Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS

Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution (<30 m) data are used to assess their accuracy with little regard to the accuracy of the higher spatial resolution reference data. In this study we aimed to investigate whether Landsat Enhanced Thematic Mapper (ETM+)‐derived reference imagery can be more accurately produced using such spectrally informed methods. The efficacy of several spectral index methods to discriminate between burned and unburned surfaces over a series of spatial scales (ground, IKONOS, Landsat ETM+ and data from the MOderate Resolution Imaging Spectrometer, MODIS) were evaluated. The optimal Landsat ETM+ reference image of burned area was achieved using a charcoal fraction map derived by linear spectral unmixing (k = 1.00, a = 99.5%), where pixels were defined as burnt if the charcoal fraction per pixel exceeded 50%. Comparison of coincident Landsat ETM+ and IKONOS burned area maps of a neighbouring region in Mongu (Zambia) indicated that the charcoal fraction map method overestimated the area burned by 1.6%. This method was, however, unstable, with the optimal fixed threshold occurring at >65% at the MODIS scale, presumably because of the decrease in signal‐to‐noise ratio as compared to the Landsat scale. At the MODIS scale the Mid‐Infrared Bispectral Index (MIRBI) using a fixed threshold of >1.75 was determined to be the optimal regional burned area mapping index (slope = 0.99, r ² = 0.95, SE = 61.40, y = Landsat burned area, x = MODIS burned area). Application of MIRBI to the entire MODIS temporal series measured the burned area as 10 267 km² during the 2001 fire season. The char fraction map and the MIRBI methodologies, which both produced reasonable burned area maps within southern African savannah environments, should also be evaluated in woodland and forested environments.     

The potential for integrating Sentinel 2 MSI with SPOT 5 HRG and Landsat 8 OLI imagery for monitoring semi-arid savannah woody cover

Multitemporal archived imagery enables the monitoring of savannah woody cover, for ecological purposes. Compatibility in multitemporal, multiple sensor image data would facilitate the monitoring. The decommissioning of SPOT 5 (Système Pour l’Observation de la Terre 5) left a void in multispectral imagery at the 10 m spatial resolution of its high-resolution geometric (HRG) sensor. The subsequent launch of Sentinel 2 presented an opportunity for data continuity to monitor the savannah woody cover, using equivalent 10 m resolution multispectral instrument (MSI) bands. This study examined the integration potential of Sentinel 2 MSI with the longer archive HRG and Landsat 8 (Land Satellite 8) Operational Land Imager (OLI) imagery, in assessing savannah woody cover. Images of three semi-arid savannah sites acquired on same season dates that excluded herbaceous vegetation from the spectral signature were used: November 2014 (HRG) and December 2015 (MSI, OLI). Using equivalent green (G), red (R), and near infrared (NIR) bands at 10 m (MSI, HRG) and 30 m (OLI) resolution, the woody cover was mapped through subpixel classification. The mapped woody cover was compared for statistical differences using χ² analysis at 10 m resolution (MSI, HRG) and at a degradation of the MSI and HRG images to the 30 m OLI pixel size. Conversion to top-of-atmosphere reflectance values facilitated inter-sensor correlation of G, R, and NIR reflectance for field sampling sites where woody cover was quantified. Inter-sensor regression functions in G, R, and NIR band MSI and HRG images were developed. The 10 m resolution classifications of woody cover were not statistically different. Due to spatial resolution similarity, SPOT 5 HRG multispectral imagery was established as suitable for integration with equivalent band MSI imagery in mapping the woody cover in a multitemporal analysis. For dense woody cover, Landsat 8 OLI imagery was more suitable for integration with MSI than HRG images due to higher radiometric sensitivity, which can permit monitoring physiology-related woody reflectance.     

