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Estimation of gross primary production in wheat from in situ measurements

文献类型： 外文期刊

作者： Wu, Chaoyang 1 ; Han, Xiuzhen 3 ; Ni, Jinsheng 4 ; Niu, Zheng 1 ; Huang, Wenjiang 1 ;

作者机构： 1.Chinese Acad Sci, Inst Remote Sensing Applicat, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

2.Chinese Acad Sci, Grad Univ, Beijing 100039, Peoples R China

3.China Meteorol Adm, Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China

4.Beijing Oriental TITAN Technol Co Ltd, Beijing 100083, Peoples R China

5.Natl Engn Res Ctr Informat Technol Agr, Beijing 100089, Peoples R China

关键词： Gross primary production; Vegetation index; LAI; Wheat; PAR

期刊名称：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION （ 影响因子：5.933； 五年影响因子：6.225 ）

ISSN： 0303-2434

年卷期： 2010 年 12 卷 3 期

页码：

收录情况： SCI

摘要： Gross primary production (GPP) is a parameter of significant importance for carbon cycle and climate change research. Remote sensing combined with other climate and meteorological data offers a convenient tool for large scale GPP estimation. This paper presents a study of GPP estimation using three methods with in situ measurements of canopy reflectance, LAI, and the photosynthetically active radiation (PAR). First, because LAI is considered as an indicator of the factor of absorbed PAR (f(APAR)), it provides reasonable estimates of GPP for all types of wheat with coefficient of determination R-2 of 0.7353. The second method uses four kinds of vegetation indices (VIs) to estimate GPP because these indices are suggested to be reliable candidates in the estimation of light use efficiency (LUE). Good determination coefficients were acquired in estimating GPP with R-2 ranging from the lowest of 0.7604 for NDVI to the highest of 0.8505 for EVI. A new method was proposed for the estimation of GPP following the Monteith logic, which considering GPP as a product of VI x VI x PAR. Results indicated that this method can provide the best estimates of GPP as determination coefficient R-2 increased largely compared to the other two methods. EVI x EVI x PAR was demonstrated to be the most suitable for the estimation of GPP with the highest R-2 of 0.9207, which was about 10% larger as compared to GPP estimated from the single EVI. These results will be helpful for the development of new models of GPP estimation with all remote sensing inputs. (C) 2010 Elsevier B.V. All rights reserved.

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