Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data
Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data
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DOI：
10.1109/tgrs.2007.904836
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发表时间：
2008-02-01
影响因子：
8.2
通讯作者：
Vigneault, Philippe
中科院分区：
文献类型：
--
作者：
Haboudane, Driss;Tremblay, Nicolas;Vigneault, Philippe
本文研究了利用模拟和测量的冠层反射率来估算作物冠层上的叶绿素。在2004年和2005年生长季组织的不同密集田间活动中，收集了玉米和小麦冠层的田间光谱测量数据。它们被用来测试和评估利用高光谱图像(紧凑型航空光谱成像仪)测定叶绿素的几个组合指数。利用PROCESS-SAILH冠层模拟光谱和野外测量的反射率研究了几种指数组合。在PROCESS-SAILH模拟数据和地面测量数据集上，叶片叶绿素含量与组合光学指数之间的关系显示出相似的趋势，这表明光谱测量和辐射传输模型在作物叶片色素的定量提取方面都具有相当的潜力。所使用的数据集表明，作物类型对预测方程的建立以及它们的验证有明显的影响。除了生成不同的预测方程外，即使对于相同的预测算法，玉米和小麦的数据也在估算和测量的叶绿素含量之间产生了对比一致的结果。在本文测试的一系列指数中，修正的叶绿素吸收比指数/最优土壤调节植被指数(OSAVI)、三角叶绿素指数/OSAVI、中分辨率成像光谱仪陆地叶绿素指数/改良土壤调节植被指数(MSAVI)和红边模型/MSAVI似乎是相对一致和更稳定的作物叶绿素含量估计器。
This paper examines the use of simulated and measured canopy reflectance for chlorophyll estimation over crop canopies. Field spectral measurements were collected over corn and wheat canopies in different intensive field campaigns organized during the growing seasons of 2004 and 2005. They were used to test and evaluate several combined indices for chlorophyll determination using hyperspectral imagery (Compact Airborne Spectrographic Imager). Several index combinations were investigated using both PROSPECT-SAILH canopy simulated spectra and field-measured reflectances. The relationships between leaf chlorophyll content and combined optical indices have shown similar trends for both PROSPECT-SAILH simulated data and ground-measured data sets, which indicates that both spectral measurements and radiative transfer models hold comparable potential for the quantitative retrieval of crop foliar pigments. The data set used has shown that crop type had a clear influence on the establishment of predictive equations as well as on their validation. In addition to generating different predictive equations, corn and wheat data yielded contrasting agreement between estimated and measured chlorophyll contents even for the same predictive algorithm. Among the set of indices tested in this paper, index combinations like Modified Chlorophyll Absorption Ratio Index/Optimized Soil-Adjusted Vegetation Index (OSAVI), Triangular Chlorophyll Index/OSAVI, Moderate Resolution Imaging Spectrometer Terrestrial Chlorophyll Index/Improved Soil-Adjusted Vegetation Index (MSAVI), and Red-Edge Model/MSAVI seem to be relatively consistent and more stable as estimators of crop chlorophyll content.