Готові списки джерел за темами / Algorithme split-window

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Добірка наукової літератури з теми "Algorithme split-window"

Автор: Grafiati

Опубліковано: 15 березня 2025

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Статті в журналах з теми "Algorithme split-window"

Pinker, Rachel T., Donglian Sun, Meng-Pai Hung, Chuan Li, and Jeffrey B. Basara. "Evaluation of Satellite Estimates of Land Surface Temperature from GOES over the United States." Journal of Applied Meteorology and Climatology 48, no. 1 (January 1, 2009): 167–80. http://dx.doi.org/10.1175/2008jamc1781.1.

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Abstract A comprehensive evaluation of split-window and triple-window algorithms to estimate land surface temperature (LST) from Geostationary Operational Environmental Satellites (GOES) that were previously described by Sun and Pinker is presented. The evaluation of the split-window algorithm is done against ground observations and against independently developed algorithms. The triple-window algorithm is evaluated only for nighttime against ground observations and against the Sun and Pinker split-window (SP-SW) algorithm. The ground observations used are from the Atmospheric Radiation Measurement Program (ARM) Central Facility, Southern Great Plains site (April 1997–March 1998); from five Surface Radiation Budget Network (SURFRAD) stations (1996–2000); and from the Oklahoma Mesonet. The independent algorithms used for comparison include the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data and Information Service operational method and the following split-window algorithms: that of Price, that of Prata and Platt, two versions of that of Ulivieri, that of Vidal, two versions of that of Sobrino, that of Coll and others, the generalized split-window algorithm as described by Becker and Li and by Wan and Dozier, and the Becker and Li algorithm with water vapor correction. The evaluation against the ARM and SURFRAD observations indicates that the LST retrievals from the SP-SW algorithm are in closer agreement with the ground observations than are the other algorithms tested. When evaluated against observations from the Oklahoma Mesonet, the triple-window algorithm is found to perform better than the split-window algorithm during nighttime.

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Meng, Xiangchen, Jie Cheng, Shaohua Zhao, Sihan Liu, and Yunjun Yao. "Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm." Remote Sensing 11, no. 2 (January 15, 2019): 155. http://dx.doi.org/10.3390/rs11020155.

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Land surface temperature (LST) is one of the key parameters in hydrology, meteorology, and the surface energy balance. The National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm is adapted to Landsat-8 data to obtain the estimate of LST. The coefficients of the Enterprise algorithm were obtained by linear regression using the analog data produced by comprehensive radiative transfer modeling. The performance of the Enterprise algorithm was first tested by simulation data and then validated by ground measurements. In addition, the accuracy of the Enterprise algorithm was compared to the generalized split-window algorithm and the split-window algorithm of Sobrino et al. (1996). The validation results indicate the Enterprise algorithm has a comparable accuracy to the other two split-window algorithms. The biases (root mean square errors) of the Enterprise algorithm were 1.38 (3.22), 1.01 (2.32), 1.99 (3.49), 2.53 (3.46), and −0.15 K (1.11 K) at the SURFRAD, HiWATER_A, HiWATER_B, HiWATER_C sites and BanGe site, respectively, whereas those values were 1.39 (3.20), 1.0 (2.30), 1.93 (3.48), 2.53 (3.35), and −0.35 K (1.16 K) for the generalized split-window algorithm, 1.45 (3.39), 1.08 (2.41), 2.16 (3.67), 2.52 (3.58), and 0.02 K (1.12 K) for the split-window algorithm of Sobrino, respectively. This study provides an alternative method to estimate LST from Landsat-8 data.

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Wang, Lijuan, Ni Guo, Wei Wang, and Hongchao Zuo. "Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval." Remote Sensing 11, no. 17 (August 27, 2019): 2016. http://dx.doi.org/10.3390/rs11172016.

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FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

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Pérez-Planells, Lluís, Raquel Niclòs, Jesús Puchades, César Coll, Frank-M. Göttsche, José A. Valiente, Enric Valor, and Joan M. Galve. "Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms." Remote Sensing 13, no. 11 (June 7, 2021): 2228. http://dx.doi.org/10.3390/rs13112228.

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Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of −0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product.

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Peng, Hong Chun, Hai Ying Li, and Hao Gao. "Study on Methods of Retrieval of Sea Surface Temperature by Using Remote Sensing Data." Advanced Materials Research 610-613 (December 2012): 3742–46. http://dx.doi.org/10.4028/www.scientific.net/amr.610-613.3742.

