Combination of the Normalized Difference Vegetation Index and surface temperature for regional scale European Forest cover mapping using AVHRR data

Forest and non-forest samples selected from an existing European forest map were classified using 8 months of cloud-screened European AVHRR data divided into 82 ecological/climatic strata. Consistently higher mean monthly forest/non-forest classification accuracies were found when the samples were classified using Normalized Difference Vegetation Index (NDVI) and surface temperature ( T ) data rather than using NDVI or T data alone.     

Studies on land surface temperature over heterogeneous areas using AVHRR data

The study addresses the use of the split-window method in tropical regions for estimation of surface temperature over heterogeneous surfaces from satellite sensor data. An attempt has been made to derive emissivity in the thermal channels using the NDVI in conjunction with fractional vegetation cover at pixel level. The estimated surface temperature values are compared with the in situ data over the region and are found to be within error limits of +/- 1.8°C. The utility of fractional vegetation cover in controlling surface temperature has been studied for the selected features over the area. The results suggest the utility of emissivity estimated from the NDVI in land surface temperature estimation.     

Land cover mapping and monitoring from NOAA AVHRR data in Bangladesh

NOAA AVHRR HRPT data consisting of two time frames i.e., 1985–86 and 1992–93 were analysed to determine the status of major land cover types of Bangladesh and to monitor change. The data were radiometrically corrected to spectral reflectance and mapped to a consistent Plate Caree projection followed by cloud masking and country masking. The satellite data and the methodology adopted was found to be useful for assessment and monitoring of major land cover types and their dynamics at small scale. The nature and pattern of land cover change derived from the analysis forms a valuable resource for planners and decision-makers in formulating policies, allocating scarce resources and in evaluation of the practical effects of land use policies.     

Integrating AVHRR satellite data and NOAA ground observations to predict surface air temperature: a statistical approach

Ground station temperature data are not commonly used simultaneously with the Advanced Very High Resolution Radiometer (AVHRR) to model and predict air temperature or land surface temperature. Technology was developed to acquire near-synchronous datasets over a 1?000?000?km² region with the goal of improving the measurement of air temperature at the surface. This study compares several statistical approaches that combine a simple AVHRR split window algorithm with ground meterological station observations in the prediction of air temperature. Three spatially dependent (kriging) models were examined, along with their non-spatial counterparts (multiple linear regressions). Cross-validation showed that the kriging models predicted temperature better (an average of 0.9°C error) than the multiple regression models (an average of 1.4°C error). The three different kriging strategies performed similarly when compared to each other. Errors from kriging models were unbiased while regression models tended to give biased predicted values. Modest improvements seen after combining the data sources suggest that, in addition to air temperature modelling, the approach may be useful in land surface temperature modelling.     

Atmospheric correction for the sea surface temperature using NOAA-7 AVHRR and METEOSAT-2 infrared data

The multi-spectral method for atmospheric corrections is investigated. The analysis shows that the non-linear effects of the atmospheric absorption can be accounted for by using three channels. The results clarify and lend theoretical support to previous works. A simple correction procedure is proposed. In the case where channel 3 of the NOAA AVHRR is not available or is contaminated, simultaneous METEOSAT infrared data can be used instead. The result tested with in situ measured temperatures shows a marked improvement when three channels are used. In this procedure, the detailed knowledge of the local atmospheric profiles is not required.     

Estimation of air temperature and reference evapotranspiration using MODIS land surface temperature over Greece

Moderate Resolution Imaging Spectroradiometer (MODIS), land surface temperature data, during daytime (LST_day) or night-time (LST_night), were employed for predicting maximum (T_max) or minimum (T_min) air temperature measured at ground stations, respectively, in order to be used as alternative inputs in minimum data-based reference evapotranspiration (ET) models in 28 stations in Greece during the growing season (May–October). The deviations between daily LST_night and T_min were found to be small, but they were greater between LST_day and T_max. Furthermore, the temperature vegetation index (TVX) method was employed for achieving more accurate T_max values from LST_day, after determining the normalized difference vegetation index of a full canopy (NDVI_max). The TVX method was validated on ‘temporal’ basis, but when the method was tested spatially, the improvement on the T_max estimates from LST_day was not encouraging, for being used operationally over Greece. Thus, LST_day or LST_night MODIS data were used as inputs in three ET models [Hargreaves–Samani, Droogers–Allen, and Reference Evapotranspiration Model for Complex Terrains (REMCT)] and their estimations, as compared with ground-based Penman–Monteith estimates, indicated that the REMCT model achieved the most accurate ET predictions (r = 0.93, mean bias error = 0.44 mm day^–1 and root mean square error = 0.74 mm day^–1), which can allow the spatial analysis of ET at higher spatial resolutions in areas with lack of ground temperature data.     

