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Relationships between species composition, fractional cover and land surface temperature in a mediterranean ecosystem

机译:地中海生态系统中物种组成,覆盖率和地表温度之间的关系

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HyspIRI is a proposed satellite mission combining two 60 m spatial resolution sensors: a Visible-Shortwave Infrared (VSWIR) imaging spectrometer and a multispectral thermal infrared (TIR) scanner. HyspIRI offers the potential to combine the ability of a VSWIR sensor to discriminate plant species and estimate accurate surface fractions of biotic and abiotic materials using multispectral emissivity with improved Land Surface Temperature (LST) retrieval from the TIR sensor. We evaluate potential synergies between AVIRIS maps of species/cover and fractions and MASTER LST utilizing multiple flight lines acquired in July 2011 in the Santa Barbara area. Cover was mapped using Multiple Endmember Spectral Mixture Analysis using a spectral library derived from the 7.5 m imagery and endmembers selected using Iterative Endmember Selection. Temperature Emissivity Separation (TES) was accomplished using the MASTER TES algorithm. Pixel-based accuracy was 55% for 21 classes, but 84.7% based on pixel majority in reference polygons. An inverse relationship was observed between green vegetation (GV) fractions and LST, but varied by plant species, generating unique LST-GV clusters.
机译:HyspIRI是一项拟议的卫星任务,将两个60 m空间分辨率传感器组合在一起:一个可见短波红外(VSWIR)成像光谱仪和一个多光谱热红外(TIR)扫描仪。 HyspIRI具有将VSWIR传感器区分植物物种并使用多光谱发射率和从TIR传感器获取的改进的地面温度(LST)进行估算的生物和非生物材料的准确表面分数的潜力。我们利用2011年7月在圣塔芭芭拉地区获得的多条航线评估了AVIRIS物种/覆盖物和馏分图与MASTER LST之间的潜在协同作用。使用多端元光谱混合分析(使用来自7.5 m图像的光谱库和使用迭代端元选择选择的端元)映射封面。使用MASTER TES算法完成温度发射率分离(TES)。基于像素的精度在21类中为55%,但在参考多边形中基于像素多数为84.7%。观察到绿色植被(GV)组分与LST之间存在反比关系,但因植物种类而异,从而产生了独特的LST-GV簇。

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