An analytical four-component directional brightness temperature model for crop and forest canopies

作者:Bian, ZJ (Bian, Zunjian)[ 1,2,3 ] ; Cao, B (Cao, Biao)[ 1 ] ; Li, H (Li, Hua)[ 1 ] ; Du, YM (Du, Yongming)[ 1 ] ; Lagouarde, JP (Lagouarde, Jean-Pierre)[ 4 ] ; Xiao, Q (Xiao, Qing)[ 1 ] ; Liu, QH (Liu, Qinhuo)[ 1,2,3 ]

REMOTE SENSING OF ENVIRONMENT

卷: 209  页: 731-746

DOI: 10.1016/j.rse.2018.03.010

出版年: MAY 2018

文献类型:Article

摘要

Measurements of surface thermal infrared (TIR) radiance that are made to extract temperatures display strong directional anisotropy effects. Directional brightness temperature (BT) models that describe this anisotropic behavior of TIR emissions can be applied to separate component temperatures using multi-angle observations. The surface temperature differences that occur between sunlit and shaded areas and the leaf clumping phenomenon jointly affect the directional signatures of out-of-canopy directional BTs. However, these factors are not fully considered in existing directional BT models. This paper therefore extends the FR97 analytical model to 1) a four-component scene containing sunlit and shaded soil and leaves by incorporating the effective emissivity values of the sunlit and shaded parts and 2) row-planted crop and forest canopies by introducing a leaf clumping index. The proposed model was assessed using a synthetic dataset that was generated by the Thermal Radiosity-Graphics Combined Model (TRGM) under various conditions. The evaluation results indicated that the proposed model performed as well as the Scattering by Arbitrarily Inclined Leaves (4SAIL) model over continuous canopies with root mean squared errors (RMSEs) lower than 0.3 degrees C. Over non-continuous crops and forests, the behavior of the proposed model displayed improved agreement with the TRGM with RMSEs lower than 0.65 degrees C. The proposed model also displayed a robust performance over both the maize and pine canopies, which was evaluated against the directional anisotropy of measured datasets that were collected at the Huailai remote sensing test site and the Institut National de la Recherche Agronomique (INRA), respectively. From these points, the proposed model has potential for component temperature inversion and rapid assessment of TIR angular effects.

通讯作者地址: Du, YM; Liu, QH (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

通讯作者地址: Liu, QH (通讯作者)

JCGCS, Beijing 100875, Peoples R China.

通讯作者地址: Liu, QH (通讯作者)

Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] JCGCS, Beijing 100875, Peoples R China

[ 3 ] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China

[ 4 ] INRA, UMR 1391, ISPA, F-33140 Villenave Dornon, France