首页> 外文会议>International Conference on Remote Sensing and Geoinformation of the Environment >Effects of satellite spatial resolution on gross primary productivity estimation through light use efficiency modeling
【24h】

Effects of satellite spatial resolution on gross primary productivity estimation through light use efficiency modeling

机译:卫星空间分辨率对通过光利用效率建模的总初级生产力估算的影响

获取原文

摘要

Terrestrial Gross Primary Productivity (GPP) describes the total amount of CO2 assimilated by plants in an ecosystem during photosynthesis and is considered the largest flux component of the global carbon cycle. One of the most prominent techniques for estimating GPP at ecosystem scale is the Light Use Efficiency (LUE) approach, taking advantage of the spatiotemporal capabilities that satellite data provide. LUE expresses GPP as the product of absorbed photosynthetically active radiation (APAR) and the efficiency (ε) that APAR is converted to biomass. Although satellite imagery is the key component of such models, the effects of image spatial resolution on model performance have not been thoroughly investigated. The emergence of new satellite instruments with enhanced spatial, spectral and temporal capabilities (i.e. Copernicus Sentinels) provides the opportunity for GPP estimation in high spatial resolution and comparison with low resolution GPP products (i.e. MODIS). In this study, a LUE model is applied to three satellite instruments with different spatial resolution: MODIS (500 m), Sentinel-3 (300 m) and Sentinel-2 (10 m). The GPP estimates of the three instruments are compared over six forest sites in Greece: two deciduous (Quercus sp., Fagus sylvatica), two coniferous (Pinus nigra, Pinus halepensis) and two mixed (Pinus nigra with Fagus sylvatica). The results demonstrate that spatial resolution is not a crucial parameter for LUE modeling in wide, homogenous and fully covered forested areas. The spatial resolution is more important when applying LUE in mixed canopies or partially covered forested areas due to the effects of the different land cover types. To that purpose, Sentinel-2 presents a unique potential for accurate characterization of the land cover type and dynamics, due to the increased spatial resolution and frequent coverage, appearing as a prominent tool for future large scale GPP monitoring.
机译:地面总初级生产率(GPP)描述了光合作用期间生态系统中植物在生态系统中同化的CO2的总量,并被认为是全球碳循环的最大磁通量。估计GPP在生态系统规模中最突出的技术之一是轻率使用效率(Lue)方法,利用卫星数据提供的时空功能。 Lue表达GPP作为吸收光合作用辐射(APAR)的产物和猿猴的效率(ε)转换为生物质。虽然卫星图像是这种模型的关键组成部分,但图像空间分辨率对模型性能的影响尚未彻底研究。具有增强的空间,光谱和时间能力的新卫星仪器的出现(即Copernicus Sentinels)为高空间分辨率的GPP估计提供了机会,并与低分辨率GPP产品(即MODIS)进行比较。在这项研究中,用不同空间分辨率的三种卫星仪器应用LUE模型:MODIS(500 m),哨子-3(300 m)和哨子-2(10μm)。三种仪器的GPP估计比希腊六种森林地点比较:两个落叶(栎属SP。,Fagus Sylvatica),两个针叶树(Pinus nigra,Pinus Halepensis)和两个混合(Pinus nigra,Fagus sylvatica)。结果表明,空间分辨率不是宽,均匀和完全覆盖的地区的Lue建模的关键参数。由于不同陆地覆盖类型的效果,空间分辨率更为重要。为此目的,由于空间分辨率增加和频繁的覆盖率增加,Sentinel-2呈现了陆地覆盖类型和动态的精确表征的独特潜力,作为未来大规模GPP监控的突出工具。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号