Systematic corrections of AVHRR image composites for temporal studies

Three multispectral algorithms for determining the cloud type of previously identified cloudy pixels during the daytime, using satellite imager data, are presented. Two algorithms were developed for use with 0.65-, 1.6-/3.75-, 10.8-, and 12.0-μm data from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting satellites. The AVHRR algorithms are identical except for the near-infrared data that are used. One algorithm uses AVHRR channel 3a (1.6 μm) reflectances, and the other uses AVHRR channel 3b (3.75 μm) reflectance estimates. Both of these algorithms are necessary because the AVHRRs on NOAA-15 through NOAA-17 have the capability to transmit either channel 3a or 3b data during the day, whereas all of the other AVHRRs on NOAA-7 through NOAA-14 can only transmit channel 3b data. The two AVHRR cloud-typing schemes are used operationally in NOAA’s extended Clouds from AVHRR (CLAVR)-x processing system. The third algorithm utilizes additional spectral bands in the 1.38- and 8.5-μm regions of the spectrum that are available on the Moderate Resolution Imaging Spectroradiometer (MODIS) and will be available on the Visible–Infrared Imaging Radiometer Suite (VIIRS). The VIIRS will eventually replace the AVHRR on board the National Polar-Orbiting Operational Environmental Satellite System (NPOESS), which is currently scheduled to be launched in 2009. Five cloud-type categories are employed: warm liquid water, supercooled water–mixed phase, opaque ice, nonopaque high ice (cirrus), and cloud overlap (multiple cloud layers). Each algorithm was qualitatively evaluated through scene analysis and then validated against inferences of cloud type that were derived from ground-based observations of clouds at the three primary Atmospheric Radiation Measurement (ARM) Program sites to help to assess the potential continuity of a combined AVHRR channel 3a–AVHRR channel 3b–VIIRS cloud-type climatology. In this paper, “validation” is strictly defined as comparisons with ground-based estimates that are completely independent of the satellite retrievals. It was determined that the two AVHRR algorithms produce nearly identical results except for certain thin clouds and cloud edges. The AVHRR 3a algorithm tends to incorrectly classify the thin edges of some low- and midlevel clouds as cirrus and opaque ice more often than the AVHRR 3b algorithm. The additional techniques implemented in the VIIRS algorithm result in a significant improvement in the identification of cirrus clouds, cloud overlap, and overall phase identification of thin clouds, as compared with the capabilities of the AVHRR algorithms presented in this paper.

The Community Radiative Transfer Model (CRTM) developed at the Joint Center for Satellite Data Assimilation (JCSDA) is used in conjunction with a daily sea surface temperature (SST) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) atmospheric data and surface wind to calculate clear-sky top-of-atmosphere (TOA) brightness temperatures (BTs) in three Advanced Very High Resolution Radiometer (AVHRR) thermal infrared channels over global oceans. CRTM calculations are routinely performed by the sea surface temperature team for four AVHRR instruments on board the National Oceanic and Atmospheric Administration (NOAA) satellites NOAA-16, NOAA-17, and NOAA-18 and the Meteorological Operation (MetOp) satellite MetOp-A, and they are compared with clear-sky TOA BTs produced by the operational AVHRR Clear-Sky Processor for Oceans (ACSPO). It was observed that the model minus observation (M−O) bias in the NOAA-16 AVHRR channel 3b (Ch3b) centered at 3.7 μm experienced a discontinuity of ∼0.3 K when a new CRTM version 1.1 (v.1.1) was implemented in ACSPO processing in September 2008. No anomalies occurred in any other AVHRR channel or for any other platform. This study shows that this discontinuity is caused by the out-of-band response in NOAA-16 AVHRR Ch3b and by using a single layer to the NCEP GFS temperature profiles above 10 hPa for the alpha version of CRTM. The problem has been solved in CRTM v.1.1, which uses one of the six standard atmospheres to fill in the missing data above the top pressure level in the input NCEP GFS data. It is found that, because of the out-of-band response, the NOAA-16 AVHRR Ch3b has sensitivity to atmospheric temperature at high altitudes. This analysis also helped to resolve another anomaly in the absorption bands of the High Resolution Infrared Radiation Sounder (HIRS) sensor, whose radiances and Jacobians were affected to a much greater extent by this CRTM inconsistency.

