NOAA AVHRR Research Articles

Wide-Area Determination of Cloud Microphysical Properties from NOAA AVHRR Measurements for FIRE and ASTEX Regions

A method for satellite remote sensing of cloud optical thickness and effective particle radius has been developed to apply to NOAA AVHRR multispectral radiance data. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 1, 3, and 4 of AVHRR and subtracted from radiances in channels 1 and 3 to derive the reflected solar radiation of a cloud layer that includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semitransparent to thick clouds. This method was applied to AVHRR data acquired over oceans during the First ISCCP Regional Experiment and the Atlantic Stratocumulus Transition Experiment. The authors found good agreement between satellite-derived and in situ microphysical quantities. The presence of drizzle droplets in optically thin clouds was also confirmed from the satellite observation. Furthermore, the results show that marine stratocumulus clouds were drastically modified by ship track effluents and dust-contaminated aifflow from the continent.

Correcting atmospheric masking to retrieve the spectral albedo of land surfaces from satellite measurements

Abstract A radiative transfer approach to the problem of atmospheric correction of satellite images in the solar spectral range is presented which includes all multiple scattering processes without any approximation. The numerical solution is accepted as satisfying, if the numerical accuracy is better than I per cent. This means that the accuracy of the atmospheric correction depends almost exclusively on the quality of the auxiliary data on the atmospheric state and the surface reflection indicatrix. Byextensivemodel calculations these parameter driven error bounds have been quantified. Thus the calculation results in a corrected albedo image with specified error bounds. This seems to be the first algorithm available for atmospheric correction of real imagery data which relies on a numerical exact treatment of multiple scattering. The program EXACT (EXact Atmospheric Correction Technique) has so far been used with Landsat Thematic Mapper (TM), NOAA AVHRR (National Oceanic and Atmospheric Administration A...

Quantifying the effect of emissivity on the relations between AVHRR split window temperature difference and atmospheric precipitable water over land surfaces

The effect of emissivity on the relations between the NOAA-9 AVHRR infrared split window temperature difference (Δ T = difference of brightness of temperatures for 10.1–11.5 μm and 11.2–12.6 μm bands) and atmospheric precipitable water (W), namely, ΔT = α + βW and W = α′ + β′ ΔT, is quantified in terms of red reflectance (0.55-0.72 μm) observed by the AVHRR. Two years (1987 and 1988) of AVHRR and radiosonde observations at 44 globally distributed land surface locations have been used in the analysis. Predictions of a radiative transfer model are tested using these observations, which indicate possible discrepancies between the predicted and the observed results. Estimates of 10.1–11.5 μm band emissivity have been made by interpreting the intercept of ΔT− W regression according to the theoretical predictions, and these emissivities were found to be linearly related to the red reflectance. Precipitable water estimated from the observed ΔT and red reflectance (by the AVHRR) for the two years at 11 locations over South American and Europe when compared with the observed precipitable water (from radiosonde) gave a mean absolute error of 5 mm and a bias of 4 mm, with the explained variance of 79%. Some inherent uncertainties in the data used in the present analysis have been noted.

Forest classification of Southeast Asia using NOAA AVHRR data

Methodologies using the 1 km resolution data of the NOAA AVHRR instrument were developed for tropical forest spectral discrimination and mapping at a regional scale. Tropical Southeast Asia was selected as a case study using a multitemporal AVHRR data set of 1990–1992. This study documents first the relevance of AVHRR data to assess the extent of seasonal and dense forest and, moreover, reports on the derivation of a specific fragmented/disturbed forest class. A geographically dependent methodology is developed: For continental Southeast Asia, where generally good cloud-free images are available during the dry season and seasonal vegetation formations are present, multitemporal AVHRR mosaics were produced before the classification process. For insular Southeast Asia, which is particularly affected by the cloud cover and where only humid vegetation formations are present, a multitemporal set of single-date AVHRR images was first classified, and then the classifications were mosaicked together using a combination of two criteria (image quality and maximum occurrence). Unsupervised classifications using NDVI and Channel 3 radiance were processed in both cases. The classifications were labeled using the spectral typology derived from the spectral analysis of the dataset. For evaluation of the labeled classifications, comparisons were made at a regional level with a digital forest map database. Verification of the AVHRR class assignment was carried out locally using a few high spatial resolution satellite images. It highlights the sources of misclassification.

