College of Resources and Environment, Shandong Agricultural University, Tai'an, 271018, China.
National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai'an, 271018, China.
J Plant Res. 2021 Jul;134(4):729-736. doi: 10.1007/s10265-020-01249-1. Epub 2021 Feb 15.
To obtain accurate spatially continuous reflectance from Unmanned Aerial Vehicle (UAV) remote sensing, UAV data needs to be integrated with the data on the ground. Here, we tested accuracy of two methods to inverse reflectance, Ground-UAV-Linear Spectral Mixture Model (G-UAV-LSMM) and Minimum Noise Fraction-Pixel Purity Index-Linear Spectral Mixture Model (MNF-PPI-LSMM). At wavelengths of 550, 660, 735 and 790 nm, which were obtained by UAV multispectral observations, we calculated the canopy abundance based on the two methods to acquire the inversion reflectance. The correlation of the inversion and measured reflectance values was stronger in G-UAV-LSMM than MNF-PPI-LSMM. We conclude that G-UAV-LSMM is the better model to obtain the canopy inversion reflectance.
为了从无人机遥感中获得准确的空间连续反射率，需要将无人机数据与地面数据进行集成。在这里，我们测试了两种反演反射率的方法的准确性，即地面-无人机线性光谱混合模型（G-UAV-LSMM）和最小噪声分数-像素纯度指数-线性光谱混合模型（MNF-PPI-LSMM）。在 UAV 多光谱观测获得的 550、660、735 和 790nm 波长下，我们根据这两种方法计算冠层丰度，以获取反演反射率。与 MNF-PPI-LSMM 相比，G-UAV-LSMM 中反演和实测反射率值的相关性更强。我们得出结论，G-UAV-LSMM 是获取冠层反演反射率的更好模型。
