Potential of MSG for surface temperature and emissivity estimation: Considerations for real-time applications

Meteosat Second Generation (MSG) will provide data with an unprecedented combination of spatial, temporal, and spectral resolutions from geostationary orbit for Africa, and most of Europe and the Atlantic ocean. This article focuses on the potential of MSG's Spinning Enhanced Visible and Infrared Imager (SEVIRI) for Land Surface Temperature (LST) and emissivity estimation. SEVIRI's advantages over the Meteosat Visible and Infrared Imager (MVIRI) and the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) are outlined. On the basis of SEVIRI's spectral and temporal resolutions the Thermal Infrared Spectral Indices (TISI) day/night method is selected for estimating emissivity. The concept of using the Sun as an active source in the 3-4 w m window is summarized and SEVIRI-specific coefficients required by the TISI day/night method are supplied. The sensitivity of the method to atmospheric conditions and to surface emissivity is analysed using simulated radiances for standard atmospheres and channel emissivities derived from spectral laboratory measurements of different surface types. In order to obtain a fast and accurate procedure for the estimation of channel emissivities and LST, the combination of the TISI day/night method with a neural network (NN) for calculating atmospheric variables is proposed.     

Size distributions of clouds in real time from satellite imagery

Abstract

An instrument is proposed to gather global data on cloud size distributions. The instrument contains a broad-band visible/infrared radiometer to scan the clouds with a field of view comparable to the grid squares of circulation models, but a resolution within the field of view of approximately 100 m, adequate to resolve the fine structure of clouds. The image is digitized, discriminated to distinguish cloudy and clear pixels, and submitted to a dedicated parallel computer which calculates the size distribution of the clouds in real time. Only the size distribution is transmitted to Earth, so the data transmission rate from the satellite is low and the archival problems at the earth station are minimal. This paper examines several alternative definitions of the size distribution of clouds, and concludes that the procedure of ‘sizing by openings’ is most appropriate. It is then shown that the processing speed required by the image analyser can be achieved at realistic power levels with present day technology.     

GCP acquisition using simulated SAR and evaluation of GCP matching accuracy with texture features

Abstract

A method for ground control point (GCP) acquisition using a simulated synthetic aperture radar (SAR) image derived from a digital elevation model (DEM) is proposed. Also proposed is a method for the evaluation of the accuracy of GCP matching with texture features from a reference GCP chip image. Results from experiments with simulated GCP chip images as reference images and geometrically distorted GCP chip images, derived using simulated SAR images as current images, show good coincidence with GCP matching accuracy in terms of pixel distances between matched GCP points in reference and current chip images and texture features. In particular, the correlation coefficient between the angular second moment (ASM) and the matching accuracy is 0.757, followed by Ent (entropy), Horn (homoginiety), Dis (dissimilarity), Con (contrast), Var (variability coefficient) and Chi (Chi-square), for the skewed images while for the rotated images, Dis shows the highest correlation of 0.628, followed by Horn, Asm, Con, Ent, Chi and Var. Based on the proposed methods, one can easily generate GCP chips images from a DEM, and also evaluate GCP matching accuracy with texture features of simulated SAR from a GCP chip image.     

The effect of orbit drift on the calibration of the 3.7 µm channel of the AVHRR onboard NOAA-14 and its impact on night-time sea surface temperature retrievals

The orbit drift of National Oceanic & Atmospheric Administration (NOAA)-14 towards the terminator has caused the deterioration of the radiometric calibration of the Advanced Very High Resolution Radiometer (AVHRR) 3.7?µm channel at night. This deterioration is a result of solar contamination of the radiometric calibration system when the sun strikes the instrument from the spacecraft horizon. The long-term trend and seasonal variation of the contamination are analysed in this study based on trending data from 1995 to 2000. The calibration bias is evaluated and its effect on the sea surface temperature retrievals is quantified. The solar contamination in late 2000 affected as much as 25% of an orbit of data, compared to an average of 7% in 1995. The NOAA/NESDIS operational calibration algorithm partially corrects for the bias but residual effects can still contribute bias on the order of 0.5?K in scene brightness temperature.     

