Estimation for sparse vegetation information in desertification region based on Tiangong-1 hyperspectral image.

In order to estimate the sparse vegetation information accurately in desertification region, taking southeast of Sunite Right Banner, Inner Mongolia, as the test site and Tiangong-1 hyperspectral image as the main data, sparse vegetation coverage and biomass were retrieved based on normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), combined with the field investigation data. Then the advantages and disadvantages between them were compared. Firstly, the correlation between vegetation indexes and vegetation coverage under different bands combination was analyzed, as well as the biomass. Secondly, the best bands combination was determined when the maximum correlation coefficient turned up between vegetation indexes (VI) and vegetation parameters. It showed that the maximum correlation coefficient between vegetation parameters and NDVI could reach as high as 0.7, while that of SAVI could nearly reach 0.8. The center wavelength of red band in the best bands combination for NDVI was 630nm, and that of the near infrared (NIR) band was 910 nm. Whereas, when the center wavelength was 620 and 920 nm respectively, they were the best combination for SAVI. Finally, the linear regression models were established to retrieve vegetation coverage and biomass based on Tiangong-1 VIs. R2 of all models was more than 0.5, while that of the model based on SAVI was higher than that based on NDVI, especially, the R2 of vegetation coverage retrieve model based on SAVI was as high as 0.59. By intersection validation, the standard errors RMSE based on SAVI models were lower than that of the model based on NDVI. The results showed that the abundant spectral information of Tiangong-1 hyperspectral image can reflect the actual vegetaion condition effectively, and SAVI can estimate the sparse vegetation information more accurately than NDVI in desertification region.

[Accuracy comparison of BJ-1, HJ and Landsat data in the retrieval of grassland vegetation coverage, leaf area index and above ground biomass].

Domestic satellites BJ-1, HJ and the most widely used satellite Landsat were selected to systematically compare their abilities and differences on the estimation of the biophysical parameters of grassland in sandstorm source region in Beijing and Tianjin, with the combination of field-measured fractional coverage, leaf area index and aboveground biomass data. The result shows: (1) In terms of the surface reflectance, HJ-1B and Landsat have a higher correlation with biophysical parameters in red band, compared with BJ-1, while BJ-1's near infra-red band was obviously superior to HJ-1B and Landsat, (2) with respect to the vegetation indices, Landsat performed best, HJ-1B was the second, and BJ-1 was the worst, (3) compared with vegetation indices, multiple regression model can raise the estimation accuracy, BJ-1 based model improved significantly, while Landsat and HJ-1B based models were less obvious. Among them, the highest accuracy was acquired for leaf area index estimation through the BJ-1 based model (R2 = 0.61, RMSEP = 0.15). In general, domestic satellites have their own unique features, which remain a huge potential to be further tapped.

[Estimation of sparse vegetation coverage in arid region based on hyperspectral mixed pixel decomposition].

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.