NPP Estimation Using Time-Series GF-1 Data in Sparse Vegetation Area

Abstract

Net primary productivity (NPP) is an important ecological indicator to evaluate ecosystem, and it is useful for land degradation assessment and monitoring. However, owing to dryland's particularity, retrieving vegetation properties from satellite remote sensing presents some significant challenges in sparse vegetation area. In this study, based on the wildly used time-series GF-1 data, the NPP estimation in sparse vegetation area was analyzed. Results showed that GF-1 data have high spatial and high temporal resolution characteristics, it is useful to distinguish land cover types in semi-arid areas based on NDVI time series data, the accuracy was 83.37% and Kappa coefficient was 0.79. Some key parameters of grassland were simulated and optimized based on CASA model. Compared with the measured data, the result was R2 with 0.71, and results indicated that NPP estimation by GF-1 data based on the new parameters in semi-arid area is feasible.