为了精准地估测荒漠化地区的稀疏植被信息, 选取内蒙古苏尼特右旗为研究区, 以天宫一号高光谱数据为数据源, 结合野外实地调查数据, 通过归一化植被指数(normalized difference vegetation index, NDVI)和土壤调节植被指数(soil adjusted vegetation index, SAVI)对研究区内的植被覆盖度和生物量进行反演, 并对比两种植被指数的优劣。 首先, 分析了每种波段组合下的植被指数与覆盖度、 生物量的相关性, 确定了最大相关的波段组合。 覆盖度和生物量与NDVI的最大相关系数可达07左右, 而与SAVI的最大相关系数可达08左右。 NDVI的最佳波段组合的红光波段中心波长为630 nm, 近红外波段的中心波长为910 nm, 而SAVI的组合为620和920 nm。 其次, 分别构建了两种植被指数与覆盖度、 生物量之间的线性回归模型, 所建模型的R2均能达到05以上。 SAVI所建模型R2要比NDVI略高, 其中植被覆盖度的反演模型R2高达059。 经留一交叉验证, SAVI所建模型的均方根误差RMSE也比基于NDVI的模型小。 结果表明: 天宫一号高光谱数据丰富的光谱信息能有效地反映地表植被的真实情况, 并且SAVI比NDVI更能较为精准地估测荒漠化地区的稀疏植被信息。

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.