Department of Earth Physics and Thermodynamics, Image Processing Laboratory, Universidad de Valencia, P.O. Box 22085, Paterna E-46071,Valencia, Spain.
Sensors (Basel). 2011;11(7):7063-81. doi: 10.3390/s110707063. Epub 2011 Jul 8.
ESA's upcoming satellite Sentinel-2 will provide Earth images of high spatial, spectral and temporal resolution and aims to ensure continuity for Landsat and SPOT observations. In comparison to the latter sensors, Sentinel-2 incorporates three new spectral bands in the red-edge region, which are centered at 705, 740 and 783 nm. This study addresses the importance of these new bands for the retrieval and monitoring of two important biophysical parameters: green leaf area index (LAI) and chlorophyll content (Ch). With data from several ESA field campaigns over agricultural sites (SPARC, AgriSAR, CEFLES2) we have evaluated the efficacy of two empirical methods that specifically make use of the new Sentinel-2 bands. First, it was shown that LAI can be derived from a generic normalized difference index (NDI) using hyperspectral data, with 674 nm with 712 nm as best performing bands. These bands are positioned closely to the Sentinel-2 B4 (665 nm) and the new red-edge B5 (705 nm) band. The method has been applied to simulated Sentinel-2 data. The resulting green LAI map was validated against field data of various crop types, thereby spanning a LAI between 0 and 6, and yielded a RMSE of 0.6. Second, the recently developed "Normalized Area Over reflectance Curve" (NAOC), an index that derives Ch from hyperspectral data, was studied on its compatibility with simulated Sentinel-2 data. This index integrates the reflectance curve between 643 and 795 nm, thereby including the new Sentinel-2 bands in the red-edge region. We found that these new bands significantly improve the accuracy of Ch estimation. Both methods emphasize the importance of red-edge bands for operational estimation of biophysical parameters from Sentinel-2.
欧空局即将发射的卫星 Sentinel-2 将提供具有高空间、光谱和时间分辨率的地球图像,旨在确保 Landsat 和 SPOT 观测的连续性。与后两种传感器相比,Sentinel-2 在红色边缘区域增加了三个新的光谱波段,中心波长分别为 705nm、740nm 和 783nm。本研究探讨了这些新波段对于反演和监测两个重要的生物物理参数——绿叶面积指数(LAI)和叶绿素含量(Ch)的重要性。我们利用来自几个欧空局在农业地区开展的实地活动(SPARC、AgriSAR、CEFLES2)的数据,评估了两种专门利用新的 Sentinel-2 波段的经验方法的效果。首先,结果表明,LAI 可以从高光谱数据的通用归一化差异指数(NDI)中导出,其中 674nm 和 712nm 是表现最好的波段。这些波段与 Sentinel-2 的 B4(665nm)和新的红色边缘 B5(705nm)波段位置接近。该方法已应用于模拟的 Sentinel-2 数据。生成的绿色 LAI 图与各种作物类型的实地数据进行了验证,涵盖了 0 到 6 的 LAI 值,其均方根误差(RMSE)为 0.6。其次,我们研究了最近开发的“归一化面积过反射率曲线”(NAOC),这是一种从高光谱数据中导出 Ch 的指数,研究其与模拟 Sentinel-2 数据的兼容性。该指数整合了 643nm 到 795nm 之间的反射率曲线,从而将红色边缘区域的新 Sentinel-2 波段包括在内。我们发现这些新的波段显著提高了 Ch 估算的精度。这两种方法都强调了红色边缘波段对于从 Sentinel-2 进行生物物理参数的业务估算的重要性。