Advanced Very High Resolution Radiometer Pixels Research Articles

Validation of MODIS and AVHRR Fire Detections in Australia

The Sentinel Bushfire Monitoring System is an internet-based mapping tool which provides timely spatial information to fire agencies across Australia. The mapping system allows users to identify active fire locations that pose a potential risk to communities and property. Sentinel at Geoscience Australia currently provides hotspot information derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on a continent-wide and daily basis enabling the fire community and general public to locate active fires. There has been little validation undertaken of the Sentinel since the system began operating in November 2002. Validation datasets have been collected for this work during the 2003-2007 fire seasons. Five study areas were selected to validate the detection capabilities of the MODIS and AVHRR hotspot product with fire activity that was mapped using high resolution earth observation imagery. The objective is to evaluate the reliability with which hotspots identified in MODIS and AVHRR thermal data can be used to identify fires. This consists of comparing the accuracy of AVHRR versus MODIS and quantifying the accuracy of both products. This objective is achieved by characterising errors through a stratified random sampling technique establishing a relationship between the ‘fire’ and ‘no fire’ condition, and error assessment using multi- source reference datasets over coincident MODIS and AVHRR pixels. The validation framework comprised two key approaches including validation of AVHRR hotspots in relation to MODIS hotspots and validation of both MODIS and AVHRR hotspots using multi-sensor earth observation imagery datasets. The study identified sources of errors associated with the Sentinel hotspots which could be used to improve the performance of hotspot algorithms and provide user-friendly information for the users. Statistical analysis revealed that overall commission errors of MODIS and AVHRR hotspots over the 5% sample data were 15% and 68% respectively, and overall omission errors of MODIS and AVHRR hotspots were 17% and 23% respectively. An important outcome of this study is the production of a database of fire locations derived from high-resolution imagery, which can serve as a resource for future validation efforts as detection algorithms evolve and sensors change.

Validation of MODIS and AVHRR Fire Detections in Australia

The Sentinel Bushfire Monitoring System is an internet-based mapping tool which provides timely spatial information to fire agencies across Australia. The mapping system allows users to identify active fire locations that pose a potential risk to communities and property. Sentinel at Geoscience Australia currently provides hotspot information derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on a continent-wide and daily basis enabling the fire community and general public to locate active fires. There has been little validation undertaken of the Sentinel since the system began operating in November 2002. Validation datasets have been collected for this work during the 2003-2007 fire seasons. Five study areas were selected to validate the detection capabilities of the MODIS and AVHRR hotspot product with fire activity that was mapped using high resolution earth observation imagery. The objective is to evaluate the reliability with which hotspots identified in MODIS and AVHRR thermal data can be used to identify fires. This consists of comparing the accuracy of AVHRR versus MODIS and quantifying the accuracy of both products. This objective is achieved by characterising errors through a stratified random sampling technique establishing a relationship between the ‘fire’ and ‘no fire’ condition, and error assessment using multi- source reference datasets over coincident MODIS and AVHRR pixels. The validation framework comprised two key approaches including validation of AVHRR hotspots in relation to MODIS hotspots and validation of both MODIS and AVHRR hotspots using multi-sensor earth observation imagery datasets. The study identified sources of errors associated with the Sentinel hotspots which could be used to improve the performance of hotspot algorithms and provide user-friendly information for the users. Statistical analysis revealed that overall commission errors of MODIS and AVHRR hotspots over the 5% sample data were 15% and 68% respectively, and overall omission errors of MODIS and AVHRR hotspots were 17% and 23% respectively. An important outcome of this study is the production of a database of fire locations derived from high-resolution imagery, which can serve as a resource for future validation efforts as detection algorithms evolve and sensors change.

