporal, and multi-spectral satellite data and ancillary data, and 5) transform the NLCD project into a national land cover monitoring program. ?? 2008 IEEE.

Low Frequency Error Analysis and Calibration for High-Resolution Optical Satellite's Uncontrolled Geometric Positioning

NASA Astrophysics Data System (ADS)

Wang, Mi; Fang, Chengcheng; Yang, Bo; Cheng, Yufeng

2016-06-01

The low frequency error is a key factor which has affected uncontrolled geometry processing accuracy of the high-resolution optical image. To guarantee the geometric quality of imagery, this paper presents an on-orbit calibration method for the low frequency error based on geometric calibration field. Firstly, we introduce the overall flow of low frequency error on-orbit analysis and calibration, which includes optical axis angle variation detection of star sensor, relative calibration among star sensors, multi-star sensor information fusion, low frequency error model construction and verification. Secondly, we use optical axis angle change detection method to analyze the law of low frequency error variation. Thirdly, we respectively use the method of relative calibration and information fusion among star sensors to realize the datum unity and high precision attitude output. Finally, we realize the low frequency error model construction and optimal estimation of model parameters based on DEM/DOM of geometric calibration field. To evaluate the performance of the proposed calibration method, a certain type satellite's real data is used. Test results demonstrate that the calibration model in this paper can well describe the law of the low frequency error variation. The uncontrolled geometric positioning accuracy of the high-resolution optical image in the WGS-84 Coordinate Systems is obviously improved after the step-wise calibration.

Suppression of sun interference in the star sensor baffling stray light by total internal reflection

NASA Astrophysics Data System (ADS)

Kawano, Hiroyuki; Shimoji, Haruhiko; Yoshikawa, Shoji; Miyatake, Katsumasa; Hama, Kazumori; Nakamura, Shuji

2005-09-01

We have developed a star sensor as an experimental device onboard the SERVIS-1 satellite launched in October 2003. The in-orbit data have verified its fundamental performance. One of the advantages of our star sensor is that the baffle has a small length of 120 mm instead of 182 mm in the conventional two-stage baffle design. The key concepts for light shielding are total internal reflection phenomena inside a nearly half sphere (NHS) lens and scattering light control by gloss black paint. However, undesirable background noise by the sun outside of the field of view (FOV) was observed in the corner of the FOV in the orbital experiment. Ray trace simulations revealed that slight scattering light on the specular baffle wall entered the NHS lens and reached the corner of the image sensor through the multi-reflection path inside the lens. It was found that the stray light path can be shielded effectively if the diameter of the aperture under the NHS lens was reduced. We redesigned the baffle and evaluated the light shielding ability with our sun interference test facility on the ground, and confirmed that the stray light was reduced below the acceptable level. As a result, the light shielding technique which we have proposed was proved to be effective for a small-size baffle. The redesigned star sensor is planned to be installed as a main attitude sensor for the SERVIS-2 satellite scheduled to be launched in February 2008.

Satellite-Based Precipitation Datasets

NASA Astrophysics Data System (ADS)

Munchak, S. J.; Huffman, G. J.

2017-12-01

Of the possible sources of precipitation data, those based on satellites provide the greatest spatial coverage. There is a wide selection of datasets, algorithms, and versions from which to choose, which can be confusing to non-specialists wishing to use the data. The International Precipitation Working Group (IPWG) maintains tables of the major publicly available, long-term, quasi-global precipitation data sets (http://www.isac.cnr.it/ ipwg/data/datasets.html), and this talk briefly reviews the various categories. As examples, NASA provides two sets of quasi-global precipitation data sets: the older Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and current Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG). Both provide near-real-time and post-real-time products that are uniformly gridded in space and time. The TMPA products are 3-hourly 0.25°x0.25° on the latitude band 50°N-S for about 16 years, while the IMERG products are half-hourly 0.1°x0.1° on 60°N-S for over 3 years (with plans to go to 16+ years in Spring 2018). In addition to the precipitation estimates, each data set provides fields of other variables, such as the satellite sensor providing estimates and estimated random error. The discussion concludes with advice about determining suitability for use, the necessity of being clear about product names and versions, and the need for continued support for satellite- and surface-based observation.

Application of Artificial Neural Networks to the Development of Improved Multi-Sensor Retrievals of Near-Surface Air Temperature and Humidity Over Ocean

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Clayson, Carol Anne

2012-01-01

Improved estimates of near-surface air temperature and air humidity are critical to the development of more accurate turbulent surface heat fluxes over the ocean. Recent progress in retrieving these parameters has been made through the application of artificial neural networks (ANN) and the use of multi-sensor passive microwave observations. Details are provided on the development of an improved retrieval algorithm that applies the nonlinear statistical ANN methodology to a set of observations from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A) that are currently available from the NASA AQUA satellite platform. Statistical inversion techniques require an adequate training dataset to properly capture embedded physical relationships. The development of multiple training datasets containing only in-situ observations, only synthetic observations produced using the Community Radiative Transfer Model (CRTM), or a mixture of each is discussed. An intercomparison of results using each training dataset is provided to highlight the relative advantages and disadvantages of each methodology. Particular emphasis will be placed on the development of retrievals in cloudy versus clear-sky conditions. Near-surface air temperature and humidity retrievals using the multi-sensor ANN algorithms are compared to previous linear and non-linear retrieval schemes.

Geostationary Lightning Mapper for GOES-R

NASA Technical Reports Server (NTRS)

Goodman, Steven; Blakeslee, Richard; Koshak, William

2007-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 11 year data record of global lightning activity. Instrument formulation studies begun in January 2006 will be completed in March 2007, with implementation expected to begin in September 2007. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite, airborne science missions (e.g., African Monsoon Multi-disciplinary Analysis, AMMA), and regional test beds (e.g, Lightning Mapping Arrays) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data now being provided to selected forecast offices will lead to improved understanding of the application of these data in the severe storm warning process and accelerate the development of the pre-launch algorithms and Nowcasting applications. Proxy data combined with MODIS and Meteosat Second Generation SEVERI observations will also lead to new applications (e.g., multi-sensor precipitation algorithms blending the GLM with the Advanced Baseline Imager, convective cloud initiation and identification, early warnings of lightning threat, storm tracking, and data assimilation).

Aerosol Climate Time Series Evaluation In ESA Aerosol_cci

NASA Astrophysics Data System (ADS)

Popp, T.; de Leeuw, G.; Pinnock, S.

2015-12-01

Within the ESA Climate Change Initiative (CCI) Aerosol_cci (2010 - 2017) conducts intensive work to improve algorithms for the retrieval of aerosol information from European sensors. By the end of 2015 full mission time series of 2 GCOS-required aerosol parameters are completely validated and released: Aerosol Optical Depth (AOD) from dual view ATSR-2 / AATSR radiometers (3 algorithms, 1995 - 2012), and stratospheric extinction profiles from star occultation GOMOS spectrometer (2002 - 2012). Additionally, a 35-year multi-sensor time series of the qualitative Absorbing Aerosol Index (AAI) together with sensitivity information and an AAI model simulator is available. Complementary aerosol properties requested by GCOS are in a "round robin" phase, where various algorithms are inter-compared: fine mode AOD, mineral dust AOD (from the thermal IASI spectrometer), absorption information and aerosol layer height. As a quasi-reference for validation in few selected regions with sparse ground-based observations the multi-pixel GRASP algorithm for the POLDER instrument is used. Validation of first dataset versions (vs. AERONET, MAN) and inter-comparison to other satellite datasets (MODIS, MISR, SeaWIFS) proved the high quality of the available datasets comparable to other satellite retrievals and revealed needs for algorithm improvement (for example for higher AOD values) which were taken into account for a reprocessing. The datasets contain pixel level uncertainty estimates which are also validated. The paper will summarize and discuss the results of major reprocessing and validation conducted in 2015. The focus will be on the ATSR, GOMOS and IASI datasets. Pixel level uncertainties validation will be summarized and discussed including unknown components and their potential usefulness and limitations. Opportunities for time series extension with successor instruments of the Sentinel family will be described and the complementarity of the different satellite aerosol products (e.g. dust vs. total AOD, ensembles from different algorithms for the same sensor) will be discussed.

Quantifying Errors in TRMM-Based Multi-Sensor QPE Products Over Land in Preparation for GPM

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa D.; Tian, Yudong

2011-01-01

Determining uncertainties in satellite-based multi-sensor quantitative precipitation estimates over land of fundamental importance to both data producers and hydro climatological applications. ,Evaluating TRMM-era products also lays the groundwork and sets the direction for algorithm and applications development for future missions including GPM. QPE uncertainties result mostly from the interplay of systematic errors and random errors. In this work, we will synthesize our recent results quantifying the error characteristics of satellite-based precipitation estimates. Both systematic errors and total uncertainties have been analyzed for six different TRMM-era precipitation products (3B42, 3B42RT, CMORPH, PERSIANN, NRL and GSMap). For systematic errors, we devised an error decomposition scheme to separate errors in precipitation estimates into three independent components, hit biases, missed precipitation and false precipitation. This decomposition scheme reveals hydroclimatologically-relevant error features and provides a better link to the error sources than conventional analysis, because in the latter these error components tend to cancel one another when aggregated or averaged in space or time. For the random errors, we calculated the measurement spread from the ensemble of these six quasi-independent products, and thus produced a global map of measurement uncertainties. The map yields a global view of the error characteristics and their regional and seasonal variations, reveals many undocumented error features over areas with no validation data available, and provides better guidance to global assimilation of satellite-based precipitation data. Insights gained from these results and how they could help with GPM will be highlighted.

Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites

NASA Astrophysics Data System (ADS)

Bai, Heming; Gong, Cheng; Wang, Minghuai; Zhang, Zhibo; L'Ecuyer, Tristan

2018-02-01

Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol-cloud interactions and constraining aerosol indirect effects. However, large discrepancies exist in the previous satellite estimates of precipitation susceptibility. In this paper, multi-sensor aerosol and cloud products, including those from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) from June 2006 to April 2011 are analyzed to estimate precipitation frequency susceptibility SPOP, precipitation intensity susceptibility SI, and precipitation rate susceptibility SR in warm marine clouds. We find that SPOP strongly depends on atmospheric stability, with larger values under more stable environments. Our results show that precipitation susceptibility for drizzle (with a -15 dBZ rainfall threshold) is significantly different than that for rain (with a 0 dBZ rainfall threshold). Onset of drizzle is not as readily suppressed in warm clouds as rainfall while precipitation intensity susceptibility is generally smaller for rain than for drizzle. We find that SPOP derived with respect to aerosol index (AI) is about one-third of SPOP derived with respect to cloud droplet number concentration (CDNC). Overall, SPOP demonstrates relatively robust features throughout independent liquid water path (LWP) products and diverse rain products. In contrast, the behaviors of SI and SR are subject to LWP or rain products used to derive them. Recommendations are further made for how to better use these metrics to quantify aerosol-cloud-precipitation interactions in observations and models.

The TRMM Multi-Satellite Precipitation Analysis (TMPA)

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.; Nelkin, Eric J.

2008-01-01

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) is intended to provide a "best" estimate of quasi-global precipitation from the wide variety of modern satellite-borne precipitation-related sensors. Estimates are provided at relatively fine scales (0.25degx0.25deg, 3-hourly) in both real and post-real time to accommodate a wide range of researchers. However, the errors inherent in the finest scale estimates are large. The most successful use of the TMPA data is when the analysis takes advantage of the fine-scale data to create time/space averages appropriate to the user s application. We review the conceptual basis for the TMPA, summarize the processing sequence, and focus on two new activities. First, a recent upgrade to the real-time version incorporates several additional satellite data sources and employs monthly climatological adjustments to approximate the bias characteristics of the research quality post-real-time product. Second, an upgrade of the research quality post-real-time TMPA from Version 6 to Version 7 (in beta test at press time) is designed to provide a variety of improvements that increase the list of input data sets and correct several issues. Future enhancements for the TMPA will include improved error estimation, extension to higher latitudes, and a shift to a Lagrangian time interpolation scheme.

The TRMM Multi-Satellite Precipitation Analysis (TMPA)

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.; Nelkin, Eric J.

2010-01-01

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) is intended to provide a "best" estimate of quasi-global precipitation from the wide variety of modern satellite-borne precipitation-related sensors. Estimates are provided at relatively fine scales (0.25 deg x 0.25 deg. 3-h) in both real and post-real time to accommodate a wide range of researchers. However, the errors inherent in the finest scale estimates are large. The most successful use of the TMPA data is when the analysis takes advantage of the fine-scale data to create time/space averages appropriate to the user fs application. We review the conceptual basis for the TMPA, summarize the processing sequence, and focus on two new activities. First, a recent upgrade for the real-time version incorporates several additional satellite data sources and employs monthly climatological adjustments to approximate the bias characteristics of the research quality post-real-time product. Second, an upgrade for the research quality post-real-time TMPA from Versions 6 to 7 (in beta test at press time) is designed to provide a variety of improvements that increase the list of input data sets and correct several issues. Future enhancements for the TMPA will include improved error estimation, extension to higher latitudes, and a shift to a Lagrangian time interpolation scheme.

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study

PubMed Central

Hosseinyalamdary, Siavash

2018-01-01

Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy. PMID:29695119

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study.

PubMed

Hosseinyalamdary, Siavash

2018-04-24

Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.

Investigation into the use of satellite remote sensing data products as part of a multi-modal marine environmental monitoring network

NASA Astrophysics Data System (ADS)

O'Connor, Edel; Smeaton, Alan F.; O'Connor, Noel E.; Regan, Fiona

2012-09-01

In this paper it is investigated how conventional in-situ sensor networks can be complemented by the satellite data streams available through numerous platforms orbiting the earth and the combined analyses products available through services such as MyOcean. Despite the numerous benefits associated with the use of satellite remote sensing data products, there are a number of limitations with their use in coastal zones. Here the ability of these data sources to provide contextual awareness, redundancy and increased efficiency to an in-situ sensor network is investigated. The potential use of a variety of chlorophyll and SST data products as additional data sources in the SmartBay monitoring network in Galway Bay, Ireland is analysed. The ultimate goal is to investigate the ability of these products to create a smarter marine monitoring network with increased efficiency. Overall it was found that while care needs to be taken in choosing these products, there was extremely promising performance from a number of these products that would be suitable in the context of a number of applications especially in relation to SST. It was more difficult to come to conclusive results for the chlorophyll analysis.

An Overview of SIMBIOS Program Activities and Accomplishments. Chapter 1

NASA Technical Reports Server (NTRS)

Fargion, Giulietta S.; McClain, Charles R.

2003-01-01

The SIMBIOS Program was conceived in 1994 as a result of a NASA management review of the agency's strategy for monitoring the bio-optical properties of the global ocean through space-based ocean color remote sensing. At that time, the NASA ocean color flight manifest included two data buy missions, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Earth Observing System (EOS) Color, and three sensors, two Moderate Resolution Imaging Spectroradiometers (MODIS) and the Multi-angle Imaging Spectro-Radiometer (MISR), scheduled for flight on the EOS-Terra and EOS-Aqua satellites. The review led to a decision that the international assemblage of ocean color satellite systems provided ample redundancy to assure continuous global coverage, with no need for the EOS Color mission. At the same time, it was noted that non-trivial technical difficulties attended the challenge (and opportunity) of combining ocean color data from this array of independent satellite systems to form consistent and accurate global bio-optical time series products. Thus, it was announced at the October 1994 EOS Interdisciplinary Working Group meeting that some of the resources budgeted for EOS Color should be redirected into an intercalibration and validation program (McClain et al., 2002).

Heading Toward Launch with the Integrated Multi-Satellite Retrievals for GPM (IMERG)

NASA Technical Reports Server (NTRS)

Huffman, George J.; Bolvin, David T.; Nelkin, Eric J.; Adler, Robert F.

2012-01-01

The Day-l algorithm for computing combined precipitation estimates in GPM is the Integrated Multi-satellitE Retrievals for GPM (IMERG). We plan for the period of record to encompass both the TRMM and GPM eras, and the coverage to extend to fully global as experience is gained in the difficult high-latitude environment. IMERG is being developed as a unified U.S. algorithm that takes advantage of strengths in the three groups that are contributing expertise: 1) the TRMM Multi-satellite Precipitation Analysis (TMPA), which addresses inter-satellite calibration of precipitation estimates and monthly scale combination of satellite and gauge analyses; 2) the CPC Morphing algorithm with Kalman Filtering (KF-CMORPH), which provides quality-weighted time interpolation of precipitation patterns following cloud motion; and 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS), which provides a neural-network-based scheme for generating microwave-calibrated precipitation estimates from geosynchronous infrared brightness temperatures. In this talk we summarize the major building blocks and important design issues driven by user needs and practical data issues. One concept being pioneered by the IMERG team is that the code system should produce estimates for the same time period but at different latencies to support the requirements of different groups of users. Another user requirement is that all these runs must be reprocessed as new IMERG versions are introduced. IMERG's status at meeting time will be summarized, and the processing scenario in the transition from TRMM to GPM will be laid out. Initially, IMERG will be run with TRMM-based calibration, and then a conversion to a GPM-based calibration will be employed after the GPM sensor products are validated. A complete reprocessing will be computed, which will complete the transition from TMPA.

Joint Polar Satellite System (JPSS) Common Ground System (CGS) Technical Performance Measures of the Block 2 Architecture

NASA Astrophysics Data System (ADS)

Grant, K. D.; Panas, M.

2016-12-01

NOAA and NASA are jointly acquiring the next-generation civilian weather satellite system: the Joint Polar Satellite System (JPSS). JPSS replaced the afternoon orbit component and ground processing of NOAA's old POES system. JPSS satellites carry sensors that collect meteorological, oceanographic, climatological, and solar-geophysical observations of the earth, atmosphere, and space. The ground processing system for JPSS is known as the JPSS Common Ground System (JPSS CGS). Developed and maintained by Raytheon Intelligence, Information and Services (IIS), the CGS is a globally distributed, multi-mission system serving NOAA, NASA and their national and international partners. The CGS has demonstrated its scalability and flexibility to incorporate multiple missions efficiently and with minimal cost, schedule and risk, while strengthening global partnerships in weather and environmental monitoring. The CGS architecture has been upgraded to Block 2.0 to satisfy several key objectives, including: "operationalizing" the first satellite, Suomi NPP, which originally was a risk reduction mission; leveraging lessons learned in multi-mission support, taking advantage of newer, more reliable and efficient technologies and satisfying constraints due of the continually evolving budgetary environment. To ensure the CGS meets these needs, we have developed 48 Technical Performance Measures (TPMs) across 9 categories: Data Availability, Data Latency, Operational Availability, Margin, Scalability, Situational Awareness, Transition (between environments and sites), WAN Efficiency, and Data Recovery Processing. This paper will provide an overview of the CGS Block 2.0 architecture, with particular focus on the 9 TPM categories listed above. We will describe how we ensure the deployed architecture meets these TPMs to satisfy our multi-mission objectives with the deployment of Block 2.0.

Ground-based microwave radar and optical lidar signatures of volcanic ash plumes: models, observations and retrievals

NASA Astrophysics Data System (ADS)

Mereu, Luigi; Marzano, Frank; Mori, Saverio; Montopoli, Mario; Cimini, Domenico; Martucci, Giovanni

2013-04-01

The detection and quantitative retrieval of volcanic ash clouds is of significant interest due to its environmental, climatic and socio-economic effects. Real-time monitoring of such phenomena is crucial, also for the initialization of dispersion models. Satellite visible-infrared radiometric observations from geostationary platforms are usually exploited for long-range trajectory tracking and for measuring low level eruptions. Their imagery is available every 15-30 minutes and suffers from a relatively poor spatial resolution. Moreover, the field-of-view of geostationary radiometric measurements may be blocked by water and ice clouds at higher levels and their overall utility is reduced at night. Ground-based microwave radars may represent an important tool to detect and, to a certain extent, mitigate the hazard from the ash clouds. Ground-based weather radar systems can provide data for determining the ash volume, total mass and height of eruption clouds. Methodological studies have recently investigated the possibility of using ground-based single-polarization and dual-polarization radar system for the remote sensing of volcanic ash cloud. A microphysical characterization of volcanic ash was carried out in terms of dielectric properties, size distribution and terminal fall speed, assuming spherically-shaped particles. A prototype of volcanic ash radar retrieval (VARR) algorithm for single-polarization systems was proposed and applied to S-band and C-band weather radar data. The sensitivity of the ground-based radar measurements decreases as the ash cloud is farther so that for distances greater than about 50 kilometers fine ash might be not detected anymore by microwave radars. In this respect, radar observations can be complementary to satellite, lidar and aircraft observations. Active remote sensing retrieval from ground, in terms of detection, estimation and sensitivity, of volcanic ash plumes is not only dependent on the sensor specifications, but also on the range and ash cloud distribution. The minimum detectable signal can be increased, for a given system and ash plume scenario, by decreasing the observation range and increasing the operational frequency using a multi-sensor approach, but also exploiting possible polarimetric capabilities. In particular, multi-wavelengths lidars can be complementary systems useful to integrate radar-based ash particle measurement. This work, starting from the results of a previous study and from above mentioned issues, is aimed at quantitatively assessing the optimal choices for microwave and millimeter-wave radar systems with a dual-polarization capability for real-time ash cloud remote sensing to be used in combination with an optical lidar. The physical-electromagnetic model of ash particle distributions is systematically reviewed and extended to include non-spherical particle shapes, vesicular composition, silicate content and orientation phenomena. The radar and lidar scattering and absorption response is simulated and analyzed in terms of self-consistent polarimetric signatures for ash classification purposes and correlation with ash concentration and mean diameter for quantitative retrieval aims. A sensitivity analysis to ash concentration, as a function of sensor specifications, range and ash category, is carried out trying to assess the expected multi-sensor multi-spectral system performances and limitations. The multi-sensor multi-wavelength polarimetric model-based approach can be used within a particle classification and estimation scheme, based on the VARR Bayesian metrics. As an application, the ground-based observation of the Eyjafjallajökull volcanic ash plume on 15-16 May 2010, carried out at the Atmospheric Research Station at Mace Head, Carna (Ireland) with MIRA36 35-GHz Ka-Band Doppler cloud radar and CHM15K lidar/ceilometer at 1064-nm wavelength, has been considered. Results are discussed in terms of retrievals and intercomparison with other ground-based and satellite-based sensors.

Concept and design of a fiber-optic and an I2C hybrid sensor bus system for telecommunication satellites

NASA Astrophysics Data System (ADS)

Putzer, P.; Hurni, A.; Manhart, M.; Tiefenbeck, C.; Plattner, M.; Koch, A. W.

2012-04-01

In this paper the concept and design of the Hybrid Sensor Bus (HSB) system for telecommunication satellites is presented. The HSB development in the frame of an ESA-ARTES project has been started in 2011 and the system will be tested as flight demonstrator onboard the German Heinrich Hertz communication satellite (H2Sat) in 2016. In state-of-the-art telecommunication platforms hundreds of sensors are necessary for satellite control and monitoring. The sensors are wired point-to-point (p2p) to the satellite management unit (SMU) which results in a high mass impact but preliminary increases AIT effort and thereby the overall satellite costs. Sensor bus architectures reduce AIT cost by reduction of wiring effort, reduction in required test time and by providing a flexible sensor network topology. The HSB system is based on a modular concept including a controller module, a fiber-optic interrogator module and an I²C electric interrogator module The HSB system provides advanced performance which includes programmable and sensor specific alarm functions, averaging of dedicated sensor values and thereby a reduction of SMU processor load. The combination of electrical I2C sensors for punctual resolved measurements and fiber-optic sensors for e.g. thermal mapping of panels by embedding sensor fibers in the satellite structures results in a versatile system. In this paper we present the design of the HSB system taking into account the requirements from European platform manufacturers. The HSB design yields a product which can be implemented as replacement of standard p2p systems to build up a more cost efficient sensor system for geostationary satellites.

Multi-sensor Observations of the SpinSat Satellite

DTIC Science & Technology

2015-10-18

through the high-radiation environment of the South Atlantic Anomaly ( SAA ). SpinSat’s status logs indicate no reset occurred after the third and...Unfortunately, the combined scheduling constraints discussed above produced a pass sequence accompanied traversals through the SAA region. So in this...during SpinSat’s traversals through the SAA , the command sequence had to be uplinked again during a second pass (middle panel) at 2015-03-30 0602-0612 EST

Ocean Drifters Get the Facts

NASA Technical Reports Server (NTRS)

2001-01-01

With the help of Small Business Innovation Research (SBIR) funding from NASA's Goddard Space Flight Center, of Greenbelt, Maryland, Clearwater Instrumentation, of Watertown, Massachusetts, created the ClearSat-Autonomous Drifting Ocean Station (ADOS). The multi-sensor array ocean drifting station was developed to support observations of Earth by NASA satellites. It is a low-cost device for gathering an assortment of data necessary to the integration of present and future satellite measurements of biological and physical processes. Clearwater Instrumentation developed its ADOS technology based on Goddard's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) project, but on a scale that is practical for commercial use. ADOS is used for the in situ measuring of ocean surface layer properties such as ocean color, surface thermal structure, and surface winds. Thus far, multiple ADOS units have been sold to The Scripps Institution of Oceanography, where they are being applied in the field of academic science research. Fisheries can also benefit, because ADOS can locate prime cultivation conditions for this fast-growing industry.

CubeSat Nighttime Earth Observations

NASA Astrophysics Data System (ADS)

Pack, D. W.; Hardy, B. S.; Longcore, T.

2017-12-01

Satellite monitoring of visible emissions at night has been established as a useful capability for environmental monitoring and mapping the global human footprint. Pioneering work using Defense Meteorological Support Program (DMSP) sensors has been followed by new work using the more capable Visible Infrared Imaging Radiometer Suite (VIIRS). Beginning in 2014, we have been investigating the ability of small visible light cameras on CubeSats to contribute to nighttime Earth science studies via point-and-stare imaging. This paper summarizes our recent research using a common suite of simple visible cameras on several AeroCube satellites to carry out nighttime observations of urban areas and natural gas flares, nighttime weather (including lighting), and fishing fleet lights. Example results include: urban image examples, the utility of color imagery, urban lighting change detection, and multi-frame sequences imaging nighttime weather and large ocean areas with extensive fishing vessel lights. Our results show the potential for CubeSat sensors to improve monitoring of urban growth, light pollution, energy usage, the urban-wildland interface, the improvement of electrical power grids in developing countries, light-induced fisheries, and oil industry flare activity. In addition to orbital results, the nighttime imaging capabilities of new CubeSat sensors scheduled for launch in October 2017 are discussed.

Polar2Grid 2.0: Reprojecting Satellite Data Made Easy

NASA Astrophysics Data System (ADS)

Hoese, D.; Strabala, K.

2015-12-01

Polar-orbiting multi-band meteorological sensors such as those on the Suomi National Polar-orbiting Partnership (SNPP) satellite pose substantial challenges for taking imagery the last mile to forecast offices, scientific analysis environments, and the general public. To do this quickly and easily, the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin has created an open-source, modular application system, Polar2Grid. This bundled solution automates tools for converting various satellite products like those from VIIRS and MODIS into a variety of output formats, including GeoTIFFs, AWIPS compatible NetCDF files, and NinJo forecasting workstation compatible TIFF images. In addition to traditional visible and infrared imagery, Polar2Grid includes three perceptual enhancements for the VIIRS Day-Night Band (DNB), as well as providing the capability to create sharpened true color, sharpened false color, and user-defined RGB images. Polar2Grid performs conversions and projections in seconds on large swaths of data. Polar2Grid is currently providing VIIRS imagery over the Continental United States, as well as Alaska and Hawaii, from various Direct-Broadcast antennas to operational forecasters at the NOAA National Weather Service (NWS) offices in their AWIPS terminals, within minutes of an overpass of the Suomi NPP satellite. Three years after Polar2Grid development started, the Polar2Grid team is now releasing version 2.0 of the software; supporting more sensors, generating more products, and providing all of its features in an easy to use command line interface.

Multi-spectral image analysis for improved space object characterization

NASA Astrophysics Data System (ADS)

Glass, William; Duggin, Michael J.; Motes, Raymond A.; Bush, Keith A.; Klein, Meiling

2009-08-01

The Air Force Research Laboratory (AFRL) is studying the application and utility of various ground-based and space-based optical sensors for improving surveillance of space objects in both Low Earth Orbit (LEO) and Geosynchronous Earth Orbit (GEO). This information can be used to improve our catalog of space objects and will be helpful in the resolution of satellite anomalies. At present, ground-based optical and radar sensors provide the bulk of remotely sensed information on satellites and space debris, and will continue to do so into the foreseeable future. However, in recent years, the Space-Based Visible (SBV) sensor was used to demonstrate that a synthesis of space-based visible data with ground-based sensor data could provide enhancements to information obtained from any one source in isolation. The incentives for space-based sensing include improved spatial resolution due to the absence of atmospheric effects and cloud cover and increased flexibility for observations. Though ground-based optical sensors can use adaptive optics to somewhat compensate for atmospheric turbulence, cloud cover and absorption are unavoidable. With recent advances in technology, we are in a far better position to consider what might constitute an ideal system to monitor our surroundings in space. This work has begun at the AFRL using detailed optical sensor simulations and analysis techniques to explore the trade space involved in acquiring and processing data from a variety of hypothetical space-based and ground-based sensor systems. In this paper, we briefly review the phenomenology and trade space aspects of what might be required in order to use multiple band-passes, sensor characteristics, and observation and illumination geometries to increase our awareness of objects in space.

Development of a Transportable Gravity Gradiometer Based on Atom Interferometry

NASA Astrophysics Data System (ADS)

Yu, N.; Kohel, J. M.; Aveline, D. C.; Kellogg, J. R.; Thompson, R. J.; Maleki, L.

2007-12-01

JPL is developing a transportable gravity gradiometer based on light-pulse atom interferometers for NASA's Earth Science Technology Office's Instrument Incubator Program. The inertial sensors in this instrument employ a quantum interference measurement technique, analogous to the precise phase measurements in atomic clocks, which offers increased sensitivity and improved long-term stability over traditional mechanical devices. We report on the implementation of this technique in JPL's gravity gradiometer, and on the current performance of the mobile instrument. We also discuss the prospects for satellite-based gravity field mapping, including high-resolution monitoring of time-varying fields from a single satellite platform and multi-component measurements of the gravitational gradient tensor, using atom interferometer-based instruments.

A Multi-Frequency Polarimetric SAR Sensors Analysis over the UNESCO Archaeological Site of Djebel Barkal (Sudan)

NASA Astrophysics Data System (ADS)

Patruno, Jolanda; Dore, Nicole; Pottier, Eric; Crespi, Mattia

2013-08-01

Differences in vegetation growth and in soil moisture content generate ground anomalies which can be linked to subsurface anthropic structures. Such evidences have been studied by means of aerial photographs and of historical II World War acquisitions first, and of very high spatial resolution of optical satellites later. This work aims to exploit the technique of SAR Polarimetry for the detection of surface and subsurface archaeological structures, comparing ALOS P ALSAR L-band (central frequency 1.27 GHz), with RADARSAT-2 C-band sensor (central frequency 5.405 GHz). The great potential of the two polarimetric sensors with different frequency for the detection of archaeological remains has been demonstrated thanks to the sand penetration capability of both C-band and L- band sensors. The choice to analyze radar sensors is based on their 24-hour observations, independent from Sun illumination and meteorological conditions and on the electromagnetic properties of the target they could provide, information not derivable from optical images.

Global Sea Surface Temperature: A Harmonized Multi-sensor Time-series from Satellite Observations

NASA Astrophysics Data System (ADS)

Merchant, C. J.

2017-12-01

This paper presents the methods used to obtain a new global sea surface temperature (SST) dataset spanning the early 1980s to the present, intended for use as a climate data record (CDR). The dataset provides skin SST (the fundamental measurement) and an estimate of the daily mean SST at depths compatible with drifting buoys (adjusting for skin and diurnal variability). The depth SST provided enables the CDR to be used with in situ records and centennial-scale SST reconstructions. The new SST timeseries is as independent as possible from in situ observations, and from 1995 onwards is harmonized to an independent satellite reference (namely, SSTs from the Advanced Along Track Scanning Radiometer (Advanced ATSR)). This maximizes the utility of our new estimates of variability and long-term trends in interrogating previous datasets tied to in situ observations. The new SSTs include full resolution (swath, level 2) data, single-sensor gridded data (level 3, 0.05 degree latitude-longitude grid) and a multi-sensor optimal analysis (level 4, same grid). All product levels are consistent. All SSTs have validated uncertainty estimates attached. The sensors used include all Advanced Very High Resolution Radiometers from NOAA-6 onwards and the ATSR series. AVHRR brightness temperatures (BTs) are calculated from counts using a new in-flight re-calibration for each sensor, ultimately linked through to the AATSR BT calibration by a new harmonization technique. Artefacts in AVHRR BTs linked to varying instrument temperature, orbital regime and solar contamination are significantly reduced. These improvements in the AVHRR BTs (level 1) translate into improved cloud detection and SST (level 2). For cloud detection, we use a Bayesian approach for all sensors. For the ATSRs, SSTs are derived with sufficient accuracy and sensitivity using dual-view coefficients. This is not the case for single-view AVHRR observations, for which a physically based retrieval is employed, using a hybrid maximum a posteriori / maximum likelihood retrieval, which optimises retrieval uncertainty and SST sensitivity for climate applications. Validation results will be presented along with examples of the variability and trends in SST evident in the dataset.

Comprehensive Evaluation of GPM and TRMM: A Case Study of the Winter 2015-2016 over California

NASA Astrophysics Data System (ADS)

Li, J.; Liu, H.

2016-12-01

The Global Precipitation Measurement (GPM) has been established to provide the next-generation observations of precipitation globally. It gives the opportunities to measure the snow and lighter rainfall rates, which are relatively difficult to be retrieved by the previous missions. Recently, the state of California experienced with El Nino in the winter of 2015-2016, which brought more-than-average rainfall and snow to the much of areas in the state. This study focused on the state of California to examine how well GPM can capture the winter precipitation compared to the Tropical Rainfall Measuring Mission (TRMM). The Integrated Multi-satellitE Retrievals for GPM (IMERG) final-run and TRMM Multi-satellite Precipitation Analysis (TMPA) version 7 were evaluated against the ground reference of NOAA stage IV multi-sensor composite rain analysis. This study employed both the pixel-based and object-based verification measures to conduct a comprehensive evaluation for GPM and TRMM in the winter season. Probability of Detection, False Alarm Ratio, Bias Ratio, Taylor Diagram, Object-based Missing Ratio, Object-based False Alarm Ratio and Overall Interest Score were used as evaluation metrics. We found the IMERG-final has a better overall performance. We anticipate that the IMERG will benefit the applications of satellite remote-sensed precipitation, such as, hydrological flood modeling, watershed management and climate studies.

Multi-Sensor Aerosol Products Sampling System

NASA Technical Reports Server (NTRS)

Petrenko, M.; Ichoku, C.; Leptoukh, G.

2011-01-01

Global and local properties of atmospheric aerosols have been extensively observed and measured using both spaceborne and ground-based instruments, especially during the last decade. Unique properties retrieved by the different instruments contribute to an unprecedented availability of the most complete set of complimentary aerosol measurements ever acquired. However, some of these measurements remain underutilized, largely due to the complexities involved in analyzing them synergistically. To characterize the inconsistencies and bridge the gap that exists between the sensors, we have established a Multi-sensor Aerosol Products Sampling System (MAPSS), which consistently samples and generates the spatial statistics (mean, standard deviation, direction and rate of spatial variation, and spatial correlation coefficient) of aerosol products from multiple spacebome sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS. Samples of satellite aerosol products are extracted over Aerosol Robotic Network (AERONET) locations as well as over other locations of interest such as those with available ground-based aerosol observations. In this way, MAPSS enables a direct cross-characterization and data integration between Level-2 aerosol observations from multiple sensors. In addition, the available well-characterized co-located ground-based data provides the basis for the integrated validation of these products. This paper explains the sampling methodology and concepts used in MAPSS, and demonstrates specific examples of using MAPSS for an integrated analysis of multiple aerosol products.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

PubMed

Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

2017-10-01

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Making every gram count - Big measurements from tiny platforms (Invited)

NASA Astrophysics Data System (ADS)

Fish, C. S.; Neilsen, T. L.; Stromberg, E. M.

2013-12-01

The most significant advances in Earth, solar, and space physics over the next decades will originate from new, system-level observational techniques. The most promising technique to still be fully developed and exploited requires conducting multi-point or distributed constellation-based observations. This system-level observational approach is required to understand the 'big picture' coupling between disparate regions such as the solar-wind, magnetosphere, ionosphere, upper atmosphere, land, and ocean. The national research council, NASA science mission directorate, and the larger heliophysics community have repeatedly identified the pressing need for multipoint scientific investigations to be implemented via satellite constellations. The NASA Solar Terrestrial Probes Magnetospheric Multiscale (MMS) mission and NASA Earth Science Division's 'A-train', consisting of the AQUA, CloudSat, CALIPSO and AURA satellites, are examples of such constellations. However, the costs to date of these and other similar proposed constellations have been prohibitive given the 'large satellite' architectures and the multiple launch vehicles required for implementing the constellations. Financially sustainable development and deployment of multi-spacecraft constellations can only be achieved through the use of small spacecraft that allow for multiple hostings per launch vehicle. The revolution in commercial mobile and other battery powered consumer technology has helped enable researchers in recent years to build and fly very small yet capable satellites, principally CubeSats. A majority of the CubeSat activity and development to date has come from international academia and the amateur radio satellite community, but several of the typical large-satellite vendors have developed CubeSats as well. Recent government-sponsored CubeSat initiatives, such as the NRO Colony, NSF CubeSat Space Weather, NASA Office of Chief Technologist Edison and CubeSat Launch Initiative (CSLI) Educational Launch of Nanosatellites Educational Launch of Nano-satellites (ELaNa), the Air Force Space Environmental NanoSat Experiment (SENSE), and the ESA QB50 programs have spurred the development of very proficient miniature space sensors and technologies that enable technology demonstration, space and earth science research, and operational CubeSat based missions. In this paper we will review many of the small, low cost sensor and instrumentation technologies that have been developed to date as part of the CubeSat movement and examine how these new CubeSat based technologies are helping us do more with less.

Leveraging Commercial Communication Satellites to support the Space Situational

NASA Astrophysics Data System (ADS)

Deaver, T.

The majority of USSTRATCOM detect and track requirements in the geosynchronous regime could be met via strategic placement of medium grade optical sensors on select geosynchronous satellites at relatively low cost in less than 48 months. An architecture which includes hosting SSA sensors on eight to ten commercial communication satellites could provide for highly accurate, timely and relatively inexpensive detect and track capabilities. The major factors considered when hosting any sensor on a commercial communications satellite are size, weight (mass) and power or SWAP. Additional sensor specific items must also be considered to form a complete feasibility analysis. These include data rate, mounting constraints, thermal balance, timing accuracy, and attitude stability requirements. All of these factors directly impact the cost and flexibility of hosting such a sensor on a geosynchronous communication satellite. By choosing a relatively light weight, low power consumption sensor which requires a small amount of bandwidth to transmit its data, the cost of hosting the sensor is kept to a minimum. Once the type of sensor or sensors is identified, the next step is to identify idea geosynchronous locations for the "hosted" sensors. Once these locations are identified, then one would identify a potential host which needs to be replaced within the desired timeframe. Once the host is identified, then the satellite owner / operator should be approached about hosting a "neighborhood" watch sensor aboard their spacecraft. Commercial satellites are routinely replaced based on age, lack of available station keeping fuel or to allow a service provider to upgrade its capabilities. Each commercial communication satellite operator maintains a plan of replacing spacecraft. Between the two largest commercial SATCOM providers, INTELSAT and SES, six to eight spacecraft will be replaced each year (100 plus spacecraft with 15 year average lifetimes). The satellites are usually procured, designed, built, launched and operational within 36 months. In order for the US Government to adapt to this timeline, a sensor specification would need to be established as well as a sensor procurement pipeline. The sensors would then be provided to the satellite bus manufacturer for integration onto the bus. The spacecraft would then be launched and operated by the commercial SATCOM operator for the life of the spacecraft. Based on this approach, it is highly conceivable that a complete geosynchronous "neighborhood" watch program could be completed within 48 months of initiation.

Multi-agent robotic systems and applications for satellite missions

NASA Astrophysics Data System (ADS)

Nunes, Miguel A.

A revolution in the space sector is happening. It is expected that in the next decade there will be more satellites launched than in the previous sixty years of space exploration. Major challenges are associated with this growth of space assets such as the autonomy and management of large groups of satellites, in particular with small satellites. There are two main objectives for this work. First, a flexible and distributed software architecture is presented to expand the possibilities of spacecraft autonomy and in particular autonomous motion in attitude and position. The approach taken is based on the concept of distributed software agents, also referred to as multi-agent robotic system. Agents are defined as software programs that are social, reactive and proactive to autonomously maximize the chances of achieving the set goals. Part of the work is to demonstrate that a multi-agent robotic system is a feasible approach for different problems of autonomy such as satellite attitude determination and control and autonomous rendezvous and docking. The second main objective is to develop a method to optimize multi-satellite configurations in space, also known as satellite constellations. This automated method generates new optimal mega-constellations designs for Earth observations and fast revisit times on large ground areas. The optimal satellite constellation can be used by researchers as the baseline for new missions. The first contribution of this work is the development of a new multi-agent robotic system for distributing the attitude determination and control subsystem for HiakaSat. The multi-agent robotic system is implemented and tested on the satellite hardware-in-the-loop testbed that simulates a representative space environment. The results show that the newly proposed system for this particular case achieves an equivalent control performance when compared to the monolithic implementation. In terms on computational efficiency it is found that the multi-agent robotic system has a consistent lower CPU load of 0.29 +/- 0.03 compared to 0.35 +/- 0.04 for the monolithic implementation, a 17.1 % reduction. The second contribution of this work is the development of a multi-agent robotic system for the autonomous rendezvous and docking of multiple spacecraft. To compute the maneuvers guidance, navigation and control algorithms are implemented as part of the multi-agent robotic system. The navigation and control functions are implemented using existing algorithms, but one important contribution of this section is the introduction of a new six degrees of freedom guidance method which is part of the guidance, navigation and control architecture. This new method is an explicit solution to the guidance problem, and is particularly useful for real time guidance for attitude and position, as opposed to typical guidance methods which are based on numerical solutions, and therefore are computationally intensive. A simulation scenario is run for docking four CubeSats deployed radially from a launch vehicle. Considering fully actuated CubeSats, the simulations show docking maneuvers that are successfully completed within 25 minutes which is approximately 30% of a full orbital period in low earth orbit. The final section investigates the problem of optimization of satellite constellations for fast revisit time, and introduces a new method to generate different constellation configurations that are evaluated with a genetic algorithm. Two case studies are presented. The first is the optimization of a constellation for rapid coverage of the oceans of the globe in 24 hours or less. Results show that for an 80 km sensor swath width 50 satellites are required to cover the oceans with a 24 hour revisit time. The second constellation configuration study focuses on the optimization for the rapid coverage of the North Atlantic Tracks for air traffic monitoring in 3 hours or less. The results show that for a fixed swath width of 160 km and for a 3 hour revisit time 52 satellites are required.

Low cost, multiscale and multi-sensor application for flooded area mapping

NASA Astrophysics Data System (ADS)

Giordan, Daniele; Notti, Davide; Villa, Alfredo; Zucca, Francesco; Calò, Fabiana; Pepe, Antonio; Dutto, Furio; Pari, Paolo; Baldo, Marco; Allasia, Paolo

2018-05-01

Flood mapping and estimation of the maximum water depth are essential elements for the first damage evaluation, civil protection intervention planning and detection of areas where remediation is needed. In this work, we present and discuss a methodology for mapping and quantifying flood severity over floodplains. The proposed methodology considers a multiscale and multi-sensor approach using free or low-cost data and sensors. We applied this method to the November 2016 Piedmont (northwestern Italy) flood. We first mapped the flooded areas at the basin scale using free satellite data from low- to medium-high-resolution from both the SAR (Sentinel-1, COSMO-Skymed) and multispectral sensors (MODIS, Sentinel-2). Using very- and ultra-high-resolution images from the low-cost aerial platform and remotely piloted aerial system, we refined the flooded zone and detected the most damaged sector. The presented method considers both urbanised and non-urbanised areas. Nadiral images have several limitations, in particular in urbanised areas, where the use of terrestrial images solved this limitation. Very- and ultra-high-resolution images were processed with structure from motion (SfM) for the realisation of 3-D models. These data, combined with an available digital terrain model, allowed us to obtain maps of the flooded area, maximum high water area and damaged infrastructures.

Integrated multi-channel nano-engineered optical hydrogen and temperature sensor detection systems for launch vehicles

NASA Astrophysics Data System (ADS)

Alam, M. Z.; Moreno, J.; Aitchison, J. S.; Mojahedi, M.; Kazemi, A. A.

2008-08-01

Launch vehicles and other satellite users need launch services that are highly reliable, less complex, easier to test, and cost effective. Being a very small molecule, hydrogen is prone to leakage through seals and micro-cracks. Hydrogen detection in space application is very challenging; public acceptance of hydrogen fuel would require the integration of a reliable hydrogen safety sensor. For detecting leakage of cryogenic fluids in spaceport facilities, launch vehicle industry and aerospace agencies are currently relying heavily on the bulky mass spectrometers, which fill one or more equipment racks, and weigh several hundred kilograms. Therefore, there is a critical need for miniaturized sensors and instruments suitable for use in space applications. This paper describes a novel multi-channel integrated nano-engineered optical sensor to detect hydrogen and monitor the temperature. The integrated optic sensor is made of multi-channel waveguide elements that measure hydrogen concentration in real Time. Our sensor is based on the use of a high index waveguide with a Ni/Pd overlay to detect hydrogen. When hydrogen is absorbed into the Ni/Pd alloy there is a change in the absorption of the material and the optical signal in the waveguide is increased. Our design uses a thin alloy (few nanometers thick) overlay which facilitates the absorption of the hydrogen and will result in a response time of approximately few seconds. Like other Pd/Pd-Ni based sensors the device response varies with temperature and hence the effects of temperature variations must be taken into account. One solution to this problem is simultaneous measurement of temperature in addition to hydrogen concentration at the same vicinity. Our approach here is to propose a temperature sensor that can easily be integrated on the same platform as the hydrogen sensor reported earlier by our group. One suitable choice of material system is silicon on insulator (SOI). Here, we propose a micro ring resonators (MRR) based temperature sensor designed on SOI that measures temperature by monitoring the output optical power.

Joint Polar Satellite System (JPSS) Common Ground System (CGS) Architecture Overview and Technical Performance Measures

NASA Astrophysics Data System (ADS)

Grant, K. D.; Johnson, B. R.; Miller, S. W.; Jamilkowski, M. L.

2014-12-01

The National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) are jointly acquiring the next-generation civilian weather and environmental satellite system: the Joint Polar Satellite System (JPSS). The Joint Polar Satellite System will replace the afternoon orbit component and ground processing system of the current Polar-orbiting Operational Environmental Satellites (POES) managed by NOAA. The JPSS satellites will carry a suite of sensors designed to collect meteorological, oceanographic, climatological and geophysical observations of the Earth. The ground processing system for JPSS is known as the JPSS Common Ground System (JPSS CGS). Developed and maintained by Raytheon Intelligence, Information and Services (IIS), the CGS is a multi-mission enterprise system serving NOAA, NASA and their national and international partners. The CGS provides a wide range of support to a number of missions. Originally designed to support S-NPP and JPSS, the CGS has demonstrated its scalability and flexibility to incorporate all of these other important missions efficiently and with minimal cost, schedule and risk, while strengthening global partnerships in weather and environmental monitoring. The CGS architecture will be upgraded to Block 2.0 in 2015 to satisfy several key objectives, including: "operationalizing" S-NPP, which had originally been intended as a risk reduction mission; leveraging lessons learned to date in multi-mission support; taking advantage of newer, more reliable and efficient technologies; and satisfying new requirements and constraints due to the continually evolving budgetary environment. To ensure the CGS meets these needs, we have developed 48 Technical Performance Measures (TPMs) across 9 categories: Data Availability, Data Latency, Operational Availability, Margin, Scalability, Situational Awareness, Transition (between environments and sites), WAN Efficiency, and Data Recovery Processing. This paper will provide an overview of the CGS Block 2.0 architecture, with particular focus on the 9 TPM categories listed above. We will describe how we ensure the deployed architecture meets these TPMs to satisfy our multi-mission objectives with the deployment of Block 2.0 in 2015.

Analysis of Decadal Vegetation Dynamics Using Multi-Scale Satellite Images

NASA Astrophysics Data System (ADS)

Chiang, Y.; Chen, K.

2013-12-01

This study aims at quantifying vegetation fractional cover (VFC) by incorporating multi-resolution satellite images, including Formosat-2(RSI), SPOT(HRV/HRG), Landsat (MSS/TM) and Terra/Aqua(MODIS), to investigate long-term and seasonal vegetation dynamics in Taiwan. We used 40-year NDVI records for derivation of VFC, with field campaigns routinely conducted to calibrate the critical NDVI threshold. Given different sensor capabilities in terms of their spatial and spectral properties, translation and infusion of NDVIs was used to assure NDVI coherence and to determine the fraction of vegetation cover at different spatio-temporal scales. Based on the proposed method, a bimodal sequence of intra-annual VFC which corresponds to the dual-cropping agriculture pattern was observed. Compared to seasonal VFC variation (78~90%), decadal VFC reveals moderate oscillations (81~86%), which were strongly linked with landuse changes and several major disturbances. This time-series mapping of VFC can be used to examine vegetation dynamics and its response associated with short-term and long-term anthropogenic/natural events.

