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Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer

机译:使用连续去除和辐射转移从机载图像数据中检索云杉叶叶绿素含量

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摘要

We investigate combined continuum removal and radiative transfer (RT) modeling to retrieve leaf chlorophyll a & b content (C_(ab)) from the AISA Eagle airborne imaging spectrometer data of sub-meter (0.4 m) spatial resolution. Based on coupled PROSPECT-DART RT simulations of a Norway spruce (Picea abies (L.) Karst.) stand, we propose a new C_(ab) sensitive index located between 650 and 720nm and termed ANCB_(650-720). The performance of ANCB_(650-720) was validated against ground-measured C_(ab) of ten spruce crowns and compared with C_(ab) estimated by a conventional artificial neural network (ANN) trained with continuum removed RT simulations and also by three previously published chlorophyll optical indices: normalized difference between reflectance at 925 and 710nm (ND_(925&710)), simple reflectance ratio between 750 and 710nm (SR_(750/710)) and the ratio of TCARI/OSAVI indices. Although all retrieval methods produced visually comparable C_(ab) spatial patterns, the ground validation revealed that the ANCB_(650-720) and ANN retrievals are more accurate than the other three chlorophyll indices (R~2=0.72 for both methods). ANCB_(650-720) estimated C_(ab) with an RMSE=2.27μgcm~(-2) (relative RRMSE=4.35%) and ANN with an RMSE=2.18μgcm~(-2) (RRMSE=4.18%), while SR_(750/710) with an RMSE=4.16μgcm~(-2) (RRMSE=7.97%), ND_(925&710) with an RMSE=9.07μgcm~(-2) (RRMSE=17.38%) and TCARI/OSAVI with an RMSE=12.30μgcm~(-2) (RRMSE=23.56%). Also the systematic RMSE_S was lower than the unsystematic one only for the ANCB_(650-720) and ANN retrievals. Our results indicate that the newly proposed index can provide the same accuracy as ANN except for C_(ab) values below 30μgcm~(-2), which are slightly overestimated (RMSE=2.42μgcm~(-2)). The computationally efficient ANCB_(650-720) retrieval provides accurate high spatial resolution airborne C_(ab) maps, considerable as a suitable reference data for validating satellite-based C_(ab) products.
机译:我们研究了连续去除和辐射传输(RT)组合模型,以从亚米(0.4 m)空间分辨率的AISA Eagle机载成像光谱仪数据中检索叶绿素a和b含量(C_(ab))。基于挪威云杉(Picea abies(L.)喀斯特地区)林分的耦合PROSPECT-DART RT模拟,我们提出了一个新的C_(ab)敏感指数,位于650至720nm之间,称为ANCB_(650-720)。 ANCB_(650-720)的性能已针对地面测量的十个云杉冠的C_(ab)进行了验证,并与通过连续移除RT模拟训练的传统人工神经网络(ANN)估算的C_(ab)进行了比较先前发布的叶绿素光学指数:925和710nm(ND_(925&710))的反射率之间的归一化差,750和710nm(SR_(750/710))的简单反射率之比以及TCARI / OSAVI指数之比。尽管所有检索方法都产生了视觉上可比的C_(ab)空间模式,但地面验证表明,ANCB_(650-720)和ANN检索比其他三个叶绿素指数更准确(两种方法的R〜2 = 0.72)。 ANCB_(650-720)用RMSE =2.27μgcm〜(-2)(相对RRMSE = 4.35%)和ANN用RMSE =2.18μgcm〜(-2)(RRMSE = 4.18%)估算C_(ab),而RMS_(750/710)的RMSE =4.16μgcm〜(-2)(RRMSE = 7.97%),ND_(925&710)的RMSE =9.07μgcm〜(-2)(RRMSE = 17.38%)和TCARI / OSAVI RMSE =12.30μgcm·(-2)(RRMSE = 23.56%)。同样,仅对于ANCB_(650-720)和ANN检索,系统的RMSE_S低于非系统的RMSE_S。我们的结果表明,新提出的指标可以提供与ANN相同的精度,只是C_(ab)值低于30μgcm〜(-2),而C_(ab)值被高估了(RMSE =2.42μgcm〜(-2))。计算有效的ANCB_(650-720)检索提供了准确的高空间分辨率机载C_(ab)地图,可作为验证基于卫星的C_(ab)产品的合适参考数据。

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