Subpixel measurement of tropical forest cover using AVHRR data

Abstract

Tropical forest assessment using data from the Advanced Very High Resolution Radiometer (AVHRR) may lead to inaccurate estimates of forest cover in regions of small subpixel forest or non-forest patches and in regions where the pattern of clearance is particularly convoluted. Test sites typifying these two patterns were chosen in Ghana and Rondonia, respectively. To capture the subpixel proportions of forest cover, a linear mixture model was applied to two AVHRR test images over the test sites. The model produced image outputs in which pixel intensities indicated the proporton of forest cover per km². For comparison, supervised maximum likelihood classifications were also performed. The outputs were assessed against classified Landsat TM scenes, converted to proportions maps and coregistered to the AVHRR images. An empirical method was applied for determining the critical forest cover per km² needed for an AVHRR pixel to be classified as forest. The critical values exceeded 50 per cent, indicating a tendency for AVHRR classification to underestimate forest cover. This was confirmed by comparing estimates of total forest cover obtained from the AVHRR and TM classifications. In the case of Ghana, a more accurate estimate of forest cover was obtained from the AVHRR mixture model than from the classification. Both mixture model outputs were found to be well correlated with those from Landsat TM. Further work should test the robustness of the approach adopted here when applied to much larger areas.     

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

This work extends the previous study of Trishchenko et al. [Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 81 (1), 1-18] that analyzed the spectral response function (SRF) effect for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA-16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The developed approach is now applied to cover three new AVHRR sensors launched in recent years on NOAA-17, 18, and METOP-A platforms. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. The differences in reflectance among these three radiometers relative to the AVHRR NOAA-9 are similar to each other and range from − 0.015 to 0.015 (− 20% to + 2% relative) for visible (red) channel, and from − 0.03 to 0.02 (− 5% to 5%) for the near infrared (NIR) channel. The absolute change in the Normalized Difference Vegetation Index (NDVI) ranged from − 0.03 to + 0.06. Due to systematic biases of the visible channels toward smaller values and the NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The polynomial approximations are provided for the bulk spectral correction with respect to the AVHRR NOAA-9 for consistency with previous study. Analysis was also conducted for the SRF effect only among the AVHRR-3 type of radiometer on NOAA-15, 16, 17, 18 and METOP-A using AVHRR NOAA-18 as a reference. The results show more consistency between sensors with typical correction being under 5% (or 0.01 in absolute values). The AVHRR METOP-A reveals the most different behavior among the AVHRR-3 group with generally positive bias for visible channel (up to + 5%, relative), slightly negative bias for the NIR channel (1%-2% relative), and negative NDVI bias (− 0.02 to + 0.005). Polynomial corrections are also suggested for normalization of AVHRR on NOAA-15, 16, 17 and METOP-A to AVHRR NOAA-18.     

In-flight calibration of NOAA AVHRR visible and near-IR bands over Greenland and Antarctica

A new method for in-flight calibration of NOAA AVHRR visible and near-IR bands is presented. The approach involves using calibrated NOAA-9 near-nadir reflectances over spatially and temporally uniform ice-surfaces from Greenland and Antarctica to produce reflectance calibration curves for AVHRR instruments in all orbits. The reflectance calibration curves consist of second order polynomial regressions of reflectance on solar zenith angle, derived from observations that are spatially uniform in all AVHRR channels over sub-regions of area 68 km by 68 km. By comparing reflectances from uncalibrated AVHRR instruments with these calibration curves, new channel 1 and 2 calibration coefficients are obtained with an accuracy of 5 per cent. The main advantages of this calibration method are: (1) calibration targets are large; (2) it can be applied over multiple years; (3) it is applicable for a wide range of solar zenith angles, and can therefore be used year-round. When calibration coefficients inferred from NOAA-11 (1994) and NOAA-14 (1995) observations over Greenland and Antarctica are compared with those from the formulae of Rao and Chen (1995, 1996), the two methods are in excellent agreement in channel 1 (within 3 per cent). In channel 2, they agree to within 4 per cent for NOAA-14, but are significantly different for NOAA-11 ( 9 per cent). When applied to NOAA-12 AVHRR observations for 1994-95, channel 1 and 2 calibration coefficients are 20 per cent and 35 per cent larger than prelaunch values, respectively.     