Developments of Highly Multiplexed, Multi-chroic Pixels for Balloon-Borne Platforms

NASA Astrophysics Data System (ADS)

Aubin, F.; Hanany, S.; Johnson, B. R.; Lee, A.; Suzuki, A.; Westbrook, B.; Young, K.

2018-02-01

We present our work to develop and characterize low thermal conductance bolometers that are part of sinuous antenna multi-chroic pixels (SAMP). We use longer, thinner and meandered bolometer legs to achieve 9 pW/K thermal conductance bolometers. We also discuss the development of inductor-capacitor chips operated at 4 K to extend the multiplexing factor of the frequency domain multiplexing to 105, an increase of 60% compared to the factor currently demonstrated for this readout system. This technology development is motivated by EBEX-IDS, a balloon-borne polarimeter designed to characterize the polarization of foregrounds and to detect the primordial gravity waves through their B-mode signature on the polarization of the cosmic microwave background. EBEX-IDS will operate 20,562 transition edge sensor bolometers spread over 7 frequency bands between 150 and 360 GHz. Balloon and satellite platforms enable observations at frequencies inaccessible from the ground and with higher instantaneous sensitivity. This development improves the readiness of the SAMP and frequency domain readout technologies for future satellite applications.

Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites

PubMed Central

Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

2016-01-01

This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons. PMID:27092508

Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites.

PubMed

Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

2016-04-15

This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons.

Creating Orthographically Rectified Satellite Multi-Spectral Imagery with High Resolution Digital Elevation Model from LiDAR: A Tutorial

DTIC Science & Technology

2014-08-15

challenges. ERDC develops innovative solutions in civil and military engineering, geospatial sciences, water resources, and environmental sciences for...GRL TR-14-1 iv Abstract Orthoimages are used to produce image- map products for navigation and planning, and serve as source data for advanced...resulting mosaic covers a wider area and contains less visible seams, which makes the map easier to understand. RPC replace the actual sensor model while

Real-time Integration of Biological, Optical and Physical Oceanographic Data from Multiple Vessels and Nearshore Sites using a Wireless Network

DTIC Science & Technology

1997-09-30

field experiments in Puget Sound . Each research vessel will use multi- sensor profiling instrument packages which obtain high-resolution physical...field deployment of the wireless network is planned for May-July, 1998, at Orcas Island, WA. IMPACT We expect that wireless communication systems will...East Sound project to be a first step toward continental shelf and open ocean deployments with the next generation of wireless and satellite

Characterize Aerosols from MODIS/MISR/OMI/MERRA-2: Dynamic Image Browse Perspective

NASA Astrophysics Data System (ADS)

Wei, J. C.; Yang, W.; Shen, S.; Zhao, P.; Albayrak, A.; Johnson, J. E.; Kempler, S. J.; Pham, L.

2016-12-01

Among the known atmospheric constituents, aerosols still represent the greatest uncertainty in climate research. To understand the uncertainty is to bring altogether of observational (in-situ and remote sensing) and modeling datasets and inter-compare them synergistically for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if these earth science data (satellite and modeling) are well utilized and interpreted. Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite-borne sensors routinely measure aerosols. There is often disagreement between similar aerosol parameters retrieved from different sensors, leaving users confused as to which sensors to trust for answering important science questions about the distribution, properties, and impacts of aerosols. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) have developed multiple MAPSS (Multi-sensor Aerosol Products Sampling System) applications as a part of Giovanni (Geospatial Interactive Online Visualization and Analysis Interface) data visualization and analysis tool since 2007. The MAPSS database provides spatio-temporal statistics for multiple spatial spaceborne Level 2 aerosol products (MODIS Terra, MODIS Aqua, MISR, POLDER, OMI, CALIOP, SeaWiFS Deep Blue, and VIIRS) sampled over AERONET ground stations. In this presentation, I will demonstrate a new visualization service (NASA Level 2 Data Quality Visualization, DQViz) supporting various visualization and data accessing capabilities from satellite Level 2 (MODIS/MISR/OMI) and long term assimilated aerosols from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2 displaying at their own native physical-retrieved spatial resolution. Functionality will include selecting data sources (e.g., multiple parameters under the same measurement), defining area-of-interest and temporal extents, zooming, panning, overlaying, sliding, and data subsetting and reformatting.

Geospatial analysis of creeks evolution in the Indus Delta, Pakistan using multi sensor satellite data

NASA Astrophysics Data System (ADS)

Ijaz, Muhammad Wajid; Mahar, Rasool Bux; Siyal, Altaf Ali; Anjum, Muhammad Naveed

2018-01-01

Sea level rise (SLR) in response to looming climate change is being considered as a major impediment to coastal areas. Acute wave activities and tidal propagations of semi-diurnal to mixed type are impairing the morphology of the Indus Delta in Pakistan. In this study a synthetic approach has been adopted using multi sensor satellite and ground data in order to integrate the individual effect of topography, oceanic activities and vegetative canopy for deduction of a synergic impact over the morphology of the Indus Delta creeks system from 1972 to 2017. Geomorphologic anomalies in the planform of fourteen major creeks were explored. Spatiotemporal variations suggested that a substantial amount of the delta alluvium had been engulfed by the Arabian Sea. On average, the creeks located on the right side of the Indus River were relatively less wide (3.9 km) than those of on the left side (5.2 km). Zonal statistics calculated with topographic position index (TPI) enabled to understand the tide induced inundation extents. The mangrove canopy on the right side was found greater, which is why tidal basins on that side experienced less erosive activities. Thus, it could be maintained that the coastal sedimentary processes may be monitored effectively with the remotely sensed data and temporal pattern of changes can be quantified for future planning and mitigation of adverse effects.

Multi-source Geospatial Data Analysis with Google Earth Engine

NASA Astrophysics Data System (ADS)

Erickson, T.

2014-12-01

The Google Earth Engine platform is a cloud computing environment for data analysis that combines a public data catalog with a large-scale computational facility optimized for parallel processing of geospatial data. The data catalog is a multi-petabyte archive of georeferenced datasets that include images from Earth observing satellite and airborne sensors (examples: USGS Landsat, NASA MODIS, USDA NAIP), weather and climate datasets, and digital elevation models. Earth Engine supports both a just-in-time computation model that enables real-time preview and debugging during algorithm development for open-ended data exploration, and a batch computation mode for applying algorithms over large spatial and temporal extents. The platform automatically handles many traditionally-onerous data management tasks, such as data format conversion, reprojection, and resampling, which facilitates writing algorithms that combine data from multiple sensors and/or models. Although the primary use of Earth Engine, to date, has been the analysis of large Earth observing satellite datasets, the computational platform is generally applicable to a wide variety of use cases that require large-scale geospatial data analyses. This presentation will focus on how Earth Engine facilitates the analysis of geospatial data streams that originate from multiple separate sources (and often communities) and how it enables collaboration during algorithm development and data exploration. The talk will highlight current projects/analyses that are enabled by this functionality.https://earthengine.google.org

An Autonomous Navigation Algorithm for High Orbit Satellite Using Star Sensor and Ultraviolet Earth Sensor

PubMed Central

Baohua, Li; Wenjie, Lai; Yun, Chen; Zongming, Liu

2013-01-01

An autonomous navigation algorithm using the sensor that integrated the star sensor (FOV1) and ultraviolet earth sensor (FOV2) is presented. The star images are sampled by FOV1, and the ultraviolet earth images are sampled by the FOV2. The star identification algorithm and star tracking algorithm are executed at FOV1. Then, the optical axis direction of FOV1 at J2000.0 coordinate system is calculated. The ultraviolet image of earth is sampled by FOV2. The center vector of earth at FOV2 coordinate system is calculated with the coordinates of ultraviolet earth. The autonomous navigation data of satellite are calculated by integrated sensor with the optical axis direction of FOV1 and the center vector of earth from FOV2. The position accuracy of the autonomous navigation for satellite is improved from 1000 meters to 300 meters. And the velocity accuracy of the autonomous navigation for satellite is improved from 100 m/s to 20 m/s. At the same time, the period sine errors of the autonomous navigation for satellite are eliminated. The autonomous navigation for satellite with a sensor that integrated ultraviolet earth sensor and star sensor is well robust. PMID:24250261

An autonomous navigation algorithm for high orbit satellite using star sensor and ultraviolet earth sensor.

PubMed

Baohua, Li; Wenjie, Lai; Yun, Chen; Zongming, Liu

2013-01-01

An autonomous navigation algorithm using the sensor that integrated the star sensor (FOV1) and ultraviolet earth sensor (FOV2) is presented. The star images are sampled by FOV1, and the ultraviolet earth images are sampled by the FOV2. The star identification algorithm and star tracking algorithm are executed at FOV1. Then, the optical axis direction of FOV1 at J2000.0 coordinate system is calculated. The ultraviolet image of earth is sampled by FOV2. The center vector of earth at FOV2 coordinate system is calculated with the coordinates of ultraviolet earth. The autonomous navigation data of satellite are calculated by integrated sensor with the optical axis direction of FOV1 and the center vector of earth from FOV2. The position accuracy of the autonomous navigation for satellite is improved from 1000 meters to 300 meters. And the velocity accuracy of the autonomous navigation for satellite is improved from 100 m/s to 20 m/s. At the same time, the period sine errors of the autonomous navigation for satellite are eliminated. The autonomous navigation for satellite with a sensor that integrated ultraviolet earth sensor and star sensor is well robust.

Global, Persistent, Real-time Multi-sensor Automated Satellite Image Analysis and Crop Forecasting in Commercial Cloud

NASA Astrophysics Data System (ADS)

Brumby, S. P.; Warren, M. S.; Keisler, R.; Chartrand, R.; Skillman, S.; Franco, E.; Kontgis, C.; Moody, D.; Kelton, T.; Mathis, M.

2016-12-01

Cloud computing, combined with recent advances in machine learning for computer vision, is enabling understanding of the world at a scale and at a level of space and time granularity never before feasible. Multi-decadal Earth remote sensing datasets at the petabyte scale (8×10^15 bits) are now available in commercial cloud, and new satellite constellations will generate daily global coverage at a few meters per pixel. Public and commercial satellite observations now provide a wide range of sensor modalities, from traditional visible/infrared to dual-polarity synthetic aperture radar (SAR). This provides the opportunity to build a continuously updated map of the world supporting the academic community and decision-makers in government, finanace and industry. We report on work demonstrating country-scale agricultural forecasting, and global-scale land cover/land, use mapping using a range of public and commercial satellite imagery. We describe processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work combining this imagery with time-series SAR collected by ESA Sentinel 1. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. We apply remote sensing science and machine learning algorithms to detect and classify agricultural crops and then estimate crop yields and detect threats to food security (e.g., flooding, drought). The software platform and analysis methodology also support monitoring water resources, forests and other general indicators of environmental health, and can detect growth and changes in cities that are displacing historical agricultural zones.

Compact SAR and Small Satellite Solutions for Earth Observation

NASA Astrophysics Data System (ADS)

LaRosa, M.; L'Abbate, M.

2016-12-01

Requirements for near and short term mission applications (Observation and Reconnaissance, SIGINT, Early Warning, Meteorology,..) are increasingly calling for spacecraft operational responsiveness, flexible configuration, lower cost satellite constellations and flying formations, to improve both the temporal performance of observation systems (revisit, response time) and the remote sensing techniques (distributed sensors, arrays, cooperative sensors). In answer to these users' needs, leading actors in Space Systems for EO are involved in development of Small and Microsatellites solutions. Thales Alenia Space (TAS) has started the "COMPACT-SAR" project to develop a SAR satellite characterized by low cost and reduced mass while providing, at the same time, high image quality in terms of resolution, swath size, and radiometric performance. Compact SAR will embark a X-band SAR based on a deployable reflector antenna fed by an active phased array feed. This concept allows high performance, providing capability of electronic beam steering both in azimuth and elevation planes, improving operational performance over a purely mechanically steered SAR system. Instrument provides both STRIPMAP and SPOTLIGHT modes, and thanks to very high gain antenna, can also provide a real maritime surveillance mode based on a patented Low PRF radar mode. Further developments are in progress considering missions based on Microsatellites technology, which can provide effective solutions for different user needs, such as Operational responsiveness, low cost constellations, distributed observation concept, flying formations, and can be conceived for applications in the field of Observation, Atmosphere sensing, Intelligence, Surveillance, Reconnaissance (ISR), Signal Intelligence. To satisfy these requirements, flexibility of small platforms is a key driver and especially new miniaturization technologies able to optimize the performance. An overview new micros-satellite (based on NIMBUS platform) and mission concepts is provided, such as passive SAR for multi-static imaging and tandem, Medium swath/medium resolution dual pol MICROSAR for in L-C-X band multi-application for maritime surveillance and land monitoring, applications for Space Debris monitoring, precision farming, Atmosphere sensing.

Comparing different approaches for an effective monitoring of forest fires based on MSG/SEVIRI images

NASA Astrophysics Data System (ADS)

Laneve, Giovanni

2010-05-01

The remote sensing sensors on board of geostationary satellite, as consequence of the high frequency of the observations, allow, in principle, the monitoring of these phenomena characterized by a fast dynamics. The only condition for is that the events to be monitored should be enough strong to be recognizable notwithstanding the low spatial resolution of the present geostationary systems (MSG/SEVIRI, GOES Imager, MTSAT). Apart from meteorological phenomena other events, like those associated with forest fires and/or volcanic eruption, are characterized by a very fast dynamics. These events are also associated with a very strong signal that make them observable by geostationary satellite in a quasi-continuous way. However, in order to make possible the detection of small fires by using the low resolution multi-spectral imagery provided by geostationary sensor like SEVIRI (3x3 km2 at the equator) new algorithms, capable to exploit it high observation frequency, has been developed. This paper is devoted to show the results obtained by comparing some of these algorithms trying to highlight their advantages and limits. The algorithms herein considered are these developed by CRPSM (SFIDE®), UNIBAS/CNR (RST-FIRES) and ESA-ESRIN (MDIFRM). In general, the new approaches proposed by each one of them are capable to promptly detect small fires making possible an operational utilization of the satellite based fire detection system in the fire fighting phases. In fact, these algorithms are quite different from these introduced in the past and specifically devoted to fire detection using low resolution multi-spectral imagery on LEO (Low Earth Orbit) satellite. Thanks to these differences they are capable of detecting sub-hectare (0.2 ha) forest fires providing an useful instrument for monitoring quasi-continuously forest fires, estimating the FRP (Fire Radiative Power), evaluating the burned biomass, retrieving the emission in the atmosphere.

Combined Landsat-8 and Sentinel-2 Burned Area Mapping

NASA Astrophysics Data System (ADS)

Huang, H.; Roy, D. P.; Zhang, H.; Boschetti, L.; Yan, L.; Li, Z.

2017-12-01

Fire products derived from coarse spatial resolution satellite data have become an important source of information for the multiple user communities involved in fire science and applications. The advent of the MODIS on NASA's Terra and Aqua satellites enabled systematic production of 500m global burned area maps. There is, however, an unequivocal demand for systematically generated higher spatial resolution burned area products, in particular to examine the role of small-fires for various applications. Moderate spatial resolution contemporaneous satellite data from Landsat-8 and the Sentinel-2A and -2B sensors provide the opportunity for detailed spatial mapping of burned areas. Combined, these polar-orbiting systems provide 10m to 30m multi-spectral global coverage more than once every three days. This NASA funded research presents results to prototype a combined Landsat-8 Sentinel-2 burned area product. The Landsat-8 and Sentinel-2 pre-processing, the time-series burned area mapping algorithm, and preliminary results and validation using high spatial resolution commercial satellite data over Africa are presented.

Radiometric gains of satellite sensors of reflected solar radiation - Results from NASA ER-2 aircraft measurements

NASA Technical Reports Server (NTRS)

Abel, Peter; Galimore, Reginald; Cooper, John

1992-01-01

A method for using congruent aircraft-satellite observations to calibrate a satellite sensor is presented. A calibrated spectroradiometer at an altitude of 19 km above White Sands, NM, is oriented to view White Sands at the satellite overpass time along the same view vector as the satellite sensor. Collected data are transformed into corresponding estimates of sensor band radiance at the satellite (derived from the aircraft measurements), and average count (from the sensor measurements). These are both averaged across the footprint of the spectroradiometer. Results are presented for the evolution of NOAA-11 Advanced Very High Resolution Radiometer (AVHRR) (Bands 1 and 2) gain between November 1988 and October 1990, and for GOES-6 and GOES-7 VISSR/VAS visible bands during the same period. Estimates of uncertainty in the results are presented, as well as ideas for their reduction in future flights.

Adaptation of an aerosol retrieval algorithm using multi-wavelength and multi-pixel information of satellites (MWPM) to GOSAT/TANSO-CAI

NASA Astrophysics Data System (ADS)

Hashimoto, M.; Takenaka, H.; Higurashi, A.; Nakajima, T.

2017-12-01

Aerosol in the atmosphere is an important constituent for determining the earth's radiation budget, so the accurate aerosol retrievals from satellite is useful. We have developed a satellite remote sensing algorithm to retrieve the aerosol optical properties using multi-wavelength and multi-pixel information of satellite imagers (MWPM). The method simultaneously derives aerosol optical properties, such as aerosol optical thickness (AOT), single scattering albedo (SSA) and aerosol size information, by using spatial difference of wavelegths (multi-wavelength) and surface reflectances (multi-pixel). The method is useful for aerosol retrieval over spatially heterogeneous surface like an urban region. In this algorithm, the inversion method is a combination of an optimal method and smoothing constraint for the state vector. Furthermore, this method has been combined with the direct radiation transfer calculation (RTM) numerically solved by each iteration step of the non-linear inverse problem, without using look up table (LUT) with several constraints. However, it takes too much computation time. To accelerate the calculation time, we replaced the RTM with an accelerated RTM solver learned by neural network-based method, EXAM (Takenaka et al., 2011), using Rster code. And then, the calculation time was shorternd to about one thouthandth. We applyed MWPM combined with EXAM to GOSAT/TANSO-CAI (Cloud and Aerosol Imager). CAI is a supplement sensor of TANSO-FTS, dedicated to measure cloud and aerosol properties. CAI has four bands, 380, 674, 870 and 1600 nm, and observes in 500 meters resolution for band1, band2 and band3, and 1.5 km for band4. Retrieved parameters are aerosol optical properties, such as aerosol optical thickness (AOT) of fine and coarse mode particles at a wavelenth of 500nm, a volume soot fraction in fine mode particles, and ground surface albedo of each observed wavelength by combining a minimum reflectance method and Fukuda et al. (2013). We will show the results and discuss the accuracy of the algorithm for various surface types. Our future work is to extend the algorithm for analysis of GOSAT-2/TANSO-CAI-2 and GCOM/C-SGLI data.

Multi-sensor satellite monitoring of ash and SO2 volcanic plume in support to aviation control

NASA Astrophysics Data System (ADS)

Brenot, Hugues; Theys, Nicolas; Clarisse, Lieven; van Geffen, Jos; van Gent, Jeroen; Van Roozendael, Michel; van der A, Ronald; Hurtmans, Daniel; Coheur, Pierre-Francois; Clerbaux, Cathy; Valks, Pieter; Hedelt, Pascal; Prata, Fred; Rasson, Olivier; Sievers, Klaus; Zehner, Claus

2014-05-01

The 'Support to Aviation Control Service' (SACS; http://sacs.aeronomie.be) is an ESA-funded project hosted by the Belgian Institute for Space Aeronomy since 2007. The service provides near real-time (NRT) global volcanic ash and SO2 observations, as well as notifications in case of volcanic eruptions (success rate >95% for ash and SO2). SACS is based on the combined use of UV-visible (OMI, GOME-2 MetOp-A, GOME-2 MetOp-B) and infrared (AIRS, IASI MetOp-A, IASI MetOp-B) satellite instruments. The SACS service is primarily designed to support the Volcanic Ash Advisory Centers (VAACs) in their mandate to gather information on volcanic clouds and give advice to airline and air traffic control organisations. SACS also serves other users that subscribe to the service, in particular local volcano observatories, research scientists and airliner pilots. When a volcanic eruption is detected, SACS issues a warning that takes the form of a notification sent by e-mail to users. The SACS notification points to a dedicated web page where all relevant information is available and can be visualised with user-friendly tools. Information about the volcanic plume height from GOME-2 (MetOp-A and MetOp-B) are also available. The strength of a multi-sensor approach relies in the use of satellite data with different overpasses times, minimising the time-lag for detection and enhancing the reliability of such alerts. This presentation will give an overview of the SACS service, and of the different techniques used to detect volcanic plumes (ash, SO2 and plume height). It will also highlight the strengths and limitations of the service and measurements, and some perspectives.

A new star tracker concept for satellite attitude determination based on a multi-purpose panoramic camera

NASA Astrophysics Data System (ADS)

Opromolla, Roberto; Fasano, Giancarmine; Rufino, Giancarlo; Grassi, Michele; Pernechele, Claudio; Dionisio, Cesare

2017-11-01

This paper presents an innovative algorithm developed for attitude determination of a space platform. The algorithm exploits images taken from a multi-purpose panoramic camera equipped with hyper-hemispheric lens and used as star tracker. The sensor architecture is also original since state-of-the-art star trackers accurately image as many stars as possible within a narrow- or medium-size field-of-view, while the considered sensor observes an extremely large portion of the celestial sphere but its observation capabilities are limited by the features of the optical system. The proposed original approach combines algorithmic concepts, like template matching and point cloud registration, inherited from the computer vision and robotic research fields, to carry out star identification. The final aim is to provide a robust and reliable initial attitude solution (lost-in-space mode), with a satisfactory accuracy level in view of the multi-purpose functionality of the sensor and considering its limitations in terms of resolution and sensitivity. Performance evaluation is carried out within a simulation environment in which the panoramic camera operation is realistically reproduced, including perturbations in the imaged star pattern. Results show that the presented algorithm is able to estimate attitude with accuracy better than 1° with a success rate around 98% evaluated by densely covering the entire space of the parameters representing the camera pointing in the inertial space.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

PubMed Central

Pan, Jianjun

2018-01-01

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively. PMID:29382073

Improving Quantitative Precipitation Estimation via Data Fusion of High-Resolution Ground-based Radar Network and CMORPH Satellite-based Product

NASA Astrophysics Data System (ADS)

Cifelli, R.; Chen, H.; Chandrasekar, V.; Xie, P.

2015-12-01

A large number of precipitation products at multi-scales have been developed based upon satellite, radar, and/or rain gauge observations. However, how to produce optimal rainfall estimation for a given region is still challenging due to the spatial and temporal sampling difference of different sensors. In this study, we develop a data fusion mechanism to improve regional quantitative precipitation estimation (QPE) by utilizing satellite-based CMORPH product, ground radar measurements, as well as numerical model simulations. The CMORPH global precipitation product is essentially derived based on retrievals from passive microwave measurements and infrared observations onboard satellites (Joyce et al. 2004). The fine spatial-temporal resolution of 0.05o Lat/Lon and 30-min is appropriate for regional hydrologic and climate studies. However, it is inadequate for localized hydrometeorological applications such as urban flash flood forecasting. Via fusion of the Regional CMORPH product and local precipitation sensors, the high-resolution QPE performance can be improved. The area of interest is the Dallas-Fort Worth (DFW) Metroplex, which is the largest land-locked metropolitan area in the U.S. In addition to an NWS dual-polarization S-band WSR-88DP radar (i.e., KFWS radar), DFW hosts the high-resolution dual-polarization X-band radar network developed by the center for Collaborative Adaptive Sensing of the Atmosphere (CASA). This talk will present a general framework of precipitation data fusion based on satellite and ground observations. The detailed prototype architecture of using regional rainfall instruments to improve regional CMORPH precipitation product via multi-scale fusion techniques will also be discussed. Particularly, the temporal and spatial fusion algorithms developed for the DFW Metroplex will be described, which utilizes CMORPH product, S-band WSR-88DP, and X-band CASA radar measurements. In order to investigate the uncertainties associated with each individual product and demonstrate the precipitation data fusion performance, both individual and fused QPE products are evaluated using rainfall measurements from a disdrometer and gauge network.

APhoRISM FP7 project: the Multi-platform volcanic Ash Cloud Estimation (MACE) infrastructure

NASA Astrophysics Data System (ADS)

Merucci, Luca; Corradini, Stefano; Bignami, Christian; Stramondo, Salvatore

2014-05-01

APHORISM is an FP7 project that aims to develop innovative products to support the management and mitigation of the volcanic and the seismic crisis. Satellite and ground measurements will be managed in a novel manner to provide new and improved products in terms of accuracy and quality of information. The Multi-platform volcanic Ash Cloud Estimation (MACE) infrastructure will exploit the complementarity between geostationary, and polar satellite sensors and ground measurements to improve the ash detection and retrieval and to fully characterize the volcanic ash clouds from source to the atmosphere. The basic idea behind the proposed method consists to manage in a novel manner, the volcanic ash retrievals at the space-time scale of typical geostationary observations using both the polar satellite estimations and in-situ measurements. The typical ash thermal infrared (TIR) retrieval will be integrated by using a wider spectral range from visible (VIS) to microwave (MW) and the ash detection will be extended also in case of cloudy atmosphere or steam plumes. All the MACE ash products will be tested on three recent eruptions representative of different eruption styles in different clear or cloudy atmospheric conditions: Eyjafjallajokull (Iceland) 2010, Grimsvotn (Iceland) 2011 and Etna (Italy) 2011-2012. The MACE infrastructure will be suitable to be implemented in the next generation of ESA Sentinels satellite missions.

Application of Satellite Altimeter Data to Studies of Ocean Surface Heat Flux and Upper Ocean Thermal Processes

NASA Technical Reports Server (NTRS)

Yan, Xiao-Hal

2003-01-01

This is a one-year cost extension of previous grant but carrying a new award number for the administrative purpose. Supported by this one-year extension, the following research has continued and obtained significant results. 20 papers have been published (9) or submitted (11) to scientific journals in this one-year period. A brief summary of scientific results on: 1. A new method for estimation of the sensible heat flux using satellite vector winds, 2. Pacific warm pool excitation, earth rotation and El Nino Southern Oscillations, 3. A new study of the Mediterranean outflow and Meddies at 400-meter isopycnal surface using multi-sensor data, 4. Response of the coastal ocean to extremely high wind, and 5. Role of wind on the estimation of heat flux using satellite data, are provided below as examples of our many research results conducted in the last year,

The Italian contribution to the CSES satellite

NASA Astrophysics Data System (ADS)

Conti, Livio

2016-04-01

We present the Italian contribution to the CSES (China Seismo-Electromagnetic Satellite) mission. The CSES satellite aims at investigating electromagnetic field, plasma and particles in the near-Earth environment in order to study in particular seismic precursors, particles fluxes (from Van Allen belts, cosmic rays, solar wind, etc.), anthropogenic electromagnetic pollution and more in general the atmosphere-ionosphere-magnetosphere coupling mechanisms that can affect the climate changes. The launch of CSES - the first of a series of several satellite missions - is scheduled by the end of 2016. The CSES satellite has been financed by the CNSA (China National Space Agency) and developed by CEA (China Earthquake Administration) together with several Chinese research institutes and private companies such as the DFH (that has developed the CAST2000 satellite platform). Italy participates to the CSES satellite mission with the LIMADOU project funded by ASI (Italian Space Agency) in collaboration with the Universities of Roma Tor Vergata, Uninettuno, Trento, Bologna and Perugia, as well as the INFN (Italian National Institute of Nuclear Physics), INGV (Italian National Institute of Geophysics and Volcanology) and INAF-IAPS (Italian National Institute of Astrophysics and Planetology). Many analyses have shown that satellite observations of electromagnetic fields, plasma parameters and particle fluxes in low Earth orbit may be useful in order to study the existence of electromagnetic emissions associated with the occurrence of earthquakes of medium and high magnitude. Although the earthquakes forecasting is not possible today, it is certainly a major challenge - and perhaps even a duty - for science in the near future. The claims that the reported anomalies (of electromagnetic, plasma and particle parameters) are seismic precursors are still intensely debated and analyses for confirming claimed correlations are still lacking. In fact, ionospheric currents, plasma parameters and stability of Van Allen belt are constantly modified by natural non-seismic and man-made processes. Therefore, in order to identify seismo-associated perturbations, it is needed to reject the "normal" background effects of the e.m. emissions due to: geomagnetic storms, tropospheric phenomena, and artificial sources (such as power lines, VLF transmitters, HF stations, etc.). Currently, the only available large database is that collected by the Demeter satellite and by rare observations made by some previous space missions, non-dedicated to this purpose. The CSES satellite aims at continuing the exploration started by Demeter with advanced multi-parametric measurements. The configuration of the CSES sensors foresees measurements of energetic particle fluxes, ionospheric plasma parameters and electromagnetic fields, in a wide range of energy and frequencies. The main sensors onboard the satellite are: the HEPD (High Energy Particle Detector) developed by the Italian participants, and the following Chinese sensors: LEPD (Low Energy Particle Detector), LP (Langmuir Probes), IDM (Ion Drift Meter), ICM (Ion Capture Meter), RPA (Retarding Potential Analyzer), EFD (Electric Field Detectors) developed in collaboration with Italian team, HPM (High Precision Magnetometer) and SCM (Search-Coil Magnetometer). The research activity is at an advanced phase, being the various payloads already built and, right now, an intense activity is going on for calibration of the various sensors. In particular, the Italian payload HEPD is under test at the laboratories of the National Institute for Nuclear Physics (INFN) and the Chinese payloads LP, IDM, ICM, RPA and EFD are tested at the INAF-IAPS "Plasma Chamber" in Rome, which is a facility where the response of the sensors, and their compatibility with ionospheric plasma, can be verified in environmental conditions very similar to those met by the satellite in orbit.

IPS - a vision aided navigation system

NASA Astrophysics Data System (ADS)

Börner, Anko; Baumbach, Dirk; Buder, Maximilian; Choinowski, Andre; Ernst, Ines; Funk, Eugen; Grießbach, Denis; Schischmanow, Adrian; Wohlfeil, Jürgen; Zuev, Sergey

2017-04-01

Ego localization is an important prerequisite for several scientific, commercial, and statutory tasks. Only by knowing one's own position, can guidance be provided, inspections be executed, and autonomous vehicles be operated. Localization becomes challenging if satellite-based navigation systems are not available, or data quality is not sufficient. To overcome this problem, a team of the German Aerospace Center (DLR) developed a multi-sensor system based on the human head and its navigation sensors - the eyes and the vestibular system. This system is called integrated positioning system (IPS) and contains a stereo camera and an inertial measurement unit for determining an ego pose in six degrees of freedom in a local coordinate system. IPS is able to operate in real time and can be applied for indoor and outdoor scenarios without any external reference or prior knowledge. In this paper, the system and its key hardware and software components are introduced. The main issues during the development of such complex multi-sensor measurement systems are identified and discussed, and the performance of this technology is demonstrated. The developer team started from scratch and transfers this technology into a commercial product right now. The paper finishes with an outlook.

Tools and Data Services from the NASA Earth Satellite Observations for Climate Applications

NASA Technical Reports Server (NTRS)

Vicente, Gilberto A.

2005-01-01

Climate science and applications require access to vast amounts of archived high quality data, software tools and services for data manipulation and information extraction. These on the other hand require gaining detailed understanding of the data's internal structure and physical implementation to data reduction, combination and data product production. This time-consuming task must be undertaken before the core investigation can begin and is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets of different formats, structures, and resolutions. In order to address these issues the Goddard Space Flight Center (GSFC) Earth Sciences (GES), Data and Information Service Center (DISC) Distributed Active Archive Center (DAAC) has made great progress in facilitating science and applications research by developing innovative tools and data services applied to the Earth sciences atmospheric and climate data. The GES/DISC/DAAC has successfully implemented and maintained a long-term climate satellite data archive and developed tools and services to a variety of atmospheric science missions including AIRS, AVHRR, MODIS, SeaWiFS, SORCE, TOMS, TOVS, TRMM, and UARS and Aura instruments providing researchers with excellent opportunities to acquire accurate and continuous atmospheric measurements. Since the number of climate science products from these various missions is steadily increasing as a result of more sophisticated sensors and new science algorithms, the main challenge for data centers like the GES/DISC/DAAC is to guide users through the variety of data sets and products, provide tools to visualize and reduce the volume of the data and secure uninterrupted and reliable access to data and related products. This presentation will describe the effort at the GES/DISC/DAAC to build a bridge between multi-sensor data and the effective scientific use of the data, with an emphasis on the heritage satellite observations and science products for climate applications. The intent is to inform users of the existence of this large collection of data and products; suggest starting points for cross-platform science projects and data mining activities and provide data services and tools information. More information about the GES/DISC/DAAC satellite data and products, tools, and services can be found at http://daac.gsfc.nasa.gov.

The Cloudsat Mission and the EOS Constellation: A New Dimension of Space-Based Observation of Clouds and Precipitation

NASA Technical Reports Server (NTRS)

Stephens, Graeme L.; Vane, Deborah G.; Boain, Ronald; Mace, Gerald; Sassen, Kenneth; Wang, Zhien; Illingworth, Anthony; OConnor, Ewan; Rossow, William; Durden, Stephen L.;

A Prototype Land Information Sensor Web: Design, Implementation and Implication for the SMAP Mission

NASA Astrophysics Data System (ADS)

Su, H.; Houser, P.; Tian, Y.; Geiger, J. K.; Kumar, S. V.; Gates, L.

2009-12-01

Land Surface Model (LSM) predictions are regular in time and space, but these predictions are influenced by errors in model structure, input variables, parameters and inadequate treatment of sub-grid scale spatial variability. Consequently, LSM predictions are significantly improved through observation constraints made in a data assimilation framework. Several multi-sensor satellites are currently operating which provide multiple global observations of the land surface, and its related near-atmospheric properties. However, these observations are not optimal for addressing current and future land surface environmental problems. To meet future earth system science challenges, NASA will develop constellations of smart satellites in sensor web configurations which provide timely on-demand data and analysis to users, and can be reconfigured based on the changing needs of science and available technology. A sensor web is more than a collection of satellite sensors. That means a sensor web is a system composed of multiple platforms interconnected by a communication network for the purpose of performing specific observations and processing data required to support specific science goals. Sensor webs can eclipse the value of disparate sensor components by reducing response time and increasing scientific value, especially when the two-way interaction between the model and the sensor web is enabled. The study of a prototype Land Information Sensor Web (LISW) is sponsored by NASA, trying to integrate the Land Information System (LIS) in a sensor web framework which allows for optimal 2-way information flow that enhances land surface modeling using sensor web observations, and in turn allows sensor web reconfiguration to minimize overall system uncertainty. This prototype is based on a simulated interactive sensor web, which is then used to exercise and optimize the sensor web modeling interfaces. The Land Information Sensor Web Service-Oriented Architecture (LISW-SOA) has been developed and it is the very first sensor web framework developed especially for the land surface studies. Synthetic experiments based on the LISW-SOA and the virtual sensor web provide a controlled environment in which to examine the end-to-end performance of the prototype, the impact of various sensor web design trade-offs and the eventual value of sensor webs for a particular prediction or decision support. In this paper, the design, implementation of the LISW-SOA and the implication for the Soil Moisture Active and Passive (SMAP) mission is presented. Particular attention is focused on examining the relationship between the economic investment on a sensor web (space and air borne, ground based) and the accuracy of the model predicted soil moisture, which can be achieved by using such sensor observations. The Study of Virtual Land Information Sensor Web (LISW) is expected to provide some necessary a priori knowledge for designing and deploying the next generation Global Earth Observing System of systems (GEOSS).

Lightning and 85-GHz MCSs in the Global Tropics

NASA Technical Reports Server (NTRS)

Toracinta, E. Richard; Zipser, E. J.

1999-01-01

Numerous observations of tropical convection show that tropical continental mesoscale convective systems (MCSs) are much more prolific lightning producers than their oceanic counterparts. Satellite-based climatologies using 85-GHz passive microwave ice-scattering signatures from the Special Sensor Microwave/Imager (SSM/I) indicate that MCSs of various size and intensity are found throughout the global tropics. In contrast, global lightning distributions show a strong land bias with an order of magnitude difference between land and ocean lightning. This is somewhat puzzling, since 85-GHz ice-scattering and the charge separation processes that lead to lightning are both thought to depend upon the existence of large graupel particles. The fact that low 85-GHz brightness temperatures are observed in tropical oceanic MCSs containing virtually no lightning leads to the postulate that tropical oceanic and tropical continental MCSs have fundamentally different hydrometeor profiles through the mixed phase region of the cloud (0 C <= T <= 20 C). Until recently, validation of this postulate has not been practicable on a global scale. Recent deployment of the Tropical Rainfall Measuring Mission (TRMM) satellite presents a unique opportunity for MCS studies. The multi-sensor instrument ensemble aboard TRMM, including a multi-channel microwave radiometer, the Lightning Imaging Sensor (LIS), and the first space-borne radar, facilitates high-resolution case studies of MCS structure throughout the global tropics. An important precursor, however, is to better understand the distribution of MCSs and lightning in the tropics. With that objective in mind, this research undertakes a systematic comparison of 85-GHz-defined MCSs and lightning over the global tropics for a full year, as an initial step toward quantifying differences between land and ocean convective systems.

Automated sensor networks to advance ocean science

NASA Astrophysics Data System (ADS)

Schofield, O.; Orcutt, J. A.; Arrott, M.; Vernon, F. L.; Peach, C. L.; Meisinger, M.; Krueger, I.; Kleinert, J.; Chao, Y.; Chien, S.; Thompson, D. R.; Chave, A. D.; Balasuriya, A.

2010-12-01

The National Science Foundation has funded the Ocean Observatories Initiative (OOI), which over the next five years will deploy infrastructure to expand scientist’s ability to remotely study the ocean. The deployed infrastructure will be linked by a robust cyberinfrastructure (CI) that will integrate marine observatories into a coherent system-of-systems. OOI is committed to engaging the ocean sciences community during the construction pahse. For the CI, this is being enabled by using a “spiral design strategy” allowing for input throughout the construction phase. In Fall 2009, the OOI CI development team used an existing ocean observing network in the Mid-Atlantic Bight (MAB) to test OOI CI software. The objective of this CI test was to aggregate data from ships, autonomous underwater vehicles (AUVs), shore-based radars, and satellites and make it available to five different data-assimilating ocean forecast models. Scientists used these multi-model forecasts to automate future glider missions in order to demonstrate the feasibility of two-way interactivity between the sensor web and predictive models. The CI software coordinated and prioritized the shared resources that allowed for the semi-automated reconfiguration of assett-tasking, and thus enabled an autonomous execution of observation plans for the fixed and mobile observation platforms. Efforts were coordinated through a web portal that provided an access point for the observational data and model forecasts. Researchers could use the CI software in tandem with the web data portal to assess the performance of individual numerical model results, or multi-model ensembles, through real-time comparisons with satellite, shore-based radar, and in situ robotic measurements. The resulting sensor net will enable a new means to explore and study the world’s oceans by providing scientists a responsive network in the world’s oceans that can be accessed via any wireless network.

Climate Change Mitigation: Can the U.S. Intelligence Community Help?

DTIC Science & Technology

2013-06-01

satellite sensors to establish the concentration of atmospheric CO2 parts per million (ppm mole fraction) in samples collected at multiple...measurements. Spatial sampling density, the number of sensors or—in the case of satellite imagery the number and resolution of the images—likewise influences...Somewhat paradoxically, sensor accuracy from either remote ( satellites ) or in situ sensors is an important consideration, but it must also be evaluated

A SERS-active sensor based on heterogeneous gold nanostar core-silver nanoparticle satellite assemblies for ultrasensitive detection of aflatoxinB1

NASA Astrophysics Data System (ADS)

Li, Aike; Tang, Lijuan; Song, Dan; Song, Shanshan; Ma, Wei; Xu, Liguang; Kuang, Hua; Wu, Xiaoling; Liu, Liqiang; Chen, Xin; Xu, Chuanlai

2016-01-01

A surface-enhanced Raman scattering (SERS) sensor based on gold nanostar (Au NS) core-silver nanoparticle (Ag NP) satellites was fabricated for the first time to detect aflatoxinB1 (AFB1). We constructed the SERS sensor using AFB1 aptamer (DNA1)-modified Ag satellites and a complementary sequence (DNA2)-modified Au NS core. The Raman label (ATP) was modified on the surface of Ag satellites. The SERS signal was enhanced when the satellite NP was attached to the Au core NS. The AFB1 aptamer on the surface of Ag satellites would bind to the targets when AFB1 was present in the system, Ag satellites were then removed and the SERS signal decreased. This SERS sensor showed superior specificity for AFB1 and the linear detection range was from 1 to 1000 pg mL-1 with the limit of detection (LOD) of 0.48 pg mL-1. The excellent recovery experiment using peanut milk demonstrated that the sensor could be applied in food and environmental detection.A surface-enhanced Raman scattering (SERS) sensor based on gold nanostar (Au NS) core-silver nanoparticle (Ag NP) satellites was fabricated for the first time to detect aflatoxinB1 (AFB1). We constructed the SERS sensor using AFB1 aptamer (DNA1)-modified Ag satellites and a complementary sequence (DNA2)-modified Au NS core. The Raman label (ATP) was modified on the surface of Ag satellites. The SERS signal was enhanced when the satellite NP was attached to the Au core NS. The AFB1 aptamer on the surface of Ag satellites would bind to the targets when AFB1 was present in the system, Ag satellites were then removed and the SERS signal decreased. This SERS sensor showed superior specificity for AFB1 and the linear detection range was from 1 to 1000 pg mL-1 with the limit of detection (LOD) of 0.48 pg mL-1. The excellent recovery experiment using peanut milk demonstrated that the sensor could be applied in food and environmental detection. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr08372a

Exploring the potential of Sentinel-1 data for regional scale slope instability detection using multi-temporal interferometry

NASA Astrophysics Data System (ADS)

Wasowski, Janusz; Bovenga, Fabio; Nutricato, Raffaele; Nitti, Davide Oscar; Chiaradia, Maria Teresa; Refice, Alberto; Pasquariello, Guido

2016-04-01

Launched in 2014, the European Space Agency (ESA) Sentinel-1 satellite carrying a medium resolution (20 m) C-Band Synthetic Aperture Radar (SAR) sensor holds much promise for new applications of multi-temporal interferometry (MTI) in landslide assessment. Specifically, the regularity of acquisitions, timeliness of data delivery, shorter repeat cycle (currently 12 days with Sentinel-1A sensor), and flexible incidence angle geometry, all imply better practical utility of MTI relying on Sentinel-1 with respect to MTI based on data from earlier ESA's satellite radar C-band sensors (ERS1/2, ENVISAT). Furthermore, the upcoming launch of Sentinel-1B will cut down the repeat cycle to 6 days, thereby further improving temporal coherence and quality and coverage of MTI products. Taking advantage of the Interferometric Wide (IW) Swath acquisition mode of Sentinel-1 (images covering a 250 km swath on the ground), in this work we test the potential of such data for regional scale slope instability detection through MTI. Our test area includes the landslide-prone Apennine Mountains of Southern Italy. We rely on over 30 Sentinel-1 images, most of which acquired in 2015, and MTI processing through the SPINUA algorithm (Stable Points INterferometry in Un-urbanized Areas). The potential of MTI results based on Sentinel-1 data is assessed by comparing the detected ground surface displacements with the MTI results obtained for the same test area using the C-Band data acquired by ERS1/2 and ENVISAT in 1990s and 2000s. Although the initial results are encouraging, it seems evident that longer-term (few years) acquisitions of Sentinel-1 are necessary to reliably detect some extremely slow movements, which were observed in the last two decades and are likely to be still present in peri-urban areas of many hilltop towns in the Apennine Mts. The MTI results obtained from Sentinel-1 data are also locally compared with the MTI outcomes based on the high resolution (3 m) TerraSAR-X imagery. Again, even though there is lack of temporal overlap in the two datasets, the comparison shows some potential benefits of the exploitation different resolution sensor datasets. For example, when considering the costs of MTI applications, an effective approach to slope hazard assessment could rely on the use of coarser imagery MTI to secure long-term wide-area coverage, to be integrated by higher resolution MTI with more focus on urbanized or greater value areas (cf., Wasowski and Bovenga et al., 2014a,b). Now these approaches are facilitated by the regular global coverage and free medium resolution imagery guaranteed by the background satellite radar mission of Sentinel-1. Acknowledgments Study carried out in the framework of the Apulia Space project (PON&REC 2007-2013, Cod: PON03PE_00067_6). We also thank ESA and the German Space Agency (DLR) for providing us radar data. References Wasowski J., Bovenga F. 2014a. Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives. Engineering Geology 174: 103-138. http://dx.doi.org/10.1016/j.enggeo.2014.03.003 Wasowski J., Bovenga F. 2014. Remote Sensing of Landslide Motion with Emphasis on Satellite Multitemporal Interferometry Applications: An Overview. In T. Davies (Ed). Landslide Hazards, Risks and Disasters. p. 345-403. http://dx.doi.org/10.1016/B978-0-12-396452-6.00011-2

Multi-Temporal Multi-Sensor Analysis of Urbanization and Environmental/Climate Impact in China for Sustainable Urban Development

NASA Astrophysics Data System (ADS)

Ban, Yifang; Gong, Peng; Gamba, Paolo; Taubenbock, Hannes; Du, Peijun

2016-08-01

The overall objective of this research is to investigate multi-temporal, multi-scale, multi-sensor satellite data for analysis of urbanization and environmental/climate impact in China to support sustainable planning. Multi- temporal multi-scale SAR and optical data have been evaluated for urban information extraction using innovative methods and algorithms, including KTH- Pavia Urban Extractor, Pavia UEXT, and an "exclusion- inclusion" framework for urban extent extraction, and KTH-SEG, a novel object-based classification method for detailed urban land cover mapping. Various pixel- based and object-based change detection algorithms were also developed to extract urban changes. Several Chinese cities including Beijing, Shanghai and Guangzhou are selected as study areas. Spatio-temporal urbanization patterns and environmental impact at regional, metropolitan and city core were evaluated through ecosystem service, landscape metrics, spatial indices, and/or their combinations. The relationship between land surface temperature and land-cover classes was also analyzed.The urban extraction results showed that urban areas and small towns could be well extracted using multitemporal SAR data with the KTH-Pavia Urban Extractor and UEXT. The fusion of SAR data at multiple scales from multiple sensors was proven to improve urban extraction. For urban land cover mapping, the results show that the fusion of multitemporal SAR and optical data could produce detailed land cover maps with improved accuracy than that of SAR or optical data alone. Pixel-based and object-based change detection algorithms developed with the project were effective to extract urban changes. Comparing the urban land cover results from mulitemporal multisensor data, the environmental impact analysis indicates major losses for food supply, noise reduction, runoff mitigation, waste treatment and global climate regulation services through landscape structural changes in terms of decreases in service area, edge contamination and fragmentation. In terms ofclimate impact, the results indicate that land surface temperature can be related to land use/land cover classes.

MultiSpec—a tool for multispectral hyperspectral image data analysis

NASA Astrophysics Data System (ADS)

Biehl, Larry; Landgrebe, David

2002-12-01

MultiSpec is a multispectral image data analysis software application. It is intended to provide a fast, easy-to-use means for analysis of multispectral image data, such as that from the Landsat, SPOT, MODIS or IKONOS series of Earth observational satellites, hyperspectral data such as that from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and EO-1 Hyperion satellite system or the data that will be produced by the next generation of Earth observational sensors. The primary purpose for the system was to make new, otherwise complex analysis tools available to the general Earth science community. It has also found use in displaying and analyzing many other types of non-space related digital imagery, such as medical image data and in K-12 and university level educational activities. MultiSpec has been implemented for both the Apple Macintosh ® and Microsoft Windows ® operating systems (OS). The effort was first begun on the Macintosh OS in 1988. The GLOBE ( http://www.globe.gov) program supported the development of a subset of MultiSpec for the Windows OS in 1995. Since then most (but not all) of the features in the Macintosh OS version have been ported to the Windows OS version. Although copyrighted, MultiSpec with its documentation is distributed without charge. The Macintosh and Windows versions and documentation on its use are available from the World Wide Web at URL: http://dynamo.ecn.purdue.edu/˜biehl/MultiSpec/ MultiSpec is copyrighted (1991-2001) by Purdue Research Foundation, West Lafayette, Indiana 47907.

A Bayesian Approach to Estimating Detection Performance in a Multi-Sensor Environment

DTIC Science & Technology

2014-09-01

National Defence, 2014 © Sa Majesté la Reine (en droit du Canada), telle que représentée par le ministre de la Défense nationale, 2014 DRDC...emphasis can be seen in CF publications such as the Maritime Capability Planning Guidance (MCPG) 2010 [3]. To make effective use of these new remote...fixed installation, ship, aircraft, or satellite. Ships of a certain size or type are required by the International Maritime Organization (IMO) to

Optical and Electrical Sensor Busses for the Heinrich Hertz Satellite

NASA Astrophysics Data System (ADS)

Heyer, Heinz-Voker; Zeh, Thomas; Reutlinger, Arnd; Kammer, Susanne; Voigt, Siegfried

2010-08-01

Germany is planning the geostationary communication satellite Heinrich Hertz. The used platform will be the Small Geo platform of OHB. Phase A for this satellite has been performed successfully and two sensor busses in addition to the conventional harness have been selected for housekeeping measurement. *Sensor bus (wire related) *Optical bus (fiber related). The satellite will be launched in 2014. The payload will be a newly developed telecommunication equipment for in-orbit demonstration.

Comparison of satellite precipitation products with Q3 over the CONUS

NASA Astrophysics Data System (ADS)

Wang, J.; Petersen, W. A.; Wolff, D. B.; Kirstetter, P. E.

