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用于冠层出射太阳诱导叶绿素荧光近感反演的氧气透过率效应补偿

Compensation of Oxygen Transmittance Effects for Proximal Sensing Retrieval of Canopy-Leaving Sun-Induced Chlorophyll Fluorescence.

作者信息

Sabater Neus, Vicent Jorge, Alonso Luis, Verrelst Jochem, Middleton Elizabeth M, Porcar-Castell Albert, Moreno José

机构信息

Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, València, Spain.

NASA, Goddard Space Flight Centre (GSFC), Greenbelt, MD 20771, USA.

出版信息

Remote Sens (Basel). 2018 Sep 26;10(10):1551. doi: 10.3390/rs10101551.

Abstract

Estimates of Sun-Induced vegetation chlorophyll Fluorescence (SIF) using remote sensing techniques are commonly determined by exploiting solar and/or telluric absorption features. When SIF is retrieved in the strong oxygen (O) absorption features, atmospheric effects must always be compensated. Whereas correction of atmospheric effects is a standard airborne or satellite data processing step, there is no consensus regarding whether it is required for SIF proximal-sensing measurements nor what is the best strategy to be followed. Thus, by using simulated data, this work provides a comprehensive analysis about how atmospheric effects impact SIF estimations on proximal sensing, regarding: (1) the sensor height above the vegetated canopy; (2) the SIF retrieval technique used, e.g., Fraunhofer Line Discriminator (FLD) family or Spectral Fitting Methods (SFM); and (3) the instrument's spectral resolution. We demonstrate that for proximal-sensing scenarios compensating for atmospheric effects by simply introducing the O transmittance function into the FLD or SFM formulations improves SIF estimations. However, these simplistic corrections still lead to inaccurate SIF estimations due to the multiplication of spectrally convolved atmospheric transfer functions with absorption features. Consequently, a more rigorous oxygen compensation strategy is proposed and assessed by following a classic airborne atmospheric correction scheme adapted to proximal sensing. This approach allows compensating for the O absorption effects and, at the same time, convolving the high spectral resolution data according to the corresponding Instrumental Spectral Response Function (ISRF) through the use of an atmospheric radiative transfer model. Finally, due to the key role of O absorption on the evaluated proximal-sensing SIF retrieval strategies, its dependency on surface pressure (p) and air temperature (T) was also assessed. As an example, we combined simulated spectral data with p and T measurements obtained for a one-year period in the Hyytiälä Forestry Field Station in Finland. Of importance hereby is that seasonal dynamics in terms of and , if not appropriately considered as part of the retrieval strategy, can result in erroneous SIF seasonal trends that mimic those of known dynamics for temperature-dependent physiological responses of vegetation.

摘要

利用遥感技术估算太阳诱导植被叶绿素荧光(SIF)通常是通过利用太阳和/或地气吸收特征来确定的。当在强氧(O)吸收特征中反演SIF时,必须始终补偿大气效应。虽然大气效应的校正是标准的机载或卫星数据处理步骤,但对于SIF近感测量是否需要进行校正以及应遵循的最佳策略尚无共识。因此,通过使用模拟数据,本研究对大气效应如何影响近感SIF估算进行了全面分析,涉及以下方面:(1)植被冠层上方的传感器高度;(2)使用的SIF反演技术,例如夫琅禾费线鉴别器(FLD)系列或光谱拟合方法(SFM);以及(3)仪器的光谱分辨率。我们证明,对于近感场景,通过简单地将O透过率函数引入FLD或SFM公式中来补偿大气效应可改善SIF估算。然而,由于光谱卷积的大气传输函数与吸收特征相乘,这些简单的校正仍然会导致SIF估算不准确。因此,通过遵循适用于近感的经典机载大气校正方案,提出并评估了一种更严格的氧补偿策略。这种方法可以补偿O吸收效应,同时通过使用大气辐射传输模型根据相应的仪器光谱响应函数(ISRF)对高光谱分辨率数据进行卷积。最后,由于O吸收在评估的近感SIF反演策略中起着关键作用,还评估了其对地表压力(p)和气温(T)的依赖性。例如,我们将模拟光谱数据与芬兰Hyytiälä林业野外站一年内获得的p和T测量值相结合。在此重要的是,如果在反演策略中没有适当考虑p和T的季节动态,可能会导致错误的SIF季节趋势,这些趋势模仿了植被温度依赖性生理响应的已知动态。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cac3/7613352/02e6e9d3dfc5/EMS152624-f001.jpg

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