Land surface temperature is one of the most important parameters in hydrology and agricultural production research. split-window algorithm based on MODIS data was briefly introduced in this paper and applied in Hetao ...
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ISBN:
(纸本)9781510600492
Land surface temperature is one of the most important parameters in hydrology and agricultural production research. split-window algorithm based on MODIS data was briefly introduced in this paper and applied in Hetao Irrigation District. Comparison between data retrieval and field collected data showed that data retrieval could reflect land surface temperature basic accurately. Linear fitting of different time series data can improve retrieval precision effectively. The results provide support for drought forecast, soil moisture monitoring etc in the future.
The National Meteorological Satellite Center in Korea retrieves land surface temperature (LST) by applying the split-window LST algorithm (CSW_v1.0) to Communication, Ocean, and Meteorological Satellite (COMS) data. C...
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The National Meteorological Satellite Center in Korea retrieves land surface temperature (LST) by applying the split-window LST algorithm (CSW_v1.0) to Communication, Ocean, and Meteorological Satellite (COMS) data. Considerable errors were detected under conditions of high water vapor content or temperature lapse rates during validation with Moderate Resolution Imaging Spectroradiometer (MODIS) LST because of the too simplified LST algorithm. In this study, six types of LST retrieval equations (CSW_v2.0) were developed to upgrade the CSW_v1.0. These methods were developed by classifying "dry," "normal," and "wet" cases for day and night and considering the relative sizes of brightness temperature difference (BTD) values. Similar to CSW_v1.0, the LST retrieved by CSW_v2.0 had a correlation coefficient of 0.99 with the prescribed LST and a slightly larger bias of -0.03 K from 0.00K;the root mean square error (RMSE) improved from 1.41 K to 1.39 K. In general, CSW_v2.0 improved the retrieval accuracy compared to CSW_v1.0, especially when the lapse rate was high (mid-day and dawn) and the water vapor content was high. The spatial distributions of LST retrieved by CSW_v2.0 were found to be similar to the MODIS LST independently of the season, day/night, and geographic locations. The validation using one year's MODIS LST data showed that CSW_v2.0 improved the retrieval accuracy of LST in terms of correlations (from 0.988 to 0.989), bias (from -1.009 K to 0.292 K), and RMSEs (from 2.613 K to 2.237 K).
Scientific interest in geophysical information about land surface temperature （LST） is ever increasing, as such information provides a base for a large number of applications, including environmental and agricultura...
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Scientific interest in geophysical information about land surface temperature （LST） is ever increasing, as such information provides a base for a large number of applications, including environmental and agricultural monitoring. Therefore, the research of LST retrieval has become a hot topic. Recent availability of Landsat-8 satel- lite imagery provides a new data source for LST retrieval. Hence, exploring an adaptive method with reliable ac- curacy seems to be essential. In this study, basing on features of Landsat-8 TIRS thermal infrared channels, we re-calculated parameters in the atmospheric transmittance empirical models of the existing split-window algorithm, and estimated the ground emissivity with the help of the land cover classification map of the study area. Further- more, a split-window algorithm was rebuilt by virtual of the estimation model of the updated atmospheric transmit- tance and the ground emissivity, and then a remote sensing retrieval for the LST of Shihezi city in Xinjiang Uygur autonomous region of Northwest China was conducted on the basis of this modified algorithm. Finally, precision validation of the new model was implemented by using the MODIS LST products. The results showed that the LST retrieval from Landsat-8 TIRS data based on our algorithm has a higher credibility, and the retrieved LST is more consistent with the MODIS LST products. This indicated that the modified algorithm is suitable for retrieving LST with competitive accuracy. With higher resolutions, Landsat-8 TIRS data may provide more accurate observation for LST retrieval.
The Extreme Universe Space Observatory (EUSO) is an astronomical telescope that will be hosted by the Japan Experiment Module (JEM) on the International Space Station. The telescope will determine ultrahigh-energy cos...