Relationship between AVHRR surface temperature and NDVI in Arctic tundra ecosystems

A negative, linear relationship between thermal emissions and a spectral vegetation index has been demonstrated for numerous mid‐latitude ecosystems. In this study, it is hypothesized that the relationship between surface temperature and the normalized difference vegetation index (NDVI) will be linear, but positive in Arctic tundra ecosystems due to the contrast between warm vegetation and the cold soil/moss background. This hypothesis is tested using Advanced Very High Resolution Radiometer (AVHRR) data collected over the North Slope of Alaska on three days during the summer of 1999. Results of the study generally provide support for this hypothesis. However, a consistent relationship observed across two contrasting physiographic provinces on one study day was shown to change the following day and could not be readily explained by differences in satellite zenith angle or observed air temperature. Surface temperatures are shown to respond directly to spatial and temporal variations in air temperature.     

A simple method for Are growth mapping using AVHRR channel 3 data

AHVRR Channel 3 data have been used widely for forest fire detection and mapping. However, little attention has been paid to the use of these data for daily fire growth monitoring. A simple method for fire growth mapping using channel 3 data is presented. An 18000 hectare forest fire affecting the Mediterranean coast of Spain is used as a case study. Discrimination of burned area was performed on every image after multitemporal registration. A thermal threshold was established to mask out fire pixels in both diurnal and nocturnal images. GIS overlay techniques were used lo obtain a synthesis map of the daily evolution of the fire. This product can generate valuable input for fire behaviour programmes to improve our understanding of the factors affecting fire spread and fire severity.     

Correcting the orbit drift effect on AVHRR land surface skin temperature measurements

The orbital drift of the National Oceanic and Atmospheric Administration (NOAA)-7, -9, -11, -14 series of satellites results in a significant cooling effect on their afternoon path Advanced Very High Resolution Radiometer (AVHRR) land surface skin temperature (T_s ) measurements. This effect mixes with the signal of true variations in the climate system, and thus prevents T_s from being directly used in climate change and global warming studies. This paper applies a physically based ‘typical pattern technique’ to remove the orbit drift effect from T_s . The technique utilizes a lookup table of representative land skin temperature diurnal cycles derived from the National Center for Atmospheric Research (NCAR) Climate Community Model (CCM3) coupled with the land surface model, Biosphere–Atmosphere Transfer Scheme (BATS). The generated typical patterns of T_s diurnal cycle are functions of vegetation type, season and latitude, and are combined with satellite observations to remove the cooling effect. Applying this methodology to eighteen years of AVHRR (1981–1998) T_s observations yields an improved skin temperature dataset. Analysis of the drift-corrected skin temperature illustrates a warming trend at the surface over the past two decades, a result which agrees well with the observed surface air temperature trend. A discussion of uncertainties in this technique is also presented.     

Size estimates of the factors controlling sea surface temperature with AVHRR data

NOAA AVHRR thermal infrared images, meteorological measurements and model calculations have been used to estimate the magnitude of the processes controlling the sea surface temperature variability in the Bay of Biscay. The estimates are based on the equation of temperature conservation and the equation for the total derivative. The results show that local time change, advection, horizontal diffusion and vertical turbulent flux vary with time and space. The sea surface temperature variability is mainly controlled by vertical turbulent flux and advection. For an average of five-day estimates, the order of the relative importance of the processes is vertical turbulence (38 per cent), advection (32 per cent), local time change (22 per cent) and horizontal diffusion (8 per cent).     