Inference of the climatic efficiency of clouds from satellite measurements

Abstract The influence of clouds over the North Sea on the radiation field and on climate is investigated by analysing satellite measurements. The main interest is on high clouds due to their ambivalent behaviour in the radiation field. To quantify the influence of clouds on climate, the cloud-climate efficiency is introduced. The cloud-climate efficiency allows us to estimate the gain or respectively loss of energy of the earth/atmosphere system in the presence of a cloud, which can be specified by a cloud classification. The spatial integration of the cloud-climate efficiencies results in the cloud forcing defined by the difference of the radiative fluxes within a clear sky and a cloudy satellite image pixel. The first step is an accurate detailed cloud classification based on the maximum likelihood method. The method developed for National Oceanograph and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR)data and Meteosat data can be used to discriminate 24 clouds, especia...

A correlation based cloud-detection algorithm combined with an examination of some split-window assumptions

Abstract Twenty-six daytime NOAA-11 AVHRR images covering the Danish waters are analysed together with 99 cloud-free bulk temperature measurements. The images are cloud screened with a cloud-detection algorithm, which in excess of the usual threshold and variation tests consists of: a min_max-routine which among suspicious pixels points out the cloud contaminated pixels, and a separation of cloud pixels from cold water pixels by means of the correlation coefficient between the channel 4 brightness temperature and the channel 4-channel 5 brightness temperature. In the cloud screened images, the channel 4-channel 5 brightness temperatures do not increase with the satellite zenith angle and do not seem to carry any significant information about the true sea surface temperature (SST). Local meteorological and oceanographic effects are believed to be more important than the channel 4-channel 5 brightness temperatures.

Adaptive least squares method for estimation of partial cloud coverage within a pixel

Abstract A study of the estimation of partial cloud cover within a pixel has been conducted in order to be able to use pixels partially contaminated with cloud in sea surface temperature determination. The existing estimation methods based on the least squares method with constraints of minimizing the mixing ratio and observation vector, are theoretically compared and then an adaptive least squares method is proposed. In a comparative study the estimation accuracies for the proposed and other existing methods, including the maximum likelihood method, are compared with simulated and real satellite image data of NOAA AVHRR and MOS-1 VTIR. The results with the simulation data show that the maximum likelihood method is best followed by the adaptive least squares method, the least squares method and the observation vector, while the results with the real VTIR data show that the proposed adaptive least squares method is best followed by the least squares method, the maximum likelihood method and the observation...

ASTER Observations for the Monitoring of Land Surface Fluxes

A recurring eddy which located at the terminal end of the Korean East Warm Current was captured on ocean color and sea surface temperature imagery from satellite in spring and autumn. During late April, 1997 thermal infrared imagery from the NOAA AVHRR sensor and ocean color data from the Japanese ADEOS-I OCTS sensor, revealed this feature. The cold core had elevated chlorophyll concentrations, based on OCTS estimates, of greater than 3 mg/m while the warmer surrounding waters had chlorophyll concentrations of 1 mg/m or less. The elevated cholophyll accociated with this eddy has not been previously described. The eddy is also evident in SST images from autumn, but the SST in the core is warmer than in spring, and the warm jet flowing to the west of the eddy is also warmer is autumn compared to spring. A reccurring eddy and the high chlorophyll_a concentration area which surround around the eddy show on NOAA and SeaWiFS images in March 2, 1998. The eddy forms at the northern extent of the Korean East Warm Current as those waters collide with the cold, south-flowing Liman Current over a topographic shelf about 1500 m deep. This region of the eddy formation appears to have a strong connection with the dynamics of the western part of the polar front eddy field that dominates surface mesoscale structure in the central East (Japan) Sea. Interaction of the eddy with ARGOW tracked drifters, and evidence for its persistence are discussed.