2016-12-01

The Global Precipitation Measurement (GPM) is an international satellite mission that provides a new-generation of global precipitation observations. A wealth of precipitation products have been generated since the launch of the GPM Core Observatory in February of 2014. However, the accuracy of the satellite-based precipitation products is affected by discrete temporal sampling and remote spaceborne retrieval algorithms. The GPM Ground Validation (GV) program is currently underway to independently verify the satellite precipitation products, which can be carried out by comparing satellite products with ground measurements. This study compares four Day-1 GPM surface precipitation products derived from the GPM Microwave Imager (GMI), Ku-band Precipitation Radar (KU), Dual-Frequency Precipitation Radar (DPR) and DPR-GMI CoMBined (CMB) algorithms, as well as the near-real-time Integrated Multi-satellitE Retrievals for GPM (IMERG) Late Run product and precipitation retrievals from Microwave Humidity Sounders (MHS) flown on NOAA and METOPS satellites, with the NOAA Multi-Radar Multi-Sensor suite (MRMS; now called "Q3"). The comparisons are conducted over the conterminous United States (CONUS) at various spatial and temporal scales with respect to different precipitation intensities, and filtered with radar quality index (RQI) thresholds and precipitation types. Various versions of GPM products are evaluated against Q3. The latest Version-04A GPM products are in reasonably good overall agreement with Q3. Based on the mission-to-date (March 2014 - May 2016) data from all GPM overpasses, the biases relative to Q3 for GMI and DPR precipitation estimates at 0.5o resolution are negative, whereas the biases for CMB and KU precipitation estimates are positive. Based on all available data (March 2015 - April 2016 at this writing), the CONUS-averaged near-real-time IMERG Late Run hourly precipitation estimate is about 46% higher than Q3. Preliminary comparison of 1-year (2015) MHS precipitation estimates with Q3 shows the MHS is bout 30% lower than Q3. Detailed comparison results are available at http://wallops-prf.gsfc.nasa.gov/NMQ/.

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

NASA Astrophysics Data System (ADS)

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

2017-12-01

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, ELR yields promising results compared to PLSR and SVR in this study. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment.

Multi-sensor Efforts to Detect Oil slicks at the Ocean Surface — An Applied Science Project

NASA Astrophysics Data System (ADS)

Gallegos, S. C.; Pichel, W. G.; Hu, Y.; Garcia-Pineda, O. G.; Kukhtarev, N.; Lewis, D.

2012-12-01

In 2008, The Naval Research Laboratory at Stennis Space Center (NRL-SSC), NASA-Langley Space Center (LaRC) and NOAA Center for Satellite Applications and Research (STAR) with the support of the NASA Applied Science Program developed the concept for an operational oil detection system to support NOAA's mission of oil spill monitoring and response. Due to the current lack of a spaceborne sensor specifically designed for oil detection, this project relied on data and algorithms for the Synthetic Aperture Radar (SAR) and the Moderate Resolution Imaging Spectroradiometer (MODIS). NOAA/Satellite Analyses Branch (NOAA/SAB) was the transition point of those algorithms. Part of the research also included the evaluation of the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) capabilities for detection of surface and subsurface oil. In April 2010, while conducting the research in the Gulf of Mexico, the Deep Water Horizon (DWH) oil spill, the largest accidental marine oil spill in the history of the petroleum industry impacted our area. This incident provided opportunities to expand our efforts to the field, the laboratory, and to the data of other sensors such as the Hyperspectral Imager of the Coastal Zone (HICO). We summarize the results of our initial effort and describe in detail those efforts carried out during the DWH oil spill.

Validation of ocean color sensors using a profiling hyperspectral radiometer

NASA Astrophysics Data System (ADS)

Ondrusek, M. E.; Stengel, E.; Rella, M. A.; Goode, W.; Ladner, S.; Feinholz, M.

2014-05-01

Validation measurements of satellite ocean color sensors require in situ measurements that are accurate, repeatable and traceable enough to distinguish variability between in situ measurements and variability in the signal being observed on orbit. The utility of using a Satlantic Profiler II equipped with HyperOCR radiometers (Hyperpro) for validating ocean color sensors is tested by assessing the stability of the calibration coefficients and by comparing Hyperpro in situ measurements to other instruments and between different Hyperpros in a variety of water types. Calibration and characterization of the NOAA Satlantic Hyperpro instrument is described and concurrent measurements of water-leaving radiances conducted during cruises are presented between this profiling instrument and other profiling, above-water and moored instruments. The moored optical instruments are the US operated Marine Optical BuoY (MOBY) and the French operated Boussole Buoy. In addition, Satlantic processing versions are described in terms of accuracy and consistency. A new multi-cast approach is compared to the most commonly used single cast method. Analysis comparisons are conducted in turbid and blue water conditions. Examples of validation matchups with VIIRS ocean color data are presented. With careful data collection and analysis, the Satlantic Hyperpro profiling radiometer has proven to be a reliable and consistent tool for satellite ocean color validation.

Robust Targeting for the Smartphone Video Guidance Sensor

NASA Technical Reports Server (NTRS)

Carter, Christopher

2017-01-01

The Smartphone Video Guidance Sensor (SVGS) is a miniature, self-contained autonomous rendezvous and docking sensor developed using a commercial off the shelf Android-based smartphone. It aims to provide a miniaturized solution for rendezvous and docking, enabling small satellites to conduct proximity operations and formation flying while minimizing interference with a primary payload. Previously, the sensor was limited by a slow (2 Hz) refresh rate and its use of retro-reflectors, both of which contributed to a limited operating environment. To advance the technology readiness level, a modified approach was developed, combining a multi-colored LED target with a focused target-detection algorithm. Alone, the use of an LED system was determined to be much more reliable, though slower, than the retro-reflector system. The focused target-detection system was developed in response to this problem to mitigate the speed reduction of using color. However, it also improved the reliability. In combination these two methods have been demonstrated to dramatically increase sensor speed and allow the sensor to select the target even with significant noise interfering with the sensor, providing millimeter level accuracy at a range of two meters with a 1U target.

Robust Targeting for the Smartphone Video Guidance Sensor

NASA Technical Reports Server (NTRS)

Carter, C.

2017-01-01

The Smartphone Video Guidance Sensor (SVGS) is a miniature, self-contained autonomous rendezvous and docking sensor developed using a commercial off the shelf Android-based smartphone. It aims to provide a miniaturized solution for rendezvous and docking, enabling small satellites to conduct proximity operations and formation flying while minimizing interference with a primary payload. Previously, the sensor was limited by a slow (2 Hz) refresh rate and its use of retro-reflectors, both of which contributed to a limited operating environment. To advance the technology readiness level, a modified approach was developed, combining a multi-colored LED target with a focused target-detection algorithm. Alone, the use of an LED system was determined to be much more reliable, though slower, than the retro-reflector system. The focused target-detection system was developed in response to this problem to mitigate the speed reduction of using color. However it also improved the reliability. In combination these two methods have been demonstrated to dramatically increase sensor speed and allow the sensor to select the target even with significant noise interfering with the sensor, providing millimeter level precision at a range of two meters with a 1U target.

On the Land-Ocean Contrast of Tropical Convection and Microphysics Statistics Derived from TRMM Satellite Signals and Global Storm-Resolving Models

NASA Technical Reports Server (NTRS)

Matsui, Toshihisa; Chern, Jiun-Dar; Tao, Wei-Kuo; Lang, Stephen E.; Satoh, Masaki; Hashino, Tempei; Kubota, Takuji

2016-01-01

A 14-year climatology of Tropical Rainfall Measuring Mission (TRMM) collocated multi-sensor signal statistics reveal a distinct land-ocean contrast as well as geographical variability of precipitation type, intensity, and microphysics. Microphysics information inferred from the TRMM precipitation radar and Microwave Imager (TMI) show a large land-ocean contrast for the deep category, suggesting continental convective vigor. Over land, TRMM shows higher echo-top heights and larger maximum echoes, suggesting taller storms and more intense precipitation, as well as larger microwave scattering, suggesting the presence of morelarger frozen convective hydrometeors. This strong land-ocean contrast in deep convection is invariant over seasonal and multi-year time-scales. Consequently, relatively short-term simulations from two global storm-resolving models can be evaluated in terms of their land-ocean statistics using the TRMM Triple-sensor Three-step Evaluation via a satellite simulator. The models evaluated are the NASA Multi-scale Modeling Framework (MMF) and the Non-hydrostatic Icosahedral Cloud Atmospheric Model (NICAM). While both simulations can represent convective land-ocean contrasts in warm precipitation to some extent, near-surface conditions over land are relatively moisture in NICAM than MMF, which appears to be the key driver in the divergent warm precipitation results between the two models. Both the MMF and NICAM produced similar frequencies of large CAPE between land and ocean. The dry MMF boundary layer enhanced microwave scattering signals over land, but only NICAM had an enhanced deep convection frequency over land. Neither model could reproduce a realistic land-ocean contrast in in deep convective precipitation microphysics. A realistic contrast between land and ocean remains an issue in global storm-resolving modeling.

Multi-parameter Observations and Validation of Pre-earthquake Atmospheric Signals

NASA Astrophysics Data System (ADS)

Ouzounov, D.; Pulinets, S. A.; Hattori, K.; Mogi, T.; Kafatos, M.

2014-12-01

We are presenting the latest development in multi-sensors observations of short-term pre-earthquake phenomena preceding major earthquakes. We are exploring the potential of pre-seismic atmospheric and ionospheric signals to alert for large earthquakes. To achieve this, we start validating anomalous ionospheric /atmospheric signals in retrospective and prospective modes. The integrated satellite and terrestrial framework (ISTF) is our method for validation and is based on a joint analysis of several physical and environmental parameters (Satellite thermal infrared radiation (OLR), electron concentration in the ionosphere (GPS/TEC), VHF-bands radio waves, radon/ion activities, air temperature and seismicity patterns) that were found to be associated with earthquakes. The science rationale for multidisciplinary analysis is based on concept Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) [Pulinets and Ouzounov, 2011], which explains the synergy of different geospace processes and anomalous variations, usually named short-term pre-earthquake anomalies. Our validation processes consist in two steps: (1) A continuous retrospective analysis preformed over two different regions with high seismicity- Taiwan and Japan for 2003-2009 The retrospective tests (100+ major earthquakes, M>5.9, Taiwan and Japan) show OLR anomalous behavior before all of these events with no false negatives. False alarm ratio for false positives is less then 25%. (2) Prospective testing using multiple parameters with potential for M5.5+ events. The initial testing shows systematic appearance of atmospheric anomalies in advance (days) to the M5.5+ events for Taiwan and Japan (Honshu and Hokkaido areas). Our initial prospective results suggest that our approach show a systematic appearance of atmospheric anomalies, one to several days prior to the largest earthquakes That feature could be further studied and tested for advancing the multi-sensors detection of pre-earthquake atmospheric signals.

Monitoring Powdery Mildew of Winter Wheat by Using Moderate Resolution Multi-Temporal Satellite Imagery

PubMed Central

Zhang, Jingcheng; Pu, Ruiliang; Yuan, Lin; Wang, Jihua; Huang, Wenjiang; Yang, Guijun

2014-01-01

Powdery mildew is one of the most serious diseases that have a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study attempted to use multi-temporal moderate resolution satellite-based data of surface reflectances in blue (B), green (G), red (R) and near infrared (NIR) bands from HJ-CCD (CCD sensor on Huanjing satellite) to monitor disease at a regional scale. In a suburban area in Beijing, China, an extensive field campaign for disease intensity survey was conducted at key growth stages of winter wheat in 2010. Meanwhile, corresponding time series of HJ-CCD images were acquired over the study area. In this study, a number of single-stage and multi-stage spectral features, which were sensitive to powdery mildew, were selected by using an independent t-test. With the selected spectral features, four advanced methods: mahalanobis distance, maximum likelihood classifier, partial least square regression and mixture tuned matched filtering were tested and evaluated for their performances in disease mapping. The experimental results showed that all four algorithms could generate disease maps with a generally correct distribution pattern of powdery mildew at the grain filling stage (Zadoks 72). However, by comparing these disease maps with ground survey data (validation samples), all of the four algorithms also produced a variable degree of error in estimating the disease occurrence and severity. Further, we found that the integration of MTMF and PLSR algorithms could result in a significant accuracy improvement of identifying and determining the disease intensity (overall accuracy of 72% increased to 78% and kappa coefficient of 0.49 increased to 0.59). The experimental results also demonstrated that the multi-temporal satellite images have a great potential in crop diseases mapping at a regional scale. PMID:24691435

Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery.

PubMed

Zhang, Jingcheng; Pu, Ruiliang; Yuan, Lin; Wang, Jihua; Huang, Wenjiang; Yang, Guijun

2014-01-01

Powdery mildew is one of the most serious diseases that have a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study attempted to use multi-temporal moderate resolution satellite-based data of surface reflectances in blue (B), green (G), red (R) and near infrared (NIR) bands from HJ-CCD (CCD sensor on Huanjing satellite) to monitor disease at a regional scale. In a suburban area in Beijing, China, an extensive field campaign for disease intensity survey was conducted at key growth stages of winter wheat in 2010. Meanwhile, corresponding time series of HJ-CCD images were acquired over the study area. In this study, a number of single-stage and multi-stage spectral features, which were sensitive to powdery mildew, were selected by using an independent t-test. With the selected spectral features, four advanced methods: mahalanobis distance, maximum likelihood classifier, partial least square regression and mixture tuned matched filtering were tested and evaluated for their performances in disease mapping. The experimental results showed that all four algorithms could generate disease maps with a generally correct distribution pattern of powdery mildew at the grain filling stage (Zadoks 72). However, by comparing these disease maps with ground survey data (validation samples), all of the four algorithms also produced a variable degree of error in estimating the disease occurrence and severity. Further, we found that the integration of MTMF and PLSR algorithms could result in a significant accuracy improvement of identifying and determining the disease intensity (overall accuracy of 72% increased to 78% and kappa coefficient of 0.49 increased to 0.59). The experimental results also demonstrated that the multi-temporal satellite images have a great potential in crop diseases mapping at a regional scale.

Solar Weather Ice Monitoring Station (SWIMS). A low cost, extreme/harsh environment, solar powered, autonomous sensor data gathering and transmission system

NASA Astrophysics Data System (ADS)

Chetty, S.; Field, L. A.

2013-12-01

The Arctic ocean's continuing decrease of summer-time ice is related to rapidly diminishing multi-year ice due to the effects of climate change. Ice911 Research aims to develop environmentally respectful materials that when deployed will increase the albedo, enhancing the formation and/preservation of multi-year ice. Small scale deployments using various materials have been done in Canada, California's Sierra Nevada Mountains and a pond in Minnesota to test the albedo performance and environmental characteristics of these materials. SWIMS is a sophisticated autonomous sensor system being developed to measure the albedo, weather, water temperature and other environmental parameters. The system (SWIMS) employs low cost, high accuracy/precision sensors, high resolution cameras, and an extreme environment command and data handling computer system using satellite and terrestrial wireless communication. The entire system is solar powered with redundant battery backup on a floating buoy platform engineered for low temperature (-40C) and high wind conditions. The system also incorporates tilt sensors, sonar based ice thickness sensors and a weather station. To keep the costs low, each SWIMS unit measures incoming and reflected radiation from the four quadrants around the buoy. This allows data from four sets of sensors, cameras, weather station, water temperature probe to be collected and transmitted by a single on-board solar powered computer. This presentation covers the technical, logistical and cost challenges in designing, developing and deploying these stations in remote, extreme environments. Image captured by camera #3 of setting sun on the SWIMS station One of the images captured by SWIMS Camera #4

Contributions of the SDR Task Network tool to Calibration and Validation of the NPOESS Preparatory Project instruments

NASA Astrophysics Data System (ADS)

Feeley, J.; Zajic, J.; Metcalf, A.; Baucom, T.

2009-12-01

The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) Calibration and Validation (Cal/Val) team is planning post-launch activities to calibrate the NPP sensors and validate Sensor Data Records (SDRs). The IPO has developed a web-based data collection and visualization tool in order to effectively collect, coordinate, and manage the calibration and validation tasks for the OMPS, ATMS, CrIS, and VIIRS instruments. This tool is accessible to the multi-institutional Cal/Val teams consisting of the Prime Contractor and Government Cal/Val leads along with the NASA NPP Mission team, and is used for mission planning and identification/resolution of conflicts between sensor activities. Visualization techniques aid in displaying task dependencies, including prerequisites and exit criteria, allowing for the identification of a critical path. This presentation will highlight how the information is collected, displayed, and used to coordinate the diverse instrument calibration/validation teams.

ISTP SBIR phase 1 Full-Sky Scanner: A feasibility study

NASA Technical Reports Server (NTRS)

1986-01-01

The objective was to develop a Full-Sky Sensor (FSS) to detect the Earth, Sun and Moon from a spinning spacecraft. The concept adopted has infinitely variable resolution. A high-speed search mode is implemented on the spacecraft. The advantages are: (1) a single sensor determines attitude parameters from Earth, Sun and Moon, thus eliminating instrument mounting errors; (2) the bias between the actual spacecraft spin axis and the intended spin axis can be determined; (3) cost is minimized; and (4) ground processing is straightforward. The FSS is a modification of an existing flight-proven sensor. Modifications to the electronics are necessary to accommodate the amplitude range and signal width range of the celestial bodies to be detected. Potential applications include ISTP missions, Multi-Spacecraft Satellite Program (MSSP), dual-spin spacecraft at any altitude, spinning spacecraft at any altitude, and orbit parameter determination for low-Earth orbits.

HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing

PubMed Central

Takahashi, Yukihiro; Sakamoto, Yuji; Kuwahara, Toshinori

2018-01-01

Although nano/microsatellites have great potential as remote sensing platforms, the spatial and spectral resolutions of an optical payload instrument are limited. In this study, a high spatial resolution multispectral sensor, the High-Precision Telescope (HPT), was developed for the RISING-2 microsatellite. The HPT has four image sensors: three in the visible region of the spectrum used for the composition of true color images, and a fourth in the near-infrared region, which employs liquid crystal tunable filter (LCTF) technology for wavelength scanning. Band-to-band image registration methods have also been developed for the HPT and implemented in the image processing procedure. The processed images were compared with other satellite images, and proven to be useful in various remote sensing applications. Thus, LCTF technology can be considered an innovative tool that is suitable for future multi/hyperspectral remote sensing by nano/microsatellites. PMID:29463022

Smart-Pixel Array Processors Based on Optimal Cellular Neural Networks for Space Sensor Applications

NASA Technical Reports Server (NTRS)

Fang, Wai-Chi; Sheu, Bing J.; Venus, Holger; Sandau, Rainer

1997-01-01

A smart-pixel cellular neural network (CNN) with hardware annealing capability, digitally programmable synaptic weights, and multisensor parallel interface has been under development for advanced space sensor applications. The smart-pixel CNN architecture is a programmable multi-dimensional array of optoelectronic neurons which are locally connected with their local neurons and associated active-pixel sensors. Integration of the neuroprocessor in each processor node of a scalable multiprocessor system offers orders-of-magnitude computing performance enhancements for on-board real-time intelligent multisensor processing and control tasks of advanced small satellites. The smart-pixel CNN operation theory, architecture, design and implementation, and system applications are investigated in detail. The VLSI (Very Large Scale Integration) implementation feasibility was illustrated by a prototype smart-pixel 5x5 neuroprocessor array chip of active dimensions 1380 micron x 746 micron in a 2-micron CMOS technology.

ISTP SBIR phase 1 Full-Sky Scanner: A feasibility study

NASA Astrophysics Data System (ADS)

1986-08-01

The objective was to develop a Full-Sky Sensor (FSS) to detect the Earth, Sun and Moon from a spinning spacecraft. The concept adopted has infinitely variable resolution. A high-speed search mode is implemented on the spacecraft. The advantages are: (1) a single sensor determines attitude parameters from Earth, Sun and Moon, thus eliminating instrument mounting errors; (2) the bias between the actual spacecraft spin axis and the intended spin axis can be determined; (3) cost is minimized; and (4) ground processing is straightforward. The FSS is a modification of an existing flight-proven sensor. Modifications to the electronics are necessary to accommodate the amplitude range and signal width range of the celestial bodies to be detected. Potential applications include ISTP missions, Multi-Spacecraft Satellite Program (MSSP), dual-spin spacecraft at any altitude, spinning spacecraft at any altitude, and orbit parameter determination for low-Earth orbits.

Spectrometric Characterization of Active Geosynchronous Satellites

NASA Astrophysics Data System (ADS)

Bedard, D.; Monin, D.; Scott, R.; Wade, G.

2012-09-01

Spectrometric characterization of artificial space objects for the purposes of Space Situational Awareness (SSA) has demonstrated great potential since this technique was first reported at this conference over a decade ago. Yet, much scientific work remains to be done before this tool can be used reliably in an operational context. For example, a detailed study of the impacts of a dynamic illumination-object-sensor geometry during individual spectrometric observations has yet to be described. A thorough understanding of this last problem is considered critical if reflectance spectroscopy will be used to characterize active low Earth orbiting spacecraft, in which the Sun-object-sensor geometry varies considerably over the course of a few seconds, or to study space debris that have uncontrolled and varying attitude. It is with the above questions in mind that two observation campaigns were conducted. The first consisted in using small-aperture telescopes to obtain multi-color photometric light curves of active geosynchronous satellites over a wide range of phase angles. The second observation campaign was conducted at the Dominion Astrophysical Observatory (DAO) using the 1.8-metre Plaskett telescope and its Cassegrain spectrograph. The objective of this experiment was to gather time-resolved spectrometric measurements of active geosynchronous satellites as a function of phase angle. This class of satellites was selected because their attitude is controlled and can be estimated to a high level of confidence. This paper presents the two observation campaigns and provides a summary of the key results of this experiment.

An airborne thematic thermal infrared and electro-optical imaging system

NASA Astrophysics Data System (ADS)

Sun, Xiuhong; Shu, Peter

2011-08-01

This paper describes an advanced Airborne Thematic Thermal InfraRed and Electro-Optical Imaging System (ATTIREOIS) and its potential applications. ATTIREOIS sensor payload consists of two sets of advanced Focal Plane Arrays (FPAs) - a broadband Thermal InfraRed Sensor (TIRS) and a four (4) band Multispectral Electro-Optical Sensor (MEOS) to approximate Landsat ETM+ bands 1,2,3,4, and 6, and LDCM bands 2,3,4,5, and 10+11. The airborne TIRS is 3-axis stabilized payload capable of providing 3D photogrammetric images with a 1,850 pixel swathwidth via pushbroom operation. MEOS has a total of 116 million simultaneous sensor counts capable of providing 3 cm spatial resolution multispectral orthophotos for continuous airborne mapping. ATTIREOIS is a complete standalone and easy-to-use portable imaging instrument for light aerial vehicle deployment. Its miniaturized backend data system operates all ATTIREOIS imaging sensor components, an INS/GPS, and an e-Gimbal™ Control Electronic Unit (ECU) with a data throughput of 300 Megabytes/sec. The backend provides advanced onboard processing, performing autonomous raw sensor imagery development, TIRS image track-recovery reconstruction, LWIR/VNIR multi-band co-registration, and photogrammetric image processing. With geometric optics and boresight calibrations, the ATTIREOIS data products are directly georeferenced with an accuracy of approximately one meter. A prototype ATTIREOIS has been configured. Its sample LWIR/EO image data will be presented. Potential applications of ATTIREOIS include: 1) Providing timely and cost-effective, precisely and directly georeferenced surface emissive and solar reflective LWIR/VNIR multispectral images via a private Google Earth Globe to enhance NASA's Earth science research capabilities; and 2) Underflight satellites to support satellite measurement calibration and validation observations.

SO2 plume height retrieval from UV satellite measurements in support to aviation control

NASA Astrophysics Data System (ADS)

van Gent, Jeroen; Brenot, Hugues; Lerot, Christophe; Theys, Nicolas; Van Roozendael, Michel

2014-05-01

The Support to Aviation Control Service (SACS), operated at our institute, uses multi-sensor UV-visible and infrared satellite measurements to provide near real-time information on volcanic ash and SO2 concentrations. In case of enhanced SO2 concentrations, notifications are send out to subscribing organisations and individuals, with details regarding the volcanic event. This information may be used by aviation control organisations to judge the risc to air traffic and provide possible alternative routing. One of the latest additions to the system is information on the altitude of SO2 plumes, based on UV measurements of the GOME-2 sensors on the platforms METOP-A and METOP-B. Further improvement of this system is ongoing. This poster shows examples of plume height retrieval from GOME-2 (METOP-A and -B) and OMI (EOS-AURA). Results are shown for a number of recent major volcanic eruptions, each with different characteristics. The applied technique to retrieve altitude information will be discussed, as well as the applicability, quality and limitations of the method.

Miniature star tracker for small remote sensing satellites

NASA Astrophysics Data System (ADS)

Cassidy, Lawrence W.; Schlom, Leslie

1995-01-01

Designers of future remote sensing spacecraft, including platforms for Mission to Planet Earth and small satellites, will be driven to provide spacecraft designs that maximize data return and minimize hardware and operating costs. The attitude determination subsystems of these spacecraft must likewise provide maximum capability and versatility at an affordable price. Hughes Danbury Optical Systems (HDOS) has developed the Model HD-1003 Miniature Star Tracker which combines high accuracy, high reliability and growth margin for `all-stellar' capability in a compact, radiation tolerant design that meets these future spacecraft needs and whose cost is competitive with horizon sensors and digital fine sum sensors. Begun in 1991, our HD-1003 development program has now entered the hardware qualification phase. This paper acquaints spacecraft designers with the design and performance capabilities of the HD- 1003 tracker. We highlight the tracker's unique features which include: (1) Very small size (165 cu. in.). (2) Low weight (7 lbs). (3) Multi-star tracking (6 stars simultaneously). (4) Eighteen arc-sec (3-sigma) accuracy. (5) Growth margin for `all-stellar' attitude reference.

Regional Glacier Mapping by Combination of Dense Optical and SAR Satellite Image Time-Series

NASA Astrophysics Data System (ADS)

Winsvold, S. H.; Kääb, A.; Andreassen, L. M.; Nuth, C.; Schellenberger, T.; van Pelt, W.

2016-12-01

Near-future dense time series from both SAR (Sentinel-1A and B) and optical satellite sensors (Landsat 8, Sentinel-2A and B) will promote new multisensory time series applications for glacier mapping. We assess such combinations of optical and SAR data among others by 1) using SAR data to supplement optical time series that suffer from heavy cloud cover (chronological gap-filling), 2) merging the two data types based on stack statistics (Std.dev, Mean, Max. etc.), or 3) better explaining glacier facies patterns in SAR data using optical satellite images. As one example, summer SAR backscatter time series have been largely unexplored and even neglected in many glaciological studies due to the high content of liquid melt water on the ice surface and its intrusion in the upper part of the snow and firn. This water content causes strong specular scattering and absorption of the radar signal, and little energy is scattered back to the SAR sensor. We find in many scenes of a Sentinel-1 time series a significant temporal backscatter difference between the glacier ice surface and the seasonal snow as it melts up glacier. Even though both surfaces have typically wet conditions, we suggest that the backscatter difference is due to different roughness lengths of the two surfaces. Higher backscatter is found on the ice surface in the ablation area compared to the firn/seasonal snow surface. We find and present also other backscatter patterns in the Sentinel-1 time series related to glacier facies and weather events. For the Ny Ålesund area, Svalbard we use Radarsat-2 time series to explore the glacier backscatter conditions in a > 5 year period, discussing distinct temporal signals from among others refreezing of the firn in late autumn, or temporal lakes. All these examples are analyzed using the above 3 methods. By this multi-temporal and multi-sensor approach we also explore and describe the possible connection between combined SAR/optical time series and surface mass balance.

Differences in the day and night longwave fluxes at satellite altitude for sun-synchronous NOAA-9 nonscanning sensors

NASA Technical Reports Server (NTRS)

Pandey, Dhirendra K.; Paden, Jack; Lee, Robert B., III

1990-01-01

The outgoing LW flux determined by using the data measured by four nonscanning sensors at satellite altitude is reported. The outgoing LW fluxes for MFOV and WFOV sensors at satellite altitude are determined by subtracting the SW fluxes from the total sensors. Results for 1985 and 1986 are discussed. The nighttime LW flux determined by using the MFOV-T channel at the satellite altitude is found to be constant from month to month within 1 W/sq m, while the LW flux from WFOV-T channel varies within 2 to 3 W sq m. The high value for the WFOV-T channel is attributed to the effects of sun-blips on the measurements involved. The main advantage of using day/night longwave flux differences at satellite altitude is that the consistencies of nonscanner sensors can be checked very quickly.

Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors.

PubMed

Yang, Aixia; Zhong, Bo; Wu, Shanlong; Liu, Qinhuo

2017-01-22

The radiometric capability of on-orbit sensors should be updated on time due to changes induced by space environmental factors and instrument aging. Some sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have onboard calibrators, which enable real-time calibration. However, most Chinese remote sensing satellite sensors lack onboard calibrators. Their radiometric calibrations have been updated once a year based on a vicarious calibration procedure, which has affected the applications of the data. Therefore, a full evaluation of the sensors' radiometric capabilities is essential before quantitative applications can be made. In this study, a comprehensive procedure for evaluating the radiometric capability of several Chinese optical satellite sensors is proposed. In this procedure, long-term radiometric stability and radiometric accuracy are the two major indicators for radiometric evaluation. The radiometric temporal stability is analyzed by the tendency of long-term top-of-atmosphere (TOA) reflectance variation; the radiometric accuracy is determined by comparison with the TOA reflectance from MODIS after spectrally matching. Three Chinese sensors including the Charge-Coupled Device (CCD) camera onboard Huan Jing 1 satellite (HJ-1), as well as the Visible and Infrared Radiometer (VIRR) and Medium-Resolution Spectral Imager (MERSI) onboard the Feng Yun 3 satellite (FY-3) are evaluated in reflective bands based on this procedure. The results are reasonable, and thus can provide reliable reference for the sensors' application, and as such will promote the development of Chinese satellite data.

Scheduling algorithm for data relay satellite optical communication based on artificial intelligent optimization

NASA Astrophysics Data System (ADS)

Zhao, Wei-hu; Zhao, Jing; Zhao, Shang-hong; Li, Yong-jun; Wang, Xiang; Dong, Yi; Dong, Chen

2013-08-01

Optical satellite communication with the advantages of broadband, large capacity and low power consuming broke the bottleneck of the traditional microwave satellite communication. The formation of the Space-based Information System with the technology of high performance optical inter-satellite communication and the realization of global seamless coverage and mobile terminal accessing are the necessary trend of the development of optical satellite communication. Considering the resources, missions and restraints of Data Relay Satellite Optical Communication System, a model of optical communication resources scheduling is established and a scheduling algorithm based on artificial intelligent optimization is put forwarded. According to the multi-relay-satellite, multi-user-satellite, multi-optical-antenna and multi-mission with several priority weights, the resources are scheduled reasonable by the operation: "Ascertain Current Mission Scheduling Time" and "Refresh Latter Mission Time-Window". The priority weight is considered as the parameter of the fitness function and the scheduling project is optimized by the Genetic Algorithm. The simulation scenarios including 3 relay satellites with 6 optical antennas, 12 user satellites and 30 missions, the simulation result reveals that the algorithm obtain satisfactory results in both efficiency and performance and resources scheduling model and the optimization algorithm are suitable in multi-relay-satellite, multi-user-satellite, and multi-optical-antenna recourses scheduling problem.

An integrated multi-sensors approach for volcanic cloud retrievals and source characterization

NASA Astrophysics Data System (ADS)

Corradini, Stefano; Merucci, Luca

2017-04-01

Volcanic eruptions are one the most important sources of natural pollution. In particular the volcanic clouds represent a severe threat for aviation safety. Worldwide the volcanic activity is monitored by using satellite and ground-based instruments working at different spectral ranges, with different spatial resolutions and sensitivities. Here the complementarity between geostationary and polar satellites and ground based measurements is exploited, in order to significantly improve the volcanic cloud detection and retrievals and to fully characterize the eruption source. The integration procedure named MACE (Multi-platform volcanic Ash Cloud Estimation), has been developed during the EU-FP7 APhoRISM project aimed to develop innovative products to support the management and mitigation of the volcanic and the seismic crisis. The proposed method integrates in a novel manner the volcanic ash retrievals at the space-time scale of typical geostationary observations using both the polar satellite estimations and in-situ measurements. On MACE the typical volcanic cloud retrievals in the thermal infrared are integrated by using a wider spectral range from visible to microwave. Moreover the volcanic cloud detection is extended in case of cloudy atmosphere or steam plumes. As example, the integrated approach is tested on different recent eruptions, occurred on Etna (Italy) in 2013 and 2015 and on Calbuco (Chile) in 2015.

Comparison of Hyperspectral and Multispectral Satellites for Discriminating Land Cover in Northern California

NASA Astrophysics Data System (ADS)

Clark, M. L.; Kilham, N. E.

2015-12-01

Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Most land-cover maps at regional to global scales are produced with remote sensing techniques applied to multispectral satellite imagery with 30-500 m pixel sizes (e.g., Landsat, MODIS). Hyperspectral, or imaging spectrometer, imagery measuring the visible to shortwave infrared regions (VSWIR) of the spectrum have shown impressive capacity to map plant species and coarser land-cover associations, yet techniques have not been widely tested at regional and greater spatial scales. The Hyperspectral Infrared Imager (HyspIRI) mission is a VSWIR hyperspectral and thermal satellite being considered for development by NASA. The goal of this study was to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping relative to multispectral satellites. We mapped FAO Land Cover Classification System (LCCS) classes over 22,500 km2 in the San Francisco Bay Area, California using 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery simulated from data acquired by NASA's AVIRIS airborne sensor. Random Forests (RF) and Multiple-Endmember Spectral Mixture Analysis (MESMA) classifiers were applied to the simulated images and accuracies were compared to those from real Landsat 8 images. The RF classifier was superior to MESMA, and multi-temporal data yielded higher accuracy than summer-only data. With RF, hyperspectral data had overall accuracy of 72.2% and 85.1% with full 20-class and reduced 12-class schemes, respectively. Multispectral imagery had lower accuracy. For example, simulated and real Landsat data had 7.5% and 4.6% lower accuracy than HyspIRI data with 12 classes, respectively. In summary, our results indicate increased mapping accuracy using HyspIRI multi-temporal imagery, particularly in discriminating different natural vegetation types, such as spectrally-mixed woodlands and forests.

Standardized Access and Processing of Multi-Source Earth Observation Time-Series Data within a Regional Data Middleware

NASA Astrophysics Data System (ADS)

Eberle, J.; Schmullius, C.

2017-12-01

Increasing archives of global satellite data present a new challenge to handle multi-source satellite data in a user-friendly way. Any user is confronted with different data formats and data access services. In addition the handling of time-series data is complex as an automated processing and execution of data processing steps is needed to supply the user with the desired product for a specific area of interest. In order to simplify the access to data archives of various satellite missions and to facilitate the subsequent processing, a regional data and processing middleware has been developed. The aim of this system is to provide standardized and web-based interfaces to multi-source time-series data for individual regions on Earth. For further use and analysis uniform data formats and data access services are provided. Interfaces to data archives of the sensor MODIS (NASA) as well as the satellites Landsat (USGS) and Sentinel (ESA) have been integrated in the middleware. Various scientific algorithms, such as the calculation of trends and breakpoints of time-series data, can be carried out on the preprocessed data on the basis of uniform data management. Jupyter Notebooks are linked to the data and further processing can be conducted directly on the server using Python and the statistical language R. In addition to accessing EO data, the middleware is also used as an intermediary between the user and external databases (e.g., Flickr, YouTube). Standardized web services as specified by OGC are provided for all tools of the middleware. Currently, the use of cloud services is being researched to bring algorithms to the data. As a thematic example, an operational monitoring of vegetation phenology is being implemented on the basis of various optical satellite data and validation data from the German Weather Service. Other examples demonstrate the monitoring of wetlands focusing on automated discovery and access of Landsat and Sentinel data for local areas.

Hydrologic Evaluation of Integrated Multi-satellite Retrivals for GPM over Nanliu River Basin in Southern China

NASA Astrophysics Data System (ADS)

Zhenqing, L.; Sheng, C.; Chaoying, H.

2017-12-01

The core satellite of Global Precipitation Measurement (GPM) mission was launched on 27 February2014 with two core sensors dual-frequency precipitation radar (DPR) and microwave imager (GMI). The algorithm of Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) blends the advantages of currently most popular satellite-based quantitative precipitation estimates (QPE) algorithms, i.e. TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH) ADDIN EN.CITE ADDIN EN.CITE.DATA , Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS).Therefore, IMERG is deemed to be the state-of-art precipitation product with high spatio-temporal resolution of 0.1°/30min. The real-time and post real-time IMERG products are now available online at https://stormpps.gsfc.nasa.gov/storm. Early studies about assessment of IMERG with gauge observations or analysis products show that the current version GPM Day-1 product IMERG demonstrates promising performance over China [1], Europe [2], and United States [3]. However, few studies are found to study the IMERG' potentials of hydrologic utility.In this study, the real-time and final run post real-time IMERG products are hydrologically evaluated with gauge analysis product as reference over Nanliu River basin (Fig.1) in Southern China since March 2014 to February 2017 with Xinanjiang model. Statistics metrics Relative Bias (RB), Root-Mean-Squared Error (RMSE), Correlation Coefficient (CC), Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), and Nash-Sutcliffe (NSCE) index will be used to compare the stream flow simulated with IMERG to the observed stream flow. This timely hydrologic evaluation is expected to offer insights into IMERG' potentials in hydrologic utility and thus provide useful feedback to the IMERG algorithm developers and the hydrologic users.

Passive Microwave Rainfall Estimates from the GPM Mission

NASA Astrophysics Data System (ADS)

Kummerow, Christian; Petkovic, Veljko

2017-04-01

The Global Precipitation Measurement (GPM) mission was launched in February 2014 as a joint mission between JAXA from Japan and NASA from the United States. GPM carries a state of the art dual-frequency precipitation radar and a multi-channel passive microwave radiometer that acts not only to enhance the radar's retrieval capability, but also as a reference for a constellation of existing satellites carrying passive microwave sensors. In March of 2016, GPM released Version 4 of its precipitation products that consists of radar, radiometer, and combined radar/radiometer products. The precipitation products from these sensors or sensor combination are consistent by design and show relatively minor differences in the mean global sense. Closer examination of the biases, however, reveals regional biases between active and passive sensors that can be directly related top the nature of the convection. By looking at cloud systems instead of individual satellite pixels, the relationship between biases and the large scale environmental state become obvious. Organized convection, which occurs more readily in regimes with large Convective Available Potential Energy (CAPE) and shear tend to drive biases in different directions than isolated convection. This is true over both land and ocean. This talk will present the latest findings and explore these discrepancies from a physical perspective in order to gain some understanding between cloud structures, information content, and retrieval differences. This analysis will be used to then drive a bigger picture of how GPM's latest results inform the Global Water and Energy budgets.

Capacity Model and Constraints Analysis for Integrated Remote Wireless Sensor and Satellite Network in Emergency Scenarios

PubMed Central

Zhang, Wei; Zhang, Gengxin; Dong, Feihong; Xie, Zhidong; Bian, Dongming

2015-01-01

This article investigates the capacity problem of an integrated remote wireless sensor and satellite network (IWSSN) in emergency scenarios. We formulate a general model to evaluate the remote sensor and satellite network capacity. Compared to most existing works for ground networks, the proposed model is time varying and space oriented. To capture the characteristics of a practical network, we sift through major capacity-impacting constraints and analyze the influence of these constraints. Specifically, we combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Our objective in analyzing these trends is to provide insights and design guidelines for optimizing the integrated remote wireless sensor and satellite network schedules. Simulation results validate the theoretical analysis of capacity trends and show the optimization opportunities of the IWSSN. PMID:26593919

Capacity Model and Constraints Analysis for Integrated Remote Wireless Sensor and Satellite Network in Emergency Scenarios.

PubMed

Zhang, Wei; Zhang, Gengxin; Dong, Feihong; Xie, Zhidong; Bian, Dongming

2015-11-17

This article investigates the capacity problem of an integrated remote wireless sensor and satellite network (IWSSN) in emergency scenarios. We formulate a general model to evaluate the remote sensor and satellite network capacity. Compared to most existing works for ground networks, the proposed model is time varying and space oriented. To capture the characteristics of a practical network, we sift through major capacity-impacting constraints and analyze the influence of these constraints. Specifically, we combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Our objective in analyzing these trends is to provide insights and design guidelines for optimizing the integrated remote wireless sensor and satellite network schedules. Simulation results validate the theoretical analysis of capacity trends and show the optimization opportunities of the IWSSN.

Concept and Design of the Hybrid Sensor Bus System for Telecommunication Satellites

NASA Astrophysics Data System (ADS)

Hurni, Andreas; Tiefenbeck, Christoph; Manhart, Markus; Heyer, Heinz-Volker; Plattner, Markus; Putzer, Philipp; Roßner, Max; Koch, Alexander W.; Furano, Gianluca; McKenzie, Iain; Lam, King

2012-08-01

The Hybrid Sensor Bus (HSB) is a system for sensor interrogation in telecommunication satellites, which will be developed in the frame of the ESA ARTES program. The main target of the HSB system is the replacement of classical point-to-point wired sensors by sensors connected on bus networks. This will save mass and reduces efforts in assembly, integration and testing (AIT). The HSB system is able to manage an electrical I2C and a fiber-optical sensor network. The system consists of an intelligent power module, an electrical and a fiber-optical interrogator module in cold redundancy. Additional features of the HSB system are its modularity and the adaptability to different satellite platforms. The implementation of a HSB system allows platform manufacturers to build a more cost efficient satellite.This paper presents the concept and the design status of the HSB system.

Satellite remote sensing of landscape freeze/thaw state dynamics for complex Topography and Fire Disturbance Areas Using multi-sensor radar and SRTM digital elevation models

NASA Technical Reports Server (NTRS)

Podest, Erika; McDonald, Kyle; Kimball, John; Randerson, James

2003-01-01

We characterize differences in radar-derived freeze/thaw state, examining transitions over complex terrain and landscape disturbance regimes. In areas of complex terrain, we explore freezekhaw dynamics related to elevation, slope aspect and varying landcover. In the burned regions, we explore the timing of seasonal freeze/thaw transition as related to the recovering landscape, relative to that of a nearby control site. We apply in situ biophysical measurements, including flux tower measurements to validate and interpret the remotely sensed parameters. A multi-scale analysis is performed relating high-resolution SAR backscatter and moderate resolution scatterometer measurements to assess trade-offs in spatial and temporal resolution in the remotely sensed fields.

Global Precipitation Measurement. Report 7; Bridging from TRMM to GPM to 3-Hourly Precipitation Estimates

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Smith, Eric A.; Adams, W. James (Editor)

2002-01-01

Historically, multi-decadal measurements of precipitation from surface-based rain gauges have been available over continents. However oceans remained largely unobserved prior to the beginning of the satellite era. Only after the launch of the first Defense Meteorological Satellite Program (DMSP) satellite in 1987 carrying a well-calibrated and multi-frequency passive microwave radiometer called Special Sensor Microwave/Imager (SSM/I) have systematic and accurate precipitation measurements over oceans become available on a regular basis; see Smith et al. (1994, 1998). Recognizing that satellite-based data are a foremost tool for measuring precipitation, NASA initiated a new research program to measure precipitation from space under its Mission to Planet Earth program in the 1990s. As a result, the Tropical Rainfall Measuring Mission (TRMM), a collaborative mission between NASA and NASDA, was launched in 1997 to measure tropical and subtropical rain. See Simpson et al. (1996) and Kummerow et al. (2000). Motivated by the success of TRMM, and recognizing the need for more comprehensive global precipitation measurements, NASA and NASDA have now planned a new mission, i.e., the Global Precipitation Measurement (GPM) mission. The primary goal of GPM is to extend TRMM's rainfall time series while making substantial improvements in precipitation observations, specifically in terms of measurement accuracy, sampling frequency, Earth coverage, and spatial resolution. This report addresses four fundamental questions related to the transition from current to future global precipitation observations as denoted by the TRMM and GPM eras, respectively.

Development of the Multi-Angle Stratospheric Aerosol Radiometer (MASTAR) Instrument

NASA Astrophysics Data System (ADS)

DeLand, M. T.; Colarco, P. R.; Kowalewski, M. G.; Gorkavyi, N.; Ramos-Izquierdo, L.

2017-12-01

Aerosol particles in the stratosphere ( 15-25 km altitude), both produced naturally and perturbed by volcanic eruptions and anthropogenic emissions, continue to be a source of significant uncertainty in the Earth's energy budget. Stratospheric aerosols can offset some of the warming effects caused by greenhouse gases. These aerosols are currently monitored using measurements from the Ozone Mapping and Profiling Suite (OMPS) Limb Profiler (LP) instrument on the Suomi NPP satellite. In order to improve the sensitivity and spatial coverage of these aerosol data, we are developing an aerosol-focused compact version of the OMPS LP sensor called Multi-Angle Stratospheric Aerosol Radiometer (MASTAR) to fly on a 3U Cubesat satellite, using a NASA Instrument Incubator Program (IIP) grant. This instrument will make limb viewing measurements of the atmosphere in multiple directions simultaneously, and uses only a few selected wavelengths to reduce size and cost. An initial prototype version has been constructed using NASA GSFC internal funding and tested in the laboratory. Current design work is targeted towards a preliminary field test in Spring 2018. We will discuss the scientific benefits of MASTAR and the status of the project.

Assessment and Prediction of Natural Hazards from Satellite Imagery

PubMed Central

Gillespie, Thomas W.; Chu, Jasmine; Frankenberg, Elizabeth; Thomas, Duncan

2013-01-01

Since 2000, there have been a number of spaceborne satellites that have changed the way we assess and predict natural hazards. These satellites are able to quantify physical geographic phenomena associated with the movements of the earth’s surface (earthquakes, mass movements), water (floods, tsunamis, storms), and fire (wildfires). Most of these satellites contain active or passive sensors that can be utilized by the scientific community for the remote sensing of natural hazards over a number of spatial and temporal scales. The most useful satellite imagery for the assessment of earthquake damage comes from high-resolution (0.6 m to 1 m pixel size) passive sensors and moderate resolution active sensors that can quantify the vertical and horizontal movement of the earth’s surface. High-resolution passive sensors have been used to successfully assess flood damage while predictive maps of flood vulnerability areas are possible based on physical variables collected from passive and active sensors. Recent moderate resolution sensors are able to provide near real time data on fires and provide quantitative data used in fire behavior models. Limitations currently exist due to atmospheric interference, pixel resolution, and revisit times. However, a number of new microsatellites and constellations of satellites will be launched in the next five years that contain increased resolution (0.5 m to 1 m pixel resolution for active sensors) and revisit times (daily ≤ 2.5 m resolution images from passive sensors) that will significantly improve our ability to assess and predict natural hazards from space. PMID:25170186

Developing the remote sensing-based water environmental model for monitoring alpine river water environment over Plateau cold zone

NASA Astrophysics Data System (ADS)

You, Y.; Wang, S.; Yang, Q.; Shen, M.; Chen, G.

2017-12-01

Alpine river water environment on the Plateau (such as Tibetan Plateau, China) is a key indicator for water security and environmental security in China. Due to the complex terrain and various surface eco-environment, it is a very difficult to monitor the water environment over the complex land surface of the plateau. The increasing availability of remote sensing techniques with appropriate spatiotemporal resolutions, broad coverage and low costs allows for effective monitoring river water environment on the Plateau, particularly in remote and inaccessible areas where are lack of in situ observations. In this study, we propose a remote sense-based monitoring model by using multi-platform remote sensing data for monitoring alpine river environment. In this study some parameterization methodologies based on satellite remote sensing data and field observations have been proposed for monitoring the water environmental parameters (including chlorophyll-a concentration (Chl-a), water turbidity (WT) or water clarity (SD), total nitrogen (TN), total phosphorus (TP), and total organic carbon (TOC)) over the china's southwest highland rivers, such as the Brahmaputra. First, because most sensors do not collect multiple observations of a target in a single pass, data from multiple orbits or acquisition times may be used, and varying atmospheric and irradiance effects must be reconciled. So based on various types of satellite data, at first we developed the techniques of multi-sensor data correction, atmospheric correction. Second, we also built the inversion spectral database derived from long-term remote sensing data and field sampling data. Then we have studied and developed a high-precision inversion model over the southwest highland river backed by inversion spectral database through using the techniques of multi-sensor remote sensing information optimization and collaboration. Third, take the middle reaches of the Brahmaputra river as the study area, we validated the key water environmental parameters and further improved the inversion model. The results indicate that our proposed water environment inversion model can be a good inversion for alpine water environmental parameters, and can improve the monitoring and warning ability for the alpine river water environment in the future.

Monitoring flood extent and area through multi-sensor, multi-temporal remote sensing: the Strymonas (Greece) river flood

NASA Astrophysics Data System (ADS)

Refice, Alberto; Tijani, Khalid; Lovergine, Francesco P.; D'Addabbo, Annarita; Nutricato, Raffaele; Morea, Alberto

2017-04-01

Satellite monitoring of flood events at high spatial and temporal resolution is considered a difficult problem, mainly due to the lack of data with sufficient acquisition frequency and timeliness. The problem is worsened by the typically cloudy weather conditions associated to floods, which obstacle the propagation of e.m. waves in the optical spectral range, forbidding acquisitions by optical sensors. This problem is not present for longer wavelengths, so that radar imaging sensors are recognized as viable solutions for long-term flood monitoring. In selected cases, however, weather conditions may remain clear for sufficient amounts of time, enabling monitoring of the evolution of flood events through long time series of satellite images, both optical and radar. In this contribution, we present a case study of long-term integrated monitoring of a flood event which affected part of the Strymonas river basin, a transboundary river with source in Bulgaria, which flows then through Greece up to the Aegean Sea. The event, which affected the floodplain close to the river mouth, started at the beginning of April 2015, due to heavy rain, and lasted for several months, with some water pools still present at the beginning of September. Due to the arid climate characterizing the area, weather conditions were cloud-free for most of the period covering the event. We collected one high-resolution, X-band, COSMO-SkyMed, 5 C-band, Sentinel-1 SAR images, and 11 optical Landsat-8 images of the area. SAR images were calibrated, speckle-filtered and precisely geocoded; optical images were radiometrically corrected to obtain ground reflectance values from which NDVI maps were derived. The images were then thresholded to obtain binary flood maps for each day. Threshold values for microwave and optical data were calibrated by comparing one SAR and one optical image acquired on the same date. Results allow to draw a multi-temporal map of the flood evolution with high temporal resolution. The extension of flooded area can also be tracked in time, allowing to envisage testing of evapotranspiration/absorption models.

