Paper
9 June 2006 Simulation of Land ETs of China with CoLM
Author Affiliations +
Proceedings Volume 6200, Remote Sensing of the Environment: 15th National Symposium on Remote Sensing of China; 62000N (2006) https://doi.org/10.1117/12.681768
Event: Remote Sensing of the Environment: 15th National Symposium on Remote Sensing of China, 2005, Guiyan City, China
Abstract
It has become a great demand and a difficult task to study regional evapo-transpiration (ETs) in the field of geology and geography nowadays. As one of the most important contents in studying the interaction between land surface and atmosphere, the precise estimation of various land surface evapo-transpiration makes great sense to study the global climate change, as well as the reasonable utilization and distribution of water resources. Firstly, this paper analyzed several methods which are used to study ETs nowadays, especially the advantages and disadvantages of the prevailing method of RS for ETs, and proposed the new idea of combining the remote sensing method with the land-surface process model based on the previous work of Remote Sensing model for ETs. Then we drove CoLM (Common Land Model), which is the most advanced land surface process model in the world with GSWP-2 re-analyses meteorological data, and compared the ETs calculated by CoLM with the result calculated by a remote sensing model SEBS-China in 1991 of China. The result indicates that both models can simulate the monthly ETs however uncertainties exist in both of the models, which shows the significance and feasibility of the combination of two models in estimation of ETs. At last this paper analyzed the cause of the uncertainties of CoLM and prospected our future work, which is a preparation for the calibration to land surface model with RS model for ETs.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lingling Ma, Lingli Tang, and Zhaoliang Li "Simulation of Land ETs of China with CoLM", Proc. SPIE 6200, Remote Sensing of the Environment: 15th National Symposium on Remote Sensing of China, 62000N (9 June 2006); https://doi.org/10.1117/12.681768
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KEYWORDS
Atmospheric modeling

Data modeling

Remote sensing

Process modeling

Vegetation

Meteorology

Atmospheric physics

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