Cloud classification from visible and infrared SMS-1 data

The success of a statistical classification-design sample model in discriminating cloud-type samples of visible and infrared meteorological satellite data depends on the selection of the design parameters for the system and the ability of the labelled design samples to characterize and discriminate class patterns within the given geographical region. In a companion study by Parikh (1977), pattern recognition design parameters were examined for a four-class problem and a three-class problem for NOAA-1 cloud data. The purpose of this study was to evaluate pattern recognition systems designed in the previous study on SMS-1 design and test sets. Experiments were conducted for both a four-class problem (separation of “low”, “mix”, “cirrus”, and “cumulonimbus” samples) and a three-class problem (separation of “low”, “cirrus”, and “cumulonimbus” samples). For the four-class problem, decreases in classification accuracy ranging from 4% to 11% occurred when the pattern recognition systems were designed and tested on two different data sets selected from the same satellite orbit. A similar decrease was not observed for the well-defined three-class problem.     

Hourly evapotranspiration derived from NOAA-AVHRR visible and GOES-IMAGER thermal infrared data

In previous studies, remotely sensed values of evapotranspiration are generally computed using a simplified surface energy budget model that employs a semi-empirical coefficient with combinations of Sun-synchronous satellite data and ground-based data. This approach has two main limitations, however: Sun-synchronous satellites have low temporal resolution and the estimation is limited only to a local point around the meteorological station because the models require the aid of ground-base measurements, especially air temperature. This study reduced both limitations through the supplemental use of geostationary satellite (GOES-8) data and remotely sensed estimates of all necessary parameters, including net radiation, air temperature and surface temperature. In particular, air temperature, which is an important meteorological parameter in evapotranspiration estimation, was reproduced through third-order polynomial multiple regressions (R² = 0.88; root mean square (rms) error = 2.21 °C). The coefficient needed for the hourly estimate of evapotranspiration was represented through both a Gaussian model and a plane model. The models were constructed using surface roughness length and Sun hour angle, which replaced wind speed – a parameter that is difficult to estimate remotely over land. Assessments show that the models can depict the temporal distributions of empirical coefficients over various land-cover types. The standard error of this coefficient estimate was 0.002 mm h^?1 K^?1 for both time periods. A strong correlation (R² > 0.87; rms. error <0.17 mm h^?1) was found in comparisons between the selected potential and remotely sensed actual evapotranspiration for four land-cover classes.     

基于融合的微光云图的云分割

首先应用基于变分的图像融合方法对DMSP气象卫星夜间微光云图和红外云图进行融合,在此基础上应用快速C-V模型图像分割方法对图像进行分割,将云分割出来。结果显示,与源图像相比,融合图像中包含了更多的关于云的信息,云体清晰度提高,纹理细致。在此基础上云体分割比较完整,比直接在各通道图像中进行分割的效果优越。     

Variability of the Columbia River plume observed in visible and infrared satellite imagery

Abstract

Variability of the Columbia River plume in coastal waters off the northwestern United States, 1979-1985, was observed in sea surface temperature and phytopiankton pigment images derived from Advanced Very High Resolution Radiometer and Coastal Zone Colour Scanner data. The orientation, shape, intensity and relative temperature of the plume vary in response to coastal winds and wind-driven surface currents. From October to April, plume water is oriented northward along the coast. Following the spring transition in April or May, the plume is oriented southward, either adjacent to the coast or offshore. Transition between the winter and summer forms can be observed in the satellite imagery. Brief reversals of the prevailing seasonal winds cause rapid changes in the orientation and shape of the plume. Remote sensing of the Columbia River plume offers valuable information for oceanographic studies and fisheries management in the region. Derivation of an appropriate visible-infrared signature for plume waters and tracking of tidal pulses in the plume is suggested as a promising direction for future research.     

气象卫星夜间微光云图和红外云图的融合技术研究

将多分辨率分析融合方法和多尺度几何分析融合方法应用于DMSP气象卫星夜间微光云图和红外云图的融合中,并对各种融合算法的性能进行了客观评价.融合实验结果显示,融合图像与源图像相比取得了良好的视觉效果.从平均互信息和Xydeas-Petrovic指标看,拉普拉斯金字塔融合算法和非下采样Contourlet融合算法的融合效果较其他方法优越.     