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The Extreme Universe Space Observatory (EUSO) is an astronomical telescope that will be hosted by the Japan Experiment Module (JEM) on the International Space Station. The telescope will determine ultrahigh-energy cosmic ray properties by measuring the UV fluorescence light generated in the interaction between the cosmic rays and the atmosphere. Therefore, cloud information is crucial for a proper interpretation of the data. To obtain the cloud top height, an infrared (IR) camera is being designed. The design is constrained by JEM-EUSO requirements, which have led to a bi-spectral camera option (10.8- and 12-mu m bands). The bi-spectral design has allowed us to develop a split-window algorithm to correct the atmospheric effects and retrieve the cloud temperature from the brightness temperatures (BTs) in the bands aforementioned. The algorithm has been validated in synthetic scenarios at pixel level. The results show that the algorithm is able to retrieve the temperature with accuracy much better than the requirement of 3K. It has also been tested in two-dimensional scenarios by applying it to moderate resolution imaging spectroradiometer (MODIS) images of BTs in bands 31 similar to those of the IR camera. The retrieved temperatures are in a very good agreement with the temperatures given by MODIS. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE).
This objective of this paper is to estimate atmospheric water vapor (m)) from the latest Landsat 8 Thermal InfRared Sensor (TIRS) image by using a new modified split-window covariance-variance ratio (MSWCVR) method. M...
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ISBN:
(纸本)9781479957750
This objective of this paper is to estimate atmospheric water vapor (m)) from the latest Landsat 8 Thermal InfRared Sensor (TIRS) image by using a new modified split-window covariance-variance ratio (MSWCVR) method. Model analysis showed that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.45g/cm(2) for most atmospheric moisture conditions. The MSWCVR was evaluated by using AERONET ground-measured data and cross-compared with MODIS products in 2013 at forty two ground sites, and results presented that the retrieved wv from TIRS data was highly correlated with but generally larger (about 1.0 g/cm(2)) than two others. The reasons for this uncertainty were mainly ascribed to data systematic noise and radiative calibration error. Future work must pay more attention to the data quality and radiative calibration of Landsat 8 TIRS data.
This objective of this paper is to estimate atmospheric water vapor (wv) from the latest Landsat 8 Thermal InfRared Sensor (TIRS) image by using a new modified split-window covariance-variance ratio (MSWCVR) method. M...
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ISBN:
(纸本)9781479953141
This objective of this paper is to estimate atmospheric water vapor (wv) from the latest Landsat 8 Thermal InfRared Sensor (TIRS) image by using a new modified split-window covariance-variance ratio (MSWCVR) method. Model analysis showed that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.45g/cm~2 for most atmospheric moisture conditions. The MSWCVR was evaluated by using AERONET ground-measured data and cross-compared with MODIS products in 2013 at forty two ground sites, and results presented that the retrieved wv from TIRS data was highly correlated with but generally larger (about 1.0 g/cm~2) than two others. The reasons for this uncertainty were mainly ascribed to data systematic noise and radiative calibration error. Future work must pay more attention to the data quality and radiative calibration of Landsat 8 TIRS data.
We evaluated the precision of land surface temperature (LST) operationally retrieved from the Korean multipurpose geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS). The split-window (SW...
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We evaluated the precision of land surface temperature (LST) operationally retrieved from the Korean multipurpose geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS). The split-window (SW)-type retrieval algorithm was developed through radiative transfer model simulations under various atmospheric profiles, satellite zenith angles, surface emissivity values and surface lapse rate conditions using Moderate Resolution Atmospheric Transmission version 4 (MODTRAN4). The estimation capabilities of the COMS SW (CSW) LST algorithm were evaluated for various impacting factors, and the retrieval accuracy of COMS LST data was evaluated with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The surface emissivity values for two SW channels were generated using a vegetation cover method. The CSW algorithm estimated the LST distribution reasonably well (averaged bias = 0.00 K, Root Mean Square Error (RMSE) = 1.41 K, correlation coefficient = 0.99);however, the estimation capabilities of the CSW algorithm were significantly impacted by large brightness temperature differences and surface lapse rates. The CSW algorithm reproduced spatiotemporal variations of LST comparing well to MODIS LST data, irrespective of what month or time of day the data were collected from. The one-year evaluation results with MODIS LST data showed that the annual mean bias, RMSE and correlation coefficient for the CSW algorithm were -1.009 K, 2.613 K and 0.988, respectively.
