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Assessment of Canopy Chlorophyll Content Retrieval in Maize and Soybean: Implications of Hysteresis on the Development of Generic Algorithms

机译:玉米和大豆冠层叶绿素含量的评估:滞后对通用算法开发的影响

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Canopy chlorophyll content (Chl) closely relates to plant photosynthetic capacity, nitrogen status and productivity. The goal of this study is to develop remote sensing techniques for accurate estimation of canopy Chl during the entire growing season without re-parameterization of algorithms for two contrasting crop species, maize and soybean. These two crops represent different biochemical mechanisms of photosynthesis, leaf structure and canopy architecture. The relationships between canopy Chl and reflectance, collected at close range and resampled to bands of the Multi Spectral Instrument (MSI) aboard Sentinel-2, were analyzed in samples taken across the entirety of the growing seasons in three irrigated and rainfed sites located in eastern Nebraska between 2001 and 2005. Crop phenology was a factor strongly influencing the reflectance of both maize and soybean. Substantial hysteresis of the reflectance vs. canopy Chl relationship existed between the vegetative and reproductive stages. The effect of the hysteresis on vegetation indices (VI), applied for canopy Chl estimation, depended on the bands used and their formulation. The hysteresis greatly affected the accuracy of canopy Chl estimation by widely-used VIs with near infrared (NIR) and red reflectance (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and simple ratio (SR)). VIs that use red edge and NIR bands (e.g., red edge chlorophyll index (CIred edge), red edge NDVI and the MERIS terrestrial chlorophyll index (MTCI)) were minimally affected by crop phenology (i.e., they exhibited little hysteresis) and were able to accurately estimate canopy Chl in two crops without algorithm reparameterization and, thus, were found to be the best candidates for generic algorithms to estimate crop Chl using the surface reflectance products of MSI Sentinel-2.
机译:冠层叶绿素含量(Chl)与植物的光合作用能力,氮素状态和生产力密切相关。这项研究的目的是开发一种遥感技术,以准确估算整个生长季节的冠层Chl,而无需对两种对比作物的玉米和大豆算法进行重新参数化。这两种农作物代表了光合作用,叶片结构和冠层结构的不同生化机制。在Sentinel-2上近距离收集并重新采样到多光谱仪器(MSI)波段的冠层Chl与反射率之间的关系,在位于东部东部三个灌溉和降雨养育地点的整个生长季节的样本中进行了分析。内布拉斯加州在2001年至2005年之间。作物物候是强烈影响玉米和大豆反射率的一个因素。营养期和生殖期之间存在反射率与冠层Chl关系的明显滞后。滞后对植被指数(VI)的影响(用于冠层Chl估计)取决于所使用的波段及其公式。迟滞极大地影响了具有近红外(NIR)和红色反射率(例如归一化差异植被指数(NDVI),增强植被指数(EVI)和简单比率(SR))的VI广泛使用的VI的冠层Chl估计的准确性。使用红边和NIR带(例如,红边叶绿素指数(CIred edge),红边NDVI和MERIS陆地叶绿素指数(MTCI))的VI受作物物候影响最小(即,表现出很小的滞后性),并且能够无需算法重新参数化即可准确估计两种作物的冠层Chl,因此,它是使用MSI Sentinel-2的表面反射率产品估算作物Chl的通用算法的最佳人选。

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