Abstract: Based on Hyperion hyperspectral image data, the image-derived shifting sand, false Gobi spectra, and field-measured sparse vegetation spectra were taken as endmembers, and the sparse vegetation coverage (<40%) in Minqin oasisdesert transitional zone of Gansu Province was estimated by using fully constrained linear spectral mixture model (LSMM) and non constrained LSMM, respectively. The results showed that the sparse vegetation fraction based on fully constrained LSMM described the actual sparse vegetation distribution. The differences between sparse vegetation fraction and field-measured vegetation coverage were less than 5% for all samples, and the RMSE was 3.0681. However, the sparse vegetation fraction based on non-constrained LSMM was lower than the field-measured vegetation coverage obviously, and the correlation between them was poor, with a low R² of 0.5855. Compared with McGwire’s corresponding research, the sparse vegetation coverage estimation in this study was more accurate and reliable, having expansive prospect for application in the future.

Key words: hyperspectral, endmember, mixed pixel decomposition, sparse vegetation coverage, tillage method, soil physicochemical properties, biological characteristics, conservation tillage.