Split-window Channels Research Articles

Development and evaluation of SST algorithms for GOES-R ABI using MSG SEVIRI as a proxy

Cross-evaluation of sea surface temperature (SST) algorithms was undertaken using split-window channels of Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) as a proxy for the Geostationary Operational Environmental Satellites-R (GOES-R) Advanced Baseline Imager (ABI). The goal of the study was to select the algorithm which provides the highest and the most uniform SST accuracy within the area observed by the geostationary sensor. The previously established algorithms, such as Non-Linear Regression (NLR) and Optimal Estimation (OE) were implemented along with two new algorithms, Incremental Regression (IncR) and Corrected Non-Linear Regression (CNLR), developed within preparations for the GOES-R ABI mission. OE, IncR and CNLR adopt the first guesses for SST and brightness temperatures (BT) and retrieve deviations of SST from the first guess (increments). OE retrieves SST increments with inversion of the radiative transfer model, whereas CNLR and IncR use regression equations. The difference between CNLR and IncR is that CNLR uses NLR coefficients, whereas IncR implies optimization of coefficients specifically for incremental formulation. Accuracy and precision of SST retrievals were evaluated by comparison with drifting buoys. The major observations from this study are as follows: 1) all algorithms adopting first guesses for SST and BTs are capable of improving SST accuracy and precision over NLR; and 2) IncR delivers the highest overall SST precision and the most uniform distributions of regional SST accuracy and precision. This paper also addresses implementation and validation issues such as bias correction in simulated BTs; preserving sensitivity of incremental SST retrievals to true SST variations; and selection of criteria for optimization and validation of incremental algorithms.

The analysis of water vapor distribution over Taklimakan Desert

Up to now, all analysis of the distribution of water vapor over the Taklimakan desert area only depends on limited ground measurements and radio soundings setting mostly on the outer margin area. This paper establishes an approach to retrieve the water vapor over the desert at high temporal and spatial resolutions by the use of FY2C geostationary satellite split-window channels in cooperation with ground-based GPS water vapor measurement. Results show that the water vapor distribution over the Taklimakan desert is affected highly by topography and surface properties. The outer margin area has generally more water vapor than the inner area. Over the outer margin area, the western part has more water vapor than the eastern part, and the northern part has more than the southern part. The driest area lies to the south of Tazhong, east of Hotan River, and extended to the south boundary of the desert. Similar to elsewhere, water vapor over the desert area shows diurnal, monthly, seasonal and annual variations even at the driest inner area of the desert. In summer, the water vapor is transported from west to east over a long distance along the westerlies at a height between 700–400 hPa and with the average speed of 50 km h−1.

Fog detection using geostationary satellite data: Temporally continuous algorithm

A fog detection algorithm that uses geostationary satellite data has been developed and tested. This algorithm focuses on continuous fog detection since temporal discontinuities, especially at dawn and dusk, are a major problem with current fog detection algorithms that use satellite imagery data. This is because the spectral radiance at 3.7 μm contains overlapping emissive and reflectance components. In order to determine the radiance at 3.7 μm under fog conditions, radiative transfer model simulations were performed. The results showed that the radiance at 3.7 μm obviously varies with the solar zenith angle, and the brightness temperature differences between 3.7 μm and 10.8 μm are completely dissimilar between day and night (positive and varying with the angle during the daytime, but negative and constant at night). In this algorithm, a dynamic threshold is used as a function of the solar zenith angle. Moreover, additional criteria such as infrared, split-window channels, and a water vapor channel are used to remove high-level clouds. Also, the visible reflectance (0.67 μm) channel is used in the daytime algorithm because visible channel images are very practical for confirming a fog area with the high reflectivity and the smooth texture. The clear-sky visible reflectance for the previous 15 days was also employed to eliminate the surface effect that appeared during dawn and dusk. As the results, fog areas were estimated continuously, allowing the lifecycle of the fog system, from its development to decline, to appear obviously in the resulting images. Moreover, the estimated fog areas matched well with surface observations, except in a high latitude region that was covered by thin cirrus clouds.