On the basis of the radiative transfer theory, this paper addressed the estimate of Land Surface Temperature (LST) from the Chinese first operational geostationary meteorological satellite-FengYun-2C (FY-2C) data in t...
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On the basis of the radiative transfer theory, this paper addressed the estimate of Land Surface Temperature (LST) from the Chinese first operational geostationary meteorological satellite-FengYun-2C (FY-2C) data in two thermal infrared channels (IR1, 10.3-11.3 mu m and IR2, 11.5-12.5 mu m), using the Generalized split-window (GSW) algorithm proposed by Wan and Dozier (1996). The coefficients in the GSW algorithm corresponding to a series of overlapping ranging of the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST were derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The simulation analysis showed that the LST could be estimated by the GSW algorithm with the Root Mean Square Error (RMSE) less than 1 K for the sub-ranges with the Viewing Zenith Angle (VZA) less than 30 degrees or for the sub-rangs with VZA less than 60 degrees and the atmospheric WVC less than 3.5 g/cm(2) provided that the Land Surface Emissivities (LSEs) are known. In order to determine the range for the optimum coefficients of the GSW algorithm, the LSEs could be derived from the data in MODIS channels 31 and 32 provided by MODIS/Terra LST product MOD11B1, or be estimated either according to the land surface classification or using the method proposed by Jiang et al. (2006);and the WVC could be obtained from MODIS total precipitable water product MOD05, or be retrieved using Li et al.' method (2003). The sensitivity and error analyses in term of the uncertainty of the LSE and WVC as well as the instrumental noise were performed. In addition, in order to compare the different formulations of the split-window algorithms, several recently proposed split-window algorithms were used to estimate the LST with the same simulated FY-2C data. The result of the intercomparsion showed that most of the algorithms give comparable result
For argumentation of feasibility of LST (Land Surface Temperature) retrieval using 8-10 mu m infrared band, this paper focuses on design of long-wave infrared band based on theory research. Basis of thermal infrared r...
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ISBN:
(纸本)9780819493170
For argumentation of feasibility of LST (Land Surface Temperature) retrieval using 8-10 mu m infrared band, this paper focuses on design of long-wave infrared band based on theory research. Basis of thermal infrared radiative transfer and atmospheric simulation, the paper analyses atmospheric effect on different long-wave infrared and obtain a preliminary selection of potential spectral channels. Several configurations of long-wave infrared spectral band were selected to perform in split-window algorithm and the relation of LST retrieval precision with error source was analyzed. Results indicate the scheme of LST retrieval using 8.0-9.0 mu m long-wave infrared is feasibility for needed retrieval precision.
Retrieving land-surface temperature with split-window algorithm was firstly applied to NOAA-AVHRR data. With the application of MODIS sensor, its data has been used more and more widely. Since MODIS sensor is able to ...
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ISBN:
(纸本)9780819473356
Retrieving land-surface temperature with split-window algorithm was firstly applied to NOAA-AVHRR data. With the application of MODIS sensor, its data has been used more and more widely. Since MODIS sensor is able to observe vapor in the air, it can provide the parameters including vapor content and atmospheric transmissivity for split-window algorithm which can thus be applied more conveniently. The article, adopting the split-window algorithms of Becker-Li(1990), Sobrino(1991) and Qin Zhihao(2005), retrieves the surface temperature at daytime and nighttime with MODIS1B data and compares with the surface temperature products of NASA. Finally, the algorithm of Qin Zhihao is demonstrated to be the one with higher accuracy at daytime and nighttime and the algorithm for surface temperature at nighttime is simple with acceptable accuracy.
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