A ground spectral model for estimating biomass at the peak of the growing season in Hulunbeier grassland, Inner Mongolia, China

Abstract

To investigate the application of hyperspectral remote sensing to estimate grassland biomass at the peak of the growing season, hyperspectral data were measured with an analytical spectral device (ASD) Fieldspec3 spectroradiometer, and harvested aboveground net primary productivity (ANPP) was recorded simultaneously in Hulunbeier grassland, Inner Mongolia, China. Ground spectral models were developed to estimate ANPP from the normalized difference vegetation index (NDVI) measured in the field following the same method as that of the National Aeronautic and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS-NDVI). Regression analysis was used to assess the relationship between ANPP and NDVI. Based on coefficients of determination (R 2) and error analysis, we determined that each vegetation type and the entire study area had unique optimal regression models. A linear equation best fit the arid steppe data, an exponential equation was best suited to wetland vegetation and power equations were optimal for meadow steppe and sand vegetation. After considering all factors, an exponential model between ANPP and NDVI (ANPP = 20.1921e3.2154(NDVI); standard error (SE) = 62.50 g m–2, R 2 = 0.7445, p < 0.001) was selected for the entire Hulunbeier grassland study area. Ground spectral models could become the foundation for yield estimation over large areas of Hulunbeier grassland.