Aircraft validation of ERS-1 ATSR and NOAA-14 AVHRR sea surface temperature measurements

Infrared brightness temperature (BT) measurements obtained from the UK Meteorological Research Flight C-130 Hercules aircraft, spatially coincident and near contemporaneous with ERS-1 Along Track Scanning Radiometer (ATSR) and NOAA-14 Advanced Very High Resolution Radiometer (AVHRR), are presented. These data have been used to obtain much needed on-going validation of the ERS-1 ATSR prior to the de-commissioning of the ATSR instrument in March 1997. BT comparisons between the ATSR and AVHRR nadir channels show negligible differences of 0.3 deg K, indicating that both radiometers are well calibrated. However, significant differences are found when common sea surface temperature (SST) algorithms are applied to the BT data. The original dual view ERS-1 ATSR skin SST (SSST) algorithm has a 0.6K cool bias relative to the in situ observations, which is consistent with other in situ validation studies. New SSST coefficients derived using the same atmospheric transmission model show that, when the appropriate pixel noise contribution is considered in the algorithm derivation, substantially improved SSST is derived from the ERS-1 ATSR. Comparing the NOAA-14 AVHRR non-linear SST (NLSST) and multi-channel SST (MCSST) algorithms to the aircraft data, the non-linear nature of the NLSST algorithm results in a small bias of 0.3 deg K compared to a substantial cool bias of 1 deg K in the MCSST case. This result highlights deficiencies in the MCSST.     

Comparisons of regional satellite sea surface temperature gradients derived from MODIS and AVHRR sensors

Regional sea surface temperature (SST) gradients were examined for a 6-year (2003–2008) period using data from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on two NOAA satellite platforms. Two regions, one in the California Current System and the other in the Gulf Stream, representing an eastern boundary upwelling region and strong western boundary current, respectively, were chosen to investigate the seasonal variability, statistical differences and similarities, and correlations with respect to the two sets of SST gradients. Results indicated higher gradient magnitudes using MODIS SST in relative comparison to those derived from AVHRR that are attributed to instrument and algorithm differences. These observed differences are important for any studies that employ SST gradients, such as fisheries investigations that have traditionally relied on AVHRR SST gradients only.     

Air-sea interacting effects to the sea surface temperature observation by NOAA/AVHRR

Abstract

Abstract. By combining the brightness temperatures by NOAA-9/AVHRR and the sea surface temperature by Mutsu Bay automatic marine monitoring buoy system, the total number of 390 match-ups was set up. The temporal and spatial coincidence in each match-up is within 30 minutes and one pixel resolution. Following to the split-window method, a regression function for the sea surface temperature estimation was calculated and its standard deviation of estimation errors was 0-59°C. The residues were carefully examined with respect to the sensor calibration data and the meteorological data at match-up collections. Then it was found that large errors appeared when differences between the air temperature and the buoy temperature were large. Those were estimated to be caused by the air-sea interacting effects. By removing the match-ups with larger errors, a selected data set with 334 match-ups was prepared and the SST estimation function was recalculated. The standard deviation of errors reduced to 0-34 °C.     

Movements of migrating green turtles in relation to AVHRR derived sea surface temperature

Previous studies have shown that for some populations of marine turtle, individuals move along narrow migration corridors in the open ocean. It has been suggested that these migration corridors may correspond with nearsurface oceanographic features that can be detected by remote sensing. This idea is examined by superimposing the tracks of green turtles (Chelonia mydas) migrating from Ascension Island to Brazil, on sea surface temperature (SST) data derived from Advanced Very High Resolution Radiometer (AVHRR) images. The turtles did not follow specific isotherms during migration nor make turns en-route where specific thermal cues were encountered. These results suggest that for this population, SST plays a minimal role in influencing the exact route that individuals follow.     

Effects of orbital drift on land surface temperature measured by AVHRR thermal sensors

The NOAA series of meteorological satellites that carry the Advanced Very High Resolution Radiometer (AVHRR) suffer from orbital drift so that during each satellite's duty period the overpass time occurs later in the day. Replacement satellites restore the overpass time temporarily, but then it gradually decays. The goals of this paper are to document the effects of variable observation time owing to orbital drift on brightness temperatures (BT) and land surface temperature (LST) calculated from them in the NOAA/NASA Pathfinder AVHRR Land (PAL) data set and to consider possible corrections for the resulting trends and discontinuities in the PAL BT data. The drift effects were found to be greater for bare ground than for vegetated land cover classes, however, significant effects were found for most vegetated classes. The magnitude of the orbital drift effect for most global cover types was at least as large as the other errors that affect LST measurement. A simple empirical correction for observation time based on solar zenith angle (SZA) was used to correct the PAL BT time series following Gutman [Int. J. Remote Sens. 20 (1999a) 3407]. The correction from this method was compared with that predicted by a physically based model and was found to differ in the early part of each satellite's duty cycle. Finally, the impacts of correction on the effective observation time are analyzed and the simple statistical correction was found to suffer from greater variability than has hitherto been recognized. A modification to the statistical correction to adjust the effective observation time is described.     