An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data

Daily daytime Advanced Very High Resolution Radiometer (AVHRR) 4‐km global area coverage data have been processed to produce a Normalized Difference Vegetation Index (NDVI) 8‐km equal‐area dataset from July 1981 through December 2004 for all continents except Antarctica. New features of this dataset include bimonthly composites, NOAA‐9 descending node data from August 1994 to January 1995, volcanic stratospheric aerosol correction for 1982–1984 and 1991–1993, NDVI normalization using empirical mode decomposition/reconstruction to minimize varying solar zenith angle effects introduced by orbital drift, inclusion of data from NOAA‐16 for 2000–2003 and NOAA‐17 for 2003–2004, and a similar dynamic range with the MODIS NDVI. Two NDVI compositing intervals have been produced: a bimonthly global dataset and a 10‐day Africa‐only dataset. Post‐processing review corrected the majority of dropped scan lines, navigation errors, data drop outs, edge‐of‐orbit composite discontinuities, and other artefacts in the composite NDVI data. All data are available from the University of Maryland Global Land Cover Facility (http://glcf.umiacs.umd.edu/data/gimms/).     

Stray light modelling and analysis for the FY‐2 meteorological satellite

Based on the analysis of the mechanism of stray light of the FY‐2 meteorological satellite, the main component of the stray light, generated from the folding mirror direct reflections, is modelled physically. Through the analysis of the stray light outside of the Earth region as well as extracting the high‐order statistical eigenvalues of the designated areas, the stray light function matrix, A, can be calculated using the total least squares (TLS) method, which is applied to the inside of the Earth region, and the full field‐of‐view (FFOV) stray light estimation can be produced efficiently. It is shown that the mean restoration errors of the stray light outside of the Earth region of the infrared (IR), water vapour (WV) and visible (VIS) channels are less than two scaled units, while the visual images are improved greatly. The images of FY‐2B IR with stray light removed and the FY‐2B raw images are inter‐calibrated with the NOAA‐17 Advanced Very High Resolution Radiometer (AVHRR) Ch4 data. The results show that, from 180 to 300?K of the brightness temperature area, the standard deviation of the data with stray light removed is from 1.3 to 3.8?K relative to the NOAA‐17 data, which is improved by 3–10 times in calibration accuracy compared with the raw data inter‐calibration results. The model has been running for testing in the FY‐2B ground operational system and will be applied operationally to the follow‐up satellite system. With the adjustment of a few parameters, the principle of the model can be used for stray light analysis of other instruments on geostationary satellite platform.     

Technical note. Agricultural,hydrologic and oceanographic studies in Bangladesh with NOAA AVHRR data

Abstract

This paper presents some results of studies made with NOAA Advanced Very High Resolution Radiometer (AVHRR) data in the fields of agriculture, hydrology and oceanography in Bangladesh. The techniques of processing the raw image data are also discussed. The data for four different dates in 1984 and 1985 have been used. The 1984 data were obtained from NOAA/NESDIS of U.S. A. and the 1985 data were received in real time at the SPARRSO Meteorological Satellite Ground Station. Data from the AVHRR channels 1 and 2 were used to calculate the vegetation index (normalized) which was found to be a useful tool for monitoring vegetation conditions from time to time. These data have also application in monitoring and studying flood conditions in and outside the country. The thermal channels of the AVHRR (channels 4 and 5) have been used for calculating the surface temperatures of the Bay of Bengal. These channels revealed some noteworthy surface features in the Bay.     

