A Remote Sensing Based Forage Biomass Yield Inversion Model of Alpine-cold Meadow during Grass-withering Period in Sanjiangyuan Area

Estimating forage biomass yield remotely from space is still challenging nowadays. Field experiments were conducted and ground measurements correlated to remote sensing data to estimate the forage biomass yield of Alpine-cold meadow grassland during the grass and grass-withering period in Sanjiangyuan area in Yushu county. Both Shapiro-Wilk and Kolmogorov-Smirnov two-tailed tests showed that the field training samples are normally distributed, the Spearman coefficient indicated that the parametric correlation analysis had significant differences. The optimal regression models were developed based on the Landsat Thematic Mapper Normalized Difference Vegetation Index (TM-NDVI) and the forage biomass field data during the grass and the grass-withering periods, respectively. Then an integration model was used to predict forage biomass yield of alpine-cold meadow in the grasswithering period. The model showed good prediction accuracy and reliability. It was found that this approach can not only estimate forage yield in large scale efficiently but also overcome the seasonal limitation of remote sensing inversion. This technique can provides valuable guidance to animal husbandry to resource more efficiently in winter.