A technique for extrapolating and validating forest cover across large regions Calibrating AVHRR data with TM data

Abstract

An approach to extending high-resolution forest cover information across large regions is presented and validated. Landsat Thematic Mapper (TM) data were classified into forest and nonforest for a portion of Jackson County, Illinois. The classified TM image was then used to determine the relationship between forest cover and the spectral signature of Advanced Very High Resolution Radiometer (AVHRR) pixels covering the same location. Regression analysis was used to develop an empirical relationship between AVHRR spectral signatures and forest cover. The regression equation developed from data from the single county calibration area in southern Illinois was then applied to the entire AVHRR scene, which covered all or parts of ten states, to produce a regional map of forest cover. This map was used to derive estimates of forest cover, within a geographical information system (GIS), for each of the 428 counties located within the boundaries of the original AVHRR scene. The validity of the overall regional map was tested by comparing the AVHRR/TM-derived estimates of county forest cover with independent estimates of county forest cover developed by the U.S. Forest Service (USFS). The overall correlation coefficient of the AVHRR/TM and USFS county forest cover estimates was r=0-89 (n=428 counties). Not surpris0ingly, some individual states and the areas nearer to the southern Illinois calibration centre had higher correlation coefficients. Absolute estimates of forest cover percentages were also significantly well predicted. With the future inclusion of multiple calibration centres representing a number of physiographic regions, the method shows promise for predicting continental and global estimates of forest cover.     

A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model

A feasible method for mapping the fraction of Snow Covered Area (SCA) in the boreal zone is presented. The method (SCAmod) is based on a semi-empirical model, where three reflectance contributors (wet snow, snow-free ground and forest canopy), interconnected by an effective canopy transmissivity and SCA, constitute the observed reflectance from the target area. Given the reflectance observation, SCA is solved from the model. The predetermined values for the reflectance contributors can be adjusted to an optional wavelength region, which makes SCAmod adaptable to various optical sensors. The effective forest canopy transmissivity specifies the effect of forests on the local reflectance observation; it is estimated using Earth observation data similar to that employed in the actual SCA estimation. This approach enables operational snow mapping for extensive areas, as auxiliary forest data are not needed.Our study area covers 404 000 km², comprising all drainage basins of Finland (with an average area of 60 km²) and some transboundary drainage basins common with Russia, Norway and Sweden. Applying SCAmod to NOAA/AVHRR cloud-free data acquired during melting periods 2001-2003, we estimated the areal fraction of snow cover for all the 5845 basins. The validation against in situ SCA from the Finnish snow course network indicates that with SCAmod, 15% (absolute SCA-units) accuracy for SCA is gained. Good results were also obtained from the validation against snow cover information provided by the Finnish weather station network, for example, 94% of snow-free and fully snow-covered basins were recognized. A general formula for deriving the statistical accuracy of SCA estimates provided by SCAmod is presented, complemented by the results when the AVHRR data are employed.Snow melting in Finland has been operatively monitored with SCAmod in Finnish Environment Institute (SYKE) since year 2001. The SCA estimates have been assimilated to the Finnish national hydrological modelling and forecasting system since 2003, showing a substantial improvement in forecasts.     

Land surface air temperature mapping using TOVS and AVHRR

Surface air temperature is an important variable in land surface hydrological studies. This paper evaluates the ability of satellites to map air temperature across large land surface areas. Algorithms recently have been developed that derive surface air temperature using observations from the TOVS (TIROS Operational Vertical Sounder) suite of instruments and also from the AVHRR (Advanced Very High Resolution Radiometer), which have flown on the NOAA operational sun synchronous satellites TIROS-N NOAA-14. In this study we evaluate TOVS soundings from NOAA-10 (nominal local time of overpass 7:30 a.m./p.m.) and data from AVHRR aboard NOAA-9 (nominal local time 2:30 a.m./p.m.). Instantaneous estimates from the AVHRR and TOVS were compared with the hourly ground observations collected from 26 meteorological stations in the Red River-Arkansas River basin for a 3-month period from May to July 1987. Detailed comparisons between the satellite and ground estimates of surface air temperatures are reported and the feasibility of estimating the diurnal variation is explored. The comparisons are interpreted in the geographical context, i.e. land cover and topography, and in the seasonal context, i.e. early and midsummer. The results show that the average bias over the 3-month period compared with ground-based observations is approximately 2°C or less for the three times of day with TOVS having lower biases than AVHRR. Knowledge of these error estimates will greatly benefit use of satellite data in hydrological modelling.     

