Comparison of Two Remote Sensing Based Models for Regional Net Primary Productivity Estimation—A Case Study in Semi-Arid Central Kazakhstan

Modelling net primary productivity (NPP) is an important instrument for analysing carbon exchange between atmosphere and vegetation as well as for quantification of carbon sinks and sources. Remote-sensing-based models allow for regional NPP estimation and are potentially transferable to new regions. Comparative model analyses, however, are lacking, especially for semi-arid environments. In this study, two recent remote-sensing-based NPP models were applied for the first time to a study region in semi-arid Kazakhstan: RBM, a light-use-efficiency model based on MODIS products, and BETHY/DLR, a soil-vegetation-atmosphere-transfer model. Differences in intermediate products, their influence on calculated NPP, as well as output products are evaluated and discussed. BETHY/DLR calculates higher NPP (mean annual NPP 2010 and 2011: 136.87 g C m^-2 and 106.69 g C m^-2) than RBM (62.14 g C m^-2 and 54.61 g C m^-2) and shows stronger inter-annual changes. Spatial and seasonal patterns present well phenological differences. Comparison to field data from 2011 showed better results for BETHY/DLR, though both results were highly correlated to the field observations (BETHY/DLR: R²=0.95, RMSE=8.36 g C m^-2; RBM: R²=0.98, RMSE=22.49 g C m^-2). The parameterization of the light use efficiency is critical for RBM; also MODIS based 16-day time steps might be too long to capture variable climatic conditions. For BETHY/DLR, the MODIS land cover product applied in this study differentiates insufficient classes within the semi-arid environment; a more detailed land cover map is needed to improve the regional analysis.