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Top-of-atmosphere hyperspectral remote sensing of solar-induced chlorophyll fluorescence: A review of methods

  • role： First author第一作者
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zhanglf@radi.ac.cn
  • Introduction：张立福(1967—　)，男，研究员，研究方向为高光谱遥感技术和应用。E-mail：zhanglf@radi.ac.cn
Lifu ZHANG1,  
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

Siheng WANG12,  
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Changping HUANG1

Resümee

Solar-Induced chlorophyll Fluorescence (SIF) is directly related to photosynthesis and therefore considered a promising tool for grossprimary productivityestimation and vegetation environmental stress monitoring. Interest in SIF has increased since satellite remote sensing of SIF became feasible, especially after the first global SIF map was depicted in 2011. However, methods for retrieving SIF at Top-Of-Atmosphere (TOA) are still under investigation and argumentation, as decoupling SIF from total at-sensor radiance is challenging in the presence of atmospheric scattering and absorption. This paper aims to review the methods proposed for SIF retrieval at TOA over the past 10 years, to illustratethe advantages/disadvantages of those methods and to provide a technical instruction for remote sensingof SIF at airborne/space level. All the methods were categorized into three types: methods based on Radiative Transfer (RT) calculations, simplified physically-based methods and data-driven approaches. Methods based on RT calculations, including improved Fraunhofer Line Discrimination (FLD) methods and Spectral Fitting Methods (SFM), aim to retrieve SIF using atmospheric absorption lines. Atmosphere is characterized through RT calculations and then the TOA problem is converted to Bottom-Of-Atmosphere (BOA). These methods are applicable in situations with medium to low spectral resolution (0.3—5 nm) whereas imperfect characterization of atmosphere and RT process will lead to retrieval errors. Physically-based methods utilize single or several solar Fraunhofer Lines located in atmospheric windows, using solar irradiance spectra (measured or simulated through spectra convolution) as reference, decoupling SIF signal from earth radiances. Atmosphere scattering and absorption are neglected under these situations. Physically-based methods were developed for high spectral resolution measurements (e.g. 0.025 nm for GOSAT) and are sensitive to noise. Data driven approaches consider any fluorescent radiance spectrum consists non-fluorescent portion and SIF signal. Features extracted from large training dataset consisting non-fluorescent spectra (cloud, ice/snow, desert…) are used to express the non-fluorescent portion in the fluorescent spectra while SIF signal is expressed as mathematical function or spectra with fixed shape. Data driven approaches are popular because they do not require RT calculations while are applicable for medium to high spectral resolution situations. The performance of data driven approaches depends on the representativeness of training dataset and other empirical settings of the model, including the number of features used, function used to describe SIF spectrum and retrieval window selected. With several satellite sensors with medium spectral resolution (0.3—0.5 nm) being available for SIF retrieval (including currently available ones and scheduled to be launched in near future ones), including MetOp-GOME-2, Sentinel-5-TROPOMI and the FLuorescence EXplorer (FLEX) mission, RT-based methods and data-driven approaches are considered most promising SIF retrieval methods in the future. The RT-based methods are mainly developed by the FLEX team and are applicable for low spectral resolution and airborne data, but the performance of these methods on global SIF retrieval needs to be validated with real satellite data. On the other hand, several global SIF products have been generated using data driven approaches. However, representative training dataset needs to be built carefully and optimal parameters need to be determined according to different sensors.

Schlüsselwort

SIF retrieval;hyperspectral remote sensing;radiative transfer;data-driven approaches

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