Relevant bibliographies by topics / Split-window algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Split-window algorithm'

Author: Grafiati
Published: 15 March 2025
Last updated: 26 July 2025

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Journal articles on the topic "Split-window algorithm"

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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 (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 Measur
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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 (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
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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 (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 repr
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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 app
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Vincent, R. F. "The Case for a Single Channel Composite Arctic Sea Surface Temperature Algorithm." Remote Sensing 11, no. 20 (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 e
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Pérez-Planells, Lluís, Raquel Niclòs, Jesús Puchades, et al. "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 (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
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Na, Yin Tai. "Study on the Application and Comparative Analysis of Land Surface Temperature Retrieval Method Based on Multi-Sensor Remote Sensing Data." Advanced Materials Research 1010-1012 (August 2014): 1276–79. http://dx.doi.org/10.4028/www.scientific.net/amr.1010-1012.1276.

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The three commonly used remote sensing land surface temperature retrieval methods are described, namely single-window algorithm, split window algorithm and multi-channel algorithm, which have their advantages and disadvantages. The land surface temperature (LST) of study area was retrieved with multi-source remote sensing data. LST of study area was retrieved with the split window algorithm on January 10, 2003 and November 19, 2003 which is comparatively analyzed with the LST result of ETM+data with the single-window algorithm and the LST result of ASTER data with multi channel algorithm in th
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Gerace, Aaron, Tania Kleynhans, Rehman Eon, and Matthew Montanaro. "Towards an Operational, Split Window-Derived Surface Temperature Product for the Thermal Infrared Sensors Onboard Landsat 8 and 9." Remote Sensing 12, no. 2 (2020): 224. http://dx.doi.org/10.3390/rs12020224.

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The split window technique has been used for over thirty years to derive surface temperatures of the Earth with image data collected from spaceborne sensors containing two thermal channels. The latest NASA/USGS Landsat satellites contain the Thermal Infrared Sensor (TIRS) instruments that acquire Earth data in two longwave infrared bands, as opposed to a single band with earlier Landsats. The United States Geological Survey (USGS) will soon begin releasing a surface temperature product for Landsats 4 through 8 based on the single spectral channel methodology. However, progress is being made to
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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 (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
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Heidinger, Andrew K., and Michael J. Pavolonis. "Gazing at Cirrus Clouds for 25 Years through a Split Window. Part I: Methodology." Journal of Applied Meteorology and Climatology 48, no. 6 (2009): 1100–1116. http://dx.doi.org/10.1175/2008jamc1882.1.

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Abstract This paper demonstrates that the split-window approach for estimating cloud properties can improve upon the methods commonly used for generating cloud temperature and emissivity climatologies from satellite imagers. Because the split-window method provides cloud properties that are consistent for day and night, it is ideally suited for the generation of a cloud climatology from the Advanced Very High Resolution Radiometer (AVHRR), which provides sampling roughly four times per day. While the split-window approach is applicable to all clouds, this paper focuses on its application to ci
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Dissertations / Theses on the topic "Split-window algorithm"

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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, l
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Book chapters on the topic "Split-window algorithm"

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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. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7731-1_9.

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Conference papers on the topic "Split-window algorithm"

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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. 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. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642876.

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Li, Fengguang, Huazhong Ren, Baozhen Wang, et al. "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. 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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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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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 sh
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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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Zhao, Shaohua, Qiming Qin, Yujiu Xiong, Guoyu Qiu, and Yonghui Yang. "Application of split window algorithm to retrieve land surface temperature over northwestern China." In Second International Conference on Earth Observation for Global Changes, edited by Xianfeng Zhang, Jonathan Li, Guoxiang Liu, and Xiaojun Yang. SPIE, 2009. http://dx.doi.org/10.1117/12.836766.

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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. 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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