Variability of fire‐induced changes in MODIS surface reflectance by land‐cover type in Borneo

This study investigates fire‐induced spectral changes detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) in different land‐cover types in Borneo. Linear discriminant analysis is used to determine the most powerful band combinations among the MODIS reflective bands for discrimination between burnt and unburnt areas in each land‐cover type. The results show that the nature of fire‐induced changes is dependent on pre‐fire vegetation characteristics in this region. Bands 1 (0.64 µm), 2 (0.86 µm), and 7 (2.14 µm) are found to be the most sensitive bands in land‐cover types dominated by green vegetation, and consequently indices or combinations of indices using these three bands are potentially effective for burnt‐area detection in the majority of areas. In land‐cover types dominated by dry vegetation and soil, MODIS band 5 (1.24 µm) alone showed the greatest statistical separability and could not be significantly improved by any multiband index.     

Assessing vegetation condition in the presence of biomass burning smoke by applying the Aerosol‐free Vegetation Index (AFRI) on MODIS images

Vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI) are widely used for assessing vegetation cover and condition. One of the NDVI's significant disadvantages is its sensitivity to aerosols in the atmosphere, hence several atmospherically resistant VIs were formulated using the difference in the radiance between the blue and the red spectral bands. The state‐of‐the‐art atmospherically resistant VI, which is a standard Moderate Resolution Imaging Spectroradiometer (MODIS) product, together with the NDVI, is the Enhanced Vegetation Index (EVI). A different approach introduced the Aerosol‐free Vegetation Index (AFRI) that is based on the correlation between the shortwave infrared (SWIR) and the visible red bands. The AFRI main advantage is in penetrating an opaque atmosphere influenced by biomass burning smoke, without the need for explicit correction for the aerosol effect. The objective of this research was to compare the performance of these three VIs under smoke conditions. The AFRI was applied to the 2.1 µm SWIR channel of the MODIS sensor onboard the Earth Observing System (EOS) Terra and Aqua satellites in order to assess its functionality on these imaging platforms. The AFRI performance was compared with those of NDVI and EVI. All VIs were calculated on images with and without present smoke, using the surface‐reflectance MODIS product, for three case studies of fires in Arizona, California, and Zambia. The MODIS Fire Product was embedded on the images in order to identify the exact location of the active fires. Although good correlations were observed between all VIs in the absence of smoke (in the Arizona case R ² = 0.86, 0.77, 0.88 for the NDVI–EVI, AFRI–EVI, and AFRI–NDVI, respectively) under smoke conditions a high correlation was maintained between the NDVI and the EVI, while low correlations were found for the AFRI–EVI and AFRI–NDVI (0.21 and 0.16, for the Arizona case, respectively). A time series of MODIS images recorded over Zambia during the summer of 2000 was tested and showed high NDVI fluctuations during the study period due to oscillations in aerosol optical thickness values despite application of aerosol corrections on the images. In contrast, the AFRI showed smoother variations and managed to better assess the vegetation condition. It is concluded that, beneath the biomass burning smoke, the AFRI is more effective than the EVI in observing the vegetation conditions.     

Spectral identification of ozone‐damaged pine needles

Needles were collected from ponderosa and Jeffrey pine trees at three sites in the Sierra Nevada, and were assembled into 504 samples and grouped according to five dominant live needle conditions – green, winter fleck, sucking insect damage, scale insect damage, and ozone damage – and a random mixture. Reflectance and transmittance measurements of abaxial and adaxial surfaces were obtained at ca 0.3 nm spectral resolution from 400–800 nm, and binned to simulate Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data. There were no significant differences in optical properties between the two surfaces. Ozone‐damaged needles were collected from Jeffrey pine trees at one site, and exhibited significantly different (family‐wise α = 0.01) reflectance and transmittance signatures – and significantly different signature slopes – at both spectral resolutions, from green and winter fleck needles from the same site. Ozone‐damaged needles had significantly different (family‐wise α = 0.01) abaxial surface reflectance and reflectance slope signatures from all other groups of needles, at both spectral resolutions. In comparison with three chlorophyll reflectance indices, a new red fall index (RFI) provides high classification accuracies for ozone‐damaged and non‐ozone‐damaged pine needles (overall acc. = 94%; κ = 59%). Thus, ozone‐damaged Jeffrey pine needles have a unique spectral signature in relation to dominant needle conditions of ponderosa and Jeffrey pine trees.     