Assessment of Surface Urban Heat Island in Three Cities Surrounded by Different Types of Land-Cover Using Satellite Images

Land-cover change is the main factor of Land Surface Temperature (LST) variation in urban areas. The temperature of man-made classes within cities is normally higher than natural classes in the surrounding areas which leads to the Urban Heat Island (UHI) phenomenon. In this study, LST maps of three cities (i.e. Paris, Riyadh and Manama) are analyzed to investigate how SUHI reacts in different cities. The SUHI intensity during summer and winter is also assessed to understand the seasonal changes of SUHI. The Advanced Very High Resolution Radiometer (AVHRR) images have been used as the main data source for investigating temperature variability in the urban areas. However, in order to define different types of land-cover and correlate those with AVHRR pixel values, one of Landsat sensors have also been used in each case. These include Thematic Mapper, Enhanced Thematic Mapper or Operational Land Imager, depending on their synchronization with the AVHRR images. The analysis of the temperature variation showed that the behavior of SUHI is not the same in different cities and is dependent on the land-covers surrounding the city.

On-Orbit Calibration Assessment of AVHRR Longwave Channels on MetOp-A Using IASI

Accurate and precise satellite radiance measurements are important for data assimilations in numerical weather prediction models and for climate-change detection. After the successful launch of the infrared atmospheric sounding interferometer (IASI), several studies have indicated that the IASI radiance measurements are well calibrated and maintain superb spectral and radiometric calibration accuracy. Owing to its hyperspectral nature and high-quality measurements, the IASI radiance can serve as a relative reference to independently assess the radiance measurements of broad- or narrow-band instruments that share the same spectral region. In this paper, we demonstrate the utility of the IASI radiances to evaluate the Advanced Very High Resolution Radiometer (AVHRR) infrared (IR) channel measurements. The coregistered AVHRR pixels inside each IASI pixel are averaged spatially. We then compared the spatially averaged radiance from AVHRR IR channels with IASI by convolving the IASI-measured spectra with the AVHRR spectral response functions. It was found that, statistically, the temperature observed from AVHRR channels 4 and 5 is slightly warmer than that in IASI for the brightness temperature (BT) range of 200 K-300 K. The mean BT difference (IASI minus AVHRR) is less than 0.4 K with a standard deviation of ~0.3 K for AVHRR channels 4 and 5. The BT difference between IASI and AVHRR IR channels is scene-temperature dependent for both channels 4 and 5, which is probably caused by the nonlinearity of the AVHRR detectors. Both AVHRR channels 4 and 5 show slight scan-dependent bias with maximum differences of approximately ~0.2 K (with AVHRR being warmer than IASI) at both ends of the scan.

Assessing the geometric accuracy of AVHRR data processed with a state vector based navigation system

We evaluate the geometric accuracy of Advanced Very High Resolution Radiometer (AVHRR) data processed using the Common AVHRR Processing System (CAPS) software. Accurate geometric correction, to known standards, of satellite images is crucial for many remote sensing applications, especially those using a time series of images and integrating other spatially referenced datasets. It is critical that data acquired by satellites with short repeat intervals (e.g., daily) are geolocated using methods that are both automatic and reliable. Landsat Enhanced Thematic Mapper Plus (ETM+) high-resolution imagery was used to establish ground control point (GCP) networks for two coastal regions of Australia, one tropical with 40 GCPs and one temperate with 60 GCPs. The geolocation accuracy was assessed for a total of 12 and 13 AVHRR images, respectively, against the GCPs for each region, respectively, totalling 1039 cloud-free GCP comparisons. Results showed that when the view zenith angle was less than 40°, the average (±1 standard deviation) accuracies are 0.36 ± 0.31 and 0.44 ± 0.35 of an at-nadir AVHRR pixel (1.1 km × 1.1 km) in the cross-track direction for the tropical and temperate regions, respectively, and 0.30 ± 0.26 and 0.36 ± 0.29 at-nadir AVHRR pixel in the along-track direction for the tropical and temperate regions, respectively. For higher satellite view zenith angles, the geometric accuracy decreases systematically with an increase in the pixel size towards the edges of the AVHRR swath.

