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Proceedings Paper

Remote sensing application in the carbon flux modeling of the terrestrial ecosystem
Author(s): Junbang Wang; Zheng Niu; Binmin Hu; Changyao Wang
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Paper Abstract

The determination of terrestrial ecosystem carbon source/sink spatial pattern is becoming one of the hottest problems and many environment politics focus on it. As a new tool for terrestrial ecosystem carbon modelling at large scale from field plot, to region, to global, remote sensing is applied to initialize, drive, and validate the model, combined with geophysics information system (GIS) and computer modelling. Carbon flux models with remote sensing data as input may be classified as light use efficiency model, process model, and eco-physiological model based on “big leaf” hypothesis. The model generally includes two parts: NPP and soil respiration model to estimate carbon flux based on the principle that the carbon flux of ecosystem equal NPP minus heterogeneity respiration (soil respiration). Remote sensing, however, is more applied in NPP modeling but little in soil respiration estimation. The latter mostly based on relationship between soil respiration and soil temperature and is highly developed. Since remote sensing is applied to retrieve land surface temperature (LST) with infrared waveband, a hypothesis was put forward, that is, land surface temperature retrieved from infrared waveband can substitute soil temperature to estimate soil respiration. The hypothesis was validated with a field experiment and result was given in this article. The experiment located in a winter wheat field at Quzhou experiment station, Hebei province, China, from Apr 19 to May 20, in 2002. The soil respiration rate was measured with CID photosynthesis system, and canopy infrared temperature, soil surface temperature were measured respectively at same time. The station provided us soil moisture content data of whole growth of winter wheat. The result shows that the soil CO2 efflux from winter wheat field is -0.03~1.38μmolm-2s-1. Its diurnal variation is well fitted with univariate quartic curve. Its variation in winter wheat heading growth period well coincide with temperature and soil moisture content. The Pearson correlation analysis shows that, on the averaged sense, for a day, soil CO2 efflux significantly correlated with the temperature of the air (Tair), the soil surface (Tsur), the averaged thermal (Tinf) temperature respectively at the p-level<0.001. The relation between soil respiration and canopy thermal temperature (Tinf) and soil surface temperature (Tsur) was modeled with equations from Fang and Moncrieff (2001) respectively. On the whole, the performance of models with Tinf as independent is better than one with Tsur as independent for the data on May 8. The max multiple correlation coefficient (MCC) of the former is 0.95118 large than the MCC 0.92338 of the later, which provide a better fundament for the hypothesis above. The result of model analysis shows that the one of Schlentner and Van Cleve (1985) is best candidate in this study because of its high coefficient of determination and its principle. However some problem should be improved in the future. Firstly, soil respiration was measured with CID photosynthesis system and chamber which demand to consider the disturbance of chamber and the precision of the instrument. Secondly, the research focus on a point not on whole area comparing with the resolution remote sensing image, such as NOAA/AVHRR, TM, MODIS, since the result can not be directly applied to satellite image, that is, the experiment on a large spatial scale should be done for satellite image application.

Paper Details

Date Published: 24 February 2004
PDF: 11 pages
Proc. SPIE 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V, (24 February 2004); doi: 10.1117/12.524332
Show Author Affiliations
Junbang Wang, Institue of Remote Sensing Applications (China)
Zhejiang University (China)
Zheng Niu, Institute of Remote Sensing Applications (China)
Binmin Hu, Zhejiang Univ. (China)
Changyao Wang, Institute of Remote Sensing Applications (China)

Published in SPIE Proceedings Vol. 5232:
Remote Sensing for Agriculture, Ecosystems, and Hydrology V
Manfred Owe; Guido D'Urso; Jose F. Moreno; Alfonso Calera, Editor(s)

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