Water vapor correction to improve the operational calibration for NOAA AVHRR/3 channel 2 (0.85 µm) over a desert target

The Advanced Very High Resolution Radiometers (AVHRRs) flown on the National Oceanic and Atmospheric Administration (NOAA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) polar satellites have been providing invaluable data for Earth system science and global change studies. However, the use of AVHRR solar reflectance products (e.g., normalized difference vegetation index (NDVI)) is constrained by calibration uncertainty, largely owing to the lack of an on-orbit calibration device for solar reflectance channels. Since the mid-1990s, NOAA has been applying operational calibration to these channels using the time series of top-of-atmosphere (TOA) measurements of the Libyan Desert site. However, the assumed radiometric stability of Libyan Desert TOA measurements can be disrupted by short-term variations in atmospheric components, especially for AVHRR channel 2, which has a spectral response function covering water vapor absorption lines in the near-infrared (NIR) spectra. This study aims to improve the calibration accuracy of AVHRR channel 2 data by applying water vapor correction over the homogeneous desert target surface using the water vapor content (W) derived from the linear relationships between the AVHRR split-window temperature difference (ΔT) and the Moderate Resolution Imaging Spectroradiometer (MODIS) NIR water vapor product. Results show that the linear ΔT–W relationship is affected by the temperature lapse rate at the satellite over-passing time and the spectral response functions of AVHRR split-window channels. Water vapor correction reduces the calibration uncertainty from 2.6%–3.5% to 1.7%–1.8%, and about 1.0% surface-based relative calibration accuracy is independent of the water vapor uncertainty introduced by the ΔT–W regression. Furthermore, the short-term variations in the channel 2 operational degradation rate are largely reduced after water vapor correction. Thus, implementing water vapor correction can improve the operational calibration accuracy for AVHRR channel 2.

Using CALIPSO to explore the sensitivity to cirrus height in the infrared observations from NPOESS/VIIRS and GOES‐R/ABI

This paper demonstrates how the availability of specific infrared channels impacts the ability of two future meteorological satellite imagers to estimate cloud‐top pressure. Both of the imagers are planned for launch by the United States, one for a geostationary platform and the other for a polar‐orbiting platform. The geostationary imager, the Advanced Baseline Imager (ABI), will be flown first on the GOES‐R platform. In addition to the split window channels at 11 and 12 μm, it has one spectral channel located at 13.3 μm where there is relatively strong absorption of H2O and CO2. The polar‐orbiting imager, called the Visible/Infrared Imager Radiometer Suite (VIIRS) and flown on the National Polar‐Orbiting Environmental satellite Suite (NPOESS), has spectral channels in window regions only. The lack of an absorbing channel on VIIRS is shown to have negative consequences for the inference of cloud‐top pressure. This paper investigates the impact on the ability of a satellite imager such as VIIRS to confidently estimate cloud‐top pressure due to the absence of infrared absorption channels. The solution space is defined as the depth of the atmospheric layer in which a cloud can be placed where the calculated top‐of‐atmosphere radiances match the measurements used in the cloud‐top pressure retrieval. For optically thin cirrus, the channels used by the operational VIIRS algorithm provide a solution space of over 200 hPa. However, the inclusion of the single CO2 channel at 13.3 μm on the ABI narrows the solution space to under 30 hPa. Our imager‐based analysis is performed using Moderate Resolution Imaging Spectroradiometer (MODIS) data, which provides the relevant channel information with sufficient spatial resolution and radiometric accuracy. Additional results are provided using data from the current GOES and POES imagers. Active lidar data from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation/Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIPSO/CALIOP) observations are used to provide cloud boundaries for verification.