An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah

We report on the numerical separation of burned and unburned vegetation classes using different bi-spectral spaces, based on the analysis of spectroradiometric data collected in situ and convolved to five spectral bands at red to mid-infrared (MIR) wavelengths. A combination of two MIR bands was found to have strong spectral separation of burned and unburned samples. Using these bands, a spectral index was formulated which is highly sensitive to spectral changes due to burning and relatively insensitive to intrinsic variability. Results have implications for the remote sensing of burned shrub-savannah using bands available on high- and low-spatial resolution sensors, in particular, Landsat TM and MODIS.     

Soil adjusted vegetation water content retrievals in grasslands

Soil contamination of canopy reflectance over grasslands can cause errors in empirical vegetation water content (VWC) retrievals using the NDII (Normalized Difference Infrared Index, [ρ_0.86?ρ_1.64]/[ρ_0.86+ρ_1.64]). Minimization of soil contamination by NDII relies on the existence of a quasi straight soil line and quasi straight VWC isolines (lines of equal VWC) in the 1.64–0.86 µm reflectance space. Further the VWC isolines are expected to meet at the origin of the 1.64–0.86 µm reflectance space. Considering soil moisture as the primary determinant of soil reflectance variation at a given location, this study investigates the effect of soil moisture on the nature of soil lines and VWC isolines under grassland conditions. Reflectance simulations from coupled soil‐leaf‐canopy reflectance models under grassland conditions show that soil lines and VWC isolines are expected to be curved and may not converge at the origin. This behaviour is attributed to disproportionate soil moisture related absorption processes operating at 1.64 µm and 0.86 µm. A new technique that accounts for these inconsistencies in NDII assumptions is proposed for VWC retrievals. The technique consists of using separate regression relationships between VWC and a Soil Adjusted NDII (SANDII) based on the volumetric soil moisture category of the background. SANDII, based on the idea borrowed from the Soil Adjusted Vegetation Index (SAVI) is an origin shifted transformation of NDII. The optimum origin that reduces VWC retrieval errors is shown to be soil moisture category specific. The proposed technique requires categorical soil moisture information in order to decide which regression relationship to apply for VWC retrievals. Climatology, meteorological models or microwave observations are expected to be reliable resources for such categorical soil moisture information. Evaluations of the proposed technique using simulated reflectances showed that absolute errors in VWC retrievals were reduced by an average 20% as compared to the traditional NDII regression method. Such improvements are expected to be significant for fire‐risk applications. Finally supporting evidence for the need of an origin translated NDII is provided using data collected over pastures during the Soil Moisture Experiment 2003 (SMEX03) field campaign.     

A methodology to evaluate the aerosol effective radius based on MODIS aerosol products applicable to other satellite platforms

A strategy to evaluate the effective radius (r _eff) as a function of aerosol retrievals is provided in this work. This methodology is based on the MODerate resolution Imaging Spectroradiometer (MODIS) aerosol products, using the 0.66 and 0.87 µm bands. These data have been studied from February 2000 to December 2005 in a grid situated at Subtropical North‐east Atlantic region. To reduce the number of MODIS useful variables a Factorial Analysis by Principal Components has been applied, decreasing the aerosol parameters from 18 to five. With these parameters, backscattering ratios and asymmetry factors at 0.66 and 0.87 µm besides the Ångström parameter, a lineal multivariate analysis technique has been applied to find the combination of variables that better evaluate the r _eff. The standard error for the predicted value of r _eff is ±0.09 µm. The expression obtained here has the advantage that it can be applied to other remote sensors like AVHRR/NOAA, HRV/SPOT, TM/LANDSAT, and so on, with long time series.     