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This article taking coastal waters of Lianyungang as the research area, and by using MODIS images during 15 April and 1 May, 2012 as source data, and the results of sea surface temperature were extracted by band operation, and by using changes in the different time of SST, spatial variation and comparative analysis to verify the accuracy of the two algorithms. Both the two split-window algorithm can get the sea surface temperature of coastal waters of Lianyungang well, and the result was reliable, and the inversion precision of SST can meet the application requirements in the general ocean applications. Not enough is the two split-window algorithm only worked under clear sky conditions, and can not get the sea surface temperature where the sea under cloud; at the zone of connected to the land and sea, universal SST algorithm was not as good as the Qin Zhihao split-window algorithm, but it has a few parameters, and the advantages of easy operation process of the desired intermediate.

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Vincent, R. F. "The Case for a Single Channel Composite Arctic Sea Surface Temperature Algorithm." Remote Sensing 11, no. 20 (October 16, 2019): 2393. http://dx.doi.org/10.3390/rs11202393.

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Surface temperatures derived from satellite thermal infrared (TIR) data are critical inputs for assessing climate change in polar environments. Sea and ice surface temperature (SST, IST) are commonly determined with split window algorithms that use the brightness temperature from the 11 μm channel (BT11) as the main estimator and the difference between BT11 and the 12 μm channel (BTD11–12) to correct for atmospheric water vapor absorption. An issue with this paradigm in the Arctic maritime environment is the occurrence of high BTD11–12 that is not indicative of atmospheric absorption of BT11 energy. The Composite Arctic Sea Surface Temperature Algorithm (CASSTA) considers three regimes based on BT11 pixel value: seawater, ice, and marginal ice zones. A single channel (BT11) estimator is used for SST and a split window algorithm for IST. Marginal ice zone temperature is determined with a weighted average between the SST and IST. This study replaces the CASSTA split window IST with a single channel (BT11) estimator to reduce errors associated with BTD11–12 in the split window algorithm. The single channel IST returned improved results in the CASSTA dataset with a mean average error for ice and marginal ice zones of 0.142 K and 0.128 K, respectively.

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Guo, Jinxin, Huazhong Ren, Yitong Zheng, Shangzong Lu, and Jiaji Dong. "Evaluation of Land Surface Temperature Retrieval from Landsat 8/TIRS Images before and after Stray Light Correction Using the SURFRAD Dataset." Remote Sensing 12, no. 6 (March 22, 2020): 1023. http://dx.doi.org/10.3390/rs12061023.

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Landsat 8/thermal infrared sensor (TIRS) is suffering from the problem of stray light that makes an inaccurate radiance for two thermal infrared (TIR) bands and the latest correction was conducted in 2017. This paper focused on evaluation of land surface temperature (LST) retrieval from Landsat 8 before and after the correction using ground-measured LST from five surface radiation budget network (SURFRAD) sites. Results indicated that the correction increased the band radiance at the top of the atmosphere for low temperature but decreased such radiance for high temperature. The root-mean-square error (RMSE) of LST retrieval decreased by 0.27 K for Band 10 and 0.78 K for Band 11 using the single-channel algorithm. For the site with high temperature, the LST retrieval RMSE of the single-channel algorithm for Band 11 even reduced by 1.4 K. However, the accuracy of two of three split-window algorithms adopted in this paper decreased. After correction, the single-channel algorithm for Band 10 and the linear split-window algorithm had the least RMSE (approximately 2.5 K) among five adopted algorithms. Moreover, besides SURFRAD sites, it is necessary to validate using more robust and homogeneous ground-measured datasets to help solely clarify the effect of the correction on LST retrieval.

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Su, Qinghua, Xiangchen Meng, and Lin Sun. "Investigation and Validation of Split-Window Algorithms for Estimating Land Surface Temperature from Landsat 9 TIRS-2 Data." Remote Sensing 16, no. 19 (September 29, 2024): 3633. http://dx.doi.org/10.3390/rs16193633.

