比较利用辐射传递模型和大麦作物的机载昼夜测量估算太阳诱导荧光效率的方法

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Remote Sensing of Environment Pub Date : 2024-11-26 DOI:10.1016/j.rse.2024.114521
Juliane Bendig , Zbynĕk Malenovský , Bastian Siegmann , Julie Krämer , Uwe Rascher
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引用次数: 0

摘要

遥感太阳诱导的叶绿素荧光(SIF)作为植被生产力和胁迫指标的能力受到各种干扰因素的影响,如作物特定冠层结构的变化、太阳照射角度的变化以及 SIF 与土壤光学的相互作用。本研究探讨了两种归一化方法,以校正夏季大麦作物 760 纳米波长(以下简称 F760)O2-A 吸收特征所获取的昼夜冠层顶部 SIF 观测数据,消除这些干扰效应。机载成像光谱仪(HyPlant)和无人机高性能点光谱仪(AirSIF)同时采集了九块育种实验田的昼夜SIF数据。辅助测量包括从无人机高光谱图像中获取的叶片色素含量、破坏性采样的叶面积指数(LAI)以及叶片水分和干物质含量,用于测试基于以下两个方面的两种归一化方法:i)荧光校正植被指数(FCVI);ii)三种版本的植被近红外反射率(NIRV)。在离散各向异性辐射传递(DART)模型中建模,将校正后的冠层 SIF 与模拟的叶片总叶绿素荧光进行比较,发现基于 NIRv 的方法非常匹配(R2 = 0.99)。用 FCVI 进行归一化的结果也可以接受(R2 = 0.93),但与叶片发出的叶绿素荧光效率相比,它对 LAI 的变化很敏感。根据 DART 建模的结果,发现 NIRvH1 归一化比其他 NIRv 变化和 FCVI 归一化性能更优。对 SIF 逃逸分数的比较表明,用 NIRvH1 估算的逃逸分数与 DART 提取的逃逸分数更接近。将 NIRvH1 应用于实验无人机和机载天顶冠层 SIF 数据时,NIRvH1 与 FCVI 生成的叶绿素荧光效率之间的一致性非常高(R2 = 0.93)。然而,在植被覆盖率较低的地区,NIRvH1 的不确定性较高,这表明 SIF 与土壤之间的相互作用未被考虑在内。这两种方法的叶绿素荧光效率昼夜变化过程与简单的入射和表观光合有效辐射归一化差异不大。总之,用 NIRvH1 对 SIF 进行归一化可更准确地补偿冠层结构对冠层顶部远红外 SIF 的影响,但当应用于春大麦的冠层顶部原位数据时,NIRvH1 和 FCVI 对 SIF 日变化的影响相似。
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Comparing methods for solar-induced fluorescence efficiency estimation using radiative transfer modelling and airborne diurnal measurements of barley crops
Ability of remotely sensed solar-induced chlorophyll fluorescence (SIF) to serve as a vegetation productivity and stress indicator is impaired by confounding factors, such as varying crop-specific canopy structure, changing solar illumination angles, and SIF-soil optical interactions. This study investigates two normalisation approaches correcting diurnal top-of-canopy SIF observations retrieved from the O2-A absorption feature at 760 nm (F760 hereafter) of summer barley crops for these confounding effects. Nadir SIF data was acquired over nine breeding experimental plots simultaneously by an airborne imaging spectrometer (HyPlant) and a drone-based high-performance point spectrometer (AirSIF). Ancillary measurements, including leaf pigment contents retrieved from drone hyperspectral imagery, destructively sampled leaf area index (LAI), and leaf water and dry matter contents, were used to test the two normalisation methods that are based on: i) the fluorescence correction vegetation index (FCVI), and ii) three versions of the near-infrared reflectance of vegetation (NIRV). Modelling in the discrete anisotropic radiative transfer (DART) model revealed close matches for NIRv-based approaches when corrected canopy SIF was compared to simulated total chlorophyll fluorescence emitted by leaves (R2 = 0.99). Normalisation with the FCVI also performed acceptably (R2 = 0.93), however, it was sensitive to variations in LAI when compared to leaf emitted chlorophyll fluorescence efficiency. Based on the results modelled in DART, the NIRvH1 normalisation was found to have a superior performance over the other NIRv variations and the FCVI normalisation. Comparison of the SIF escape fractions suggests that the escape fraction estimated with NIRvH1 matched escape fraction extracted from DART more closely. When applied to the experimental drone and airborne nadir canopy SIF data, the agreement between NIRvH1 and FCVI produced chlorophyll fluorescence efficiency was very high (R2 = 0.93). Nevertheless, NIRvH1 showed higher uncertainties for areas with low vegetation cover indicating an unaccounted contribution of SIF-soil interactions. The diurnal courses of chlorophyll fluorescence efficiency for both approaches differed not significantly from simple normalisation by incoming and apparent photosynthetically active radiation. In conclusion, SIF normalisation with NIRvH1 more accurately compensates the effects of canopy structure on top of canopy far red SIF, but when applied to top of canopy in-situ data of spring barley, the effects of NIRvH1 and FCVI on the diurnal course of SIF had a similar influence.
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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