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Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity

机译:评估基于Sentinel-2的红边反射使用植被指数的有效性来估计总初级生产力

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摘要

Gross primary productivity (GPP) is the most important component of terrestrial carbon flux. Red-edge (680−780 nm) reflectance is sensitive to leaf chlorophyll content, which is directly correlated with photosynthesis as the pigment pool, and it has the potential to improve GPP estimation. The European Space Agency (ESA) Sentinel-2A and B satellites provide red-edge bands at 20-m spatial resolution on a five-day revisit period, which can be used for global estimation of GPP. Previous studies focused mostly on improving cropland GPP estimation using red-edge bands. In this study, we firstly evaluated the relationship between eight vegetation indices (VIs) retrieved from Sentinel-2 imagery in association with incident photosynthetic active radiation (PARin) and carbon flux tower GPP (GPPEC) across three forest and two grassland sites in Australia. We derived a time series of five red-edge VIs and three non-red-edge VIs over the CO2 flux tower footprints at 16-day time intervals and compared both temporal and spatial variations. The results showed that the relationship between the red-edge index (CIr, ρ 783 ρ 705 − 1 ) multiplied by PARin and GPPEC had the highest correlation (R2 = 0.77, root-mean-square error (RMSE) = 0.81 gC∙m−2∙day−1) at the two grassland sites. The CIr also showed consistency (rRMSE defined as RMSE/mean GPP, lower than 0.25) across forest and grassland sites. The high spatial resolution of the Sentinel-2 data provided more detailed information to adequately characterize the GPP variance at spatially heterogeneous areas. The high revisit period of Sentinel-2 exhibited temporal variance in GPP at the grassland sites; however, at forest sites, the flux-tower-based GPP variance could not be fully tracked by the limited satellite images. These results suggest that the high-spatial-resolution red-edge index from Sentinel-2 can improve large-scale spatio-temporal GPP assessments.
机译:总初级生产力(GPP)是陆地碳通量最重要的成分。红边(680-780nm)反射率对叶片叶绿素含量敏感,与颜料池的光合作用直接相关,具有改善GPP估计的可能性。欧洲航天局(ESA)Sentinel-2A和B卫星在五天的重新访问期间提供20米空间分辨率的红边频段,可用于全球GPP的全局估算。以前的研究主要集中在使用红边频段改善农田GPP估计。在这项研究中,我们首先评估了在澳大利亚三种森林和两个草地网站上与事件光合作用辐射(Parin)和碳通量塔GPP(GPPEC)相关联的八个植被指数(VI)之间的关系。我们在16天的时间间隔中衍生出五个红边VIS和三个非红边,并在CO2磁通塔脚印上进行三个非红线VIS,并比较了时间和空间变化。结果表明,红边索引(CIR,ρ7837705-1)之间的关系乘以Parin和GPPEC具有最高的相关性(R2 = 0.77,根均方误差(RMSE)= 0.81 GC∙M -2∙日1)在两个草地网站。 CIR还显示森林和草原地点的一致性(RRMSE定义为RMSE /均值GPP,低于0.25)。 Sentinel-2数据的高空间分辨率提供了更详细的信息,以充分表征空间异构区域的GPP方差。 Sentinel-2的高回归时期在草地位点显示了GPP的时间差异;然而,在森林网站,有限卫星图像无法完全跟踪基于磁通塔的GPP方差。这些结果表明,Sentinel-2的高空间分辨率红边指数可以改善大规模的时空GPP评估。

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