title: Estimation for sparse vegetation information in desertification region based on Tiangong-1 hyperspectral image.
abstract: 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.
authors.forename: Jun-Jun
authors.initials: JJ
authors.lastname: Wu
authors.forename: Zhi-Hai
authors.initials: ZH
authors.lastname: Gao
authors.forename: Zeng-Yuan
authors.initials: ZY
authors.lastname: Li
authors.forename: Hong-Yan
authors.initials: HY
authors.lastname: Wang
authors.forename: Yong
authors.initials: Y
authors.lastname: Pang
authors.forename: Bin
authors.initials: B
authors.lastname: Sun
authors.forename: Chang-Long
authors.initials: CL
authors.lastname: Li
authors.forename: Xu-Zhi
authors.initials: XZ
authors.lastname: Li
authors.forename: Jiu-Xing
authors.initials: JX
authors.lastname: Zhang
mesh_terms: D018533:Biomass; D002681:China; D003247:Conservation of Natural Resources; D003889:Desert Climate; D016014:Linear Models; D008962:Models, Theoretical; D010944:Plants; D012044:Regression Analysis; D012987:Soil; D013057:Spectrum Analysis
chemical_list: D012987:Soil