Temporal burn scar evolution in tallgrass prairie based on field spectroscopy

In situ field spectroscopy samples were used to simulate several Moderate Resolution Imaging Spectroradiometer (MODIS) bands and indices commonly used for burned area detection. Each band or index was tested for its ability to differentiate between burned and unburned tallgrass prairie during several time periods from spring (when burning took place) to late summer (peak biomass) with three analysis of variance tests. The normalized difference vegetation index (NDVI), global environmental monitoring index (GEMI), global environmental monitoring index – burn scar (GEMI-B), and normalized burn ratio (NBR) indices, as well as MODIS band 7 (longwave mid-infrared; LWMIR), showed virtually no promise for differentiating burned from unburned areas for more than several days after the burn. Others, including the burned area index (BAI), Mid-infrared burn index (MIRBI), and MODIS bands 3 (red), 4 (near-infrared; NIR), 5 (longwave near-infrared; LWNIR), and 6 (shortwave mid-infrared; SWMIR) were able to differentiate between burned and unburned areas well into the growing season – in some cases, even through its entire length. The performance of particular bands and indices often depended on grazing, vegetation phenology, ash/char/soil reflectance, and factors that influenced pre-burn biomass.     

Hydrocarbon Index – an algorithm for hyperspectral detection of hydrocarbons

Previous studies have shown that spectral signatures of hydrocarbon-bearing materials are characterized by prominent absorption features at 1.73 and 2.31?µm. Many other materials also show absorption features at wavelengths in the interval from 2.0 to 2.5?µm, yielding a mixed response in spectral signatures. In contrast to this wavelength range, most materials show similar spectral characteristics in the 1.73?µm range. Mainly hydrocarbon-bearing materials produce an absorption feature which is unique and prominent at 1.73?µm. A Hydrocarbon Index (HI) was developed and tested for the direct detection of hydrocarbons. The HI transforms multispectral data into a single image band that shows the distribution of hydrocarbons on the ground surface. The HI takes advantage of reflection differences around the 1.73?µm feature in hydrocarbon spectra. The HI indicates the presence of the 1.73?µm hydrocarbon absorption feature in a pixel spectrum. HI values can be easily calculated from radiance and reflectance data recorded by high signal-to-noise ratio hyperspectral scanners.     

Effects of adaxial and abaxial surface on the estimation of leaf chlorophyll content using hyperspectral vegetation indices

Vegetation indices are frequently used for the non-destructive assessment of leaf chemistry, especially chlorophyll content. However, most vegetation indices were developed based on the statistical relationship between the spectral reflectance of the adaxial leaf surface and chlorophyll content, even though abaxial leaf surfaces may influence reflectance spectra because of canopy structure or the inclination of leaves. In the present study, reflectance spectra from both adaxial and abaxial leaf surfaces of Populus alba and Ulmus pumila var. pendula were measured. The results showed that structural differences of the two leaf surfaces may result in differences in reflectance and hyperspectral vegetation indices. Among 30 vegetation indices tested, R₆₇₂/(R₅₅₀ × R₇₀₈) had the smallest difference (4.66% for P. alba, 2.30% for U. pumila var. pendula) between the two blade surfaces of the same leaf in both species. However, linear regression analysis showed that several vegetation indices (R₈₅₀ ? R₇₁₀)/(R₈₅₀ ? R₆₈₀), VOG₂, D₇₃₀, and D₇₄₀, had high coefficients of determination (R² > 0.8) and varied little between the two leaf surfaces of the plants we sampled. This demonstrated that these four vegetation indices had relatively stable accuracy for estimating leaf chlorophyll content. The coefficients of determination (R²) for the calibration of P. alba leaves were 0.92, 0.98, 0.93, and 0.95 on the adaxial surfaces, and 0.88, 0.87, 0.88, and 0.92 on the abaxial surfaces. The coefficients of determination (R²) for the calibration of U. pumila var. pendula leaves were 0.85, 0.91, 0.86, and 0.90 on adaxial surface, and 0.80, 0.80, 0.84, and 0.88 on abaxial surface. These four vegetation indices were readily available and were little influenced by the differences in the two leaf surfaces during the estimation of leaf chlorophyll content.     