Advanced Very High Resolution Radiometer (AVHRR) data are used to extract broadband sea ice surface albedos from narrowband channel 1 and 2 top of the atmosphere (TOA) radiances. Corrections for the intervening atmosphere, viewing geometry and sensor spectral response are applied to the satellite data. Atmospheric correction increases TOA albedos by 27 to 32%. After removing the effects of viewing geometry, surface albedo variability between orbits decreases. The satellite-derived surface albedo over snow-covered sea ice corrected for viewing geometry ranged from 0.68 to 0.82. The residual diurnal variability is attributed to uncertainties in the atmospheric and anisotropic corrections of the satellite data. After comparison with coincidental in situ measurements, AVHRR pixel. In order to develop a reliable methodology for using these satellite data to derive sea ice albedo, an improved understanding of both the atmosphere's behavior over the long path lengths common to the Arctic and the anisotropic nature of snow-covered sea ice reflectance is required. Furthermore, any seasonal characteristics of these factors must be addressed.Key words: sea ice, albedo, remote sensing, AVHRR, anisotropy

This paper proposes a novel approach to detect hotspots using NOAA advanced very high resolution radiometer (AVHRR) for the Jharia, Jharkhand (India) region. Jharia coalfield in Jharkhand is the richest coal bearing area in India that contains a large number of mine fires which have been burning for several decades. In this paper, a fuzzy based methodology has been applied for the determination of hotspots to Jharia AVHRR images based on a theoretical model that establishes relationship among AVHRR channel 4, channel 5 and different vegetation indices. The algorithm consists of four stages: data preprocessing, multi-channel information fusion, hotspot detection using fuzzy logic approach and validation of result. The most commonly used existing algorithms like contextual algorithms, multi-thresholding, entropy based thresholding, and genetic algorithms have limitation that they need some mathematical model for training in order to get the required result. The employed fuzzy logic approach overcomes this requirement and in addition, it is flexible, tolerant of imprecise data and is based on natural language. The results were compared with the results obtained by ground survey and a good agreement has been obtained between observed and predicted hotspots.

Vegetation in northern high-latitudes plays an important role in energy exchange and carbon dynamics, thereby influencing regional and global climate. Vegetation indices derived from the space-borne Advanced Very High Resolution Radiometers (AVHRR) have suggested decreased photosynthetic activity during recent decades within some continental regions of the pan-arctic boreal forests. The purpose of this research was to determine associations between the normalized difference vegetation index (NDVI), as derived by both AVHRR and Moderate Resolution Imaging Spectroradiometers (MODIS), and inter-annual variations in radial stem growth in high-latitude coniferous forests. During 2008 and 2009, tree core samples were collected at 12 sites in northeast Russia and at 10 sites in northwest Canada. Ring-width indices (RWI; n = 27) were generated for larch, spruce, and pine genera and these were correlated with summer NDVI derived from the AVHRR sensors over the 1982 to 2008 period. The correlations between NDVI and RWI were then examined between 2000 and 2008 using both MODIS and AVHRR. The sensors showed similar abilities to proxy radial growth and NDVI-RWI correlations appeared mostly insensitive to changes in MODIS grain sizes between 250 m and 24 km. Over the 27 year period RWI and NDVI showed positive, though variable, correlations (r = 0.43 ± 0.19, n = 27). For pine and spruce, both evergreen conifers, the annual rate of radial growth was significantly correlated with growth during previous years, as was canopy development, as proxied by NDVI. Larch, however, did not show year to year persistence in either radial growth or canopy development, a finding that points to differences in growth patterns between functionally-distinct tree genera. These findings suggest that negative trends in NDVI may reflect decreased radial growth at some locations and that attempts to model tree growth and carbon uptake using NDVI need to take into account multi-year persistence in tree growth. Additionally, the work shows similarities between AVHRR and MODIS, suggesting potential to bridge the historical AVHRR record with the newer and finer resolution MODIS record.

The retrieval of the distribution of aerosol properties and determination of trends in their temporal variation can only be achieved by the use of long-term satellite measurements. The Advanced Very High Resolution Radiometer (AVHRR) carried on board the National Oceanic and Atmospheric Administration (NOAA) and the EUMETSAT Meteorological Operational Satellite (Metop) polar orbiting satellites offers more than 35 years of satellite data since 1978 which makes it worthwhile to explore their use for the analysis of aerosols on a daily basis. So in this paper we applied the algorithm for the land aerosol and bidirectional reflectance inversion by times series technique (LABITS) to data from AVHRR Channel 1 on the NOAA-12 and NOAA-14 satellites using a model for the earth-atmosphere system which couples an atmospheric radiative transfer model with the Ross - Thick - Li-sparse bidirectional reflectance factor (BRF) model. Preliminary results show that LABITS provides good results over China for different surface type. Comparing our results with the with SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) aerosol product AOD products shows good agreement. The algorithm has the potential to retrieve global AOD over land for long time series of NOAA AVHRR data going back to the 1980s, which are urgently needed for studies on aerosol climatology and global climate change.