Terra and Aqua MODIS inter‐comparison of three reflective solar bands using AVHRR onboard the NOAA‐KLM satellites

Cross‐sensor inter‐comparison is important to assess calibration quality and consistency and ensure continuity of observational datasets. This study conducts an inter‐comparison of Terra and Aqua MODIS (the MODerate Resolution Imaging Spectroradiometer) to examine the overall calibration consistency of the reflective solar bands. Observations obtained from AVHRR (the Advanced Very High Resolution Radiometer) onboard the NOAA‐KLM series of satellites are used as a transfer radiometer to examine three MODIS bands at 0.65 (visible), 0.85 (near‐IR) and 1.64 µm (far near‐IR) that match spectrally with AVHRR channels. Coincident events are sampled at a frequency of about once per month with each containing at least 3000 pixel‐by‐pixel matched data points. Multiple AVHRR sensors on‐board NOAA‐15 to 18 satellites are used to check the repeatability of the Terra/Aqua MODIS inter‐comparison results. The same approach applied in previous studies is used with defined criteria to generate coincident and co‐located near nadir MODIS and AVHRR pixel pairs matched in footprint. Terra and Aqua MODIS to AVHRR reflectance ratios are derived from matched pixel pairs with the same AVHRR used as a transfer radiometer. The ratio differences between Terra and Aqua MODIS/AVHRR give an indication of the calibration biases between the two MODIS instruments. Effects due to pixel footprint mismatch, band spectral differences and surface and atmospheric bi‐directional reflectance distributions (BRDFs) are discussed. Trending results from 2002 to 2006 show that Terra and Aqua MODIS reflectances agree with each other within 2% for the three reflective solar bands.

Relationship between herbaceous biomass and 1‐km2 Advanced Very High Resolution Radiometer (AVHRR) NDVI in Kruger National Park, South Africa

The relationship between multi‐year (1989–2003), herbaceous biomass and 1‐km2 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data in Kruger National Park (KNP), South Africa is considered. The objectives were: (1) to analyse the underlying relationship between NDVI summed for the growth season (ΣNDVI) and herbaceous biomass in field sites (n = 533) through time and (2) to investigate the possibility of producing reliable herbaceous biomass maps for each growth season from the satellite ΣNDVI observations. Landsat Enhanced Thematic Mapper Plus (ETM+) and Thematic Mapper (TM) data were used to identify highly heterogeneous field sites and exclude them from the analyses. The average R 2 for the ΣNDVI–biomass relationship at individual sites was 0.42. The growth season mean biomass and ΣNDVI of most landscape groups were strongly correlated with rainfall and each other. Although measured tree cover and MODIS estimates of tree cover did not have a detectable effect on the ΣNDVI–biomass relationship, other observations suggest that tree cover should not be ignored. The ΣNDVI was successful at estimating inter‐annual variations in the biomass at single sites, but on an annual basis the relationship derived from all the sites was not strong enough (average R 2 = 0.36) to produce reliable growth season biomass maps. This was mainly attributed to the fact that the biomass data were sampled from very small field sites that were not fully representative of 1‐km2 AVHRR pixels. Supplementary field surveys that sample a larger area for each field site (e.g. 1 km2 or larger) should account for the variability in biomass and may improve the strength of ΣNDVI–biomass relationships observed in a single growth season.

Spaceborne observations of the 2000 Bezymianny, Kamchatka eruption: the integration of high-resolution ASTER data into near real-time monitoring using AVHRR