Estimation of Leaf Area Index in dry deciduous forests from IRS‐WiFS in central India

Leaf Area Index (LAI) is an important biophysical parameter necessary to infer vegetation vigour, seasonal vegetation variability and different physiological and biochemical processes of vegetation. Gap fraction analysis has been carried out to estimate plot‐wise LAI. Tropical dry deciduous forests of the study area can be categorized into two prominent phases—growing and senescent phases. Efforts have been made to observe the relationship between ground based LAI values and satellite derived parameters, such as radiance values and also different vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), maximum, minimum, amplitude, sum and radiance NDVI values for both the phases. Multi‐temporal IRS 1C WiFS data have been used. WiFS provides information in two bands: red (0.62–0.68 µm) and near‐infrared (0.77–0.86 µm). All the images from the representative months have been corrected radiometrically and geometrically. NDVI values have been derived for all the representative months. Among various parameters maximum NDVI values showed better relationships with LAI for both the phases. For the growing phase R ² (coefficient of determination) was 0.79, whereas for the senescent phase it was 0.48. These empirical relationships have been used to estimate LAI at a regional level. The LAI estimate for growing and senescent phases ranged from <1 to 4.25.     

Relationship between spatio-temporal characteristics of leaf-fall phenology and seasonal variations in near surface- and satellite-observed vegetation indices in a cool-temperate deciduous broad-leaved forest in Japan

We examined the relationship between the spatio-temporal distribution of leaf litter for each species and the seasonal patterns of in situ and satellite-observed daily vegetation indices in a cool-temperate deciduous broad-leaved forest. The timing and distribution of leaf-fall revealed spatio-temporal relationships with species and topography. Values of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and green–red vegetation index (GRVI), measured both in situ and by satellite, and those of the in situ-measured leaf area index (LAI), rapidly declined at the peak of leaf-fall. At the late stage of leaf-fall, in situ-measured values of NDVI, EVI, and LAI declined but those of GRVI changed from decreasing to increasing. The peak timing of leaf-fall, when 50–73% of the leaf litter had fallen, corresponds to LAI = 1.80–0.81, NDVI = 0.61–0.54, EVI = 0.29–0.25, and GRVI = 0.01 ～ ?0.07. Although the distribution of leaf litter among species displayed spatial characteristics at the peak of leaf-fall, spatial heterogeneity of amount of leaf litter at the peak timing of leaf-fall was less than that at the beginning and end. These facts suggest that the criterion for determining the timing of leaf-fall from vegetation indices should be a value corresponding to the peak of leaf-fall rather than its end. In a high-biodiversity forest, such as this study forest, the effect of spatial heterogeneity on the timing and patterns of leaf-fall on vegetation indices can be reduced by observing only the seasonal variation in colour on the canopy surface by using GRVI, which consists of visible reflectance bands, rather than that of both leaf area and colour of the canopy surface by using NDVI and EVI, which consist of visible and near-infrared reflectance bands.     

Simultaneous determination of aerosol optical thickness and surface reflectance using ASTER visible to near-infrared data over land

An innovative method for the determination of aerosol optical thickness (AOT) and surface reflectance for operational use of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible to near-infrared data is presented. This method is designed to obtain the atmospheric parameters needed in the correction of the image. This method is based on a simplified radiative transfer equation describing the relation between the ground surface reflectance, AOT and top-of-atmosphere reflectance. By exploiting the ASTER dual-angle view capabilities in band 3N (Nadir) and band 3B (Backwards), surface reflectance and AOT can be retrieved synchronously. Thus, it solves the problem of separating atmospheric radiance from the transmitted radiance of the surface to some extent. After applying this new atmospheric correction method to three areas of ASTER images, Beijing urban city, the Heihe River Basin and Hong Kong of China, ASTER surface reflectance products (AST07) were obtained. AOT values from in situ measurements of CIMEL Electronique 318 Sun Photometers or AERONET (AErosol RObotic NETwork) and surface reflectance in situ measured using an Analytical Spectral Device (ASD) Field Spec spectral radiometer are used for validation. AOT derived from the new method is consistent with in situ station measurements from CIMEL Electronique 318 Sun Photometer and level 2.0 data from AERONET, with correlation coefficient (R ²) of 0.98 and root mean square error of 0.05, whereas Multi-angle Imaging Spectroradiometer AOT products underestimate AERONET AOT and Moderate-Resolution Imaging Spectroradiometer AOT products overestimate AERONET AOT in these regions. More encouraging is the comparison between the corrected surface reflectance, AST07 and ASD measurements. Root mean square error of AST07 and retrieved surface reflectance are as follows: band 1 (556 nm) = 0.04 and 0.05; band 2 (661 nm) = 0.036 and 0.035; band 3 (807 nm) = 0.056 and 0.038, which suggests that compared with AST07 in bands 2 and 3, retrieved surface reflectance has better agreement with measured reflectance from ASD.     