Seasonal and interannual variability of oceanographic processes in the Gulf of Guinea: An investigation using AVHRR sea surface temperature data

The Gulf of Guinea is situated in a critical position for understanding Atlantic equatorial dynamics. This study investigates seasonal and interannual variability in sea surface temperature (SST) throughout this region, focusing on dynamical ocean processes. A 10.5-year time series of remotely sensed SST data with 4 km spatial resolution from the Advanced Very High Resolution Radiometer (AVHRR) were used for this investigation, as they are sufficient to resolve shelf processes. Firstly, patterns of cloud cover were assessed, then spatio-temporal variability in SST patterns was investigated. Features identified in climatological SST images were the Senegalese upwelling influence, coastal upwelling, tropical surface water, river run-off and fronts. Of particular interest is a shelf-edge cooling along the coast of Liberia and Sierra Leone in February. Interannual variability, assessed using annual mean images, time series decomposition and spectral analysis, showed a quasi-cyclic pattern of warm and cool years, perhaps related to El Niño-type forcing. The results of this study show the usefulness of infrared remote sensing for tropical oceanography, despite high levels of cloud cover and atmospheric water vapour contamination, and they provide evidence for theories of westward movement of the upwelling against the Guinea current and remote forcing of the upwelling.     

Improvements in the estimation of daily minimum air temperature in peninsular Spain using MODIS land surface temperature

Air temperature (T_a) is a key variable in many environmental risk models and plays a very important role in climate change research. In previous studies we developed models for estimating the daily maximum (T_max), mean (T_mean), and minimum air temperature (T_min) in peninsular Spain over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Those models were obtained empirically through linear regressions between daily T_a and daytime Terra-MODIS land surface temperature (LST), and then optimized by including spatio-temporal variables. The best T_mean and T_max models were satisfactory (coefficient of determination (R²) of 0.91–0.93; and residual standard error (RSE) of 1.88–2.25 K), but not the T_min models (R² = 0.80–0.81 and RSE = 2.83–3.00 K). In this article T_min models are improved using night-time Aqua LST instead of daytime Terra LST, and then refined including total precipitable water (W) retrieved from daytime Terra-MODIS data and the spatio-temporal variables curvature (c), longitude (λ), Julian day of the year (JD) and elevation (h). The best T_min models are based on the National Aeronautics and Space Administration (NASA) standard product MYD11 LST; and on the direct broadcast version of this product, the International MODIS/AIRS Processing Package (IMAPP) LST product. Models based on Sobrino’s LST1 algorithm were also tested, with worse results. The improved T_min models yield R² = 0.91–0.92 and RSE = 1.75 K and model validations obtain similar R² and RSE values, root mean square error of the differences (RMSD) of 1.87–1.88 K and bias = 0.11 K. The main advantage of the T_min models based on the IMAPP LST product is that they can be generated in nearly real-time using the MODIS direct broadcast system at the University of Oviedo.     

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     

Impact of AVHRR filter functions on surface temperature estimation from the split window approach

With the development of long-term remotely-sensed data sets through the National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) Pathfinder programme, decadal length studies of the land surface are being conducted. However, data from five different Advanced Very High Resolution Radiometer (AVHRR) instruments and thus five different filter functions for each thermal channel must be used. If variations in sensor filter functions are not taken into account when the split window technique is applied, considerable error in the estimation of land surface temperature can be produced. For example, the Becker and Li (1990) equation was designed to be used with data from the AVHRR instrument on the NOAA-9 satellite. If it is applied to data from an AVHRR sensor other than NOAA-9's, the error in surface temperature can be as large as 2.3 degK.