基于红光波段SIF的高寒草甸GPP遥感监测方法

Remote Sensing Monitoring of GPP in Alpine Meadows Based on Red SIF

摘要:

高寒草甸生态系统具有强大的碳汇能力，准确估计其总初级生产力(Gross primary productivity，GPP)对掌握全球碳循环具有重要意义。日光诱导叶绿素荧光（Solar-induced chlorophyll fluorescence, SIF）是指示植被光合作用过程的无损探针，红光波段SIF（Red SIF，RSIF）包含了更多的PS Ⅱ信息。为了探究RSIF对高寒草甸生态系统GPP的响应特性，本文综合RSIF、环境变量以及冠层结构参数，分别基于随机森林回归（Random forest regression，RFR）、多元线性回归（Multiple linear regression，MLR）和简单线性回归（Simple linear regression，SLR）方法构建了高寒草甸GPP预测模型，并将其与远红光波段SIF（Far-red SIF，FRSIF）的预测结果进行对比分析。结果表明：冠层RSIF和FRSIF均与GPP呈显著正相关关系，RSIF与GPP的相关系数较FRSIF提高23.53%，在高寒草甸GPP预测中较FRSIF具有更大的优势。在训练数据集中，综合RSIF、环境变量以及冠层结构参数构建的RFR和MLR模型预测GPP与实测GPP间的平均R2较FRSIF分别提高5.79%和12.69%，平均RMSE分别降低16.37%和30.56%，以单一RSIF为自变量构建的SLR模型预测GPP与实测GPP间的平均R2较FRSIF提高31.02%，平均RMSE降低34.28%。在验证数据集中以RSIF、环境变量以及冠层结构参数为自变量构建的RFR模型预测GPP和实测GPP之间的平均R2较MLR提高1.86%、较单一以RSIF为自变量的SLR模型提高6.62%，其对应平均RMSE分别降低1.04%和17.13%。RSIF比FRSIF在高寒草甸生态系统GPP监测方面具有更大的潜力，研究结果亦对其它生态系统GPP遥感监测具有重要参考价值。

Abstract:

The alpine meadow ecosystem has a strong carbon sink capacity, and accurately estimating the gross primary productivity (GPP) is essential to grasp the global carbon cycle. Solar-induced chlorophyll fluorescence (SIF) is a nondestructive probe indicating the photosynthetic process of plants, and red SIF (RSIF) contains more information about PSⅡ. To explore the response characteristics of RSIF to the GPP of the alpine meadow ecosystem, integrating RSIF, environmental variables and canopy structure parameters, and respectively constructed GPP prediction models based on random forest regression (RFR), multiple linear regression (MLR), and simple linear regression (SLR) methods. The results showed that both canopy RSIF and FRSIF were significantly positively correlated with GPP, and the correlation between RSIF and GPP was 23.53% higher than that of FRSIF, which had greater advantages than FRSIF in the prediction of alpine meadow GPP. In the training data set, the RFR and MLR models constructed by combining RSIF, environmental variables and canopy structure parameters increased the average R2 between predicted GPP and measured GPP by 5.79% and 12.69%, respectively, compared with FRSIF, and the average RMSE was decreased by 16.37% and 30.56%, respectively. Compared with FRSIF, the average R2 between the predicted GPP and the measured GPP was increased by 31.02% and the average RMSE was decreased by 34.28% in SLR model with a single RSIF as the independent variable. In the validation data set, the average R2 between predicted GPP and measured GPP predicted by the RFR model with RSIF, environment variables and canopy structure parameters as independent variables was increased by 1.86% compared with MLR, and by 6.62% compared with the single SLR model with RSIF as independent variable, and the corresponding average RMSE was decreased by 1.04% and 17.13%, respectively. RSIF had greater potential than FRSIF for GPP monitoring in alpine meadow ecosystem, and the results also had important reference value for GPP monitoring in other ecosystems.