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通过将多传感器卫星,现场和联变量数据集成到基于效率的模型中来估计作物生产率

Shivani Kalra¹, N R Patel¹, Shweta Pokhariyal²

¹Agriculture & Soils Department, Indian Institute of Remote Sensing, ISRO, Govt. of India, 4, Kalidas Road, Dehradun, Uttarakhand, 248001, India.

Environmental monitoring and assessment

|November 20, 2023

概括

本研究使用印度卫星数据的光利用效率 (LUE) 模型估计了作物生产率. 该LUE模型准确地捕获了作物系统的初级生产率 (GPP),表现优于全球模型.

科学领域:

农业科学农业科学
遥感遥感遥感遥感
生态系统建模生态系统建模

背景情况:

精确的碳预算估计需要将状共变量 (EC) 流量塔数据与遥感和生态系统模型进行整合.
光利用效率 (LUE) 模型提供了一种基于辐射吸收和生物质转换的初级生产率估计方法.

研究的目的:

用遥感驱动的LUE模型估计印度主要作物系统的初级生产率.
将模型的性能与地面EC塔观测和全球GPP产品进行比较.

主要方法:

利用多时间的Sentinel-2和Landsat 8卫星数据用于印度萨哈兰普尔的作物旋转.
估计每月光合作用活性辐射 (PAR) 和吸收PAR (fAPAR) 分数,使用卫星衍生的植被指数和阳光照射数据.
通过通过LSWI和EC塔数据得出的水和温度应激因子下调最大LUE来开发空间LUE地图.

主要成果:

在LUE模型中,甘-小麦系统 (C4作物) 的总初级生产率 (GPP) 比大米-小麦系统 (C3作物) 高.
甘-小麦系统的模拟GPP与观察到的EC塔GPP (一致性指数=0.93) 非常相匹配.
与MODIS GPP产品相比,区域校准的LUE模型在捕获耕地生态系统上的GPP方面表现优越.

结论:

基于遥感的LUE模型,当区域校准时,可以准确地估计耕地生态系统中的GPP.
该研究强调了作物类型 (C4与C3) 在确定光合作用效率和GPP方面的重要性.
结合EC塔数据和遥感,为区域碳预算评估提供了可靠的方法.

关键词:

埃迪的共变性埃迪共变性毛初级生产率的生产率.灯光使用效率的效率.遥感是一种远程传感.哨兵-2 卫星卫星-2 卫星

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