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Land surface temperature (LST) is important in a variety of applications, such as urban thermal environment monitoring and water resource management. In this paper, eleven candidate split-window (SW) algorithms were adapted to Thermal Infrared Sensor-2 (TIRS-2) data of the Landsat 9 satellite for estimating the LST. The simulated dataset produced by extensive radiative transfer modeling and five global atmospheric profile databases was used to determine the SW algorithm coefficients. Ground measurements gathered at Surface Radiation Budget Network sites were used to confirm the efficiency of the SW algorithms after their performance was initially examined using the independent simulation dataset. Five atmospheric profile databases perform similarly in training accuracy under various subranges of total water vapor. The candidate SW algorithms demonstrate superior performance compared to the radiative transfer equation algorithm, exhibiting a reduction in overall bias and RMSE by 1.30 K and 1.0 K, respectively. It is expected to provide guidance for the generation of the Landsat 9 LST using the SW algorithms.

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Wang, Lei, Yao Lu, and Yunlong Yao. "Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images." Sensors 19, no. 22 (November 19, 2019): 5049. http://dx.doi.org/10.3390/s19225049.

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The successful launch of the Landsat 8 satellite provides important data for the monitoring of urban heat island effects. Since the Landsat 8 TIRS data has two thermal infrared bands, it is suitable for many algorithms to retrieve the land surface temperature (LST). However, the selection of algorithms for retrieving the LST, the acquisition of algorithm input parameters, and the verification of the results are problems without obvious solutions. Taking Changchun City as an example, this paper used the mono-window algorithm (MWA), the split window algorithm (SWA), and the single-channel (SC) method to extract the LST from the Landsat 8 image and compared the three algorithms in terms of input parameters, accuracy, and sensitivity. The results show that all three algorithms can achieve good results in retrieving the LST. The SWA is the least sensitive to the error of the input parameters. The MWA and the SC method are sensitive to the error of the input parameters, and compared with the error of the LSE, these two algorithms are more sensitive to the error of atmospheric water vapor content. In addition, the MWA is also very sensitive to the error of the effective mean atmospheric temperature.

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Sun, Donglian, Yunyue Yu, Li Fang, and Yuling Liu. "Toward an Operational Land Surface Temperature Algorithm for GOES." Journal of Applied Meteorology and Climatology 52, no. 9 (September 2013): 1974–86. http://dx.doi.org/10.1175/jamc-d-12-0132.1.

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AbstractFor most land surface temperature (LST) regression algorithms, a set of optimized coefficients is determined by manual separation of the different subdivisions of atmospheric and surface conditions. In this study, a machine-learning technique, the regression tree (RT) technique, is introduced with the aim of automatically finding these subranges and the thresholds for the stratification of regression coefficients. The use of RT techniques in LST retrieval has the potential to contribute to the determination of optimal regression relationships under different conditions. Because of the lack of split-window channels for the Geostationary Operational Environmental Satellite (GOES) M–Q series (GOES-12–GOES-15, plus GOES-Q), a dual-window LST algorithm was developed by combining the infrared 11-μm channel with the shortwave-infrared (SWIR) 3.9-μm channel, which presents lower atmospheric absorption than does the infrared split-window channels (11 and 12 μm). The RT technique was introduced to derive the regression models under different conditions. The algorithms were used to derive the LST product from GOES observations and were evaluated against the 2004 Surface Radiation budget network. The results indicate that the RT technique outperforms the traditional regression method.

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Більше джерел

Дисертації з теми "Algorithme split-window"

Zhang, Shuting. "Angular effects of surface brightness temperature observed from Sentinel-3A/SLSTR data." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD055.

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Ce travail de thèse utilise les données TIR de SLSTR comme source principale pour extraire la température de brillance de la surface (SBT) en appliquant l’algorithme split-window, afin d’analyser l’effet angulaire sur la SBT. En se basant sur une base de données de simulation, une méthode d’extraction de la SBT a été développée et appliquée aux observations à double angle de SLSTR. L’étude a ensuite examiné l’amplitude et les caractéristiques des différences de SBT entre les vues nadir et obliques, en tenant compte de facteurs tels que l’occupation du sol /la couverture terrestre, la saison, la latitude et le climat. Enfin, l’outil GeoDetector a été utilisé pour effectuer une analyse d’attribution des effets angulaires sur la SBT
This study adopts SLSTR TIR data as the main data source and retrieves surface brightness temperature using split-window algorithm to analyze the angular effect of surface brightness temperature (SBT). Based on the simulation database, SBT retrieval method is developed and applied to SLSTR dual-angle SBT extraction. Then the magnitude and characteristics of SBT differences between nadir and oblique views were observed, considering factors such as land use/land cover, season, latitude and climate. Finally, GeoDetector tool was used to perform attribution analysis of SBT angular effects

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Частини книг з теми "Algorithme split-window"

Thakur, Pawan Kumar, Manish Kumar, R. B. Singh, Vaibhav E. Gosavi, Bhim Chand, and Sarika Sharma. "Land Surface Temperature Retrieval of Landsat-8 Data Using Split-Window Algorithm Over Delhi City, India." In Remote Sensing and Geographic Information Systems for Policy Decision Support, 191–218. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7731-1_9.

