Wang Fu-Min, Huang Jing-Feng, Xu Jun-Feng, Wang Xiu-Zhen
Institute of Agriculture Remote Sensing & Information System Application, Zhejiang University, Hangzhou 310029, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2008 May;28(5):1098-101.
The hyperspectral remote sensing data usually involve hundreds or even thousands of narrow bands, which may be crucial for providing additional information with significant improvements over broad bands in quantifying biophysical and biochemical variables of agricultural crop. However, the huge data generated by hyperspectral systems, and the problems this presents for storage and analysis, have far prevented the routine use of such data. The objective of the present research was to identify the spectral bands in the visible and near-infrared range that were suitable for the study of rice. The hyperspectral reflectance of canopy in different development stages was measured in experimental field using a 1 nm-wide spectroradiometer but was aggregated to 10 nm-wide bandwidths to match the first spaceborne hyperspectral sensor, Hyperion. The correlation coefficients(r) between all the combinations of spectral bands were computed, and then they were converted to R2 , which constituted R2 matrices. The matrices were plotted against wavebands. The criterion of band selection is that the lower the R2 value, the less the redundancy between two wavebands while the higher R2 indicates that there is redundant information between two wavebands. According to the criterion, the wavebands corresponding to the first 100 minimum R2 values were selected from all canopy spectra collected on different dates. And then these bands were analyzed. The results indicate that the visible and infrared (NIR and SWIR) themselves contain redundant information. The wavebands containing abundant information of rice are located in specific bands in the longer wavelength portion of the visible region, with secondary clusters in red edge region, in strongly reflective near-infrared region with relatively higher reflectance, in one particular section of short wave near-infrared (SWIR) (1 530 nm) and in the second maximum reflectance region of SWIR (2 215 nm). Compared with the selected bands with other vegetation, rice seems to have three spectral regions of 400-410 nm, 630-650 nm and 1 520-1 540 nm, which exclusively depict the characteristics of rice. Moreover, this research identified 17 spectral bands in the visible and near-infrared region, which were 405, 565, 585, 605, 620, 640, 660, 680, 695, 705, 720, 740, 865, 910, 1 085, 1 530 and 2 215 nm. These bands contain the majority of the rice information content. A reduction in band number without significant information loss is important because it makes it possible to achieve fine spatial resolution without sacrificing the ability to characterize rice status.
高光谱遥感数据通常包含数百甚至数千个窄波段,这对于在量化农作物的生物物理和生化变量方面提供比宽波段有显著改进的额外信息可能至关重要。然而,高光谱系统产生的海量数据及其在存储和分析方面带来的问题,极大地阻碍了此类数据的常规使用。本研究的目的是识别可见光和近红外范围内适合水稻研究的光谱波段。在试验田中使用1纳米宽的光谱辐射计测量了不同发育阶段冠层的高光谱反射率,但将其聚合为10纳米宽的带宽,以匹配首个星载高光谱传感器Hyperion。计算了所有光谱波段组合之间的相关系数(r),然后将其转换为R²,构成R²矩阵。将这些矩阵相对于波段进行绘制。波段选择的标准是,R²值越低,两个波段之间的冗余度越小,而R²值越高表明两个波段之间存在冗余信息。根据该标准,从不同日期收集的所有冠层光谱中选择对应前100个最小R²值的波段。然后对这些波段进行分析。结果表明,可见光和红外波段(近红外和短波红外)本身包含冗余信息。包含水稻丰富信息的波段位于可见光区域较长波长部分的特定波段,在红边区域有次级聚类,在反射率相对较高的强反射近红外区域,在短波近红外(SWIR)的一个特定部分(1530纳米)以及在SWIR的第二最大反射率区域(2215纳米)。与其他植被的选定波段相比,水稻似乎有三个光谱区域,即400 - 410纳米、630 - 650纳米和1520 - 1540纳米,它们专门描绘了水稻的特征。此外,本研究在可见光和近红外区域识别出17个光谱波段,分别为405、565、585、605、620、640、660、680、695、705、720、740、865、910、1085、1530和2215纳米。这些波段包含了水稻的大部分信息内容。在不损失显著信息的情况下减少波段数量很重要,因为这使得在不牺牲表征水稻状况能力的前提下实现精细空间分辨率成为可能。