Digital elevation model (DEM) generation and accuracy assessment from ASTER stereo data

Digital elevation models (DEM) are the indispensable quantitative environmental variable in most of the research studies in remote sensing. The improvement of sensor and satellite imaging technologies enabled the researchers to generate DEM using remotely sensed data. These data can be started to use as not only the two-dimensional (2-D) but also three-dimensional (3-D) information sources with usage of the DEM. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is one of the sensor systems capable of DEM generation and during the study, ASTER level 1A (L1A) data were used. Due to presence of many geological features and different landcover types, the test site is selected as the watershed of Asarsuyu River, located in north-western Anatolia in between Duzce and Bolu plains. The aim of this study is to check the best effort of 15 m spatial resolution DEM generation from ASTER L1A data by collecting different numbers of ground control points (GCP) (30, 45, and 60) and tie points (TP). During the study, three different techniques—spatial correlation, image differencing and profiling—were used for both planimetric and vertical accuracy assessment. The obtained results from both of the techniques show that the accuracy of the DEM increases by increasing the number of GCP. However, there is an only slight difference between the result of 45 GCPs and 60 GCPs.     

一种新的SAR图像斜距多普勒定位模型的直接解法

斜距多普勒定位模型是实现SAR图像无控制点定位的基本模型,因此如何高效准确地获得RD方程组的解成为实现SAR图像无控制点几何定位的根本问题,也是实现SAR自动图像定位的关键。首先利用地固坐标系下目标速度为零的特性,简化RD模型并整理为一元四次方程,然后利用一元四次方程求根方法,推导出斜距多普勒定位模型的明确数学解析解,并与常用的数值迭代求解方法进行比较分析,仿真结果验证了推导的正确性。新解法具有无需设置初始值,易于编程实现,鲁棒性高的特点。     

NOAA polar orbiting sensor systems Today and tomorrow

Abstract

For almost a quarter of a century, imagery and sounding data have been directly broadcast to users worldwide from polar orbiting satellites operated by the U.S. National Oceanic and Atmospheric Administration (NOAA). For more than a decade, almost the same payloads and data transmissions have been utilized. However, in the decade of the 1990s, several sensor improvements are planned or projected. This paper outlines payloads planned for NOAA Series spacecraft through the present century and the resulting changes in direct broadcast services.     

Evaluating the performance of multitemporal image compositing algorithms for burned area analysis

The main objective of this study was to compare the adequacy of various multitemporal image compositing algorithms to produce composite images suitable for burned area analysis. Satellite imagery from the NOAA Advanced Very High Resolution Radiometer (AVHRR) from three different regions (Portugal, central Africa, and South America) were used to compare six algorithms, two of which involve the sequential application of two criteria. Performance of the algorithms was assessed with the Jeffries-Matusita distance, to quantify spectral separability of the burned and unburned classes in the composite images. The ability of the algorithms to avoid the retention of cloud shadows was assessed visually with red-green-blue colour composites, and the level of radiometric speckle in the composite images was quantified with the Moran's I spatial autocorrelation statistic. The commonly used NDVI maximum value compositing procedure was found to be the least appropriate to produce composites to be used for burned area mapping, from all standpoints. The best spectral separability is provided by the minimum channel 2 (m2) compositing approach which has, however, the drawback of retaining cloud shadows. A two-criterion approach which complements m2 with maximization of brightness temperature in a subset of the data (m2M4) is considered the better method.     

Regional boundary layer airflow patterns derived from digital NOAA-AVHRR data

Abstract

NOAA AVHRR (Advanced Very High Resolution Radiometer) data make a significant contribution to information about boundary layer air flow patterns, especially over complex terrain, where wind fields are very often totally disconnected from upper air flows. An evaluation procedure which is based on different approaches, depending on the regional meteorological situation, is presented in this paper. It-includes (1) mapping of local cold air pools, (2) mapping of the vertical extent of temperature inversions and (3) mapping the extent and dynamics of fog layers. All the examples refer to the Swiss Alps and the surrounding basins.     

