NOAA AVHRR image referencing

Abstract

A simple and fast algorithm for image referencing of the NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data has been derived to facilitate the identification of geographic co-ordinates corresponding to any pixel on an NOAA image and vice versa. The procedure assumes a spherical Earth and circular orbit and takes into account the effects due to the Earth's rotation and oblateness and the scan skew. Inputs to the procedure are the ascending nodal longitude and lime, the time of the first scan line and one ground control point (GCP). The effects of an ellipsoid Earth and an elliptical orbit are corrected by using the GCP to adjust the spacecraft altitude and inclination angle. No detailed emphemeris data arc required. The average r.m.s. errors obtained by comparing with independent sets of well-distributed GCPs for each image are about 2 pixels and 2 lines or 3 km displacement. Results from the procedure are illustrated by the rectification of NOAA images over France.     

Validation of MISR land surface broadband albedo

Land surface broadband albedo is a critical variable for many scientific applications. Due to the scarcity of spectral albedo measurements of the Earth's surface environments, it is useful to construct broadband albedo from spectral albedo data obtained by multi‐angle satellite observations. The Multi‐angle Imaging SpectroRadiometer (MISR) onboard NASA's Earth Observing System (EOS) Terra satellite provides land surface albedo products from multi‐angular observations; however, the products have not been comprehensively validated. We convert MISR spectral albedos to total shortwave albedos and validate them using ground measurements at different validation sites. For most surface types, a published narrowband to broadband conversion formula was used, but a new conversion formula for snow and ice covered sites is developed in this study where the spectral range of the instrument is different. Several comparisons are made: (1) between MISR directional‐hemispherical reflectance (DHR) or albedo and MODIS (Moderate Resolution Imaging Spectroradiometer) DHR; and (2) between MISR spectral DHR and bi‐hemispherical reflectance (BHR). The results show that: (1) both the value and the temporal trends of the MISR shortwave albedo and the ground measured shortwave albedo are in good agreement, with the exception of the snow and ice sites; (2) the MISR DHR conforms well to MODIS DHR; and (3) the values of MISR DHR and BHR are nearly identical.     

Identification of land surface temperature and albedo trends in AVHRR Pathfinder data from 1982 to 2005 for northern Siberia

The arctic regions are highly vulnerable to climate change. Climate models predict an increase in global mean temperatures for the upcoming century. The arctic environment is subject to significant changes of the land surface. Especially the changes of vegetation pattern and the phenological cycle in the taiga–tundra transition area are of high importance in climate change research. This study focuses on time series and trend analysis of land surface temperature, albedo, snow water equivalent, and normalized difference vegetation index information in the time period of 1982–2005 for northern Siberia. The findings show strong dependencies between these parameters and their inter-annual dynamics, which indicate changes in vegetation growing period. We found a strong negative correlation between land surface temperature and albedo conditions for the beginning (60–90%) of the growing season for selected hot spot trend regions in northern Siberia.     

Retrieval and validation of land surface temperature (LST) from NOAA AVHRR thermal images of Gujarat,India

A study was undertaken to retrieve land (soil-vegetation complex) surface temperature (LST) over a 100 km 2 100 km area in Gujarat (India) using thermal bands (channel 4 and 5) and estimated emissivity from atmospherically corrected NDVI, derived from NOAA-14 AVHRR data. The LST values were compared with near synchronous soil and air temperature measurements over five sites in December and May 1997 during Land Surface Processes Experiment (LASPEX) in Gujarat, India. The estimated LST of a semi-arid mixed agricultural barren landscape at 10.00 GMT was found to vary from 302 to 305.6 K on 13 December 1997 (winter) and from 317.5 to 328.5 K at 08.30 GMT on 15 May 1997 (Summer). During December, the LST values were near midway between air temperature (AT) and soil surface temperature (ST) with mean bias of m 2.9 K and 7.0 K respectively. However, in May, the LST values were found to be closer to ST, which may be due to lower fractional vegetation cover and NDVI.     

On the monitoring of land surface temperatures with the NOAA/AVHRR: Removing the effect of satellite orbit drift

To study the inter-annual variability of land surface temperature with NOAA Advanced Very High Resolution Radiometer (AVHRR) data, one must account for changes in the observed radiances due to changes in the observation time caused by satellite orbit drift (SOD). This study proposes a simple method to remove the SOD component from the AVHRR thermal IR. Spurious trends in these data should be corrected for to prevent their misidentification as real trends in the Earth's climate system and to infer more reliable conclusions from the inter-annual land surface variability studies, such as monitoring droughts. The proposed correction requires information on the observation solar zenith angle and normalized difference vegetation index for the region of interest.     

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.     

