NOAA Advanced Very High Resolution Radiometer Research Articles

Using NOAA AVHRR data to assess flood damage in China.

The article used two NOAA-14 Advanced Very High Resolution Radiometer (AVHRR) datasets to assess flood damage in the middle and lower reaches of China's Changjiang River (Yangtze River) in 1998. As the AVHRR is an optical sensor, it cannot penetrate the clouds that frequently cover the land during the flood season, and this technology is greatly limited in flood monitoring. However the widely used normalized difference vegetation index (NDVI) can be used to monitor flooding, since water has a much lower NDVI value than other surface features. Though many factors other than flooding (e.g. atmospheric conditions, different sun-target-satellite angles, and cloud) can change NDVI values, inundated areas can be distinguished from other types of ground cover by changes in the NDVI value before and after the flood after eliminating the effects of other factors on NDVI. AVHRR data from 26 May and 22 August, 1998 were selected to represent the ground conditions before and after flooding. After accurate geometric correction by collecting GCPs, and atmospheric and angular corrections by using the 6S code, NDVI values for both days and their differences were calculated for cloud-free pixels. The difference in the NDVI values between these two times, together with the NDVI values and a land-use map, were used to identify inundated areas and to assess the area lost to the flood. The results show a total of 358,867 ha, with 207,556 ha of cultivated fields (paddy and non-irrigated field) inundated during the flood of 1998 in the middle and lower reaches of the Changjiang River Catchment; comparing with the reported total of 321,000 and 197,000 ha, respectively. The discrimination accuracy of this method was tested by comparing the results from two nearly simultaneous sets of remote-sensing data (NOAA's AVHRR data from 10 September, 1998, and JERS-1 synthetic aperture radar (SAR) data from 11 September, 1998, with a lag of about 18.5 hr) over a representative flooded region in the study area. The results showed that 67.26% of the total area identified as inundated using the NOAA data was also identified as inundated using the SAR data.

An integrative classification of vegetation in China based on NOAA AVHRR and vegetation-climate indices of the Holdridge life zone

We developed a method for integrated analysis of multi-source data for vegetation classification at the continental scale, and applied it to China. Multi-temporal 1 km NOAA Advanced Very High Resolution Radiometer (AVHRR) Holdridge's life zone system and its vegetation-climate classification indices such as bio-temperature (BT), potential evapotranspiration rate (PER) and precipitation ( P ) correspond better with undisturbed vegetation types all over the world. We generated 1 km images of BT, PER and P using the quantitative model of Holdridge's life zone system with climate data of China. They were processed with principal component analysis (PCA) to produce an ancillary image. This image and 12 monthly images of maximum Normalized Difference Vegetation Index (NDVI) values at 1 km resolution were input into an ISODATA clustering algorithm to carry out a vegetation classification. As a result, 47 information classes were obtained. Seasonal NDVI parameters derived through time series analysis (TSA) of the NDVI temporal profile and a set of quantitative vegetation-climate parameters of Holdrige's life zone model were synthetically utilized to label information classes. In this method, climate, terrain and spectral data were integrated; separability between vegetation types and classification accuracy were improved. A total of 47 land cover classes were obtained. Validation data collected in the field using GPS indicated that an overall classification accuracy of 71.4% was reached, an 8.1% improvement to the map derived only from multi-temporal NDVI images. To compare our results with the International Geosphere-Biosphere Programme (IGBP) DISCover land cover dataset, we aggregated our land cover classes according to the IGBP classification system. The overall classification accuracy for the aggregated vegetation map from our classification results improved IGBP land cover map from 75.5% to 86.3%.

Remote sensing of cloud cover in the Arctic region from AVHRR data during the ARTIST experiment

In this paper we present a cloud detection algorithm developed for the Arctic region using Advanced Very High Resolution Radiometer (AVHRR) data. Our approach is a simplified version of the Ebert method to discriminate between clouds, ice and open water in the Arctic Sea. The algorithm is tuned to work on an AVHRR scene typical of the winter to spring transition period. The algorithm has been applied to 1 month (154 scenes) of NOAA-14 AVHRR images (from 16 March to 15 April 1998) covering the region of the Arctic Sea near the Svalbard Islands. The cloud detection results are analysed using various check procedures. The algorithm's pixel classification performance was verified by a satellite image expert. The misclassified pixels were digitalized on the image and counted by the expert in order to quantify the algorithm's accuracy. The cloud classification results are quite accurate: 70% of the images (109) have an error less than 5% and only 11% of the image results have an error greater than 10%. The method's performance is also tested against independent cloud and ice observations obtained, respectively, from the Ny-Ålesund meteorological base and from the Special Sensor Microwave/Imager (SSM/I) dataset. The comparison with these independent sources of data confirms the algorithm's good performance.

