Evaluating atmospheric correction models for retrieving surface temperatures from the AVHRR over a tallgrass prairie

Recent progress in canopy bidirectional reflectance distribution function (BRDF) model inversions has allowed accurate estimates of vegetation biophysical characteristics from remotely sensed multi-angle optical data. Since most current BRDF inversion methods utilize one-dimensional (1D) models, surface homogeneity within an image pixel is implied. The Advanced Very High Resolution Radiometer (AVHRR) is one of the few spaceborne sensors capable of acquiring radiometric data over the range of view angles required for BRDF inversions. However, its relatively coarse spatial resolution often results in measurements of mixed landcovers, and thus the data may not be ideal for BRDF inversions. The authors present a three-step spectral unmixing method for retrieving AVHRR sub-pixel directional reflectances in regions of high spatial heterogeneity. The reflectances of individual vegetation types are deconvolved using co-located Landsat TM and AVHRR data. The three major steps in the model include: (1) unmixing of vegetation endmember concentrations in TM imagery; (2) correction of dissimilar shadow fractions between TM and AVHRR data; and (3) unmixing of AVHRR sub-pixel reflectances of vegetation types for any Sun-sensor geometry. The authors tested the method using simulated TM and AVHRR data. A savannah landscape simulation, comprised of a canopy radiative transfer model and a crown geometric-optical model was used to create images containing mixed pixels of tree, grass, and shade endmembers. TM and AVHRR spectral response functions, viewing geometrics, and off-nadir pixel shape calculations were incorporated into the simulations. Following the successful testing of the unmixing method on error-free simulations, random noise representing atmospheric perturbations and co-registration inaccuracies was added to the data. The method is stable when errors resulting from either the first unmixing step or image co-registration inaccuracies are introduced. Potential errors in the AVHRR data may result in inaccurately retrieved reflectances if the image scene contains a spatially homogeneous mix of landcovers. A method for detecting and mitigating this problem is presented.

The Bureau of Meteorology continuously acquires low resolution multispectral image data, with continental coverage of Australia in near-real time, from both the Advanced Very High Resolution Radiometer (AVHRR) on the US National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites and from the geostationary imagers on Japan’s MTSAT-1R and China’s Fengyun-2C satellites. The Bureau routinely derives several products from these satellites which can serve as continuous data streams that can contribute to operational land surface monitoring, either directly or as inputs (drivers or constraints) to land surface models. Solar radiation, in the form of fields of integrated daily solar horizontal exposure, is produced daily from MTSAT-1R visible-band data. A 17-year climatology of daily solar radiation has recently been produced by processing archived satellite data from 1990 to 2006, and will find application in agriculture, solar energy planning, building design, and surface energy balance studies. The Bureau is implementing operational production of Normalised Difference Vegetation Index (NDVI) and land surface temperature (LST) from AVHRR data using the Common AVHRR Processing System (CAPS) software developed by CSIRO, for use in applications that require national monitoring of vegetation condition. All of these products are produced on a 0.05° national grid: at least once per day for LST, daily for solar exposure, and weekly for NDVI. The AVHRR data products and solar exposure can together serve as near-real time continental inputs to systems for the assessment of surface moisture status across Australia, based either on simple surface energy balance models or more complex land surface models. The Australian Water Availability Project, a collaboration between the Bureau of Rural Sciences, CSIRO and the Bureau of Meteorology, is using such an approach with the data streams described to establish national monitoring of land surface water availability.