风云三号微波湿度计遥感图像地理定位方法

风云三号（FY-3）微波湿度计采用45°天线绕轴旋转形成垂直于卫星飞行轨迹的360°连续变速圆周扫描方式。依据这种扫描几何,给出了适用于风云三号微波湿度计遥感图像地理定位的方法,定义了完善的坐标系及坐标系转换关系,根据微波湿度计观测几何、卫星空间位置和姿态、仪器空间位置和指向建立了天线观测矢量与地面位置之间关系的模型。风云三号微波湿度计遥感图像地理定位方法在风云三号地面应用系统业务运行中,对微波湿度计遥感图像地理定位精度达到了像元级。     

NOAA operational sounding products for advanced TOVS

The National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite Data and Information Service (NESDIS) operates a fleet of civilian, polar‐orbiting, environmental satellites that provide users and researchers with a continuous suite of atmospheric and surface products on a global scale. The first advanced TIROS operational vertical sounder (ATOVS) radiometer configuration, onboard NOAA‐15, was successfully launched into an early evening orbit on 13 May 1998. The ATOVS featured the new 13 channel advanced microwave sounding unit (AMSU) module A1, the 2 channel AMSU‐A2 and 5 channel AMSU‐B radiometers, which replaced the MSU and SSU, along with a similar HIRS and AVHRR instrument payload (Goodrun G., Kidwell, K.B. and Winston, W., 2000, NOAA‐KLM users guide: September 2000 revision, Technical Document, National Oceanic and Atmospheric Administration, National Environmental Satellite Data and Information Service, National Climatic Data Center) that had been operational since 1979. The ATOVS onboard NOAA‐16 was successfully launched into an afternoon orbit on 21 September 2000, followed by NOAA‐17 into a late evening orbit on 24 June 2002, creating the first ever three‐satellite constellation of operational polar satellites (ATOVS). The following report summarizes the online and offline scientific processing systems, respectively, for deriving the NESDIS operational ATOVS sounding products and presents a series of results for the unique three‐operational‐satellite configuration of NOAA‐15, 16 and 17. Results focus on the value of the derived products in the analyses of global and regional scale weather, including direct comparisons against collocated numerical weather prediction (NWP) forecast and radiosonde data. The report is prefaced by a brief history of satellite weather products and concludes with future plans to meet expanding user requirements.     

Visualization of A-Train vertical profiles using Google Earth

Online tools, such as those pioneered by Google Earth (GE), are changing the way in which scientists and the general public interact with three-dimensional geospatial data in a virtual environment. However, while GE provides a number of features to facilitate geospatial data visualization, there is currently no readily available method for rendering vertical geospatial data derived from Earth—viewing remote sensing satellites as an orbit curtain seen from above. Here, a solution (one of many possible) is demonstrated to render vertical profiles of atmospheric data from the A-Train satellite formation in GE, using as a proof-of-concept data from one of the instruments—the NASA CloudSat satellite. CloudSat carries a nadir-viewing Cloud Profiling Radar that produces data revealing the vertical distribution of cloud characteristics along the satellite track. These data are first rendered into a long vertical image for a user-selected spatial range through the NASA Goddard Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) system (http://giovanni.gsfc.nasa.gov/). The vertical image is then chopped into small slices representing 15 s of satellite time (～103 km long ground distance). Each small piece, as a texture, is fed into a generalized COLLAborative Design Activity (COLLADA) three-dimensional (3-D) model. Using the satellite orbit coordinates, the repeated 15 s “3-D model slices” are spliced together to form a vertical “curtain” image in Keyhole Markup Language (KML) format. Each model slice is geolocated along the CloudSat orbit path based on its size, scale and angle with the longitude line that are precisely calculated on the fly. The resulting vertical cloud data can be viewed in GE, either transparently or opaquely, superimposed above the Earth's surface with an exaggerated vertical scale. Since CloudSat is just a part of the A-Train formation, the full utility of this tool can be explored within the context of the A-Train Data Depot (ATDD, http://disc.gsfc.nasa.gov/atdd/) and the corresponding Giovanni instance (http://disc1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=atrain). The latter portal allows scientists and the general public to access and visualize complex A-Train datasets without having to delve into data formats specific to a given mission.     