Monitoring pasture productivity in New Zealand—an investigation using NOAA-11 AVHRR data and dairy farm milk fat production

Abstract A major issue when using NOAA AVHRR derived NDVI data in terrestrial applications is the acquisition of sufficient ground information lo relate to NDVI. The usual method of acquiring pasture productivity ground data is from pasture-cuts beneath movable cages. In this study, milk fat production data collected daily from dairy farms in three 25 km2 study sites in New Zealand is used as a surrogate for pasture production. The results show a probable causal relation between the AVHRR-derived NDVI and milk fat production and identify a need to use farm production models to further quantify the relationship. Our aim is to use successive years NOAA AVHRR data to monitor trends in pasture and milk fat production.

Size estimates of the factors controlling sea surface temperature with AVHRR data

Abstract NOAA AVHRR thermal infrared images, meteorological measurements and model calculations have been used to estimate the magnitude of the processes controlling the sea surface temperature variability in the Bay of Biscay. The estimates are based on the equation of temperature conservation and the equation for the total derivative. The results show that local time change, advection, horizontal diffusion and vertical turbulent flux vary with time and space. The sea surface temperature variability is mainly controlled by vertical turbulent flux and advection. For an average of five-day estimates, the order of the relative importance of the processes is vertical turbulence (38 per cent), advection (32 per cent), local time change (22 per cent) and horizontal diffusion (8 per cent).

Modeled and observed relations between the AVHRR split window temperature difference and atmospheric precipitable water over land surfaces

Atmospheric correction to near-infrared reflectances for land surface studies requires information about precipitable water. A relation between the split window temperature difference ( ΔT) and atmosphere precipitable water ( W) over land surfaces is developed from the radiative transfer equation considering spectral variation of the emissivity. This relation is found to be essentially linear with slope and intercept being dependent on the emissivity. The observed ΔT (from NOAA-9 AVHRR data for 10.1–11.5 μm and 11.2–12.6 μm channels) and W (from radiosonde data) for 1 year (1988) at 24 globally distributed locations of various surface types (hot and temperate deserts, savannas, temperature deciduous forest, rainforest, boreal forest, etc.) were analyzed to assess the relation between ΔT and W and compared with the theoretical results. These globally distributed data were segregated into specified ranges of visible reflectance (from the AVHRR corresponding to ΔT data) so as to minimize emissivity variations in determining the slope and the intercept of the ΔT-W relationship and in estimating emissivity. Both the theory and the observations demonstrate the feasibility of estimating W from ΔT over vegetated surfaces, although a priori knowledge of emissivity will be needed for a wider application.

The variability of cloud cover and cloud forcing inferred from NOAA AVHRR data for the North Sea

From August 1990 until September 1994, NOAA AVHRR HRPT data were analysed to determine the variability of cloud cover and of cloud forcing at top of atmosphere (TOA) for the North Sea (1300×1300 km 2). The first step was a detailed cloud classification based on the maximum likelihood method. That classification scheme allows a discrimination of 24 cloud classes, especially high clouds with different optical properties. The validation of these results is based on synoptical observations. Therefore, the annual variability of cloud cover can be shown for individual synoptical ground stations and for larger areas inferred from satellite data. Due to an overlapping period with ISCCP C2 data, a comparison with the high-resolution results was carried out. That led to a 10 year cloud cover climatology (1983–1993) for the North Sea, where 24 grid areas (each 2.5°×2.5°) could be investigated. A further step is the computation of cloud-climate efficiency and cloud forcing at top of atmosphere, where cloud forcing is the area mean of cloud-climate efficiencies. The cloud-climate efficiency is the cloud forcing of an individual classified cloud. For the analysed period, the variability of cloud forcing depends strongly on the cloud cover variability. Thus, the drought 1992 in Schleswig-Holstein and in northern Germany can be detected and the influence of high clouds, their amplifying heating effect, can be shown.

How accurate is a classification of thin clouds using NOAA AVHRR data?