ABSOLUTE RADIOMETRIC CALIBRATION OF FY-2C INFRARED SPLIT-WINDOW CHANNELS BY USING SEA BUOY DATA AND NCEP REANALYSIS DATA

A method of absolute radiometric calibration for FY-2C infrared split-window channels by using sea buoy data and NCEP reanalysis data was introduced.In 2006,ten times remote sensing data were selected to obtain the radiometric calibration coefficients.The calibration results obtained from this new method were compared with the product brightness temperature provided by FY-2C.The results show that the biggest differences of the brightness temperatures at the top of atmosphere(TOA) obtained from these two suits of calibration coefficients are mainly located at the low surface temperature pixels,such as the top of clouds and snow covered areas,however,the calibration differences are small in the normal temperature pixels of land and sea surface areas,and the differences of TOA brightness temperature are mainly around 2K.This new vicarious calibration method can increase the calibration frequency and provide a methodological foundation for the real-time radiometric calibration of FY-2C infrared split-window channels.

Intercalibration of the split-window channels of SVISSR/FY-2C against AIRS/Aqua channels

Intercalibration of the split-window channels of SVISSR/FY-2C against AIRS/Aqua channels

Evaluating the calibration of MTSAT‐1R infrared channels using collocated Terra MODIS measurements

The calibration of four MTSAT‐1R infrared channels was evaluated by comparing MTSAT measurements with Terra/MODIS inferred MTSAT‐equivalent brightness temperatures during August 2005 and August 2006. Theoretical relationships converting MODIS brightness temperatures to MTSAT‐equivalent values were obtained and used for the comparison. Results indicate that MTSAT two split window channels are well calibrated, and no serious systematic errors or biases are found; and the MTSAT water‐vapour channel shows a good linear relationship but with a warm bias up to 2 K. The significant cold bias of MTSAT 3.7 µm channel about −6.7 K in August 2005 is found to be much removed in August 2006, after correction of the electrical crosstalk between MTSAT‐1R SWIR channel and WV channel starting from March 2006. Since then, calibration performances of MTSAT‐1R split window channels and shortwave IR channel seem to be comparable with MODIS calibration, while the water‐vapour channel shows more uncertainties up to 2 K of bias.

Operational retrieval of Asian sand and dust storm from FY-2C geostationary meteorological satellite and its application to real time forecast in Asia

Abstract. This paper describes an operational retrieval algorithm for the sand/dust storm (SDS) from FY-2C/S-VISSR (Stretched-Visible and Infrared Spin-Scan Radiometer) developed at the National Satellite Meteorological Center (NSMC) of China. This algorithm, called Dust Retrieval Algorithm based on Geostationary Imager (DRAGI), is based on the optical and radiative physical properties of SDS in mid-infrared and thermal infrared spectral regions as well as the observation of all bands in the geostationary imager, which include the Brightness Temperature Difference (BTD) in split window channels, Infrared Difference Dust Index (IDDI) and the ratio of middle infrared reflectance to visible reflectance. It also combines the visible and water vapor bands observation of the geostationary imager to identify the dust clouds from the surface targets and meteorological clouds. The output product is validated by and related to other dust aerosol observations such as the synoptic weather reports, surface visibility, aerosol optical depth (AOD) and ground-based PM10 observations. Using the SDS-IDD product and a data assimilation scheme, the dust forecast model CUACE/Dust achieved a substantial improvement to the SDS predictions in spring 2006.

