Using multiple radiometric correction images to estimate leaf area index
Using multiple radiometric correction images to estimate leaf area index
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DOI：
10.1080/01431161.2011.562251
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发表时间：
2011-11
影响因子：
3.4
通讯作者：
Z. Gu;Xuezheng Shi;Lin Li;Dongsheng Yu;Liu Liu-Liu;Wentai Zhang
中科院分区：
文献类型：
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作者：
Z. Gu;Xuezheng Shi;Lin Li;Dongsheng Yu;Liu Liu-Liu;Wentai Zhang
遥感技术的生态应用一般限于大气校正后的图像，虽然其他辐射校正数据可能是有价值的。本文利用SPOT 5遥感影像，在4个辐射校正水平（数字数（DN）、传感器辐射亮度（SR）、大气顶反射率（TOA）和大气校正后反射率（PAC））下，提取了6个光谱植被指数（维斯）。这些维斯指数包括归一化植被指数（NDVI）、比值植被指数（RVI）、辐射曲线斜率比（K）、总辐射水平（L）、可见光-红外辐射平衡（B）和波段辐射变化（V）。然后，它们与叶面积指数（LAI），获得从现场测量，福建省河田镇，中国。植被类型、维斯指数和影像辐射校正水平对VI-LAI相关系数的影响较大，影像辐射校正并不能提高VI-LAI相关系数。在所建立的330个VI-LAI模型中，多变量模型的R2普遍高于单变量模型。最佳VI-LAI模型的自变量包含了所有辐射校正水平下的所有维斯指数，显示了多辐射校正图像在估算LAI中的潜力。结果表明，利用多幅辐射校正图像的维斯指数，可以更好地发挥遥感信息的能力，从而提高叶面积指数的估算精度。
Ecological applications of remote-sensing techniques are generally limited to images after atmospheric correction, though other radiometric correction data are potentially valuable. In this article, six spectral vegetation indices (VIs) were derived from a SPOT 5 image at four radiometric correction levels: digital number (DN), at-sensor radiance (SR), top of atmosphere reflectance (TOA) and post-atmospheric correction reflectance (PAC). These VIs include the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), slope ratio of radiation curve (K), general radiance level (L), visible-infrared radiation balance (B) and band radiance variation (V). They were then related to the leaf area index (LAI), acquired from in situ measurement in Hetian town, Fujian Province, China. The VI–LAI correlation coefficients varied greatly across vegetation types, VIs as well as image radiometric correction levels, and were not surely increased by image radiometric corrections. Among all 330 VI–LAI models established, the R 2 of multi-variable models were generally higher than those of the single-variable ones. The independent variables of the best VI–LAI models contained all VIs from all radiometric correction levels, showing the potentials of multi-radiometric correction images in LAI estimating. The results indicated that the use of VIs from multiple radiometric correction images can better exploit the capabilities of remote-sensing information, thus improving the accuracy of LAI estimating.