Monitoring the effects of arsenic and chromium accumulation in Chinese brake fern (Pteris vittata)

The objectives of this study were (i) to investigate the feasibility of using spectral reflectance for monitoring As and Cr accumulation in Chinese brake fern (Pteris vitatta), and (ii) to search for spectral indices sensitive to structural changes caused by metal accumulation during the process of phytoremediation. Potted Chinese brake fern plants were exposed to As (100 and 300 ppm) and Cr (300 and 600 ppm) treatments for 22 days. The plants were then harvested and analysed for metal accumulation. Diffuse reflectance spectra (350–2500 nm) of the plant canopies were collected regularly throughout the metal treatment period using a portable spectroradiometer. Leaf reflectance is governed by leaf surface properties, internal structure, and foliar pigments and biochemical components. Leaf samples were collected and analysed for structural changes through microscopic observations. Our microscopic studies on changes of leaf structure provide insight into the physical changes that are remotely detected as changes in reflectance, and may permit extrapolation of these results to other plant species. Cr accumulation resulted in a decrease in biomass, relative water content (RWC), and changes in the internal structure of the leaf. The structural and spectral results show significant changes in Cr‐treated plants while the changes were minimal in As‐treated plants compared to untreated plants. Our spectral analysis revealed that a unique ratio index R ₁₁₁₀/R ₈₁₀ can be used to monitor structural changes in plants due to accumulation of Cr. This index distinguished Cr‐treated plants from untreated and As‐treated plants. The Normalized Difference Vegetative Index (NDVI) distinguished stressed plants, but NDVI cannot distinguish Cr‐stressed plants from As‐stressed plants. Our results show that brake fern can accumulate significant amounts of Cr in shoots (2108 mg kg^?1 dry weight), but it is not a hyperaccumulator for Cr because much higher Cr accumulation was found in roots (7686 mg kg^?1 dry weight). This study suggests that the infrared reflectance spectrum (800–1300 nm) of plant canopy may provide a non‐intrusive monitoring method to access the physiological status of plants grown in heavy metal‐contaminated soil.     

Spatial distribution of atmospheric CO2 absorption detected around 2 µm on the reflectance spectra derived from Hyperion images

This study explores the detectability of atmospheric carbon dioxide (CO₂) absorption at the short wave infrared bands of Hyperion EO-1 hyperspectral images. Two CO₂ absorption bands at around 2.002–2.012 and 2.052 µm, respectively, were identified on the reflectance spectra derived from the images. Considering the principle of differential optical absorption spectroscopy (DOAS) and assuming the O₂-A band around 0.762 µm as reference, the spatial variation of the column density was estimated for Kolkata, a region around 22° 31′ N 88° 21′ E. The ratio of the individual absorption depth of CO₂ to that of O₂, instead of the ratio of their column density, was found to be more suitable to express the spatial distribution of CO₂ concentration over short distance, of the order of metres. The methodology was validated with Orbiting Carbon Observatory-2 (OCO-2) data. The work suggests the inclusion of 2–3 narrowband channels corresponding to O₂-A and CO₂ absorptions in future moderate resolution multispectral sensors in order to facilitate the monitoring of atmospheric CO₂.     

Multi year satellite remote sensing of particulate matter air quality over Sydney,Australia

Particulate matter (PM) air‐quality information is usually derived from ground‐based instruments. These measurements, while valuable, are not well suited to provide air‐quality information over large spatial scales. In this study, using 4 years of satellite aerosol optical thickness (AOT) at 0.55 µm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Terra and Aqua satellites, we present a multi‐year air analysis of PM air quality over Sydney, Australia. We then compare the satellite data with PM_2.5 mass concentration measurements from six ground‐based stations in the area. Our results indicate significant diurnal variations and an overall increase in PM_2.5 during Southern Hemisphere spring and summer seasons due to bush fires. The air quality in Sydney, Australia is good throughout the year except during major bushfires when PM_2.5 mass loading can increase from normal (<20 µg m^?3) to unhealthy conditions (>70 µg m^?3). The satellite data also show corresponding AOT changes from less than 0.1 to greater than 1.0 during bushfire events. We conclude that satellite data are an excellent tool for studying PM air quality over large areas, especially when ground measurements are not available. While this is the first multi‐year combined satellite and ground‐based air quality analysis over Sydney, ancillary information from lidars, sun photometers, and size‐resolved chemistry measurements will further enhance our capability to monitor and forecast air quality in and around Sydney.     

