A Novel Nonlinear Algorithm for Area-Wide Near Surface Air Temperature Retrieval
| العنوان: | A Novel Nonlinear Algorithm for Area-Wide Near Surface Air Temperature Retrieval |
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| المؤلفون: | Fu He, Jiang-Lin Qin, Xiu-Feng Lei, Xiu-Hao Yang |
| المصدر: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 9, Iss 7, Pp 3283-3296 (2016) |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2016. |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | Atmospheric Science, 010504 meteorology & atmospheric sciences, Computer science, Computation, Geophysics. Cosmic physics, 0211 other engineering and technologies, 02 engineering and technology, Atmospheric model, computer.software_genre, 01 natural sciences, Approximation error, Inverse distance weighting, GIS spatial analysis, Computers in Earth Sciences, TC1501-1800, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, moderate-resolution imaging spectroradiometer (MODIS), QC801-809, Area-wide retrieving, multivariable analysis, high-performance computation (HPC), Ocean engineering, Support vector machine, Data point, Cover (topology), Data mining, computer, Algorithm, digital elevation model (DEM), Interpolation |
| الوصف: | This paper reports a novel nonlinear algorithm for retrieving near surface air temperature over a large area using support vector machines with satellite remote sensing and other types of data. The steps include the following. 1) Establish the 1 $^{\text{st}}$ sub model learning dataset and validation dataset, then obtain the 2 $^{\text{nd}}$ to f $^{\text{th}}$ sub model learning datasets and validation datasets, using unmanned weather station data and predefined influential variables. 2) Retrieve Ta of the target area. 3) Correct the generated Ta images based on prediction errors using the inverse distance weighting interpolation. The novelty of this algorithm is to apply multiple sources of remote sensing data combined with data of unmanned weather stations, topography, ground cover, DEM, and astronomy and calendar rules. The results indicated that the model has high accuracy, reliability, and generalization ability. Factors such as cloudiness, ground vegetation, and water vapor show little interference, so the model seems suitable for large area retrieving under natural conditions. The required high-performance computation was achieved by a ${\text{CPU}} + {\text{GPU}}$ isomery and synergy parallel computation system that improved computing speed by more than 1000-fold, with easily extendable computing capability. We found that the current algorithm is superior to seven major split-window algorithms and their best combined algorithms based on prediction errors, root-mean-square errors, and the percentage of data points with < 3 °C absolute error. Our SVM approach overcomes shortcomings of classical temperature remote sensing technologies, and is the first report of such application. |
| تدمد: | 2151-1535 1939-1404 |
| DOI: | 10.1109/jstars.2016.2536745 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82be6c00ccc6af1f1418d68c5673c853 https://doi.org/10.1109/jstars.2016.2536745 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....82be6c00ccc6af1f1418d68c5673c853 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21511535 19391404 |
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| DOI: | 10.1109/jstars.2016.2536745 |