Use of satellite infrared data for geomorphology studies

Enhanced nighttime thermal infrared imagery and digital data from a NOAA polar orbiting satellite were used to map drainage patterns and landforms in North and South Dakota. Features delineated include the Missouri Escarpment from Saskatchewan to the Nebraska border, the Manitoba Escarpment, Coteau des Prairies, recessional moraines on the Coteau du Missouri and partial drainage boundaries for the following rivers: James, Big Sioux, Minnesota, Red of the North, Souris, and the main stem of the Missouri plus its western tributaries in the Dakotas. In several instances drainage from gentle slopes in the Midwest was discerned and correlated with local relief. Analyses of satellite digital thermal data for western tributaries of the Missouri River revealed north-facing slopes to be warmer than south-facing slopes by an average of 1.5°C. Comparisons of ground and satellite temperatures for 11 stations in South Dakota showed good agreement.     

Detection and tracking of oil slicks on sun-glittered visible and near infrared satellite imagery

The use of the visible and near infrared (VNIR) bands of MODIS and MERIS imaging sensors acquired in sunglint conditions to reveal smoothed regions such as those affected by oil pollution is investigated. The underlying physical mechanism that enables oil slick detection is based on the modification of the surface slope distribution composing the wind-roughened sea due to the action of mineral oils. The role of sunglint as the chief mechanism that allows the imaging of oil slick features with VNIR wavelengths is assessed for selected case studies in the Mediterranean Sea. The high rate of acquisition and the frequent occurrence of MODIS and MERIS imagery affected by sunglint, especially in low-latitude seas, can thus significantly contribute to increase the actual oil slick detection capability offered by synthetic aperture radar (SAR) systems. We also show how the combined observations from any of the microwave and optical sensors permit the slick to be followed during its movement. Finally, a simulation study specific to the Mediterranean Sea was carried out in order to demonstrate the feasibility of such an approach supporting SAR observations.     

Non-stationary eddies in the Black Sea as seen by satellite infrared and visible imagery

Examination of non-stationary eddies in the Black Sea is presented based on satellite visible and infrared imagery. The images were obtained by the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectrometer (MODIS) between September 2004 and December 2010. As a result of the analysis performed, it was discovered that there are four main non-stationary eddy types which can be frequently observed in such imagery: near-shore anticyclonic eddies, mushroom-like currents, eddies of the Anatolian coast, and eddy chains. For each type of eddy, spatio-temporal parameters were retrieved such as areas of the most frequent generation and typical length scale, as well as their seasonality. Every non-stationary eddy type was shown to have its own area of location. Analysis of eddy spatial scale revealed that anticyclonic eddies of all types were greater in size than cyclonic ones (on average 52 km for anticyclones and 36 km for cyclones).     

Radiometric calibration of satellite sensors in the visible and near infrared: History and outlook

Documentation for radiometric calibration of visible and near infrared sensors viewing the earth has been quite limited in the past. Recent quantitative use of satellite radiometry suggests the need for better information concerning instrumentation, estimation of atmospheric effects, and ground verification of satellite measured radiances. An example from the thermal infrared spectral region illustrates that analysis algorithms provide the link between satellite-derived results and the required accuracy of satellite calibration.     

An advanced algorithm for operational retrieval of water quality from satellite data in the visible

We present a new operational algorithm for the retrieval of water quality from optical remote sensing data for both clear and turbid waters. It contains an array of neural networks providing input for the Levenberg–Marquardt multivariate optimization procedure as the final retrieval tool. With a given accuracy threshold, the developed algorithm is sufficiently robust to data with noise up to 15% for certain hydro‐optical conditions. To avoid inadequate retrieval results, the algorithm identifies and eventually discards the pixels with inadequate atmospheric correction and/or water optical properties incompatible with the applied hydro‐optical model. This procedure also identifies coccolith expressions. Examples of practical applications of the developed algorithm are given.     

Gender recognition from visible and thermal infrared facial images

Most present research of gender recognition focuses on visible facial images, which are sensitive to illumination changes. In this paper, we proposed hybrid methods for gender recognition by fusing visible and thermal infrared images. First, the active appearance model is used to extract features from visible images, as well as local binary pattern features and several statistical temperature features are extracted from thermal infrared images. Then, feature selection is performed by using the F-test statistic. Third, we propose using Bayesian Networks to perform explicit and implicit fusion of visible and thermal infrared image features. For explicit fusion, we propose two Bayesian Networks to perform decision-level and feature-level fusion. For implicit fusion, we propose using features from one modality as privileged information to improve gender recognition by another modality. Finally, we evaluate the proposed methods on the Natural Visible and Infrared facial Expression spontaneous database and the Equinox face database. Experimental results show that both feature-level and decision-level fusion improve the gender recognition performance, compared to that achieved from one modality. The proposed implicit fusion methods successfully capture the role of privileged information of one modality, thus enhance the gender recognition from another modality.     