Thermal Land Surface Emissivity Retrieved From SEVIRI/Meteosat

The methodologies used by the Satellite Application Facility on Land Surface Analysis (Land SAF) for retrieving emissivity are presented here. In the first approach, i.e., the vegetation cover method (VCM), the land surface emissivity (EM) is computed for Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared channels and for the 3- to 14- range using information on the pixel fraction of vegetation cover (FVC). The VCM uses a lookup table, which takes into account the channel's spectral response function, and laboratory reflectance spectra for different materials. The accuracy of the VCM depends on the reliability of FVC and the land cover classification. The EM for SEVIRI split-window channels is primarily used as an internal product by Land SAF for land surface temperature (LST) estimations. However, sensitivity studies show that LST often fails to meet the required accuracy of 2 K over desert and semiarid regions, where the VCM is unable to model the EM spatial variability, which is mostly associated with soil composition. Moreover, it is also over such areas where the atmosphere is generally dry that the impact of EM uncertainties on LST is largest. A second approach to determine the EM for SEVIRI split-window channels is currently being tested. This methodology allows the simultaneous retrieval of LST and channel EMs with the assumption that the latter remain constant. The channel EMs are then averaged over a 22-day period to filter out the noise in the retrievals. A first analysis of the maps obtained for an area within Northern Africa shows spatial patterns with features also present in the surface albedo.

Total water vapor column retrieval from MSG-SEVIRI split window measurements exploiting the daily cycle of land surface temperatures

A physically based algorithm for the retrieval of total water vapor column (TWC) over cloud-free land surfaces proposed by Kleespies and McMillin [Kleespies, J.T., McMillin L.M. (1990). Retrieval of precipitable water from observations in the Split Window over varying surface temperatures. Journal of Applied Meteorology, 29, 851–862.] is evaluated and extended for use in atmospheric correction and surface irradiance calculation schemes. Thermal infrared split window channels at 10.8 and 12.0 μm of the MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager) instrument are used. The proposed algorithm takes advantage of the improved measurement capabilities of the MSG-SEVIRI instrument with its 15 min temporal resolution and its radiometric accuracy of 0.25 K and 0.37 K in the 10.8 and 12.0 μm channels. The temporal resolution allows exploitation of the daily land surface temperature variation. There is no further need for explicit auxiliary information on air and land surface temperatures, which is difficult to obtain on an operational basis. Updated coefficients for the split window parameterization are derived based on simulations of ‘top-of-atmosphere’ SEVIRI brightness temperatures for the globally representative Thermodynamic Initial Guess Retrieval (TIGR3) set of radiosonde profiles. It turns out that the linear dependency on the transmission ratio in both split window channels as originally proposed by Kleespies & McMillin [Kleespies, J.T., McMillin L.M. (1990). Retrieval of precipitable water from observations in the Split Window over varying surface temperatures. Journal of Applied Meteorology, 29, 851–862.] has to be extended towards a non-linear approach in order to make it applicable to the full range of global atmospheric conditions. Sensitivity studies reveal that the parameterization relies on the availability of input brightness temperatures with a variation larger than approximately 5 K during the daily cycle. The new TWC algorithm was tested with MSG-SEVIRI data for European and African regions for the period March–August 2004 and compared with radiosonde data. The results show that the algorithm is capable of producing TWC values with a mean bias of − 0.2 mm and an RMSE of 6.8 mm. From the total amount of 2583 coincidences for all viewing zenith angles both for winter and summer conditions, 82% were within a ± 5 mm and 94% were within a ± 10 mm difference interval between MSG-based and radiosonde-based TWC. A second comparison to European GPS measurements for the same period from March to August 2004 reveals a bias of − 3.0 mm and an RMSE of 6.0 mm. This result is obtained for 11 UTC GPS measurements which proved to match best the MSG-TWC values. Comparing MSG-TWC to daily cloud-free mean GPS values shows a lower bias of − 2.56 mm and an increased RMSE of 6.6 mm. These findings support the usefulness of the new MSG-based algorithm for surface irradiance calculations and atmospheric correction purposes.

