Abstract
Quantitative estimations of the fractional cover of photosynthetic vegetation (f(PV)), non-photosynthetic vegetation (f(NPV)) and bare soil (f(BS)) are critical for soil wind erosion, desertification, grassland grazing, grassland fire, and grassland carbon storage studies. At present, regional and large-scale f(PV), f(NPV) and f(BS) estimations have been carried out in many areas. However, few studies have used moderate resolution imaging spectroradiometer (MODIS) data to perform large-scale, long-term f(PV), f(NPV) and f(BS) estimations in the Xilingol grassland of China. The objective of this study was to quantitatively estimate the time series of f(PV), f(NPV) and f(BS) in the typical grassland region of Xilingol from MODIS image data. Field measurement spectral and coverage data from May and September 2017 were combined with the 8-day composite product (MOD09A1) acquired during 2017. We established an empirical linear model of different non-photosynthetic vegetation indices (NPVIs) and f(NPV) based on the sample scale. The linear correlation between the dead fuel index (DFI) and f(NPV) was best (R-2 = 0.60, RMSE = 0.15). A normalized difference vegetation index (NDVI)-DFI model based on MODIS data was proposed to accurately estimate the f(PV), f(NPV) and f(BS) (estimation accuracies of 44%, 71%, and 74%, respectively) in the typical grasslands of Xilingol in China. The f(PV), f(NPV) and f(BS) values for the typical grassland time series estimated by the NDVI-DFI model were consistent with the phenological characteristics of the grassland vegetation. The results show that the application of the NDVI-DFI model to the Xilingol grassland is reasonable and appropriate, and it is of great significance to the monitoring of soil wind erosion and fires in grasslands.
NSTL
Elsevier