Errors in high-latitude SSTs and other geophysical products linked to NOAA-14 AVHRR channel 4 problems

Errors in brightness temperatures for channel 4 in the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA‐14 spacecraft are examined. The errors involve a low frequency of occurrence for some values, and a corresponding enhancement of frequency for others. Errors appear to be related to the conversion of analog to digital values. Unfortunately, it is not possible to identify and separate erroneous values. The most apparent errors in geophysical products derived from AVHRR's channel 4 occur at low brightness temperatures, therefore sea surface temperatures in high latitudes (below about 6°C) and cloud‐related products must be used with caution, as they may have systematic errors as large as 0.5°C.

Devising a new Digital Vegetation Model for eco-climatic analysis in Africa using GIS and NOAA AVHRR data

Large-scale land cover information is a crucial input in numerical climatic change modelling. Traditionally, this information is encoded as vegetation maps based on ground surveys made at selected points, and is of poor resolution. Nowadays, improvements in land cover mapping/monitoring are obtained by using the Normalized Difference Vegetation Index (NDVI) calculated from NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite sensor data. In this paper, a Digital Vegetation Model (DVM) was derived in an attempt to get an accurate and upgradable digital image of African land cover types, using 1985–1991 NOAA AVHRR data. First, the main seasonal features of Africa are extracted from the NDVI data. Then (unsupervised) classification, using only the principal components analysis (PCA) first components of the NOAA multi-spectral data (the visible channels 1 and 2, and the thermal channel 4) on the IDRISI32 GIS platform, yields our macro-scale seven-category African DVM, consistent with Köppen climatic classification. Subsequent reclassifications, referring to known African sub-regional or local features (vegetation, eco-region maps, altitude, geomorphology), finally lead us to our fine-tuned regional-scale 30 category DVM. Including channel 4 data in our scheme has been pivotal for allowing full-scale classification from dense forests to sparsely or non-vegetated areas. Usually, this is barely possible in NDVI-only classifications.

A Critical Examination of Satellite Cloud Retrieval from AVHRR in the Arctic Using SHEBA Data

Abstract This study examines the validity and limitations associated with retrieval of cloud optical depth τ and effective droplet size re in the Arctic from Advanced Very High Resolution Radiometer (AVHRR) channels 2 (0.725–1.10 μm), 3 (3.55–3.93 μm), and 4 (10.3–11.3 μm). The error in re is found to be normally less than 10%, but the uncertainty in τ can be more than 50% for a 10% uncertainty in the satellite-measured radiance. Model simulations show that the satellite-retrieved cloud optical depth τsat is overestimated by up to 20% if the vertical cloud inhomogeneity is ignored and is underestimated by more than 50% if overlap of cirrus and liquid water clouds is ignored. Under partially cloudy conditions, τsat is larger than that derived from surface-measured downward solar irradiance (τsurf) by 40%–130%, depending on cloud-cover fraction. Here, τsat derived from NOAA-14 AVHRR data agrees well with τsurf derived from surface measurements of solar irradiance at the Surface Heat Budget of the Arctic Oce...