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 advanced very high resolution radiometer (AVHRR) data acquired from the National Oceanic and Atmospheric Administration (NOAA) satellites have been widely applied to a variety of environmental research. A single AVHRR scene is seldom completely cloud‐free. Maximum value compositing (MVC) to create a single image from multiple orbits and dates has become the most valuable method to minimize cloud contamination. Composite images are not absolutely cloud‐free. Postcomposite cloud screening of the composite aggregates was developed to overcome the residual cloud contamination problem, but this is not possible for real‐time delivery of composite data or not suitable for compositing based on AVHRR data from multiple NOAA satellites. Another approach is to detect and remove cloud‐contaminated pixels from daily AVHRR scenes prior to applying the MVC method to provide real‐time composite images. This study developed an automated cloud detection method for daily NOAA 16 AVHRR scenes over the state of Texas and Mexico. The accuracy of the cloud detection algorithm was greater than 93% based on a random test sample from 36 images. Unidentified cloud shadow pixels as well as misidentified barren land pixels and water pixels contributed to more than 5% of the accumulated errors. The error from misidentification of water pixels can be reduced by assigning different threshold values for channel 4 brightness temperature according to the geographical latitude of the data. The resulting daily cloud‐free AVHRR data can be used to construct short‐time period composite images valuable for detecting subtle but critical environment changes. In addition, compositing methods other than MVC, such as multidate averaging or minimum value selection, can be applied for various research purposes, once the daily AVHRR data are cloud‐free.

Satellite retrieved aerosol optical properties are compared to aircraft measurements for a case study during the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX). Two satellite instruments are used: the Along Track Scanning Radiometer 2 (ATSR‐2) and the advanced very high resolution radiometer (AVHRR). The aerosol optical depth in the mid‐visible (0.555 μm) retrieved from the ATSR‐2 data agrees within 0.03 with colocated sunphotometer measurements. Also, the spectral behavior of the aerosol optical depth is retrieved accurately. Good correlation is found between aerosol optical depths for AVHRR channel 1 (0.64 μm) and sunphotometer derived values, but the satellite retrieved values are 0.05 to 0.15 lower. The Ångström wavelength exponent is determined both from the ATSR‐2 and the AVHRR data. The ATSR‐2 derived Ångström exponents are in good agreement with the values computed from the sunphotometer data. The Angstrom exponents determined from AVHRR data show very large variations. Both the ATSR‐2 and the AVHRR aerosol optical depth images show a large gradient. Vertical profile data of temperature, relative humidity, and particle scattering indicate that this gradient is probably caused by changes in the dry aerosol properties, rather than a change in the relative humidity.

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.

In the framework of the Ocean Colour European Archive Network (OCEAN) programme of the European Commission and the European Space Agency, a joint team composed of ACRI, GM-Images and Ecole des Mines de Paris proposed to analyse jointly Coastal Zone Color Scanner (CZCS) (chlorophyll) and Advanced Very High Resolution Radiometer (AVHRR) (temperature) data in order to study mesoscale dynamics features in the Gulf of Biscay. A large archive of AVHRR data, between 1978-1990, was analysed with the support of EPSHOM (Etablissement Principal du Service Hydrographique et Oceanographique de la Marine), together with about one hundred pairs of AVHRR and CZCS images. The method used to process AVHRR and CZCS data is presented in this paper. Interesting oceanographic results have been obtained. Mesoscale features such as eddies and plumes have been observed and detailed. Thestructures have been identified and classified according to an accurate typology. The appearance and evolution of mushroom-like structures have been noted, which may promote a high efficiency in the mixing of water masses. The benefit of the fusion of images is discussed. Although the locations of structures observed in both image types differ perceptibly, the increased number of observations leads to better mapping and monitoring of the features, both in space and time.

