Dr. Kai Qin Profile

Dr. Kai Qin

Lecture at China Univ of Mining and Technology

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SPIE Journal Paper | 11 October 2018

Remote sensing estimation of urban surface evapotranspiration based on a modified Penman–Monteith model

Yu Zhang, Long Li, Kai Qin, Yuchen Wang, Longqian Chen, Xiaoyan Yang

JARS, Vol. 12, Issue 04, 046006, (October 2018) https://doi.org/10.1117/12.10.1117/1.JRS.12.046006

KEYWORDS: Vegetation, Remote sensing, Data modeling, Heat flux, Earth observing sensors, Soil science, Landsat, Resistance, Data acquisition, Atmospheric modeling

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To date, remote sensing-based algorithms for inferring urban surface evapotranspiration (ET) remain little studied. Based on the modifications of the remote sensing Penman–Monteith (RS-PM) model, we propose an urban RS-PM model for estimating urban surface ET. Compared with the traditional RS-PM model, our urban RS-PM model is specifically developed for urban areas and is characterized by the following improvements: (1) excluding the interference of impervious surface components in urban areas by replacing the vegetation cover fraction index with land surface component fraction parameters inversed through linear spectral mixture analysis for calculating the area proportions of vegetation and soil; (2) considering the effect of the component fractions of vegetation or soil on all energy components of the surface energy balance by applying the modified multisource parallel model for estimating the component latent heat flux; and (3) optimizing the calculation of the component net radiation flux by considering the component surface characteristics. This urban RS-PM model was tested on an urban area of Xuzhou in the eastern Chinese province of Jiangsu. Landsat 8 operational land imager and thermal infrared sensor satellite images acquired between 2014 and 2016, together with their corresponding meteorological data and flux observation data, were used for estimating the ET of the study area for eight dates with the model. The results were validated by the latent heat flux data observed by an open path eddy covariance system. Validation shows the goodness of fit (R2), the root-mean-square error, the mean relative error, and the correlation coefficient (r) between estimated ET and observed ET for the eight dates were 0.8965, 24.14 W · m − 2, 18.5%, and 0.9546, respectively. The results prove that the urban RS-PM model is effective in estimating ET of urban areas with an acceptable accuracy.

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