文献类型: 外文期刊
作者: Jing, Xia 1 ; Ye, Qixing 1 ; Chen, Bing 2 ; Li, Bingyu 1 ; Du, Kaiqi 3 ; Xue, Yiyang 1 ;
作者机构: 1.Xian Univ Sci & Technol, Coll Geomat, Xian 710054, Peoples R China
2.Xinjiang Acad Agr & Reclamat Sci, Shihezi 832000, Peoples R China
3.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China
关键词: solar-induced chlorophyll fluorescence; full-spectrum SIF; modified SIFB; pseudokurtosis; wheat stripe rust
期刊名称:AGRONOMY-BASEL ( 影响因子:3.4; 五年影响因子:3.8 )
ISSN:
年卷期: 2024 年 14 卷 8 期
页码:
收录情况: SCI
摘要: Red solar-induced chlorophyll fluorescence (SIFB) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIFB by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIFB using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PKB) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PKB and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPAR(chl)) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIFB by the vegetation canopy. Consequently, we constructed an expression of PKB to modify SIFB. To evaluate the modified SIFB (MSIFB) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIFB, MSIFB, SIFB-VIs (a fusion of the vegetation index and SIFB), and MSIFB-VIs (a fusion of the vegetation index and MSIFB) with the severity level (SL), respectively. The results show that the correlation between MSIFB and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIFB. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIFB, MSIFB, SIFB-VIs, and MSIFB-VIs as independent variables. PKB significantly improves the accuracy and robustness of models based on SIFB and its fusion index SIFB-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIFB-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIFB-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIFB modified by PKB can weaken the effects of chlorophyll reabsorption and canopy architecture on SIFB and improve the ability of SIFB to detect stress information.
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