Mapping and monitoring of Arctic vegetation biochemical and biophysical properties is gaining importance as global climate change is disproportionately affecting this region. Previous studies using remote sensing to model Arctic vegetation biochemical and biophysical properties have generally involved empirical modelling with nadir looking broadband sensors and have typically been conducted at the field scale in one study area. Satellite hyperspectral remote sensing has not been previously investigated for retrieving leaf and canopy biochemical and biophysical properties of Arctic vegetation across multiple sites using either empirical or physically-based modelling approaches. Furthermore, multi-angle hyperspectral sensors (CHRIS/PROBA), which can provide insight into vegetation reflectance anisotropy and potentially improve vegetation parameter estimation, have also not been investigated for this purpose. In this study, three modelling approaches previously investigated with field spectroscopy data (Kennedy et al., 2020) were used with CHRIS Mode-1 imagery to predict leaf chlorophyll content, plant area index and canopy chlorophyll content across a bioclimatic gradient in the Western Canadian Arctic. Modelling approaches included: parametric linear regression based on vegetation indices (VI), non-parametric machine learning Gaussian processes regression (GPR) and inversion of the PROSAIL radiative transfer model using a look-up table approach (LUT). CHRIS imagery was acquired with −55°, −36°, 0°, +36°, +55° view zenith angles (VZA) between 2011 and 2014 over three field sites extending from the Richardson Mountains in central Yukon, Canada to the north end of Banks Island, Northwest Territories, Canada. Field measurements were acquired within several weeks of satellite acquisitions. GPR had the best model fit (mean cross-validated (_cv) coefficient of determination, r²_cv = 0.61 across all vegetation variables, sites and VZAs vs. 0.59 for the simple ratio, SR) and predictive performance (normalized root mean square error, NRMSE_cv = 0.13 vs. 0.14 for SR). The revised optimized soil adjusted VI (ROSAVI) performance was slightly poorer (r²_cv = 0.51; NRMSE_cv = 0.15). The physically-based PROSAIL model performed poorer than all empirical models (r² = 0.50; NRMSE = 0.18). This ranking of model performance is similar to that found in the previous field spectroscopy study, where empirical model fits and predictive performance were only slightly worse. With respect to view angle performance, NRMSE varied only slightly, indicating no distinct advantage for any one VZA. Overall, strong potential has been demonstrated for empirical modelling of Arctic vegetation chlorophyll and plant area index using hyperspectral data combined with band selection/optimization procedures in the Arctic. Recently launched and future hyperspectral satellites, including next generation airborne sensors, will likely provide improvements to the model performance reported here.

中文翻译：

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利用经验和物理模型从CHRIS / PROBA多角度影像中检索北极植被的生物物理和生化特性

随着全球气候变化不成比例地影响该地区，对北极植被生化和生物物理特性进行制图和监测变得越来越重要。以前使用遥感对北极植被的生化和生物物理特性进行建模的研究通常涉及使用最低点宽带传感器进行的经验建模，并且通常是在一个研究区域的实地规模上进行的。以前尚未对卫星高光谱遥感进行过经验研究或基于物理的建模方法来检索多个地点的北极植被的叶片和冠层生化和生物物理特性的研究。此外，多角度高光谱传感器（CHRIS / PROBA）可以提供关于植被反射率各向异性的见解并可能改善植被参数估计的方法，尚未为此目的进行研究。在这项研究中，先前使用现场光谱数据研究的三种建模方法（Kennedy等人，2020年）与CHRIS Mode-1影像一起用于预测加拿大西部生物气候梯度下的叶绿素含量，植物面积指数和冠层叶绿素含量。北极。建模方法包括：基于植被指数（VI）的参数线性回归，非参数机器学习高斯过程回归（GPR）以及使用查找表方法（LUT）进行PROSAIL辐射传递模型的反演。CHRIS影像是在-55°，-36°，0°，+ 36°，从加拿大育空地区中部的理查森山脉一直延伸到加拿大西北地区班克斯岛的北端的三个野外站点，2011年至2014年之间的+ 55°天顶角（VZA）。实地测量是在卫星采集后的几周内获得的。GPR具有最佳模型拟合度（均值交叉验证（_cv）的确定系数，所有植被变量，位点和VZA的r ² _cv = 0.61，简单比率SR为0.59）和预测性能（归一化均方根误差，NRMSE _cv = 0.13对SR为0.14）。修改后的优化土壤调节VI（ROSAVI）性能稍差（ r ² _cv = 0.51； NRMSE _cv = 0.15）。基于物理的PROSAIL模型的绩效比所有经验模型都要差（ r ²= 0.50; NRMSE = 0.18）。该模型性能的排名与先前的现场光谱研究中发现的相似，在该研究中，经验模型的拟合和预测性能仅稍差一些。在视角性能方面，NRMSE仅略有变化，这表明任何一个VZA都没有明显的优势。总体而言，利用高光谱数据结合北极地区的波段选择/优化程序，已经证明了对北极植被叶绿素和植物面积指数进行经验建模的强大潜力。最近发射和未来发射的高光谱卫星，包括下一代机载传感器，可能会改善此处报告的模型性能。