Leaf pigment retrieval using the PROSAIL model: Influence of uncertainty in prior canopy-structure information

文献类型： 外文期刊

第一作者： Sun, Jia

作者： Sun, Jia;Wang, Lunche;Yang, Jian;Wang, Shaoqiang;Sun, Jia;Tagesson, Torbern;Shi, Shuo;Gong, Wei;Li, Zhenhai;Tagesson, Torbern

作者机构：

关键词： Leaf pigment; PROSAIL model; Canopy structure; Chlorophyll content; Leaf area index; Leaf angle distribution

期刊名称：CROP JOURNAL （ 影响因子：4.647； 五年影响因子：5.781 ）

ISSN： 2095-5421

年卷期： 2022 年 10 卷 5 期

页码：

收录情况： SCI

摘要： Leaf pigments are critical indicators of plant photosynthesis, stress, and physiological conditions. Inversion of radiative transfer models (RTMs) is a promising method for robustly retrieving leaf biochemical traits from canopy observations, and adding prior information has been effective in alleviating the "ill-posed" problem, a major challenge in model inversion. Canopy structure parameters, such as leaf area index (LAI) and average leaf inclination angle (ALA), can serve as prior information for leaf pigment retrieval. Using canopy spectra simulated from the PROSAIL model, we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll (C-ab) and carotenoid (C-ar). The retrieval accuracies of the two pigments were increased by use of the priors of LAI (RMSE of C(ab )from 7.67 to 6.32 mu g cm(2) , C-ar from 2.41 to 2.28 mu g cm(2)) and ALA (RMSE of C-ab from 7.67 to 5.72 mu g cm(2) , C-ar from 2.41 to 2.23 mu g cm(2)). However, this improvement deteriorated with an increase of additive and multiplicative uncertainties, and when 40% and 20% noise was added to LAI and ALA respectively, these priors ceased to increase retrieval accuracy. Validation using an experimental winter wheat dataset also showed that compared with C-ar , the estimation accuracy of C-ab increased more or deteriorated less with uncertainty in prior canopy structure. This study demonstrates possible limitations of using prior information in RTM inversions for retrieval of leaf biochemistry, when large uncertainties are present. (C) 2022 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.

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