Relationships between percent vegetation cover and vegetation indices

In this paper, percent vegetation cover is estimated from vegetation indices using simulated Advanced Very High Resolution Radiometer (AVHRR) data derived from in situ spectral reflectance data. Spectral reflectance measurements were conducted on grasslands in Mongolia and Japan. Vegetation indices such as the normalized difference, soil-adjusted, modified soil-adjusted and transformed soil-adjusted vegetation indices (NDVI, SAVI, MSAVI and TSAVI) were calculated from the spectral reflectance of various vegetation covers. Percent vegetation cover was estimated using pixel values of red, green and blue bands of digitized colour photographs. Relationships between various vegetation indices and percent vegetation cover were compared using a second-order polynomial regression. TSAVI and NDVI gave the best estimates of vegetation cover for a wide range of grass densities.     

Radiometric comparison of the LANDSAT-5 TM and MSS sensors

Abstract

This paper analyses the radiometric accuracy of LANDSAT-5 Thematic Mapper (TM) data and of LANDSAT-5 Multispectral Scanner (MSS) data, using concurrent TM and MSS images recorded simultaneously over the city of Montreal, Quebec, Canada. The data sets were obtained from the Canada Centre for Remote Sensing (CCRS), and have been preprocessed for geometric correction, and for radiometric 23calibration utilizing the in-flight calibration lamp data. The comparison of the TM and MSS normalized apparent reflectances computed for 12 different typical cover types using the post-launch calibration dynamic ranges shows the relevance of the CCRS processing systems. The significant linear regressions, obtained between channels from the two sensors, and the analysis of the ground reflectance corrected for atmospheric absorption and scattering as well as for pixel adjacency effects, can serve both to assess detector degradation with time and to rescale data to match those from other LANDSAT sensors.     

Leaf area index mapping in northern Canada

Leaf area index (LAI) is an important structural vegetation parameter that is commonly derived from remotely sensed data. It has been used as a reliable indicator for vegetation's cover, status, health and productivity. In the past two decades, various Canada-wide LAI maps have been generated by the Canada Centre for Remote Sensing (CCRS). These products have been produced using a variety of very coarse satellite data such as those from SPOT VGT and NOAA AVHRR satellite data. However, in these LAI products, the mapping of the Canadian northern vegetation has not been performed with field LAI measurements due in large part to scarce in situ measurements over northern biomes. The coarse resolution maps have been extensively used in Canada, but finer resolution LAI maps are needed over the northern Canadian ecozones, in particular for studying caribou habitats and feeding grounds.

In this study, a new LAI algorithm was developed with particular emphasis over northern Canada using a much finer resolution of remotely sensed data and in situ measurements collected over a wide range of northern arctic vegetation. A statistical relationship was developed between the in situ LAI measurements collected over vegetation plots in northern Canada and their corresponding pixel spectral information from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Furthermore, all Landsat TM and ETM+ data have been pre-normalized to NOAA AVHRR and SPOT VGT data from the growing season of 2005 to reduce any seasonal or temporal variations. Various spectral vegetation indices developed from the Landsat TM and ETM?+?data were analysed in this study. The reduced simple ratio index (RSR) was found to be the most robust and an accurate estimator of LAI for northern arctic vegetation. An exponential relationship developed using the Theil–Sen regression technique showed an R ² of 0.51 between field LAI measurement and the RSR. The developed statistical relationship was applied to a pre-existing Landsat TM 250 m resolution mosaic for northern Canada to produce the final LAI map for northern Canada ecological zones. Furthermore, the 250 m resolution LAI estimates, per ecological zone, were almost generally lower than those of the CCRS Canada-wide VGT LAI maps for the same ecozones. Validation of the map with LAI field data from the 2008 season, not used in the derivation of the algorithm, shows strong agreement between the in situ LAI measurement values and the map-estimated LAI values.     

SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum

This paper describes a computationally fast and accurate technique for the atmospheric correction of satellite measurements in the solar spectrum. The main advantage of the method is that it is several hundred times faster than more detailed radiative transfer models like 5S and that it does not require precalculated look-up tables. The method is especially useful for correcting the huge amounts of data acquired by large-field-of-view high-repetitivity sensors, like the ones on board polar orbiting and geostationary meteorological satellites.