Extending the use and interpretation of ocean satellite data using Lagrangian modelling

We propose a new methodology for synthesizing satellite or in situ observations with ocean circulation velocity fields from an operational model. This is done by attaching values taken from the satellite observations to virtual particles seeded at the surface in the domain of a circulation model and advecting them in a Lagrangian fashion. It is then possible to track the fate and change in composition of individual water parcels between two satellite images, and hence estimate the change in satellite‐derived properties along the trajectories of water parcels. The power of the method lies in deciphering the change in sea surface properties from satellite data in the Lagrangian (advective) frame. We use this to estimate rates of biological processes. Further, we generate a dynamically correct time‐interpolation of satellite fields by considering the temporal change in water properties as occurring along trajectories of moving water parcels, rather than in a static medium. We use the methodology to interpret and interpolate MODIS satellite fields in the Gulf of Maine, which has notoriously intermittent satellite coverage. The dynamic interpretation is made possible for this region by the availability of time‐specific velocity fields from an operational coastal circulation model.     

A blended global snow product using visible,passive microwave and scatterometer satellite data

A joint US Air Force/National Aeronautics and Space Administration (NASA) blended global snow product that uses Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and Quick Scatterometer (QuikSCAT or QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by using a newly developed Air Force Weather Agency (AFWA)/NASA Snow Algorithm (ANSA). This initial blended snow product uses minimal modelling to expeditiously yield improved snow products, which include, or will include, snow-cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the USA, from Colorado obtained during the Cold Land Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow-cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or diurnal-amplitude variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.     

Estimating surface temperatures from satellite thermal infrared data—A simple formulation for the atmospheric effect

In recent years the availability of high spatial resolution thermal infrared data from satellites has prompted the use of energy budget models relating satellite-derived surface temperatures to surface moisture, near-surface thermal inertia, energy exchange with the atmosphere, etc. However, correction of the high spatial resolution satellite data for atmospheric water vapor effects can represent a substantial computational burden unless simplifying assumptions are utilized. A simple formulation is developed and its applicability tested by application to standard meteorological soundings at a time near the overpass of an NOAA operational satellite. It appears that reasonable estimates of surface temperature (±2–3°C) are readily obtained for areas of order 100–300 km².     

Nearshore sea temperature variability off Rottnest Island (Western Australia) derived from satellite data

While satellite‐derived sea‐surface temperatures (SSTs) have been widely used in open ocean monitoring, they have rarely been applied to the nearshore region. In this study, full‐resolution Advanced Very High Resolution Radiometer (AVHRR) satellite data were used to derive a sea temperature climatology for three nearshore sites at Rottnest Island (Western Australia) for the 7‐year period 1995 to 2001. Because of the proximity to the land, image geolocation and careful pixel selection were crucial. The mean annual cycle shows the influence of both air–sea heat flux in the shallow waters and the seasonal strengthening of the tropical Leeuwin Current. Self‐recording temperature loggers were installed on near‐surface buoys for a few months in 2001 to assess how reliably the satellite temperatures could be used in such a dynamic nearshore environment. About 80% of the individual satellite temperatures were within ±0.5°C of the logger measurements (at the same time of day) and over 95% were within ±1°C. Occasionally, however, the satellite temperatures differed from the in situ measurements by more than 2°C and possible reasons for this are discussed. The monthly mean satellite temperatures were generally within 0.3°C of the monthly logger averages, which may be taken as a practical reliability limit for the climatology.     

