| 引用本文: | 徐加龙,李亚威,徐俊增,等.水分调控下水稻冠层叶绿素荧光响应与高光谱预测[J].灌溉排水学报,2026,45(8):17-24. |
| Xu Jialong,Li Yawei,Xu Junzeng,et al.水分调控下水稻冠层叶绿素荧光响应与高光谱预测[J].灌溉排水学报,2026,45(8):17-24. |
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| 摘要: |
| 【目的】阐明水分胁迫下水稻叶绿素荧光参数的响应特征及其与冠层高光谱的关系，并评估高光谱预测的可行性。【方法】以盆栽水稻为研究对象，在孕穗期设置持续淹水（CK）、轻度干旱（MS）和重度干旱（HS）3个水分调控处理，结合高通量植物表型平台获取冠层高光谱反射率和叶绿素荧光数据，分析叶绿素荧光参数对土壤水分变化的响应特征，并基于特征波段构建偏最小二乘回归（PLSR）和反向传播神经网络（BPNN）预测模型。【结果】①叶绿素荧光参数最大光化学效率Fv/Fm、实际光化学效率Y(II)、光化学淬灭系数qL和调节性能量耗散量子产量Y(NPQ)对干旱胁迫表现出一致响应特征，各参数在落干后约3~4 d内发生显著变化并随后趋于稳定，HS处理在落干第6天时其Fv/Fm、Y(II)和qL较CK显著降低41.3%、46.9%和53.1%，Y(NPQ)显著升高117.5%；②经Savitzky-Golay平滑与多元散射校正（MSC）预处理后，高光谱数据中由冠层结构差异引起的散射效应得到有效削弱，不同荧光参数的特征波段虽有差异，但主要分布在蓝光区（400~500 nm）及红光和近红外区域；③与PLSR模型相比，BPNN模型能够更有效描述高光谱与叶绿素荧光参数之间的非线性关系，整体预测精度更高。BPNN模型对Y(NPQ)和qL的预测能力较好，验证集R2分别达0.867和0.845，对Y(II)的预测能力次之，而对Fv/Fm的预测能力较弱。【结论】利用冠层高光谱数据预测叶绿素荧光参数具有较好可行性，可为作物水分胁迫的快速、无损、精准监测提供技术支持。 |
| 关键词: 水分胁迫；水稻；高光谱；叶绿素荧光参数；预测模型 |
| DOI：10.13522/j.cnki.ggps.2026082 |
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| Using hyperspectral reflectance to explore the responses of rice canopy chlorophyll fluorescence to water stress |
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Xu Jialong, Li Yawei, Xu Junzeng, Chen Shengyu, Yin Aojie, Liu Xiaoyin, Wei Qi, Zhou Xue
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1. College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China;
2. Institute of Advanced Studies, Nanjing Agricultural University, Nanjing 210095, China
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| Abstract: |
| 【Objective】Chlorophyll fluorescence is a physiological indicator reflecting crop photosynthesis and water stress. Non-destructively monitoring the changes in chlorophyll fluorescence under water stress is critical for improving irrigation management. This paper explores the applicability of canopy hyperspectral reflectance for elucidating the response of rice canopy chlorophyll fluorescence to water stress.【Method】The experiment was conducted in pots and the measurements were taken during the booting stage of rice. Three water treatments were set, including continuous flooding irrigation (CK), mild drought (MS) and severe drought (HS). Canopy hyperspectral reflectance and chlorophyll fluorescence were synchronously measured using a high-throughput phenotyping platform, from which we analysed the responses of chlorophyll fluorescence traits to soil water change. Prediction models were developed to estimate chlorophyll fluorescence traits using partial least squares regression (PLSR) and backpropagation neural network (BPNN), based on characteristic spectral bands.【Result】①The chlorophyll fluorescence traits Fv/Fm, Y(II), qL and Y(NPQ) varied with water stress, with significant changes observed 3-4 days after cessation of irrigation, and detectable variation identified up to day 6 after terminating irrigation. On day 6 after irrigation cessation, the HS treatment reduced Fv/Fm, Y(II) and qL by 41.3%, 46.9% and 53.1%, respectively, whereas increased Y(NPQ) by 117.5% compared with CK. ②Savitzky-Golay smoothing and multiplicative scatter correction (MSC) preprocessing effectively reduced the scattering effects on canopy hyperspectral data induced by structural variation. The characteristic spectral bands selected from the hyperspectral data were mainly distributed in the blue (400-500 nm), red and near-infrared regions. ③Compared with PLSR, the BPNN was more effective in capturing the nonlinear relationships between hyperspectral data and chlorophyll fluorescence traits. The BPNN was most accurate for estimating Y(NPQ) and qL, with the associated R2 values being 0.867 and 0.845, respectively, and less accurate for estimating Fv/Fm.【Conclusion】Canopy hyperspectral data can be used to estimate rice chlorophyll fluorescence traits. This approach provides a rapid, cost-effective, and non-destructive method for monitoring crop physiological responses to water stress. |
| Key words: water stress; rice; hyperspectral reflectance; chlorophyll fluorescence parameters; prediction models |
