本文選題：總初級(jí)生產(chǎn)力 + 遙感��； 參考：《植物生態(tài)學(xué)報(bào)》2017年03期

【摘要】：在區(qū)域和全球尺度上估算植被總初級(jí)生產(chǎn)力(GPP)對(duì)理解陸地生態(tài)系統(tǒng)的碳循環(huán)具有重要意義。由于地表異質(zhì)性的存在,局限在站點(diǎn)尺度上的觀測(cè)數(shù)據(jù)無(wú)法直接擴(kuò)展到更大空間尺度的區(qū)域上。通過(guò)與地面觀測(cè)數(shù)據(jù)相結(jié)合,遙感成為實(shí)現(xiàn)植被GPP空間擴(kuò)展的主要工具。但是現(xiàn)有模型對(duì)氣象數(shù)據(jù)依賴較多,且在不同氣象數(shù)據(jù)集的驅(qū)動(dòng)下,模擬結(jié)果間會(huì)有差異,進(jìn)而產(chǎn)生不確定性。建立以遙感數(shù)據(jù)為主的GPP模型(簡(jiǎn)稱遙感GPP模型),使其易于在區(qū)域和全球尺度上應(yīng)用,是解決上述問(wèn)題的一個(gè)可行方案。該研究使用TG(temperature and greenness model)和VI(vegetation index model)兩個(gè)遙感GPP模型,結(jié)合中國(guó)通量觀測(cè)研究聯(lián)盟(China FLUX)的臺(tái)站數(shù)據(jù),對(duì)中國(guó)典型植被類型的GPP進(jìn)行了模擬、比較與評(píng)估,旨在進(jìn)一步提高遙感GPP模型在中國(guó)區(qū)域的適用性。結(jié)果表明:(1)TG和VI模型選用的遙感參數(shù)均與GPP觀測(cè)值有較高的相關(guān)性,都可以得到可信的光合轉(zhuǎn)換系數(shù)m和a�；谂c夜間地表溫度平均值的相關(guān)關(guān)系,m和a在空間尺度上得到了擴(kuò)展,這使得TG和VI都可以應(yīng)用到區(qū)域尺度上。(2)TG和VI模型的模擬值與實(shí)測(cè)值間的相關(guān)性大多較高,決定系數(shù)(R~2)多在0.67以上。但不同臺(tái)站間的誤差變動(dòng)較大,TG模型的均方根誤差為0.29 6.40 g·m~( 2)·d~( 1),VI模型的均方根誤差為0.31 7.09 g·m~( 2)·d~( 1)。(3)總體而言,TG模型的表現(xiàn)優(yōu)于VI,尤其在海拔或緯度較高、以溫度限制為主的臺(tái)站,TG模型的模擬效果較好。上述結(jié)果初步揭示遙感GPP模型具備了在區(qū)域尺度上應(yīng)用的潛力。
[Abstract]:The estimation of total primary productivity (GPP) of vegetation at regional and global scales is of great significance in understanding the carbon cycle of terrestrial ecosystems. Because of the heterogeneity of the surface, the observation data confined to the site scale can not be directly extended to the larger spatial scale area. By combining with ground observation data, remote sensing has become the main tool for vegetation GPP spatial expansion. However, the existing models depend more on meteorological data, and driven by different meteorological data sets, the simulation results will be different, which will lead to uncertainty. The GPP model based on remote sensing data (GPP model for short), which is easy to be applied in regional and global scale, is a feasible solution to the above problems. In this study, two remote sensing GPP models, TG(temperature and greenness model) and VI(vegetation index model, were used to simulate, compare and evaluate the GPP of typical vegetation types in China. The purpose of this paper is to improve the applicability of remote sensing GPP model in China. The results show that the remote sensing parameters selected by the TG and VI models are highly correlated with the observed values of GPP, and reliable photosynthetic conversion coefficients m and a can be obtained. Based on the correlation between the mean value of surface temperature at night and the average value of surface temperature, m and a have been extended on spatial scale, which makes TG and VI can be applied to the regional scale. The correlation between simulated values and measured values of TG and VI models is mostly high. The determination coefficient is more than 0.67. However, the root mean square error of TG model is 0.29 ~ 6.40 g / m ~ (-1) 路m ~ (-1), the root mean square error of VI model is 0.31 ~ 7.09 g / m ~ (-1) / m ~ (2) / m ~ (-1) / m ~ (-1) ~ (-1) / t ~ (3), and the performance of TG model is better than that of Vi in general, especially at high altitude or latitude. The simulation effect of TG model with temperature limitation is better. The above results reveal that the remote sensing GPP model has the potential to be applied in the regional scale.
【作者單位】： 北京林業(yè)大學(xué)林學(xué)院;
【基金】：國(guó)家林業(yè)公益性行業(yè)科研專項(xiàng)(201404201) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)(BLX2015-16)
【分類號(hào)】：Q948;TP79

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