Quantitative dynamic flood monitoring with NOAA AVHRR

The analysis of the spatial extent and temporal pattern of flood inundation from remotely sensed imagery is of critical importance to flood mitigation. With a high frequency of global coverage, NOAA/AVHRR has the advantage of detecting flood dynamics during devastating floods. In this paper, we describe a systematic approach to flood monitoring using AVHRR data. Four critical issues for successful flood monitoring with AVHRR were identified: correct identification of water bodies, effective reduction of cloud contamination, accurate area estimation of flood extent, and dynamic monitoring of flood processes. In accordance with the spectral characteristics of water and land in AVHRR channels, a simple but effective water identification method was developed with the ability to reduce cloud influences. The area of flooded regions was calculated with the consideration of areal distortion due to map projection, and mixed pixels at water/land boundaries. Flood dynamics were analysed from flood distributions in both space and time. The maximum spatial extent of floods, generated by compiling the time series of flood maps, was informative about flood damages. We report a successful use of this approach to monitor the Huaihe river flood, a centennial devastating disaster occurred in the Huaihe river basin of China in the summer of 1991.     

An automatic system for AVHRR land surface product generation

Making products from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar‐orbiting satellites can be time consuming and an automated technique for image processing is required to generate long time series of AVHRR imagery. This paper aims to describe the development of a system for fully‐automated AVHRR image processing, including radiometric calibration, precise geo‐registration and generating land‐surface products, such as vegetation indices, maximum value composites and cloud masks. Tests for crop monitoring purposes were carried out using High Resolution Picture Transmission (HRPT) images between October 2003 and April 2004. The region used to evaluate the system was the State of Paraná, one of the primary soybean producers in Brazil. Results have shown that for severely cloud‐filtered images, the system was effective in generating geometrically precise image products, with geolocation errors less than a pixel. The developed system can be operated with no human intervention and can be used as an important tool for NOAA‐AVHRR image users especially those who need to use long time series.     

Bidirectional reflectance effects in NOAA AVHRR data

The bidirectional reflectance effects in NOAA AVHRR data have been investigated for forest and pasture sites in New Zealand. The impact of surface anisotropy has been examined for channel 1, channel 2, and the derived Normalized Difference Vegetation Index (NDVI) over a 14-day period in the southern hemisphere summer of 1992/1993. Results show a bidirectional effect which persists through atmospheric correction processing and the generation of the NDVI. Comparison is made with previously published results and models, which show consistency for this limited data set.     

Estimating bidirectional angles in NOAA AVHRR images

In studies concerning the surface bidirectional reflectance distribution function (BRDF) and thermal-infrared multiangular emissions, Sun-sensor geometry must be known. This Letter provides a potential and simple method for NOAA Advanced Very High Resolution Radiometer (AVHRR) users to estimate the imaging configuration of each pixel in a geometrically corrected image. Our formulas were tested with example AVHRR data and their precision was shown to be comparatively high with a maximum error of either the satellite zenith or azimuth angle less than 4°. The standard deviation for the zenith is 2.07° and azimuth is 2.47°.     

Multi-spectral classification of snow using NOAA AVHRR imagery

Abstract

Problems of accurate discrimination between snow and cloud, together with the detection of the snow pack boundary, have handicapped the use of satellite data in operational snow-cover mapping systems. A technique, involving an unsupervised clustering procedure, is described which allows the removal of cloud areas using NOAA-9 Advanced Very High Resolution Radiometer (AVHRR) channel-1, channel-3 and channel-4 data in conditions of recent snow lie and a difference channel (channel-2 —channel-1 with channel-3 and channel-4) during periods of advanced snow melt. Accurate delineation of snow extent is provided by the techniques if these specified snow conditions are taken into account. A method for the identification of areas of marginal snow melt is also presented, based on comparisons with Landsat Thematic Mapper data. The classifications also enable the determination of snow areas influenced by cloud shadows and conifer forest in addition to separating areas of differing snow depth and percentage cover.     

Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation

Surface urban heat island (SUHI) is a phenomenon of both high spatial and temporal variability. In this context, studying and monitoring the SUHIs of urban areas through the satellite remote sensing technology, requires land surface temperature (LST) image data from satellite-borne thermal sensors of high spatial resolution as well as temporal resolution. However, due to technical constrains, satellite-borne thermal sensors yield a trade-off between their spatial and temporal resolution; a high spatial resolution is associated with a low temporal resolution and vice versa. To resolve this drawback, we applied in this study four downscaling techniques using different scaling factors to downscale 1-km LST image data provided by the Advanced Very High Resolution Radiometer (AVHRR) sensor, given that AVHRR can offer the highest temporal resolution currently available. The city of Athens in Greece was used as the application site. Downscaled 120-m AVHRR LSTs simulated by the downscaling techniques, were then used for SUHI intensity estimation based on LST differences observed between the main urban land covers of Athens and the city's rural background. For the needs of the study, land cover information for Athens was obtained from the Corine Land Cover (CLC) 2000 database for Greece. Validation of the downscaled 120-m AVHRR LSTs as well of the retrieved SUHI intensities was performed by comparative analysis with time-coincident observations of 120-m LST and SUHI intensities generated from the band 6 of the Thermal Mapper (TM) sensor onboard the Landsat 5 platform. The spatial pattern of the downscaled AVHRR LST was found to be visually improved when compared to that of the original AVHRR LST and to resemble more that of TM6 LST. Statistical results indicated that, when compared to 120-m TM6 LST, the root mean square error (RMSE) in 120-m AVHRR LST generated by the downscaling techniques ranged from 4.9 to 5.3 °C. However, the accuracy in SUHI intensity was found to have significantly improved, with a RMSE value decreasing from 2.4 °C when the original AVHRR LST was utilized, down to 0.94 °C in case that downscaling was applied.     

