研究浑善达克沙地光合/非光合植被(photosynthetic/non-photosynthetic vegetation, PV/NPV)及裸土(bared soil, BS)光谱混合机理, 对于构建沙地最佳光谱混合模型、 准确估算沙地地表植被覆盖信息具有重要意义。 本研究通过两景覆盖研究区的Hyperion高光谱影像获取47个典型混合样地对应混合光谱信息, 利用地面实测获取PV/NPV及BS端元光谱和每个样地各端元丰度信息, 然后分别尝试采用线性光谱混合模型和非线性光谱混合模型对所有样地混合光谱进行分解计算光合植被覆盖度(fractional cover of photosynthetic vegetation, fpv)和非光合植被覆盖度(fractional cover of non-photosynthetic vegetation, fnpv), 通过比较不同模型分解均方根误差及PV/NPV覆盖度估算精度来探索浑善达克沙地PV/NPV及BS之间光谱混合形成机理, 寻求适合其fpv与fnpv估算的最佳光谱混合模型。 结果表明: 对于浑善达克沙地来说, 基于PV/NPV及BS的线性光谱混合模型可以实现fpv与fnpv的较好估算, fpv估算的均方根误差为0.12(R2=0.84), fnpv估算的均方根误差为0.13(R2=0.66); 考虑多重散射影响的非线性光谱混合模型无论在模型分解精度还是在fpv与fnpv估算精度上均没有明显提升, 其中各端元之间的多重散射作用对fpv估算精度的影响不大, 但会导致fnpv估算精度的明显降低。

Analysis of spectral mixing mechanism of photosynthetic vegetation (PV)/non-photosynthetic vegetation (NPV) and bared soil (BS) mixture would be essential to establish the optimal spectral mixture model and further improve the estimation accuracy of sparse vegetation coverage in Hunshandake (Otindag) sandy land, Inner Mongolia of China. Over the past several decades, remote sensing has been widely utilized for estimating the fractional cover of vegetation. However, most efforts have been devoted to the estimation of fractional cover of photosynthetic vegetation (fpv) rather than fractional cover of non-photosynthetic vegetation (fnpv), although the latter is equally important, especially in desertified regions. Among of which, linear spectral mixture analysis was the most popular approach since its simplicity and operability, while the effects of the multiple scattering and the resulting nonlinear mixing problem is seldom concerned. Therefore, taking the Otindag sandy land as the study area, the mixed spectra were acquired over 47 sample plots through utilizing two Hyperion images, and the endmember(PV/NPV and BS) spectral and fraction were acquired through field investigation. Then, linear spectral mixture model(LSMM)and non-linear spectral mixture model (NSMM) including different multiple scattering combinations were adopted to decompose the mixed spectra to determine the optimal spectral mixture model based on the root mean square error (RMSE) of the unmixing and estimation accuracy of fpv and fnpv. The results show: (1) The LSMM consists of PV/NPV and BS endmembers performs fairly well in Hunshandake (Otindag) sandy land, with a RMSE of 0.12 for fpv(R2=0.84) and a RMSE of 0.13 for fnpv (R2=0.66); (2) The performance of NSMMs, which consider different multiple photon scattering effects scenarios, do not improve significantly whether in unmixing RMSE or estimation accuracy of fpv and fnpv. Moreover, non-linear mixing effects among different endmembers has little effect on the estimation accuracy of fpv, but will result in a significant reduction of estimation accuracy on fnpv.