Detection of blue-absorbing aerosols using near infrared and visible (ocean color) remote sensing observations

An algorithm is presented, which is designed to identify blue-absorbing aerosols from near infrared and visible remote-sensing observations, as they are in particular collected by satellite ocean color sensors. The technique basically consists in determining an error budget at one wavelength around 510 nm, based on a first-guess estimation of the atmospheric path reflectance as if the atmosphere was of a maritime type, and on a reasonable hypothesis about the marine signal at this wavelength. The budget also includes the typical calibration uncertainty and the natural variability in the ocean optical properties. Identification of blue-absorbing aerosols is then achieved when the error budget demonstrates a significant over-correction of the atmospheric signal when using non-absorbing maritime aerosols. Implementation of the algorithm is presented, and its application to real observations by the MERIS and SeaWiFS ocean color sensors is discussed. The results demonstrate the skill of the algorithm in various regions of the ocean where absorbing aerosols are present, and for two different sensors. A validation of the results is also performed against in situ data from the AERONET, and further illustrates the skill of the algorithm and its general applicability.     

Aircraft experiments with visible and infrared sensors

Abstract

Visible and near-visible sensors, which have been widely used in the beginning of the period of interest, have been later overshadowed by the availability of sensors using the microwave part of the spectrum. However, the latter years of this period have shown an obvious come-back with numerous experiments flown with new generation equipment. This paper describes briefly the first generation sensors and how they were used in airborne experiments, explains the developments being conducted from mechanical to push-broom scanners and to non-scanning sensors, shows how some sensors were being integrated in comprehensive systems and what is the trend found in the later years of the period of reference. This is illustrated by examples of existing equipment in France and in Europe, and two case studies: one on the Dutch CAESAR push-broom scanner, the other on the Oceanographic Lidar System developed by the University of Oldenburg in Germany.     

Spatiotemporal modelling of urban quality of life (UQoL) using satellite images and GIS

ABSTRACT

In modern cities, life is primarily formed in interaction with various environmental, social, economic, infrastructural, hygienic, safety, political and cultural conditions. Urban quality of life (UQoL) is the result of such interactions. In general, both objective and subjective approaches are used in the study and modelling of UQoL. So far the studies have been conducted in forms of social and large-scale geographical studies mainly ignoring spatial differences of quality of life (QoL) in complicated urban environments. Moreover, QoL as one of the attributes of geographical environment is a dynamic and variable concept which has received little attention. Spatiotemporal modelling of this concept can contribute to monitoring the UQoL and planning for its improvement. The current study aims to develop a method for spatiotemporal modelling of UQoL. For this purpose, multi criteria decision making (MCDM) method and fuzzy logic are used. Moreover, given the variability of some indicators, temporal modelling of UQoL was conducted based on snapshot method in seasonal scale. In order to evaluate the proposed procedure, modelling of QoL was done at urban blocks scale in regions 3, 6, and 11 of Tehran, Iran. The results show the existence of a fairly regular pattern as an increase in desirability of UQoL from south to north of the area. The seasonal changes of UQoL show the improvement of environmental condition in spring and autumn compared to winter and summer. The result of sensitivity analysis shows the reliability of the modelling results. Since the effect of inputs on the fuzzy gamma output (2.5%) are less than the corresponding amount in VIKOR-fuzzy method (48%), it can be concluded that the output of fuzzy-gamma is more reliable.     

The use of measured sky radiance data to improve infrared signature modelling

In this paper we discuss the complications of modelling the infrared signature of objects, for example ships and land‐vehicles. Specifically we focus on the difficulties of accounting correctly for the effect of the environment on the signature. We attribute many of the inaccuracies in modelling signature to the fact that key input data to the models are often not known or poorly defined. We propose that an experimental measurement of solar irradiance and sky radiance provides better input to signature models than estimation of these values through atmospheric radiation models such as MODTRAN. We illustrate this with experimental data. The prediction and measurement process(es) are complicated by the need to convert signature between different sensor response functions and further study into this area is required. The taking of narrow‐band spectral data must also be considered ideal when signature measurements are made.     

Using sea surface temperature measurements from microwave and infrared satellite measurements

The Tropical Rainfall Mapping Mission Microwave Imager (TMI) instrument Sea Surface Temperature (SST) product (v1.0) is compared with in situ observations obtained in the Atlantic Ocean. The TMI SST has a mean warm bias of 0.25?K±0.7?K when compared to in situ SST at a depth of 7?m. When TMI SST are compared to in situ skin SST measurements, the bias is 0.6?K±0.5?K. A limited global comparison between TMI SST and co-incident ERS-2 Along-Track Scanning Radiometer (ATSR/2) skin SST demonstrates a bias of 0.6?K±0.6?K consistent with the result obtained using in situ observations. These results are consistent with the predicted accuracy of the TMI SST data products. Based on these results, a simple method to merge the TMI and ATSR data is proposed.