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Тези доповідей конференцій з теми "Algorithme split-window"

Heidarian, Peyman, Hua Li, Zelin Zhang, Ruibo Li, Qinhuo Liu, and Tan Yumin. "High-Resolution Land Surface Temperature Retrieval from GF5-02 VIMI Data using an Operational Split-Window Algorithm." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 2713–16. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641359.

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Tan, Mingming, Hua Li, Xiangrong Xin, Ruibo Li, Yifan Lu, and Qing Xiao. "High-Resolution Sea Surface Temperature Retrieval from GF5-02 VIMI Data Using A Nonlinear Split-Window Algorithm." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 5865–69. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642876.

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Li, Fengguang, Huazhong Ren, Baozhen Wang, Jinshun Zhu, Songyi Lin, Wenjie Fan, Zian Wang, and Qiming Qin. "An Angle-Dependent Non-Linear Split-Window Algorithm for Estimating Sea Surface Temperature from Chinese HY-1D Satellite." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 5855–59. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641508.

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Mito, C. O., Giovanni Laneve, and Marco M. Castronuovo. "General split window algorithm for land surface temperature estimation." In International Symposium on Remote Sensing, edited by Manfred Owe and Guido D'Urso. SPIE, 2002. http://dx.doi.org/10.1117/12.454192.

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V., Ionca, Bogliolo M. P., Laneve G., Liberti G., Palombo A., and Pignatti S. "Split Window Algorithm Calibration and Validation for TASI Sensor." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8898750.

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Sharma, Jivitesh, Bernt-Vigo Matheussen, Sondre Glimsdal, and Ole-Christoffer Granmo. "Hydropower Optimization Using Split-Window, Meta-Heuristic and Genetic Algorithms." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00153.

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Saad, Sameh M., and Ramin Bahadori. "Pollution routing problem with time window and split delivery." In The 7th International Workshop on Simulation for Energy, Sustainable Development & Environment. CAL-TEK srl, 2019. http://dx.doi.org/10.46354/i3m.2019.sesde.004.

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In most classic vehicle routing problems, the main goal is to minimise the total travel time or distance while, the green vehicle routing problem, in addition to the stated objectives, also focuses on minimising fuel costs and greenhouse gas emissions, including carbon dioxide emissions. In this research, a new approach in Pollution Routing Problem (PRP) is proposed to minimise the CO2 emission by investigating vehicle weight fill level in length of each route. The PRP with a homogeneous fleet of vehicles, time windows, considering the possibility of split delivery and constraint of minimum shipment weight that must be on the vehicle in each route is investigated simultaneously. The mathematical model is developed and implemented using a simulated annealing algorithm which is programmed in MATLAB software. The generated results from all experiments demonstrated that the application of the proposed mathematical model led to the reduction in CO2 emission.

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Guillory, Anthony R., Henry E. Fuelberg, and Gary J. Jedlovec. "A Physical Split Window Technique for Deriving Precipitable Water Utilizing Vas Data." In Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/orsa.1991.omb3.

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A new algorithm, developed by Jedlovec (1987), is examined which uses Visible Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) 11 and 12 μm (split window) data to derive precipitable water (PW) at mesoscale resolution. The algorithm is physically based and derives its first guess information from radiosonde data. It has several advantages: 1) it can be applied to multispectral imaging (MSI) data, which are available half hourly, 2) it uses only limited spatial averaging, and 3) it can be applied to instruments which lack sounding channels (e.g., GOES-Next imager).

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Na-na, Liu, Li Jing-wen, and Cui Yan-feng. "Cluster-Based Split-Window Radon Transform Algorithm for Ship Wake Detection." In 2009 WRI World Congress on Computer Science and Information Engineering. IEEE, 2009. http://dx.doi.org/10.1109/csie.2009.521.

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Chen Du, Huazhong Ren, Qiming Qin, Jinjie Meng, and Jing Li. "Split-Window algorithm for estimating land surface temperature from Landsat 8 TIRS data." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6947256.

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