基于MODIS的中国草地NPP综合估算模型

草地生态系统是陆地生态系统分布最广的生态系统类型之一,其碳储量的估算在全球变化中的作用越来越受到重视。为了快速、便捷地实现中国草地净初级生产力(NPP)的估算,在获取野外调查资料与同期遥感影像数据的基础上,利用归一化植被指数(NDVI)以及气候数据,构建了草地NPP综合估算模型。模型包括叶面积指数(LAI)和光合累积量(PA)两个子模型,其中LAI子模型利用了遥感数据NDVI,PA子模型利用了温度、降水和辐射等气候数据。通过建模以外独立的实测数据的验证,模拟值与实测值之间有很好的相关性,R2为0.8519,相关性达到极显著水平。RMSE和RRMSE均较小,表明模型的模拟结果比较可靠。同时模拟值与实测值之间的平均相对误差仅为1.97%,模拟结果的准确度较高,因此利用上述模型估算中国草地NPP是可行的。以上结果为中国草地NPP估算提供了新的方法。

Comprehensive estimation model of grassland NPP based on MODIS in China

Grassland ecosystem is one of the most widely distributed types in the terrestrial ecosystems. Estimating carbon storage in grassland ecosystem has been a central focus of global change researches. In order to estimate the grassland net primary productivity (NPP) quickly and reliably, based on the field survey data and the remote sensing image data of the same period, the comprehensive estimation model of grassland NPP in China was developed by using normalized difference vegetation index (NDVI) and climate data. According to the basic principles of grassland genetic and the relationship between the single factor and the NPP, through statistical analysis, the model structural factors were put forward, and then integrated together. The comprehensive model included two sub models of leaf area index (LAI) and photosynthetic accumulation (PA), and it was NPP= LAI×PA. The remote sensing data NDVI was used as a driving factor for constructing LAI sub model and it was LAI=ln(5.79×NDVI+5.91)/(2.73-2.46×NDVI). The climate data such as temperature, precipitation, and radiation were used as driving factors to construct PA sub model. In the PA sub model, there was a logarithmic relationship between the grassland NPP and mean monthly temperature, and the correlation coefficient r=0.4382 (P<0.01, n=95). There was a linear positive correlation between the grassland NPP and monthly rainfall, and the correlation coefficient r=0.6626 (P<0.01, n=95). There was an exponential relationship between the grassland NPP and radiation, and the correlation coefficient r=-0.7047 (P<0.01, n=95). So PA sub model was described as PA=ln(2+T/18.1)×Sqrt(W/89.3)×110/Exp(R/603-0.8), where T was mean monthly temperature, W was monthly rainfall, and R was monthly radiation. The model was validated by independent measured data which was not used for constructing the model. There was a good correlation between the simulated and observed NPP values, and the R² was 0.8519 (P<0.01). The root mean square error (RMSE) and the relative root mean square error (RRMSE) were 59.955 gC/m² and 0.358, respectively. The small values of RMSE and RRMSE indicated that the model was reliable. The average relative error between the simulated and measured values was only 1.97%, and the model can accurately predict NPP. So it was feasible to estimate grassland NPP in China by using this model, and this model provided a new method for estimating of grassland NPP in China.