To classify different cloud types June 1991 NOAA AVHRR data were analysed. Applying the maximum likelihood classification method, it was difficult to detect thin clouds over land due to the variability of surface reflectance and to the unknown amount and type of aerosols. This study is about the accuracy by which clouds can be detected considering the two aforementioned difficulties. Thus AVIRIS data with high spatial and spectral resolution were used to determine the surface reflectance characteristics of a chosen test site. A pixel by pixel comparison between AVHRR and spatially and spectrally integrated AVIRIS data showed a systematic error less than 2 %. Starting with the test site's surface reflectances inferred from AVIRIS data the signal (corresponding to a NOAA pixel) at top of atmosphere (TOA) has been simulated using a radiative transfer scheme (5S). The aerosol amounts were varied in these simulations. The maximal error in the reflectance at TOA due to these variations of aerosol amount was found in an order of 3 % in channel 1 and 4 % in channel 2. This corresponds to an error in the optical depth of a cloud of about 0.5 or about 30 W/m 2 in the downward net shortware flux at surface.

The effects of higher-order resampling on AVHRR data

Abstract Before using remotely-sensed data for any scientific research, the data have to pass through several stages of pre-processing such as navigation and registration, rectification, replacement of dropouts and primary calibration. It is necessary to know the effects of these pre-processing operations on the information content in the raw data. In this article the effects of higher-order resampling methods on AVHRR data are analysed and discussed. An exemplar taken from Normalised Difference Vegetation Index (NDVI) data sets derived from NOAA AVHRR data is presented to show the possible artefacts introduced by higher-order resampling strategies.

Using satellite remote sensing to extrapolate evapotranspiration estimates in time and space over a semiarid Rangeland basin

Remote sensing data from the NOAA-11 AVHRR satellite were collected over the USDA-Agricultural Research Service Walnut Gulch Experimental Watershed in southeastern Arizona during the MONSOON 90 field campaigns. An energy balance model which relies primarily on remotely sensed inputs was used to extrapolate evapotranspiration (ET) estimates from one location containing near-surface meteorological data to other areas in the basin. Satisfactory results were obtained under a wide range of environmental conditions. However, the ET values are essentially instantaneous and therefore do not necessarily provide reliable estimates of daytime or daily ET fluxes required for many hydrological and resource management applications. An operational technique was developed to extrapolate one time of day ET estimates to daytime averages using the evaporative fraction concept and empirical methods for converting midday available energy to daytime average values. Model derived daytime average ET fluxes were in reasonable agreement with local ground-based measurements. The technique also was used to estimate daily ET at the basin scale.

Operational use of environmental satellite remote sensing and satellite communications technology for global food security and locust control by FAO: The ARTEMIS and DIANA systems

Since August 1988, after a development period of 3 years, the FAO Remote Sensing Centre has been operating the Africa Real Time Environmental Monitoring Information System (ARTEMIS) in support of the Global Information and Early Warning System on Food and Agriculture and the Desert Locust Plague Prevention Programme of FAO. The ARTEMIS system was implemented by FAO in close co-operation with NASA Goddard Space Flight Center, U.S.A.; the National Aerospace Laboratory of The Netherlands and the University of Reading, U.K. ARTEMIS is a highly automated data acquisition, pre- and thematic processing, production and archiving system for real-time precipitation assessment and near real-time vegetation condition monitoring of Africa, the Near East and Southwest Asia, based on hourly Meteosat thermal infrared and NOAA AVHRR data. The vegetation condition assessment capability is currently being expanded to include the rest of Asia and Latin America. ARTEMIS data products, generated by the system on a 10-day and monthly basis, are currently used operationally by a variety of users at FAO Headquarters and by regional and national food security early warning systems in sixteen Eastern and Southern African countries. The ARTEMIS system plays an important role in the generation and archiving of global satellite derived environmental data sets for use by FAO and other organizations with global monitoring and assessment mandates. An extensive ARTEMIS rainfall estimation calibration programme, covering Sahelian countries as well as Eastern and Southern Africa, has been developed in co-operation with the University of Reading, U.K. and regional FAO field projects. This programme was started in 1990 and will continue through 1995 to obtain statistically valid regression parameters for homogeneous climatic zones to allow automated translation of satellite derived cold cloud duration data into quantities of estimated rainfall. Jointly with the European Space Agency, FAO Has been implementing during 1989–1992 a dedicated satellite communications system, DIANA (Direct Information Access Network for Africa), which allows real-time transmission of high volume ARTEMIS digital products to user terminals in Nairobi, Kenya: Accra, Ghana and Harare, Zimbabwe. The DIANA system, which operates through the Intelsat satellite over the Indian Ocean and the italian Intelsat groundstation of Telespazio in Fucino, is currently being tested and demonstrated for a wide variety of applications of an operational, technical and administrative nature.