In the framework of the Satellite Application Facility on Climate Monitoring (CM‐SAF) an algorithm was developed to retrieve Cloud Physical Properties (CPP) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (METEOSAT−8) and the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) satellites. This paper presents the CPP algorithm and determines if SEVIRI can be used together with AVHRR to build a consistent and accurate data set of cloud optical thickness (COT) and cloud liquid water path (CLWP) over Europe for climate research purposes. After quantifying the differences in 0.6 and 1.6 μm operational calibrated reflectances of SEVIRI and AVHRR, a recalibration procedure is proposed to normalize and absolutely calibrate these reflectances. The effects of recalibration, spatial resolution, and viewing geometry differences on the SEVIRI and AVHRR cloud property retrievals are evaluated. The intercomparison of 0.6 and 1.6 μm operationally calibrated reflectances indicates ∼6 and ∼26% higher reflectances for SEVIRI than for AVHRR. These discrepancies result in retrieval differences between AVHRR and SEVIRI of ∼8% for COT and ∼60% for CLWP. Owing to recalibration these differences reduce to ∼5%, while the magnitude of the median COT and CLWP values of AVHRR decrease ∼2 and ∼60% and the SEVIRI values increase ∼10 and ∼55%, respectively. The differences in spatial resolution and viewing geometry slightly influence the retrieval precision. Thus the CPP algorithm can be used to build a consistent and high‐quality data set of SEVIRI and AVHRR retrieved cloud properties for climate research purposes, provided the instrument reflectances are recalibrated, preferably guided by the satellite operators.

Global climate change studies require long-term, radiometrically accurate, and stable observations from a number of satellites. Spatially uniform and radiometrically/spectrally stable vicarious calibration sites can be used to quantify the sensor gain change over time, monitor the instrument performance, and compare measurements from multiple instruments to maintain consistent radiometric calibration. This study uses AQUA moderate resolution imaging spectroradiometer (MODIS), MetOp-A advanced very high resolution radiometer (AVHRR), and Earth Observing-1 Hyperion to analyze the radiometric and spectral characteristics of the Dome C and Sonoran Desert sites and perform intercomparisons. The radiometric stability of both sites over a period of eight years as evaluated using AQUA MODIS is found to be better than 2% in the visible (0.64 μm) and near-infrared regions (0.86 μm), assuming the MODIS calibration is stable. The bidirectional reflectance distribution (BRDF) effect over Dome C is large, with greater than 5% seasonal variation, compared to the Sonoran Desert with less than 2% variation. However, the BRDF impact can be reduced to less than 2% for Dome C after normalization by an appropriate BRDF model. For water vapor absorption channels, such as AVHRR channel 2 (0.86 μm), this study suggests that the Sonoran Desert is largely affected with greater than 6% absorption variability compared to that of Dome C with less than 2%. The study also reveals that the operationally calibrated AVHRR top-of-atmosphere reflectance is lower than that of MODIS by about 8%.

MetOp-A satellite-based hyper-spectral Infrared Atmospheric Sounding Interferometer (IASI) observations are used to evaluate the accuracy of the broadband short-wave infrared (SWIR) atmospheric window channel (channel 3B) centred at 3.74 μm of the Advanced Very High Resolution Radiometer (AVHRR) carried on the same platform. To complement the partial spectral coverage of IASI, line-by-line radiative transfer model (LBLRTM)-simulated IASI spectra are used. The comparisons result in significant negative AVHRR minus IASI bias in radiance (∼–0.04 mW m–2 sr–1 cm–1) with scene temperature dependency in which the absolute value of the bias linearly increases with increasing temperature. It is demonstrated that the negative bias and the scene temperature dependency of the bias are the results of significant absorption in the portion of AVHRR spectral band not seen by IASI, leading to the conclusion that MetOp-A AVHRR channel 3B is not purely an ‘atmospheric window’ channel.

Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses. The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR–AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.