Perspectives in using a remotely sensed dryness index in distributed hydrological models at the river‐basin scale

AbstractIn a previous study a spatially distributed hydrological model, based on the MIKE SHE code, was constructed and validated for the 375 000 km2 Senegal River basin in West Africa. The model was constructed using spatial data on topography, soil types and vegetation characteristics together with time‐series of precipitation from 112 stations in the basin. The model was calibrated and validated based on river discharge data from nine stations in the basin for 11 years. Calibration and validation results suggested that the spatial resolution of the input data in parts of the area was not sufficient for a satisfactory evaluation of the modelling performance. The study further examined the spatial patterns in the model input and output, and it was found that particularly the spatial resolution of the precipitation input had a major impact on the model response.In an attempt to improve the model performance, this study examines a remotely sensed dryness index for its relationship to simulated soil moisture and evaporation for six days in the wet season 1990. The index is derived from observations of surface temperature and vegetation index as measured by the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor. The correlation results between the index and the simulation results are of mixed quality. A sensitivity analysis, conducted on both estimates, reveals significant uncertainties in both. The study suggests that the remotely sensed dryness index with its current use of NOAA AVHRR data does not offer information that leads to a better calibration or validation of the simulation model in a spatial sense. The method potentially may become more suitable with the use of the upcoming high‐resolution temporal Meteosat Second Generation data. Copyright © 2002 John Wiley & Sons, Ltd.

Assessment of NDVI Composites Using Merged NOAA-14 and NOAA-15 AVHRR Data

The Advanced Very High Resolution Radiometer (AVHRR) data acquired by the National Oceanic and Atmospheric Administration's (NOAA) satellites provide daily observations of the Earth's surface. While NOAA-14 data from ascending orbits are operationally used for global vegetation monitoring; NOAA-15 data from descending orbits could provide additional daily coverage. The normalized difference vegetation index (NDVI), derived from the solar reflective data, is linked to vegetation condition and plant biomass production on land surfaces. Because a single AVHRR scene is seldom completely cloud-free, maximum value compositing (MVC) of multi-date NDVI data is widely used to minimize cloud contamination. While ten-day composites from NOAA-14 AVHRR data may not be cloud-free; critical short-term changes in vegetation condition may be lost in composites created over longer time periods. AVHRR data of Texas obtained from NOAA-15 (morning satellite) and NOAA-14 (afternoon satellite) were used in our study on the potential benefits of merged morning and afternoon NDVI data sets for crop monitoring. A strong correlation was observed between NOAA-14 and NOAA-15 NDVI data derived from top-of-atmosphere (TOA) reflectance.

Thermal features of the Mackenzie basin from NOAA AVHRR observations for summer 1994

A series of mid‐afternoon Advanced Very High Resolution Radiometer (AVHRR) thermal radiance scenes were assembled in order to develop a better understanding of the complex energy and water processes leading to variations in surface temperature. An in‐depth knowledge of the temperature variability is of interest to land surface process modelling and its application to the Mackenzie Global Energy and Water Cycle Experiment (GEWEX) Study (MAGS). Clear‐sky land surface temperatures are estimated by applying a split window technique to remove atmospheric effects. A maximum land surface temperature map of the Mackenzie basin at 1‐km scale for summer 1994 is produced. The patterns are related to land surface features and elevation. The basin's maximum land surface temperature patterns can be subdivided into three land zones (≥ 35°C, 33–34°C and 27–32°C) and a water dominated zone (20.5°C on average). The highest maximum temperature zone (≥35°C) corresponds to a combination of minimal vegetation, drier soils and low terrain. This zone is not in the southern part of the basin as might be speculated in the absence of these data, but in a wide low elevation corridor from west of Great Bear Lake along the Mackenzie River down to 50°N, 120°W. The maximum land surface temperatures tend to decrease with increasing vegetation density and surface moisture; they also decrease with elevation at a rate of –4.5°C km–1. This is confirmed by weather station data. The AVHRR data extend this relationship to the 1200 – 2200 m altitude ranges, where there are no station data. The data suggest that elevation and land cover should be taken into account in the objective analysis (spatial interpolation) of station data.

Surface Albedo over the Arctic Ocean Derived from AVHRR and Its Validation with SHEBA Data