The technique is based on a set of equations with coefficients which depend on the spectral band of the sensor. Semi-empirical formulations are used to describe the different interactions (absorption, scattering, etc.) of solar radiation with atmospheric constituents during its traverse through the atmosphere. Sensor specific coefficients of each equation are determined using a best fit technique against the computations of the 5S code (Simulation of Satellite Signal in the Solar Spectrum, Tanré et al. 1990). Other radiative transfer models could be used. Once coefficients for a specific spectral band are determined, the inputs of the model are vertically integrated gaseous contents, aerosol optical depth at 550 nm, geometric conditions and reflectance at the top of the atmosphere (TOA). TOA reflectances were calculated using our method and then compared to the TOA reflectances calculated by 5S for a wide range of gaseous and aerosol contents, illumination and observation conditions for various sensor spectral bands. In the case of NOAA-9 AVHRR visible data the maximum relative error is 2·35 per cent (i.e. 0·01 for a reflectance value of 0·4) and the corresponding rmse is 0·0018. For NOAA-9 AVHRR near-infrared, Meteosat-1 visible, Landsat-5 TM band 1 and Landsat-5 TM band 4 the maximum relative errors are 3·11, 4·0, 1·65 and 2·37per cent respectively. The corresponding values of the rmse are 0·0022, 0·0015, 0·0017 and 0·0012.

The method can be used both in the direct and in the inverse mode, i.e., to compute TOA reflectance knowing the surface reflectance (e.g., for fast sensitivity studies), or conversely to retrieve surface reflectance from the TOA reflectance. It can easily be implemented in operational data preprocessing computer code, since only band specific coefficients need to be updated when new sensors are flown, while the routines remain the same.     

Technical note: On a slight mis-registration of AVHRR channels 3 and 4

Abstract

When using multispectral techniques that exploit the channels of the Advanced Very-High Resolution Radiometer (AVHRR), it is assumed that the radiation measured in the different channels emanates from the same place. Evidence is presented to suggest that for one particular pass of NOAA-7, there was a mis-registration of about one-quarter of a pixel between AVHRR channels 3 and 4. A simple correction is shown to be effective in this case.     

Thermal remote sensing of near-surface water vapor

In this study, four approaches to estimate atmospheric water vapor from Advanced Very High Resolution Radiometer (AVHRR) observations were tested with data from the Boreal Ecosystem–Atmosphere Study (BOREAS) and the Oklahoma Mesonetwork. The approaches studied were (i) the split-window difference of the thermal channels (Channel 4: 10.3–11.3 μm and Channel 5: 11.5–12.5 μm) by Dalu [Int. J. Remote Sens. 7 (1986) 1089.], (ii) the ratio of variances by Jedlovec [J. Appl. Meteorol. 29 (1990) 863.], (iii) the regression slope by Goward et al. [Ecol. Appl. 4 (1994) 322.], and (iv) a look-up table derived from radiative transfer model output. Although these techniques were primarily developed to estimate total column precipitable water, we used them to estimate near-surface water vapor, within a few meters of the surface. Near-surface water vapor is needed for hydrologic and biospheric modeling. Analysis showed the total column precipitable water to be highly correlated (r²=.79) with near-surface absolute humidity for clear-sky conditions at the BOREAS and the Oklahoma study sites. Correlation of all the retrieval techniques with ground observations was very low. For the split-window approach, water vapor can only be estimated on a per pixel basis and is ambiguous for anything but a single site. The regression slope and variance ratio techniques showed very little correlation with ground observations with r²=.02 when compared with data from BOREAS, and .17 for the variance ratio and .24 for the regression slope when compared with Mesonet data. The spatial variability of water vapor across the landscape hampers the use of these contextual approaches. The highest correlation was for the look-up table approach, with r²=.36 when compared with data from the BOREAS site. The look-up table was applied using AVHRR Channels 4 and 5 brightness temperatures, surface temperature, and near-surface air temperature. Surface temperature and air temperature were both estimated from the satellite readings. Combining the satellite data with air temperature measured at meteorological ground stations improved the correlation to .50. The relatively low r² values were at least partly due to spatial and temporal mismatches between surface and satellite measurements. Simulation of Moderate Resolution Imaging Spectrometer (MODIS) thermal Channels 29 (8.4–8.7 μm), 31 (10.78–11.28 μm), and 32 (11.77–12.27 μm) brightness temperatures showed that Channels 31 and 32 provide similar information as AVHRR Channels 4 and 5. The additional thermal information provided by Channel 29 shows promise for future water vapor detection efforts.     