The long-term data record (LTDR) has the goal of developing a quality and consistent Advanced Very High Resolution Radiometer (AVHRR) surface reflectance and albedo products dating back to 1982 at 0.05° spatial resolution. Distinguishing between cloud and snow is of critical importance when analyzing global albedo trends, for they influence the Earth’s energy balance. However, this task is specially challenging when working with AVHRR given its limited spectral bands. Therefore, the current version of the LTDR does not distinguish between snow and clouds. To this end, we propose the Moderate Resolution Imaging Spectroradiometer (MODIS)-based AVHRR Class Separation Algorithm (MACSSA), whose goal is to identify clear land and snow pixels using AVHRR data. We make use of a combination of optical and thermal information from satellite and reanalysis data, along with monthly climatology information. These are used as inputs for two different support vector machine (SVM) models, which are then applied to AVHRR data to retrieve the MACSSA predicted tags. These are compared first against reference tags retrieved from the MYD10C1 product over pixels with less than 2-min overpass time difference between MODIS Aqua and NOAA16–19, distributed all around the world, and second against the Climate Change Initiative Cloud (Cloud_cci AVHRR) project. We found the product to be highly accurate in identifying clear land pixels, with a probability of detection of clear pixels (PODclear) of 97%. The discrimination of snow and clouds shows a PODsnow of 89%, which is encouraging given the spectral limitations of the AVHRR sensor.

We describe the validation of surface albedos of snow and glacier ice as derived from Advanced Very High Resolution Radiometer (AVHRR) and MOderate Resolution Imaging Spectrometer (MODIS) satellite data. For this purpose we measured surface albedos from a helicopter over Vatnajokull, Iceland, and the Kangerlussuaq transect (western part of the Greenland ice sheet) in Thematic Mapper (TM) bands 2 and 4 and AVHRR bands 1 and 2, and converted these values to ‘measured albedos’ in three MODIS bands. Relative to other validation methods, our helicopter measurements have the advantages of larger spatial coverage and of (almost) direct measurements in satellite-sensor spectral bands. We found the smallest differences between the satellite-derived and helicopter albedos for the Kangerlussuaq transect: for AVHRR data a mean difference of 0.01 in both bands (with the satellite in near-nadir position) and for two MODIS images a mean difference of 0. 00-0.02 for bands 2 and 4, and 0.03 for band 1. For two AVHRR images of Vatnajokull, we found mean differences of up to 0.06. Differences are primarily due to errors in the satellite-derived albedos, which, in turn, are mainly caused by errors in the calibration coefficients of the satellite sensors and insufficient knowledge of the angular distribution of the radiation reflected by snow and ice. Satellite data obtained from view zenith angles larger than ~50-55° appeared to be unsuitable.

Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A

The AVHRR (Advanced Very High Resolution Radiometer) data record comprises the longest existing daily satellite dataset. The CSIRO AVHRR time series includes data over Australasia from all of the NOAA polar orbiters since NOAA-6 in 1981 through the currently operational NOAA-17. Both inter- and intrasatellite factors affect the usability of these data for the generation of consistent time series of meteorological and geo/bio-physical parameters. Not only do the particular AVHRR instruments on the individual satellites differ in terms of spectral response and sensitivity, but the sensitivity varies with time, and the evolution of each satellite's orbit results in changes in target illumination and viewing geometry that must be taken into account. To improve our knowledge of the measurement variation of the different AVHRR instruments we have attempted to develop a fully operational method, based on multivariate alternate detection in comparison with pseudo-invariant features. A comparison of the operability and effectiveness of these methods is discussed and their relative impact on the overall consistency and long term calibration of the time series is described.

The relationship between climate change and vegetation dynamics in the southwestern karst region of China has been identified by recent studies. Based on previous researches and AVHRR (Advanced Very High Resolution Radiometer) GIMMS (Global Inventory Monitoring and Modeling Studies) NDVI (Normalized Difference Vegetation Index) (1982–2003) and AVHRR GloPEM (Global Production Efficiency Model) NPP (Net Primary Production) (1981–2000) datasets, vegetation dynamics impacted by climate change in the southwestern karst region of China were assessed. The results show that: (1) since the early 1980s, both vegetation cover density and net primary production have insignificant ascending tendencies. However, the inter-annual variation rates of vegetation indexes have apparent spatial differentiations; (2) the correlation coefficients between the inter-annual variations of vegetation indexes and the inter-annual variations of climate factors vary geographically; (3) as indicated by NDVI and NPP, various vegetation types have different responses to climate change, and the annual mean temperature variation has more significant impact on vegetation dynamics than the annual precipitation variation in the study area; (4) distribution laws of correlation coefficients between the inter-annual variations of vegetation indexes and the inter-annual variations of climate factors in different climate conditions are apparent. All these findings will enrich our knowledge of the natural forces which impact the stability of the karst ecosystems and provide scientific basis for the management of the karst ecosystems.