草地绿色生物量是监测草地生态系统的重要指标。 高效高精度估算草地绿色生物量对草地生态系统具有重要意义。 遥感技术因方便快捷、 成本较低等优势, 已被广泛应用于生物量估算, 而传统光学遥感技术易受云层、 气候条件等因素影响, 不适用于高密度植被区。 因此, 受外界环境影响较小且具有一定穿透性的合成孔径雷达技术在生物量估算中得到了推广; 但当前SAR技术多用于估算森林生物量与作物生物量, 鲜有估算草地绿色生物量的研究。 故选取内蒙古草原为研究区, 基于Sentinel-1A SLC影像提取后向散射系数、 纹理特征、 极化分解量共11种雷达指数, 并根据已有雷达植被指数(σ0和σ′0)引入2种雷达植被指数(σ１和σ′１), 结合草地绿色生物量实测数据分别对15种雷达指数进行建模分析。 结果表明纹理特征中的均值、 后向散射系数σＶＨ为估算草地绿色生物量最佳雷达指数, 其估算模型R2分别为0.54和0.60, RMSE分别为47.3和44.3 g·m-2, 此外, 雷达植被指数σ0和σ1估算草地绿色生物量也可获得较高精度, 其估算模型R2分别为0.53和0.42, RMSE分别为47.6和53.0 g·m-2。 研究证明SAR技术在高效高精度草地绿色生物量估算中具有较强应用潜力, 但在误差消除方面仍需改进。

Grassland green biomass is an important index for monitoring the grassland ecosystem, and it is of great significance to estimate green biomass efficiently and accurately. Remote sensing technology has been widely used in biomass estimation due to its convenience and low cost advantages. However, traditional optical remote sensing technology is susceptible to the cloud and climatic conditions and unsuitable for high-density vegetation areas. Therefore, Synthetic Aperture Radar (SAR) technology, which is less affected by the external environment and has certain penetration, has been promoted in biomass estimation. However, the current SAR technology is mostly used to estimate forest biomass and crop biomass, and there are few studies on estimating grassland green biomass. Therefore, the Inner Mongolia grassland was selected as the research area, and 11 radar indices, including backscattering coefficient, texture characteristics and polarization decomposition, were extracted from Sentinel-1A SLC images. Two radar vegetation indices (σ１ and σ′１) were introduced based on the existing radar vegetation indices (σ0 and σ′0). Based on the measured data of grassland green biomass, 15 radar indices were modeled and analyzed respectively. The results showed that the mean value and the backscattering coefficient σＶＨ in the texture feature were the best radar indices for estimating grassland green biomass, and their estimation models R2 were 0.54 and 0.60, respectively. RMSE were 47.3 and 44.3 g·m-2, respectively. In addition, radar vegetation indices σ0 and σ１ can also be used to estimate green biomass of grassland with high accuracy, with R2 of 0.53 and 0.42, RMSE of 47.6 and 53.0 g·m-2, respectively. This study proved that SAR technology has strong application potential in high-efficiency and high-precision estimation of grassland green biomass, but it still needs improvement in error elimination.