Estimating soil thermal properties from sequences of land surface temperature using hybrid Genetic Algorithm–Finite Difference method |
| |
| Authors: | SM Bateni D-S Jeng SM Mortazavi Naeini |
| |
| Affiliation: | 1. State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China;2. Key Laboratory of Mechanics on Disaster and Environment in Western China, the Ministry of Education of China and School of Civil Engineering and Mechanics, Lanzhou University, Lanzhou 730000, China;3. University of Chinese Academy of Sciences, Beijing 100049, China;1. State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221008, China;2. School of Mechanics and Civil Engineering, China University of Mining and Technology, Jiangsu 221116, China;3. State Key Laboratory of Frozen Soil Engineering, Cold and Arid Regions Environmental and Engineering Research Institute, Lanzhou 730000, China;1. State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China;2. GeoEnergy Research Centre, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK;3. School of Chemistry, University of Nottingham, Nottingham NG7 2RD, UK;1. Department of Civil Engineering, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada;2. School of Civil Engineering, Wuhan University, Wuhan, Hubei 430072, China |
| |
| Abstract: | Most models used in land surface hydrology, vadose zone hydrology, and hydro-climatology require an accurate representation of soil thermal properties (soil thermal conductivity and volumetric heat capacity). Various empirical relations have been suggested to estimate soil thermal properties. However, they require many input parameters such as soil texture, mineralogical composition, porosity and water content, which are not always available from laboratory experiments and field measurements. In this paper, to overcome the above challenge, a hybrid numerical method, Genetic Algorithm–Finite Difference (GA–FD), is proposed to estimate soil thermal properties using land surface temperature (LST) as the only input. The genetic algorithm (GA) optimization method coupled with the finite difference (FD) modeling technique is a viable hybrid approach for estimating soil thermal properties. The finite difference method is employed to solve the heat diffusion equation and simulate LST, while a robust optimization technique (GA) is used to retrieve soil thermal properties by minimizing the difference between observed and simulated LST. Furthermore, a generalization of the hybrid model is developed for inhomogeneous soil, in which soil thermal properties are not constant throughout the soil slab. The proposed model is applied to the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). The results show that the proposed hybrid numerical method is able to estimate soil thermal properties accurately, and therefore effectively eliminate the need for the unavailable soil parameters which are required by empirical methods for determining the soil thermal conductivity and volumetric heat capacity. Remarkably, the temporal variation of the retrieved soil thermal conductivity is consistent with the volumetric water content, even though no water content information is used in the model. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
