Zhu Lei, Xu Jun-feng, Huang Jing-feng, Wang Fu-min, Liu Zhan-yu, Wang Yuan
Institute of Agriculture Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2008 Aug;28(8):1827-31.
In order to boost the study and application of hyperspectral remote sensing for the estimation of crop vegetation coverage percentage, an ASD FieldSpec Pro FRTM spectroradiometer was used for canopy spectral measurements of rape, corn and rice at different vegetation cover levels and photos of individual plants were taken simultaneously in order to calculate the vegetation cover percentage in computer. Firstly, data of three crops respectively and the mixed data of them were used to make correlation analysis between vegetation coverage percentage and reflectance spectra There was a high correlation between them and no obvious difference in correlation coefficient among different types of crop in the region of blue, red and near-infrared band. This indicated that it was feasible to make correlation analysis and build estimation model using mixed data Secondly, mixed data were used as unique analytical data to calculate red edge variables and pair combination of bands in the region of blue, red and near-infrared band was used to calculate normal difference vegetation index (NDVI). Hyperspectral estimation models with NDVI and red edge variable as independent variable were built individually. The correlation coefficient of the former was larger than the latter, which indicated that NDVI was most effective for the estimation of vegetation coverage percentage. Effective wavelength combinations of NDVI for vegetation cover percentage estimation were determined based on the principle of higher correlation coefficient. NDVI combined with bands in the regions from 350 to 590 nm and from 710 to 1150 nm or bands in the regions from 590 to 710 nm and from 710 to 1300 nm are most effective for vegetation coverage percentage estimation. The best estimation model is simple quadratic equation using NDVI(696-921) as independent variable. The correlation coefficient matrix shows that most of the correlation coefficients of vegetation coverage percentage and NDVI combined with bands in the regions from 630 to 690 nm and from 760 to 900 nm are larger than 0.8. These two band regions correspond to TM3 and TM4 of landsat 4,5,7. It proves that NDVI(TM3-TM4) can be used to and has been used to simulate vegetation coverage percentage. In order to further the study, TM3 and TM4 of Landsat5 was modeled according to spectral response function to calculate NDVI. Correlation analysis was made with NDVI and corresponding vegetation coverage percentage. The correlation coefficient of them was 0.80 and the regression equation was verified by experimental data. This is exploratory research for the calculation of vegetation coverage percentage using TM data in large area.
为推动高光谱遥感在作物植被覆盖度估算方面的研究与应用，采用美国ASD公司的FieldSpec Pro FRTM光谱辐射仪对不同植被覆盖度水平下的油菜、玉米和水稻进行冠层光谱测量，并同步拍摄单株照片，以便在计算机上计算植被覆盖度。首先，分别利用三种作物的数据以及它们的混合数据，对植被覆盖度与反射光谱进行相关性分析。在蓝、红和近红外波段区域，它们之间具有较高的相关性，且不同作物类型之间的相关系数无明显差异。这表明利用混合数据进行相关性分析和建立估算模型是可行的。其次，将混合数据作为唯一的分析数据来计算红边变量，并利用蓝、红和近红外波段区域的波段对组合来计算归一化植被指数（NDVI）。分别建立了以NDVI和红边变量为自变量的高光谱估算模型。前者的相关系数大于后者，这表明NDVI对植被覆盖度的估算最为有效。基于相关系数较高的原则，确定了用于植被覆盖度估算的NDVI有效波长组合。NDVI与350至590nm和710至1150nm区域的波段组合，或590至710nm和710至1300nm区域的波段组合对植被覆盖度估算最为有效。最佳估算模型是以NDVI(696 - 921)为自变量的简单二次方程。相关系数矩阵表明，植被覆盖度与NDVI和630至690nm以及760至900nm区域波段组合的大多数相关系数大于0.8。这两个波段区域对应于陆地卫星4、5、7的TM3和TM4。证明了NDVI(TM3 - TM4)可用于并已用于模拟植被覆盖度。为进一步开展研究，根据光谱响应函数对陆地卫星5的TM3和TM4进行建模以计算NDVI。对NDVI与相应的植被覆盖度进行相关性分析。它们的相关系数为0.80，并通过实验数据对回归方程进行了验证。这是利用TM数据大面积计算植被覆盖度的探索性研究。