A method is presented for retrieving the broadband albedo over the Arctic Ocean using advanced very high resolution radiometer (AVHRR) data obtained from NOAA polar-orbiting satellites. Visible and near-infrared albedos over snow and ice surfaces are retrieved from AVHRR channels 1 and 2, respectively, and the broadband shortwave albedo is derived through narrow-to-broadband conversion (NTBC). It is found that field measurements taken under different conditions yield different NTBC coefficients. Model simulations over snow and ice surfaces based on rigorous radiative transfer theory support this finding. The lack of a universal set of NTBC coefficients implies a 5%‐10% error in the retrieved broadband albedo. An empirical formula is derived for converting albedo values from AVHRR channels 1 and 2 into a broadband albedo under different snow and ice surface conditions. Uncertain calibration of AVHRR channels 1 and 2 is the largest source of uncertainty, and an error of 5% in satellite-measured radiance leads to an error of 5%‐10% in the retrieved albedo. NOAA-14 AVHRR data obtained over the Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp are used to derive the seasonal variation of the surface albedo over the Arctic Ocean between April and August of 1998. Comparison with surface measurements of albedo by Perovich and others near the SHEBA ice camp shows very good agreement. On average, the retrieval error of albedo from AVHRR is 5%‐10%.

Development of a Global Validation Package for Satellite Oceanic Aerosol Optical Thickness Retrieval Based on AERONET Observations and Its Application to NOAA/NESDIS Operational Aerosol Retrievals

In this paper, a global validation package for satellite aerosol optical thickness retrieval using the Aerosol Robotic Network (AERONET) observations as ground truth is described. To standardize the validation procedure, the optimum time–space match-up window, the ensemble statistical analysis method, the best selection of AERONET channels, and the numerical scheme used to interpolate/extrapolate these observations to satellite channels have been identified through sensitivity studies. The package is shown to be a unique tool for more objective validation and intercomparison of satellite aerosol retrievals, helping to satisfy an increasingly important requirement of the satellite aerosol remote sensing community. Results of applying the package to the second-generation operational aerosol observational data (AEROBS) from the NOAA-14 Advanced Very High Resolution Radiometer (AVHRR) in 1998 and to the same year aerosol observation data [Clouds and the Earth's Radiant Energy System-Single Scanner Foodprint version 4 (CERES-SSF4)] from the Tropical Rainfall Measuring Mission (TRMM) Visible Infrared Scanner (VIRS) are presented as examples of global validation. The usefulness of the package for identifying improvements to the aerosol optical thickness τ retrieval algorithm is also demonstrated. The principal causes of systematic errors in the current National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite, Data, and Information Service (NESDIS) operational aerosol optical thickness retrieval algorithm have been identified and can be reduced significantly, if the correction and adjustment suggested from the global validation are adopted. Random error in the τ retrieval is identified to be a major source of error on deriving the effective Ångström wavelength exponent α and may be associated with regional differences in aerosol particles, which are not accounted for in the current second-generation operational algorithm. Adjustments to the nonaerosol and aerosol radiative transfer model parameters that reduce systematic errors in τ retrievals are suggested for consideration in the next-generation algorithm. Basic features that should be included in the next-generation algorithm to reduce random error in τ retrievals and the resulting error in the effective Ångström wavelength exponent have also been discussed. Compared to the AERONET observation, the NOAA-14 AVHRR (AEROBS) τ values for mean conditions are biased high by 0.05 and 0.08, with random errors of 0.08 and 0.05, at 0.63 and 0.83 μm, respectively. Correspondingly, the TRMM VIRS (CERES-SSF4) values for mean conditions are biased high by 0.06 and 0.02, with random errors of 0.06 and 0.04 at 0.63 and 1.61 μm, respectively. After corrections and adjustments to the retrieval algorithm, the biases in both channels of AVHRR and VIRS are reduced significantly to values close to zero, although random error is almost unchanged. The α exponent derived directly from the aerosol optical thicknesses (τs) has been shown to be poorly correlated both before and after adjustments, indicating that random error in the τ measurement (possibly related to aerosol model parameter variations or cloud–surface reflectance contamination) needs to be reduced.

