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作者	单位	地址
任延穗	西安科技大学	陕西省西安市碑林区雁塔中路58号西安科技大学
薛一阳
竞霞^*	西安科技大学	陕西省西安市碑林区雁塔中路58号西安科技大学
张咏
程前进

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中文摘要:

日光诱导叶绿素荧光（solar-induced chlorophyll fluorescence，SIF）与植被的光合生理及受胁迫状况密切相关。为解决直接利用原始全波段SIF数据构建模型过程中出现的数据冗余问题，在利用相关性分析从全波段SIF光谱中选择对小麦条锈病严重度（severity level，SL）敏感波段的基础上，分别构建了倒数SIF光谱指数（reciprocal SIF spectrum index，RSISIF）、对数SIF光谱指数（log SIF spectrum index，LSISIF）、倒数对数SIF光谱指数（reciprocal logarithmic SIF spectrum index，RLSISIF）、一阶微分SIF光谱指数（first order differential SIF spectrum index，FDSISIF）、和值SIF光谱指数（sum SIF spectrum index，SSISIF）以及差值SIF光谱指数（differential SIF spectrum index，DSISIF），对各指数与SL的相关性进行评估，筛选与SL相关性较强的指数（|r| > 0.5）采用随机森林回归（random forest regression，RFR）与支持向量回归（support vector regression，SVR）构建小麦条锈病遥感监测模型，并利用独立样本进行验证。结果表明，除FDSISIF外，其余SIF指数与SL的相关性相较于全波段SIF和单一波段SIF均不同程度提高，其中LSISIF与SL的相关性最高，分别较远红光SIF（far-red SIF，FRSIF）和原始全波段SIF分别提高了91%和72%。估算模型中，基于SIF指数构建的模型精度均优于单一波段SIF及原始全波段SIF，且RFR模型的总体表现优于SVR模型。在小区试验中，以全波段SIF为自变量构建的RFR模型决定系数（r2）较FRSIF提高了42%，均方根误差（RMSE）降低了22%，分别以RSISIF、LSISIF、RLSISIF、SSISIF为自变量的RFR模型r2较全波段SIF分别提高了19%、21%、22%和21%；RMSE分别降低了19%、22%、23%和22%。在大田试验中，以RSISIF、LSISIF、RLSISIF、SSISIF为自变量的RFR模型r2较全波段SIF分别提高了28%、27%、31%和23%；RMSE分别降低了21%、20%、22%和19%。综上，对全波段SIF进行数学变换构建出的SIF指数在监测小麦条锈病上具备一定的稳定性与可迁移性。

英文摘要:

Solar-induced chlorophyll fluorescence (SIF) is tightly coupled with plant photosynthetic functioning and provides a sensitive indicator of vegetation stress. To address the issue of data redundancy that arises during the process of building models directly using raw full-band SIF data, this study first performed correlation analysis on the full-spectrum SIF signal to identify wavelength regions that are most responsive to wheat stripe rust severity level (SL). Guided by the selected sensitive bands, six SIF spectrum indices were subsequently developed through mathematical transformations, including the reciprocal SIF spectrum index(RSISIF )、logarithmic SIF spectrum index (LSISIF), reciprocal logarithmic SIF spectrum index (RLSISIF), first order differential SIF spectrum index(FDSISIF), sum SIF spectrum index (SSISIF), and differential SIF spectrum index (DSISIF). Subsequently, the correlations between each index and SL were evaluated, and indices showing stronger associations with SL (|r| > 0.5) were selected to develop wheat stripe rust remote sensing monitoring models using random forest regression (RFR) and support vector regression (SVR), which were further validated with independent samples. The results indicated that, with the exception of FDSISIF, the rest of SIF spectrum indices consistently strengthened the association with SL compared with both raw full-spectrum SIF and single-band SIF metrics. Among them, LSISIF exhibited the highest sensitivity to disease severity, achieving correlation improvements of 91% relative to far-red SIF (FRSIF) and 72% relative to the original full-spectrum SIF. In terms of predictive performance, models driven by the SIF spectrum indices generally outperformed those based on FRSIF or untransformed full-spectrum SIF, while RFR delivered superior overall accuracy compared with SVR across experiments. In the controlled plot experiment, the RFR model using full-spectrum SIF as predictors increased r2by 42% and reduced RMSE by 22% compared with the FRSIF-based model. Moreover, RFR models incorporating RSISIF, LSISIF, RLSISIF, and SSISIF further improved r2by 19%, 21%, 22%, and 21% over the full-spectrum SIF model, accompanied by RMSE reductions of 19%, 22%, 23%, and 22%, respectively. In the field experiment, the corresponding RFR models achieved additional gains in r2of 28%, 27%, 31%, and 23% relative to the full-spectrum SIF model, while decreasing RMSE by 21%, 20%, 22%, and 19%, respectively. Overall, these findings suggest that SIF spectrum indices constructed through mathematical transformations of full-spectrum SIF can effectively enhance the disease-related signal, leading to more accurate and robust estimates of wheat stripe rust severity. The consistent improvements observed across the controlled plot experiment and the field experiment further highlight the stability and potential transferability of the proposed indices, supporting their applicability for operational remote sensing-based crop disease monitoring.

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