Since its launch in December 1999, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument has been observing over 1300 of the world's volcanoes during the day and night and at different times of the year. At the onset of an eruption, the temporal frequency of these regularly scheduled observations can be increased to as little as 1–3 days at higher latitudes. However, even this repeat time is not sufficient for near real-time monitoring, which is on the order of minutes to hours using poorer spatial resolution (>1 km/pixel) instruments. The eruption of Bezymianny Volcano (Kamchatkan Peninsula, Russia) in March 2000 was detected by the Alaska Volcano Observatory (AVO) and also initiated an increased observation frequency for ASTER. A complete framework of the eruptive cycle from April 2000 to January 2001 was established, with the Advanced Very High Resolution Radiometer (AVHRR) data used to monitor the large eruptions and produce the average yearly background state for the volcano. Twenty, nearly cloud-free ASTER scenes (2 days and 18 nights) show large thermal anomalies covering tens to hundreds of pixels and reveal both the actively erupting and restive (background) state of the volcano. ASTER short-wave infrared (SWIR) and thermal infrared (TIR) data were also used to validate the recovered kinetic temperatures from the larger AVHRR pixels, as well as map the volcanic products and monitor the thermal features on the summit dome and surrounding small pyroclastic flows. These anomalies increase to greater than 90 °C prior to a larger eruption sequence in October 2000. In addition, ASTER has the first multispectral spaceborne TIR capability, which allowed for the modeling of micrometer-scale surface roughness (vesicularity) on the active lava dome. Where coupled with ongoing operational monitoring programs like those at AVO, ASTER data become extremely useful in discrimination of small surface targets in addition to providing enhanced volcanic mapping capabilities.

Characterization of the Cloud-Topped Boundary Layer at the Synoptic Scale Using AVHRR Observations during the SEMAPHORE Experiment

Abstract Satellite platforms NOAA-11 and -12 Advanced Very High Resolution Radiometer (AVHRR) data are used during the daytime to study large sheets of stratocumulus over the North Atlantic Ocean. The application concerns an anticyclonic period of the Structure des Echanges Mer–Atmosphere, Proprietes des Heterogeneites Oceaniques: Recherche Experimentale (SEMAPHORE) campaign (10–17 November 1993). In the region of interest, the satellite images are recorded under large solar zenith angles. Extending the SEMAPHORE area, a region of about 3000 × 3000 km2 is studied to characterize the atmospheric boundary layer. A statistical cloud classification method is applied to discriminate for low-level and optically thick clouds. For AVHRR pixels covered with thick clouds, brightness temperatures are used to evaluate the boundary layer cloud-top temperature (CTT). The objective is to obtain accurate CTT maps for evaluation of a global model. In this application, the full-resolution fields are reduced to match model ...

Systematic corrections of AVHRR image composites for temporal studies

For quantitative studies of vegetation dynamics, satellite data need to be corrected for spurious effects. In this study, we have applied several changes to an earlier advanced very high resolution radiometer (AVHRR) processing methodology (ABC3; [Remote Sens. Environ. 60 (1997) 35; J. Geophys. Res.-Atmos. 102 (1997) 29625; Can. J. Remote Sens. 23 (1997) 163]), to better represent the various physical processes causing contamination of the AVHRR measurements. These included published recent estimates of the NOAA-11 and NOAA-14 AVHRR calibration trajectories for channels 1 and 2; the best available estimates for the water vapour, aerosol and ozone amounts at the time of AVHRR data acquisition; an improved bidirectional reflectance algorithm that also takes into consideration surface topography; and an improved image screening algorithm for contaminated pixels. Unlike the previous study that compared the composite images to a single-date AVHRR image, we employed coincident TM images to approximate the AVHRR pixel field of view during the data acquisition. Compared to ABC3, the modified procedure ABC3V2 was found to improve the accuracy of AVHRR pixel reflectance estimates, both in the sensitivity (slope) of the regression and in r 2. The improvements were especially significant in AVHRR channel 1. In comparison with reference values derived from two full TM scenes, the corrected AVHRR surface reflectance estimates had average standard errors values of ±0.009 for AVHRR C1, ±0.019 for C2, and ±0.04 for NDVI; the corresponding r 2 values were 0.55, 0.80, and 0.50, respectively. The changes in ABC3V2 were not able to completely remove interannual variability for land cover types with little or no vegetation cover, which would be expected to remain stable over time, and they increased the interannual variability of mixed forest and grassland. These results are attributed to a combination of increased sensitivity to interannual dynamics on one hand, and the inability to remove all sources of noise for barren or sparsely vegetated northern land cover types on the other.

