目录
目录
摘要 II
关键词 II
Abstract III
引言
引言 1
1 材料与方法 2
1．1 试验设计 2
1．2 数据获取 3
1．2．1 日光诱导的叶绿素荧光(SIF) 3
1．2．2 叶片高光谱反射率 3
1．2．3 光合参数 4
1．2．4 PSII最大光化学效率 4
1．2．5 叶片生化参数 4
1．2．6 细胞结构 4
1．3 数据分析 4
1．3．1 SIF产率指数(FY指数) 4
1．3．2 植被指数(VIs) 5
1．3．3 指标的敏感性分析 6
1．3．4 回归和分类模型构建 6
2 结果与分析 7
2．1 盐胁迫对水稻生物学形态的影响 7
2．2 水稻单叶尺度的SIF在盐胁迫下的时序变化 9
2．2．1 盐胁迫对SIF产率曲线的影响 9
2．2．2 SIF产率（FY）指数随盐胁迫的动态变化 9
2．3 FY指数、植被指数和光合相关参数对盐胁迫的敏感性比较 11
2．4 构建并比较基于SIF和VIs的水稻盐胁迫监测诊断模型 12
3 讨论 13
3．1 盐胁迫不同阶段对水稻光合功能的影响机理 14
3．2 SIF与VIs对盐胁迫敏感性的差异 15
3．3 SIF用于水稻盐胁迫监测诊断的可行性 16
致谢 17
参考文献 18
基于日光诱导叶绿素荧光的水稻盐胁迫监测诊断
摘 要
盐胁迫是影响水稻可持续生产的主要非生物胁迫之一，早期、无损、精确地监测水稻盐胁迫有利于及时采取有效的防治手段。近年来，日光诱导的叶绿素荧光（SIF）作为光合作用的探针在作物胁迫监测诊断方面表现出很大的潜力。本文旨在明确SIF对盐胁迫的响应规律，比较并筛选SIF产率指数（FY指数）、生理生化指标和植被指数（ *51今日免费论文网|www.jxszl.com +Q: &351916072& 
VIs）中对盐胁迫最敏感的指标，构建并比较基于SIF和VIs的水稻盐胁迫早期监测诊断模型。通过设置不同的盐胁迫水平，在2个水稻品种的不同生长阶段采集了2年的水稻盐胁迫试验数据，分析获得9个FY指数、若干生理生化参数和9个VIs。结果表明SIF指标中，FY687s和FY739s随着盐胁迫梯度和时间的增加而下降，FY687/FY739s则相反。在上述3大类指标中，Pn是对水稻盐胁迫最敏感的指标，其次为FY687s和FY739s，能够指示早期和轻度盐胁迫，其敏感性大于VIs和FY687/FY739s。基于上述结果，我们筛选了3个FY739s、↑FY687和PRI、CIrededge、CIgreen、WI分别作为基于SIF和VIs建模的特征参数。生理生化指标的回归模型中，基于SIF的模型与VI模型相比显著提高了Pn和K+/Na+的监测精度（分别为0.92和0.61），其它生理生化指标的监测精度都大于0.80。在水稻盐胁迫早期诊断模型中，SIF模型整体表现优于VI模型，盐处理后第一天病叶和健康叶的诊断精度大于77%。因此，SIF作为水稻盐胁迫的早期监测和诊断方法具有很好的应用前景。
MONITORING AND DIAGNOSIS OF SALT STRESS IN RICE BASED ON SOLARINDUCED CHLOROPHYLL FLUORESCENCE
ABSTRACT
Salt stress is one of the main abiotic stress that affect the sustainable production of rice. Monitoring rice salt stress early, nondestructively and accurately is conducive to timely and effective control measures. Recently, solarinduced chlorophyll fluorescence (SIF) has shown great potential as a probe of photosynthesis in plant stress monitoring and diagnosis. The purpose of this paper is to clarify the response of SIF to salt stress, compare and screen the most sensitive indicators among SIF yield index (FY index), physiological and biochemical index and vegetation index (VIs), to build and compare the early monitoring and diagnosis model of rice salt stress based on SIF and VIs. By setting different salt stress levels, we collected two years’ data at different growth stages of two rice varieties. Simultaneously, we obtained 9 FY indices, some physiological and biochemical parameters and 9 VIs. The results showed that FY687s and FY739s decreased with the increase of salt stress levels and time, while FY687/FY739s was the opposite. Pn was the most sensitive index to rice salt stress, followed by FY687s and FY739s which were able to indicate early and mild salt stress. And their sensitivity were higher than VIs and FY687/FY739s. Based on the above results, we selected three FY739s, ↑FY687 and PRI, CIrededge, CIgreen, WI as the feature parameters based on SIF and VIs, respectively. In the regression model of physiological and biochemical indices, the model based on SIF significantly improves the monitoring accuracy of Pn and K+/Na+(0.92 and 0.61, respectively) compared with VI model, and the accuracy of other indices were all higher than 0.80. The early diagnosis model based on SIF also performed better than that based on VIs, and SIF model’s accuracy of diseased leaves and healthy leaves were higher than 77% at 1DAT. Therefore, SIF has a great prospect as an early detection and diagnosis method of salt stress in rice.

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