Visual transformation for interactive spatiotemporal data mining

Analytical models intend to reveal inner structure, dynamics, or relationship of things. However, they are not necessarily intuitive to humans. Conventional scientific visualization methods are intuitive, but limited by depth, dimension, and resolution. The purpose of this study is to bridge the gap with transformation algorithms for mapping the data from an abstract space to an intuitive one, which include shape correlation, periodicity, multiphysics, and spatial Bayesian. We tested this approach with the oceanographic case study. We found that the interactive visualization increases robustness in object tracking and positive detection accuracy in object prediction. We also found that the interactive method enables the user to process the image data at less than 1 min per image versus 30 min per image manually. As a result, our test system can handle at least 10 times more data sets than traditional manual analyses. The results also suggest that minimal human interactions with appropriate computational transformations or cues may significantly increase the overall productivity. Yang Cai is Director of Ambient Intelligence Laboratory and Faculty of Cylab and Institute of Complex Engineered Systems (ICES), Carnegie Mellon University, and Professor of Industrial Design at Modern Industrial Design Institute, Zhejiang University, P.R. China. He was Systems Scientist at Human–Computer Interaction Institute, Senior Scientist in CMRI at CMU, and Senior Designer for Daimler Chrysler. Cai’s interests include pattern recognition, visualization, and Ambient Intelligence. He cochaired international workshops in Ambient Intelligence for Scientific Discovery, Vienna, 2004 and AmI for Everyday Life, Spain, 2005 and Digital Human Modeling, UK, 2006. He is Editor of the Lecture Notes in Artificial Intelligence, LNAI 3345 and LNAI 3864, published by Springer. He was NASA Faculty Fellow in 2003 and 2004. Richard Stumpf is a Senior Oceanographer and Team Leader of Remote Sensing National Oceanic and Atmospheric Administration (NOAA), Center for Coastal Monitoring and Assessment, Silver Spring, MD, where he leads 6–10 team members developing remote sensing capabilities for NOAA. Dr. Stumpf has extensively published papers on remote sensing for monitoring and forecasting harmful algal blooms and river plumes. He received his Ph.D. in Oceanography. Timothy Wynne is an oceanographer with I.M. Systems Group and NOAA. Primarily his work at NOAA has involved ocean color imagery with an emphasis on algal bloom detection. He has also used remotely sensed data to quantify resuspension events. He has a M.S. in Oceanography from Old Dominion University and a B.S. in Marine Science from the Richard Stockton College of New Jersey. Michelle Tomlinson has been an Oceanographer with the Center for Coastal Monitoring and Assessment, National Ocean Service, NOAA since 2002. Her current research focuses on the application of satellite-derived ocean color sensors (SeaWiFS, MODIS, MERIS) to detect, monitor, and forecast the occurrence of harmful algal blooms. This work has led to the development of an operational forecast system for harmful Karenia brevis blooms in the Gulf of Mexico. She received her B.S. in Marine Science Biology from Southampton College of Long Island University, and a M.S. in Oceanography from Old Dominion University. Daniel Sai Ho (Daniel) Chung is a Master of Science Degree Student at the Institute of Networked Information, Carnegie Mellon University. He has been a Research Assistant in the Ambient Intelligence Laboratory since 2004, where he developed data mining and wireless video streaming systems for NASA and TRB-sponsored projects. Xavier Boutonnier is a Research Assistant at Carnegie Mellon University, CYLAB—Ambient Intelligence Laboratory, Pittsburgh, PA, USA. He is a Master of Science Degree Student at the National Superior School of Electronics of Toulouse (ENSEEIHT) in France. He specialized in Signal, Image, Acoustic, and optimization. He has been working with Dr. Yang Cai on the NASA-sponsored data mining project. His favorite fields of application are Video, Image, acoustic, and other signal processing. Matthias Ihmig is pursuing his Ph.D. in the area of software-defined radio at Munich Technical University, while he is working at BMW, Germany. He was an Intern Graduate Student at Carnegie Mellon University, USA. His interests include stereo vision, wireless networks, and intelligent systems. Rafael Franco is a Master’s Degree Student in Electronics and Telecommunications at the Engineering School of ENSEEIHT in France. Currently, he is an Intern at Cylab working on assignments related to information visualization and wireless user positioning. Nathaniel Bauernfeind is a Research Assistant at Carnegie Mellon University, CYLAB—Ambient Intelligence Laboratory, Pittsburgh, PA, USA. He is a Computer Science and Mathematics student in the School of Computer Science at CMU. His research interests include computational algorithms, 3D graphics programming, and artificial intelligence. He is working with Dr. Yang Cai on a driving simulator that focuses on algorithmic automation for General Motors.     