Two years of operational use of Subpixel Automatic Navigation of AVHRR scheme: accuracy assessment and validation

Automated techniques for satellite imagery navigation and co-location are especially required for environmental monitoring activities intensively using satellite data. In this work are presented the results obtained after 2 years of operational use of the Subpixel Automatic Navigation of AVHRR (SANA) scheme. An automatic method for accuracy assessment of satellite navigation techniques, which permits a preliminary evaluation of their performances, dealing with a large collection of test images is also proposed. The navigation accuracy assessment, performed by using a selection of small islands as reference points, is discussed. Results achieved over more than 400 Advanced Very-High-Resolution Radiometer (AVHRR) scenes confirm that the SANA scheme is a very accurate one (computed mean navigation error is generally about one AVHRR pixel). Furthermore, because of its high processing speed, it can be considered a suitable tool for intensive satellite data processing in multitemporal analyses, especially required for environmental studies as well as for operational monitoring purposes.

Using MODIS to Estimate Cloud Contamination of the AVHRR Data Record

Abstract A study of the improvement in cloud-masking capability of data from a Moderate Resolution Imaging Spectroradiometer (MODIS) relative to data from an Advanced Very High Resolution Radiometer (AVHRR) is performed. MODIS offers significant advances over AVHRR in spatial resolution and spectral information. Three MODIS scenes that present a range of cloudiness, surface type, and illumination conditions are analyzed. AVHRR local area coverage (LAC) and global area coverage (GAC) data were synthesized from the most spectrally comparable MODIS channels. This study explores the benefits to cloud masking offered by MODIS beyond that offered by AVHRR. No global generalization can be inferred from this limited analysis, but this study does attempt to quantify the added benefit of MODIS over AVHRR for three scenes. The sole focus is on the levels of cloud contamination in clear AVHRR pixels; the misclassification of clear pixels as cloudy is not addressed. For the scenes studied, the results of the additiona...

Sensitivity of multitemporal NOAA AVHRR data of an urbanizing region to land-use/land-cover changes and misregistration

Our objectives were to: (1) investigate the sensitivity of multitemporal image data from the Advanced Very High Resolution Radiometer (AVHRR) satellite data for detecting land-use/land-cover changes primarily associated with urbanization and (2) test the effectiveness of a misregistration compensation model on the same data set. Empirical analyses were conducted using two near-anniversary, single-date NOAA AVHHR images of a rapidly urbanizing region of southern and Baja California. Analyses were facilitated by reference data from detailed GIS data layers of land-use/land-cover types for the 2 years corresponding to image acquisition dates (1990 and 1995). Almost all AVHRR pixels containing land-use/land-cover changes were mixed with nonchange areas, even when the extent of change features was greater than the nominal 1 km 2 ground sampling area. The strongest signals of image brightness change were detected by temporal differences of NDVI and Channel 4 surface temperature. “Undeveloped to urban” and “undeveloped to water” were the land-use/land-cover transition sequences with the most definitive AVHRR change signals. Mean magnitudes of misregistration errors were estimated to be around 0.2 pixel units in x and y directions. Mean values for misregistration noise equivalent in brightness change (MNEΔ B) were 0.02, 0.02, and 1.96 K for image differences of Channel 1 reflectance, NDVI, and Channel 4 surface temperature, respectively. The misregistration compensation model reduced false detection of change, but improvements in detection of land-use/land-cover changes were not conclusive.

Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements

Leaf area index (LAI) is one of the surface parameters that has importance in climate, weather, and ecological studies, and has been routinely estimated from remote sensing measurements. Canada-wide LAI maps are now being produced using cloud-free Advanced Very High-Resolution Radiometer (AVHRR) imagery every 10 days at 1-km resolution. The archive of these products began in 1993. LAI maps at the same resolution are also being produced with images from the SPOT VEGETATION sensor. To improve the LAI algorithms and validate these products, a group of Canadian scientists acquired LAI measurements during the summer of 1998 in deciduous, conifer, and mixed forests, and in cropland. Common measurement standards using the commercial Tracing Radiation and Architecture of Canopies (TRAC) and LAI-2000 instruments were followed. Eight Landsat Thematic Mapper (TM) scenes at 30-m resolution were used to locate ground sites and to facilitate spatial scaling to 1-km pixels. In this paper, examples of Canada-wide LAI maps are presented after an assessment of their accuracy using ground measurements and the eight Landsat scenes. Methodologies for scaling from high- to coarse-resolution images that consider surface heterogeneity in terms of mixed cover types are evaluated and discussed. Using Landsat LAI images as the standard, it is shown that the accuracy of LAI values of individual AVHRR and VEGETATION pixels was in the range of 50–75%. Random and bias errors were both considerable. Bias was mostly caused by uncertainties in atmospheric correction of the Landsat images, but surface heterogeneity in terms of mixed cover types were also found to cause bias in AVHRR and SPOT VEGETATION LAI calculations. Random errors come from many sources, but pixels with mixed cover types are the main cause of random errors. As radiative signals from different vegetation types were quite different at the same LAI, accurate information about subpixel mixture of the various cover types is identified as the key to improving the accuracy of LAI estimates.

Forest Mapping of Central America and Mexico with AVHRR Data

Concerns over changes in global forest resource distributions have prompted a number of studies to examine and map forest areas at continental scales with various types of satellite data. The work described here details the use of Advanced Very High Resolution Radiometer (AVHRR) data in concert with Landsat Thematic Mapper (TM) and Systeme Probatoire d'Observation de la Terre (SPOT) imagery to identify and map forest land in Mexico and Central America. A two stage approach was used to accomplish the study objectives. First, a modeling procedure was used to estimate percent forest cover in AVHRR pixels based on enumeration of forest area with Landsat TM and SPOT data of the same region covered by the AVHRR data. This product was used to subset the original AVHRR data into areas of probable forest lands. The AVHRR spectral data of the subset area was then classified by forest type and the results were compiled to produce the final product. The implications of this work are discussed in the context of global forest monitoring and landscape management.

Fast coastal algorithm for automatic geometric correction of AVHRR images

A coastal algorithm for fully automatic geometric correction of Advanced Very High Resolution Radiometer (AVHRR) images is presented. Inputs are the AVHRR image and updated ephemeris data and outputs are the georeference image and a cloud image mask. Its principal advantage and novelty is that it requires only manual control in the first stage of the process. Particularly, the detection of Ground Control Points (GCPs), usually rather time consuming, is performed with this method in an automatic way. The procedure only requires the previous existence of a coastal reference-windows database. To find the exact location of the GCPs, the routine searches the best match of these referencewindows with the image. The complete automation of the process makes the routine very fast, then allowing its operative application on a large volume of images. The process provides accuracies to within 1-1.5 AVHRR pixels.

Retrieval of temporal profiles of reflectances from simulated and real NOAA-AVHRR data over heterogeneous landscapes

To carry out functioning and dynamic vegetation studies, a temporal analysis is needed. So far, only data provided by the National Oceanic and Atmospheric Administration (NOAA) satellites with Advanced Very High Resolution Radiometer (AVHRR) sensors offer the required temporal resolution, but their spatial resolution is coarse (1.1 km). But, in many situations, the vegetation cover is heterogeneous and the 1.1 km AVHRR pixel contains several types of land use radiometrically different and is, in fact, a mixed pixel. Thus, the reflectance and consequently deduced parameters (NDVI, LAI, etc.) measured by AVHRR is actually average value and does not represent a value for each vegetation class present in the pixel. The objective is to extract the reflectance of each vegetation class from the mixed pixel using NOAA-AVHRR data and SPOT-HRV data simultaneously which give the proportions of each type of vegetation inside the mixed pixel through a classification map. The paper presents a method for radiometrically unmixing coarse resolution signals through the inversion of linear mixture modelling on heterogeneous regions of natural vegetation (Bidi-Bahn) in Burkina-Faso and in Niger (Hapex site). In a first step, simulated coarse resolution data (NOAA-AVHRR) obtained from the degradation of SPOT images are used to assess the method. In a second step, real NOAA-AVHRR data are used and some elements of validation are given by comparing the results to airborne reflectance measurements.