Aerosol Retrievals from Individual AVHRR Channels. Part I: Retrieval Algorithm and Transition from Dave to 6S Radiative Transfer Model

The present second-generation aerosol retrieval algorithm over oceans used at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) separately retrieves two values of aerosol optical depth, τ1 and τ2, from Advanced Very High Resolution Radiometer (AVHRR) channels 1 and 2 centered at λ1 = 0.63 (operational) and λ2 = 0.83 μm (experimental), respectively. From these, an effective Ångström exponent α, related to particle size, can be derived as α = −ln(τ1/τ2)/ln(λ1/λ2). The single-channel lookup tables, relating reflectance to optical depth in the retrievals, have been precalculated with the Dave (1973) scalar radiative transfer (RT) model. This first part of a two-part paper describes the retrieval algorithm, with emphasis on its RT modeling related elements, and documents the transition to the Second Simulation of the Satellite Signal in the Solar Spectrum (6S; 1997) RT model. The new 6S RT model has the capability to account for reflection from wind-roughened sea surface, offers a wide choice of flexible aerosol and gaseous absorption models, and allows easy convolution with the sensor's spectral response. The value of these new features for aerosol remote sensing from AVHRR is discussed in detail. The transition effect is quantified by directly applying the Dave- and 6S-based algorithms to four large datasets of NOAA-14 AVHRR measurements, collected between February 1998 and May 1999 over the latitudinal belt of 5°–25°S. Statistics of the differences (δτ = τDave − τ6S and δα = αDave − α6S) are as follows: averages − 〈δτ1〉 < 1 × 10−3, 〈δτ2〉 ≈ −4 × 10−3, and 〈δα〉 ≈ +8 × 10−2; and standard deviations are στ1 ∼ 6 × 10−3, στ2 ∼ 4 × 10−3, and σα ≈ 9 × 10−2. These are found to be well within a few percent of typical values of τ and α and their respective ranges of variability, thus ensuring a smooth transition and continuity in the operational aerosol retrieval. On the other hand, the 6S model provides a much more flexible RT modeling tool compared to the previously used Dave code.

Aerosol Retrievals from Individual AVHRR Channels. Part II: Quality Control, Probability Distribution Functions, Information Content, and Consistency Checks of Retrievals

This second part of a two-part study evaluates retrievals of aerosol optical depths, τ1 and τ2, in Advanced Very High Resolution Radiometer (AVHRR) channels 1 and 2 centered at λ1 = 0.63 and λ2 = 0.83 μm, and an effective Ångström exponent, α, derived therefrom as α = −ln(τ1/τ2)/ln(λ1/λ2). The retrievals are made with the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model from four NOAA-14 AVHRR datasets, collected between February 1998 and May 1999 in the latitudinal belt of 5°–25°S. A series of quality control (QC) checks applied to the retrievals to identify outliers are described. These remove a total of ∼1% of points, which presumably originate from channel misregistration, residual cloud in AVHRR cloud-screened pixels, and substantial deviations from the assumptions used in the retrieval model (e.g., bright coastal and high altitude inland waters). First, from examining histograms of the derived parameters it is found that τ and α are accurately fit by lognormal and normal probability distribution functions (PDFs), respectively. Second, the scattergrams τ1 versus τ2 are analyzed to see if they form a coherent pattern. They do indeed converge at the origin, as expected, but frequently are outside of the expected domain in τ1–τ2 space, defined by two straight lines corresponding to α = 0 and α = 2. This results in a low bias in α, which tends to fill in an interval of α ∈ [−1, 1] rather than α ∈ [0, 2]. Third, scattergrams of α versus τ are used to empirically confirm a previously drawn theoretical conclusion that errors in α are inversely proportional to τ. More in-depth quantitative analyses suggest that the AVHRR-derived Ångström exponent becomes progressively more meaningful when τ > 0.2. Geographical trends are studied to demonstrate that the selected ocean area is reasonably uniform to justify application of consistency checks to reveal angular trends in the retrievals. These checks show that in most cases, the artifacts in the retrieved τ and α are statistically insignificant. On average, the analyses suggest that the retrieved τ1, τ2, and α show a high degree of self- and interconsistency, with the exception of a troublesome May 1999 dataset. The most prominent problem noticed so far is the inconsistency between τ1 and τ2, persistent from one dataset to another, which calls for fine-tuning some (not aerosol-model related) elements of the retrieval algorithm. These adjustments will be discussed elsewhere.

