Advanced Very High Resolution Radiometer Data Research Articles

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.

Investigation of flood inundation on playas within the Zone of Chotts, using a time-series of AVHRR

Our principal objectives were to examine the application of a time-series of advanced very high-resolution radiometer (AVHRR) data to contrast the inundation regimes of a range of large, climatically sensitive playas in the Zone of Chotts regions of southern Tunisia and northeastern Algeria. In doing so, we aimed to: (i) refine methods for monitoring monthly changes in lake areas and seasonal water balance, building from previous studies; (ii) explore the extent to which simple hydrological models can be developed for individual playas; and (iii) compare the hydrologic response of individual playas to seasonal rainfall inputs within North Africa. Here, analyses were conducted using two time-series of AVHRR data (c.2 images per month) for southern Tunisia and northern Algeria (1983–1993 and 1987–1999), with coincident meteorological data. These data were used to detect and monitor inundation events within specific playa catchments in the Zone of Chotts region. Key analyses methods included: (a) the use of reflectance profiles for each time-series to infer inundation processes for each playa and (b) the extraction of lake areas from sequential image data to determine the extent of inundation with an accuracy of between 2% and 6% (depending on lake size). For those playas which were found to inundate as a direct result of precipitation records of lake area change were used to drive simple hydrological models to allowed mean monthly precipitation ( P), monthly effective precipitation (P-E), and monthly evaporation ( E) to be estimated. The techniques presented here make an important contribution to our understanding of the hydrology of playas in this region. Results for individual playas also suggest that these techniques may be useful in highlighting the dynamic response of these environments to the forcing mechanisms for regional precipitation (e.g., North Atlantic Oscillation [NAO]). In addition, the findings suggest that more widespread monitoring of playa inundation processes in this manner may impact on our understanding of the precipitation/evaporation status of a number of dryland regions.

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.

Improved semi-arid community type differentiation with the NOAA AVHRR via exploitation of the directional signal

Mapping semi-arid vegetation types at the community level is extremely difficult for optical sensors with large ground footprints such as the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). Attempts to use solar wavelength AVHRR data in community type differentiation have often resulted in unacceptable classification errors which are usually attributed to noise from topographic and soil background variations, inaccurate reflectance retrieval and poor registration. One source of variation which is rarely accounted for adequately is the directional signal resulting from the combined effects of the surface bidirectional reflectance distribution function (BRDF) and the variation of viewing and illumination geometry as a function of scan angle, season, latitude and orbital overpass time. In this study, a linear semiempirical kernel-driven BRDF model is used to examine the utility:of the directional signal in community and cover type differentiation over discontinuous but statistically homogeneous semi-arid canopies in Inner Mongolia Autonomous Region, China, and New Mexico, USA. This research shows that the directional signal resulting from the physical structure of the canopy-soil complex can be retrieved to provide information which is highly complementary to that obtained in the spectral domain.

Land surface albedo from the synergistic use of polar (EPS) and geo-stationary (MSG) observing systems: An assessment of physical uncertainties

This investigation aims at quantifying the various sources of uncertainties in the derivation of albedo products from geo-stationary and polar orbiting optical systems with emphasis on the sensor and surface type (soil, snow, vegetation) spectral characteristics, atmospheric condition, and angular sampling issues. This research specifically takes into account the uncertainties in albedo that we can expect due to the synergistic use of the European Polar System (EPS)/Advanced Very High Resolution Radiometer (AVHRR) and Meteosat Second Generation (MSG)/Scanning Enhanced Visible and Infrared Imager (SEVIRI) instruments data. Satellite orbital models and a Scattering by Arbitrarily Inclined Leaves (SAIL)-hotspot canopy radiative transfer model were used to simulate synthetic bidirectional reflectance data sets for a broad range of vegetation canopies. The surface bidirectional reflectance distribution function (BRDF) database derived from the Polarization and Directionality of Earth Reflectance (POLDER) data was used in support of the simulations. Spectral atmospheric effects were generated using the 6S atmospheric radiative transfer code. Linear BRDF models, which are candidates for operational use, were inverted with the synthetic reflectance data sets of MSG, AVHRR and MSG–AVHRR combined. The BRDF parameters were then used to derive albedo by hemispherical angular integration. The retrieved model coefficients are discussed with regard to angular sampling and the implications these yield on spectral and broadband albedo determinations. It appears that the sampling problem is better conditioned thanks to the synergistic nature of MSG and AVHRR data. This synergy contributed to enhanced albedo retrievals, but can introduce larger uncertainties due to issues related to the differences in spectral bands and atmospheric state. Hence, the quality of the operational albedo products derived from one or more sources of satellite data will depend to a large extent on sensor characteristics (spectral, radiometric, and geometric), cloud detection, atmospheric correction, the angular distribution of the observations, and finally, the narrow-to-broadband albedo conversion.