Infrared Spectral Radiance Intercomparisons With Satellite and Aircraft Sensors

NASA Technical Reports Server (NTRS)

Larar, Allen M.; Zhou, Daniel K.; Liu, Xu; Smith, William L.

2014-01-01

Measurement system validation is critical for advanced satellite sounders to reach their full potential of improving observations of the Earth's atmosphere, clouds, and surface for enabling enhancements in weather prediction, climate monitoring capability, and environmental change detection. Experimental field campaigns, focusing on satellite under-flights with well-calibrated FTS sensors aboard high-altitude aircraft, are an essential part of the validation task. Airborne FTS systems can enable an independent, SI-traceable measurement system validation by directly measuring the same level-1 parameters spatially and temporally coincident with the satellite sensor of interest. Continuation of aircraft under-flights for multiple satellites during multiple field campaigns enables long-term monitoring of system performance and inter-satellite cross-validation. The NASA / NPOESS Airborne Sounder Testbed - Interferometer (NAST-I) has been a significant contributor in this area by providing coincident high spectral/spatial resolution observations of infrared spectral radiances along with independently-retrieved geophysical products for comparison with like products from satellite sensors being validated. This presentation gives an overview of benefits achieved using airborne sensors such as NAST-I utilizing examples from recent field campaigns. The methodology implemented is not only beneficial to new sensors such as the Cross-track Infrared Sounder (CrIS) flying aboard the Suomi NPP and future JPSS satellites but also of significant benefit to sensors of longer flight heritage such as the Atmospheric InfraRed Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI) on the AQUA and METOP-A platforms, respectively, to ensure data quality continuity important for climate and other applications. Infrared spectral radiance inter-comparisons are discussed with a particular focus on usage of NAST-I data for enabling inter-platform cross-validation.

A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network

PubMed Central

Tokumitsu, Masahiro; Ishida, Yoshiteru

2014-01-01

This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing. PMID:24803190

A space weather forecasting system with multiple satellites based on a self-recognizing network.

PubMed

Tokumitsu, Masahiro; Ishida, Yoshiteru

2014-05-05

This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

Adding Semantics and OPM Ontology for the Provenance of Multi-sensor Merged Climate Data Records. Now What About Reproducibility?

NASA Astrophysics Data System (ADS)

Hua, H.; Wilson, B. D.; Manipon, G.; Pan, L.; Fetzer, E.

2011-12-01

Multi-decadal climate data records are critical to studying climate variability and change. These often also require merging data from multiple instruments such as those from NASA's A-Train that contain measurements covering a wide range of atmospheric conditions and phenomena. Multi-decadal climate data record of water vapor measurements from sensors on A-Train, operational weather, and other satellites are being assembled from existing data sources, or produced from well-established methods published in peer-reviewed literature. However, the immense volume and inhomogeneity of data often requires an "exploratory computing" approach to product generation where data is processed in a variety of different ways with varying algorithms, parameters, and code changes until an acceptable intermediate product is generated. This process is repeated until a desirable final merged product can be generated. Typically the production legacy is often lost due to the complexity of processing steps that were tried along the way. The data product information associated with source data, processing methods, parameters used, intermediate product outputs, and associated materials are often hidden in each of the trials and scattered throughout the processing system(s). We will discuss methods to help users better capture and explore the production legacy of the data, metadata, ancillary files, code, and computing environment changes used during the production of these merged and multi-sensor data products. By leveraging existing semantic and provenance tools, we can capture sufficient information to enable users to track, perform faceted searches, and visualize the provenance of the products and processing lineage. We will explore if sufficient provenance information can be captured to enable science reproducibility of these climate data records.

Flower elliptical constellation of millimeter-wave radiometers for precipitating cloud monitoring at geostationary scale

NASA Astrophysics Data System (ADS)

Marzano, F. S.; Cimini, D.; Montopoli, M.; Rossi, T.; Mortari, D.; di Michele, S.; Bauer, P.

2009-04-01

Millimeter-wave observation of the atmospheric parameters is becoming an appealing goal within satellite radiometry applications. The major technological advantage of millimeter-wave (MMW) radiometers is the reduced size of the overall system, for given performances, with respect to microwave sensor. On the other hand, millimeter-wave sounding can exploit window frequencies and various gaseous absorption bands at 50/60 GHz, 118 GHz and 183 GHz. These bands can be used to estimate tropospheric temperature profiles, integrated water vapor and cloud liquid content and, using a differentia spectral mode, light rainfall and snowfall. Millimeter-wave radiometers, for given observation conditions, can also exhibit relatively small field-of-views (FOVs), of the order of some kilometers for low-Earth-orbit (LEO) satellites. However, the temporal resolution of LEO millimeter-wave system observations remains a major drawback with respect to the geostationary-Earth-orbit (GEO) satellites. An overpass every about 12 hours for a single LEO platform (conditioned to a sufficiently large swath of the scanning MMW radiometer) is usually too much when compared with the typical temporal scale variation of atmospheric fields. This feature cannot be improved by resorting to GEO platforms due to their high orbit altitude and consequent degradation of the MMW-sensor FOVs. A way to tackle this impasse is to draw our attention at the regional scale and to focus non-circular orbits over the area of interest, exploiting the concept of micro-satellite flower constellations. The Flower Constellations (FCs) is a general class of elliptical orbits which can be optimized, through genetic algorithms, in order to maximize the revisiting time and the orbital height, ensuring also a repeating ground-track. The constellation concept nicely matches the choice of mini-satellites as a baseline choice, due to their small size, weight (less than 500 kilograms) and relatively low cost (essential when deploying several identical speceborne platforms). Moreover, the micro-satellite solution clearly addresses the choice of small passive sensors with small size, low weight and power consumption, features which cannot be usually satisfied by active sensors. In this respect, MMW technology is the most compatible with the specifications and constraints of micro-satellites. In this work, we will discuss the numerical results of a feasibility study aimed at designing a Flower elliptical constellation of 3 micro-satellite millimeter-wave radiometers for pseudo-geostationary atmospheric observations over the Mediterranean region. The Flower constellation will be optimized in such a way to simulate a pseudo-geostationary observation of the Mediterranean area with an observation repetition time less than 2 hours. The mission requirements request the retrieval of thermodinamical and hydrological properties of the troposphere, specifically temperature profiles, integrated water vapor and cloud liquid content, rainfall and snowfall. Several configurations of the MMW radiometer multi-band channels will be discussed, pointing out the trade-off between performances and complexity. Integrated estimation algorithms, based on a Bayesian approache, will be illustrated to retrieve the requested atmospheric parameters, discussing its sensitivity to sensor radiometric precision and accuracy within each frequency-set configuration. After this numerical study, a review of the mission requirements and specifications will be also proposed.

Ionospheric and receiver DCB-constrained multi-GNSS single-frequency PPP integrated with MEMS inertial measurements

NASA Astrophysics Data System (ADS)

Gao, Zhouzheng; Ge, Maorong; Shen, Wenbin; Zhang, Hongping; Niu, Xiaoji

2017-11-01

Single-frequency precise point positioning (SF-PPP) is a potential precise positioning technique due to the advantages of the high accuracy in positioning after convergence and the low cost in operation. However, there are still challenges limiting its applications at present, such as the long convergence time, the low reliability, and the poor satellite availability and continuity in kinematic applications. In recent years, the achievements in the dual-frequency PPP have confirmed that its performance can be significantly enhanced by employing the slant ionospheric delay and receiver differential code bias (DCB) constraint model, and the multi-constellation Global Navigation Satellite Systems (GNSS) data. Accordingly, we introduce the slant ionospheric delay and receiver DCB constraint model, and the multi-GNSS data in SF-PPP modular together. In order to further overcome the drawbacks of SF-PPP in terms of reliability, continuity, and accuracy in the signal easily blocking environments, the inertial measurements are also adopted in this paper. Finally, we form a new approach to tightly integrate the multi-GNSS single-frequency observations and inertial measurements together to ameliorate the performance of the ionospheric delay and receiver DCB-constrained SF-PPP. In such model, the inter-system bias between each two GNSS systems, the inter-frequency bias between each two GLONASS frequencies, the hardware errors of the inertial sensors, the slant ionospheric delays of each user-satellite pair, and the receiver DCB are estimated together with other parameters in a unique Kalman filter. To demonstrate its performance, the multi-GNSS and low-cost inertial data from a land-borne experiment are analyzed. The results indicate that visible positioning improvements in terms of accuracy, continuity, and reliability can be achieved in both open-sky and complex conditions while using the proposed model in this study compared to the conventional GPS SF-PPP.

Satellite remote sensing of the ocean

NASA Technical Reports Server (NTRS)

Fu, Lee-Lueng; Liu, W. T.; Abbott, Mark R.

1990-01-01

A concise description of the principles and applications of several selected instruments that have been utilized most frequently in remote sensing of the ocean from satellites is presented. Emphasis is placed on the current progress in oceanographic applications and the outlook of the instruments in future oceanographic satellite missions is discussed. The instruments under discussion are placed into three groups: active microwave sensors, passive ocean color and infrared sensors, and passive microwave sensors.

Validation Test Report for the Automated Optical Processing System (AOPS) Version 4.10

DTIC Science & Technology

2015-08-25

Geostationary Ocean Color Imager (GOCI) sensors. AOPS enables exploitation of multiple space-borne ocean color satellite sensors to provide optical...package as well as from the Geostationary Ocean Color Imager (GOCI) sensor aboard the Communication Ocean and Meteorological Satellite (COMS) satellite... GEOstationary Coastal and Air Pollution Events (GEO-CAPE) mission and provided to NRL courtesy of Mike Ondrusek and Zhongping Lee. AOP and IOP data were

Standardized Photometric Calibrations for Panchromatic SSA Sensors

NASA Astrophysics Data System (ADS)

Castro, P.; Payne, T.; Battle, A.; Cole, Z.; Moody, J.; Gregory, S.; Dao, P.

2016-09-01

Panchromatic sensors used for Space Situational Awareness (SSA) have no standardized method for transforming the net flux detected by a CCD without a spectral filter into an exo-atmospheric magnitude in a standard magnitude system. Each SSA data provider appears to have their own method for computing the visual magnitude based on panchromatic brightness making cross-comparisons impossible. We provide a procedure in order to standardize the calibration of panchromatic sensors for the purposes of SSA. A technique based on theoretical modeling is presented that derives standard panchromatic magnitudes from the Johnson-Cousins photometric system defined by Arlo Landolt. We verify this technique using observations of Landolt standard stars and a Vega-like star to determine empirical panchromatic magnitudes and compare these to synthetically derived panchromatic magnitudes. We also investigate color terms caused by differences in the quantum efficiency (QE) between the Landolt standard system and panchromatic systems. We evaluate calibrated panchromatic satellite photometry by observing several GEO satellites and standard stars using three different sensors. We explore the effect of satellite color terms by comparing the satellite signatures. In order to remove other variables affecting the satellite photometry, two of the sensors are at the same site using different CCDs. The third sensor is geographically separate from the first two allowing for a definitive test of calibrated panchromatic satellite photometry.

Cloud Forecasting and 3-D Radiative Transfer Model Validation using Citizen-Sourced Imagery

NASA Astrophysics Data System (ADS)

Gasiewski, A. J.; Heymsfield, A.; Newman Frey, K.; Davis, R.; Rapp, J.; Bansemer, A.; Coon, T.; Folsom, R.; Pfeufer, N.; Kalloor, J.

2017-12-01

Cloud radiative feedback mechanisms are one of the largest sources of uncertainty in global climate models. Variations in local 3D cloud structure impact the interpretation of NASA CERES and MODIS data for top-of-atmosphere radiation studies over clouds. Much of this uncertainty results from lack of knowledge of cloud vertical and horizontal structure. Surface-based data on 3-D cloud structure from a multi-sensor array of low-latency ground-based cameras can be used to intercompare radiative transfer models based on MODIS and other satellite data with CERES data to improve the 3-D cloud parameterizations. Closely related, forecasting of solar insolation and associated cloud cover on time scales out to 1 hour and with spatial resolution of 100 meters is valuable for stabilizing power grids with high solar photovoltaic penetrations. Data for cloud-advection based solar insolation forecasting with requisite spatial resolution and latency needed to predict high ramp rate events obtained from a bottom-up perspective is strongly correlated with cloud-induced fluctuations. The development of grid management practices for improved integration of renewable solar energy thus also benefits from a multi-sensor camera array. The data needs for both 3D cloud radiation modelling and solar forecasting are being addressed using a network of low-cost upward-looking visible light CCD sky cameras positioned at 2 km spacing over an area of 30-60 km in size acquiring imagery on 30 second intervals. Such cameras can be manufactured in quantity and deployed by citizen volunteers at a marginal cost of 200-400 and operated unattended using existing communications infrastructure. A trial phase to understand the potential utility of up-looking multi-sensor visible imagery is underway within this NASA Citizen Science project. To develop the initial data sets necessary to optimally design a multi-sensor cloud camera array a team of 100 citizen scientists using self-owned PDA cameras is being organized to collect distributed cloud data sets suitable for MODIS-CERES cloud radiation science and solar forecasting algorithm development. A low-cost and robust sensor design suitable for large scale fabrication and long term deployment has been developed during the project prototyping phase.

Using Satellite Observations to Infer the Relationship Between Cold Pools and Subsequent Convection Development

NASA Technical Reports Server (NTRS)

Elsaesser, Gregory

2015-01-01

Cold pools are increasingly being recognized as important players in the evolution of both shallow and deep convection; hence, the incorporation of cold pool processes into a number of recently developed convective parameterizations. Unfortunately, observations serving to inform cold pool parameterization development are limited to select field programs and limited radar domains. However, a number of recent studies have noted that cold pools are often associated with arcs-lines of shallow clouds traversing 10 100 km in visible satellite imagery. Boundary layer thermodynamic perturbations are plausible at such scales, coincident with such mesoscale features. Atmospheric signatures of features at these spatial scales are potentially observable from satellites. In this presentation, we discuss recent work that uses multi-sensor, high-resolution satellite products for observing mesoscale wind vector fluctuations and boundary layer temperature depressions attributed to cold pools produced by antecedent convection. The relationship to subsequent convection as well as convective system longevity is discussed. As improvements in satellite technology occur and efforts to reduce noise in high-resolution orbital products progress, satellite pixel level (10 km) thermodynamic and dynamic (e.g. mesoscale convergence) parameters can increasingly serve as useful benchmarks for constraining convective parameterization development, including for regimes where organized convection contributes substantially to the cloud and rainfall climatology.

Soil moisture downscaling using a simple thermal based proxy

NASA Astrophysics Data System (ADS)

Peng, Jian; Loew, Alexander; Niesel, Jonathan

2016-04-01

Microwave remote sensing has been largely applied to retrieve soil moisture (SM) from active and passive sensors. The obvious advantage of microwave sensor is that SM can be obtained regardless of atmospheric conditions. However, existing global SM products only provide observations at coarse spatial resolutions, which often hamper their applications in regional hydrological studies. Therefore, various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of a simple Vegetation Temperature Condition Index (VTCI) downscaling scheme over different climates and regions. Both polar orbiting (MODIS) and geostationary (MSG SEVIRI) satellite data are used to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture in-situ measurements, spatial pattern comparison, as well as seasonal and land use analyses show that the downscaling method can significantly improve the spatial details of CCI soil moisture while maintain the accuracy of CCI soil moisture. The application of the scheme with different satellite platforms and over different regions further demonstrate the robustness and effectiveness of the proposed method. Therefore, the VTCI downscaling method has the potential to facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.

Monitoring and Modeling Crop Health and Water Use via in-situ, Airborne and Space-based Platforms

NASA Astrophysics Data System (ADS)

McCabe, M. F.

2014-12-01

The accurate retrieval of plant water use, health and function together with soil state and condition, represent key objectives in the management and monitoring of large-scale agricultural production. In regions of water shortage or stress, understanding the sustainable use of available water supplies is critical. Unfortunately, this need is all too often limited by a lack of reliable observations. Techniques that balance the demand for reliable ground-based data with the rapid retrieval of spatially distributed crop characteristics represent a needed line of research. Data from in-situ monitoring coupled with advances in satellite retrievals of key land surface variables, provide the information necessary to characterize many crop health and water use features, including evaporation, leaf-chlorophyll and other common vegetation indices. With developments in UAV and quadcopter solutions, the opportunity to bridge the spatio-temporal gap between satellite and ground based sensing now exists, along with the capacity for customized retrievals of crop information. While there remain challenges in the routine application of autonomous airborne systems, the state of current technology and sensor developments provide the capacity to explore the operational potential. While this presentation will focus on the multi-scale estimation of crop-water use and crop-health characteristics from satellite-based sensors, the retrieval of high resolution spatially distributed information from near-surface airborne and ground-based systems will also be examined.

Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment.

PubMed

Guay, Kevin C; Beck, Pieter S A; Berner, Logan T; Goetz, Scott J; Baccini, Alessandro; Buermann, Wolfgang

2014-10-01

Satellite-derived indices of photosynthetic activity are the primary data source used to study changes in global vegetation productivity over recent decades. Creating coherent, long-term records of vegetation activity from legacy satellite data sets requires addressing many factors that introduce uncertainties into vegetation index time series. We compared long-term changes in vegetation productivity at high northern latitudes (>50°N), estimated as trends in growing season NDVI derived from the most widely used global NDVI data sets. The comparison included the AVHRR-based GIMMS-NDVI version G (GIMMSg ) series, and its recent successor version 3g (GIMMS3g ), as well as the shorter NDVI records generated from the more modern sensors, SeaWiFS, SPOT-VGT, and MODIS. The data sets from the latter two sensors were provided in a form that reduces the effects of surface reflectance associated with solar and view angles. Our analysis revealed large geographic areas, totaling 40% of the study area, where all data sets indicated similar changes in vegetation productivity over their common temporal record, as well as areas where data sets showed conflicting patterns. The newer, GIMMS3g data set showed statistically significant (α = 0.05) increases in vegetation productivity (greening) in over 15% of the study area, not seen in its predecessor (GIMMSg ), whereas the reverse was rare (<3%). The latter has implications for earlier reports on changes in vegetation activity based on GIMMSg , particularly in Eurasia where greening is especially pronounced in the GIMMS3g data. Our findings highlight both critical uncertainties and areas of confidence in the assessment of ecosystem-response to climate change using satellite-derived indices of photosynthetic activity. Broader efforts are required to evaluate NDVI time series against field measurements of vegetation growth, primary productivity, recruitment, mortality, and other biological processes in order to better understand ecosystem responses to environmental change over large areas. © 2014 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Timely detection and monitoring of oil leakage by satellite optical data.

NASA Astrophysics Data System (ADS)

Grimaldi, C. S. L.; Coviello, I.; Lacava, T.; Pergola, N.; Tramutoli, V.

2009-04-01

Sea oil pollution can derive from different sources. Accidental release of oil into the oceans caused by "human errors" (tankers collisions and/or shipwrecks) or natural hazards (hurricanes, landslides, earthquakes) have remarkable ecological impact on maritime and coastal environments. Katrina Hurricane, for example, hitting oil and gas infrastructures off USA coasts caused the destruction of more than 100 platforms and the release into the sea of more than 10,000 gallons of crude oil. In order to reduce the environmental impact of such kind of technological hazards, timely detection and continuously updated information are fundamental. Satellite remote sensing can give a significant contribution in such a direction. Nowadays, SAR (Synthetic Aperture Radar) technology has been recognized as the most efficient for oil spill detection and mapping, thanks to the high spatial resolution and all-time/weather capability of the present operational sensors. Anyway, due to their current revisiting cycles, SAR systems cannot be profitably used for a rapid detection and for a continuous and near real-time monitoring of these phenomena. Until COSMO-Skymed SAR constellation, that will be able to improve SAR observational frequency, will not be fully operational, passive optical sensors on board meteorological satellites, thanks to their high temporal resolution, may represent a suitable alternative for early detection and continuous monitoring of oil spills, provided that adequate and reliable data analysis techniques exist. Recently, an innovative technique for oil spill detection and monitoring, based on the general Robust Satellite Techniques (RST) approach, has been proposed. It exploits the multi-temporal analysis of optical data acquired by both AVHRR (Advanced Very High Resolution Radiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) sensors in order to detect, automatically and timely, the presence of oil spill over the sea surface, trying to minimize the "false-detections" possibly caused by spurious effects (e.g. clouds). In this paper, preliminary results obtained applying the proposed methodology to different test-cases are shown and discussed.

Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment

PubMed Central

Guay, Kevin C; Beck, Pieter S A; Berner, Logan T; Goetz, Scott J; Baccini, Alessandro; Buermann, Wolfgang

2014-01-01

Satellite-derived indices of photosynthetic activity are the primary data source used to study changes in global vegetation productivity over recent decades. Creating coherent, long-term records of vegetation activity from legacy satellite data sets requires addressing many factors that introduce uncertainties into vegetation index time series. We compared long-term changes in vegetation productivity at high northern latitudes (>50°N), estimated as trends in growing season NDVI derived from the most widely used global NDVI data sets. The comparison included the AVHRR-based GIMMS-NDVI version G (GIMMSg) series, and its recent successor version 3g (GIMMS3g), as well as the shorter NDVI records generated from the more modern sensors, SeaWiFS, SPOT-VGT, and MODIS. The data sets from the latter two sensors were provided in a form that reduces the effects of surface reflectance associated with solar and view angles. Our analysis revealed large geographic areas, totaling 40% of the study area, where all data sets indicated similar changes in vegetation productivity over their common temporal record, as well as areas where data sets showed conflicting patterns. The newer, GIMMS3g data set showed statistically significant (α = 0.05) increases in vegetation productivity (greening) in over 15% of the study area, not seen in its predecessor (GIMMSg), whereas the reverse was rare (<3%). The latter has implications for earlier reports on changes in vegetation activity based on GIMMSg, particularly in Eurasia where greening is especially pronounced in the GIMMS3g data. Our findings highlight both critical uncertainties and areas of confidence in the assessment of ecosystem-response to climate change using satellite-derived indices of photosynthetic activity. Broader efforts are required to evaluate NDVI time series against field measurements of vegetation growth, primary productivity, recruitment, mortality, and other biological processes in order to better understand ecosystem responses to environmental change over large areas. PMID:24890614

Perception-oriented fusion of multi-sensor imagery: visible, IR, and SAR

NASA Astrophysics Data System (ADS)

Sidorchuk, D.; Volkov, V.; Gladilin, S.

2018-04-01

This paper addresses the problem of image fusion of optical (visible and thermal domain) data and radar data for the purpose of visualization. These types of images typically contain a lot of complimentary information, and their joint visualization can be useful and more convenient for human user than a set of individual images. To solve the image fusion problem we propose a novel algorithm that utilizes some peculiarities of human color perception and based on the grey-scale structural visualization. Benefits of presented algorithm are exemplified by satellite imagery.

Utilization of Precipitation and Moisture Products Derived from Satellites to Support NOAA Operational Precipitation Forecasts

NASA Astrophysics Data System (ADS)

Ferraro, R.; Zhao, L.; Kuligowski, R. J.; Kusselson, S.; Ma, L.; Kidder, S. Q.; Forsythe, J. M.; Jones, A. S.; Ebert, E. E.; Valenti, E.

2012-12-01

NOAA/NESDIS operates a constellation of polar and geostationary orbiting satellites to support weather forecasts and to monitor the climate. Additionally, NOAA utilizes satellite assets from other U.S. agencies like NASA and the Department of Defense, as well as those from other nations with similar weather and climate responsibilities (i.e., EUMETSAT and JMA). Over the past two decades, through joint efforts between U.S. and international government researchers, academic partners, and private sector corporations, a series of "value added" products have been developed to better serve the needs of weather forecasters and to exploit the full potential of precipitation and moisture products generated from these satellites. In this presentation, we will focus on two of these products - Ensemble Tropical Rainfall Potential (eTRaP) and Blended Total Precipitable Water (bTPW) - and provide examples on how they contribute to hydrometeorological forecasts. In terms of passive microwave satellite products, TPW perhaps is most widely used to support real-time forecasting applications, as it accurately depicts tropospheric water vapor and its movement. In particular, it has proven to be extremely useful in determining the location, timing, and duration of "atmospheric rivers" which contribute to and sustain flooding events. A multi-sensor approach has been developed and implemented at NESDIS in which passive microwave estimates from multiple satellites and sensors are merged to create a seamless, bTPW product that is more efficient for forecasters to use. Additionally, this product is being enhanced for utilization for television weather forecasters. Examples will be shown to illustrate the roll of atmospheric rivers and contribution to flooding events, and how the bTPW product was used to improve the forecast of these events. Heavy rains associated with land falling tropical cyclones (TC) frequently trigger floods that cause millions of dollars of damage and tremendous loss of lives. To provide observations-based forecast guidance for TC heavy rain, the Tropical Rainfall Potential (TRaP), an extrapolation forecast generated by accumulating rainfall estimates from satellites with microwave sensors as the storm is translated along the forecast track, was originally developed to predict the maximum rainfall at landfall, as well as the spatial pattern of precipitation. More recently, an enhancement has been made to combine the TRaP forecasts from multiple sensors and various start times into an ensemble (eTRaP). The ensemble approach provides not only more accurate quantitative precipitation forecasts, including more skillful maximum rainfall amount and location, it also produces probabilistic forecasts of rainfall exceeding various thresholds that decision makers can use to make critical risk assessments. Examples of the utilization and performance of eTRaP will be given in the presentation.

Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness

NASA Technical Reports Server (NTRS)

Leptoukh, Gregory; Zubko, V.; Gopalan, A.

2007-01-01

The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.

Uncued Low SNR Detection with Likelihood from Image Multi Bernoulli Filter

NASA Astrophysics Data System (ADS)

Murphy, T.; Holzinger, M.

2016-09-01

Both SSA and SDA necessitate uncued, partially informed detection and orbit determination efforts for small space objects which often produce only low strength electro-optical signatures. General frame to frame detection and tracking of objects includes methods such as moving target indicator, multiple hypothesis testing, direct track-before-detect methods, and random finite set based multiobject tracking. This paper will apply the multi-Bernoilli filter to low signal-to-noise ratio (SNR), uncued detection of space objects for space domain awareness applications. The primary novel innovation in this paper is a detailed analysis of the existing state-of-the-art likelihood functions and a likelihood function, based on a binary hypothesis, previously proposed by the authors. The algorithm is tested on electro-optical imagery obtained from a variety of sensors at Georgia Tech, including the GT-SORT 0.5m Raven-class telescope, and a twenty degree field of view high frame rate CMOS sensor. In particular, a data set of an extended pass of the Hitomi Astro-H satellite approximately 3 days after loss of communication and potential break up is examined.

Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters - Part 2: Aerosols

NASA Astrophysics Data System (ADS)

Wind, Galina; da Silva, Arlindo M.; Norris, Peter M.; Platnick, Steven; Mattoo, Shana; Levy, Robert C.

2016-07-01

The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a "simulated radiance" product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land-ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

Multi-Sensor Cloud and Aerosol Retrieval Simulator and Remote Sensing from Model Parameters . Part 2; Aerosols

NASA Technical Reports Server (NTRS)

Wind, Galina; Da Silva, Arlindo M.; Norris, Peter M.; Platnick, Steven; Mattoo, Shana; Levy, Robert C.

2016-01-01

The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a simulated radiance product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers. This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled. In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model sub grid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms. Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (MOYD04). TheMOYD04 product is of course normally produced from MOYD021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a MOYD021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source. We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

JPSS Data Product Applications for Monitoring Severe Weather and Environmental Hazards

NASA Astrophysics Data System (ADS)

Liu, X.; Zhou, L.; Divakarla, M. G.; Atkins, T.

2016-12-01

The Joint Polar Satellite System (JPSS) is the National Oceanic and Atmospheric Administration's (NOAA's) next-generation polar-orbiting operational environmental satellite system. The Suomi National Polar-orbiting Partnership (S-NPP) is the first satellite in the JPSS series. One of the JPSS supported key mission areas is to reduce the loss of life from high-impact weather events while improving efficient economies through environmental information. Combining with the sensors on other polar and geostationary satellite platforms, JPSS observations provided much enhanced capabilities for the Nation's essential products and services, including forecasting severe weather like hurricanes, potential tornadic outbreaks, and blizzards days in advance, and assessing environmental hazards such as droughts, floods, forest fires, poor air quality and harmful coastal waters. Sensor and Environmental Data Records (SDRs/EDRs) derived from S-NPP and follow-on JPSS satellites provide critical data for environmental assessments, forecasts and warnings. This paper demonstrates the use of S-NPP science data products towards analysis events of severe weather and environmental hazards, such as Paraguay Flooding, Hurricane Iselle, the record-breaking winter storm system that impacted the US East Coast area early this year, and Fort McMurray wildfire. A brief description of these examples and a detailed discussion of the winter storm event are presented in this paper. VIIRS (Visible Infrared Imaging Radiometer Suite) and ATMS (Advanced Technology Microwave Sounder) SDR/EDR products collected from multiple days of S-NPP observations are analyzed to study the progression of the winter storm and illustrate how JPSS products captured the storm system. The products used for this study included VIIRS day/night band (DNB) and true color images, ocean turbidity images, snow cover fraction, and the multi-sensor snowfall rates. Quantitative evaluation of the ATMS derived snowfall rates with the radar estimates revealed good agreement. Use of STAR JPSS product monitoring and visualization tools to evaluate these events, and applications of these tools for anomaly detection, mitigation, and science maintenance of the long-term stability of the data products is also presented in this paper.

Statistical Evaluation of VIIRS Ocean Color Products

NASA Astrophysics Data System (ADS)

Mikelsons, K.; Wang, M.; Jiang, L.

2016-02-01

Evaluation and validation of satellite-derived ocean color products is a complicated task, which often relies on precise in-situ measurements for satellite data quality assessment. However, in-situ measurements are only available in comparatively few locations, expensive, and not for all times. In the open ocean, the variability in spatial and temporal scales is longer, and the water conditions are generally more stable. We use this fact to perform extensive statistical evaluations of consistency for ocean color retrievals based on comparison of retrieved data at different times, and corresponding to various retrieval parameters. We have used the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system for ocean color product data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS). We show the results for statistical dependence of normalized water-leaving radiance spectra with respect to various parameters of retrieval geometry, such as solar- and sensor-zenith angles, as well as physical variables, such as wind speed, air pressure, ozone amount, water vapor, etc. In most cases, the results show consistent retrievals within the relevant range of retrieval parameters, showing a good performance with the MSL12 in the open ocean. The results also yield the upper bounds of solar- and sensor-zenith angles for reliable ocean color retrievals, and also show a slight increase of VIIRS-derived normalized water-leaving radiances with wind speed and water vapor concentration.

Evaluation of multi-resolution satellite sensors for assessing water quality and bottom depth of Lake Garda.

PubMed

Giardino, Claudia; Bresciani, Mariano; Cazzaniga, Ilaria; Schenk, Karin; Rieger, Patrizia; Braga, Federica; Matta, Erica; Brando, Vittorio E

2014-12-15

In this study we evaluate the capabilities of three satellite sensors for assessing water composition and bottom depth in Lake Garda, Italy. A consistent physics-based processing chain was applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat-8 Operational Land Imager (OLI) and RapidEye. Images gathered on 10 June 2014 were corrected for the atmospheric effects with the 6SV code. The computed remote sensing reflectance (Rrs) from MODIS and OLI were converted into water quality parameters by adopting a spectral inversion procedure based on a bio-optical model calibrated with optical properties of the lake. The same spectral inversion procedure was applied to RapidEye and to OLI data to map bottom depth. In situ measurements of Rrs and of concentrations of water quality parameters collected in five locations were used to evaluate the models. The bottom depth maps from OLI and RapidEye showed similar gradients up to 7 m (r = 0.72). The results indicate that: (1) the spatial and radiometric resolutions of OLI enabled mapping water constituents and bottom properties; (2) MODIS was appropriate for assessing water quality in the pelagic areas at a coarser spatial resolution; and (3) RapidEye had the capability to retrieve bottom depth at high spatial resolution. Future work should evaluate the performance of the three sensors in different bio-optical conditions.

Soil moisture observations using L-, C-, and X-band microwave radiometers

NASA Astrophysics Data System (ADS)

Bolten, John Dennis

The purpose of this thesis is to further the current understanding of soil moisture remote sensing under varying conditions using L-, C-, and X-band. Aircraft and satellite instruments are used to investigate the effects of frequency and spatial resolution on soil moisture sensitivity. The specific objectives of the research are to examine multi-scale observed and modeled microwave radiobrightness, evaluate new EOS Aqua Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperature and soil moisture retrievals, and examine future satellite-based technologies for soil moisture sensing. The cycling of Earth's water, energy and carbon is vital to understanding global climate. Over land, these processes are largely dependent on the amount of moisture within the top few centimeters of the soil. However, there are currently no methods available that can accurately characterize Earth's soil moisture layer at the spatial scales or temporal resolutions appropriate for climate modeling. The current work uses ground truth, satellite and aircraft remote sensing data from three large-scale field experiments having different land surface, topographic and climate conditions. A physically-based radiative transfer model is used to simulate the observed aircraft and satellite measurements using spatially and temporally co-located surface parameters. A robust analysis of surface heterogeneity and scaling is possible due to the combination of multiple datasets from a range of microwave frequencies and field conditions. Accurate characterization of spatial and temporal variability of soil moisture during the three field experiments is achieved through sensor calibration and algorithm validation. Comparisons of satellite observations and resampled aircraft observations are made using soil moisture from a Numerical Weather Prediction (NWP) model in order to further demonstrate a soil moisture correlation where point data was unavailable. The influence of vegetation, spatial scaling, and surface heterogeneity on multi-scale soil moisture prediction is presented. This work demonstrates that derived soil moisture using remote sensing provides a better coverage of soil moisture spatial variability than traditional in-situ sensors. Effects of spatial scale were shown to be less significant than frequency on soil moisture sensitivity. Retrievals of soil moisture using the current methods proved inadequate under some conditions; however, this study demonstrates the need for concurrent spaceborne frequencies including L-, C, and X-band.

Satellite passive remote sensing of off-shore pollutants, volume 2

NASA Technical Reports Server (NTRS)

1979-01-01

Satellite detection and monitoring of off-shore dumped pollutants, other than oil, are discussed. Summaries of satellite sensor performance in three spectral bands (visible, infrared, and microwave) are presented. The bulk of the report gives all the calculations, trade-offs and limitations of the three sensor systems. It is asserted that the problem of pollution monitoring is not a sensor problem but a problem of mathematical modeling and data processing.

Space sensors for global change

DOE Office of Scientific and Technical Information (OSTI.GOV)

Canavan, G.H.

1994-02-15

Satellite measurements should contribute to a fuller understanding of the physical processes behind the radiation budget, exchange processes, and global change. Climate engineering requires global observation for early indications of predicted effects, which puts a premium on affordable, distributed constellations of satellites with effective, affordable sensors. Defense has a requirement for continuous global surveillance for warning of aggression, which could evolve from advanced sensors and satellites in development. Many climate engineering needs match those of defense technologies.

The GPM Common Calibrated Brightness Temperature Product

NASA Technical Reports Server (NTRS)

Stout, John; Berg, Wesley; Huffman, George; Kummerow, Chris; Stocker, Erich

2005-01-01

The Global Precipitation Measurement (GPM) project will provide a core satellite carrying the GPM Microwave Imager (GMI) and will use microwave observations from a constellation of other satellites. Each partner with a satellite in the constellation will have a calibration that meets their own requirements and will decide on the format to archive their brightness temperature (Tb) record in GPM. However, GPM multi-sensor precipitation algorithms need to input intercalibrated Tb's in order to avoid differences among sensors introducing artifacts into the longer term climate record of precipitation. The GPM Common Calibrated Brightness Temperature Product is intended to address this problem by providing intercalibrated Tb data, called "Tc" data, where the "c" stands for common. The precipitation algorithms require a Tc file format that is both generic and flexible enough to accommodate the different passive microwave instruments. The format will provide detailed information on the processing history in order to allow future researchers to have a record of what was done. The format will be simple, including the main items of scan time, latitude, longitude, and Tc. It will also provide spacecraft orientation, spacecraft location, orbit, and instrument scan type (cross-track or conical). Another simplification is to store data in real numbers, avoiding the ambiguity of scaled data. Finally, units and descriptions will be provided in the product. The format is built on the concept of a swath, which is a series of scans that have common geolocation and common scan geometry. Scan geometry includes pixels per scan, sensor orientation, scan type, and incidence angles. The Tc algorithm and data format are being tested using the pre-GPM Precipitation Processing System (PPS) software to generate formats and 1/0 routines. In the test, data from SSM/I, TMI, AMSR-E, and WindSat are being processed and written as Tc products.

Assessment of Various Remote Sensing Technologies in Biomass and Nitrogen Content Estimation Using AN Agricultural Test Field

NASA Astrophysics Data System (ADS)

Näsi, R.; Viljanen, N.; Kaivosoja, J.; Hakala, T.; Pandžić, M.; Markelin, L.; Honkavaara, E.

2017-10-01

Multispectral and hyperspectral imaging is usually acquired by satellite and aircraft platforms. Recently, miniaturized hyperspectral 2D frame cameras have showed great potential to precise agriculture estimations and they are feasible to combine with lightweight platforms, such as drones. Drone platform is a flexible tool for remote sensing applications with environment and agriculture. The assessment and comparison of different platforms such as satellite, aircraft and drones with different sensors, such as hyperspectral and RGB cameras is an important task in order to understand the potential of the data provided by these equipment and to select the most appropriate according to the user applications and requirements. In this context, open and permanent test fields are very significant and helpful experimental environment, since they provide a comparative data for different platforms, sensors and users, allowing multi-temporal analyses as well. Objective of this work was to investigate the feasibility of an open permanent test field in context of precision agriculture. Satellite (Sentinel-2), aircraft and drones with hyperspectral and RGB cameras were assessed in this study to estimate biomass, using linear regression models and in-situ samples. Spectral data and 3D information were used and compared in different combinations to investigate the quality of the models. The biomass estimation accuracies using linear regression models were better than 90 % for the drone based datasets. The results showed that the use of spectral and 3D features together improved the estimation model. However, estimation of nitrogen content was less accurate with the evaluated remote sensing sensors. The open and permanent test field showed to be suitable to provide an accurate and reliable reference data for the commercial users and farmers.

Yield variability prediction by remote sensing sensors with different spatial resolution

NASA Astrophysics Data System (ADS)

Kumhálová, Jitka; Matějková, Štěpánka

2017-04-01

Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

Instrumentation and data acquisition for satellite testing in nuclear environments

NASA Astrophysics Data System (ADS)

Samyal, B.; Naumann, W.

1982-06-01

Electro-optic and magnetic-optic sensors for measurement of SGEMP-induced electromagnetic fields in and around a satellite in a UGT environment and a fiber optic data link suitable for relaying analog measurements inside the satellite to outside data collection devices are described. The electro-optic and magneto-optic sensors are based on the Pockels and Faraday Effects, respectively. The former has a sensitivity range of 10 to the second power - 10 to the 6th power v/m and the latter 1 x 10 to the minus 6th power - 34 x 10 to the minus 4th power Weber/meters square. Brief theoretical reviews and optical systems for the application of these sensors are presented. These sensors have several advantages over the conventional electrical sensors and they exhibit a great potential for measurement of electromagenetic fields. However, the effects of radiation on these sensors are uncertain and need to be assessed for any future development of these sensors. The fiber optic data link consists of several transmitter modules, located at the satellite, connected by optical fibers to the corresponding receiver modules located at a radiation safe alcove.

Small Total Dose Measurement System for SOHLA-1 and SDS-1

NASA Astrophysics Data System (ADS)

Kimoto, Yugo; Satoh, Yohei; Tachihara, Hiroshi

The Japanese Aerospace Exploration Agency (JAXA) uses monitors on board satellites to measure and record in-flight data about ionization effects in space. A compact, total-dose measurement system for small satellites—Space-Oriented Higashiosaka Leading Association -1 (SOHLA-1) and Small Demonstration-Satellite -1 (SDS-1)—was developed based on a prior system for measuring total ionizing dose effects. Especially, the sensor for SDS-1 is much smaller than the sensor for SOHLA-1. The sensor for SDS-1 is 8 mm wide × 3 mm high × 19 mm long and weighs approximately 4 g with 500 mm with its wire harness. An 8-pin Lead less Chip Carrier (LCC) RADFET and temperature sensor are arranged on it. Seven sensors are mounted on some components inside the SDS-1. The sensor for SOHLA-1 is a 14-pin Dual Inline Package (DIP) type RADFET. The four sensors, which have RADFET on a printed board covered with an aluminum chassis, are mounted both inside and outside the satellite. This report presents small total dose measurement systems and ground irradiation test results for two small satellites.

Precision analysis of autonomous orbit determination using star sensor for Beidou MEO satellite

NASA Astrophysics Data System (ADS)

Shang, Lin; Chang, Jiachao; Zhang, Jun; Li, Guotong

2018-04-01

This paper focuses on the autonomous orbit determination accuracy of Beidou MEO satellite using the onboard observations of the star sensors and infrared horizon sensor. A polynomial fitting method is proposed to calibrate the periodic error in the observation of the infrared horizon sensor, which will greatly influence the accuracy of autonomous orbit determination. Test results show that the periodic error can be eliminated using the polynomial fitting method. The User Range Error (URE) of Beidou MEO satellite is less than 2 km using the observations of the star sensors and infrared horizon sensor for autonomous orbit determination. The error of the Right Ascension of Ascending Node (RAAN) is less than 60 μrad and the observations of star sensors can be used as a spatial basis for Beidou MEO navigation constellation.

Satellite Observation Systems for Polar Climate Change Studies

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.

2012-01-01

The key observational tools for detecting large scale changes of various parameters in the polar regions have been satellite sensors. The sensors include passive and active satellite systems in the visible, infrared and microwave frequencies. The monitoring started with Tiros and Nimbus research satellites series in the 1970s but during the period, not much data was stored digitally because of limitations and cost of the needed storage systems. Continuous global data came about starting with the launch of ocean color, passive microwave, and thermal infrared sensors on board Nimbus-7 and Synthetic Aperture Radar, Radar Altimeter and Scatterometer on board SeaSat satellite both launched in 1978. The Nimbus-7 lasted longer than expected and provided about 9 years of useful data while SeaSat quit working after 3 months but provided very useful data that became the baseline for follow-up systems with similar capabilities. Over the years, many new sensors were launched, some from Japan Aeronautics and Space Agency (JAXA), some from the European Space Agency (ESA) and more recently, from RuSSia, China, Korea, Canada and India. For polar studies, among the most useful sensors has been the passive microwave sensor which provides day/night and almost all weather observation of the surface. The sensor provide sea surface temperature, precipitation, wind, water vapor and sea ice concentration data that have been very useful in monitoring the climate of the region. More than 30 years of such data are now available, starting with the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7, the Special Scanning Microwave/Imager (SSM/I) on board a Defense Meteorological Satellite Program (DMSP) and the Advanced Microwave Scanning Radiometer on board the EOS/ Aqua satellite. The techniques that have been developed to derive geophysical parameters from data provided by these and other sensors and associated instrumental and algorithm errors and validation techniques will be discussed. An important issue is the organization and storage of hundreds of terabytes of data collected by even just a few of these satellite sensors. Advances in mass storage and computer technology have made it possible to overcome many of the collection and archival problems and the availability of comprehensive satellite data sets put together by NASA's Earth Observing System project will be discussed.

Next-Generation Satellite Precipitation Products for Understanding Global and Regional Water Variability

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2011-01-01

A major challenge in understanding the space-time variability of continental water fluxes is the lack of accurate precipitation estimates over complex terrains. While satellite precipitation observations can be used to complement ground-based data to obtain improved estimates, space-based and ground-based estimates come with their own sets of uncertainties, which must be understood and characterized. Quantitative estimation of uncertainties in these products also provides a necessary foundation for merging satellite and ground-based precipitation measurements within a rigorous statistical framework. Global Precipitation Measurement (GPM) is an international satellite mission that will provide next-generation global precipitation data products for research and applications. It consists of a constellation of microwave sensors provided by NASA, JAXA, CNES, ISRO, EUMETSAT, DOD, NOAA, NPP, and JPSS. At the heart of the mission is the GPM Core Observatory provided by NASA and JAXA to be launched in 2013. The GPM Core, which will carry the first space-borne dual-frequency radar and a state-of-the-art multi-frequency radiometer, is designed to set new reference standards for precipitation measurements from space, which can then be used to unify and refine precipitation retrievals from all constellation sensors. The next-generation constellation-based satellite precipitation estimates will be characterized by intercalibrated radiometric measurements and physical-based retrievals using a common observation-derived hydrometeor database. For pre-launch algorithm development and post-launch product evaluation, NASA supports an extensive ground validation (GV) program in cooperation with domestic and international partners to improve (1) physics of remote-sensing algorithms through a series of focused field campaigns, (2) characterization of uncertainties in satellite and ground-based precipitation products over selected GV testbeds, and (3) modeling of atmospheric processes and land surface hydrology through simulation, downscaling, and data assimilation. An overview of the GPM mission, science status, and synergies with HyMex activities will be presented

High-resolution Mapping of Permafrost and Soil Freeze/thaw Dynamics in the Tibetan Plateau Based on Multi-sensor Satellite Observations

NASA Astrophysics Data System (ADS)

Zhang, W.; Yi, Y.; Yang, K.; Kimball, J. S.

2016-12-01

The Tibetan Plateau (TP) is underlain by the world's largest extent of alpine permafrost ( 2.5×106 km2), dominated by sporadic and discontinuous permafrost with strong sensitivity to climate warming. Detailed permafrost distributions and patterns in most of the TP region are still unknown due to extremely sparse in-situ observations in this region characterized by heterogeneous land cover and large temporal dynamics in surface soil moisture conditions. Therefore, satellite-based temperature and moisture observations are essential for high-resolution mapping of permafrost distribution and soil active layer changes in the TP region. In this study, we quantify the TP regional permafrost distribution at 1-km resolution using a detailed satellite data-driven soil thermal process model (GIPL2). The soil thermal model is calibrated and validated using in-situ soil temperature/moisture observations from the CAMP/Tibet field campaign (9 sites: 0-300 cm soil depth sampling from 1997-2007), a multi-scale soil moisture and temperature monitoring network in the central TP (CTP-SMTMN, 57 sites: 5-40 cm, 2010-2014) and across the whole plateau (China Meteorology Administration, 98 sites: 0-320 cm, 2000-2015). Our preliminary results using the CAMP/Tibet and CTP-SMTMN network observations indicate strong controls of surface thermal and soil moisture conditions on soil freeze/thaw dynamics, which vary greatly with underlying topography, soil texture and vegetation cover. For regional mapping of soil freeze/thaw and permafrost dynamics, we use the most recent soil moisture retrievals from the NASA SMAP (Soil Moisture Active Passive) sensor to account for the effects of temporal soil moisture dynamics on soil thermal heat transfer, with surface thermal conditions defined by MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature records. Our study provides the first 1-km map of spatial patterns and recent changes of permafrost conditions in the TP.

Multi-Modalities Sensor Science

DTIC Science & Technology

2015-02-28

enhanced multi-mode sensor science. bio -sensing, cross-discipling, multi-physics, nano-technology sailing He +46-8790 8465 1 Final Report for SOARD Project...spectroscopy, nano-technology, biophotonics and multi-physics modeling to produce adaptable bio -nanostructure enhanced multi-mode sensor science. 1...adaptable bio -nanostructure enhanced multi-mode sensor science. The accomplishments includes 1) A General Method for Designing a Radome to Enhance

Teamwork Reasoning and Multi-Satellite Missions

NASA Technical Reports Server (NTRS)

Marsella, Stacy C.; Plaunt, Christian (Technical Monitor)

2002-01-01

NASA is rapidly moving towards the use of spatially distributed multiple satellites operating in near Earth orbit and Deep Space. Effective operation of such multi-satellite constellations raises many key research issues. In particular, the satellites will be required to cooperate with each other as a team that must achieve common objectives with a high degree of autonomy from ground based operations. The multi-agent research community has made considerable progress in investigating the challenges of realizing such teamwork. In this report, we discuss some of the teamwork issues that will be faced by multi-satellite operations. The basis of the discussion is a particular proposed mission, the Magnetospheric MultiScale mission to explore Earth's magnetosphere. We describe this mission and then consider how multi-agent technologies might be applied in the design and operation of these missions. We consider the potential benefits of these technologies as well as the research challenges that will be raised in applying them to NASA multi-satellite missions. We conclude with some recommendations for future work.

Study on the multi-sensors monitoring and information fusion technology of dangerous cargo container

NASA Astrophysics Data System (ADS)

Xu, Shibo; Zhang, Shuhui; Cao, Wensheng

2017-10-01

In this paper, monitoring system of dangerous cargo container based on multi-sensors is presented. In order to improve monitoring accuracy, multi-sensors will be applied inside of dangerous cargo container. Multi-sensors information fusion solution of monitoring dangerous cargo container is put forward, and information pre-processing, the fusion algorithm of homogenous sensors and information fusion based on BP neural network are illustrated, applying multi-sensors in the field of container monitoring has some novelty.

Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors

PubMed Central

Yang, Aixia; Zhong, Bo; Wu, Shanlong; Liu, Qinhuo

2017-01-01

The radiometric capability of on-orbit sensors should be updated on time due to changes induced by space environmental factors and instrument aging. Some sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have onboard calibrators, which enable real-time calibration. However, most Chinese remote sensing satellite sensors lack onboard calibrators. Their radiometric calibrations have been updated once a year based on a vicarious calibration procedure, which has affected the applications of the data. Therefore, a full evaluation of the sensors’ radiometric capabilities is essential before quantitative applications can be made. In this study, a comprehensive procedure for evaluating the radiometric capability of several Chinese optical satellite sensors is proposed. In this procedure, long-term radiometric stability and radiometric accuracy are the two major indicators for radiometric evaluation. The radiometric temporal stability is analyzed by the tendency of long-term top-of-atmosphere (TOA) reflectance variation; the radiometric accuracy is determined by comparison with the TOA reflectance from MODIS after spectrally matching. Three Chinese sensors including the Charge-Coupled Device (CCD) camera onboard Huan Jing 1 satellite (HJ-1), as well as the Visible and Infrared Radiometer (VIRR) and Medium-Resolution Spectral Imager (MERSI) onboard the Feng Yun 3 satellite (FY-3) are evaluated in reflective bands based on this procedure. The results are reasonable, and thus can provide reliable reference for the sensors’ application, and as such will promote the development of Chinese satellite data. PMID:28117745

Estimating Snow Water Equivalent over the American River in the Sierra Nevada Basin Using Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Welch, S. C.; Kerkez, B.; Glaser, S. D.; Bales, R. C.; Rice, R.

2011-12-01

We have designed a basin-scale (>2000 km2) instrument cluster, made up of 20 local-scale (1-km footprint) wireless sensor networks (WSNs), to measure patterns of snow depth and snow water equivalent (SWE) across the main snowmelt producing area within the American River basin. Each of the 20 WSNs has on the order of 25 wireless nodes, with over 10 nodes actively sensing snow depth, and thus snow accumulation and melt. When combined with existing snow density measurements and full-basin satellite snowcover data, these measurements are designed to provide dense ground-truth snow properties for research and real-time SWE for water management. The design of this large-scale network is based on rigorous testing of previous, smaller-scale studies, permitting for the development of methods to significantly, and efficiently scale up network operations. Recent advances in WSN technology have resulted in a modularized strategy that permits rapid future network deployment. To select network and sensor locations, various sensor placement approaches were compared, including random placement, placement of WSNs in locations that have captured the historical basin mean, as well as a placement algorithm leveraging the covariance structure of the SWE distribution. We show that that the optimal network locations do not exhibit a uniform grid, but rather follow strategic patterns based on physiographic terrain parameters. Uncertainty estimates are also provided to assess the confidence in the placement approach. To ensure near-optimal coverage of the full basin, we validated each placement approach with a multi-year record of SWE derived from reconstruction of historical satellite measurements.

Analysis of Multi-Scale Radiometric Data Collected during the Cold Land Processes Experiment-1 (CLPX-1)

NASA Technical Reports Server (NTRS)

Tedesco, M.; Kim, E. J.; Gasiewski, A.; Stankov, B.

2005-01-01

Brightness temperature maps at 18.7 and 37 GHz collected at the Fraser and North Park Meso-Scale Areas during the Cold Land Processes Experiment by the NOAA Polarimetric Scanning Radiometer (PSWA) airborne sensor are analyzed. The Fraser site is mostly covered by forest with a typical snowpack depth of 1 m while North Park has no forest cover and is characterized by patches of shallow snow. We examine histograms of the brightness temperatures at 500 m resolution for both the Fraser and North Park areas. The histograms can be modelled by a log-normal distribution in the case of the Fraser MSA and by a bi-modal distribution in the case of the North Park MSA. The histograms of the brightness temperatures at coarser resolutions are also plotted to study the effects of sensor resolution on the shape of the distribution, on the values of the average brightness temperatures and standard deviations. Finally, the values of brightness temperatures obtained by re-sampling (aggregating) the data at 25 km resolution are compared with the values of the brightness temperatures collected by the Advanced Microwave Scanning Radiometer (AMSR-E) and Special Sensor Microwave/Imager (SSMII) satellite radiometers. The results show that in both areas for sensor footprint larger than 5000 m, the brightness temperatures show a flat distribution and the memory of the initial distribution is lost. The values of the brightness temperatures measured by the satellite radiometers are in good agreement with the values obtained averaging the airborne data, even if some discrepancies occur.

Instrument Noise Simulation for GRACE Follow-On

NASA Astrophysics Data System (ADS)

Darbeheshti, N.; Mueller, V.; Wegener, H.; Hewitson, M.; Heinzel, G.; Naeimi, M.; Flury, J.

2016-12-01

The quality of the temporal gravity field from GRACE Follow-On mission depends on its multi-sensor system consisting of inter-satellite ranging with microwave and laser ranging instrument, GNSS orbit tracking, accelerometry, and attitude sensing. In this presentation, the noise models for GRACE Follow-On major instruments are described and their effect on the estimation of Earth's gravity field accuracy are discussed. To do this the spectrum of the instruments noise models has been related to the spectrum of the disturbing potential of the Earth's gravity field. The instrument noise models are available to the geodesy community through GRACE Follow-On mock data challenges. The performance of gravity field recovery approaches can be tested by comparing observation residuals to the simulated instrument noises. The instrument noise models will also provide valuable insight for inter-satellite ranging configurations beyond GRACE Follow-On.

Upper Ocean Response to Hurricanes Katrina and Rita (2005) from Multi-sensor Satellites

NASA Astrophysics Data System (ADS)

Gierach, M. M.; Bulusu, S.

2006-12-01

Analysis of satellite observations and model simulations of the mixed layer provided an opportunity to assess the biological and physical effects of hurricanes Katrina and Rita (2005) in the Gulf of Mexico. Oceanic cyclonic circulation was intensified by the hurricanes' wind field, maximizing upwelling, surface cooling, and deepening the mixed layer. Two areas of maximum surface chlorophyll-a concentration and sea surface cooling were detected with peak intensities ranging from 2-3 mg m-3 and 4-6°C, along the tracks of Katrina and Rita. The temperature of the mixed layer cooled approximately 2°C and the depth of the mixed layer deepened by approximately 33-52 m. The forced deepening of the mixed layer injected nutrients into the euphotic zone, generating phytoplankton blooms 3-5 days after the passage of Katrina and Rita (2005).

On the potential of a multi-temporal AMSR-E data analysis for soil wetness monitoring

NASA Astrophysics Data System (ADS)

Lacava, T.; Coviello, I.; Calice, G.; Mazzeo, G.; Pergola, N.; Tramutoli, V.

2009-12-01

Soil moisture is a critical element for both global water and energy budget. The use of satellite remote sensing data for a characterizations of soil moisture fields at different spatial and temporal scales has more and more increased during last years, thanks also to the new generation of microwave sensors (both active and passive) orbiting around the Earth. Among microwave radiometers which could be used for soil moisture retrieval, the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR-E), is the one that, for its spectral characteristics, should give more reliable results. The possibility of collect information in five observational bands in the range 6.9 - 89 GHz (with dual polarization), make it currently, waiting for the next ESA Soil Moisture and Ocean Salinity Mission (SMOS - scheduled for September 2009) and the NASA Soil Moisture Active Passive Mission (SMAP - scheduled for 2013), the best radiometer for soil moisture retrieval. Unfortunately, after its launch (AMSR-E is flying aboard EOS-AQUA satellite since 2002) diffuse C-band Radio-Frequency Interferences (RFI) were discovered contaminating AMSR-E radiances over many areas in the world. For this reason, often X-band (less RFI affected) based soil moisture retrieval algorithms, instead of the original based on C-band, have been preferred. As a consequence, the sensitivity of such measurements is decreased, because of the lower penetrating capability of the X band wavelengths than C-band, as well as for their greater noisiness, due to their high sensitivity to the presence of vegetation in the sensor field of view. In order to face all these problems, in this work a general methodology for multi-temporal satellite data analysis (Robust Satellite Techniques, RST) will be used. RST approach, already successfully applied in the framework of hydro-meteorological risk mitigation, should help us in managing AMSR-E data for several purposes. In this paper, in particular, we have looked into the possible improvement, both in terms of quality and reliability, of AMSR-E C-band soil moisture retrieval which, a differential approach like RST, may produce. To reach this aim, a multi-temporal analysis of long-term historical series of AMSR-E C-band data has been performed. Preliminary results of such an analysis will be shown in this work and discussed also by a comparison with the standard AMSR-E soil moisture products, daily provided by NASA. In detail, achievements obtained investigating several flooding events happened in the past over different areas of the world will be presented.

Satellite, climatological, and theoretical inputs for modeling of the diurnal cycle of fire emissions

NASA Astrophysics Data System (ADS)

Hyer, E. J.; Reid, J. S.; Schmidt, C. C.; Giglio, L.; Prins, E.

2009-12-01

The diurnal cycle of fire activity is crucial for accurate simulation of atmospheric effects of fire emissions, especially at finer spatial and temporal scales. Estimating diurnal variability in emissions is also a critical problem for construction of emissions estimates from multiple sensors with variable coverage patterns. An optimal diurnal emissions estimate will use as much information as possible from satellite fire observations, compensate known biases in those observations, and use detailed theoretical models of the diurnal cycle to fill in missing information. As part of ongoing improvements to the Fire Location and Monitoring of Burning Emissions (FLAMBE) fire monitoring system, we evaluated several different methods of integrating observations with different temporal sampling. We used geostationary fire detections from WF_ABBA, fire detection data from MODIS, empirical diurnal cycles from TRMM, and simple theoretical diurnal curves based on surface heating. Our experiments integrated these data in different combinations to estimate the diurnal cycles of emissions for each location and time. Hourly emissions estimates derived using these methods were tested using an aerosol transport model. We present results of this comparison, and discuss the implications of our results for the broader problem of multi-sensor data fusion in fire emissions modeling.

The New MODIS-Terra, and the Proposed COBRA Mission: First Global Aerosol Distribution and Properties Over Land and Ocean, and Plans to Measure Global Black Carbon Absorption Over the Ocean Glint

NASA Technical Reports Server (NTRS)

Kaufman, Yoram J.; Tanre, Didier; Remer, Lorraine; Martins, Vanderlei; Schoeberl, Mark; Lau, William K. M. (Technical Monitor)

2001-01-01

The MODIS instrument was launched on the NASA Terra satellite in Dec. 1999. Since last Oct, the sensor and the aerosol algorithm reached maturity and provide global daily retrievals of aerosol optical thickness and properties. MODIS has 36 spectral channels in the visible to IR with resolution down to 250 m. This allows accurate cloud screening and multi-spectral aerosol retrievals. We derive the aerosol optical thickness over the ocean and most of the land areas, distinguishing between fine (mainly man-made aerosol) and coarse (mainly natural) aerosol particles. New methods to derive the aerosol absorption of sunlight are also being developed. These measurements are use to track different aerosol sources, transport and the radiative forcing at the top and bottom of the atmosphere. However MODIS or any present satellite sensor cannot measure absorption by Black Carbon over the oceans, a critical component in studying climate change and human health. For this purpose we propose the COBRA mission that observes the ocean at glint and off glint simultaneously measuring the spectral polarized light and deriving precisely the aerosol absorption.

A Multi-spacecraft Study of the Magnetospheric Influence on Ionospheric Chemistry - a Detailed Examination of Recent Geomagnetically Active Periods

NASA Astrophysics Data System (ADS)

Petrinec, S. M.; Chenette, D. L.; Imhof, W. L.; Baker, D. N.; Barth, C. A.; Mankoff, K. D.; Luhmann, J. G.; Mason, G. M.; Mazur, J. E.; Evans, D. S.

2001-12-01

A detailed analysis of the particle precipitation into the auroral regions during specific storm intervals is performed. The global energetic particle input to the ionosphere and lower thermosphere is provided by several monitors; namely the Polar Ionospheric X-ray Experiment (PIXIE) on board the NASA/GGS Polar satellite (for inferred electron energies greater than about 3 keV); the TED sensor system on board the NOAA/Polar Orbiting Environmental Satellite (POES) (particle energies between about 50 eV and 20 keV), and the sensor system (LICA) on board the Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX) spacecraft (for electron energies greater then 25 keV). Changes in nitric oxide (NO) densities at altitudes between 97 and 150 km during these storm intervals are studied using observations from the Student Nitric Oxide Explorer (SNOE). Solar wind observations are also used to provide important information regarding the external drivers for the magnetospheric input to the upper atmosphere. Specific intervals of examination include the recent large geomagnetic event of March 31-April 1, 2001, and other events from the most recent solar maximum.

Technology Advancements Enhance Aircraft Support of Experiment Campaigns

NASA Technical Reports Server (NTRS)

Vachon, Jacques J.

2009-01-01

For over 30 years, the NASA Airborne Science Program has provided airborne platforms for space bound instrument development, for calibrating new and existing satellite systems, and for making in situ and remote sensing measurements that can only be made from aircraft. New technologies have expanded the capabilities of aircraft that are operated for these missions. Over the last several years a new technology investment portfolio has yielded improvements that produce better measurements for the airborne science communities. These new technologies include unmanned vehicles, precision trajectory control and advanced telecommunications capabilities. We will discuss some of the benefits of these new technologies and systems which aim to provide users with more precision, lower operational costs, quicker access to data, and better management of multi aircraft and multi sensor campaigns.

Sensor-scheduling simulation of disparate sensors for Space Situational Awareness

NASA Astrophysics Data System (ADS)

Hobson, T.; Clarkson, I.

2011-09-01

The art and science of space situational awareness (SSA) has been practised and developed from the time of Sputnik. However, recent developments, such as the accelerating pace of satellite launch, the proliferation of launch capable agencies, both commercial and sovereign, and recent well-publicised collisions involving man-made space objects, has further magnified the importance of timely and accurate SSA. The United States Strategic Command (USSTRATCOM) operates the Space Surveillance Network (SSN), a global network of sensors tasked with maintaining SSA. The rapidly increasing number of resident space objects will require commensurate improvements in the SSN. Sensors are scarce resources that must be scheduled judiciously to obtain measurements of maximum utility. Improvements in sensor scheduling and fusion, can serve to reduce the number of additional sensors that may be required. Recently, Hill et al. [1] have proposed and developed a simulation environment named TASMAN (Tasking Autonomous Sensors in a Multiple Application Network) to enable testing of alternative scheduling strategies within a simulated multi-sensor, multi-target environment. TASMAN simulates a high-fidelity, hardware-in-the-loop system by running multiple machines with different roles in parallel. At present, TASMAN is limited to simulations involving electro-optic sensors. Its high fidelity is at once a feature and a limitation, since supercomputing is required to run simulations of appreciable scale. In this paper, we describe an alternative, modular and scalable SSA simulation system that can extend the work of Hill et al with reduced complexity, albeit also with reduced fidelity. The tool has been developed in MATLAB and therefore can be run on a very wide range of computing platforms. It can also make use of MATLAB’s parallel processing capabilities to obtain considerable speed-up. The speed and flexibility so obtained can be used to quickly test scheduling algorithms even with a relatively large number of space objects. We further describe an application of the tool by exploring how the relative mixture of electro-optical and radar sensors can impact the scheduling, fusion and achievable accuracy of an SSA system. By varying the mixture of sensor types, we are able to characterise the main advantages and disadvantages of each configuration.

Parameterization of gaseous constituencies concentration profiles in the planetary boundary layer as required in support of airborne and satellite borne sensors

NASA Technical Reports Server (NTRS)

Kindle, E. C.; Condon, E.; Casas, J.

1976-01-01

The research to develop the capabilities for sensing air pollution constituencies using satellite or airborne remote sensors is reported. Sensor evaluation and calibration are analyzed including data reduction. The proposed follow-on research is presented.

Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multi-sensor data

NASA Astrophysics Data System (ADS)

Pitarch, Jaime; Volpe, Gianluca; Colella, Simone; Krasemann, Hajo; Santoleri, Rosalia

2016-03-01

A 15-year (1997-2012) time series of chlorophyll a (Chl a) in the Baltic Sea, based on merged multi-sensor satellite data was analysed. Several available Chl a algorithms were sea-truthed against the largest in situ publicly available Chl a data set ever used for calibration and validation over the Baltic region. To account for the known biogeochemical heterogeneity of the Baltic, matchups were calculated for three separate areas: (1) the Skagerrak and Kattegat, (2) the central Baltic, including the Baltic Proper and the gulfs of Riga and Finland, and (3) the Gulf of Bothnia. Similarly, within the operational context of the Copernicus Marine Environment Monitoring Service (CMEMS) the three areas were also considered as a whole in the analysis. In general, statistics showed low linearity. However, a bootstrapping-like assessment did provide the means for removing the bias from the satellite observations, which were then used to compute basin average time series. Resulting climatologies confirmed that the three regions display completely different Chl a seasonal dynamics. The Gulf of Bothnia displays a single Chl a peak during spring, whereas in the Skagerrak and Kattegat the dynamics are less regular and composed of highs and lows during winter, progressing towards a small bloom in spring and a minimum in summer. In the central Baltic, Chl a follows a dynamics of a mild spring bloom followed by a much stronger bloom in summer. Surface temperature data are able to explain a variable fraction of the intensity of the summer bloom in the central Baltic.<

A Multi-Band Analytical Algorithm for Deriving Absorption and Backscattering Coefficients from Remote-Sensing Reflectance of Optically Deep Waters

NASA Technical Reports Server (NTRS)

Lee, Zhong-Ping; Carder, Kendall L.

2001-01-01

A multi-band analytical (MBA) algorithm is developed to retrieve absorption and backscattering coefficients for optically deep waters, which can be applied to data from past and current satellite sensors, as well as data from hyperspectral sensors. This MBA algorithm applies a remote-sensing reflectance model derived from the Radiative Transfer Equation, and values of absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. There are only limited empirical relationships involved in the algorithm, which implies that this MBA algorithm could be applied to a wide dynamic range of waters. Applying the algorithm to a simulated non-"Case 1" data set, which has no relation to the development of the algorithm, the percentage error for the total absorption coefficient at 440 nm a (sub 440) is approximately 12% for a range of 0.012 - 2.1 per meter (approximately 6% for a (sub 440) less than approximately 0.3 per meter), while a traditional band-ratio approach returns a percentage error of approximately 30%. Applying it to a field data set ranging from 0.025 to 2.0 per meter, the result for a (sub 440) is very close to that using a full spectrum optimization technique (9.6% difference). Compared to the optimization approach, the MBA algorithm cuts the computation time dramatically with only a small sacrifice in accuracy, making it suitable for processing large data sets such as satellite images. Significant improvements over empirical algorithms have also been achieved in retrieving the optical properties of optically deep waters.

Optical Passive Sensor Calibration for Satellite Remote Sensing and the Legacy of NOAA and NIST Cooperation

PubMed Central

Datla, Raju; Weinreb, Michael; Rice, Joseph; Johnson, B. Carol; Shirley, Eric; Cao, Changyong

2014-01-01

This paper traces the cooperative efforts of scientists at the National Oceanic and Atmospheric Administration (NOAA) and the National Institute of Standards and Technology (NIST) to improve the calibration of operational satellite sensors for remote sensing of the Earth’s land, atmosphere and oceans. It gives a chronological perspective of the NOAA satellite program and the interactions between the two agencies’ scientists to address pre-launch calibration and issues of sensor performance on orbit. The drive to improve accuracy of measurements has had a new impetus in recent years because of the need for improved weather prediction and climate monitoring. The highlights of this cooperation and strategies to achieve SI-traceability and improve accuracy for optical satellite sensor data are summarized1. PMID:26601030

Optical Passive Sensor Calibration for Satellite Remote Sensing and the Legacy of NOAA and NIST Cooperation.

PubMed

Datla, Raju; Weinreb, Michael; Rice, Joseph; Johnson, B Carol; Shirley, Eric; Cao, Changyong

2014-01-01

This paper traces the cooperative efforts of scientists at the National Oceanic and Atmospheric Administration (NOAA) and the National Institute of Standards and Technology (NIST) to improve the calibration of operational satellite sensors for remote sensing of the Earth's land, atmosphere and oceans. It gives a chronological perspective of the NOAA satellite program and the interactions between the two agencies' scientists to address pre-launch calibration and issues of sensor performance on orbit. The drive to improve accuracy of measurements has had a new impetus in recent years because of the need for improved weather prediction and climate monitoring. The highlights of this cooperation and strategies to achieve SI-traceability and improve accuracy for optical satellite sensor data are summarized.

Observing a Severe Dust Storm Event over China using Multiple Satellite Data

NASA Astrophysics Data System (ADS)

Xu, Hui; Xue, Yong; Guang, Jie; Mei, Linlu

2013-04-01

A severe dust storm (SDS) event occurred from 19 to 21 March 2010 in China, originated in western China and Mongolia and propagated into eastern/southern China, affecting human's life in a large area. As reported by National Meteorological Center of CMA (China Meteorological Administration), 16 provinces (cities) of China were hit by the dust storm (Han et al., 2012). Satellites can provide global measurements of desert dust and have particular importance in remote areas where there is a lack of in situ measurements (Carboni et al., 2012). To observe a dust, it is necessary to estimate the spatial and temporal distributions of dust aerosols. An important metric in the characterisation of aerosol distribution is the aerosol optical depth (AOD) (Adhikary et al., 2008). Satellite aerosol retrievals have improved considerably in the last decade, and numerous satellite sensors and algorithms have been generated. Reliable retrievals of dust aerosol over land were made using POLarization and Directionality of the Earth's Reflectance instrument-POLDER (Deuze et al., 2001), Moderate Resolution Imaging Spectroradiometer-MODIS (Kaufman et al., 1997; Hsu et al., 2004), Multiangle Imaging Spectroradiometer-MISR (Martonchik et al., 1998), and Cloud-aerosol Lidar and infrared pathfinder satellite observations (CALIPSO). However, intercomparison exercises (Myhre et al., 2005) have revealed that discrepancies between satellite measurements are particularly large during events of heavy aerosol loading. The reason is that different AOD retrieval algorithms make use of different instrument characteristics to obtain retrievals over bright surfaces. For MISR, POLDER and MODIS instrument, the multi-angle approaches, the polarization measurements and single-view approaches were used to retrieval AOD respectively. Combining of multi-sensor AOD data can potentially create a more consistent, reliable and complete picture of the space-time evolution of dust storms (Ehlers, 1991). In order to make use of all useful satellite data to observe one severe dust procedure, multi-sensor and multi-algorithm AOD data were combined. In this paper, the satellite instruments considered are MISR, MODIS, POLDER and CALIPSO. In addition, air pollution index (API) data were used to validate the satellite AOD data. We chose the study region with a longitude range from 76°N to 136°N and a latitude range from 15°E to 60°E. Index Terms—aerosol optical depth, dust, satellite REFERENCES Adhikary, B., Kulkarni, S., Dallura A., Tang, Y., Chai, T., Leung, L. R., Qian, Y., Chung, C. E., Ramanathan,V. and Carmichael, G. R., 2008, A regional scale chemical transport modeling of Asian aerosols with data assimilation of AOD observations using optimal interpolation technique, Atmospheric Environment, 42(37), 8600-8615. Carboni, E., Thomas, G. E., Sayer, A. M., Siddans, R., Poulsen, C. A., Grainger, R. G., Ahn, C., Antoine, D., Bevan, S., Braak, R., Brindley, H., DeSouza-Machado, S., Deuz'e, J. L., Diner, D., Ducos, F., Grey, W., Hsu, C., Kalashnikova, O. V., Kahn, R., North, P. R. J., Salustro, C., Smith, A., Tanr'e, D., Torres, O., and Veihelmann, B., 2012, Intercomparison of desert dust optical depth from satellite measurements, Atmospheric Measurement Techniques, 5, 1973-2002. Deuze', J. L., Bre'on, F. M., Devaux, C., Goloub, Herman, M., Lafrance, B., Maignan, F., Marchand, A.,Nadal, F., Perry, G., and Tanre', D., 2001, Remote sensing of aerosols over land surfaces from POLDER-ADEOS-1 polarized measurements, Journal of Geophysical Research, 106(D5), 4913-4926. Ehlers, M., 1991, Multisensor image fusion techniques in remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 46, 19-30. Han, X., Ge. C., Tao, J. H., Zhang, M. G., Zhang, R. J., 2012, Air Quality Modeling for a Strong Dust Event in East Asia in March 2010, Aerosol and Air Quality Research, 12: 615-628. Hsu, N. C., Tsay, S. C., King, M. D. and Herman, J. R., 2004, Aerosol Properties over Bright-Reflecting Source Regions, IEEE Transactions on Geoscience and Remote Sensing, 42(3), 557-569. Martonchik, J. V., Diner, D. J., Kahn, R., Ackerman, T. P., Verstraete, M. M., Pinty, B., and Gordon, H. R., 1998, Techniques for the retrieval of aerosol properties over land and ocean using multiangle imaging, IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1212-1227. Myhre, G., Stordal, F., Johnsrud, M., Diner, D. J., Geogdzhayev, I. V., Haywood, J. M., Holben, B. N., Holzer-Popp, T., Ignatov, A., Kahn, R. A., Kaufman, Y. J., Loeb, N., Martonchik, J. V., Mishchenko, M. I., Nalli, N. R., Remer, L. A., Schroedter-Homscheidt, M., Tanr'e, D., Torres, O., and Wang, M., 2005, Intercomparison of satellite retrieved aerosol optical depth over ocean during the period September 1997 to December 2000, Atmospheric Chemistry and Physics, 5, 1697-1719. Kaufman, Y.J., Tanre', D., Remer, L.A., Vermote, E.F., Chu, A., and Holben, B.N., 1997, Operationalremote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer, Journal of Geophysical Research, 102(D14), 17,051-17,067.

Detection of Neolithic Settlements in Thessaly (Greece) Through Multispectral and Hyperspectral Satellite Imagery

PubMed Central

Alexakis, Dimitrios; Sarris, Apostolos; Astaras, Theodoros; Albanakis, Konstantinos

2009-01-01

Thessaly is a low relief region in Greece where hundreds of Neolithic settlements/tells called magoules were established from the Early Neolithic period until the Bronze Age (6,000 – 3,000 BC). Multi-sensor remote sensing was applied to the study area in order to evaluate its potential to detect Neolithic settlements. Hundreds of sites were geo-referenced through systematic GPS surveying throughout the region. Data from four primary sensors were used, namely Landsat ETM, ASTER, EO1 - HYPERION and IKONOS. A range of image processing techniques were originally applied to the hyperspectral imagery in order to detect the settlements and validate the results of GPS surveying. Although specific difficulties were encountered in the automatic classification of archaeological features composed by a similar parent material with the surrounding landscape, the results of the research suggested a different response of each sensor to the detection of the Neolithic settlements, according to their spectral and spatial resolution. PMID:22399961

Detection of neolithic settlements in thessaly (Greece) through multispectral and hyperspectral satellite imagery.

PubMed

Alexakis, Dimitrios; Sarris, Apostolos; Astaras, Theodoros; Albanakis, Konstantinos

2009-01-01

Thessaly is a low relief region in Greece where hundreds of Neolithic settlements/tells called magoules were established from the Early Neolithic period until the Bronze Age (6,000 - 3,000 BC). Multi-sensor remote sensing was applied to the study area in order to evaluate its potential to detect Neolithic settlements. Hundreds of sites were geo-referenced through systematic GPS surveying throughout the region. Data from four primary sensors were used, namely Landsat ETM, ASTER, EO1 - HYPERION and IKONOS. A range of image processing techniques were originally applied to the hyperspectral imagery in order to detect the settlements and validate the results of GPS surveying. Although specific difficulties were encountered in the automatic classification of archaeological features composed by a similar parent material with the surrounding landscape, the results of the research suggested a different response of each sensor to the detection of the Neolithic settlements, according to their spectral and spatial resolution.

Remote sensing of land degradation: experiences from Latin America and the Caribbean.

PubMed

Metternicht, G; Zinck, J A; Blanco, P D; del Valle, H F

2010-01-01

Land degradation caused by deforestation, overgrazing, and inappropriate irrigation practices affects about 16% of Latin America and the Caribbean (LAC). This paper addresses issues related to the application of remote sensing technologies for the identification and mapping of land degradation features, with special attention to the LAC region. The contribution of remote sensing to mapping land degradation is analyzed from the compilation of a large set of research papers published between the 1980s and 2009, dealing with water and wind erosion, salinization, and changes of vegetation cover. The analysis undertaken found that Landsat series (MSS, TM, ETM+) are the most commonly used data source (49% of the papers report their use), followed by aerial photographs (39%), and microwave sensing (ERS, JERS-1, Radarsat) (27%). About 43% of the works analyzed use multi-scale, multi-sensor, multi-spectral approaches for mapping degraded areas, with a combination of visual interpretation and advanced image processing techniques. The use of more expensive hyperspectral and/or very high spatial resolution sensors like AVIRIS, Hyperion, SPOT-5, and IKONOS tends to be limited to small surface areas. The key issue of indicators that can directly or indirectly help recognize land degradation features in the visible, infrared, and microwave regions of the electromagnetic spectrum are discussed. Factors considered when selecting indicators for establishing land degradation baselines include, among others, the mapping scale, the spectral characteristics of the sensors, and the time of image acquisition. The validation methods used to assess the accuracy of maps produced with satellite data are discussed as well.

Satellite stratospheric aerosol measurement validation

NASA Technical Reports Server (NTRS)

Russell, P. B.; Mccormick, M. P.

1984-01-01

The validity of the stratospheric aerosol measurements made by the satellite sensors SAM II and SAGE was tested by comparing their results with each other and with results obtained by other techniques (lider, dustsonde, filter, and impactor). The latter type of comparison required the development of special techniques that convert the quantity measured by the correlative sensor (e.g. particle backscatter, number, or mass) to that measured by the satellite sensor (extinction) and quantitatively estimate the uncertainty in the conversion process. The results of both types of comparisons show agreement within the measurement and conversion uncertainties. Moreover, the satellite uncertainty is small compared to aerosol natural variability (caused by seasonal changes, volcanoes, sudden warmings, and vortex structure). It was concluded that the satellite measurements are valid.

Research on orbit prediction for solar-based calibration proper satellite

NASA Astrophysics Data System (ADS)

Chen, Xuan; Qi, Wenwen; Xu, Peng

2018-03-01

Utilizing the mathematical model of the orbit mechanics, the orbit prediction is to forecast the space target's orbit information of a certain time based on the orbit of the initial moment. The proper satellite radiometric calibration and calibration orbit prediction process are introduced briefly. On the basis of the research of the calibration space position design method and the radiative transfer model, an orbit prediction method for proper satellite radiometric calibration is proposed to select the appropriate calibration arc for the remote sensor and to predict the orbit information of the proper satellite and the remote sensor. By analyzing the orbit constraint of the proper satellite calibration, the GF-1solar synchronous orbit is chose as the proper satellite orbit in order to simulate the calibration visible durance for different satellites to be calibrated. The results of simulation and analysis provide the basis for the improvement of the radiometric calibration accuracy of the satellite remote sensor, which lays the foundation for the high precision and high frequency radiometric calibration.

The German joint research project "concepts for future gravity satellite missions"

NASA Astrophysics Data System (ADS)

Reubelt, Tilo; Sneeuw, Nico; Fichter, Walter; Müller, Jürgen

2010-05-01

Within the German joint research project "concepts for future gravity satellite missions", funded by the Geotechnologies programme of the German Federal Ministry of Education and Research, options and concepts for future satellite missions for precise (time-variable) gravity field recovery are investigated. The project team is composed of members from science and industry, bringing together experts in geodesy, satellite systems, metrology, sensor technology and control systems. The majority of team members already contributed to former gravity missions. The composition of the team guarantees that not only geodetic aspects and objectives are investigated, but also technological and financial constraints are considered. Conversely, satellite, sensor and system concepts are developed and improved in a direct exchange with geodetic and scientific claims. The project aims to develop concepts for both near and mid-term future satellite missions, taking into account e.g. advanced satellite formations and constellations, improved orbit design, innovative metrology and sensor systems and advances in satellite systems.

Sensor Web Interoperability Testbed Results Incorporating Earth Observation Satellites

NASA Technical Reports Server (NTRS)

Frye, Stuart; Mandl, Daniel J.; Alameh, Nadine; Bambacus, Myra; Cappelaere, Pat; Falke, Stefan; Derezinski, Linda; Zhao, Piesheng

2007-01-01

This paper describes an Earth Observation Sensor Web scenario based on the Open Geospatial Consortium s Sensor Web Enablement and Web Services interoperability standards. The scenario demonstrates the application of standards in describing, discovering, accessing and tasking satellites and groundbased sensor installations in a sequence of analysis activities that deliver information required by decision makers in response to national, regional or local emergencies.

Oil Spill Disasters Detection and Monitoring by RST Analysis of Optical Satellite Radiances: the Case of Deepwater Horizon Platform in the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Pergola, N.; Grimaldi, S. C.; Coviello, I.; Faruolo, M.; Lacava, T.; Tramutoli, V.

2010-12-01

Marine oil spill disasters may have devastating effects on the marine and coastal environment. For monitoring and mitigation purposes, timely detection and continuously updated information on polluted areas are required. Satellite remote sensing can give a significant contribution in such a direction. Nowadays, SAR (Synthetic Aperture Radar) technology has been recognized as the most efficient for oil spill detection and mapping, thanks to the high spatial resolution and all-time/all-weather capability of the present operational sensors. Anyway, the present SARs revisiting time does not allow for a rapid detection and a near real-time monitoring of these phenomena at global scale. Passive optical sensors, on board meteorological satellites, thanks to their high temporal resolution (from a few hours to 15 minutes, depending on the characteristics of the platform/sensor), may represent, at this moment, a suitable SAR alternative/complement for oil spill detection and monitoring. Up to now, some techniques, based on optical satellite data, have been proposed for “a posteriori” mapping of already known oil spill discharges. On the other hand, reliable satellite methods for an automatic and timely detection of oil spills, for surveillance and warning purposes, are still currently missing. Recently, an innovative technique for automatic and near real time oil spill detection and monitoring has been proposed. The technique is based on the general RST (Robust Satellite Technique) approach which exploits multi-temporal satellite records in order to obtain a former characterization of the measured signal, in terms of expected value and natural variability, providing a further identification of signal anomalies by an automatic, unsupervised change detection step. Results obtained by using AVHRR (Advanced Very High Resolution Radiometer) Thermal Infrared data, in different geographic areas and observational conditions, demonstrated excellent detection capabilities both in term of sensitivity (to the presence even of thin/old oil films) and reliability (up to zero occurrence of false alarms), mainly due to the RST invariance regardless of local and environmental conditions. Exploiting its complete independence on the specific satellite platform, RST approach has been successfully exported to the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites. In this paper, results obtained applying the proposed methodology to the recent oil spill disaster of Deepwater Horizon Platform in the gulf of Mexico, that discharged over 5 million barrels (550 million litres) in the ocean, will be shown. A dense temporal series of RST-based oil spill maps, obtained by using MODIS TIR records, are commented, emphasizing and discussing main peculiarities and specific characteristics of this event. Preliminary findings, possible residual limits and future perspectives will be also presented and discussed.

Predicting Near Real-Time Inundation Occurrence from Complimentary Satellite Microwave Brightness Temperature Observations

NASA Astrophysics Data System (ADS)

Fisher, C. K.; Pan, M.; Wood, E. F.

2017-12-01

Throughout the world, there is an increasing need for new methods and data that can aid decision makers, emergency responders and scientists in the monitoring of flood events as they happen. In many regions, it is possible to examine the extent of historical and real-time inundation occurrence from visible and infrared imagery provided by sensors such as MODIS or the Landsat TM; however, this is not possible in regions that are densely vegetated or are under persistent cloud cover. In addition, there is often a temporal mismatch between the sampling of a particular sensor and a given flood event, leading to limited observations in near real-time. As a result, there is a need for alternative methods that take full advantage of complimentary remotely sensed data sources, such as available microwave brightness temperature observations (e.g., SMAP, SMOS, AMSR2, AMSR-E, and GMI), to aid in the estimation of global flooding. The objective of this work was to develop a high-resolution mapping of inundated areas derived from multiple satellite microwave sensor observations with a daily temporal resolution. This system consists of first retrieving water fractions from complimentary microwave sensors (AMSR-2 and SMAP) which may spatially and temporally overlap in the region of interest. Using additional information in a Random Forest classifier, including high resolution topography and multiple datasets of inundated area (both historical and empirical), the resulting retrievals are spatially downscaled to derive estimates of the extent of inundation at a scale relevant to management and flood response activities ( 90m or better) instead of the relatively coarse resolution water fractions, which are limited by the microwave sensor footprints ( 5-50km). Here we present the training and validation of this method for the 2015 floods that occurred in Houston, Texas. Comparing the predicted inundation against historical occurrence maps derived from the Landsat TM record and MODIS imagery, we find good agreement for most areas and are able to provide a daily mapping given the increased temporal coverage. These results illustrate the feasibility of a near real-time inundation prediction system driven by multi-sensor satellite microwave observations, which can be extended to provide a daily estimate of global flooding.

Using satellite microwave sensors to develop climate data records

NASA Astrophysics Data System (ADS)

Ferraro, Ralph; Meng, Huan; Luo, Zhengzhao

2011-08-01

NOAA Workshop on Climate Data Records From Satellite Passive Microwave Sounders: AMSU/MHS/SSMT2; College Park, Maryland, 2-3 March 2011 ; The National Oceanic and Atmospheric Administration's (NOAA) Climate Data Record (CDR) program (http://www.ncdc.noaa.gov/cdr/index.html) is an effort to create long-term homogeneous records of satellite measurements and derived products. As part of this effort, scientists at two related projects that focus on passive microwave sensors with the goal of hydrological applications—one led by a National Environmental Satellite, Data, and Information Service/Center for Satellite Applications and Research (STAR) team and one led by the City College of New York (CCNY)—held a joint workshop with the following objectives: To allow the CDR teams to interact with satellite data and product users and other CDR developers on relevant aspects of sensor characteristics and intercalibration that will lead to mature CDRs; To provide a formal mechanism for input by subject matter experts, in particular, sensor scientists and engineers; and> To move toward a community consensus approach for NOAA microwave sounder CDRs.

Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity

NASA Astrophysics Data System (ADS)

Bach, H.; Klug, P.; Ruf, T.; Migdall, S.; Schlenz, F.; Hank, T.; Mauser, W.

2015-04-01

To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2's continuous and manifold data stream.

The GPM Ground Validation Program: Pre to Post-Launch

NASA Astrophysics Data System (ADS)

Petersen, W. A.

2014-12-01

NASA GPM Ground Validation (GV) activities have transitioned from the pre to post-launch era. Prior to launch direct validation networks and associated partner institutions were identified world-wide, covering a plethora of precipitation regimes. In the U.S. direct GV efforts focused on use of new operational products such as the NOAA Multi-Radar Multi-Sensor suite (MRMS) for TRMM validation and GPM radiometer algorithm database development. In the post-launch, MRMS products including precipitation rate, types and data quality are being routinely generated to facilitate statistical GV of instantaneous and merged GPM products. To assess precipitation column impacts on product uncertainties, range-gate to pixel-level validation of both Dual-Frequency Precipitation Radar (DPR) and GPM microwave imager data are performed using GPM Validation Network (VN) ground radar and satellite data processing software. VN software ingests quality-controlled volumetric radar datasets and geo-matches those data to coincident DPR and radiometer level-II data. When combined MRMS and VN datasets enable more comprehensive interpretation of ground-satellite estimation uncertainties. To support physical validation efforts eight (one) field campaigns have been conducted in the pre (post) launch era. The campaigns span regimes from northern latitude cold-season snow to warm tropical rain. Most recently the Integrated Precipitation and Hydrology Experiment (IPHEx) took place in the mountains of North Carolina and involved combined airborne and ground-based measurements of orographic precipitation and hydrologic processes underneath the GPM Core satellite. One more U.S. GV field campaign (OLYMPEX) is planned for late 2015 and will address cold-season precipitation estimation, process and hydrology in the orographic and oceanic domains of western Washington State. Finally, continuous direct and physical validation measurements are also being conducted at the NASA Wallops Flight Facility multi-radar, gauge and disdrometer facility located in coastal Virginia. This presentation will summarize the evolution of the NASA GPM GV program from pre to post-launch eras and highlight early evaluations of GPM satellite datasets.

[Inversion of organic matter content of the north fluvo-aquic soil based on hyperspectral and multi-spectra].

PubMed

Wang, Yan-Cang; Gu, Xiao-He; Zhu, Jin-Shan; Long, Hui-Ling; Xu, Peng; Liao, Qin-Hong

2014-01-01

The present study aims to assess the feasibility of multi-spectral data in monitoring soil organic matter content. The data source comes from hyperspectral measured under laboratory condition, and simulated multi-spectral data from the hyperspectral. According to the reflectance response functions of Landsat TM and HJ-CCD (the Environment and Disaster Reduction Small Satellites, HJ), the hyperspectra were resampled for the corresponding bands of multi-spectral sensors. The correlation between hyperspectral, simulated reflectance spectra and organic matter content was calculated, and used to extract the sensitive bands of the organic matter in the north fluvo-aquic soil. The partial least square regression (PLSR) method was used to establish experiential models to estimate soil organic matter content. Both root mean squared error (RMSE) and coefficient of the determination (R2) were introduced to test the precision and stability of the modes. Results demonstrate that compared with the hyperspectral data, the best model established by simulated multi-spectral data gives a good result for organic matter content, with R2=0.586, and RMSE=0.280. Therefore, using multi-spectral data to predict tide soil organic matter content is feasible.

Skylab

NASA Image and Video Library

1973-09-01

This Earth Resource Experiment Package (EREP) photograph of the Uncompahgre area of Colorado was electronically acquired in September of 1973 by the Multi-spectral Scarner, Skylab Experiment S192. EREP images were used to analyze the vegetation conditions and landscape characteristic of this area. Skylab's Earth sensors played the dual roles of gathering information about the planet and perfecting instruments and techniques for future satellites and manned stations. An array of six fixed cameras, another for high resolution, and the astronauts' handheld cameras photographed surface features. Other instruments, recording on magnetic tape, measured the reflectivity of plants, soils, and water. Radar measured the altitude of land and water surfaces. The sensors' objectives were to survey croplands and forests, identify soils and rock types, map natural features and urban developments, detect sediments and the spread of pollutants, study clouds and the sea, and determine the extent of snow and ice cover.

Downscaling Land Surface Temperature in an Urban Area: A Case Study for Hamburg, Germany

NASA Astrophysics Data System (ADS)

Bechtel, Benjamin; Zakšek, Klemen

2013-04-01

Land surface temperature (LST) is an important parameter for the urban radiation and heat balance and a boundary condition for the atmospheric urban heat island (UHI). The increase in urban surface temperatures compared to the surrounding area (surface urban heat island, SUHI) has been described and analysed with satellite-based measurements for several decades. Besides continuous progress in the development of new sensors, an operational monitoring is still severely limited by physical constraints regarding the spatial and temporal resolution of the satellite data. Essentially, two measurement concepts must be distinguished: Sensors on geostationary platforms have high temporal (several times per hour) and poor spatial resolution (~ 5 km) while those on low earth orbiters have high spatial (~ 100-1000 m) resolution and a long return period (one day to several weeks). To enable an observation with high temporal and spatial resolution, a downscaling scheme for LST from the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard the geostationary meteorological Meteosat 9 to spatial resolutions between 100 and 1000 m was developed and tested for Hamburg in this case study. Therefore, various predictor sets (including parameters derived from multi-temporal thermal data, NDVI, and morphological parameters) were tested. The relationship between predictors and LST was empirically calibrated in the low resolution domain and then transferred to the high resolution domain. The downscaling was validated with LST data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) for the same time. Aggregated parameters from multi-temporal thermal data (in particular annual cycle parameters and principal components) proved particularly suitable. The results for the highest resolution of 100 m showed a high explained variance (R² = 0.71) and relatively low root mean square errors (RMSE = 2.2 K). Larger predictor sets resulted in higher errors, because they tended to overfit. As expected the results were better for coarser spatial resolutions (R² = 0.80, RMSE = 1.8 K for 500 m). These results are similar or slightly better than in previous studies, although we are not aware of any study with a comparably large downscaling factor. A considerable percentage of the error is systematic due to the different viewing geometry of the sensors (the high resolution LST was overestimated about 1.3 K). The study shows that downscaling of SEVIRI LST is possible up to a resolution of 100 m for urban areas and that multi-temporal thermal data are particularly suitable as predictors.

MEMS for pico- to micro-satellites

NASA Astrophysics Data System (ADS)

Shea, H. R.

2009-02-01

MEMS sensors, actuators, and sub-systems can enable an important reduction in the size and mass of spacecrafts, first by replacing larger and heavier components, then by replacing entire subsystems, and finally by enabling the microfabrication of highly integrated picosats. Very small satellites (1 to 100 kg) stand to benefit the most from MEMS technologies. These small satellites are typically used for science or technology demonstration missions, with higher risk tolerance than multi-ton telecommunication satellites. While MEMS are playing a growing role on Earth in safety-critical applications, in the harsh and remote environment of space, reliability is still the crucial issue, and the absence of an accepted qualification methodology is holding back MEMS from wider use. An overview is given of the range of MEMS applications in space. An effective way to prove that MEMS can operate reliably in space is to use them in space: we illustrate how Cubesats (1 kg, 1 liter, cubic satellites in a standardized format to reduce launch costs) can serve as low-cost vectors for MEMS technology demonstration in space. The Cubesat SwissCube developed in Switzerland is used as one example of a rapid way to fly new microtechnologies, and also as an example of a spacecraft whose performance is only possible thanks to MEMS.

Convolutional Neural Network for Multi-Source Deep Learning Crop Classification in Ukraine

NASA Astrophysics Data System (ADS)

Lavreniuk, M. S.

2016-12-01

Land cover and crop type maps are one of the most essential inputs when dealing with environmental and agriculture monitoring tasks [1]. During long time neural network (NN) approach was one of the most efficient and popular approach for most applications, including crop classification using remote sensing data, with high an overall accuracy (OA) [2]. In the last years the most popular and efficient method for multi-sensor and multi-temporal land cover classification is convolution neural networks (CNNs). Taking into account presence clouds in optical data, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of optical imagery from Landsat-8 satellite. After missing data restoration, optical data from Landsat-8 was merged with Sentinel-1A radar data for better crop types discrimination [3]. An ensemble of CNNs is proposed for multi-temporal satellite images supervised classification. Each CNN in the corresponding ensemble is a 1-d CNN with 4 layers implemented using the Google's library TensorFlow. The efficiency of the proposed approach was tested on a time-series of Landsat-8 and Sentinel-1A images over the JECAM test site (Kyiv region) in Ukraine in 2015. Overall classification accuracy for ensemble of CNNs was 93.5% that outperformed an ensemble of multi-layer perceptrons (MLPs) by +0.8% and allowed us to better discriminate summer crops, in particular maize and soybeans. For 2016 we would like to validate this method using Sentinel-1 and Sentinel-2 data for Ukraine territory within ESA project on country level demonstration Sen2Agri. 1. A. Kolotii et al., "Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine," The Int. Arch. of Photogram., Rem. Sens. and Spatial Inform. Scie., vol. 40, no. 7, pp. 39-44, 2015. 2. F. Waldner et al., "Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity," Int. Journal of Rem. Sens. vol. 37, no. 14, pp 3196-3231, 2016. 3. S. Skakun et al., "Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine," IEEE Journal of Selected Topics in Applied Earth Observ. and Rem. Sens., 2015, DOI: 10.1109/JSTARS.2015.2454297.

Assessment of Satellite-Derived Surface Reflectances by NASA's CAR Airborne Radiometer over Railroad Valley, Nevada

NASA Technical Reports Server (NTRS)

Kharbouche, Said; Muller, Jan-Peter; Gatebe, Charles K.; Scanlon, Tracy; Banks, Andrew C.

2017-01-01

CAR (Cloud Absorption Radiometer) is a multi-angular and multi-spectral airborne radiometer instrument, whose radiometric and geometric characteristics are well calibrated and adjusted before and after each flight campaign. CAR was built by NASA (National Aeronautics and Space Administration) in 1984. On 16 May 2008, a CAR flight campaign took place over the well-known calibration and validation site of Railroad Valley in Nevada (38.504 deg N, 115.692 deg W).The campaign coincided with the overpasses of several key EO (Earth Observation) satellites such as Landsat-7, Envisat and Terra. Thus, there are nearly simultaneous measurements from these satellites and the CAR airborne sensor over the same calibration site. The CAR spectral bands are close to those of most EO satellites. CAR has the ability to cover the whole range of azimuth view angles and a variety of zenith angles depending on altitude and, as a consequence, the biases seen between satellite and CAR measurements due to both unmatched spectral bands and unmatched angles can be significantly reduced. A comparison is presented here between CARs land surface reflectance (BRF or Bidirectional Reflectance Factor) with those derived from Terra/MODIS (MOD09 and MAIAC), Terra/MISR, Envisat/MERIS and Landsat-7. In this study, we utilized CAR data from low altitude flights (approx. 180 m above the surface) in order to minimize the effects of the atmosphere on these measurements and then obtain a valuable ground-truth data set of surface reflectance. Furthermore, this study shows that differences between measurements caused by surface heterogeneity can be tolerated, thanks to the high homogeneity of the study site on the one hand, and on the other hand, to the spatial sampling and the large number of CAR samples. These results demonstrate that satellite BRF measurements over this site are in good agreement with CAR with variable biases across different spectral bands. This is most likely due to residual aerosol effects in the EO derived reflectances.

Rayleigh radiance computations for satellite remote sensing: accounting for the effect of sensor spectral response function.