Derivation of a threshold function for the Advanced Very High Resolution Radiometer 3.75 µm channel and its application in automatic cloud discrimination over snow/ice surfaces

The distinct contrast between the reflectance of solar radiation in Advanced Very High Resolution Radiometer (AVHRR) channel 3 (3.75?µm) by clouds and by bright surfaces provides an effective means of cloud discrimination over snow/ice surfaces. A threshold function for the top-of-atmosphere (TOA) albedo in channel 3 (r ₃) is derived and used to develop an improved method for cloud discrimination over snow/ice surfaces that makes explicit use of TOA r ₃. Corrections for radiance anisotropy and temperature effects are required to derive accurate values of r ₃ from satellite measurements and to utilize the threshold function. It has been used to retrieve cloud cover fractions from National Oceanic and Atmospheric Administration (NOAA)-14 AVHRR data over the Arctic Ocean and over the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site in Barrow, Alaska. The retrieved cloud fractions are in good agreement with SHEBA (Surface HEat Budget of the Arctic Ocean) surface visual observations and with NSA cloud radar and lidar observations, respectively. This method can be utilized to improve cloud discrimination over snow/ice surfaces for any satellite sensor with a channel near 3.7?µm.     

Hyperspectral indices for detecting changes in canopy reflectance as a result of underground natural gas leakage

Natural gas leakage from underground pipelines is known to affect vegetation adversely, probably by displacement of the soil oxygen needed for respiration. This causes changes in plant and canopy reflectance, which may serve as indicators of gas leakage. In this study, a covariance analysis was performed between reflectance indices of maize (Zea mays) and wheat (Triticum aestivum) canopies and oxygen concentrations in a simulated natural gas leak. Twenty‐nine days after oxygen shortage occurred, the reflectance indices had the highest correlation with oxygen concentrations in the soil, for both species. The effect was consistent within species but the absolute values varied between the species. Normalization by adding a constant value to the control index of one species resulted in significant linear regression models for several indices. The indices with the highest regression coefficients were used to predict the oxygen concentration in the soil. This showed that the gas leakage caused reflectance changes up to 0.5 m from the source. As it could not be proven that oxygen shortage was the cause of the reflectance changes, further work is needed to study the side‐effects of gas leakage, such as bacterial oxygen depletion, on plant growth and reflectance.     

The integrated radiometric correction of optical remote sensing imageries

Abstract Environmental analysis, management and modelling require detailed and precise land‐use/land‐cover discrimination as initial conditions of land surface characteristics. With the ultimate goal of accurate land surface classification analysis, we devised a fully image‐based and physically based correction method (the Integrated Radiometric Correction (IRC) method) considering both the atmospheric and the topographic effects simultaneously, using the information deduced from the satellite images and 5 m resolution DEM data. The overall process is carried out in four steps: (i) calculation of the radiance/irradiance relational expression for horizontal surfaces, (ii) devising the radiance/irradiance relational expression for inclined surfaces, (iii) derivation of solar and land geometric parameters from DEM data, as well as the calculation of the topographic correction factor (A‐factor) and the atmospheric transmittance functions, and (iv) retrieval of the corrected surface reflectance and radiance. Using Landsat/ETM+ satellite data, the performance of the formulated IRC method is evaluated visually and statistically. Visual evaluation of radiometrically corrected images shows significant improvements for each band as well as for various bands composites, while the independence between the corrected surface reflectance and radiance, and the topography (incidence angle (i) or solar illumination (cos i)) is shown by very weak correlation coefficients as compared with non‐corrected data.