Quantification of surface suspended sediments along a river dominated coast with NOAA AVHRR and SeaWiFS measurements: Louisiana, USA

The ability to quantify suspended sediment concentrations accurately over both time and space using satellite data has been a goal of many environmental researchers over the past few decades. This study utilizes data acquired by the NOAA Advanced Very High Resolution Radiometer (AVHRR) and the Orbview-2 Sea-viewing wide field-of-view (SeaWiFS) ocean colour sensor, coupled with field measurements to develop statistical models for the estimation of nearsurface suspended sediments and suspended solids. 'Ground truth' water samples were obtained via helicopter, small boat and automatic water sampler within a few hours of satellite overpasses. The NOAA AVHRR atmospheric correction was modified for the high levels of turbidity along the Louisiana coast. Models were developed based on the field measurements and reflectance/radiance measurements in the visible and near infrared Channels of NOAA-14 and Orbview-2 SeaWiFS. The best models for predicting surface suspended sediment concentrations were obtained with a NOAA AVHRR Channel 1 (580-680 nm) cubic model, Channel 2 (725-1100 nm) linear model and SeaWiFS Channel 6 (660-680 nm) power model. The suspended sediment models developed using SeaWiFS Channel 5 (545-565 nm) were inferior, a result that we attribute mainly to the atmospheric correction technique, the shallow depth of the water samples and absorption effects from non-sediment water constituents.

Observations of oceanic mesoscale features and variability in the Canary Islands area from ERS-1 altimeter data, satellite infrared imagery and hydrographic measurements

The main purposes of this paper are to identify and evaluate the oceanic mesoscale features that appear in the Atlantic Ocean eastern boundary, 20-40° N, 19-9° W, using ERS-1 (1992-1993) satellite altimeter data. The sea surface height anomalies and the eddy kinetic energy fields are calculated. High energy values, between 0.03 and 0.05 m 2 s -2, are observed with the altimeter data in the Canary region mainly in summer. These maximum values are associated with eddies located downstream of Gran Canaria and Tenerife (Canary Islands). Comparison with in situ measurements provided by the hydrographic surveys from a cruise in summer 1993 around the Canary Islands showed a good agreement. Dynamic heights relative to 300 dbar for August and the altimetric heights from ERS-1 data were averaged over 0.125° boxes for the duration of the cruise. The correlation coefficient was 0.7. Negative anomalies of the sea level calculated from ERS-1 between Cape Yubi and Cape Bojador (26.0°-27.5° N) were obtained in August 1993. Possibly these correspond with filaments from the north-west African upwelling coast. Also, the results of the altimetric data were compared with NOAA AVHRR (Advanced Very High Resolution Radiometer) sea surface temperature (SST) satellite images. A cyclonic and an anticyclonic eddy to the south-west of the island of Gran Canaria were identified during the same period in the SST images.

An automated cloud detection method for daily NOAA-14 AVHRR data for Texas, USA

A variety of cloud types appears in each Advanced Very High Resolution Radiometer (AVHRR) image. Clouds may contaminate solar reflectance data to be used for vegetation studies. This may jeopardize the accuracy of any quantitative results from data analysis. Published cloud detection algorithms for AVHRR data to date have mainly used data over Europe received from the National Oceanic and Atmospheric Administration (NOAA)-12 or earlier satellites. This study examined the previously published cloud detection methods with the intent to develop an automated cloud detection algorithm for NOAA-14 AVHRR data for Texas. Through testing a whole year of AVHRR scenes, the Texas automated cloud detection algorithm was capable of correctly identifying most of the cloudcontaminated pixels except for cloud shadow pixels. The overall accuracy reached 89%. The developed algorithm includes three major steps, top-of-the-atmosphere reflectance of channel 1, temperature difference of channels 3 and 4, and a combination of ratio of channel 2 to channel 1 and temperature in channel 4.

GOES-8 and NOAA-14 AVHRR retrieval of smoke aerosol optical thickness during SCAR-B

Using the NOAA-14 1-km Advanced Very High Resolution Radiometer (AVHRR) and the Geostationary Operational Environmental Satellite (GOES-8) imager data, smoke aerosol optical thickness ( ) is retrieved over land during the Smoke, Clouds and Radiation-Brazil (SCAR-B) experiment in Brazil during August-September 1995. The satellite-retrieved values are then compared against ground-based sunphotometer derived values from the Aerosol Robotic Network (AERONET) program. Both the AVHRR and GOES-8 retrieved values are in excellent agreement with the AERONET derived values with linear correlation coefficients of 0.93. A single scattering albedo of 0.90 (at 0.67 w m) provides the best fit between the GOES-8 and AERONET values. The sensitivity of the retrieved to assumed surface albedo and aerosol single scattering albedo are also examined. A simple multi-spectral thresholding algorithm is used to separate smoke from other features from GOES-8 satellite imagery and regional maps of are provided. Our results show that the aerosol properties used in this paper are adequate to characterize biomass burning aerosols and can be used in studies that model the role of biomass burning on regional climate.

