title: Determination of optimum viewing angles for the angular normalization of land surface temperature over vegetated surface.
abstract: Multi-angular observation of land surface thermal radiation is considered to be a promising method of performing the angular normalization of land surface temperature (LST) retrieved from remote sensing data. This paper focuses on an investigation of the minimum requirements of viewing angles to perform such normalizations on LST. The normally kernel-driven bi-directional reflectance distribution function (BRDF) is first extended to the thermal infrared (TIR) domain as TIR-BRDF model, and its uncertainty is shown to be less than 0.3 K when used to fit the hemispheric directional thermal radiation. A local optimum three-angle combination is found and verified using the TIR-BRDF model based on two patterns: the single-point pattern and the linear-array pattern. The TIR-BRDF is applied to an airborne multi-angular dataset to retrieve LST at nadir (Te-nadir) from different viewing directions, and the results show that this model can obtain reliable Te-nadir from 3 to 4 directional observations with large angle intervals, thus corresponding to large temperature angular variations. The Te-nadir is generally larger than temperature of the slant direction, with a difference of approximately 0.5~2.0 K for vegetated pixels and up to several Kelvins for non-vegetated pixels. The findings of this paper will facilitate the future development of multi-angular thermal infrared sensors.
authors.affiliation: State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China. f.nerry@unistra.fr.|Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China. f.nerry@unistra.fr.|ICube Laboratory, Université de Strasbourg, 67412 Illkirch, France. f.nerry@unistra.fr.
authors.forename: Huazhong
authors.initials: H
authors.lastname: Ren
authors.affiliation: State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China. gjyan@bnu.edu.cn.
authors.forename: Guangjian
authors.initials: G
authors.lastname: Yan
authors.affiliation: State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China. rongyuanliu@mail.bnu.edu.cn.|ICube Laboratory, Université de Strasbourg, 67412 Illkirch, France. rongyuanliu@mail.bnu.edu.cn.
authors.forename: Rongyuan
authors.initials: R
authors.lastname: Liu
authors.affiliation: Key Laboratory of Agri-Informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China. f.nerry@unistra.fr.|ICube Laboratory, Université de Strasbourg, 67412 Illkirch, France. f.nerry@unistra.fr.
authors.forename: Zhao-Liang
authors.initials: ZL
authors.lastname: Li
authors.affiliation: Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China. qmqinpku@163.com.
authors.forename: Qiming
authors.initials: Q
authors.lastname: Qin
authors.affiliation: ICube Laboratory, Université de Strasbourg, 67412 Illkirch, France. f.nerry@unistra.fr.
authors.forename: Françoise
authors.initials: F
authors.lastname: Nerry
authors.affiliation: College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China. toliuqiang@bnu.edu.cn.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Liu
references.citation: Opt Express. 2009 Mar 2;17(5):3173-82
references.pmid: 19259154
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references.pmid: 19582025