Spatial Scale Transformation-based Estimation Model for Fresh Grass Yield: A Case Study of the Xilingol Grassland, Inner Mongolia, China

Abstract Estimating the grass yield of a grassland is of vital theoretical and practical significance for reasonably determining its grazing capacity and maintaining its ecological balance. On that account, this paper first compares model precision by adopting normalized differential vegetation index (NDVI) and net primary productivity (NPP) as grass yield estimation factors, and then proposes a spatial scale transformation (SST)-based estimation model for fresh grass yield (FGY) adopting NPP as its estimation factor. Next, it takes the grassland in Xilingol League, Inner Mongolia as the study area for precision verification and grass yield estimation. Results indicated that: (1) The precision of the model adopting NPP as the estimation factor was clearly higher than that of the model adopting NDVI. (2) Through modifying NPP, the SST-based FGY estimation model could greatly improve estimation precision. The relative precisions of the estimation models constructed using linear and power functions were 18.16% and 18.35%, respectively. (3) The estimation models constructed using linear and power functions were employed to estimate the grass yield of the grassland in Xilingol League, and the total FGYs estimated by them were 8.777×10 10 kg and 8.583×10 10 kg, respectively. The two models obtained roughly the same estimates, but there were significant differences between them in the spatial distributions of FGY per unit.