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%.

Study of the 1997 Borneo fires: Quantitative analysis using global area coverage (GAC) satellite data

In 1997 a drought commenced in Southeast Asia, this being directly related to the then ongoing El Niño‐Southern Oscillation (ENSO) event. Interaction between land clearance activities and this drought led to massive, uncontrolled vegetation fires that burned large areas of forest and agricultural land, most severely on the Indonesian island of Borneo. A similar situation in 1982–1983 led to the largest uncontrolled forest fire ever documented, damaging around 50,000 km2 of Borneo's forest. This paper investigates the extent to which nighttime advanced very high resolution radiometer (AVHRR) global area coverage (GAC) data can be used to detail the spatial and temporal evolution of these large‐scale Indonesian fire events, and particularly the 1997 activity. GAC data are a spatial subsample of the full resolution local area coverage (LAC) AVHRR data, but unlike LAC, they have been archived globally on a daily basis for almost 20 years. Despite the extreme subsampling involved in GAC data production, simulation modeling and analysis of real data indicates that surprisingly reliable fire statistics can be obtained for the 1997 Borneo fire event from GAC imagery. For three dates in October 1997 where coincident LAC and GAC data are available, once numerical adjustment for the GAC subsampling procedure is included, fire counts extracted from LAC and GAC data consistently agree to within 0.15–13%. Time series analysis of the 1997 GAC data indicates that fires began in July in south Kalimantan, principally at the interface between cleared, cultivated land and lowland rain forest. Fire activity moved generally southward and peaked in September 1997. GAC data show the equivalent of 9733 LAC pixels as containing one or more active fires on 2 September. Fire activity declined significantly in November owing to the onset of the monsoon rains, with all fires ceasing by December 1997. Analysis of the cumulative fire map distinguishes peat swamp forest as the most severely affected ecosystem on Borneo, with >20% of this land cover category being identified as directly impacted by active fires. If the performance of GAC data can be shown to extend to other El Niño years and also to less intense periods of burning, then the GAC archive offers a tool for analyzing long‐term changes in the spatiotemporal pattern of fire activity in the region. Any trend can then be investigated for its relation to variations in agriculture, El Niño‐related climate, and other phenomena relevant to regional change in Southeast Asia.

Evaluating the Potential for Retrieving Aerosol Optical Depth over Land from AVHRR Pathfinder Atmosphere Data

In spite of numerous studies on the remote sensing of aerosols from satellites, the magnitude of aerosol climate forcing remains uncertain. However, data from the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder-Atmosphere (PATMOS) dataset—a statistical reduction of more than 19 yr of AVHRR data (1981–2000)—could provide nearly 20 yr of aerosol history. PATMOS data have a daily 110 × 110 km2 equal-area grid that contains means and standard deviations of AVHRR observations within each grid cell. This research is a first step toward understanding aerosols over land with PATMOS data. Herein, the aerosol optical depth is retrieved over land at numerous Aerosol Robotic Network (AERONET) sites around the globe using PATMOS cloud-free reflectances. First, the surface bidirectional reflectance distribution function (BRDF) is retrieved using a lookup table created with a radiative transfer model and the Rahman BRDF. Aerosol optical depths are then retrieved using the retrieved BRDF parameters and the PATMOS reflectances assuming a globally constant aerosol model. This method is applied to locations with ground truth measurements, where comparisons show that the best retrievals are made by estimating the surface reflectance using observations grouped by month. Random errors (i.e., correlation coefficients and standard error of estimate) in this case are lower than those where the surface BRDF is allowed year-to-year variations. By grouping the comparison results by land cover type, it was found that less noise is expected over forested regions, with a significant potential for retrieval for 80% of all land surfaces. These results and analyses suggest that the PATMOS data can provide valuable information on aerosols over land.

Capability of AVHRR data in discriminating rangeland cover mixtures

A combination of high temporal resolution Advanced Very High Resolution Radiometer (AVHRR) data and high spatial information Map Information Analysis and Display System (MIADS) landuse/landcover data from the United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS) were used to investigate the feasibility of using the combined dataset for regional evapotranspiration (ET) studies. It was shown that the biweekly maximum Normalized Difference Vegetation Index (NDVI) composite AVHRR data were capable of discriminating rangelands with different types of trees and shrubs species. AVHRR data also showed a potential to distinguish canopy cover differences within a mix of similar species. The combination of MIADS data and AVHRR data can be used to study temporal dynamics of various cover types for use in regional ET estimates.