Remote sensing of cirrus cloud parameters based on a 0.63‐3.7 µm radiance correlation technique applied to AVHRR data

Using the data gathered from the Advanced Very High Resolution Radiometer (AVHRR) 0.63 and 3.7 µm channels, an algorithm for the inference of cirrus cloud optical depth and mean effective size has been developed for the first time. This scheme is based on the correlation between the 3.7 µm (total) and 0.63 µm radiances that is constructed from radiative transfer calculations involving ice crystal clouds. Application of the algorithm to AVHRR channels has been performed by using data sets that were collected during the First ISCCP Regional Experiment, Phase II, Cirrus Intensive Field Observation (FIRE‐II‐IFO; November–December 1991) at Coffeyville, Kansas. For validation, the in‐situ data collected by the balloon‐borne replicator and airborne 2‐D probes that were collocated with AVHRR pixels were carefully analyzed for five cases involving single and multiple cirrus cloud layers. We demonstrate that the retrieved cirrus cloud optical depths and mean effective sizes compare reasonably well with those determined from the in‐situ analyses.

The Surface Albedo Of The Vatnajökull Ice Cap, Iceland: A Comparison Between Satellite-Derived And Ground-Based Measurements

The temporal and spatial variations in the surface albedo of the Vatnajokull ice cap, Iceland, are investigated. A time series of the surface albedo is composed for the summer of 1996 using satellite radiance measurements from the Advanced Very High Resolution Radiometer (AVHRR). This time series is compared with ground measurements carried out during a glacio-meteorological experiment during the same summer on the ice cap. The AVHRR is able to reproduce the development in time of the surface albedo fairly well. The large systematic differences found for some of the stations on the ice are attributed to sub-pixel-scale variations in the albedo. An attempt is made to confirm this hypothesis using satellite radiance measurements carried out by the Thematic Mapper (TM) and measurements made with a portable albedometer. The TM has a pixel size of 30 × 30 m whereas the pixel size of the AVHRR is 1 × 1 km. Although the TM measurements show greater variability in the albedo than do the AVHRR measurements, the large systematic difference remains. Measurements with the portable albedometer show a large spread in the albedo at sites with large systematic differences. This implies that the scale of the albedo variations is smaller than the scale of the AVHRR and TM pixels.

Statistical models of fragmented land cover and the effect of coarse spatial resolution on the estimation of area with satellite sensor imagery

The feasibility of correcting for errors in apparent extent of land cover types on coarse spatial resolution satellite imagery was analysed using a modelling approach. The size distributions for small burn scars mapped with two Landsat Multi-spectral Scanner (MSS) images and ponds mapped with an ERS-1 synthetic aperture radar (SAR) image were measured using geographical information system (GIS) software. Regression analysis showed that these size distributions could be modelled with two types of statistical distributions a power distribution and an exponential distribution. A comparison of the size distributions of small burn scars as observed with the Landsat MSS imagery to the distribution observed with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery indicated that distortions due to the coarse spatial resolution of AVHRR caused overestimation of the burn area. This bias was primarily caused by detection in two or three AVHRR pixels of burns whose actual size was on the order of a single AVHRR pixel. Knowledge of the type of the actual size distribution of small fragments in a scene and the causes of distortion may lead to methods for correcting area estimates involving models of the size distribution observed with coarse imagery and requiring little or no recourse to fine scale data.