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     

Noise estimation in NOAA AVHRR maximum-value composite NDVI images

Noise has been estimated in Advanced Very High Resolution Radiometer (AVHRR) maximum-value composite Normalized Difference Vegetation Index (NDVI) products using a geostatistical method calculating the noise as the square root of the nugget variance in a semi-variogram. High noise values (20-50 per cent of the standard deviation) were found in individual scenes, whereas values close to zero were found in a nine-year average image. The higher levels of noise in individual images were present in humid areas particularly during the wet season, indicating that atmospheric effects and cloud contamination may be among the main contributing factors.     

Operational bi-angle approach to retrieve the Earth surface albedo from AVHRR data in the visible band

The inference of surface spectral reflectance using visible observations is complicated mainly because of scattering effects. In the present paper we attempt a solution to the problem of retrieval of surface reflectance from satellite radiance measurements based on a solution of the radiative transfer equation. We have developed an operational method which relies on multiple view angle observations or multiple solar zenith angle observations of the surface to accomplish part of this task in a routine manner. This approach may be used for ERS-2 ATSR visible bands because there will be two view angle observations for the same area at essentially the same time.

This approach can also be used with NOAA satellite AVHRR data by assuming that the distribution of the aerosol does not vary too rapidly as a function of time. Usually it is better to select the AVHRR data from around noon and at dusk in the same day. This approach requires that the visibility of the area should be more than 5 km for multiple solar zenith angle observations applications.     

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.     

Using NOAA AVHRR imagery in assessing water quality parameters

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) instrument, carried by the NOAA-TIROS/N series of operational meteorological satellites can, on a routine basis, provide observational data which allow interpretation in terms of parameters related to water quality. In principle, some, though not all of the algorithms applied to Coastal Zone Colour Scanner data can be transformed for use with AVHRR observations. In combination with the operational character of the NOAA satellites this opens up the way to applications in monitoring of open sea and inland waters. Algorithms and results for some examples of such potential applications are presented.     

人体经络的三维数据模型和动画显示方法研究

本文叙述了人体三维形体的空间数据获得方法，提出一种基于齐次坐标变换的人体经络数据模型，为解决计算机人体经络模型的动画演示和经络穴位的透明叠加显示等问题提供了一条新思路，也为今后进一步应用数学方法或从整体综合的角度去研究经络打下了良好基础，并在分析Ｔａｒｇａ图象和ＦＬＩ动画格式的基础上，实现了它们在Ｗｉｎｄｏｗｓ环境下的显示方法。     

Atmospheric optical depth and water vapour effects on the angular characteristics of surface reflectance in NOAA AVHRR

The effects of atmospheric optical depth and water vapour content on the bidirectional surface reflectance in channel 1 (visible) and channel 2 (near-infrared) of NOAA AVHRR have been analysed using a coupled surface atmosphere reflectance model. Two different cases of surface: (i) bare soil, and (ii) vegetation cover have been considered. In the case of bare soil, both the amplitude and angular distribution of the bidirectional reflectance of the surface are modified at satellite altitude due to scattering caused by atmospheric molecules and aerosols in the two channels and thereby, the directional properties of the surface are smoothed. Whereas, in the case of lawn, in channel 1, the angular variation of surface reflectance is enlarged together with a large augmentation in reflectance amplitude, and in channel 2, a small reduction in amplitude as well as a variation in angular distribution of reflectances are caused due to scattering particularly over large viewing angles and thereby, the directional variations are smoothed. In channel 1, atmospheric scattering reduces the contrast between the soil and vegetation and is very much significant for medium to high aerosol loadings. Atmospheric water vapour reduces the amplitude of the surface bidirectional reflectance without introducing any significant changes in angular distribution of the surface reflectance for both bare soil and vegetation canopy in channel 2.     

NOAA AVHRR and its uses for rainfall and evapotranspiration monitoring

Abstract

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) Global Vegetation Indices (GVI) were used during the 1986 rainy season (June-September) over Senegal to monitor rainfall. The satellite data were used in conjunction with ground-based measurements so as to derive empirical relationships between rainfall and GVI. The regression obtained was then used to map the total rainfall corresponding to the growing season, yielding good results. Normalized Difference Vegetation Indices (NDVI) derived from High Resolution Picture Transmission (HRJT) data were also compared with actual evapotranspiration (ET) data and proved to be closely correlated with it with a time lapse of 20 days.