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.

Ground-based method for measuring thermal infrared effective emissivities: Implications and perspectives on the measurement of land surface temperature from satellite data

Thermal infrared emissivity is an important parameter for surface characterization and for determining surface temperature. The field-based measurements for ground and vegetation emissivities in 8-14 w m waveband were performed with an emissivity box. A theoretical analysis was carried out using the box and a correcting factor has been determined. The average value for thermal band emissivity of the exposed bare soil was found to be around 0.909; the average value measured for most of the varieties of vegetation present were in the range of 0.980-0.985. A theoretical model is used for obtaining effective emissivity in the 8-14 w m region from Advanced Very High Resolution Radiometer (AVHRR) data considering the proportion of vegetation cover in a pixel and the field-measured emissivity values. The error of the methodology is found to be within 1.5%. Narrow band emissivities for AVHRR channels 4 and 5 have been derived from the emissivity values in the 8-14 w m waveband. The surface temperature has been derived from AVHRR data using a split-window algorithm as a function of emissivities derived in narrow bands. The split-window algorithm accounted for absorption effects of the atmosphere by incorporating the water vapour concentration measured in the campaign. A good agreement was obtained between the satellite-derived surface temperature and the in situ observations. The result suggest that the methodology allows us to derive land surface temperature with an accuracy better than 1.5° C provided the surface emissivity is known. The paper describes the field-based emissivity measurement and approach for deriving surface temperature over land surface.

Dynamics of volcanic and meteorological clouds produced on 26 December (Boxing Day) 1997 at Soufrière Hills Volcano, Montserrat

Abstract The 26 December 1997 explosive activity of Soufrière Hills Volcano, Montserrat, provided an opportunity to study the evolution of a volcanic cloud by merging data from various satellites with wind-trajectory data. The activity involved a debris avalanche that descended SSW from the lava dome, to the coast, and a pyroclastic density current that traversed the coast and entered the sea. The slope failure and subsequent dome collapse occurred at c . 07:01 universal time (UT; 03:01 local time), lasted 15.2 minutes, and produced an upwardly convecting volcanic ash cloud that cloud temperatures suggest rose to c . 15 km. The volcanic ash cloud was unusual because the pyroclastic density current transported hot fine ash to the sea, where it rapidly transferred its heat to the sea water. The evaporation of large volumes of water produced a volcanogenic meteorological (VM) cloud that convected along with the volcanic ash cloud. The evolution of the volcanic and VM clouds was studied using an isentropic wind trajectory model and data from three satellite sensors: Geostationary Observational Environmental Satellite 8 (GOES 8), Advanced Very High Resolution Radiometer (AVHRR), and Total Ozone Mapping Spectrometer (TOMS). The high temporal resolution of the GOES 8 images filled many of the time gaps the other satellites left, and allowed quantitative retrievals to be performed using a two-band infrared retrieval method. The three-dimensional morphology of the volcanic cloud was reconstructed using GOES 8 data and by determining the heights of air parcels from wind-trajectory data. The volcanic cloud was estimated to contain up to 4.5 x 10 7 kg of silicate ash. Between c . 07:39 UT and 13:39 UT the ash signal of the volcanic cloud was masked by the VM cloud, which had a mass of up to 1.5 x 10 8 kg of ice. Ice forms when moist air is convected upwards to temperatures of less than -40°C and becomes saturated. Ice formation in volcanic clouds is especially likely when hot volcanic material is cooled by seawater rather than the atmosphere. The efficiency of evaporation of the seawater was calculated to be c . 5%, based on physical and GOES 8 data. TOMS data showed the SO 2 in the volcanic cloud rose higher than the ash in the volcanic cloud, as has occurred in several other eruptions. A comparison between GOES 8 and AVHRR data showed that AVHRR data retrieved higher fine-ash silicate masses and higher cloud areas than GOES 8 due to the finer spatial resolution of AVHRR images. The effect on retrieval data of the high water vapour content in the lower troposphere of the tropical atmosphere was quantified; the high humidity in the Montserrat region caused the characteristic ash signal to the infrared sensors to be depressed by up to 80%. This signal depression caused a corresponding underestimation of the mass and area of the volcanic cloud when the infrared brightness temperature difference retrieval technique was used.

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.

