Comparison of multiple models for estimating gross primary production using remote sensing data and fluxnet observations
Comparison of multiple models for estimating gross primary production using remote sensing data and fluxnet observations
复制标题
使用遥感数据和通量网观测估算初级总产量的多种模型的比较
DOI：
10.5194/piahs-368-75-2015
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发表时间：
2015-05
期刊：
影响因子：
--
通讯作者：
Mo Xingguo
中科院分区：
文献类型：
--
作者：
Wang Sisi;Mo Xingguo
抽象的。在这项研究中，对2003-2005年间由温度和绿度(TG)模型、绿度和辐射(GR)模型、植被光合作用模型(VPM)和MODIS产品估算的总初级生产力(GPP)与涡动协方差测量进行了比较。结果表明，FLUXNET GPP与GPP估计值之间的决定系数(R2)均大于0.74，表明所有模型都能提供可靠的GPP估计值。我们还发现，基于VPM的GPP估计的表现略好于其他模型(R2为0.82，RMSE为16.75gcm−2(8天)−1)，这主要是因为它综合考虑了来自光、温度和水的压力。MOD_GPP在非生长季高估了实际GPP，而在生长季低估了实际GPP。验证结果证实，上述三种模型均可用于估算北中国平原的作物产量，但仍存在重大不确定性。
Abstract. In this study, gross primary production (GPP) estimated from a temperature and greenness (TG) model, a greenness and radiation (GR) model, a vegetation photosynthesis model (VPM), and a MODIS product have been compared with eddy covariance measurements in cropland during 2003–2005. Results showed that the determination coefficients (R 2 ) between fluxnet GPP and estimated GPP were all greater than 0.74, indicating that all these models offered reliable estimates of GPP. We also found that the VPM-based GPP estimates performed a bit better (R 2 is 0.82, and RMSE is 16.75 gC m −2 (8 day) −1 ) than other models, mainly due to its comprehensive consideration of the stresses from light, temperature and water. The actual GPP was overestimated in the non-growing season and underestimated in the growing season by MOD_GPP. The validation confirms that the above three models may be used to estimate crop production in the North China Plain, but there are still significant uncertainties.
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影响因子：
13.5
作者：
Fangmin Zhang;Jing M. Chen;Jiquan Chen;C. Gough;T. Martin;D. Dragoni
通讯作者：
Fangmin Zhang;Jing M. Chen;Jiquan Chen;C. Gough;T. Martin;D. Dragoni
影响因子：
13.5
作者：
Chaoyang Wu;J. W. Munger;Z. Niu;D. Kuang
通讯作者：
Chaoyang Wu;J. W. Munger;Z. Niu;D. Kuang
影响因子：
1.7
作者：
Chaoyang Wu
通讯作者：
Chaoyang Wu
基于 Landsat 数据、改进的升级模型框架和涡流协方差通量测量来估计高时空分辨率下的景观网络生态系统交换
影响因子：
13.5
作者：
Fu, Dongjie;Chen, Baozhang;Verma, Shashi
通讯作者：
Verma, Shashi
影响因子：
13.5
作者：
Huete, AR;Liu, HQ;vanLeeuwen, W
通讯作者：
vanLeeuwen, W