Estimation of diurnal air temperature using MSG SEVIRI data in West Africa

Spatially distributed air temperature data with high temporal resolution are desired for several modeling applications. By exploiting the thermal split window channels in combination with the red and near infrared channels of the geostationary MSG SEVIRI sensor, multiple daily air temperature estimates can be achieved using the contextual temperature–vegetation index method. Air temperature was estimated for 436 image acquisitions during the 2005 rainy season over West Africa and evaluated against in situ data from a field test site in Dahra, Northern Senegal. The methodology was adjusted using data from the test site resulting in RMSE = 2.55 K, MBE = − 0.30 K and R 2 = 0.63 for the estimated versus observed air temperatures. A spatial validation of the method using 12 synoptic weather stations from Senegal and Mali within the Senegal River basin resulted in overall values of RMSE = 2.96 K, MBE = − 1.11 K and R 2 = 0.68. The daytime temperature curve is interpolated using a sine function based on the multiple daily air temperature estimates from the SEVIRI data. These estimates (covering the 8:00–20:00 UCT time window) were in good agreement with observed values with RMSE = 2.99 K, MBE = − 0.70 K and R 2 = 0.64. The temperature–vegetation index method was applied as a moving window technique to produce distributed maps of air temperature with 15 min intervals and 3 km spatial resolution for application in a distributed hydrological model.

Retrieval of Optical Thickness and Effective Particle Radius of Thin Low-Level Water Clouds using the Split Window of Meteosat-8

We developed a method to retrieve the optical thickness (τ) and effective particle radius (re) of water clouds using the split-window channels and the 8.7-µm channel of Meteosat-8. Valid ranges are approximately from 1 to 9 for τ and smaller than 18 µm for re. Water clouds were first identified using 8.7-µm and 11-µm data. The retrieval used the brightness temperature (TBB) and brightness temperature difference (BTD) between the split windows, as computed with the radiation code RSTAR5b for various properties of water clouds and vertical profiles of temperature and water vapor. Arch-shaped curves in the TBB and BTD domains resembled those for ice clouds, depending on τ and re. The retrieved cloud parameters were then compared to those retrieved by the solar reflection method, which uses the 0.6-, 3.9-, and 11-µm channels of Meteosat-8. Comparison of the two methods revealed that the split-window technique could capture spatial features for both τ and re, showing some agreement with re for the solar reflection but needing more improvement for τ in the current case study.

Periodic Time Variation of a Low-TBB Cloud Area in Typhoon

In this paper, the evolution of the cloud activities for six typhoons in 1997 is examined, utilizing TBB (equivalent Black Body temperature) data of GMS (Geostationary Meteorological Satellite)-5 split window channels. As the result, periods of 24-hour and about 10-hour are found in the time variation of the cloud areas whose cloud top temperature is lower. Diurnal variation is found most distinctly in the time series of the cloud area of TBB lower than -40°C, which corresponds to the one whose cloud top gets at the upper level of the troposphere. The maximum and the minimum peaks of this variation appear around at 1500 JST (Japan Standard Time) and 0800 JST. And, the maximum appears corresponding to vigorous convections at 0600-1000 JST of all the vigorous convections. About 10-hour period time variation is found most distinctly in the time series of the cloud areas of TBB lower than -60°C, which corresponds to the one whose cloud top gets at the upper level of tropopause or over. The maximum of this variation appears corresponding to a preceding vigorous convective activity which occurs in the wide region of the typhoon a few hours before.

Radiative Effects of Various Cloud Types as Classified by the Split Window Technique over the Eastern Sub-tropical Pacific Derived from Collocated ERBE and AVHRR Data.

