首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Evaluating impacts of snow, surface water, soil and vegetation on empirical vegetation and snow indices for the Utqiagvik tundra ecosystem in Alaska with the LVS3 model
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Evaluating impacts of snow, surface water, soil and vegetation on empirical vegetation and snow indices for the Utqiagvik tundra ecosystem in Alaska with the LVS3 model

机译:用LVS3模型评估雪,地表水,土壤和植被对紫砂苔原生态系统的经验植被和雪指标

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Satellite observations for the Arctic and boreal region may contain information of vegetation, soil, snow, snowmelt, and/or other surface water bodies. We investigated the impacts of vegetation, soil, snow and surface water on empirical vegetation/snow indices on a tundra ecosystem area located around Utqiagvik (formerly Barrow) of Alaska with the Moderate Resolution Imaging Spectrometer (MODIS) images in 2001-2014. Empirical vegetation indices, such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), the index of near infrared of vegetation (NIRv), and modified EVI (EVI2), have been used to monitor vegetation. Normalized difference snow index (NDSI) has been widely applied to monitor snow. The vegetation cover fraction (VGCF), the soil cover fraction (SOILCF), the snow cover fraction (SNOWCF), the surface water body cover fraction (WaterBodyCF), the fractional absorption of photosynthetically active radiation (PAR) by vegetation chlorophyll (fAPAR(chl)), the fractional absorption of PAR by non-chlorophyll components of the vegetation (fAPAR(non-chl)), and the fractional absorption of PAR by the entire canopy (fAPAR(canopy)) are retrieved with the MODIS images and a coupled Leaf-Vegetation-Soil-Snow-Surface water body radiative transfer model, LVS3. The vegetation indices (NDVI, EVI, EVI2 and NIRv) differ from VGCF, fAPAR(chl), fAPAR(non-chl), and fAPAR(canopy). In addition to vegetation, we find that soil, snow and surface water also have impacts on vegetation indices NDVI, EVI (EVI2), and NIRv. Presence of snow makes lower the observed values of NDVI, EVI2 and NIRv. After snowmelt is gone, the vegetation indices (NDVI, EVI, EVI2 and NIRv) linearly decrease with SOILCF and WaterBodyCF, and WaterBodyCF has stronger impacts on these vegetation indices than SOILCF. The relationship between EVI and snow is complicated. NDSI non-linearly increases with SNOWCF, but linearly increases with sum of SNOWCF and WaterBodyCF (sum = 0.5893 x NDSI +0.4342, R-2 = 0.976). NDSI linearly decreases with VGCF, and the relationship between NDSI and SOILCF is complex. Retrievals of VGCF, fAPAR(chl), fAPAR(non-chl) and fAPAR(canopy) with the LVS3 model provide alternatives for vegetation monitoring and ecological modeling.
机译:北极和北部地区的卫星观察可能包含植被,土壤,雪,雪花和/或其他地表水体的信息。我们调查了植被,土壤,雪和地表水对位于阿拉斯加州UTQIAGVIK(原律车)周围的苔原生态系统地区的经验植被/雪指数的影响,2001-2014中的中度分辨率成像光谱仪(MODIS)图像。经验植被指数,如归一化差异植被指数(NDVI),增强植被指数(EVI),近红外植被(NIRV)和改良EVI(EVI2)的指数,用于监测植被。归一化差异雪指数(NDSI)已被广泛应用于监控雪。植被覆盖分数(VGCF),土壤覆盖率(土壤),雪盖分数(Snowcf),地表水体盖分数(水体CF),植被叶绿素(FAPAR)的分数吸收光合作用辐射(PAR)(FAPAR)( CHL)),通过MODIS图像检索植被(FAPAR(非CHL))的非叶绿素组分(FAPAR(非CHL))和分数吸收(FAPAR(冠源(冠源(冠源))的分数吸收的分数吸收耦合叶植被 - 土壤 - 雪地雪地水体辐射转移模型,LVS3。植被指数(NDVI,EVI,EVI2和NIRV)不同于VGCF,FAPAR(CHL),FAPAR(非CHL)和FAPAR(天篷)。除了植被外,我们还发现土壤,雪和地表水也对植被索引NDVI,EVI(EVI2)和NIRV产生了影响。积雪的存在降低了NDVI,EVI2和NIRV的观察到值。在雪花之后,植被指数(NDVI,EVI,EVI2和NIRV)与土壤和水体CF线性减少,水体CF对这些植被指数的影响力强于土壤。 EVI和雪之间的关系很复杂。 NDSI与SnowCF非线性地增加,但随着SnowCF和水体CF的总和线性增加(SUM = 0.5893 x NDSI +0.4342,R-2 = 0.976)。 NDSI用VGCF线性降低,NDSI与土壤之间的关系复杂。 VGCF,FAPAR(CHL),FAPAR(非CHL)和FAPAR(冠层)的检索提供LVS3模型的植被监测和生态建模的替代品。

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