An image geometry model for METEOSAT†

METEOSAT is a spin-stabilized geostationary satellite which takes Earth images in three spectral channels, infrared, visible and water vapour absorption band. The time-dependent deviations of the satellite from nominal position, attitude and spin speed are described by an image geometry model. A new iterative procedure for refined attitude determination is used. The attitude is derived from northern and southern polar horizon scan limits which are extracted from infrared image data. The geometry model provides a deformation vector field which relates the ideal reference image to the actual image. The actual image is then mapped to the reference image by the nearest-neighbour rectification process.     

Fire recognition potential of the bi-spectral Infrared Detection (BIRD) satellite

The potential of a new small Bi-spectral Infrared Detection (BIRD) satellite system for the detection and quantitative characterization of high-temperature events on the Earth surface is discussed. BIRD was successfully piggy-back launched by an Indian Polar Satellite Launch Vehicle (PSLV-C3) in a circular Sun-synchronous orbit on 22 October 2001. Images of wildfires in the area of Sydney, Australia, obtained in January 2002, illustrate the fire recognition potential of BIRD.     

Exploring the utility of NOAA AVHRR middle infrared reflectance to monitor the impacts of ENSO-induced drought stress on Sabah rainforests

The ever-wet tropics are under threat from ENSO events and there is a need for a monitoring system to analyse and describe their responses to such events. This letter explores the relative value of using NOAA AVHRR middle infrared (MIR) reflectance data and NDVI data for the monitoring of ENSO-related drought stress of a tropical forest ecosystem in Sabah, Malaysia. Relationships between rainfall and MIR reflectance were examined. Correlation coefficients are generally large and significant (at 0.1 level) while those between rainfall and NDVI were small and insignificant. This letter concludes that there is potential in using MIR reflectance for monitoring the effects of ENSO-induced drought stress on these forests and this has a bearing on how NOAA AVHRR data may be used to further our knowledge on the impacts of ENSO events on tropical forest environments.     

Detection of stable synoptical features of sea surface from a series of infrared satellite images

Maps of the dominant orientation of thermal contrasts (statistically significant tangents to isotherms) on infrared images obtained from NOAA satellites are capable of representing the sea circulation structure in difficult cloudy conditions and, thus, can be used for quantitative analysis of synoptical-scale processes on the sea surface. For this purpose, one should plot compositional maps and estimate the lifetime of the dominant orientations. An approach to plotting such maps is described, the lifetime of the dominants is estimated, and the applicability conditions of the proposed method are investigated. The dominants stable for a week are obtained and their correlation with synoptical-scale objects is revealed. It is demonstrated that such objects can be detected using maps of the dominant orientations of thermal contrasts. Aleksanin Anatolii Ivanovich. Born 1956. Graduated from the Moscow Institute of Physics and Technology in 1979. Received candidate’s degree (in Engineering) in 1987. Works at the Institute of Automatics and Control Processes, Far-East Division, Russian Academy of Sciences, as the Head of the Laboratory of Satellite Monitoring. Scientific interests: dynamics of the atmosphere of the earth and sea, investigation of the Earth from the space, data processing. Author of 37 scientific publications. Aleksanina Marina Georgievna. Born 1960. Graduated from the Moscow Institute of Physics and Technology in 1983. Received candidate’s degree (in Engineering) in 1998. Works at the Institute of Automatics and Control Processes, Far-East Division, Russian Academy of Sciences, as Senior Researcher. Scientific interests: investigation of the Earth from the space, processing and analysis of images of sea surface and atmosphere. Author of 24 scientific publications.