The radiative effects of several cloud types as classified by the split window (11 and 12 μm) technique were studied using coincident and collocated Earth Radiation Budget Experiment (ERBE) S-8 data and Advanced Very High Resolution Radiometer (AVHRR) data from NOAA-9. The parameter investigated was cloud radiative forcing (CRF), the difference between clear and cloudy shortwave flux (SW) and longwave flux (OLR) at the top of the atmosphere. In computing the CRF, the accuracy of clear SW and OLR is essential. Clear scene IDs in the ERBE dataset were evaluated using coincident and collocated AVHRR image data. The mean visible reflectance and SW for clear footprints defined by the ERBE are reasonably small and are 3.2% and 89.0 Wm-2, respectively. However, the values computed using our technique are smaller, 2.7% and 83.9 Wm-2, respectively. The use of collocated AVHRR image data improves clear footprint definition and implies that care should be taken when computing CRF from ERBE data alone. The CRF from several cloud types classified by the split window were compared. Cumulonimbus clouds show the largest impact on top of the atmosphere radiation for both SW and OLR. Cirrus and lowlevel cumulus clouds have similar effects on OLR, but large differences between them are seen for SW. The impact of low-level cumulus clouds on SW is much larger than that of cirrus clouds. Some optically thin cirrus clouds show positive cloud radiative forcing (warming effect). The relationships between OLR and cloud types (including cloud-free) as classified by the split window technique were investigated. By using brightness temperature differences between the split window channels, OLR estimation is improved for cloud-free and low-level cumulus clouds when compared with OLR estimated by the National Oceanic and Atmospheric Administration (NOAA) operational algorithm.

Split-window and multi-angle methods of sea surface temperature determination: An analysis

A physical model of split-window and multi-angle algorithms for the retrieval of sea surface temperature has been obtained. It is shown that optical thickness in the thermal infrared window region is approximately represented by a separable function of wavelength and atmospheric variables which greatly simplify the radiative transfer model. In modelling the temperature retrieval algorithms we exploit the correlation that exists between the sea surface temperature and the average temperature of the atmosphere. It is shown that, to the extent this correlation is maintained, there is a simple relationship between surface temperature and the brightness temperatures measured in two split-window or multi-angle channels. The different atmospheric conditions which cause spread in this relationship are briefly discussed. It is shown that measurements at three optical thicknesses instead of two as in the case of the split-window method improve the SST retrieval accuracy since it can take care of non-equilibrium conditions such as inversions, surface instabilities, etc., prevailing in the atmosphere.

Simultaneous Land Surface Temperature–Emissivity Retrieval in the Infrared Split Window

Abstract A combined land surface temperature–emissivity retrieval algorithm is developed and tested for Geostationary Operational Environmental Satellite (GOES)-Imager and National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) split-window channels. By assuming that the spectral emissivities are constant over a short time period (12–24 h), two sets of split-window radiance measurements taken at two different times are used to retrieve two spectral emissivities and two land surface temperatures (LSTs) simultaneously. The algorithm employs an optimization scheme rather than a direct solver for a system of equations because of constraint requirements. The retrieved variables minimize the rms differences between measured satellite radiances and those predicted by a spectrally detailed radiative transfer model. A GOES-8 version of the algorithm is validated with in situ radiometer measurements from the Department of Energy’s Atmospheric Radiation Measurement Program Cl...

Vicarious calibration of the Moderate-Resolution Imaging Spectroradiometer Airborne Simulator thermal-infrared channels

We made an experimental vicarious calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) thermal infrared (TIR) channel data acquired in the field campaign near Mono Lake, Calif. on 10 March 1998 to demonstrate the advantage of using high-elevation sites in dry atmospheric conditions for vicarious calibration. With three lake-surface sites and one snow-field site, we estimated the MAS noise-equivalent temperature difference as 0.7-1.0 degrees C for bands 30-32 in the 3.68-4.13-microm region and 0.1-0.5 degrees C for bands 42, 45, 46, and 48 in the 8-13.5-microm region. This study shows that the MAS calibration error is within +/-0.4 degrees C in the split-window channels (at 11 and 12 microm) and larger in other TIR channels based on the MAS data over Mono Lake and in situ measurement data over the snow-field site.