PubMed

Wang, Menghua

2016-05-30

To understand and assess the effect of the sensor spectral response function (SRF) on the accuracy of the top of the atmosphere (TOA) Rayleigh-scattering radiance computation, new TOA Rayleigh radiance lookup tables (LUTs) over global oceans and inland waters have been generated. The new Rayleigh LUTs include spectral coverage of 335-2555 nm, all possible solar-sensor geometries, and surface wind speeds of 0-30 m/s. Using the new Rayleigh LUTs, the sensor SRF effect on the accuracy of the TOA Rayleigh radiance computation has been evaluated for spectral bands of the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite and the Joint Polar Satellite System (JPSS)-1, showing some important uncertainties for VIIRS-SNPP particularly for large solar- and/or sensor-zenith angles as well as for large Rayleigh optical thicknesses (i.e., short wavelengths) and bands with broad spectral bandwidths. To accurately account for the sensor SRF effect, a new correction algorithm has been developed for VIIRS spectral bands, which improves the TOA Rayleigh radiance accuracy to ~0.01% even for the large solar-zenith angles of 70°-80°, compared with the error of ~0.7% without applying the correction for the VIIRS-SNPP 410 nm band. The same methodology that accounts for the sensor SRF effect on the Rayleigh radiance computation can be used for other satellite sensors. In addition, with the new Rayleigh LUTs, the effect of surface atmospheric pressure variation on the TOA Rayleigh radiance computation can be calculated precisely, and no specific atmospheric pressure correction algorithm is needed. There are some other important applications and advantages to using the new Rayleigh LUTs for satellite remote sensing, including an efficient and accurate TOA Rayleigh radiance computation for hyperspectral satellite remote sensing, detector-based TOA Rayleigh radiance computation, Rayleigh radiance calculations for high altitude lakes, and the same Rayleigh LUTs are applicable for all satellite sensors over the global ocean and inland waters. The new Rayleigh LUTs have been implemented in the VIIRS-SNPP ocean color data processing for routine production of global ocean color and inland water products.

Experimenting with an Evolving Ground/Space-based Software Architecture to Enable Sensor Webs

NASA Technical Reports Server (NTRS)

mandl, Daniel; Frye, Stuart

2005-01-01

A series of ongoing experiments are being conducted at the NASA Goddard Space Flight Center to explore integrated ground and space-based software architectures enabling sensor webs. A sensor web, as defined by Steve Talabac at NASA Goddard Space Flight Center(GSFC), is a coherent set of distributed nodes interconnected by a communications fabric, that collectively behave as a single, dynamically adaptive, observing system. The nodes can be comprised of satellites, ground instruments, computing nodes etc. Sensor web capability requires autonomous management of constellation resources. This becomes progressively more important as more and more satellites share resource, such as communication channels and ground station,s while automatically coordinating their activities. There have been five ongoing activities which include an effort to standardize a set of middleware. This paper will describe one set of activities using the Earth Observing 1 satellite, which used a variety of ground and flight software along with other satellites and ground sensors to prototype a sensor web. This activity allowed us to explore where the difficulties that occur in the assembly of sensor webs given today s technology. We will present an overview of the software system architecture, some key experiments and lessons learned to facilitate better sensor webs in the future.

Sensor lighting considerations for earth observatory satellite missions

NASA Technical Reports Server (NTRS)

Cooley, J. L.

1972-01-01

Facets of sensor lighting conditions for Earth observatory satellite missions are considered. Assuming onboard sensors of a given width viewing perpendicular to the subsatellite ground track along sun-synchronous orbits with various nodes, the ground trace of the ends of the sensor coverage were found, as well as the variation in solar illumination on the ground across the line covered by the sensor during the day for any point along the orbit. The changes with season and variation during the year were also found.

Comparison of Hyperspectral and Multispectral Satellites for Forest Alliance Classification in the San Francisco Bay Area

NASA Astrophysics Data System (ADS)

Clark, M. L.

2016-12-01

The goal of this study was to assess multi-temporal, Hyperspectral Infrared Imager (HyspIRI) satellite imagery for improved forest class mapping relative to multispectral satellites. The study area was the western San Francisco Bay Area, California and forest alliances (e.g., forest communities defined by dominant or co-dominant trees) were defined using the U.S. National Vegetation Classification System. Simulated 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery were processed from image data acquired by NASA's AVIRIS airborne sensor in year 2015, with summer and multi-temporal (spring, summer, fall) data analyzed separately. HyspIRI reflectance was used to generate a suite of hyperspectral metrics that targeted key spectral features related to chemical and structural properties. The Random Forests classifier was applied to the simulated images and overall accuracies (OA) were compared to those from real Landsat 8 images. For each image group, broad land cover (e.g., Needle-leaf Trees, Broad-leaf Trees, Annual agriculture, Herbaceous, Built-up) was classified first, followed by a finer-detail forest alliance classification for pixels mapped as closed-canopy forest. There were 5 needle-leaf tree alliances and 16 broad-leaf tree alliances, including 7 Quercus (oak) alliance types. No forest alliance classification exceeded 50% OA, indicating that there was broad spectral similarity among alliances, most of which were not spectrally pure but rather a mix of tree species. In general, needle-leaf (Pine, Redwood, Douglas Fir) alliances had better class accuracies than broad-leaf alliances (Oaks, Madrone, Bay Laurel, Buckeye, etc). Multi-temporal data classifications all had 5-6% greater OA than with comparable summer data. For simulated data, HyspIRI metrics had 4-5% greater OA than Landsat 8 and Sentinel-2 multispectral imagery and 3-4% greater OA than HyspIRI reflectance. Finally, HyspIRI metrics had 8% greater OA than real Landsat 8 imagery. In conclusion, forest alliance classification was found to be a difficult remote sensing application with moderate resolution (30 m) satellite imagery; however, of the data tested, HyspIRI spectral metrics had the best performance relative to multispectral satellites.

Sea ice-atmosphere interaction: Application of multispectral satellite data in polar surface energy flux estimates

NASA Technical Reports Server (NTRS)

Steffen, K.; Schweiger, A.; Maslanik, J.; Key, J.; Weaver, R.; Barry, R.

1990-01-01

The application of multi-spectral satellite data to estimate polar surface energy fluxes is addressed. To what accuracy and over which geographic areas large scale energy budgets can be estimated are investigated based upon a combination of available remote sensing and climatological data sets. The general approach was to: (1) formulate parameterization schemes for the appropriate sea ice energy budget terms based upon the remotely sensed and/or in-situ data sets; (2) conduct sensitivity analyses using as input both natural variability (observed data in regional case studies) and theoretical variability based upon energy flux model concepts; (3) assess the applicability of these parameterization schemes to both regional and basin wide energy balance estimates using remote sensing data sets; and (4) assemble multi-spectral, multi-sensor data sets for at least two regions of the Arctic Basin and possibly one region of the Antarctic. The type of data needed for a basin-wide assessment is described and the temporal coverage of these data sets are determined by data availability and need as defined by parameterization scheme. The titles of the subjects are as follows: (1) Heat flux calculations from SSM/I and LANDSAT data in the Bering Sea; (2) Energy flux estimation using passive microwave data; (3) Fetch and stability sensitivity estimates of turbulent heat flux; and (4) Surface temperature algorithm.

TRIO (Triplet Ionospheric Observatory) Mission

NASA Astrophysics Data System (ADS)

Lee, D.; Seon, J.; Jin, H.; Kim, K.; Lee, J.; Jang, M.; Pak, S.; Kim, K.; Lin, R. P.; Parks, G. K.; Halekas, J. S.; Larson, D. E.; Eastwood, J. P.; Roelof, E. C.; Horbury, T. S.

2009-12-01

Triplets of identical cubesats will be built to carry out the following scientific objectives: i) multi-observations of ionospheric ENA (Energetic Neutral Atom) imaging, ii) ionospheric signature of suprathermal electrons and ions associated with auroral acceleration as well as electron microbursts, and iii) complementary measurements of magnetic fields for particle data. Each satellite, a cubesat for ion, neutral, electron, and magnetic fields (CINEMA), is equipped with a suprathermal electron, ion, neutral (STEIN) instrument and a 3-axis magnetometer of magnetoresistive sensors. TRIO is developed by three institutes: i) two CINEMA by Kyung Hee University (KHU) under the WCU program, ii) one CINEMA by UC Berkeley under the NSF support, and iii) three magnetometers by Imperial College, respectively. Multi-spacecraft observations in the STEIN instruments will provide i) stereo ENA imaging with a wide angle in local times, which are sensitive to the evolution of ring current phase space distributions, ii) suprathermal electron measurements with narrow spacings, which reveal the differential signature of accelerated electrons driven by Alfven waves and/or double layer formation in the ionosphere between the acceleration region and the aurora, and iii) suprathermal ion precipitation when the storm-time ring current appears. In addition, multi-spacecraft magnetic field measurements in low earth orbits will allow the tracking of the phase fronts of ULF waves, FTEs, and quasi-periodic reconnection events between ground-based magnetometer data and upstream satellite data.

Monitoring Snow Using Geostationary Satellite Retrievals During the SAAWSO Project

NASA Astrophysics Data System (ADS)

Rabin, Robert M.; Gultepe, Ismail; Kuligowski, Robert J.; Heidinger, Andrew K.

2016-09-01

The SAAWSO (Satellite Applications for Arctic Weather and SAR (Search And Rescue) Operations) field programs were conducted by Environment Canada near St. Johns, NL and Goose Bay, NL in the winters of 2012-13 and 2013-14, respectively. The goals of these programs were to validate satellite-based nowcasting products, including snow amount, wind intensity, and cloud physical parameters (e.g., cloud cover), over northern latitudes with potential applications to Search And Rescue (SAR) operations. Ground-based in situ sensors and remote sensing platforms were used to measure microphysical properties of precipitation, clouds and fog, radiation, temperature, moisture and wind profiles. Multi-spectral infrared observations obtained from Geostationary Operational Environmental Satellite (GOES)-13 provided estimates of cloud top temperature and height, phase (water, ice), hydrometer size, extinction, optical depth, and horizontal wind patterns at 15 min intervals. In this work, a technique developed for identifying clouds capable of producing high snowfall rates and incorporating wind information from the satellite observations is described. The cloud top physical properties retrieved from operational satellite observations are validated using measurements obtained from the ground-based in situ and remote sensing platforms collected during two precipitation events: a blizzard heavy snow storm case and a moderate snow event. The retrieved snow precipitation rates are found to be comparable to those of ground-based platform measurements in the heavy snow event.

Visualization of Space-Time Ambiguities to be Explored by the NASA GEC Mission with a Critique of Synthesized Measurements for Different GEC Mission Scenarios

NASA Technical Reports Server (NTRS)

Sojka, Jan J.; Zhu, Lie; Fuller-Rowell, Timothy J.

2005-01-01

The objective of this grant was to study how a multi-satellite mission configuration can be optimized for maximum exploratory scientific return. NASA's Solar Terrestrial Probe (STP) concept mission Geospace Electrodynamic Connections (GEC) was the target mission for this pilot study. GEC prime mission characteristics were two fold: (i) a series of three satellites in the same orbit plane with differential spacing, and (ii) a deep-dipping phase in which these satellites could dip to altitudes as low as 130 km to explore the lower ionosphere and thermosphere. Each satellite would carry a full suite of plasma and neutral in-situ sensors and have the same dipping capability. This latter aspect would be envisaged as a series, up to 10, of deep-dipping campaigns, each lasting 10 days during which the perigee would be lowered to the desired probing depth. The challenge in optimization is to establish the scientific problems that can best be addressed by varying or selecting satellite spacing during a two-year mission while also interspersing, in this two year time frame, the deep-dipping campaigns. Although this sounds like a straightforward trade-off situation, it is complicated by the orbit precession in local time, the location of perigee, and that even the dipping campaigns will have preferred satellite spacing requirements.

Improving the Forecast Accuracy of an Ocean Observation and Prediction System by Adaptive Control of the Sensor Network

NASA Astrophysics Data System (ADS)

Talukder, A.; Panangadan, A. V.; Blumberg, A. F.; Herrington, T.; Georgas, N.

2008-12-01

The New York Harbor Observation and Prediction System (NYHOPS) is a real-time, estuarine and coastal ocean observing and modeling system for the New York Harbor and surrounding waters. Real-time measurements from in-situ mobile and stationary sensors in the NYHOPS networks are assimilated into marine forecasts in order to reduce the discrepancy with ground truth. The forecasts are obtained from the ECOMSED hydrodynamic model, a shallow water derivative of the Princeton Ocean Model. Currently, all sensors in the NYHOPS system are operated in a fixed mode with uniform sampling rates. This technology infusion effort demonstrates the use of Model Predictive Control (MPC) to autonomously adapt the operation of both mobile and stationary sensors in response to changing events that are -automatically detected from the ECOMSED forecasts. The controller focuses sensing resources on those regions that are expected to be impacted by the detected events. The MPC approach involves formulating the problem of calculating the optimal sensor parameters as a constrained multi-objective optimization problem. We have developed an objective function that takes into account the spatiotemporal relationship of the in-situ sensor locations and the locations of events detected by the model. Experiments in simulation were carried out using data collected during a freshwater flooding event. The location of the resulting freshwater plume was calculated from the corresponding model forecasts and was used by the MPC controller to derive control parameters for the sensing assets. The operational parameters that are controlled include the sampling rates of stationary sensors, paths of unmanned underwater vehicles (UUVs), and data transfer routes between sensors and the central modeling computer. The simulation experiments show that MPC-based sensor control reduces the RMS error in the forecast by a factor of 380% as compared to uniform sampling. The paths of multiple UUVs were simultaneously calculated such that measurements from on-board sensors would lead to maximal reduction in the forecast error after data assimilation. The MPC controller also reduces the consumption of system resources such as energy expended in sampling and wireless communication. The MPC-based control approach can be generalized to accept data from remote sensing satellites. This will enable in-situ sensors to be regulated using forecasts generated by assimilating local high resolution in-situ measurements with wide-area observations from remote sensing satellites.

Ten Years of MISR Observations from Terra: Looking Back, Ahead, and in Between

NASA Technical Reports Server (NTRS)

Diner, David J.; Ackerman, Thomas P.; Braverman, Amy J.; Bruegge, Carol J.; Chopping, Mark J.; Clothiaux, Eugene E.; Davies, Roger; Di Girolamo, Larry; Kahn, Ralph A.; Knyazikhin, Yuri;

Airborne net-centric multi-INT sensor control, display, fusion, and exploitation systems

NASA Astrophysics Data System (ADS)

Linne von Berg, Dale C.; Lee, John N.; Kruer, Melvin R.; Duncan, Michael D.; Olchowski, Fred M.; Allman, Eric; Howard, Grant

2004-08-01

The NRL Optical Sciences Division has initiated a multi-year effort to develop and demonstrate an airborne net-centric suite of multi-intelligence (multi-INT) sensors and exploitation systems for real-time target detection and targeting product dissemination. The goal of this Net-centric Multi-Intelligence Fusion Targeting Initiative (NCMIFTI) is to develop an airborne real-time intelligence gathering and targeting system that can be used to detect concealed, camouflaged, and mobile targets. The multi-INT sensor suite will include high-resolution visible/infrared (EO/IR) dual-band cameras, hyperspectral imaging (HSI) sensors in the visible-to-near infrared, short-wave and long-wave infrared (VNIR/SWIR/LWIR) bands, Synthetic Aperture Radar (SAR), electronics intelligence sensors (ELINT), and off-board networked sensors. Other sensors are also being considered for inclusion in the suite to address unique target detection needs. Integrating a suite of multi-INT sensors on a single platform should optimize real-time fusion of the on-board sensor streams, thereby improving the detection probability and reducing the false alarms that occur in reconnaissance systems that use single-sensor types on separate platforms, or that use independent target detection algorithms on multiple sensors. In addition to the integration and fusion of the multi-INT sensors, the effort is establishing an open-systems net-centric architecture that will provide a modular "plug and play" capability for additional sensors and system components and provide distributed connectivity to multiple sites for remote system control and exploitation.

Integration of Satellite, Modeled, and Ground Based Aerosol Data for use in Air Quality and Public Health Applications

NASA Astrophysics Data System (ADS)

Garcia, V.; Kondragunta, S.; Holland, D.; Dimmick, F.; Boothe, V.; Szykman, J.; Chu, A.; Kittaka, C.; Al-Saadi, J.; Engel-Cox, J.; Hoff, R.; Wayland, R.; Rao, S.; Remer, L.

2006-05-01

Advancements in remote sensing over the past decade have been recognized by governments around the world and led to the development of the international Global Earth Observation System of Systems 10-Year Implementation Plan. The plan for the U.S. contribution to GEOSS has been put forth in The Strategic Plan for the U.S. Integrated Earth Observation System (IEOS) developed under IWGEO-CENR. The approach for the development of the U.S. IEOS is to focus on specific societal benefits that can be achieved by integrating the nation's Earth observation capabilities. One such challenge is our ability to understand the impact of poor air quality on human health and well being. Historically, the air monitoring networks put in place for the Nations air quality programs provided the only aerosol air quality data on an ongoing and systematic basis at national levels. However, scientific advances in the remote sensing of aerosols from space have improved dramatically. The MODIS sensor and GOES Imager aboard NASA and NOAA satellites, respectively, provide synoptic-scale measurements of aerosol optical depth (AOD) which have been demonstrated to correlate with high levels of PM10 and PM2.5 at the surface. The MODIS sensor has been shown to be capable of a 1 km x 1 km (at nadir) AOD product, while the GOES Imager can provide AOD at 4 km x 4 km every 30 minutes. Within the next several years NOAA and EPA will begin to issue PM2.5 air quality forecasts over the entire domain of the eastern United States, eventually extending to national coverage. These forecasts will provide continuous estimated values of PM2.5 on a daily basis. A multi-agency collaborative project among government and academia is underway to improve the spatial prediction of fine particulate matter through the integration of multi-sensor and multi-platform aerosol observations (MODIS and GOES), numerical model output, and air monitoring data. By giving more weight to monitoring data in monitored areas and relying on adjusted model output and satellite data in non-monitored areas, a Bayesian hierarchical space-time model will be used to improve the accuracy of prediction and associated prediction errors. The improved spatial predictions will be tested as estimates of exposure for input to modeling relationships between air quality and asthma/other respiratory diseases through CDC under the Environmental Public Health Tracking Network. We will also focus on the use of the predictive spatial maps within the EPA AIRNow program which provides near real-time spatial maps of daily average PM2.5 concentrations across the US. We will present the overall project plan and preliminary results with emphasis on how GEOSS framework is facilitating this effort.

An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin.

PubMed

Hu, Chuli; Li, Jie; Lin, Xin; Chen, Nengcheng; Yang, Chao

2018-05-21

Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors.

Multi Scale Multi Temporal Near Real Time Approach for Volcanic Eruptions monitoring, Test Case: Mt Etna eruption 2017

NASA Astrophysics Data System (ADS)

Buongiorno, M. F.; Silvestri, M.; Musacchio, M.

2017-12-01

In this work a complete processing chain from the detection of the beginning of eruption to the estimation of lava flow temperature on active volcanoes using remote sensing data is presented showing the results for the Mt. Etna eruption on March 2017. The early detection of new eruption is based on the potentiality ensured by geostationary very low spatial resolution satellite (3x3 km in nadiral view), the hot spot/lava flow evolution is derived by S2 polar medium/high spatial resolution (20x20 mt) while the surface temperature is estimated by polar medium/low spatial resolution such as L8, ASTER and S3 (from 90 mt up to 1km).This approach merges two outcome derived by activity performed for monitoring purposes within INGV R&D activities and the results obtained by Geohazards Exploitation Platform ESA funded project (GEP) aimed to the development of shared platform for providing services based on EO data. Because the variety of phenomena to be analyzed a multi temporal multi scale approach has been used to implement suitable and robust algorithms for the different sensors. With the exception of Sentinel 2 (MSI) data, for which the algorithm used is based on NIR-SWIR bands, we exploit the MIR-TIR channels of L8, ASTER, S3 and SEVIRI for generating automatically the surface thermal state analysis. The developed procedure produces time series data and allows to extract information from each single co-registered pixel, to highlight variation of temperatures within specific areas. The final goal is to implement an easy tool which enables scientists and users to extract valuable information from satellite time series at different scales produced by ESA and EUMETSAT in the frame of Europe's Copernicus program and other Earth observation satellites programs such as LANDSAT (USGS) and GOES (NOAA).

A multidisciplinary and multi-sensor assessment of continuous degassing at Turrialba volcano, Costa Rica; insights and their application to hazard management

NASA Astrophysics Data System (ADS)

van Manen, S. M.; Tortini, R.; Burson, B.; Carn, S. A.

2013-12-01

Turrialba is an active stratovolcano located in the Central Cordillera of Costa Rica with an elevation of 3,340 m. Located just 35 km northeast of Costa Rica's capital city San Jose it looms over Costa Rica's Central Valley, the social and economic hub of the country. After more than 100 years of quiescence Turrialba resumed activity in 1996, marked by progressive increases in degassing and seismic activity with gas emissions becoming continuous in 2007. Intermittent phreatic explosions accompanied by ash emissions that have reached the capital have been occurring since 2010. The activity has resulted in the evacuation of two villages, closure of the National Park that comprises the summit region of the volcano and devastation of the local ecosystem. In this work we present a multi-disciplinary and multi-sensor assessment of the persistent degassing and its impacts on the local ecosystem. Combining a variety of high temporal and high spatial resolution satellite-based time series with ground-based measurements of ambient gas concentrations, element deposition and surveys of species richness, enables a comprehensive assessment of SO2 emissions and changes in vegetation. Satellite-based time-series were obtained from Landsat TM and ETM+, Terra ASTER and MODIS, Aqua MODIS, EO-1 and Aura OMI, with some of the data dating back to 2000. Preliminary results show exposure to the volcanic plume results in high soil acidity and significant uptake of certain heavy metals (e.g. Cd, Co, Cu, Hg and Pb) by vegetation, in contrast other elements such as Ba, Ca and Sr are leached from the soil as a result of the acid deposition. These factors are likely to be responsible for decreased species richness and physiological damage observed downwind of Turrialba. Ambient SO2 concentrations that exceed WHO guideline values have been recorded, which has potentially important consequences for human health in the area. Analyzing and relating the remote observations to conditions and impacts on the ground provides an increased understanding of volcanic degassing, its impacts in terms of the long-term vegetation response and how satellite-based monitoring can be used to inform hazard management strategies related to land use, agricultural productivity and human health in near-real time.

Satellite Ocean Biology: Past, Present, Future

NASA Technical Reports Server (NTRS)

McClain, Charles R.

2012-01-01

Since 1978 when the first satellite ocean color proof-of-concept sensor, the Nimbus-7 Coastal Zone Color Scanner, was launched, much progress has been made in refining the basic measurement concept and expanding the research applications of global satellite time series of biological and optical properties such as chlorophyll-a concentrations. The seminar will review the fundamentals of satellite ocean color measurements (sensor design considerations, on-orbit calibration, atmospheric corrections, and bio-optical algorithms), scientific results from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate resolution Imaging Spectroradiometer (MODIS) missions, and the goals of future NASA missions such as PACE, the Aerosol, Cloud, Ecology (ACE), and Geostationary Coastal and Air Pollution Events (GeoCAPE) missions.

Health Monitoring of a Satellite System

NASA Technical Reports Server (NTRS)

Chen, Robert H.; Ng, Hok K.; Speyer, Jason L.; Guntur, Lokeshkumar S.; Carpenter, Russell

2004-01-01

A health monitoring system based on analytical redundancy is developed for satellites on elliptical orbits. First, the dynamics of the satellite including orbital mechanics and attitude dynamics is modelled as a periodic system. Then, periodic fault detection filters are designed to detect and identify the satellite's actuator and sensor faults. In addition, parity equations are constructed using the algebraic redundant relationship among the actuators and sensors. Furthermore, a residual processor is designed to generate the probability of each of the actuator and sensor faults by using a sequential probability test. Finally, the health monitoring system, consisting of periodic fault detection lters, parity equations and residual processor, is evaluated in the simulation in the presence of disturbances and uncertainty.

Hydra Rendezvous and Docking Sensor

NASA Technical Reports Server (NTRS)

Roe, Fred; Carrington, Connie

2007-01-01

The U.S. technology to support a CEV AR&D activity is mature and was developed by NASA and supporting industry during an extensive research and development program conducted during the 1990's and early 2000 time frame at the Marshall Space Flight Center. Development and demonstration of a rendezvous/docking sensor was identified early in the AR&D Program as the critical enabling technology that allows automated proxinity operations and docking. A first generation rendezvous/docking sensor, the Video Guidance Sensor (VGS) was developed and successfully flown on STS 87 and again on STS 95, proving the concept of a video-based sensor. Advances in both video and signal processing technologies and the lessons learned from the two successful flight experiments provided a baseline for the development of a new generation of video based rendezvous/docking sensor. The Advanced Video Guidance Sensor (AVGS) has greatly increased performance and additional capability for longer-range operation. A Demonstration Automatic Rendezvous Technology (DART) flight experiment was flown in April 2005 using AVGS as the primary proximity operations sensor. Because of the absence of a docking mechanism on the target satellite, this mission did not demonstrate the ability of the sensor to coltrold ocking. Mission results indicate that the rendezvous sensor operated successfully in "spot mode" (2 km acquisition of the target, bearing data only) but was never commanded to "acquire and track" the docking target. Parts obsolescence issues prevent the construction of current design AVGS units to support the NASA Exploration initiative. This flight proven AR&D technology is being modularized and upgraded with additional capabilities through the Hydra project at the Marshall Space Flight Center. Hydra brings a unique engineering approach and sensor architecture to the table, to solve the continuing issues of parts obsolescence and multiple sensor integration. This paper presents an approach to sensor hardware trades, to address the needs of future vehicles that may rendezvous and dock with the International Space Station (ISS). It will also discuss approaches for upgrading AVGS to address parts obsolescence, and concepts for modularizing the sensor to provide configuration flexibility for multiple vehicle applications. Options for complementary sensors to be integrated into the multi-head Hydra system will also be presented. Complementary sensor options include ULTOR, a digital image correlator system that could provide relative six-degree-of-freedom information independently from AVGS, and time-of-flight sensors, which determine the range between vehicles by timing pulses that travel from the sensor to the target and back. Common targets and integrated targets, suitable for use with the multi-sensor options in Hydra, will also be addressed.

Geo-spatial distribution of cloud cover and influence of cloud induced attenuation and noise temperature on satellite signal propagation over Nigeria

NASA Astrophysics Data System (ADS)

Ojo, Joseph Sunday

2017-05-01

The study of the influence of cloud cover on satellite propagation links is becoming more demanding due to the requirement of larger bandwidth for different satellite applications. Cloud attenuation is one of the major factors to consider for optimum performance of Ka/V and other higher frequency bands. In this paper, the geo-spatial distribution of cloud coverage over some chosen stations in Nigeria has been considered. The substantial scale spatial dispersion of cloud cover based on synoptic meteorological data and the possible impact on satellite communication links at higher frequency bands was also investigated. The investigation was based on 5 years (2008-2012) achieved cloud cover data collected by the Nigerian Meteorological Agency (NIMET) Federal Ministry of Aviation, Oshodi Lagos over four synoptic hours of the day covering day and night. The performances of satellite signals as they traverse through the cloud and cloud noise temperature at different seasons and over different hours of days at Ku/W-bands frequency are also examined. The overall result shows that the additional total atmospheric noise temperature due to the clear air effect and the noise temperature from the cloud reduces the signal-to-noise ratio of the satellite receiver systems, leading to more signal loss and if not adequately taken care of may lead to significant outage. The present results will be useful for Earth-space link budgeting, especially for the proposed multi-sensors communication satellite systems in Nigeria.

Joint Polar Satellite System (JPSS) Common Ground System (CGS) Block 3.0 Communications Strategies

NASA Astrophysics Data System (ADS)

Miller, S. W.; Grant, K. D.; Ottinger, K.

2015-12-01

The National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) are jointly acquiring the next-generation civilian weather and environmental satellite system: the Joint Polar Satellite System (JPSS). The JPSS program is the follow-on for both space and ground systems to the Polar-orbiting Operational Environmental Satellites (POES) managed by NOAA. The JPSS satellites will carry a suite of sensors designed to collect meteorological, oceanographic, climatological and geophysical observations of the Earth. The ground processing system for JPSS is known as the JPSS Common Ground System (JPSS CGS). Developed and maintained by Raytheon Intelligence, Information and Services (IIS), the CGS is a globally distributed, multi-mission system serving NOAA, NASA and their national and international partners. The CGS has demonstrated its scalability and flexibility to incorporate multiple missions efficiently and with minimal cost, schedule and risk, while strengthening global partnerships in weather and environmental monitoring. In a highly successful international partnership between NOAA and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), the CGS currently provides data routing from McMurdo Station in Antarctica to the EUMETSAT processing center in Darmstadt, Germany. Continuing and building upon that partnership, NOAA and EUMETSAT are collaborating on the development of a new path forward for the 2020's. One approach being explored is a concept of operations where each organization shares satellite downlink resources with the other. This paper will describe that approach, as well as modeling results that demonstrate its feasibility and expected performance.

Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll-a data.

PubMed

Mélin, F; Vantrepotte, V; Chuprin, A; Grant, M; Jackson, T; Sathyendranath, S

2017-12-15

In this work, trend estimates are used as indicators to compare the multi-annual variability of different satellite chlorophyll- a (Chl a ) data and to assess the fitness-for-purpose of multi-mission Chl a products as climate data records (CDR). Under the assumption that single-mission products are free from spurious temporal artifacts and can be used as benchmark time series, multi-mission CDRs should reproduce the main trend patterns observed by single-mission series when computed over their respective periods. This study introduces and applies quantitative metrics to compare trend distributions from different data records. First, contingency matrices compare the trend diagnostics associated with two satellite products when expressed in binary categories such as existence, significance and signs of trends. Contingency matrices can be further summarized by metrics such as Cohen's κ index that rates the overall agreement between the two distributions of diagnostics. A more quantitative measure of the discrepancies between trends is provided by the distributions of differences between trend slopes. Thirdly, maps of the level of significance P of a t -test quantifying the degree to which two trend estimates differ provide a statistical, spatially-resolved, evaluation. The proposed methodology is applied to the multi-mission Ocean Colour-Climate Change Initiative (OC-CCI) Chl a data. The agreement between trend distributions associated with OC-CCI data and single-mission products usually appears as good as when single-mission products are compared. As the period of analysis is extended beyond 2012 to 2015, the level of agreement tends to be degraded, which might be at least partly due to the aging of the MODIS sensor on-board Aqua. On the other hand, the trends displayed by the OC-CCI series over the short period 2012-2015 are very consistent with those observed with VIIRS. These results overall suggest that the OC-CCI Chl a data can be used for multi-annual time series analysis (including trend detection), but with some caution required if recent years are included, particularly in the central tropical Pacific. The study also recalls the challenges associated with creating a multi-mission ocean color data record suitable for climate research.

An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin

PubMed Central

Hu, Chuli; Li, Jie; Lin, Xin

2018-01-01

Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors. PMID:29883425

Potential Utility of the Real-Time TMPA-RT Precipitation Estimates in Streamflow Prediction

NASA Technical Reports Server (NTRS)

Su, Fengge; Gao, Huilin; Huffman, George J.; Lettenmaier, Dennis P.

2010-01-01

We investigate the potential utility of the real-time Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA-RT) data for streamflow prediction, both through direct comparisons of TMPA-RT estimates with a gridded gauge product, and through evaluation of streamflow simulations over four tributaries of La Plata Basin (LPB) in South America using the two precipitation products. Our assessments indicate that the relative accuracy and the hydrologic performance of TMPA-RT-based streamflow simulations generally improved after February 2005. The improvements in TMPA-RT since 2005 are closely related to upgrades in the TMPA-RT algorithm in early February, 2005 which include use of additional microwave sensors (AMSR-E and AMSU-B) and implementation of different calibration schemes. Our work suggests considerable potential for hydrologic prediction using purely satellite-derived precipitation estimates (no adjustments by in situ gauges) in parts of the globe where in situ observations are sparse.

Long-Term Large-Scale Bias-Adjusted Precipitation Estimates at High Spatial and Temporal Resolution Derived from the National Mosaic and Multi-Sensor QPE (NMQ/Q2) Precipitation Reanalysis over CONUS

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.; Stevens, S. E.; Seo, D. J.; Kim, B.

2014-12-01

The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (Nexrad) network over Continental United States (CONUS) is nearly completed for the period covering from 2000 to 2012. This important milestone constitutes a unique opportunity to study precipitation processes at a 1-km spatial resolution for a 5-min temporal resolution. However, in order to be suitable for hydrological, meteorological and climatological applications, the radar-only product needs to be bias-adjusted and merged with in-situ rain gauge information. Rain gauge networks such as the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), the Climate Reference Network (CRN), and the Global Historical Climatology Network - Daily (GHCN-D) are used to adjust for those biases and to merge with the radar only product to provide a multi-sensor estimate. The challenges related to incorporating non-homogeneous networks over a vast area and for a long-term record are enormous. Among the challenges we are facing are the difficulties incorporating differing resolution and quality surface measurements to adjust gridded estimates of precipitation. Another challenge is the type of adjustment technique. After assessing the bias and applying reduction or elimination techniques, we are investigating the kriging method and its variants such as simple kriging (SK), ordinary kriging (OK), and conditional bias-penalized Kriging (CBPK) among others. In addition we hope to generate estimates of uncertainty for the gridded estimate. In this work the methodology is presented as well as a comparison between the radar-only product and the final multi-sensor QPE product. The comparison is performed at various time scales from the sub-hourly, to annual. In addition, comparisons over the same period with a suite of lower resolution QPEs derived from ground based radar measurements (Stage IV) and satellite products (TMPA, CMORPH, PERSIANN) are provided in order to give a detailed picture of the improvements and remaining challenges.

Multi-scales and multi-satellites estimates of evapotranspiration with a residual energy balance model in the Muzza agricultural district in Northern Italy

NASA Astrophysics Data System (ADS)

Corbari, C.; Bissolati, M.; Mancini, M.

2015-05-01

Evapotranspiration estimates were performed with a residual energy balance model (REB) over an agricultural area using remote sensing data. REB uses land surface temperature (LST) as main input parameter so that energy fluxes were computed instantaneously at the time of data acquisition. Data from MODIS and SEVIRI sensors were used and downscaling techniques were implemented to improve their spatial resolutions. Energy fluxes at the original spatial resolutions (1000 m for MODIS and 5000 m for SEVIRI) as well as at the downscaled resolutions (250 m for MODIS and 1000 m for SEVIRI) were calculated with the REB model. Ground eddy covariance data and remote sensing information from the Muzza agricultural irrigation district in Italy from 2010 to 2012 gave the opportunity to validate and compare spatially distributed energy fluxes. The model outputs matched quite well ground observations when ground LST data were used, while differences increased when MODIS and SEVIRI LST were used. The spatial analysis revealed significant differences between the two sensors both in term of LST (around 2.8 °C) and of latent heat fluxes with values (around 100 W m-2).

Remote sensing of deep hermatypic coral reefs in Puerto Rico and the U.S. Virgin Islands using the Seabed autonomous underwater vehicle

NASA Astrophysics Data System (ADS)

Armstrong, Roy A.; Singh, Hanumant

2006-09-01

Optical imaging of coral reefs and other benthic communities present below one attenuation depth, the limit of effective airborne and satellite remote sensing, requires the use of in situ platforms such as autonomous underwater vehicles (AUVs). The Seabed AUV, which was designed for high-resolution underwater optical and acoustic imaging, was used to characterize several deep insular shelf reefs of Puerto Rico and the US Virgin Islands using digital imagery. The digital photo transects obtained by the Seabed AUV provided quantitative data on living coral, sponge, gorgonian, and macroalgal cover as well as coral species richness and diversity. Rugosity, an index of structural complexity, was derived from the pencil-beam acoustic data. The AUV benthic assessments could provide the required information for selecting unique areas of high coral cover, biodiversity and structural complexity for habitat protection and ecosystem-based management. Data from Seabed sensors and related imaging technologies are being used to conduct multi-beam sonar surveys, 3-D image reconstruction from a single camera, photo mosaicking, image based navigation, and multi-sensor fusion of acoustic and optical data.

Automatic registration of optical imagery with 3d lidar data using local combined mutual information

NASA Astrophysics Data System (ADS)

Parmehr, E. G.; Fraser, C. S.; Zhang, C.; Leach, J.

2013-10-01

Automatic registration of multi-sensor data is a basic step in data fusion for photogrammetric and remote sensing applications. The effectiveness of intensity-based methods such as Mutual Information (MI) for automated registration of multi-sensor image has been previously reported for medical and remote sensing applications. In this paper, a new multivariable MI approach that exploits complementary information of inherently registered LiDAR DSM and intensity data to improve the robustness of registering optical imagery and LiDAR point cloud, is presented. LiDAR DSM and intensity information has been utilised in measuring the similarity of LiDAR and optical imagery via the Combined MI. An effective histogramming technique is adopted to facilitate estimation of a 3D probability density function (pdf). In addition, a local similarity measure is introduced to decrease the complexity of optimisation at higher dimensions and computation cost. Therefore, the reliability of registration is improved due to the use of redundant observations of similarity. The performance of the proposed method for registration of satellite and aerial images with LiDAR data in urban and rural areas is experimentally evaluated and the results obtained are discussed.

Applicability Assessment of Uavsar Data in Wetland Monitoring: a Case Study of Louisiana Wetland

NASA Astrophysics Data System (ADS)

Zhao, J.; Niu, Y.; Lu, Z.; Yang, J.; Li, P.; Liu, W.

2018-04-01

Wetlands are highly productive and support a wide variety of ecosystem goods and services. Monitoring wetland is essential and potential. Because of the repeat-pass nature of satellite orbit and airborne, time-series of remote sensing data can be obtained to monitor wetland. UAVSAR is a NASA L-band synthetic aperture radar (SAR) sensor compact pod-mounted polarimetric instrument for interferometric repeat-track observations. Moreover, UAVSAR images can accurately map crustal deformations associated with natural hazards, such as volcanoes and earthquakes. And its polarization agility facilitates terrain and land-use classification and change detection. In this paper, the multi-temporal UAVSAR data are applied for monitoring the wetland change. Using the multi-temporal polarimetric SAR (PolSAR) data, the change detection maps are obtained by unsupervised and supervised method. And the coherence is extracted from the interfometric SAR (InSAR) data to verify the accuracy of change detection map. The experimental results show that the multi-temporal UAVSAR data is fit for wetland monitor.

Spectral Reconstruction for Obtaining Virtual Hyperspectral Images

NASA Astrophysics Data System (ADS)

Perez, G. J. P.; Castro, E. C.

2016-12-01

Hyperspectral sensors demonstrated its capabalities in identifying materials and detecting processes in a satellite scene. However, availability of hyperspectral images are limited due to the high development cost of these sensors. Currently, most of the readily available data are from multi-spectral instruments. Spectral reconstruction is an alternative method to address the need for hyperspectral information. The spectral reconstruction technique has been shown to provide a quick and accurate detection of defects in an integrated circuit, recovers damaged parts of frescoes, and it also aids in converting a microscope into an imaging spectrometer. By using several spectral bands together with a spectral library, a spectrum acquired by a sensor can be expressed as a linear superposition of elementary signals. In this study, spectral reconstruction is used to estimate the spectra of different surfaces imaged by Landsat 8. Four atmospherically corrected surface reflectance from three visible bands (499 nm, 585 nm, 670 nm) and one near-infrared band (872 nm) of Landsat 8, and a spectral library of ground elements acquired from the United States Geological Survey (USGS) are used. The spectral library is limited to 420-1020 nm spectral range, and is interpolated at one nanometer resolution. Singular Value Decomposition (SVD) is used to calculate the basis spectra, which are then applied to reconstruct the spectrum. The spectral reconstruction is applied for test cases within the library consisting of vegetation communities. This technique was successful in reconstructing a hyperspectral signal with error of less than 12% for most of the test cases. Hence, this study demonstrated the potential of simulating information at any desired wavelength, creating a virtual hyperspectral sensor without the need for additional satellite bands.

The Compact Environmental Anomaly Sensor (CEASE) III

NASA Astrophysics Data System (ADS)

Roddy, P.; Hilmer, R. V.; Ballenthin, J.; Lindstrom, C. D.; Barton, D. A.; Ignazio, J. M.; Coombs, J. M.; Johnston, W. R.; Wheelock, A. T.; Quigley, S.

2016-12-01

The Air Force Research Laboratory's Energetic Charged Particle (ECP) sensor project is a comprehensive effort to measure the charged particle environment that causes satellite anomalies. The project includes the Compact Environmental Anomaly Sensor (CEASE) III, building on the flight heritage of prior CEASE designs. CEASE III consists of multiple sensor modules. High energy particles are observed using independent unique silicon detector stacks. In addition CEASE III includes an electrostatic analyzer (ESA) assembly which uses charge multiplication for particle detection. The sensors cover a wide range of proton and electron energies that contribute to satellite anomalies.

Imager-to-Radiometer In-flight Cross Calibration: RSP Radiometric Comparison with Airborne and Satellite Sensors

NASA Technical Reports Server (NTRS)

McCorkel, Joel; Cairns, Brian; Wasilewski, Andrzej

2016-01-01

This work develops a method to compare the radiometric calibration between a radiometer and imagers hosted on aircraft and satellites. The radiometer is the airborne Research Scanning Polarimeter (RSP), which takes multi-angle, photo-polarimetric measurements in several spectral channels. The RSP measurements used in this work were coincident with measurements made by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), which was on the same aircraft. These airborne measurements were also coincident with an overpass of the Landsat 8 Operational Land Imager (OLI). First we compare the RSP and OLI radiance measurements to AVIRIS since the spectral response of the multispectral instruments can be used to synthesize a spectrally equivalent signal from the imaging spectrometer data. We then explore a method that uses AVIRIS as a transfer between RSP and OLI to show that radiometric traceability of a satellite-based imager can be used to calibrate a radiometer despite differences in spectral channel sensitivities. This calibration transfer shows agreement within the uncertainty of both the various instruments for most spectral channels.

Assembly of Landsat's TIRS Instrument

NASA Image and Video Library

2012-02-14

Aleksandra Bogunovic reaches across the instrument to affix the corners of a Multi-Layer Insulation blanket to the TIRS instrument. The Thermal Infrared Sensor (TIRS) will fly on the next Landsat satellite, the Landsat Data Continuity Mission (LDCM). TIRS was built on an accelerated schedule at NASA's Goddard Space Flight Center, Greenbelt, Md. and will now be integrated into the LDCM spacecraft at Orbital Science Corp. in Gilbert, Ariz. The Landsat Program is a series of Earth observing satellite missions jointly managed by NASA and the U.S. Geological Survey. Landsat satellites have been consistently gathering data about our planet since 1972. They continue to improve and expand this unparalleled record of Earth's changing landscapes for the benefit of all. For more information on Landsat, visit: www.nasa.gov/landsat Credit: NASA/GSFC/Rebecca Roth NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Probabilistic verification of cloud fraction from three different products with CALIPSO

NASA Astrophysics Data System (ADS)

Jung, B. J.; Descombes, G.; Snyder, C.

2017-12-01

In this study, we present how Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) can be used for probabilistic verification of cloud fraction, and apply this probabilistic approach to three cloud fraction products: a) The Air Force Weather (AFW) World Wide Merged Cloud Analysis (WWMCA), b) Satellite Cloud Observations and Radiative Property retrieval Systems (SatCORPS) from NASA Langley Research Center, and c) Multi-sensor Advection Diffusion nowCast (MADCast) from NCAR. Although they differ in their details, both WWMCA and SatCORPS retrieve cloud fraction from satellite observations, mainly of infrared radiances. MADCast utilizes in addition a short-range forecast of cloud fraction (provided by the Model for Prediction Across Scales, assuming cloud fraction is advected as a tracer) and a column-by-column particle filter implemented within the Gridpoint Statistical Interpolation (GSI) data-assimilation system. The probabilistic verification considers the retrieved or analyzed cloud fractions as predicting the probability of cloud at any location within a grid cell and the 5-km vertical feature mask (VFM) from CALIPSO level-2 products as a point observation of cloud.

Oil spill disasters detection and monitoring by optical satellite data

NASA Astrophysics Data System (ADS)

Livia Grimaldi, Caterina Sara; Coviello, Irina; Lacava, Teodosio; Pergola, Nicola; Tramutoli, Valerio

2010-05-01

Marine oil spill disasters may be related to natural hazards, when storms and hurricanes cause the sinking of tankers carrying crude or refined oil, as well as to human action, as illegal discharges, assessment errors (failures or collisions) or acts of warfare. Their consequence has a devastating effects on the marine and coastal environment. In order to reduce the environmental impact of such kind of hazard, giving to local authorities necessary information of pollution entity and evolution, timely detection and continuously updated information are fundamental. Satellite remote sensing can give a significant contribution in such a direction. Nowadays, SAR (Synthetic Aperture Radar) technology has been recognized as the most efficient for oil spill detection and description, thanks to the high spatial resolution and all-time/weather capability of the present operational sensors. Anyway, the actual SARs revisiting time does not allow a rapid detection and near real-time monitoring of these phenomena at global scale. The COSMO-Skymed Italian dual-mission (expected in the 2010) will overcome this limitation improving the temporal resolution until 12 hours by a SAR constellation of four satellites, but several open questions regarding costs and global delivery policy of such data, might prevent their use in an operational context. Passive optical sensors, on board meteorological satellites, thanks to their high temporal resolution (from a few hours to 15 minutes, depending on the characteristics of the platform/sensor), may represent, at this moment, a suitable SAR alternative/complement for oil spill detection and monitoring. Up to now, some techniques have been proposed for mapping known oil spill discharges monitoring using optical satellite data, on the other hand, reliable satellite methods for an automatic and timely detection of oil spill are still currently missing. Existing methods, in fact, can localize the presence of an oil spill only after an alert and require the presence of a qualified operator. Recently, an innovative technique for near real time oil spill detection and monitoring has been proposed. The technique is based on the general RST (Robust Satellite Technique) approach which exploits long-term multi-temporal satellite records in order to obtain a former characterization of the measured signal, in terms of expected value and natural variability, providing a further identification of signal anomalies by an automatic, unsupervised change detection step. Results obtained by using both AVHRR (Advanced Very High Resolution Radiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) data in different geographic areas and observational conditions demonstrate excellent detection capabilities both in term of sensitivity (to the presence even of very thin/old oil films) and reliability (up to zero occurrence of false alarms) mainly due to the RST invariance regardless of local and environmental conditions. Moreover, the possibility to apply RST approach to both MODIS and AVHRR sensors may ensure an improved (up to 3 hours and less) frequency of TIR (Thermal Infrared) observations as well as an increased spatial accuracy of the description of oil spills (thanks to higher spatial resolution of MODIS visible channels). In this paper, results obtained applying the proposed methodology to events of different extension and in different geographic areas are shown and discussed.

Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks

NASA Astrophysics Data System (ADS)

Fieuzal, R.; Marais Sicre, C.; Baup, F.

2017-05-01

The yield forecasting of corn constitutes a key issue in agricultural management, particularly in the context of demographic pressure and climate change. This study presents two methods to estimate yields using artificial neural networks: a diagnostic approach based on all the satellite data acquired throughout the agricultural season, and a real-time approach, where estimates are updated after each image was acquired in the microwave and optical domains (Formosat-2, Spot-4/5, TerraSAR-X, and Radarsat-2) throughout the crop cycle. The results are based on the Multispectral Crop Monitoring experimental campaign conducted by the CESBIO (Centre d'Études de la BIOsphère) laboratory in 2010 over an agricultural region in southwestern France. Among the tested sensor configurations (multi-frequency, multi-polarization or multi-source data), the best yield estimation performance (using the diagnostic approach) is obtained with reflectance acquired in the red wavelength region, with a coefficient of determination of 0.77 and an RMSE of 6.6 q ha-1. In the real-time approach the combination of red reflectance and CHH backscattering coefficients provides the best compromise between the accuracy and earliness of the yield estimate (more than 3 months before the harvest), with an R2 of 0.69 and an RMSE of 7.0 q ha-1 during the development of the central stem. The two best yield estimates are similar in most cases (for more than 80% of the monitored fields), and the differences are related to discrepancies in the crop growth cycle and/or the consequences of pests.

Evaluation of scanning earth sensor mechanism on engineering test satellite 4

NASA Technical Reports Server (NTRS)

Ikeuchi, M.; Wakabayashi, Y.; Ohkami, Y.; Kida, T.; Ishigaki, T.; Matsumoto, M.

1983-01-01

The results of the analysis and the evaluation of flight data obtained from the horizon sensor test project are described. The rotary mechanism of the scanning earth sensor composed of direct drive motor and bearings using solid lubricant is operated satisfactorily. The transmitted flight data from Engineering Test Satellite IV was evaluated in comparison with the design value.

Horizon sensors attitude errors simulation for the Brazilian Remote Sensing Satellite

NASA Astrophysics Data System (ADS)

Vicente de Brum, Antonio Gil; Ricci, Mario Cesar

Remote sensing, meteorological and other types of satellites require an increasingly better Earth related positioning. From the past experience it is well known that the thermal horizon in the 15 micrometer band provides conditions of determining the local vertical at any time. This detection is done by horizon sensors which are accurate instruments for Earth referred attitude sensing and control whose performance is limited by systematic and random errors amounting about 0.5 deg. Using the computer programs OBLATE, SEASON, ELECTRO and MISALIGN, developed at INPE to simulate four distinct facets of conical scanning horizon sensors, attitude errors are obtained for the Brazilian Remote Sensing Satellite (the first one, SSR-1, is scheduled to fly in 1996). These errors are due to the oblate shape of the Earth, seasonal and latitudinal variations of the 15 micrometer infrared radiation, electronic processing time delay and misalignment of sensor axis. The sensor related attitude errors are thus properly quantified in this work and will, together with other systematic errors (for instance, ambient temperature variation) take part in the pre-launch analysis of the Brazilian Remote Sensing Satellite, with respect to the horizon sensor performance.