Radiative Effects of Various Cloud Types as Classified by the Split Window Technique over the Eastern Sub-tropical Pacific Derived from Collocated ERBE and AVHRR Data.

The radiative effects of several cloud types as classified by the split window (11 and 12 μm) technique were studied using coincident and collocated Earth Radiation Budget Experiment (ERBE) S-8 data and Advanced Very High Resolution Radiometer (AVHRR) data from NOAA-9. The parameter investigated was cloud radiative forcing (CRF), the difference between clear and cloudy shortwave flux (SW) and longwave flux (OLR) at the top of the atmosphere. In computing the CRF, the accuracy of clear SW and OLR is essential. Clear scene IDs in the ERBE dataset were evaluated using coincident and collocated AVHRR image data. The mean visible reflectance and SW for clear footprints defined by the ERBE are reasonably small and are 3.2% and 89.0 Wm-2, respectively. However, the values computed using our technique are smaller, 2.7% and 83.9 Wm-2, respectively. The use of collocated AVHRR image data improves clear footprint definition and implies that care should be taken when computing CRF from ERBE data alone. The CRF from several cloud types classified by the split window were compared. Cumulonimbus clouds show the largest impact on top of the atmosphere radiation for both SW and OLR. Cirrus and lowlevel cumulus clouds have similar effects on OLR, but large differences between them are seen for SW. The impact of low-level cumulus clouds on SW is much larger than that of cirrus clouds. Some optically thin cirrus clouds show positive cloud radiative forcing (warming effect). The relationships between OLR and cloud types (including cloud-free) as classified by the split window technique were investigated. By using brightness temperature differences between the split window channels, OLR estimation is improved for cloud-free and low-level cumulus clouds when compared with OLR estimated by the National Oceanic and Atmospheric Administration (NOAA) operational algorithm.

AVHRR composite period selection for land cover classification

Multitemporal satellite image datasets provide valuable information on the phenological characteristics of vegetation, thereby significantly increasing the accuracy of cover type classifications compared to single date classifications. However, the processing of these datasets can become very complex when dealing with multitemporal data combined with multispectral data. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data are commonly used to classify land cover over large regions. Selecting a subset of these biweekly composite periods may be required to reduce the complexity and cost of land cover mapping. The objective of our research was to evaluate the effect of reducing the number of composite periods and altering the spacing of those composite periods on classification accuracy. Because inter-annual variability can have a major impact on classification results, 5 years of AVHRR data were evaluated. AVHRR biweekly composite images for spectral channels 1-4 (visible, nearinfrared and two thermal bands) covering the entire growing season were used to classify 14 cover types over the entire state of Colorado for each of five different years. A supervised classification method was applied to maintain consistent procedures for each case tested. Results indicate that the number of composite periods can be halved-reduced from 14 composite dates to seven composite dates-without significantly reducing overall classification accuracy (80.4% Kappa accuracy for the 14-composite dataset as compared to 80.0% for a seven-composite dataset). At least seven composite periods were required to ensure the classification accuracy was not affected by inter-annual variability due to climate fluctuations. Concentrating more composites near the beginning and end of the growing season, as compared to using evenly spaced time periods, consistently produced slightly higher classification values over the 5 years tested (average Kappa of 80.3% for the heavy early/late case as compared to 79.0% for the alternate dataset case).

AVHRR channel selection for land cover classification

Mapping land cover of large regions often requires processing of satellite images collected from several time periods at many spectral wavelength channels. However, manipulating and processing large amounts of image data increases the complexity and time, and hence the cost, that it takes to produce a land cover map. Very few studies have evaluated the importance of individual Advanced Very High Resolution Radiometer (AVHRR) channels for discriminating cover types, especially the thermal channels (channels 3, 4 and 5). Studies rarely perform a multi-year analysis to determine the impact of inter-annual variability on the classification results. We evaluated 5 years of AVHRR data using combinations of the original AVHRR spectral channels (1-5) to determine which channels are most important for cover type discrimination, yet stabilize inter-annual variability. Particular attention was placed on the channels in the thermal portion of the spectrum. Fourteen cover types over the entire state of Colorado were evaluated using a supervised classification approach on all two-, three-, four- and five-channel combinations for seven AVHRR biweekly composite datasets covering the entire growing season for each of 5 years. Results show that all three of the major portions of the electromagnetic spectrum represented by the AVHRR sensor are required to discriminate cover types effectively and stabilize inter-annual variability. Of the two-channel combinations, channels 1 (red visible) and 2 (near-infrared) had, by far, the highest average overall accuracy (72.2%), yet the inter-annual classification accuracies were highly variable. Including a thermal channel (channel 4) significantly increased the average overall classification accuracy by 5.5% and stabilized interannual variability. Each of the thermal channels gave similar classification accuracies; however, because of the problems in consistently interpreting channel 3 data, either channel 4 or 5 was found to be a more appropriate choice. Substituting the thermal channel with a single elevation layer resulted in equivalent classification accuracies and inter-annual variability.

