通过SIF和PRI建立大米树冠上的原始生产率模型
Zhanhao Zhang1, Jianmao Guo1,2, Shihui Han1
1School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Plant phenomics (Washington, D.C.)
|February 2, 2024
概括
将光化学反射率指数 (PRI) 与太阳引起的叶绿素光 (SIF) 结合起来,可以准确估计大米总初级生产率 (GPP). 双叶模型方法改善了GPP估计,显示PRI增强了SIF,以更好地跟踪作物生产率.
科学领域:
- 植物生理学的遥感.
- 生态系统碳循环建模
- 农业监测 农业监测 农业监测
背景情况:
- 太阳引起的叶绿素光 (SIF) 和光化学反射率指数 (PRI) 是估计植被碳吸收的关键.
- 现有模型面临的挑战是SIF和PRI的时间和空间变化,受照明和植被状态的影响.
- 整合SIF和PRI用于作物树冠总初级生产率 (GPP) 估计仍未得到充分探索.
研究的目的:
- 评估PRI在增强基于SIF的模型来估计作物树冠GPP方面的有效性.
- 为了比较使用热点 (PRIhs/SIFhs) 与总树冠 (PRItot/SIFtot) 测量的GPP估计准确度.
- 调查叶面积指数 (LAI),温度和蒸汽压力赤字对PRI/SIF-GPP关系的影响.
主要方法:
- 使用了PRI和SIF的多角度光谱仪数据,以及GPP的状共变量数据.
- 在热点PRI/SIF计算中采用半实证的核心驱动的BRDF模型.
- 应用了修改后的双叶模型来推导总PRI/SIF树冠,将其性能与GPP估计的热点值进行比较.
主要成果:
- PRI+SIF-GPP模型表现出卓越的性能,其中R=0.88,RMSE=3.74和RPD=2.71.
- 总 PRI/SIF (PRItot/SIFtot) 与热点测量相比,在时间系列中与GPP保持更好的一致性.
- LAI具有线性影响,而温度和蒸汽压力差异对PRI/SIF-GPP估计表现出非线性影响.
结论:
- 两叶模型方法显著提高了基于SIF和PRI的GPP对大米树冠的估计.
- PRI有效地改善了基于SIF的GPP估计,突出了其在监测作物生产率方面的价值.
- 这项研究强调了使用植被指数实时跟踪植被生产力的双叶模型的实用性.
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