翅碱蓬是辽东湾北部滨海湿地一种典型的植被, 其生物量的评估对了解滨海湿地生态系统生产力, 生态系统机构和功能的形成具有十分重要的作用。 而翅碱蓬覆盖度不均一, 特别是自然状态下的覆盖度较低, 土壤背景影响严重。 将基于模拟Landsat 8 OLI数据的转换型土壤调整指数(transformed soil adjusted vegetation index, TSAVI)作为自变量, 与地面实测生物量进行回归分析, 构建了翅碱蓬群落生物量反演模型。 结果表明： TSAVI（红光600～687 nm, 近红外820～880 nm）与生物量的相关性显著, 相关系数在0.9左右, 最高相关系数可达0.92； 线性、 二次多项式优于对数、 指数和幂模型, 模型拟合优度r2都为0.83, 再结合模型的F值和运算效率, 认为线性模型是反演成熟翅碱蓬生物量的最优模型。 最后, 实现了研究区域Landsat 8 OLI卫星遥感数据翅碱蓬群落生物量反演及模型验证, 估算值和实测值的相关系数r为0.962, 平均相对误差为0.106, 翅碱蓬覆盖度越大, 相对误差越低, 覆盖度低的翅碱蓬生物量反演的相对误差在0.18左右, 表明所建立的线性反演模型在高、 低覆盖度时均具有良好的反演精度； 此外, 还人为地将模型中土壤线系数a和b引入±5%扰动, 扰动后的反演结果平均相对误差比较稳定, 相关系数有所降低, 但都在0.9以上, 表明所建立反演模型具有较好的稳定性。

Suaeda salsa(S.salsa) is a typical vegetation of coastal wetland in the north of Liaodong Bay. The S. salsa biomass assessment plays an important role in understanding the ecosystem productivity of coastal wetland and the formation of ecosystem structure and function. Usually the S.salsa coverage is inhomogeneous. The low S.salsa coverage can be found at a natural condition, the soil background has a strong influence on S.salsa spectral data. The Transformed Soil Adjusted Vegetation Index (TSAVI) used as independent variable was derived by the Landsat 8 OLI simulation data. The S.salsa biomass inversion models were built based on the regression analysis of TSAVI and ground measured biomass in this study. The correlation between TSAVI (600~687, 820~880 nm) and biomass was significant, the correlation coefficient was about 0.9, up to 0.92. The results of linear and quadratic models were better than those of logarithmic, exponential and power models, the determination coefficient r2 of linear and quadratic models were 0.83. Combined with F value and operation efficiency, the linear model was the best option for mature S.salsa biomass inversion. The linear model was applied to invert the S.salsa biomass by using the Landsat 8 OLI data in the study area and it was further validated using in-situ data. The correlation coefficient between the in-situ value and retrieved value was 0.962, the relative error was 0.106. For higher S.salsa coverage, the relative error was lower. The relative error of the low-cover S.salsa biomass inversion was around 0.18. The results showed that the established model has good accuracy for different coverage. In addition, with the introduction of ±5% error of soil line parameters a and b, the average relative errors were relatively stable, and the correlation coefficients were reduced, but all the correlative coefficients were above 0.9. The results showed that the established model is stable.