Seasat. Volume 2: Flight systems

NASA Technical Reports Server (NTRS)

Pounder, E. (Editor)

1980-01-01

Flight systems used in the Seasat Project are described. Included are (1) launch operation; (2) satellite performance after launch; (3) sensors that collected data; and (4) the launch vehicle that placed the satellite into Earth orbit. Techniques for sensor management are explained.

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

NASA Astrophysics Data System (ADS)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

PRIMA Platform capability for satellite missions in LEO and MEO (SAR, Optical, GNSS, TLC, etc.)

NASA Astrophysics Data System (ADS)

Logue, T.; L'Abbate, M.

2016-12-01

PRIMA (Piattaforma Riconfigurabile Italiana Multi Applicativa) is a multi-mission 3-axis stabilized Platform developed by Thales Alenia Space Italia under ASI contract.PRIMA is designed to operate for a wide variety of applications from LEO, MEO up to GEO and for different classes of satellites Platform Family. It has an extensive heritage in flight heritage (LEO and MEO Satellites already fully operational) in which it has successfully demonstrated the flexibility of use, low management costs and the ability to adapt to changing operational conditions.The flexibility and modularity of PRIMA provides unique capability to satisfy different Payload design and mission requirements, thanks to the utilization of recurrent adaptable modules (Service Module-SVM, Propulsion Module-PPM, Payload Module-PLM) to obtain mission dependent configuration. PRIMA product line development is continuously progressing, and is based on state of art technology, modular architecture and an Integrated Avionics. The aim is to maintain and extent multi-mission capabilities to operate in different environments (LEO to GEO) with different payloads (SAR, Optical, GNSS, TLC, etc.). The design is compatible with a wide range of European and US equipment suppliers, thus maximising cooperation opportunity. Evolution activities are mainly focused on the following areas: Structure: to enable Spacecraft configurations for multiple launch; Thermal Control: to guarantee thermal limits for new missions, more demanding in terms of environment and payload; Electrical: to cope with higher power demand (e.g. electrical propulsion, wide range of payloads, etc.) considering orbital environment (e.g. lighting condition); Avionics : AOCS solutions optimized on mission (LEO observation driven by agility and pointing, agility not a driver for GEO). Use of sensors and actuators tailored for specific mission and related environments. Optimised Propulsion control. Data Handling, SW and FDIR mission customization, ensuring robust storage and downlink capability, long lasting autonomy and flexible operations in all mission phases, nominal and non-nominal conditions. This paper starting from PRIMA flight achievements will then outline PRIMA family multi-purpose features addressed to meet multi mission requirements.

OMMYDCLD: a New A-train Cloud Product that Co-locates OMI and MODIS Cloud and Radiance Parameters onto the OMI Footprint

NASA Technical Reports Server (NTRS)

Fisher, Brad; Joiner, Joanna; Vasilkov, Alexander; Veefkind, Pepijn; Platnick, Steven; Wind, Galina

2014-01-01

Clouds cover approximately 60% of the earth's surface. When obscuring the satellite's field of view (FOV), clouds complicate the retrieval of ozone, trace gases and aerosols from data collected by earth observing satellites. Cloud properties associated with optical thickness, cloud pressure, water phase, drop size distribution (DSD), cloud fraction, vertical and areal extent can also change significantly over short spatio-temporal scales. The radiative transfer models used to retrieve column estimates of atmospheric constituents typically do not account for all these properties and their variations. The OMI science team is preparing to release a new data product, OMMYDCLD, which combines the cloud information from sensors on board two earth observing satellites in the NASA A-Train: Aura/OMI and Aqua/MODIS. OMMYDCLD co-locates high resolution cloud and radiance information from MODIS onto the much larger OMI pixel and combines it with parameters derived from the two other OMI cloud products: OMCLDRR and OMCLDO2. The product includes histograms for MODIS scientific data sets (SDS) provided at 1 km resolution. The statistics of key data fields - such as effective particle radius, cloud optical thickness and cloud water path - are further separated into liquid and ice categories using the optical and IR phase information. OMMYDCLD offers users of OMI data cloud information that will be useful for carrying out OMI calibration work, multi-year studies of cloud vertical structure and in the identification and classification of multi-layer clouds.

Automated Glacier Surface Velocity using Multi-Image/Multi-Chip (MIMC) Feature Tracking

NASA Astrophysics Data System (ADS)

Ahn, Y.; Howat, I. M.

2009-12-01

Remote sensing from space has enabled effective monitoring of remote and inhospitable polar regions. Glacier velocity, and its variation in time, is one of the most important parameters needed to understand glacier dynamics, glacier mass balance and contribution to sea level rise. Regular measurements of ice velocity are possible from large and accessible satellite data set archives, such as ASTER and LANDSAT-7. Among satellite imagery, optical imagery (i.e. passive, visible to near-infrared band sensors) provides abundant data with optimal spatial resolution and repeat interval for tracking glacier motion at high temporal resolution. Due to massive amounts of data, computation of ice velocity from feature tracking requires 1) user-friendly interface, 2) minimum local/user parameter inputs and 3) results that need minimum editing. We focus on robust feature tracking, applicable to all currently available optical satellite imagery, that is ASTER, SPOT and LANDSAT etc. We introduce the MIMC (multiple images/multiple chip sizes) matching approach that does not involve any user defined local/empirical parameters except approximate average glacier speed. We also introduce a method for extracting velocity from LANDSAT-7 SLC-off data, which has 22 percent of scene data missing in slanted strips due to failure of the scan line corrector. We apply our approach to major outlet glaciers in west/east Greenland and assess our MIMC feature tracking technique by comparison with conventional correlation matching and other methods (e.g. InSAR).

Toward a Tighter Coupling between Models and Observations of Arctic Energy Balance

NASA Astrophysics Data System (ADS)

L'Ecuyer, T. S.

2016-12-01

The Arctic climate is changing more rapidly than almost anywhere else on Earth owing to a number of unique feedbacks that locally amplify the effects of increased greenhouse gas concentrations. While the basic theory behind these feedback mechanisms has been known for a long time, current climate models still struggle to capture observed rates of sea ice decline and ice sheet melt. This may be explained, at least partially, by a lack of observational constraints on cloud and precipitation processes owing to the challenges of making sustained, high quality atmospheric measurements in this inhospitable region. This presentation will introduce a new multi-satellite, multi-model combined Arctic dataset for probing the state of the Arctic climate and documenting and improving prediction models. Recent satellite-based reconstructions of the Arctic energy budget and its annual cycle contained within this dataset will used to demonstrate that many climate models exhibit significant biases in several key energy flows in the region. These biases, in turn, lead to discrepancies in both the magnitude and seasonality of the implied heat transport into the Arctic from lower latitudes. The potential impacts of these biases on the surface mass balance of the Greenland Ice Sheet will be explored. New estimates of downwelling radiative fluxes that explicitly account for the effects of super-cooled liquid water observed by new active satellite sensors will be used to drive a regional ice sheet model to assess the sensitivity of ice sheet dynamical processes to uncertainties in surface radiation balance.

Evaluation of topographical and seasonal feature using GPM IMERG and TRMM 3B42 over Far-East Asia

NASA Astrophysics Data System (ADS)

Kim, Kiyoung; Park, Jongmin; Baik, Jongjin; Choi, Minha

2017-05-01

The acquisition of accurate precipitation data is essential for analyzing various hydrological phenomena and climate change. Recently, the Global Precipitation Measurement (GPM) satellites were launched as a next-generation rainfall mission for observing global precipitation characteristics. The main objective in this study is to assess precipitation products from GPM, especially the Integrated Multi-satellitE Retrievals (GPM-3IMERGHH) and the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), using gauge-based precipitation data from Far-East Asia during the pre-monsoon and monsoon seasons. Evaluation was performed by focusing on three different factors: geographical aspects, seasonal factors, and spatial distributions. In both mountainous and coastal regions, the GPM-3IMERGHH product showed better performance than the TRMM 3B42 V7, although both rainfall products showed uncertainties caused by orographic convection and the land-ocean classification algorithm. GPM-3IMERGHH performed about 8% better than TRMM 3B42 V7 during the pre-monsoon and monsoon seasons due to the improvement of loaded sensor and reinforcement in capturing convective rainfall, respectively. In depicting the spatial distribution of precipitation, GPM-3IMERGHH was more accurate than TRMM 3B42 V7 because of its enhanced spatial and temporal resolutions of 10 km and 30 min, respectively. Based on these results, GPM-3IMERGHH would be helpful for not only understanding the characteristics of precipitation with high spatial and temporal resolution, but also for estimating near-real-time runoff patterns.

GEWEX cloud assessment: A review

NASA Astrophysics Data System (ADS)

Stubenrauch, Claudia; Rossow, William B.; Kinne, Stefan; Ackerman, Steve; Cesana, Gregory; Chepfer, Hélène; Di Girolamo, Larry; Getzewich, Brian; Guignard, Anthony; Heidinger, Andy; Maddux, Brent; Menzel, Paul; Minnis, Patrick; Pearl, Cindy; Platnick, Steven; Poulsen, Caroline; Riedi, Jérôme; Sayer, Andrew; Sun-Mack, Sunny; Walther, Andi; Winker, Dave; Zeng, Shen; Zhao, Guangyu

2013-05-01

Clouds cover about 70% of the Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the entire globe and across the wide range of spatial and temporal scales that comprise weather and climate variability. Satellite cloud data records now exceed more than 25 years; however, climatologies compiled from different satellite datasets can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors. The Global Energy and Water cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel, provides the first coordinated intercomparison of publicly available, global cloud products (gridded, monthly statistics) retrieved from measurements of multi-spectral imagers (some with multi-angle view and polarization capabilities), IR sounders and lidar. Cloud properties under study include cloud amount, cloud height (in terms of pressure, temperature or altitude), cloud radiative properties (optical depth or emissivity), cloud thermodynamic phase and bulk microphysical properties (effective particle size and water path). Differences in average cloud properties, especially in the amount of high-level clouds, are mostly explained by the inherent instrument measurement capability for detecting and/or identifying optically thin cirrus, especially when overlying low-level clouds. The study of long-term variations with these datasets requires consideration of many factors. The monthly, gridded database presented here facilitates further assessments, climate studies, and the evaluation of climate models.

Multi-Band Multi-Tone Tunable Millimeter-Wave Frequency Synthesizer For Satellite Beacon Transmitter

NASA Technical Reports Server (NTRS)

Simons, Rainee N.; Wintucky, Edwin G.

2016-01-01

This paper presents the design and test results of a multi-band multi-tone tunable millimeter-wave frequency synthesizer, based on a solid-state frequency comb generator. The intended application of the synthesizer is in a satellite beacon transmitter for radio wave propagation studies at K-band (18 to 26.5 GHz), Q-band (37 to 42 GHz), and E-band (71 to 76 GHz). In addition, the architecture for a compact beacon transmitter, which includes the multi-tone synthesizer, polarizer, horn antenna, and power/control electronics, has been investigated for a notional space-to-ground radio wave propagation experiment payload on a small satellite. The above studies would enable the design of robust high throughput multi-Gbps data rate future space-to-ground satellite communication links.

An FP7 "Space" project: Aphorism "Advanced PRocedures for volcanic and Seismic Monitoring"

NASA Astrophysics Data System (ADS)

Di Iorio, A., Sr.; Stramondo, S.; Bignami, C.; Corradini, S.; Merucci, L.

2014-12-01

APHORISM project proposes the development and testing of two new methods to combine Earth Observation satellite data from different sensors, and ground data. The aim is to demonstrate that this two types of data, appropriately managed and integrated, can provide new improved GMES products useful for seismic and volcanic crisis management. The first method, APE - A Priori information for Earthquake damage mapping, concerns the generation of maps to address the detection and estimate of damage caused by a seism. The use of satellite data to investigate earthquake damages is not an innovative issue. We can find a wide literature and projects concerning such issue, but usually the approach is only based on change detection techniques and classifications algorithms. The novelty of APE relies on the exploitation of a priori information derived by InSAR time series to measure surface movements, shake maps obtained from seismological data, and vulnerability information. This a priori information is then integrated with change detection map to improve accuracy and to limit false alarms. The second method deals with volcanic crisis management. The method, MACE - Multi-platform volcanic Ash Cloud Estimation, concerns the exploitation of GEO (Geosynchronous Earth Orbit) sensor platform, LEO (Low Earth Orbit) satellite sensors and ground measures to improve the ash detection and retrieval and to characterize the volcanic ash clouds. The basic idea of MACE consists of an improvement of volcanic ash retrievals at the space-time scale by using both the LEO and GEO estimations and in-situ data. Indeed the standard ash thermal infrared retrieval is integrated with data coming from a wider spectral range from visible to microwave. The ash detection is also extended in case of cloudy atmosphere or steam plumes. APE and MACE methods have been defined in order to provide products oriented toward the next ESA Sentinels satellite missions.The project is funded under the European Union FP7 program and the Kick-Off meeting has been held at INGV premises in Rome on 18th December 2013.

Principles and practical implementation for high resolution multi-sensor QPE

NASA Astrophysics Data System (ADS)

Chandra, C. V.; Lim, S.; Cifelli, R.

2011-12-01

The multi-sensor Quantitative Precipitation Estimation (MPE) is a principle and a practical concept and is becoming a well-known term in the scientific circles of hydrology and atmospheric science. The main challenge in QPE is that precipitation is a highly variable quantity with extensive spatial and temporal variability at multiple scales. There are MPE products produced from satellites, radars, models and ground sensors. There are MPE products at global scale (Heinemann et al. 2002), continental scale (Seo et al. 2010; Zhang et al. 2011) and regional scale (Kitzmiller et al. 2011). Lots of the MPE products are used to alleviate the problems of one type of sensor by another. Some multi-sensor products are used to move across scales. This paper looks at a comprehensive view of the "concept of multi sensor precipitation estimate", from different perspectives. This paper delineates the MPE problem into three categories namely, a) Scale based MPE, b) MPE for accuracy enhancement and coverage and c) Integrative across scales. For example, by introducing dual polarization radar data to the MPE system, QPE can be improved significantly. In last decade, dual polarization radars are becoming an important tool for QPE in operational networks. Dual polarization radars offer an advantage to interpret more accurate physical models by providing information of the size, shape, phase and orientation of hydrometers (Bringi and Chandrasekar 2001). In addition, these systems have the ability to provide measurements that are immune to absolute radar calibration and partial beam blockage as well as help in data quality enhancement. By integrating these characteristics of dual polarization radar, QPE performance can be improved in comparison of single polarization radar based QPE (Cifelli and Chandrasekar 2010). Dual-polarization techniques have been applied to S and C band radar systems for several decades and higher frequency system such as X band are now widely available to the radar community. One solution to the dilemma of precipitation variability across scales can be to supplement existing long-range radar networks with short-range higher frequency systems (X band). The smaller X band systems provide more portability and higher data resolution, and networks of these systems may be a cost-effective option for improved rainfall estimation for radar networks with large separation distances (McLaughlin et al. 2009). This paper will describe the principles of the MPE concept and implementation issues of within the context of the classification described above.

Modeling change from large-scale high-dimensional spatio-temporal array data

NASA Astrophysics Data System (ADS)

Lu, Meng; Pebesma, Edzer

2014-05-01

The massive data that come from Earth observation satellite and other sensors provide significant information for modeling global change. At the same time, the high dimensionality of the data has brought challenges in data acquisition, management, effective querying and processing. In addition, the output of earth system modeling tends to be data intensive and needs methodologies for storing, validation, analyzing and visualization, e.g. as maps. An important proportion of earth system observations and simulated data can be represented as multi-dimensional array data, which has received increasingly attention in big data management and spatial-temporal analysis. Study cases will be developed in natural science such as climate change, hydrological modeling, sediment dynamics, from which the addressing of big data problems is necessary. Multi-dimensional array-based database management and analytics system such as Rasdaman, SciDB, and R will be applied to these cases. From these studies will hope to learn the strengths and weaknesses of these systems, how they might work together or how semantics of array operations differ, through addressing the problems associated with big data. Research questions include: • How can we reduce dimensions spatially and temporally, or thematically? • How can we extend existing GIS functions to work on multidimensional arrays? • How can we combine data sets of different dimensionality or different resolutions? • Can map algebra be extended to an intelligible array algebra? • What are effective semantics for array programming of dynamic data driven applications? • In which sense are space and time special, as dimensions, compared to other properties? • How can we make the analysis of multi-spectral, multi-temporal and multi-sensor earth observation data easy?

Transient response measurements on a satellite system

NASA Technical Reports Server (NTRS)

Nanevicz, J. E.; Adamo, R. C.

1977-01-01

A set of instruments designed to detect the occurance of electrical breakdown was flown on a synchronous-orbit satellite. The LeRC sensors were installed on cables inside the vehicle. Accordingly, they respond to signals coupled into the satellite wiring system. The SRI sensors were located on the exterior of the vehicle and detected the RF noise pulses associated with surface breakdowns. The results of the earlier SRI program are being used to design and develop a set of intrumentation suitable for inclusion as a general piggy-back package for the detection of the onset of satellite charging and breakdowns on synchronous orbit satellites.

ASTER's First Views of Red Sea, Ethiopia - Thermal-Infrared (TIR) Image (monochrome)

NASA Technical Reports Server (NTRS)

2000-01-01

ASTER succeeded in acquiring this image at night, which is something Visible/Near Infrared VNIR) and Shortwave Infrared (SWIR) sensors cannot do. The scene covers the Red Sea coastline to an inland area of Ethiopia. White pixels represent areas with higher temperature material on the surface, while dark pixels indicate lower temperatures. This image shows ASTER's ability as a highly sensitive, temperature-discerning instrument and the first spaceborne TIR multi-band sensor in history.

The size of image: 60 km x 60 km approx., ground resolution 90 m x 90 m approximately.

The ASTER instrument was built in Japan for the Ministry of International Trade and Industry. A joint United States/Japan Science Team is responsible for instrument design, calibration, and data validation. ASTER is flying on the Terra satellite, which is managed by NASA's Goddard Space Flight Center, Greenbelt, MD.

A Decade of High-Resolution Arctic Sea Ice Measurements from Airborne Altimetry

NASA Astrophysics Data System (ADS)

Duncan, K.; Farrell, S. L.; Connor, L. N.; Jackson, C.; Richter-Menge, J.

2017-12-01

Satellite altimeters carried on board ERS-1,-2, EnviSat, ICESat, CryoSat-2, AltiKa and Sentinel-3 have transformed our ability to map the thickness and volume of the polar sea ice cover, on seasonal and decadal time-scales. The era of polar satellite altimetry has coincided with a rapid decline of the Arctic ice cover, which has thinned, and transitioned from a predominantly multi-year to first-year ice cover. In conjunction with basin-scale satellite altimeter observations, airborne surveys of the Arctic Ocean at the end of winter are now routine. These surveys have been targeted to monitor regions of rapid change, and are designed to obtain the full snow and ice thickness distribution, across a range of ice types. Sensors routinely deployed as part of NASA's Operation IceBridge (OIB) campaigns include the Airborne Topographic Mapper (ATM) laser altimeter, the frequency-modulated continuous-wave snow radar, and the Digital Mapping System (DMS). Airborne measurements yield high-resolution data products and thus present a unique opportunity to assess the quality and characteristics of the satellite observations. We present a suite of sea ice data products that describe the snow depth and thickness of the Arctic ice cover during the last decade. Fields were derived from OIB measurements collected between 2009-2017, and from reprocessed data collected during ad-hoc sea ice campaigns prior to OIB. Our bespoke algorithms are designed to accommodate the heterogeneous sea ice surface topography, that varies at short spatial scales. We assess regional and inter-annual variability in the sea ice thickness distribution. Results are compared to satellite-derived ice thickness fields to highlight the sensitivities of satellite footprints to the tails of the thickness distribution. We also show changes in the dynamic forcing shaping the ice pack over the last eight years through an analysis of pressure-ridge sail-height distributions and surface roughness conditions. Variability is linked to the geographic location and extent of multi-year sea ice. Finally, we describe accessing our high-resolution data products at the NOAA Laboratory for Satellite Altimetry.

Utility of a Two-source Energy Balance Approach for Daily Mapping of Landsat-scale Fluxes Over Irrigated Agriculture in a Desert Environment

NASA Astrophysics Data System (ADS)

Houborg, R.; McCabe, M. F.; Rosas Aguilar, J.; Anderson, M. C.; Hain, C.

2014-12-01

The Middle East and North Africa (MENA) region is an area characterized by limited fresh water resources, an often inefficient use of these, and relatively poor in-situ monitoring as a result of sparse meteorological observations. Enhanced satellite-based monitoring systems are needed for aiding local water resource and agricultural management activities in these data poor arid environments. A multi-sensor and multi-scale land-surface flux monitoring capacity is being implemented over parts of MENA in order to provide meaningful decision support at relevant spatiotemporal scales. The integrated modeling system uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI), and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in conjunction with model reanalysis data and remotely sensed data from polar orbiting (Landsat and MODIS; MODerate resolution Imaging Spectroradiometer) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate daily estimates of land surface fluxes down to sub-field scale (i.e. 30 m). Within this modeling system, thermal infrared satellite data provide information about the sub-surface moisture status and plant stress, obviating the need for precipitation input and error-prone soil surface characterizations. In this study, the integrated ALEXI-DisALEXI-STARFM framework is applied over an irrigated agricultural region in Saudi Arabia, and the daily estimates of Landsat scale water, energy and carbon fluxes are evaluated against available flux tower observations and other independent in-situ and satellite-based records. The study addresses the challenges associated with time-continuous sub-field scale mapping of land-surface fluxes in a harsh desert environment, and looks into the optimization of model descriptions and parameterizations and meteorological forcing and vegetation inputs for application over these regions.

Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques

DOE PAGES

Meng, Ran; Wu, Jin; Zhao, Feng; ...

2018-06-01

Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. For this work, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1m simultaneous airborne imaging spectroscopy and LiDAR and 2m satellite multi-spectral imagery) to separate canopy recovery from understory recovery wouldmore » enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal domains. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management, and for constraining/benchmarking fire effect schemes in ecological process models.« less

Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meng, Ran; Wu, Jin; Zhao, Feng

Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. For this work, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1m simultaneous airborne imaging spectroscopy and LiDAR and 2m satellite multi-spectral imagery) to separate canopy recovery from understory recovery wouldmore » enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal domains. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management, and for constraining/benchmarking fire effect schemes in ecological process models.« less

Development of a harmonised multi sensor retrieval scheme for HCHO within the Quality Assurance For Essential Climate Variables (QA4ECV) project

NASA Astrophysics Data System (ADS)

De Smedt, Isabelle; Richter, Andreas; Beirle, Steffen; Danckaert, Thomas; Van Roozendael, Michel; Yu, Huan; Bösch, Tim; Hilboll, Andreas; Peters, Enno; Doerner, Steffen; Wagner, Thomas; Wang, Yang; Lorente, Alba; Eskes, Henk; Van Geffen, Jos; Boersma, Folkert

2016-04-01

One of the main goals of the QA4ECV project is to define community best-practices for the generation of multi-decadal ECV data records from satellite instruments. QA4ECV will develop retrieval algorithms for the Land ECVs surface albedo, leaf area index (LAI), and fraction of active photosynthetic radiation (fAPAR), as well as for the Atmosphere ECV ozone and aerosol precursors nitrogen dioxide (NO2), formaldehyde (HCHO), and carbon monoxide (CO). Here we assess best practices and provide recommendations for the retrieval of HCHO. Best practices are established based on (1) a detailed intercomparison exercise between the QA4ECV partner's for each specific algorithm processing steps, (2) the feasibility of implementation, and (3) the requirement to generate consistent multi-sensor multi-decadal data records. We propose a fitting window covering the 328.5-346 nm spectral interval for the morning sensors (GOME, SCIAMACHY and GOME-2) and an extension to 328.5-359 nm for OMI and GOME-2, allowed by improved quality of the recorded spectra. A high level of consistency between group algorithms is found when the retrieval settings are carefully aligned. However, the retrieval of slant columns is highly sensitive to any change in the selected settings. The use of a mean background radiance as DOAS reference spectrum allows for a stabilization of the retrievals. A background correction based on the reference sector method is recommended for implementation in the QA4ECV HCHO algorithm as it further reduces retrieval uncertainties. HCHO AMFs using different radiative transfer codes show a good overall consistency when harmonized settings are used. As for NO2, it is proposed to use a priori HCHO profiles from the TM5 model. These are provided on a 1°x1° latitude-longitude grid.

The Next Landsat Satellite: The Landsat Data Continuity Mission

NASA Technical Reports Server (NTRS)

Rons, James R.; Dwyer, John L.; Barsi, Julia A.

2012-01-01

The Landsat program is one of the longest running satellite programs for Earth observations from space. The program was initiated by the launch of Landsat 1 in 1972. Since then a series of six more Landsat satellites were launched and at least one of those satellites has been in operations at all times to continuously collect images of the global land surface. The Department of Interior (DOI) U.S. Geological Survey (USGS) preserves data collected by all of the Landsat satellites at their Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. This 40-year data archive provides an unmatched record of the Earth's land surface that has undergone dramatic changes in recent decades due to the increasing pressure of a growing population and advancing technologies. EROS provides the ability for anyone to search the archive and order digital Landsat images over the internet for free. The Landsat data are a public resource for observing, characterizing, monitoring, trending, and predicting land use change over time providing an invaluable tool for those addressing the profound consequences of those changes to society. The most recent launch of a Landsat satellite occurred in 1999 when Landsat 7 was placed in orbit. While Landsat 7 remains in operation, the National Aeronautics and Space Administration (NASA) and the DOI/ USGS are building its successor satellite system currently called the Landsat Data Continuity Mission (LDCM). NASA has the lead for building and launching the satellite that will carry two Earth-viewing instruments, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The OLI will take images that measure the amount of sunlight reflected by the land surface at nine wavelengths of light with three of those wavelengths beyond the range of human vision. T1RS will collect coincident images that measure light emitted by the land surface as a function of surface temperature at two longer wavelengths well beyond the range of human vision. The DOI/USGS is developing the ground system that will command and control the LDCM satellite in orbit and manage the OLI and TIRS data transmitted by the satellite. DOI/USGS will thus operate the satellite and collect, archive, and distribute the image data as part of the EROS archive. DOI/USGS has committed to renaming LDCM as Landsat 8 following launch. By either name the satellite and its sensors will extend the 40-year archive with images sufficiently consistent with data from earlier Landsat satellites to allow multi-decadal, broad-area studies of our dynamic landscapes. The next Landsat satellite and ground system are on schedule for a January, 2013 launch.

Surface phenology and satellite sensor-derived onset of greenness: An initial comparison

USGS Publications Warehouse

Schwartz, Mark D.; Reed, Bradley C.

1999-01-01

The objective of this work was to document the utility of phenological data derived from satellite sensors by comparing them with modelled phenology. Surface phenological model outputs (first leaf and first bloom dates) were correlated positively with satellite sensor-derived start of season (SOS) dates for 1991-1995 across the eastern United States. The correlation was highest for forest (r 0.62 for deciduous trees and 0.64 for mixed woodland) and tall grass (r 0.46) and lowest for short grass (r 0.37). The average correlation over all land cover types was 0.61. Average SOS dates were consistently earlier than Spring Index dates across all land cover types. This finding and limited native tree phenology data suggest that the SOS technique detects understorey green-up in the forest rather than overstorey species. The biweekly temporal resolution of the satellite sensor data placed an upper limit on prediction accuracy; thus, year-to-year variations at individual sites were typically small. Nevertheless, the correct biweek SOS could be identified from the surface models 61% of the time, and 1 biweek 96% of the time. Further temporal refinement of the satellite sensor measurements is necessary in order to connect them with surface phenology adequately and to develop links among 'green wave' components in selected biomes.

Multi-Sensor Approach to Mapping Snow Cover Using Data From NASA's EOS Aqua and Terra Spacecraft

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M. J.

2003-12-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Over the past several decades both optical and passive microwave satellite data have been utilized for snow mapping at the regional to global scale. For the period 1978 to 2002, we have shown earlier that both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are, depending on season, less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Snow mapping using optical data is based on the magnitude of the surface reflectance while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Our previous work has defined the respective advantages and disadvantages of these two types of satellite data for snow cover mapping and it is clear that a blended product is optimal. We present a multi-sensor approach to snow mapping based both on historical data as well as data from current NASA EOS sensors. For the period 1978 to 2002 we combine data from the NOAA weekly snow charts with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend MODIS and AMSR-E data sets. An example of validation at the brightness temperature level is provided through the comparison of AMSR-E with data from the well-calibrated heritage SSM/I sensor over a large homogeneous snow-covered surface (Dome C, Antarctica). Prototype snow cover maps from AMSR-E compare well with maps derived from SSM/I. Our current blended product is being developed in the 25 km EASE-Grid while the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) allowing the blended product to indicate percent snow cover over the larger grid cell. Relationships between the percent area covered by snow as indicated by the MODIS data and the threshold for the appearance of snow as indicated by the passive microwave data are presented. Both MODIS and AMSR-E data have enhanced spatial resolution compared to the earlier data sources and examples of how this increased spatial resolution results in more accurate snow cover maps are presented. A wide range of validation data sets are being employed in this study including the NASA Cold Lands Processes Field Experiment undertaken in Colorado during 2002 and 2003.

Joint search and sensor management for geosynchronous satellites

NASA Astrophysics Data System (ADS)

Zatezalo, A.; El-Fallah, A.; Mahler, R.; Mehra, R. K.; Pham, K.

2008-04-01

Joint search and sensor management for space situational awareness presents daunting scientific and practical challenges as it requires a simultaneous search for new, and the catalog update of the current space objects. We demonstrate a new approach to joint search and sensor management by utilizing the Posterior Expected Number of Targets (PENT) as the objective function, an observation model for a space-based EO/IR sensor, and a Probability Hypothesis Density Particle Filter (PHD-PF) tracker. Simulation and results using actual Geosynchronous Satellites are presented.

Micro-satellite for space debris observation by optical sensors

NASA Astrophysics Data System (ADS)

Thillot, Marc; Brenière, Xavier; Midavaine, Thierry

2017-11-01

The purpose of this theoretical study carried out under CNES contract is to analyze the feasibility of small space debris detection and classification with an optical sensor on-board micro-satellite. Technical solutions based on active and passive sensors are analyzed and compared. For the most appropriated concept an optimization was made and theoretical performances in terms of number of detection versus class of diameter were calculated. Finally we give some preliminary physical sensor features to illustrate the concept (weight, volume, consumption,…).

GEONEX: Land Monitoring From a New Generation of Geostationary Satellite Sensors

NASA Technical Reports Server (NTRS)

Nemani, Ramakrishna; Lyapustin, Alexei; Wang, Weile; Wang, Yujie; Hashimoto, Hirofumi; Li, Shuang; Ganguly, Sangram; Michaelis, Andrew; Higuchi, Atsushi; Takaneka, Hideaki;

GEONEX: Land monitoring from a new generation of geostationary satellite sensors

NASA Astrophysics Data System (ADS)

Nemani, R. R.; Lyapustin, A.; Wang, W.; Ganguly, S.; Wang, Y.; Michaelis, A.; Hashimoto, H.; Li, S.; Higuchi, A.; Huete, A. R.; Yeom, J. M.; camacho De Coca, F.; Lee, T. J.; Takenaka, H.

2017-12-01

The latest generation of geostationary satellites carry sensors such as ABI (Advanced Baseline Imager on GOES-16) and the AHI (Advanced Himawari Imager on Himawari) that closely mimic the spatial and spectral characteristics of Earth Observing System flagship MODIS for monitoring land surface conditions. More importantly they provide observations at 5-15 minute intervals. Such high frequency data offer exciting possibilities for producing robust estimates of land surface conditions by overcoming cloud cover, enabling studies of diurnally varying local-to-regional biosphere-atmosphere interactions, and operational decision-making in agriculture, forestry and disaster management. But the data come with challenges that need special attention. For instance, geostationary data feature changing sun angle at constant view for each pixel, which is reciprocal to sun-synchronous observations, and thus require careful adaptation of EOS algorithms. Our goal is to produce a set of land surface products from geostationary sensors by leveraging NASA's investments in EOS algorithms and in the data/compute facility NEX. The land surface variables of interest include atmospherically corrected surface reflectances, snow cover, vegetation indices and leaf area index (LAI)/fraction of photosynthetically absorbed radiation (FPAR), as well as land surface temperature and fires. In order to get ready to produce operational products over the US from GOES-16 starting 2018, we have utilized 18 months of data from Himawari AHI over Australia to test the production pipeline and the performance of various algorithms for our initial tests. The end-to-end processing pipeline consists of a suite of modules to (a) perform calibration and automatic georeference correction of the AHI L1b data, (b) adopt the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce surface spectral reflectances along with compositing schemes and QA, and (c) modify relevant EOS retrieval algorithms (e.g., LAI and FPAR, GPP, etc.) for subsequent science product generation. Initial evaluation of Himawari AHI products against standard MODIS products indicate general agreement, suggesting that data from geostationary sensors can augment low earth orbit (LEO) satellite observations.

The Sentinel-2 MSI Can Increase the Temporal Resolution of 30m Satellite-Derived LAI Estimates

NASA Astrophysics Data System (ADS)

Dungan, J. L.; Li, S.; Ganguly, S.; Wang, W.; Nemani, R. R.; Ju, J.; Claverie, M.; Masek, J. G.

2016-12-01

The successful launch of the European Space Agency (ESA) Sentinel-2A (S2-A) on 23 June 2015 with its MultiSpectral Instrument (MSI) provides an important means to augment Earth-observation capabilities following the legacy of Landsat. After the three-month satellite commissioning campaign, the MSI onboard S-2A is performing very well (ESA, 2015). By 3 December 2015, the sensor data records have achieved provisional maturity status and have been accessed in level-1C Top-Of-Atmosphere (TOA) reflectance by the remote sensing community worldwide. Near-nadir observations by the MSI onboard S-2A and the Operational Land Imager (OLI) onboard Landsat 8 were collected during Simultaneous Nadir Overpasses as well as nearly coincident overpasses. This paper presents a processing chain using harmonized S-2A MSI and Landsat 8 OLI sensors to obtain increased temporal resolution in Leaf Area Index (LAI) estimates using the red-edge band B8A of MSI to replace the NIR band B08. Results demonstrate that LAI estimates from the MSI and OLI are comparable, and, given sufficient preprocessing for atmospheric correction and geometric rectification, can be used interchangeably to improve the frequency with which low LAI canopies can be monitored.

Sensor fault detection and recovery in satellite attitude control

NASA Astrophysics Data System (ADS)

Nasrolahi, Seiied Saeed; Abdollahi, Farzaneh

2018-04-01

This paper proposes an integrated sensor fault detection and recovery for the satellite attitude control system. By introducing a nonlinear observer, the healthy sensor measurements are provided. Considering attitude dynamics and kinematic, a novel observer is developed to detect the fault in angular rate as well as attitude sensors individually or simultaneously. There is no limit on type and configuration of attitude sensors. By designing a state feedback based control signal and Lyapunov stability criterion, the uniformly ultimately boundedness of tracking errors in the presence of sensor faults is guaranteed. Finally, simulation results are presented to illustrate the performance of the integrated scheme.

Minuteman 2 launched small satellite

NASA Technical Reports Server (NTRS)

Chan, Sunny; Hinders, Kriss; Martin, Trent; Mcmillian, Shandy; Sharp, Brad; Vajdos, Greg

1994-01-01

The goal of LEOSat Industries' Spring 1994 project was to design a small satellite that has a strong technology demonstration or scientific justification and incorporates a high level of student involvement. The satellite is to be launched into low earth orbit by the converted Minuteman 2 satellite launcher designed by Minotaur Designs, Inc. in 1993. The launch vehicle shroud was modified to a height of 90 inches, a diameter of 48 inches at the bottom and 35 inches at the top for a total volume of 85 cubic feet. The maximum allowable mass of the payload is about 1100 lb., depending on the launch site, orbit altitude, and inclination. The satellite designed by LEOSat Industries is TerraSat, a remote-sensing satellite that will provide information for use in space-based earth studies. It will consist of infrared and ultraviolet/visible sensors similar to the SDI-developed sensors being tested on Clementine. The sensors will be mounted on the Defense Systems, Inc. Standard Satellite-1 spacecraft bus. LEOSat has planned for two satellites orbiting the Earth with trajectories similar to that of LANDSAT 5. The semi-major axis is 7080 kilometers, the eccentricity is 0, and the inclination is 98.2 degrees. The estimated mass of TerraSat is 145 kilograms and the estimated volume is 1.8 cubic meters. The estimated cost of TerraSat is $13.7 million. The projected length of time from assembly of the sensors to launch of the spacecraft is 13 months.

ESA activities on satellite laser ranging to non-cooperative objects

NASA Astrophysics Data System (ADS)

Flohrer, Tim; Krag, Holger; Funke, Quirin; Jilete, Beatriz; Mancas, Alexandru

2016-07-01

Satellite laser ranging (SLR) to non-cooperative objects is an emerging technology that can contribute significantly to operational, modelling and mitigation needs set by the space debris population. ESA is conducting various research and development activities in SLR to non-cooperative objects. ESA's Space Situational Awareness (SSA) program supports specific activities in the Space Surveillance and Tracking (SST) segment. Research and development activities with operational aspects are run by ESA's Space Debris Office. At ESA SSA/SST comprises detecting, cataloguing and predicting the objects orbiting the Earth, and the derived applications. SST aims at facilitating research and development of sensor and data processing technologies and of related common components while staying complementary with, and in support of, national and multi-national European initiatives. SST promotes standardisation and interoperability of the technology developments. For SLR these goals are implemented through researching, developing, and deploying an expert centre. This centre shall coordinate the contribution of system-external loosely connected SLR sensors, and shall provide back calibration and expert evaluation support to the sensors. The Space Debris Office at ESA is responsible for all aspects related to space debris in the Agency. It is in charge of providing operational support to ESA and third party missions. Currently, the office studies the potential benefits of laser ranging to space debris objects to resolve close approaches to active satellites, to improve re-entry predictions of time and locations, and the more general SLR support during contingency situations. The office studies the determination of attitude and attitude motion of uncooperative objects with special focus on the combination of SLR, light-curve, and radar imaging data. Generating sufficiently precise information to allow for the acquisition of debris objects by a SLR sensor in a stare-and-chase scenario, or from externally provided orbital information, is also investigated. In our paper we will outline the motivation and objectives, as well as detail the current status of the various and parallel SLR-related SST and Space Debris Office activities at ESA. We will provide an overview on plans for SLR activities in research and development and in operational support. Current gaps in the standardisation of data exchange and sensor interfaces will be addressed, reflecting the need of coordinating multiple stations in all tasks.

Neural networks for satellite remote sensing and robotic sensor interpretation

NASA Astrophysics Data System (ADS)

Martens, Siegfried

Remote sensing of forests and robotic sensor fusion can be viewed, in part, as supervised learning problems, mapping from sensory input to perceptual output. This dissertation develops ARTMAP neural networks for real-time category learning, pattern recognition, and prediction tailored to remote sensing and robotics applications. Three studies are presented. The first two use ARTMAP to create maps from remotely sensed data, while the third uses an ARTMAP system for sensor fusion on a mobile robot. The first study uses ARTMAP to predict vegetation mixtures in the Plumas National Forest based on spectral data from the Landsat Thematic Mapper satellite. While most previous ARTMAP systems have predicted discrete output classes, this project develops new capabilities for multi-valued prediction. On the mixture prediction task, the new network is shown to perform better than maximum likelihood and linear mixture models. The second remote sensing study uses an ARTMAP classification system to evaluate the relative importance of spectral and terrain data for map-making. This project has produced a large-scale map of remotely sensed vegetation in the Sierra National Forest. Network predictions are validated with ground truth data, and maps produced using the ARTMAP system are compared to a map produced by human experts. The ARTMAP Sierra map was generated in an afternoon, while the labor intensive expert method required nearly a year to perform the same task. The robotics research uses an ARTMAP system to integrate visual information and ultrasonic sensory information on a B14 mobile robot. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. ARTMAP effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.

Determination of Primary Spectral Bands for Remote Sensing of Aquatic Environments.

PubMed

Lee, ZhongPing; Carder, Kendall; Arnone, Robert; He, MingXia

2007-12-20

About 30 years ago, NASA launched the first ocean-color observing satellite:the Coastal Zone Color Scanner. CZCS had 5 bands in the visible-infrared domain with anobjective to detect changes of phytoplankton (measured by concentration of chlorophyll) inthe oceans. Twenty years later, for the same objective but with advanced technology, theSea-viewing Wide Field-of-view Sensor (SeaWiFS, 7 bands), the Moderate-ResolutionImaging Spectrometer (MODIS, 8 bands), and the Medium Resolution ImagingSpectrometer (MERIS, 12 bands) were launched. The selection of the number of bands andtheir positions was based on experimental and theoretical results achieved before thedesign of these satellite sensors. Recently, Lee and Carder (2002) demonstrated that foradequate derivation of major properties (phytoplankton biomass, colored dissolved organicmatter, suspended sediments, and bottom properties) in both oceanic and coastalenvironments from observation of water color, it is better for a sensor to have ~15 bands inthe 400 - 800 nm range. In that study, however, it did not provide detailed analysesregarding the spectral locations of the 15 bands. Here, from nearly 400 hyperspectral (~ 3-nm resolution) measurements of remote-sensing reflectance (a measure of water color)taken in both coastal and oceanic waters covering both optically deep and optically shallowwaters, first- and second-order derivatives were calculated after interpolating themeasurements to 1-nm resolution. From these derivatives, the frequency of zero values foreach wavelength was accounted for, and the distribution spectrum of such frequencies wasobtained. Furthermore, the wavelengths that have the highest appearance of zeros wereidentified. Because these spectral locations indicate extrema (a local maximum orminimum) of the reflectance spectrum or inflections of the spectral curvature, placing the bands of a sensor at these wavelengths maximizes the potential of capturing (and then restoring) the spectral curve, and thus maximizes the potential of accurately deriving properties of the water column and/or bottom of various aquatic environments with a multi-band sensor.

Fusion of radar and satellite target measurements

NASA Astrophysics Data System (ADS)

Moy, Gabriel; Blaty, Donald; Farber, Morton; Nealy, Carlton

2011-06-01

A potentially high payoff for the ballistic missile defense system (BMDS) is the ability to fuse the information gathered by various sensor systems. In particular, it may be valuable in the future to fuse measurements made using ground based radars with passive measurements obtained from satellite-based EO/IR sensors. This task can be challenging in a multitarget environment in view of the widely differing resolution between active ground-based radar and an observation made by a sensor at long range from a satellite platform. Additionally, each sensor system could have a residual pointing bias which has not been calibrated out. The problem is further compounded by the possibility that an EO/IR sensor may not see exactly the same set of targets as a microwave radar. In order to better understand the problems involved in performing the fusion of metric information from EO/IR satellite measurements with active microwave radar measurements, we have undertaken a study of this data fusion issue and of the associated data processing techniques. To carry out this analysis, we have made use of high fidelity simulations to model the radar observations from a missile target and the observations of the same simulated target, as gathered by a constellation of satellites. In the paper, we discuss the improvements seen in our tests when fusing the state vectors, along with the improvements in sensor bias estimation. The limitations in performance due to the differing phenomenology between IR and microwave radar are discussed as well.

A micro-vibration generated method for testing the imaging quality on ground of space remote sensing

NASA Astrophysics Data System (ADS)

Gu, Yingying; Wang, Li; Wu, Qingwen

2018-03-01

In this paper, a novel method is proposed, which can simulate satellite platform micro-vibration and test the impact of satellite micro-vibration on imaging quality of space optical remote sensor on ground. The method can generate micro-vibration of satellite platform in orbit from vibrational degrees of freedom, spectrum, magnitude, and coupling path. Experiment results show that the relative error of acceleration control is within 7%, in frequencies from 7Hz to 40Hz. Utilizing this method, the system level test about the micro-vibration impact on imaging quality of space optical remote sensor can be realized. This method will have an important applications in testing micro-vibration tolerance margin of optical remote sensor, verifying vibration isolation and suppression performance of optical remote sensor, exploring the principle of micro-vibration impact on imaging quality of optical remote sensor.

The fusion of satellite and UAV data: simulation of high spatial resolution band

NASA Astrophysics Data System (ADS)

Jenerowicz, Agnieszka; Siok, Katarzyna; Woroszkiewicz, Malgorzata; Orych, Agata

2017-10-01

Remote sensing techniques used in the precision agriculture and farming that apply imagery data obtained with sensors mounted on UAV platforms became more popular in the last few years due to the availability of low- cost UAV platforms and low- cost sensors. Data obtained from low altitudes with low- cost sensors can be characterised by high spatial and radiometric resolution but quite low spectral resolution, therefore the application of imagery data obtained with such technology is quite limited and can be used only for the basic land cover classification. To enrich the spectral resolution of imagery data acquired with low- cost sensors from low altitudes, the authors proposed the fusion of RGB data obtained with UAV platform with multispectral satellite imagery. The fusion is based on the pansharpening process, that aims to integrate the spatial details of the high-resolution panchromatic image with the spectral information of lower resolution multispectral or hyperspectral imagery to obtain multispectral or hyperspectral images with high spatial resolution. The key of pansharpening is to properly estimate the missing spatial details of multispectral images while preserving their spectral properties. In the research, the authors presented the fusion of RGB images (with high spatial resolution) obtained with sensors mounted on low- cost UAV platforms and multispectral satellite imagery with satellite sensors, i.e. Landsat 8 OLI. To perform the fusion of UAV data with satellite imagery, the simulation of the panchromatic bands from RGB data based on the spectral channels linear combination, was conducted. Next, for simulated bands and multispectral satellite images, the Gram-Schmidt pansharpening method was applied. As a result of the fusion, the authors obtained several multispectral images with very high spatial resolution and then analysed the spatial and spectral accuracies of processed images.

Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake

PubMed Central

Pareeth, Sajid; Salmaso, Nico; Adrian, Rita; Neteler, Markus

2016-01-01

Availability of remotely sensed multi-spectral images since the 1980’s, which cover three decades of voluminous data could help researchers to study the changing dynamics of bio-physical characteristics of land and water. In this study, we introduce a new methodology to develop homogenised Lake Surface Water Temperature (LSWT) from multiple polar orbiting satellites. Precisely, we developed homogenised 1 km daily LSWT maps covering the last 30 years (1986 to 2015) combining data from 13 satellites. We used a split-window technique to derive LSWT from brightness temperatures and a modified diurnal temperature cycle model to homogenise data which were acquired between 8:00 to 17:00 UTC. Gaps in the temporal LSWT data due to the presence of clouds were filled by applying Harmonic ANalysis of Time Series (HANTS). The satellite derived LSWT maps were validated based on long-term monthly in-situ bulk temperature measurements in Lake Garda, the largest lake in Italy. We found the satellite derived homogenised LSWT being significantly correlated to in-situ data. The new LSWT time series showed a significant annual rate of increase of 0.020 °C yr−1 (*P < 0.05), and of 0.036 °C yr−1 (***P < 0.001) during summer. PMID:27502177

The OMI Aerosol Absorption Product: An A-train application

NASA Astrophysics Data System (ADS)

Torres, O.; Jethva, H. T.; Ahn, C.

2017-12-01

Because of the uniquely large sensitivity of satellite-measured near-UV radiances to absorption by desert dust, carbonaceous and volcanic ash aerosols, observations by a variety of UV-capable sensors have been routinely used over the last forty years in both qualitative and quantitative applications for estimating the absorption properties of these aerosol types. In this presentation we will discuss a multi-sensor application involving observations from A-train sensors OMI, AIRS and CALIOP for the creation of a 13-year record of aerosol optical depth (AOD) and single scattering albedo (SSA). Determination of aerosol type, in terms of particle size distribution and refractive index, is an important algorithmic step that requires using external information. AIRS CO measurements are used as carbonaceous aerosols tracer to differentiate this aerosol type from desert dust. On the other hand, the height of the absorbing aerosol layer, an important parameter in UV aerosol retrievals, is prescribed using a CALIOP-based climatology. The combined use of these observations in the developments of the OMI long-term AOD/SSA record will be discussed along with an evaluation of retrieval results using independent observations.

Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach

PubMed Central

Girrbach, Fabian; Hol, Jeroen D.; Bellusci, Giovanni; Diehl, Moritz

2017-01-01

The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem. PMID:28534857

Preliminary radiometric calibration assessment of ALOS AVNIR-2

USGS Publications Warehouse

Bouvet, M.; Goryl, P.; Chander, G.; Santer, R.; Saunier, S.

2008-01-01

This paper summarizes the activities carried out in the frame of the data quality activities of the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) sensor onboard the Advanced Land Observing Satellite (ALOS). Assessment of the radiometric calibration of the AVNIR-2 multi-spectral imager is achieved via three intercomparisons to currently flying sensors over the Libyan desert, during the first year of operation. AU three methodologies indicate a slight underestimation of AVNIR-2 in band 1 by 4 to 7% with respect to other sensors radiometric scale. Band 2 does not show any obvious bias. Results for band 3 are affected by saturation due to inappropriate gain setting. Two methodologies indicate no significant bias in band 4. Preliminary results indicate possible degradations of the AVNIR-2 channels, which, when modeled as an exponentially decreasing functions, have time constants of respectively 13.2 %.year-1, 8.8%.year-1 and 0.1%.year-1 in band 1, 2 and 4 (with respect to the radiometric scale of the MEdium Resolution Imaging Spectrometer, MERIS). Longer time series of AVNIR-2 data are needed to draw final conclusions. ?? 2007 IEEE.

Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach.

PubMed

Girrbach, Fabian; Hol, Jeroen D; Bellusci, Giovanni; Diehl, Moritz

2017-05-19

The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem.

Effective Utilization of Satellite Observations for Assessing Transnational Impact of Disasters

NASA Astrophysics Data System (ADS)

Alozie, J. E.; Anuforom, A. C.

2014-12-01

General meteorological observations sources for the surface, upper air and outer space are conducted using different technological equipment and instruments that meet international standards prescribed and approved by the United Nations organizations such as the International Civil Aviation Organization (ICAO) and the World Meteorological Organization (WMO). Satellite weather observations are critical for effective monitoring of the developments, propagations and disseminations of cold clouds and their expected adverse weather conditions as they move across national and transnational boundaries. The Nigerian Meteorological Agency (NiMet) which is the national weather service provider for Nigeria, utilizes an array of satellite products obtained from mainly the European Meteorological Satellite (EUMETSAT) for its routine weather and climate monitoring and forecasts. Overtime, NiMet has used weather workstations such as MSG, SYNERGIE and now PUMA for accessing satellite products such as RGB, Infra-red, Water vapour and the Multi-sensor Precipitation Estimate (MPE) obtained at near real-time periods. The satellite imageries find extensive applications in the delivery of early warning of raising of severe weather conditions such as dust storm and dust haze during the harmattan season (November - February); and thunderstorm accompanied by severe lightning and destructive strong winds. The paper will showcase some special cases of the tracking of squall lines and issuance of weather alerts through the media. The good result is that there was limited damage to infrastructure and no loss of life from the flash floods caused by the heavy rainfall from the squally thunderstorm.

A New Technique For Quantifying Effusive Volcanic Activity at Tolbachik Volcano Using Multiple Remote Sensing Platforms

NASA Astrophysics Data System (ADS)

McAlpin, D. B.; Meyer, F. J.; Dehn, J.; Webley, P. W.

2016-12-01

In 1976, "The Great Tolbachik Fissure Eruption," became the largest basaltic eruption in the recorded history of the Kamchatka Peninsula. In November 2012, after thirty-six years of quiescence, Tolbachik again erupted, and continued for nine months until its end in August, 2013. Observers of the 2012-13 eruption reported a mostly effusive eruption from two main fissures, long, rapidly moving lava flows, and ash clouds of up to 6 km. Initial estimates of effusive activity reported an approximate volume of 0.52 km³ over an area of more than 35 km². In this analysis, we provide updated effusion estimates for the Tolbachik eruption, determined by thermal data acquired by the Advanced Very High Resolution Radiometer (AVHRR) satellites. Each of the four AVHRR satellites carries a broad-band, five channel sensor that acquires data in the visible and infrared portions of the electromagnetic spectrum, with each satellite completing 14 daily Earth orbits. A critical component to the volume estimates is a determination of fissure size and the area of lava flow at different times during the eruption. For this purpose, we acquired optical satellite images obtained from three orbiting platforms: the Advanced Land Imager (ALI),) aboard the Earth Observer-1 (EO-1) satellite, the Operational Land Imager (OLI) aboard Landsat 8, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard NASA's Terra satellite. From these multiple platforms, lava flow maps were prepared from repeat acquisitions over the course of the eruption. Periodic lava flow measurements clarify effusion rates as instantaneous discharge rates, mean effusion rates over time, and an overall effusion rate over the entire eruption. Given the natural limitations of effusion estimates derived from thermal data, our results are compared to effusion estimates derived by DEM differencing to evaluate accuracy. This analysis is a true multi-sensor technique that affords a method to rapidly quantify effusive volcanic activity in terms of flow temperature, lava volume, and area on a basis coeval to the eruption, and has important implications for scientific and hazard analyses of future volcanic episodes.

Space-based IR tracking bias removal using background star observations

NASA Astrophysics Data System (ADS)

Clemons, T. M., III; Chang, K. C.

2009-05-01

This paper provides the results of a proposed methodology for removing sensor bias from a space-based infrared (IR) tracking system through the use of stars detected in the background field of the tracking sensor. The tracking system consists of two satellites flying in a lead-follower formation tracking a ballistic target. Each satellite is equipped with a narrow-view IR sensor that provides azimuth and elevation to the target. The tracking problem is made more difficult due to a constant, non-varying or slowly varying bias error present in each sensor's line of sight measurements. As known stars are detected during the target tracking process, the instantaneous sensor pointing error can be calculated as the difference between star detection reading and the known position of the star. The system then utilizes a separate bias filter to estimate the bias value based on these detections and correct the target line of sight measurements to improve the target state vector. The target state vector is estimated through a Linearized Kalman Filter (LKF) for the highly non-linear problem of tracking a ballistic missile. Scenarios are created using Satellite Toolkit(C) for trajectories with associated sensor observations. Mean Square Error results are given for tracking during the period when the target is in view of the satellite IR sensors. The results of this research provide a potential solution to bias correction while simultaneously tracking a target.

Satellite-Derived Sea Surface Temperature: Workshop 1

NASA Technical Reports Server (NTRS)

Njoku, E. G.

1983-01-01

Satellite measurements of sea surface temperature are now possible using a variety of sensors. The present accuracies of these methods are in the range of 0.5 to 2.0 C. This makes them potentially useful for synoptic studies of ocean currents and for global monitoring of climatological anomalies. To improve confidence in the satellite data, objective evaluations of sensor accuracies are necessary, and the conditions under which these accuracies degrade need to be understood. The Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite was studied. Sea surface temperatures, derived from November 1979 SMMR data, were compared globally against ship measurements and climatology, using facilities of the JPL Pilot Ocean Data System. Methods for improved data analysis and plans for additional workshops to incorporate data from other sensors were discussed.

Synergistically combining Optical and Thermal radiative transfer modelswithin the EO-LDAS data assimilation framework to estimate land surfaceand component temperatures from MODIS and Sentinel-3

NASA Astrophysics Data System (ADS)

Timmermans, J.; Gomez-Dans, J. L.; Verhoef, W.; Tol, C. V. D.; Lewis, P.

2017-12-01

Evapotranspiration (ET) cannot be directly measured from space. Instead it relies on modelling approaches that use several land surface parameters (LSP), LAI and LST, in conjunction with meteorological parameters. Such a modelling approach presents two caveats: the validity of the model, and the consistency between the different input parameters. Often this second step is not considered, ignoring that without good inputs no decent output can provided. When LSP- dynamics contradict each other, the output of the model cannot be representative of reality. At present however, the LSPs used in large scale ET estimations originate from different single-sensor retrieval-approaches and even from different satellite sensors. In response, the Earth Observation Land Data Assimilation System (EOLDAS) was developed. EOLDAS uses a multi-sensor approach to couple different satellite observations/types to radiative transfer models (RTM), consistently. It is therefore capable of synergistically estimating a variety of LSPs. Considering that ET is most sensitive to the temperatures of the land surface (components), the goal of this research is to expand EOLDAS to the thermal domain. This research not only focuses on estimating LST, but also on retrieving (soil/vegetation, Sunlit/shaded) component temperatures, to facilitate dual/quad-source ET models. To achieve this, The Soil Canopy Observations of Photosynthesis and Energy (SCOPE) model was integrated into EOLDAS. SCOPE couples key-parameters to key-processes, such as photosynthesis, ET and optical/thermal RT. In this research SCOPE was also coupled to MODTRAN RTM, in order to estimate BOA component temperatures directly from TOA observations. This paper presents the main modelling steps of integrating these complex models into an operational platform. In addition it highlights the actual retrieval using different satellite observations, such as MODIS and Sentinel-3, and meteorological variables from the ERA-Interim.

Spacecraft technology. [development of satellites and remote sensors

NASA Technical Reports Server (NTRS)

1975-01-01

Developments in spacecraft technology are discussed with emphasis on the Explorer satellite program. The subjects considered include the following: (1) nutational behavior of the Explorer-45 satellite, (2) panoramic sensor development, (3) onboard camera signal processor for Explorer satellites, and (4) microcircuit development. Information on the zero gravity testing of heat pipes is included. Procedures for cleaning heat treated aluminum heat pipes are explained. The development of a five-year magnetic tape, an accurate incremental angular encoder, and a blood freezing apparatus for leukemia research are also discussed.

Signal Conditioning for the Kalman Filter: Application to Satellite Attitude Estimation with Magnetometer and Sun Sensors

PubMed Central

Esteban, Segundo; Girón-Sierra, Jose M.; Polo, Óscar R.; Angulo, Manuel

2016-01-01

Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework. PMID:27809250

Signal Conditioning for the Kalman Filter: Application to Satellite Attitude Estimation with Magnetometer and Sun Sensors.

PubMed

Esteban, Segundo; Girón-Sierra, Jose M; Polo, Óscar R; Angulo, Manuel

2016-10-31

Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.

Roi-Orientated Sensor Correction Based on Virtual Steady Reimaging Model for Wide Swath High Resolution Optical Satellite Imagery

NASA Astrophysics Data System (ADS)

Zhu, Y.; Jin, S.; Tian, Y.; Wang, M.

2017-09-01

To meet the requirement of high accuracy and high speed processing for wide swath high resolution optical satellite imagery under emergency situation in both ground processing system and on-board processing system. This paper proposed a ROI-orientated sensor correction algorithm based on virtual steady reimaging model for wide swath high resolution optical satellite imagery. Firstly, the imaging time and spatial window of the ROI is determined by a dynamic search method. Then, the dynamic ROI sensor correction model based on virtual steady reimaging model is constructed. Finally, the corrected image corresponding to the ROI is generated based on the coordinates mapping relationship which is established by the dynamic sensor correction model for corrected image and rigours imaging model for original image. Two experimental results show that the image registration between panchromatic and multispectral images can be well achieved and the image distortion caused by satellite jitter can be also corrected efficiently.

Spatio-Temporal Variations in the Associations between Hourly PM2.5 and Aerosol Optical Depth (AOD) from MODIS Sensors on Terra and Aqua*

PubMed Central

Kim, Minho; Zhang, Xingyou; Holt, James B.; Liu, Yang

2015-01-01

Recent studies have explored the relationship between aerosol optical depth (AOD) measurements by satellite sensors and concentrations of particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5). However, relatively little is known about spatial and temporal patterns in this relationship across the contiguous United States. In this study, we investigated the relationship between US Environmental Protection Agency estimates of PM2.5 concentrations and Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements provided by two NASA satellites (Terra and Aqua) across the contiguous United States during 2005. We found that the combined use of both satellite sensors provided more AOD coverage than the use of either satellite sensor alone, that the correlation between AOD measurements and PM2.5 concentrations varied substantially by geographic location, and that this correlation was stronger in the summer and fall than that in the winter and spring. PMID:26336576

Development and Commissioning Results of the Hybrid Sensor Bus Engineering Qualification Model

NASA Astrophysics Data System (ADS)

Hurni, Andreas; Putzer, Phillipp; Roner, Markus; Gurster, Markus; Hulsemeyer, Christian; Lemke, Norbert M. K.

2016-08-01

In order to reduce mass, AIT effort and overall costs of classical point-to-point wired temperature sensor harness on-board spacecraft OHB System AGhas introduced the Hybrid Sensor Bus (HSB) system which interrogates sensors connected in a bus architecture. To use the advantages of electrical as wellas of fiber-optical sensing technologies, HSB is designed as a modular measurement system interrogating digital sensors connected on electricalsensor buses based on I2C and fiber-optical sensor buses based on fiber Bragg grating (FBG) sensors inscribed in optical fibers. Fiber-optical sensor bus networks on-board satellites are well suited for temperature measurement due to low mass, electro-magnetic insensitivity and the capability to embed them inside structure parts. The lightweight FBG sensors inscribed in radiation tolerant fibers can reach every part of the satellite. HSB has been developed in the frame of the ESA ARTES program with European and German co- funding and will be verified as flight demonstrator on- board the German Heinrich Hertz satellite (H2Sat).In this paper the Engineering Qualification Model (EQM) development of HSB and first commissioning results are presented. For the HSB development requirements applicable for telecommunication satellite platforms have been considered. This includes an operation of at least 15 years in a geostationary orbit.In Q3/2016 the qualification test campaign is planned to be carried out. The HSB EQM undergoes a full qualification according to ECSS. The paper concludes with an outlook regarding this HSB flight demonstrator development and its in-orbit verification (IOV) on board H2Sat.

Removing the solar exclusion with high altitude satellites [Orbital strategies to mitigate the Solar Exclusion Effect on Space-Based Observation of the Geosynchronous Belt

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vallado, David A.; Cefola, Paul J.; Kiziah, Rex R.

Here, observing geosynchronous satellites has numerous applications. Lighting conditions near the equinoxes routinely cause problems for traditional observations of sensors near the equator – the solar exclusion. We investigate using sensors on satellites (in polar and high- altitude orbits) to observe satellites that are in geosynchronous orbit. It is hoped that these satellite configurations will alleviate many of these problems. Assessing the orbit insertion and station-keeping requirements are important to understand. We summarize the literature to understand the relevant perturbing forces and assess the delta-v requirements.

Removing the solar exclusion with high altitude satellites [Orbital strategies to mitigate the Solar Exclusion Effect on Space-Based Observation of the Geosynchronous Belt

DOE PAGES

Vallado, David A.; Cefola, Paul J.; Kiziah, Rex R.; ...

2016-09-09

Here, observing geosynchronous satellites has numerous applications. Lighting conditions near the equinoxes routinely cause problems for traditional observations of sensors near the equator – the solar exclusion. We investigate using sensors on satellites (in polar and high- altitude orbits) to observe satellites that are in geosynchronous orbit. It is hoped that these satellite configurations will alleviate many of these problems. Assessing the orbit insertion and station-keeping requirements are important to understand. We summarize the literature to understand the relevant perturbing forces and assess the delta-v requirements.

Phased Array-Fed Reflector (PAFR) Antenna Architectures for Space-Based Sensors

NASA Technical Reports Server (NTRS)

Cooley, Michael E.

2014-01-01

Communication link and target ranges for satellite communications (SATCOM) and space-based sensors (e.g. radars) vary from approximately 1000 km (for LEO satellites) to 35,800 km (for GEO satellites). At these long ranges, large antenna gains are required and legacy payloads have usually employed large reflectors with single beams that are either fixed or mechanically steered. For many applications, there are inherent limitations that are associated with the use of these legacy antennas/payloads. Hybrid antenna designs using Phased Array Fed Reflectors (PAFRs) provide a compromise between reflectors and Direct Radiating phased Arrays (DRAs). PAFRs provide many of the performance benefits of DRAs while utilizing much smaller, lower cost (feed) arrays. The primary limitation associated with hybrid PAFR architectures is electronic scan range; approximately +/-5 to +/- 10 degrees is typical, but this range depends on many factors. For LEO applications, the earth FOV is approximately +/-55 degrees which is well beyond the range of electronic scanning for PAFRs. However, for some LEO missions, limited scanning is sufficient or the CONOPS and space vehicle designs can be developed to incorporate a combination mechanical slewing and electronic scanning. In this paper, we review, compare and contrast various PAFR architectures with a focus on their general applicability to space missions. We compare the RF performance of various PAFR architectures and describe key hardware design and implementation trades. Space-based PAFR designs are highly multi-disciplinary and we briefly address key hardware engineering design areas. Finally, we briefly describe two PAFR antenna architectures that have been developed at Northrop Grumman.

Remote Sensing of Multi-Level Wind Fields with High-Energy Airborne Scanning Coherent Doppler Lidar

NASA Technical Reports Server (NTRS)

Rothermel, Jeffry; Olivier, Lisa D.; Banta, Robert M.; Hardesty, R. Michael; Howell, James N.; Cutten, Dean R.; Johnson, Steven C.; Menzies, Robert T.; Tratt, David M.

1997-01-01

The atmospheric lidar remote sensing groups of NOAA Environmental Technology Laboratory, NASA Marshall Space Flight Center, and Jet Propulsion Laboratory have developed and flown a scanning, 1 Joule per pulse, CO2 coherent Doppler lidar capable of mapping a three-dimensional volume of atmospheric winds and aerosol backscatter in the troposphere and lower stratosphere. Applications include the study of severe and non-severe atmospheric flows, intercomparisons with other sensors, and the simulation of prospective satellite Doppler lidar wind profilers. Examples of wind measurements are given for the marine boundary layer and near the coastline of the western United States.

Remote sensing of multi-level wind fields with high-energy airborne scanning coherent Doppler lidar.

PubMed

Rothermel, J; Olivier, L; Banta, R; Hardesty, R M; Howell, J; Cutten, D; Johnson, S; Menzies, R; Tratt, D M

1998-01-19

The atmospheric lidar remote sensing groups of NOAA Environmental Technology Laboratory, NASA Marshall Space Flight Center, and Jet Propulsion Laboratory have developed and flown a scanning, 1 Joule per pulse, CO2 coherent Doppler lidar capable of mapping a three-dimensional volume of atmospheric winds and aerosol backscatter in the planetary boundary layer, free troposphere, and lower stratosphere. Applications include the study of severe and non-severe atmospheric flows, intercomparisons with other sensors, and the simulation of prospective satellite Doppler lidar wind profilers. Examples of wind measurements are given for the marine boundary layer and near the coastline of the western United States.

A Micromechanical INS/GPS System for Small Satellites

NASA Technical Reports Server (NTRS)

Barbour, N.; Brand, T.; Haley, R.; Socha, M.; Stoll, J.; Ward, P.; Weinberg, M.

1995-01-01

The cost and complexity of large satellite space missions continue to escalate. To reduce costs, more attention is being directed toward small lightweight satellites where future demand is expected to grow dramatically. Specifically, micromechanical inertial systems and microstrip global positioning system (GPS) antennas incorporating flip-chip bonding, application specific integrated circuits (ASIC) and MCM technologies will be required. Traditional microsatellite pointing systems do not employ active control. Many systems allow the satellite to point coarsely using gravity gradient, then attempt to maintain the image on the focal plane with fast-steering mirrors. Draper's approach is to actively control the line of sight pointing by utilizing on-board attitude determination with micromechanical inertial sensors and reaction wheel control actuators. Draper has developed commercial and tactical-grade micromechanical inertial sensors, The small size, low weight, and low cost of these gyroscopes and accelerometers enable systems previously impractical because of size and cost. Evolving micromechanical inertial sensors can be applied to closed-loop, active control of small satellites for micro-radian precision-pointing missions. An inertial reference feedback control loop can be used to determine attitude and line of sight jitter to provide error information to the controller for correction. At low frequencies, the error signal is provided by GPS. At higher frequencies, feedback is provided by the micromechanical gyros. This blending of sensors provides wide-band sensing from dc to operational frequencies. First order simulation has shown that the performance of existing micromechanical gyros, with integrated GPS, is feasible for a pointing mission of 10 micro-radians of jitter stability and approximately 1 milli-radian absolute error, for a satellite with 1 meter antenna separation. Improved performance micromechanical sensors currently under development will be suitable for a range of micro-nano-satellite applications.

Earth remote sensing with NPOESS: instruments and environmental data products

NASA Astrophysics Data System (ADS)

Glackin, David L.; Cunningham, John D.; Nelson, Craig S.

2004-02-01

The NPOESS (National Polar-orbiting Operational Environmental Satellite System) program represents the merger of the NOAA POES (Polar-orbiting Environmental Satellite) program and the DoD DMSP (Defense Meteorological Satellite Program) satellites. Established by presidential directive in 1994, a tri-agency Integrated Program Office (IPO) in Silver Spring, Maryland, has been managing NPOESS development, and is staffed by representatives of NOAA, DoD, and NASA. NPOESS is being designed to provide 55 atmospheric, oceanographic, terrestrial, and solar-geophysical data products, and will disseminate them to civilian and military users worldwide. The first NPOESS satellite is scheduled to be launched late in this decade, with the other two satellites of the three-satellite constellation due to be launched over the ensuing four years. NPOESS will remain operational for at least ten years. The 55 Environmental Data Records (EDRs) will be provided by a number of instruments, many of which will be briefly described in this paper. The instruments will be hosted in various combinations on three NPOESS platforms in three distinct polar sun-synchronous orbits. The instrument complement represents the combined requirements of the weather, climate, and environmental remote sensing communities. The three critical instruments are VIIRS (Visible/Infrared Imager-Radiometer Suite), CMIS (Conical Microwave Imager/Sounder), and CrIS (Cross-track Infrared Sounder). The other IPO-developed instruments are OMPS (Ozone Mapper/Profiler Suite), GPSOS (Global Positioning System Occultation Sensor), the APS (Aerosol Polarimeter Sensor), and the SESS (Space Environment Sensor Suite). NPOESS will also carry various "leveraged" instruments, i.e., ones that do not require development by the IPO. These include the ATMS (Advanced Technology Microwave Sounder), the TSIS (Total Solar Irradiance Sensor), the ERBS (Earth Radiation Budget Sensor), and the ALT (Radar Altimeter).

Performance Assessment and Geometric Calibration of RESOURCESAT-2

NASA Astrophysics Data System (ADS)

Radhadevi, P. V.; Solanki, S. S.; Akilan, A.; Jyothi, M. V.; Nagasubramanian, V.

2016-06-01

Resourcesat-2 (RS-2) has successfully completed five years of operations in its orbit. This satellite has multi-resolution and multi-spectral capabilities in a single platform. A continuous and autonomous co-registration, geo-location and radiometric calibration of image data from different sensors with widely varying view angles and resolution was one of the challenges of RS-2 data processing. On-orbit geometric performance of RS-2 sensors has been widely assessed and calibrated during the initial phase operations. Since then, as an ongoing activity, various geometric performance data are being generated periodically. This is performed with sites of dense ground control points (GCPs). These parameters are correlated to the direct geo-location accuracy of the RS-2 sensors and are monitored and validated to maintain the performance. This paper brings out the geometric accuracy assessment, calibration and validation done for about 500 datasets of RS-2. The objectives of this study are to ensure the best absolute and relative location accuracy of different cameras, location performance with payload steering and co-registration of multiple bands. This is done using a viewing geometry model, given ephemeris and attitude data, precise camera geometry and datum transformation. In the model, the forward and reverse transformations between the coordinate systems associated with the focal plane, payload, body, orbit and ground are rigorously and explicitly defined. System level tests using comparisons to ground check points have validated the operational geo-location accuracy performance and the stability of the calibration parameters.

The Community Cloud retrieval for CLimate (CC4CL) - Part 1: A framework applied to multiple satellite imaging sensors

NASA Astrophysics Data System (ADS)

Sus, Oliver; Stengel, Martin; Stapelberg, Stefan; McGarragh, Gregory; Poulsen, Caroline; Povey, Adam C.; Schlundt, Cornelia; Thomas, Gareth; Christensen, Matthew; Proud, Simon; Jerg, Matthias; Grainger, Roy; Hollmann, Rainer

2018-06-01

We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02°. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea-West Africa. The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.

Development of a fusion approach selection tool

NASA Astrophysics Data System (ADS)

Pohl, C.; Zeng, Y.

2015-06-01

During the last decades number and quality of available remote sensing satellite sensors for Earth observation has grown significantly. The amount of available multi-sensor images along with their increased spatial and spectral resolution provides new challenges to Earth scientists. With a Fusion Approach Selection Tool (FAST) the remote sensing community would obtain access to an optimized and improved image processing technology. Remote sensing image fusion is a mean to produce images containing information that is not inherent in the single image alone. In the meantime the user has access to sophisticated commercialized image fusion techniques plus the option to tune the parameters of each individual technique to match the anticipated application. This leaves the operator with an uncountable number of options to combine remote sensing images, not talking about the selection of the appropriate images, resolution and bands. Image fusion can be a machine and time-consuming endeavour. In addition it requires knowledge about remote sensing, image fusion, digital image processing and the application. FAST shall provide the user with a quick overview of processing flows to choose from to reach the target. FAST will ask for available images, application parameters and desired information to process this input to come out with a workflow to quickly obtain the best results. It will optimize data and image fusion techniques. It provides an overview on the possible results from which the user can choose the best. FAST will enable even inexperienced users to use advanced processing methods to maximize the benefit of multi-sensor image exploitation.

Production of a long-term global water vapor and liquid water data set using ultra-fast methods to assimilate multi-satellite and radiosonde observations

NASA Technical Reports Server (NTRS)

Vonderhaar, Thomas H.; Randel, David L.; Reinke, Donald L.; Stephens, Graeme L.; Ringerud, Mark A.; Combs, Cynthia L.; Greenwald, Thomas J.; Wittmeyer, Ian L.

1995-01-01

There is a well-documented requirement for a comprehensive and accurate global moisture data set to assist many important studies in atmospheric science. Currently, atmospheric water vapor measurements are made from a variety of sources including radiosondes, aircraft and surface observations, and in recent years, by various satellite instruments. Creating a global data set from a single measuring system produces results that are useful and accurate only in specific situations and/or areas. Therefore, an accurate global moisture data set has been derived from a combination of these measurement systems. Under a NASA peer-reviewed contract, STC-METSAT produced two 5-yr (1988-1992) global data sets. One is the total column (integrated) water vapor data set and the other, a global layered water vapor data set using a combination of radiosonde observations, Television and Infrared Observation Satellite (TIROS) Operational Satellite (TOVS), and Special Sensor Microwave/Imager (SSM/I) data sets. STC-METSAT also produced a companion, global, integrated liquid water data set. The complete data set (all three products) has been named NVAP, an anachronym for NASA Water Vapor Project. STC-METSAT developed methods to process the data at a daily time scale and 1 x 1 deg spatial resolution.

Energy Efficient Real-Time Scheduling Using DPM on Mobile Sensors with a Uniform Multi-Cores

PubMed Central

Kim, Youngmin; Lee, Chan-Gun

2017-01-01

In wireless sensor networks (WSNs), sensor nodes are deployed for collecting and analyzing data. These nodes use limited energy batteries for easy deployment and low cost. The use of limited energy batteries is closely related to the lifetime of the sensor nodes when using wireless sensor networks. Efficient-energy management is important to extending the lifetime of the sensor nodes. Most effort for improving power efficiency in tiny sensor nodes has focused mainly on reducing the power consumed during data transmission. However, recent emergence of sensor nodes equipped with multi-cores strongly requires attention to be given to the problem of reducing power consumption in multi-cores. In this paper, we propose an energy efficient scheduling method for sensor nodes supporting a uniform multi-cores. We extend the proposed T-Ler plane based scheduling for global optimal scheduling of a uniform multi-cores and multi-processors to enable power management using dynamic power management. In the proposed approach, processor selection for a scheduling and mapping method between the tasks and processors is proposed to efficiently utilize dynamic power management. Experiments show the effectiveness of the proposed approach compared to other existing methods. PMID:29240695

Wavelet maxima curves of surface latent heat flux associated with two recent Greek earthquakes

NASA Astrophysics Data System (ADS)

Cervone, G.; Kafatos, M.; Napoletani, D.; Singh, R. P.

2004-05-01

Multi sensor data available through remote sensing satellites provide information about changes in the state of the oceans, land and atmosphere. Recent studies have shown anomalous changes in oceans, land, atmospheric and ionospheric parameters prior to earthquakes events. This paper introduces an innovative data mining technique to identify precursory signals associated with earthquakes. The proposed methodology is a multi strategy approach which employs one dimensional wavelet transformations to identify singularities in the data, and an analysis of the continuity of the wavelet maxima in time and space to identify the singularities associated with earthquakes. The proposed methodology has been employed using Surface Latent Heat Flux (SLHF) data to study the earthquakes which occurred on 14 August 2003 and on 1 March 2004 in Greece. A single prominent SLHF anomaly has been found about two weeks prior to each of the earthquakes.

Measurement of the Quasistatic Component of Microaccelerations Using a Convection Sensor onboard a Satellite

NASA Astrophysics Data System (ADS)

Nikitin, S. A.; Polezhaev, V. I.; Sazonov, V. V.

2001-03-01

The problem of the interpretation of measurements made by means of a convection sensor is considered. The sensor is a cubic chamber filled by a viscous fluid (gas). Fixed and unequal temperatures are maintained on two opposite sides of the cube; the other sides are perfect heat conductors. Two differential thermocouples are placed inside the chamber to measure the temperature difference at two pairs of fixed points. The sensor is mounted aboard the Earth's satellite. Mathematical models of various degrees of complexity are proposed which describe processes of heat and mass transfer under the action of a quasistatic component of microaccelerations. The results of mathematical simulation of the data of sensor thermocouples presenting a response to the real quasistatic component of microaccelerations which took place aboard the Mirstation are given. It is shown that under usual conditions of an orbital mission the sensor presents a linear low-frequency filter. By combining the data of several identical sensors, tightly arranged and oriented in a certain way, it is possible to measure low-frequency components of the angular acceleration of the satellite and linear microaccelerations at the point of the sensor position.

Precise attitude rate estimation using star images obtained by mission telescope for satellite missions

NASA Astrophysics Data System (ADS)

Inamori, Takaya; Hosonuma, Takayuki; Ikari, Satoshi; Saisutjarit, Phongsatorn; Sako, Nobutada; Nakasuka, Shinichi

2015-02-01

Recently, small satellites have been employed in various satellite missions such as astronomical observation and remote sensing. During these missions, the attitudes of small satellites should be stabilized to a higher accuracy to obtain accurate science data and images. To achieve precise attitude stabilization, these small satellites should estimate their attitude rate under the strict constraints of mass, space, and cost. This research presents a new method for small satellites to precisely estimate angular rate using star blurred images by employing a mission telescope to achieve precise attitude stabilization. In this method, the angular velocity is estimated by assessing the quality of a star image, based on how blurred it appears to be. Because the proposed method utilizes existing mission devices, a satellite does not require additional precise rate sensors, which makes it easier to achieve precise stabilization given the strict constraints possessed by small satellites. The research studied the relationship between estimation accuracy and parameters used to achieve an attitude rate estimation, which has a precision greater than 1 × 10-6 rad/s. The method can be applied to all attitude sensors, which use optics systems such as sun sensors and star trackers (STTs). Finally, the method is applied to the nano astrometry satellite Nano-JASMINE, and we investigate the problems that are expected to arise with real small satellites by performing numerical simulations.

Collaborative, Rapid Mapping of Water Extents During Hurricane Harvey Using Optical and Radar Satellite Sensors

NASA Astrophysics Data System (ADS)

Muench, R.; Jones, M.; Herndon, K. E.; Bell, J. R.; Anderson, E. R.; Markert, K. N.; Molthan, A.; Adams, E. C.; Shultz, L.; Cherrington, E. A.; Flores, A.; Lucey, R.; Munroe, T.; Layne, G.; Pulla, S. T.; Weigel, A. M.; Tondapu, G.

2017-12-01

On August 25, 2017, Hurricane Harvey made landfall between Port Aransas and Port O'Connor, Texas, bringing with it unprecedented amounts of rainfall and flooding. In times of natural disasters of this nature, emergency responders require timely and accurate information about the hazard in order to assess and plan for disaster response. Due to the extreme flooding impacts associated with Hurricane Harvey, delineations of water extent were crucial to inform resource deployment. Through the USGS's Hazards Data Distribution System, government and commercial vendors were able to acquire and distribute various satellite imagery to analysts to create value-added products that can be used by these emergency responders. Rapid-response water extent maps were created through a collaborative multi-organization and multi-sensor approach. One team of researchers created Synthetic Aperture Radar (SAR) water extent maps using modified Copernicus Sentinel data (2017), processed by ESA. This group used backscatter images, pre-processed by the Alaska Satellite Facility's Hybrid Pluggable Processing Pipeline (HyP3), to identify and apply a threshold to identify water in the image. Quality control was conducted by manually examining the image and correcting for potential errors. Another group of researchers and graduate student volunteers derived water masks from high resolution DigitalGlobe and SPOT images. Through a system of standardized image processing, quality control measures, and communication channels the team provided timely and fairly accurate water extent maps to support a larger NASA Disasters Program response. The optical imagery was processed through a combination of various band thresholds by using Normalized Difference Water Index (NDWI), Modified Normalized Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and cloud masking. Several aspects of the pre-processing and image access were run on internal servers to expedite the provision of images to analysts who could focus on manipulating thresholds and quality control checks for maximum accuracy within the time constraints. The combined results of the radar- and optical-derived value-added products through the coordination of multiple organizations provided timely information for emergency response and recovery efforts

Collaborative, Rapid Mapping of Water Extents During Hurricane Harvey Using Optical and Radar Satellite Sensors

NASA Technical Reports Server (NTRS)

Muench, Rebekke; Jones, Madeline; Herndon, Kelsey; Schultz, Lori; Bell, Jordan; Anderson, Eric; Markert, Kel; Molthan, Andrew; Adams, Emily; Cherrington, Emil;

Preparing Precipitation Data Access, Value-added Services and Scientific Exploration Tools for the Integrated Multi-satellitE Retrievals for GPM (IMERG)

NASA Astrophysics Data System (ADS)

Ostrenga, D.; Liu, Z.; Kempler, S. J.; Vollmer, B.; Teng, W. L.

2013-12-01

The Precipitation Data and Information Services Center (PDISC) (http://disc.gsfc.nasa.gov/precipitation or google: NASA PDISC), located at the NASA Goddard Space Flight Center (GSFC) Earth Sciences (GES) Data and Information Services Center (DISC), is home of the Tropical Rainfall Measuring Mission (TRMM) data archive. For over 15 years, the GES DISC has served not only TRMM, but also other space-based, airborne-based, field campaign and ground-based precipitation data products to the precipitation community and other disciplinary communities as well. The TRMM Multi-Satellite Precipitation Analysis (TMPA) products are the most popular products in the TRMM product family in terms of data download and access through Mirador, the GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) and other services. The next generation of TMPA, the Integrated Multi-satellitE Retrievals for GPM (IMERG) to be released in 2014 after the launch of GPM, will be significantly improved in terms of spatial and temporal resolutions. To better serve the user community, we are preparing data services and samples are listed below. To enable scientific exploration of Earth science data products without going through complicated and often time consuming processes, such as data downloading, data processing, etc., the GES DISC has developed Giovanni in consultation with members of the user community, requesting quick search, subset, analysis and display capabilities for their specific data of interest. For example, the TRMM Online Visualization and Analysis System (TOVAS, http://disc2.nascom.nasa.gov/Giovanni/tovas/) has proven extremely popular, especially as additional datasets have been added upon request. Giovanni will continue to evolve to accommodate GPM data and the multi-sensor data inter-comparisons that will be sure to follow. Additional PDISC tool and service capabilities being adapted for GPM data include: An on-line PDISC Portal (includes user guide, etc.); Data ingest, processing, distribution from on-line archive; Google-like Mirador data search and access engine; electronic distribution, Subscriptions; Uses semantic technology to help manage large amounts of multi-sensor data and their relationships; Data drill down and search capabilities; Data access through various web services, i.e., OPeNDAP, GDS, WMS, WCS; Conversion into various formats, e.g., netCDF, HDF, KML (for Google Earth), ascii; Exploration, visualization and statistical online analysis through Giovanni; Visualization and analysis of L2 data profiles and maps; Generation of derived products, such as, daily products; Parameter and spatial subsetting; Time and temporal aggregation; Regridding; Data version control and provenance; Data Stewardship - Continuous archive verification; Documentation; Science support for proper data usage, help desk; Monitoring services for applications; Expertise in data related standards and interoperability. This presentation will further describe the data services at the PDISC that are currently being utilized by precipitation science and application researchers, and the preparation plan for IMERG. Comments and feedback are welcome.

A fiber optic multi-stress monitoring system for power transformer

NASA Astrophysics Data System (ADS)

Kim, Dae-gil; Sampath, Umesh; Kim, Hyunjin; Song, Minho

2017-04-01

A fiber-optic multi-stress monitoring system which uses 4 FBG sensors and a fiber-optic mandrel acoustic emission sensor is proposed. FBG sensors and a mandrel sensor measure different types of stresses occurring in electrical power transformer, such as temperature and acoustic signals. The sensor system uses single broadband light source to address the outputs of both sensors using single fiber-optic circuitry. An athermal-packaged FBG is used to supply quasi-coherent light for the Sagnac interferometer demodulation which processes the mandrel sensor output. The proposed sensor system could simplify the optical circuit for the multi-stress measurements and enhance the cost-effectiveness of the sensor system.

Use of multiple in situ instruments and remote sensed satellite data for calibration tests at Solfatara (Campi Flegrei volcanic area)

NASA Astrophysics Data System (ADS)

Silvestri, Malvina; Musacchio, Massimo; Fabrizia Buongiorno, Maria; Doumaz, Fawzi; Andres Diaz, Jorge

2017-04-01

Monitoring natural hazards such as active volcanoes requires specific instruments to measure many parameters (gas emissions, surface temperatures, surface deformation etc.) to determine the activity level of a volcano. Volcanoes in most cases present difficult and dangerous environment for scientists who need to take in situ measurements. Remote Sensing systems on board of satellite permit to measure a large number of parameters especially during the eruptive events but still show large limits to monitor volcanic precursors and phenomena at local scale (gas species emitted by fumarole or summit craters degassing plumes and surface thermal changes of few degrees) for their specific risk. For such reason unmanned aircraft systems (UAS) mounting a variety of multigas sensors instruments (such as miniature mass spectrometer) or single specie sensors (such as electrochemical and IR sensors) allow a safe monitoring of volcanic activities. With this technology, it is possible to perform monitoring measurements of volcanic activity without risking the lives of scientists and personnel performing analysis during the field campaigns in areas of high volcanic activity and supporting the calibration and validation of satellite data measurements. These systems allowed the acquisition of real-time information such as temperature, pressure, relative humidity, SO2, H2S, CO2 contained in degassing plume and fumaroles, with GPS geolocation. The acquired data are both stored in the sensor and transmitted to a computer for real time viewing information. Information in the form of 3D concentration maps can be returned. The equipment used during the campaigns at Solfatara Volcano (in 2014, 2015 and 2016) was miniaturized instruments allowed measurements conducted either by flying drones over the fumarolic sites and by hand carrying into the fumaroles. We present the results of the field campaign held in different years at the Solfatara of Pozzuoli, near Naples, concerning measurements of CO2, H2S and SO2. The campaigns were carried out in collaboration with the University of Costa Rica and Jet Propulsion Laboratory of the California Institute of Technology (Pasadena, California) and has allowed the acquisition of a number of measures through scientific miniaturized multi-gas, thermal cameras and spectro-radiometer. The acquired measurements have also permitted the calibration and validation of satellite data as ASTER and LANDSAT8 (in collaboration with USGS). We believe that the rapid increasing of technology developments will permit the use UAS to integrate geophysical measurements and contribute to the necessary calibration and validation of current and future satellite missions dedicated to the measurements of surface temperatures and gas emissions in volcanic areas.

Radiometric Characterization of the IKONOS, QuickBird, and OrbView-3 Sensors

NASA Technical Reports Server (NTRS)

Holekamp, Kara

2006-01-01

Radiometric calibration of commercial imaging satellite products is required to ensure that science and application communities can better understand their properties. Inaccurate radiometric calibrations can lead to erroneous decisions and invalid conclusions and can limit intercomparisons with other systems. To address this calibration need, satellite at-sensor radiance values were compared to those estimated by each independent team member to determine the sensor's radiometric accuracy. The combined results of this evaluation provide the user community with an independent assessment of these commercially available high spatial resolution sensors' absolute calibration values.

Using Sensor Web Processes and Protocols to Assimilate Satellite Data into a Forecast Model

NASA Technical Reports Server (NTRS)

Goodman, H. Michael; Conover, Helen; Zavodsky, Bradley; Maskey, Manil; Jedlovec, Gary; Regner, Kathryn; Li, Xiang; Lu, Jessica; Botts, Mike; Berthiau, Gregoire

2008-01-01

The goal of the Sensor Management Applied Research Technologies (SMART) On-Demand Modeling project is to develop and demonstrate the readiness of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities to integrate both space-based Earth observations and forecast model output into new data acquisition and assimilation strategies. The project is developing sensor web-enabled processing plans to assimilate Atmospheric Infrared Sounding (AIRS) satellite temperature and moisture retrievals into a regional Weather Research and Forecast (WRF) model over the southeastern United States.

The Purpose of the Sensor Web

NASA Technical Reports Server (NTRS)

Schoeberl, Mark R.

2004-01-01

The Sensor Web concept emerged as the number of Earth Science Satellites began to increase in the recent years. The idea, part of a vision for the future of earth science, was that the sensor systems would be linked in an active way to provide improved forecast capability. This means that a system that is nearly autonomous would need to be developed to allow the satellites to re-target and deploy assets for particular phenomena or provide on board processing for real time data. This talk will describe several elements of the sensor web.

Fiber-optic sensor demonstrator (FSD) preliminary test results on PROBA-2

NASA Astrophysics Data System (ADS)

Kruzelecky, Roman V.; Zou, Jing; Haddad, Emile; Jamroz, Wes; Ricci, Francesco; Edwards, Eric; McKenzie, Iain; Vuilleumier, Pierrik

2017-11-01

Fiber Sensor Demonstrator (FSD) developed by MPB Communications (MPBC) is the first demonstration of a full fiber-optic sensor network in the space environment on a satellite. FSD has been launched on ESA's Proba-2 satellite in November 2009. FSD contains twelve temperature sensors to measure the temperature at different locations in the satellite, and one High-Temperature sensor to measure the transient high temperature in the thruster, as well as one pressure sensor to measure the xenon tank pressure. First set of on-orbit test data were obtained in January 2010. The FSD unit successfully established the communication with Proba-2. The temperature of FSD unit was also acquired through a AD590 sensor inside the unit. The measurements of all the optical fiber sensor lines will be evaluated after the testing results obtained. The FSD contains twelve specially-packaged FBG temperature sensors to measure the temperature at different locations in the propulsion system and the spacecraft over the range of -60°C to +120°C. A high-temperature sensor is provided to measure the transient temperature response of the thruster to beyond 350°C. There is also an innovative P/T sensor that provides both temperature and pressure measurements of the Xe propellant tank. The preliminary data of on-orbit functional testing and temperature measurements are provided mainly in Section 6.

New Microwave-Based Missions Applications for Rainfed Crops Characterization

NASA Astrophysics Data System (ADS)

Sánchez, N.; Lopez-Sanchez, J. M.; Arias-Pérez, B.; Valcarce-Diñeiro, R.; Martínez-Fernández, J.; Calvo-Heras, J. M.; Camps, A.; González-Zamora, A.; Vicente-Guijalba, F.

2016-06-01

A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.

Performance of Precipitation Algorithms During IPHEx and Observations of Microphysical Characteristics in Complex Terrain

NASA Astrophysics Data System (ADS)

Erlingis, J. M.; Gourley, J. J.; Kirstetter, P.; Anagnostou, E. N.; Kalogiros, J. A.; Anagnostou, M.

2015-12-01

An Intensive Observation Period (IOP) for the Integrated Precipitation and Hydrology Experiment (IPHEx), part of NASA's Ground Validation campaign for the Global Precipitation Measurement Mission satellite took place from May-June 2014 in the Smoky Mountains of western North Carolina. The National Severe Storms Laboratory's mobile dual-pol X-band radar, NOXP, was deployed in the Pigeon River Basin during this time and employed various scanning strategies, including more than 1000 Range Height Indicator (RHI) scans in coordination with another radar and research aircraft. Rain gauges and disdrometers were also positioned within the basin to verify precipitation estimates and estimation of microphysical parameters. The performance of the SCOP-ME post-processing algorithm on NOXP data is compared with real-time and near real-time precipitation estimates with varying spatial resolutions and quality control measures (Stage IV gauge-corrected radar estimates, Multi-Radar/Multi-Sensor System Quantitative Precipitation Estimates, and CMORPH satellite estimates) to assess the utility of a gap-filling radar in complex terrain. Additionally, the RHI scans collected in this IOP provide a valuable opportunity to examine the evolution of microphysical characteristics of convective and stratiform precipitation as they impinge on terrain. To further the understanding of orographically enhanced precipitation, multiple storms for which RHI data are available are considered.

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

NASA Technical Reports Server (NTRS)

Wolff, David B.; Fisher, Brad L.

2010-01-01

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products. 3

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

NASA Technical Reports Server (NTRS)

Wolff, David B.; Fisher, Brad L.

2011-01-01

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25deg terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Assessing the Relative Performance of Microwave-based Satellite Rain Rate Retrievals using TRMM Ground Validation Data

NASA Technical Reports Server (NTRS)

Wolff, David B.; Fisher, Brad L.

2008-01-01

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecast of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (AQUA) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparison with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellites is examined via comparisons with GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm hr-1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Space-based sensor management and geostationary satellites tracking

NASA Astrophysics Data System (ADS)

El-Fallah, A.; Zatezalo, A.; Mahler, R.; Mehra, R. K.; Donatelli, D.

2007-04-01

Sensor management for space situational awareness presents a daunting theoretical and practical challenge as it requires the use of multiple types of sensors on a variety of platforms to ensure that the space environment is continuously monitored. We demonstrate a new approach utilizing the Posterior Expected Number of Targets (PENT) as the sensor management objective function, an observation model for a space-based EO/IR sensor platform, and a Probability Hypothesis Density Particle Filter (PHD-PF) tracker. Simulation and results using actual Geostationary Satellites are presented. We also demonstrate enhanced performance by applying the ProgressiveWeighting Correction (PWC) method for regularization in the implementation of the PHD-PF tracker.

Remote sensing of oceanic phytoplankton - Present capabilities and future goals

NASA Technical Reports Server (NTRS)

Esaias, W. E.

1980-01-01

A description is given of current work in the development of sensors, and their integration into increasingly powerful systems, for oceanic phytoplankton abundance estimation. Among the problems relevant to such work are phytoplankton ecology, the spatial and temporal domains, available sensor platforms, and sensor combinations. Among the platforms considered are satellites, aircraft, tethered balloons, helicopters, ships, and the Space Shuttle. Sensors discussed include microwave radiometers, laser fluorosensors, microwave scatterometers, multispectral scanners, Coastal Ocean Dynamics Radar (CODAR), and linear array detectors. Consideration is also given to the prospects for such future sensor systems as the National Oceanic Satellite System (NOSS) and the Airborne Integrated Mapping System (AIMS).

Environmental monitoring of Galway Bay: fusing data from remote and in-situ sources

NASA Astrophysics Data System (ADS)

O'Connor, Edel; Hayes, Jer; Smeaton, Alan F.; O'Connor, Noel E.; Diamond, Dermot

2009-09-01

Changes in sea surface temperature can be used as an indicator of water quality. In-situ sensors are being used for continuous autonomous monitoring. However these sensors have limited spatial resolution as they are in effect single point sensors. Satellite remote sensing can be used to provide better spatial coverage at good temporal scales. However in-situ sensors have a richer temporal scale for a particular point of interest. Work carried out in Galway Bay has combined data from multiple satellite sources and in-situ sensors and investigated the benefits and drawbacks of using multiple sensing modalities for monitoring a marine location.

A novel framework for change detection in bi-temporal polarimetric SAR images

NASA Astrophysics Data System (ADS)

Pirrone, Davide; Bovolo, Francesca; Bruzzone, Lorenzo

2016-10-01

Last years have seen relevant increase of polarimetric Synthetic Aperture Radar (SAR) data availability, thanks to satellite sensors like Sentinel-1 or ALOS-2 PALSAR-2. The augmented information lying in the additional polarimetric channels represents a possibility for better discriminate different classes of changes in change detection (CD) applications. This work aims at proposing a framework for CD in multi-temporal multi-polarization SAR data. The framework includes both a tool for an effective visual representation of the change information and a method for extracting the multiple-change information. Both components are designed to effectively handle the multi-dimensionality of polarimetric data. In the novel representation, multi-temporal intensity SAR data are employed to compute a polarimetric log-ratio. The multitemporal information of the polarimetric log-ratio image is represented in a multi-dimensional features space, where changes are highlighted in terms of magnitude and direction. This representation is employed to design a novel unsupervised multi-class CD approach. This approach considers a sequential two-step analysis of the magnitude and the direction information for separating non-changed and changed samples. The proposed approach has been validated on a pair of Sentinel-1 data acquired before and after the flood in Tamil-Nadu in 2015. Preliminary results demonstrate that the representation tool is effective and that the use of polarimetric SAR data is promising in multi-class change detection applications.

Approaches and Data Quality for Global Precipitation Estimation

NASA Astrophysics Data System (ADS)

Huffman, G. J.; Bolvin, D. T.; Nelkin, E. J.

2015-12-01

The space and time scales on which precipitation varies are small compared to the satellite coverage that we have, so it is necessary to merge "all" of the available satellite estimates. Differing retrieval capabilities from the various satellites require inter-calibration for the satellite estimates, while "morphing", i.e., Lagrangian time interpolation, is used to lengthen the period over which time interpolation is valid. Additionally, estimates from geostationary-Earth-orbit infrared data are plentiful, but of sufficiently lower quality compared to low-Earth-orbit passive microwave estimates that they are only used when needed. Finally, monthly surface precipitation gauge data can be used to reduce bias and improve patterns of occurrence for monthly satellite data, and short-interval satellite estimates can be improved with a simple scaling such that they sum to the monthly satellite-gauge combination. The presentation will briefly consider some of the design decisions for practical computation of the Global Precipitation Measurement (GPM) mission product Integrated Multi-satellitE Retrievals for GPM (IMERG), then examine design choices that maximize value for end users. For example, data fields are provided in the output file that provide insight into the basis for the estimated precipitation, including error, sensor providing the estimate, precipitation phase (solid/liquid), and intermediate precipitation estimates. Another important initiative is successive computations for the same data date/time at longer latencies as additional data are received, which for IMERG is currently done at 6 hours, 16 hours, and 3 months after observation time. Importantly, users require long records for each latency, which runs counter to the data archiving practices at most archive sites. As well, the assignment of Digital Object Identifiers (DOI's) for near-real-time data sets (at 6 and 16 hours for IMERG) is not a settled issue.