Satellite observations of circulation in the southern Great Barrier Reef, Australia

Abstract Twenty-one NOAA-9 AVHRR satellite images of the southern Great Barrier Reef, spanning the period from June 1986 to September 1988, were examined for sea surface temperature patterns in order to trace circulation within this bathymetrically complex area. Our findings are in general agreement with the few field studies of this region. The East Australian Current tended to flow outside the reefs along the shelf break until it entered the Capricorn Channel, where it either meandered westward along the narrowing shelf, adhering closely to the slope contours, or flowed directly southward. It then impinged upon the shelf break, near Fraser Island, where it bifurcated to produce a southward continuation of the current, and a cyclonic eddy within the Capricorn Channel. Cool water, which commonly occurred over the shelf between Fraser Island and Cape Clinton, has probable significance for biological production within the adjacent Capricorn/Bunker Reefs of the Great Barrier Reef Marine Park. Interpreted as ...

Assessment of AVHRR data for deforestation estimation in Mato Grosso (Amazon Basin)

Abstract A large area (∼ 900,000 km2) corresponding to Mato Grosso, a southern most State of the Brazilian Legal Amazon region, was selected for this study. All five channels of selected dates of 1988, 1989, and 1990, full resolution NOAA AVHRR data were investigated to map “forest” and “non‐forest” (“cerrado”) using on the screen visual interpretation. Maximum likelihood classifier (MLC) was used to classify deforestation for the 1988 AVHRR images. The vegetation map obtained by interpretation of AVHRR data agreed well with the available vegetation map used as reference. Results based on cells of 1° by 1.5° showed r2 = 0.94 (significant at α = 0.01) between the AVHRR map of forest and the available vegetation map. Comparisons for 1988 between AVHRR deforestation estimates and two independent estimates using TM data, i.e. Landsat TM band 5 habitat fragmentation and Landsat TM bands 3, 4, and 5 color composite deforestation estimates were within 3% of each other for the entire State (range from 71,128 km2 ...

A microwave technique for mapping thin sea ice

A technique is presented for mapping the distribution of new, young and first‐year sea ice in seasonal sea ice zones that utilizes microwave spectral and polarization information from the Defense Meteorological Satellite Program Special Sensor Microwave/Imager. The motivation for this work stems from the need for accurate estimates of open water and thin ice within the Arctic ice pack. The technique utilizes the microwave polarization and spectral characteristics of these three ice types through two microwave radiance ratios: the 19.4 GHz polarization and the spectral gradient ratio, which is a measure of the spectral difference between the 19.4‐GHz and the 37.0‐GHz vertically polarized radiance components. The combined use of the spectral gradient ratio and polarization reduces the low ice concentration bias generally associated with the presence of thin ice types. The microwave polarization, which is sensitive to changes in ice thickness and ice surface characteristics, is used to classify new, young, and first‐year ice types. The polarization varies from about 0.3 for calm ice‐free water, to about 0.15 for new ice, and to 0.03 for thick first‐year ice. On the basis of this parameterization, improved sea ice concentration maps and maps of new, young and first‐year ice type distributions are derived. Examples from the Bering Sea are presented and comparisons are made with analyzed NOAA AVHRR imagery.

Identification of icing water clouds by NOAA AVHRR satellite data

Aircraft warning of icing is made by operational meteorologists by combining humidity data with temperature information. It is well known that such warnings frequently fail [14]. Improvement of icing warning could be obtained by using satellite data additionally to the conventional data. According to Hoffmann, cloud phase, liquid water content, temperature and drop size distribution are the cloud properties relevant for icing. In this paper, cloud top temperature and cloud phase, derived from NOAA AVHRR data are compared with synchronous airborne measurements to demonstrate that satellite data may be well suited to give relevant information on icing conditions which are not available from conventional data.