An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data

Long term observations of global vegetation from multiple satellites require much effort to ensure continuity and compatibility due to differences in sensor characteristics and product generation algorithms. In this study, we focused on the bandpass filter differences and empirically investigated cross-sensor relationships of the normalized difference vegetation index (NDVI) and reflectance. The specific objectives were: 1) to understand the systematic trends in cross-sensor relationships of the NDVI and reflectance as a function of spectral bandpasses, 2) to examine/identify the relative importance of the spectral features (i.e., the green peak, red edge, and leaf liquid water absorption regions) in and the mechanism(s) of causing the observed systematic trends, and 3) to evaluate the performance of several empirical cross-calibration methods in modeling the observed systematic trends. A Level 1A Hyperion hyperspectral image acquired over a tropical forest—savanna transitional region in Brazil was processed to simulate atmospherically corrected reflectances and NDVI for various bandpasses, including Terra Moderate Resolution Imaging Spectroradiometer (MODIS), NOAA-14 Advanced Very High Resolution Radiometer (AVHRR), and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). Data were extracted from various land cover types typically found in tropical forest and savanna biomes and used for analyses. Both NDVI and reflectance relationships among the sensors were neither linear nor unique and were found to exhibit complex patterns and bandpass dependencies. The reflectance relationships showed strong land cover dependencies. The NDVI relationships, in contrast, did not show land cover dependencies, but resulted in nonlinear forms. From sensitivity analyses, the green peak (∼550 nm) and red-NIR transitional (680-780 nm) features were identified as the key factors in producing the observed land cover dependencies and nonlinearity in cross-sensor relationships. In particular, differences in the extents to which the red and/or NIR bandpasses included these features significantly influenced the forms and degrees of nonlinearity in the relationships. Translation of MODIS NDVI to “AVHRR-like” NDVI using a weighted average of MODIS green and red bands performed very poorly, resulting in no reduction of overall discrepancy between MODIS and AVHRR NDVI. Cross-calibration of NDVI and reflectance using NDVI-based quadratic functions performed well, reducing their differences to ± .025 units for the NDVI and ± .01 units for the reflectances; however, many of the translation results suffered from bias errors. The present results suggest that distinct translation equations and coefficients need to be developed for every sensor pairs and that land cover-dependency need to be explicitly accounted for to reduce bias errors.     

New radiance-based method for AVHRR thermal channel nonlinearity corrections

Thermal channels 4 and 5 of the Advanced Very High Resolution Radiometer (AVHRR) on National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites have an onboard calibration process that provides data from which incoming scene radiance is linearly related to AVHRR count output. However, prelaunch calibration tests show that the radiance is more accurately modelled as a quadratic in count value and that the actual quadratic fit depends upon the operating temperature of the AVHRR itself. NOAA has developed a new method to provide prelaunch information to operational data users that is both concise and accurate. It corrects the linear radiance estimate instead of correcting equivalent blackbody temperature values. The nonlinear correction to the linear radiance estimate is provided by a single quadratic equation, independent of the AVHRR temperature. The new method was first applied to the NOAA-14 AVHRR. When corrected radiance estimates for the NOAA-14 AVHRR are compared to precise prelaunch data, the rms difference is 0.14K in channel 4 and 0.08K in channel 5, in temperature units. At present, users of NOAA-14 AVHRR 1b data have to apply the radiance correction themselves, but for the NOAA-15 and future AVHRRs the necessary information is included in the expanded 1b datastream. Direct readout High Resolution Picture Transmission (HRPT) users have to implement the correction process themselves for NOAA-14, NOAA-15 and future AVHRRs.     

Operational AVHRR navigation results

A navigation method that combine a precise image deformation model and an automatic adjustment on coastal landmarks is operationally used at the CMS on AVHRR imagery. This paper presents the principles of the method, the error sources and a detailed analysis of the results obtained in 1996-1997 with NOAA-14 and NOAA-12 images. Both ARGOS or TBUS orbital elements were used and yield similar results. The AVHRR image navigation without any landmark adjustment, is 3.9 km for NOAA-14 and 8.7 km for NOAA-12. The navigation error before ANA, which assumes a constant attitude error, is similar for NOAA-14, 3.5 km, but is greatly reduced for NOAA-12, 2.2 km. The NOAA-14 error is mainly due to clock error temporal variations, poorly known in real time, whereas the NOAA-12 error results from attitude errors of 5 mrad roll and 1.5 mrad yaw. The AVHRR image navigation error after landmark adjustment is 1.7 km. The landmark adjustment gives better performances on NOAA-14 images (with 60.1% success on the current orbit) than on NOAA-12 images (51.7% success); these values are increased to 88.9% for NOAA-14 and 84.0% for NOAA-12 when using the attitude estimated on the preceeding orbit.     

