Estimation of land surface heat fluxes based on visible infrared imaging radiometer suite data: case study in northern China 

作者:Li, XJ (Li, Xiaojun)[ 1,2,3 ] ; Xin, XZ (Xin, Xiaozhou)[ 1,2 ] ; Peng, ZQ (Peng, Zhiqing)[ 1,2,3 ] ; Zhang, HL (Zhang, Hailong)[ 1,2 ] ; Li, L (Li, Li)[ 1,2 ] ; Shao, SS (Shao, Shanshan)[ 4 ] ; Liu, QH (Liu, Qinhuo)[ 1,2,5 ]  

JOURNAL OF APPLIED REMOTE SENSING 

卷: 11 

文献号: 046012 

DOI: 10.1117/1.JRS.11.046012 

出版年: NOV 6 2017 

摘要

Evapotranspiration (ET) plays an important role in surface-atmosphere interactions and can be monitored using remote sensing data. The visible infrared imaging radiometer suite (VIIRS) sensor is a generation of optical satellite sensors that provide daily global coverage at 375-to 750-m spatial resolutions with 22 spectral channels (0.412 to 12.05 mu m) and capable of monitoring ET from regional to global scales. However, few studies have focused on methods of acquiring ET from VIIRS images. The objective of this study is to introduce an algorithm that uses the VIIRS data and meteorological variables to estimate the energy budgets of land surfaces, including the net radiation, soil heat flux, sensible heat flux, and latent heat fluxes. A single-source model that based on surface energy balance equation is used to obtain surface heat fluxes within the Zhangye oasis in China. The results were validated using observations collected during the HiWATER (Heihe Watershed Allied Telemetry Experimental Research) project. To facilitate comparison, we also use moderate resolution imaging spectrometer (MODIS) data to retrieve the regional surface heat fluxes. The validation results show that it is feasible to estimate the turbulent heat flux based on the VIIRS sensor and that these data have certain advantages (i.e., the mean bias error of sensible heat flux is 15.23 Wm(-2)) compared with MODIS data (i.e., the mean bias error of sensible heat flux is -29.36 Wm(-2)). Error analysis indicates that, in our model, the accuracies of the estimated sensible heat fluxes rely on the errors in the retrieved surface temperatures and the canopy heights. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

通讯作者地址: Xin, XZ (通讯作者)

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

 Beijing Normal Univ, Beijing, Peoples R China.

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