Using covariates to spatially interpolate moisture availability in the Murray–Darling Basin: A novel use of remotely sensed data

Moisture availability is estimated in the 1.1 million km 2 Murray–Darling Basin (MDB) in southeast Australia. Remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) are combined with meteorological data to estimate the Normalised Difference Temperature Index (NDTI). The NDTI provides a measure of the moisture availability, the ratio of actual to potential evapotranspiration. Surface temperature minus air temperature, percent vegetation cover and net radiation explained 85% of variation in the modelled NDTI. Using these three covariates across the network of meteorological stations allows NDTI images, which maps changes in moisture availability across the MDB, to be calculated. This method uses a calculate then interpolate (CI) approach that uses the per-pixel variation present in the AVHRR data as the backbone for the spatial interpolation. Using the spatially dense AVHRR-based covariates in a CI approach avoids errors that occur between measurement points when interpolating variables for regional hydrologic modelling, most significantly the spatial pattern of rainfall. The NDTI provides a link into regional water balance modelling which does not require daily rainfall to be spatially interpolated. Assessing spatial and temporal interactions between the NDTI and the Normalised Difference Vegetation Index (NDVI) provides useful information about regional hydroecological processes, including agricultural management, within the context of Australia's highly variable climate and sparse network of meteorological stations.

The potential of Pathfinder AVHRR data for providing surrogate climatic variables across Africa and Europe for epidemiological applications

Surface climatic conditions are key determinants of arthropod vector distribution and abundance and consequently affect transmission rates of any diseases they may carry. Remotely sensed observations by satellite sensors are the only feasible means of obtaining regional- and continental-scale measurements of climate at regular intervals for real-time epidemiological applications, such as disease early warning systems. The potential of Pathfinder AVHRR Land (PAL) data to provide surrogate variables for near-surface air temperature ( T a) and vapour pressure deficit (VPD) over Africa and Europe was assessed in this context. For the years 1988–1990 and 1992, correlations were examined between meteorological ground measurements (monthly mean T a and VPD grd) and variables derived from advanced very high resolution radiometer (AVHRR) data [land surface temperature (LST) and VPD sat]. The AVHRR indices were derived from both daily and composite PAL data so that their relative performance could be determined. Furthermore, the ground observations were divided into African and European subsets, so that the relative performance of the satellite data at tropical/subtropical and temperate latitudes could be assessed. Significant correlations were shown between T a and LST in all months. Temporal variability existed in the strength of correlations throughout any 12-month period, with the pattern of variability consistent between years. The adjusted r 2 values increased when elevation and the normalised difference vegetation index (NDVI) were included, in addition to LST, as predictor variables of T a. Attempts to derive monthly estimates of atmospheric moisture availability resulted in an overestimation of VPD sat compared to ground observations, VPD grd. The use of daily PAL data to derive monthly mean climatic indices was shown to be more accurate than those obtained using monthly maximum values from 10-day composite data. A subset of the 1992 data was then used to build linear regression models for the direct retrieval of monthly mean T a from PAL data. The accuracy of retrieved estimates was greatest when NDVI was included with LST as predictor variables, with root mean square errors (RMSEs) varying from 1.83°C to 3.18°C with a mean of 2.38°C over the 12-month period.

Solar contamination effects on the infrared channels of the advanced very high resolution radiometer (AVHRR)

Solar impingement on the advanced very high resolution radiometers (AVHRRs) near the terminator can contaminate the onboard radiometric calibration system and degrade AVHRR data. The solar contamination causes disagreement between the sensor‐measured radiometric output of the onboard blackbody versus its bulk temperature measured by the platinum resistance thermometers. Despite the sun shield installed on the latest AVHRR unit, solar contamination can still contribute errors of more than 0.5 K in the 3.7 μm channel and 0.25 K in the long‐wave infrared channels. The timescale and spectral characteristics of the contamination are analyzed to find the possible causes. In addition, stray light in Earth scenes is found whenever the radiometric calibration is contaminated. These effects occur as the spacecraft moves out of the shadow of the Earth at spacecraft sunrise. The intensity of the effects varies by orbit and season and is related to the solar zenith and azimuth angles at the spacecraft.