Validation of coastal sea and lake surface temperature measurements derived from NOAA/AVHRR data

An interactive validation monitoring system is being used at the NOAA/NESDIS to validate the sea surface temperature (SST) derived from the NOAA-12 and NOAA-14 polar orbiting satellite AVHRR sensors for the NOAA CoastWatch program. In 1997, we validated the SST in coastal regions of the Gulf of Mexico, Southeast US and Northeast US and the lake surface temperatures in the Great Lakes every other month. The in situ     

Monitoring arid lands using AVHRR-observed visible reflectance and SMMR-37 GHz polarization difference

Abstract

Previous studies have shown the usefulness of visible reflectance observed by varied space-borne sensors for monitoring arid and semi-arid regions of the world, with particular reference to desertification. Visible reflectance along a transect through the Sahel and Sudan zones of Africa has been derived from observations by the advanced very high resolution radiometer (AVHRR) on board the NOAA-7 and NOAA-9 satellites and compared with concurrent observations of the 37 GHz polarization difference by the scanning multichannel microwave radiometer (SMMR) on board the Nimbus-7 satellite. The study period was January 1982 to December 1986, which included an unprecedented drought during 1984 over the Sahel zone. While spatial and temporal patterns of these two data sets are found to be highly correlated, there are also quantitative differences which need to be understood.     

Improved calibration coefficients for NOAA-14 AVHRR visible and near-infrared channels

An analysis of the calibration coefficients used to describe sensor degradation in channels 1 and 2 of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-14 spacecraft is presented. The radiometrically stable permanent ice sheet of central Antarctica is used as a calibration target to characterize sensor performance. Published calibration coefficients and the coefficients imbedded in the NOAA Level 1b data stream for the period January 1995 to November 1998 are shown to be deficient in correcting for the degradation of the sensor with time since launch. Calibration formulae constructed from NOAA-9 reflectances are used to derive improved calibration coefficients for the AVHRR visible and near-infrared channels for NOAA-14. Channel 1 reflectances for the Greenland ice sheet derived using the new coefficients are consistent with those derived previously using NOAA-9 AVHRR. In addition, improved reference AVHRR channel 2 reflectances for Greenland are derived from NOAA-14 observations. It is recommended that the coefficients derived in this study be used to calibrate reflectances for NOAA-14 AVHRR channels 1 and 2.     

The estimation of atmospheric corrections to one-channel (11 μ m) data from the AVHRR; simulation using AVHRR/ 2

It has been established that the sea-surface brightness temperatures Tb₄ in the 11 μ m channel and T_b4in the 12 μ m channel of the Advanced Very High Resolution Radiometer (AVHRR/ 2) are linearly related to a good degree of accuracy, i.e. T_b5= α+ β T_b4 Using AVHRR/ 2 data for various dates and from different parts of the world's oceans, the parameters a and 0 have been determined. The above relation may then be used for simulating T_b5 for those cases for which only Tb4 is available (e.g. for the AVHRR on TIROS-N, NOAA-6, NOAA-8, etc.). The brightness temperature TM and pseudo-brightness temperature T_b5 then enable one to use the split-window technique for estimating atmospherically-corrected sea-surface temperatures (SSTs) from the 11μ m channel data alone. Such an atmospheric correction technique should be a possibility because the 11μ m channel of the AVHRR on the various satellites in question are almost identical

This technique has been used with two split-window algorithms for correcting the data from the 11μ m channel of the AVHRR instrument on the TIROS-N satellite obtained off south-western Portugal. One of the algorithms gives ‘ skin’ temperatures and the other algorithm gives bulk temperatures. The resulting SSTs for twelve dates from 15 June 1979 to 14 June 1980 have been compared with sea-surface (skin) temperatures which were obtained with airborne radiometer data obtained on the same dates.     

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.