Evaluating reliability of grassland net primary productivity estimates using different meteorological interpolation methods

Meteorological factors are significant in researching grassland productivity, vegetation growth, and disaster assessment. This study used different spatial interpolation methods including Cokriging (CK), inverse distance weighting (IDW), and ANUSPLIN, to analyse the average July precipitation and temperature datasets of 90 meteorological stations in Xinjiang from 2000 to 2011. In addition, the mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the interpolation results, and we discussed the effects of different interpolation methods on spatial variation of precipitation and temperature. Furthermore, the spatial variations in precipitation and temperature obtained using various interpolation methods were used to calculate the grassland net primary productivity (NPP) in Xinjiang using the Carnegie-Ames-Stanford (CASA) model and verified their accuracy using field measured biomass data. We obtained the following results, 1)The interpolation results of the precipitation and temperature using the ANUSPLIN was better than those of the other methods (MAEprecipitation=6.45, RMSEprecipitation=8.77, and MAEtemperature=2.11, RMSEtemperature=3.52), which indicates that the ANUSPLIN is a superior method for interpolating meteorological factors in Xinjiang. 2) The accuracy of estimating the Xinjiang grassland NPP differed between the various methods. The calculation of the coefficients between the field measured biomass data and the simulated values obtained using CASA model, showed that the ANUSPLIN had the highest accuracy, with an R2 of 0.794 7. There was a good linear relationship between the measured and simulated values of the grassland NPP. Compared with the Cokriging and IDW, the accuracy of ANUSPLIN was higher by 13.23% and 20.13%, respectively. These results show that improving the accuracy of the interpolation results of meteorological factors could enhance the estimation of grassland NPP.