An Assessment of the Accuracy of Land Surface Temperature Determination from the GMS-5 VISSR

The Japanese Geostationary Meterological Satellite 5 (GMS-5) carries the Visible and Infrared Spin Scan Radiometer (VISSR), which has two thermal infrared channels within the “so-called” atmospheric window. These channels are similar to the split-window channels of the AVHRR-2 and are potentially useful for determining surface temperatures. The purpose of the present work is to determine the accuracy of land surface temperatures derived from the GMS-5 VISSR. For a period of 10 clear days during March 1997 hourly digital data from the GMS-5 VISSR are analyzed over two ground-truth sites in Australia. The satellite and ground-truth data are used to test the efficacy of a variety of published land surface temperature (LST) algorithms. NOAA AVHRR-2 data are also used to compare and contrast the accuracies of the LST estimates from these instruments. The results show that LST can be determined from the GMS-5 VISSR to root-mean-squared accuracy of 2–3°C with little bias. Two land surface temperature algorithms are validated and presented for use with the GMS-5 VISSR split-window radiances.

Comment on “Comparison of atmospheric correction models for thermal bands of the advanced very high resolution radiometer over FIFE” by S. N. Kalluri and R. O. Dubayah

Journal of Geophysical Research: AtmospheresVolume 103, Issue D6 p. 6237-6241 CommentariesFree Access Comment on “Comparison of atmospheric correction models for thermal bands of the advanced very high resolution radiometer over FIFE” by S. N. Kalluri and R. O. Dubayah A. J. Prata, A. J. PrataSearch for more papers by this author A. J. Prata, A. J. PrataSearch for more papers by this author First published: 01 March 1998 https://doi.org/10.1029/96JD03657Citations: 3AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat References Becker, F., The impact of spectral emissivity on the measurement of land surface temperature from a satellite, Int. J. Remote Sens., 8, 1509– 1522, 1987. Becker, F., Z. L. Li, Towards a local split window method over land surfaces, Int. J. Remote Sens., 3, 369– 393, 1990. Coll, C., V. Caselles, J. A. Sobrino, E. Valor, On the atmospheric dependence of the split-window equation for land surface temperature, Int. J. Remote Sens., 151, 105– 122, 1994. Goetz, S. J., R. N. Halthore, F. G. Hall, B. L. Markham, Surface temperature retrieval in a temperate grassland with multiresolution sensors, J. Geophys. Res., 100, 25397– 25410, 1995. Kalluri, S. N. V., R. O. Dubayah, Comparison of atmospheric correction models for thermal bands of the advanced very high resolution radiometer over FIFE, J. Geophys. Res., 100, 25411– 25418, 1995. Kneizys, F. X., et al., Users guide to LOWTRAN-7AFGL Tech. Rep. AFGL-TR-88-0177Opt./Infrared Technol. Div., U. S. Air Force Geophys. Lab., Hanscom Air Force Base, Mass., 1988. Marlatt, W. E., Remote and in situ temperature measurements of land and water surfaces, J. Appl. Meteorol., 6, 272– 279, 1967. McClain, E. P., W. G. Pichel, C. C. Walton, Z. Ahmad, J. Sutton, Multichannel improvements to satellite derived global sea surface temperatures, Adv. Space Res., 2, 43– 47, 1983. McMillin, L. M., D. S. Crosby, Theory and validation of the multiple window sea surface temperature technique, J. Geophys. Res., 89, 3655– 3661, 1984. Prata, A. J., Land surface temperatures derived from the AVHRR and ATSR, 1, Theory, J. Geophys. Res., 98, 16689– 16702, 1993. Prata, A. J., Land surface temperatures derived from AVHRR and ATSR, 2, Experimental results and validation of AVHRR algorithms, J. Geophys. Res., 99, 13025– 13058, 1994. Price, J. C., Land surface temperature measurements from the split window channels of the NOAA-7/AVHRR, J. Geophys. Res., 89, 7231– 7237, 1984. Sobrino, J. A., C. Coll, V. Caselles, Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5, Remote Sens. Environ., 38, 19– 34, 1991. Ulivieri, C., M. M. Castronuovo, R. Franctoni, A. Cardillo, A split-window algorithm for estimating land surface temperature from satellites, Adv. Space. Res., 143, 59– 65, 1994. Citing Literature Volume103, IssueD627 March 1998Pages 6237-6241 ReferencesRelatedInformation