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Spring and autumn phenology across the Tibetan Plateau inferred from normalized difference vegetation index and solar-induced chlorophyll fluorescence

机译:春季和秋季候选跨藏高原推断出归一化差异植被指数和太阳能抗叶绿素荧光

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Plant phenology is a key parameter for accurately modeling ecosystem dynamics. Limited by scarce ground observations and benefiting from the rapid growth of satellite-based Earth observations, satellite data have been widely used for broad-scale phenology studies. Commonly used reflectance vegetation indices represent the emergence and senescence of photosynthetic structures (leaves), but not necessarily that of photosynthetic activities. Leveraging data of the recently emerging solar-induced chlorophyll fluorescence (SIF) that is directly related to photosynthesis, and the traditional MODIS Normalized Difference Vegetation Index (NDVI), we investigated the similarities and differences on the start and end of the growing season (SOS and EOS, respectively) of the Tibetan Plateau. We found similar spatiotemporal patterns in SIF-based SOS (SOS_(SIF)) and NDVI-based SOS (SOS_(NDVI)). These spatial patterns were mainly driven by temperature in the east and by precipitation in the west. Yet the two satellite products produced different spatial patterns in EOS, likely due to their different climate dependencies. Our work demonstrates the value of big Earth data for discovering broad-scale spatiotemporal patterns, especially on regions with scarce field data. This study provides insights into extending the definition of phenology and fosters a deeper understanding of ecosystem dynamics from big data.
机译:植物候选是用于准确建模生态系统动态的关键参数。受到稀缺地面观测的限制并受益于卫星地球观测的快速增长,卫星数据已被广泛用于广泛的候选研究。常用的反射型植被指数代表光合结构(叶子)的出现和衰老,但不一定是光合作用活动。利用最近出现的太阳能诱导的叶绿素荧光(SIF)与光合作用直接相关的数据,以及传统的MODIS归一化差异植被指数(NDVI),我们调查了生长季节开始和结束的相似之处和差异(SOS西藏高原的EOS分别。我们在基于SOIF的SOS(SOS_(SOS_(SOS_(SOS_(SOS_(SOS_(SOS_(SOS_(SOS_(SOS_(SOS_(SOS_(SOS_(NDVI))中找到了类似的时空模式。这些空间模式主要受到东部的温度和西部沉淀的温度。然而,两颗卫星产品在EOS中产生了不同的空间模式,可能是由于其不同的气候依赖性。我们的作品展示了发现广泛的时空模式的大地球数据的价值,特别是在具有稀缺现场数据的地区。本研究提供了扩展候选的定义的见解,并促进了对大数据的深入了解生态系统动态的更深入了解。

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