This paper describes two new methods that were used to generate 26 years (1985–2010) of broadband emissivity (BBE) products with spatiotemporal continuity at the global scale from satellite data recorded by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR). On the basis of emissivity libraries, the study began with establishing relationships for converting channel emissivities of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS to BBEs for the 8–13.5- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu\hbox{m}$</tex></formula> spectral window and then developed two new algorithms from simultaneous ASTER emissivity products to estimate BBEs over vegetated surfaces using the MODIS and AVHRR data. The MODIS-data-based algorithm (MDBA) uses linear equations with MODIS normalized difference vegetation index (NDVI) and seven channels' albedo; the AVHRR-data-based algorithm uses nonlinear equations with AVHRR red and near-infrared reflectances. The proposed algorithms were first validated with ASTER emissivity products. Results indicated that the root-mean-square errors of both the proposed algorithms were less than 0.015 and their biases were less than 0.003. Comparison with MODIS emissivity products from the day/night algorithm showed that the estimated BBEs using the MDBA were generally smaller than the MODIS products. Cross-comparisons were also made between the proposed algorithms and the NDVI threshold method. Finally, strategies for mapping global BBE products from the MODIS and AVHRR data are presented, and some examples are discussed. The global BBE products are planned to be released throughout the network in the near future.

Regional simulation of ecosystem CO 2 and water vapor exchange for agricultural land using NOAA AVHRR and Terra MODIS satellite data. Application to Zealand, Denmark

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

The enhanced vegetation index (EVI) has been found useful in improving linearity with biophysical vegetation properties and in reducing saturation effects found in densely vegetated surfaces, commonly encountered in the normalized difference vegetation index (NDVI). However, EVI requires a blue band and is sensitive to variations in blue band reflectance, which limits consistency of EVI across different sensors. The objectives of this study are to develop a 2-band EVI (EVI2) without a blue band that has the best similarity with the 3-band EVI, and to investigate the crosssensor continuity of the EVI2 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). A linearity-adjustment factor (&#946;) was introduced and coupled with the soil adjustment factor (L) used in the soil-adjusted vegetation index (SAVI) in the development of the EVI2 equation. The similarity between EVI and EVI2 was validated at the global scale. After a linear adjustment, the AVHRR EVI2 was found to be comparable with the MODIS EVI2. The good agreement between the AVHRR and MODIS EVI2 suggests the possibility of extending the current MODIS EVI time series to the historical AVHRR data, providing another longterm vegetation record different from the NDVI counterpart.

A method for reducing the impact of noise in the 3.7 micron spectral channel in climate data records derived from coarse resolution (4 km) global measurements from the Advanced Very High Resolution Radiometer (AVHRR) data is presented. A dynamic size-varying median filter is applied to measurements guided by measured noise levels and scene temperatures for individual AVHRR sensors on historic National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites in the period 1982–2001. The method was used in the preparation of the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data—Second Edition (CLARA-A2), a cloud climate data record produced by the EUMETSAT Satellite Application Facility for Climate Monitoring (CM SAF), as well as in the preparation of the corresponding AVHRR-based datasets produced by the European Space Agency (ESA) Climate Change Initiative (CCI) project ESA-CLOUD-CCI. The impact of the noise filter was equivalent to removing an artificial decreasing trend in global cloud cover of 1–2% per decade in the studied period, mainly explained by the very high noise levels experienced in data from the first satellites in the series (NOAA-7 and NOAA-9).

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.

Precipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High-Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because (1) AVHRR data have been continuously collected for about four decades on multiple platforms with global coverage, and similar observations will likely continue in the future; (2) current passive microwave satellite precipitation products have several issues over snow and ice surfaces; and (3) good coincident observations between AVHRR and CloudSat are available for training ML algorithms. Using ML, snowfall rate was retrieved from AVHRR’s brightness temperature and cloud probability, as well as auxiliary information provided by numerical reanalysis. The results indicate that the ML-based retrieval algorithm is capable of detection and estimation of snowfall with comparable or better statistical scores than those obtained from the Atmospheric Infrared Sounder (AIRS) and two passive microwave sensors contributing to the Global Precipitation Measurement (GPM) mission constellation. The outcomes also suggest that AVHRR-based snowfall retrievals are spatially and temporally reasonable and can be considered as a quantitatively useful input to the merged precipitation products that require frequent sampling or long-term records.

Unmixing multiple land-cover type reflectances from coarse spatial resolution satellite data