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Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index

机译:使用MODIS和植物候选指数在北欧脱落的远程感知植物候选和雪季季节性

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摘要

Land surface phenology is frequently derived from remotely sensed data. However, over regions with seasonal snow cover, remotely-sensed land surface phenology may be dominated by snow seasonality, rather than showing true plant phenology. Overlooking snow influences may lead to inaccurate plant phenology estimation, and consequently to misinterpretation of climate-vegetation interactions. To address the problem we apply the recently developed plant phenology index (PPI) to Moderate Resolution Imaging Spectroradiometer (MODIS) data for estimating plant phenology metrics over northern Europe. We compare PPI-derived start and end of the growing season with ground observations by professionals (6 sites) and nonprofessional citizens (378 sites), with phenology metrics derived from gross primary productivity (GPP, 18 sites), and with data on the timing of snow cover. These data are also compared with land surface phenology metrics derived from the normalized difference vegetation index (NDVI) using the same MODIS data. We find that the PPI-retrieved plant phenology agrees with ground observations and GPP-derived phenology, and that the NDVI-derived phenology to a large extent agrees with the end-of-snowmelt for the start-of-season and the start-of-snowing for the end of-season. PPI is thereby useful for more accurate estimation of plant phenology from remotely sensed data over northern Europe and other regions with seasonal snow cover. (C) 2017 Elsevier Inc All rights reserved.
机译:陆地表面候选经常从远程感测的数据衍生出来。然而,在季节性雪覆盖的地区,远程感应的土地候选可能是雪季季节性的主导,而不是显示真正的植物候选。俯瞰雪影响可能导致植物候选估计不准确,从而误解气候 - 植被相互作用。为了解决问题,我们将最近开发的植物候选指数(PPI)应用于适度分辨率成像光谱仪(MODIS)数据,用于估算北欧的植物候选度量。我们比较PPI派生季节的开始和结束,通过专业人士(6个站点)和非专业公民(378个地点),具有从初级生产力(GPP,18个站点)的苯版度量,以及关于时序的数据雪盖。这些数据也与使用相同的MODIS数据的归一化差异植被指数(NDVI)导出的陆地表面签名度量。我们发现PPI检索的植物候选与地面观察和GPP衍生的候选,并且NDVI衍生的候选在很大程度上同意季节开始和开始的雪地 - 赛季结束了。因此,PPI可用于从北欧的远程感测数据和季节性雪覆盖的其他地区更准确地估算植物候选。 (c)2017年elsevier inc保留所有权利。

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