Analysis of the temporal and spatial vegetation patterns in a semi-arid environment observed by NOAA AVHRR imagery and spectral ground measurements

The borderline between Israel and Sinai is characterized by a sharp contrast that is caused by the low spectral reflectance on the Israeli side (Negev desert) and the high spectral reflectance on the bare Egyptian side (Sinai desert). This contrast across the political border has been discussed in many publications over the last two decades. In this study, satellite images acquired by NOAA Advanced Very High Resolution Radiometer (AVHRR) over a time period of 3 years (June 1995 to June 1998) were analysed. In addition, extensive field studies were carried out on the Israel side of the border. The current research shows that the reflectance values in Sinai seem to be quite stable over the entire year, however reflectance values in the Negev show a significant difference between the dry and the rainy seasons. Comparison between the AVHRR-derived Normalized Difference Vegetation Index (NDVI ) values and rainfall data from the Negev shows that the highest AVHRR-derived NDVI values occur a few weeks after the main rainfall. Field observations, based on spectrometer measurements of different surface components (bare sands, biological soil crusts, annuals, and perennials) and estimation of vegetation cover on the Israeli side of the border, show that the peak NDVI of the perennials occurs at the same time as the satellite observed peak. The spectral difference between both sides of the border during the dry season is caused by the dense cover of the higher vegetation and the biological soil crusts and by the photosynthetic activity of perennials during the dry season. The highest difference between both sides during the rainy season is caused by the photosynthetic activity and vegetation cover of the annuals and perennials.

A statistical methodology for burned area estimation using multitemporal AVHRR data

A statistical methodology based on multitemporal NOAA Advanced Very High Resolution Radiometer (AVHRR) data, for identification of burned areas has been developed and tested for an area covering the Niokolo Koba National Park in the Sudanian zone of south-east Senegal. Ten NOAA AVHRR images, acquired to coincide with a field campaign, were employed in the analysis. A SPOT HRV scene, also coinciding with the field campaign, was furthermore applied and used as validation of the developed methodology. Burned areas were identified as pixels in the AVHRR data that underwent a rapid change in time in a three-dimensional feature space defined by the Normalized Difference Vegetation Index (NDVI), surface temperature and albedo. To circumvent fluctuations in the AVHRR data due to atmospheric contamination as well as the general senescence of vegetation occurring at the start of the dry season, principal component transformations of the AVHRR data were used. Following this initial identification, all identified pixels were checked against the preceding image and were confirmed as a burned area if they exhibited relatively low levels of NDVI, high surface temperature and a low albedo. All results of the method were validated against the classified SPOT HRV image and the accuracy of the method, expressed as agreement between the applied method and the classified high resolution image, was evaluated. It was found that the accuracy of burned areas is a function of the fraction of the pixel affected by fire and that for pixels burned from 75% to 100% an accuracy of 80% is found, which decreases as the fraction decreases.

Accuracy of NOAA AVHRR-based surface reflectance overa winter-time boreal surface - comparison with aircraft measurements and land-cover information

Polar-orbiting NOAA14 AVHRR (Advanced Very-High-Resolution Radiometer) based estimates of surface reflectance, with a spatial resolution of about 1.1 km at nadir, were derived for a single almost cloud-free image of snow-covered, partly-forested boreal terrain in northern Finland. As a part of the WINTEX campaign, comparative measurements of reflectance were made along three 30 km flight legs with downward and upward-facing broadband pyranometers installed in a C-130 aircraft. After co-location of the satellite sensor pixels and the airborne samples, the two estimates of broadband (0.3–3 μm) surface reflectance matched to within 2%, with a standard error of 4.4% (absolute units). The reflectance variation obtained by the aircraft along the flight legs was well matched by the satellite-based method.