Snow-corrected vegetation indices for improved gross primary productivity assessment in North American evergreen forests
Snow-corrected vegetation indices for improved gross primary productivity assessment in North American evergreen forests
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DOI：
10.1016/j.agrformet.2023.109600
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发表时间：
2023-09
影响因子：
6.2
通讯作者：
Ran Wang;D. Bowling;J. Gamon;Kenneth R. Smith;Rong Yu;G. Hmimina;M. Ueyama;A. Noormets;T. Kolb;A. Richardson;C. Bourque;R. Bracho;P. Blanken;T. A. Black;M. A. Arain
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作者：
Ran Wang;D. Bowling;J. Gamon;Kenneth R. Smith;Rong Yu;G. Hmimina;M. Ueyama;A. Noormets;T. Kolb;A. Richardson;C. Bourque;R. Bracho;P. Blanken;T. A. Black;M. A. Arain
北美万年青森林覆盖面积大，影响全球碳循环。卫星遥感已被用于跟踪这些森林的生态系统光合作用的物候，通过检测与生理和结构特征相关的植被光学特性的变化，这些方法大多与植被的绿色度密切相关。然而，在常绿植物，卫星数据监测光合物候的应用往往是有限的缺乏敏感性的绿色指数。在这项研究中，我们确定了47个万年青森林通量站点在北美有MODIS观测重叠的通量塔记录。然后，我们计算了四个植被指数，使用MODIS MAIAC数据（MCD19A1），包括NDVI，CCI，NIRv，和kNDVI，为47个通量站点，并评估了北美万年青林的总初级生产力（GPP）和植被指数之间的关系。我们的研究结果表明，雪有实质性的影响，所有的植被指数在跟踪GPP物候的性能，特别是在早春时，发生快速变化的GPP和积雪。不同的植被指数受到不同的影响，这表明雪对这些指数的矛盾和混淆的影响。在校正雪效应后，CCI和NIRv在跟踪GPP物候方面表现良好，尽管原因不同。CCI是敏感的叶绿素和类胡萝卜素色素，这是密切相关的GPP在常绿植物的物候期的相对水平的季节性变化。NIRv对吸收的光合有效辐射和落叶组分对整体光学性质的贡献敏感。我们还发现，GPP和植被指数之间的相关性不同的生态区和气候类。在一般情况下，具有明显的季节性GPP模式的地区有较强的GPP和绿色指数之间的相关性比较弱的季节性GPP模式的地区。这些生物群落差异对于CCI来说不太明显。在使用光学卫星反射率指数进行的任何大规模GPP研究中，都应考虑本文报告的雪伪影和补充植被指数效应。
North American evergreen forests cover large areas and influence the global carbon cycle. Satellite remote sensing has been used to track the phenology of ecosystem photosynthesis of these forests by detecting variation in vegetation optical properties associated with physiological and structural features, and most of these methods have been closely tied to vegetation greenness. However, in evergreens, the application of satellite data to monitor photosynthetic phenology is often limited by the lack of sensitivity of greenness-based indices. In this study, we identified 47 evergreen forest flux sites in North America that had MODIS observation overlapping with the flux tower records. We then calculated four vegetation indices using MODIS MAIAC data (MCD19A1), including NDVI, CCI, NIRv, and kNDVI, for the 47 flux sites and evaluated relationships between gross primary productivity (GPP) and vegetation indices across the North American evergreen forests. Our results showed that snow had substantial effects on the performance of all vegetation indices in tracking GPP phenology, particularly in the early spring when rapid changes occurred to both GPP and snow cover. Different vegetation indices were affected differently, indicating contradictory and confounding effects of snow on these indices. After correcting for the snow effects, both CCI and NIRv performed well in tracking GPP phenology, albeit for different reasons. CCI is sensitive to seasonal changes in the relative levels of chlorophyll and carotenoid pigments, which are closely tied to GPP phenology in evergreens. NIRv is sensitive to the absorbed photosynthetically active radiation and to the contribution of deciduous components to the overall optical properties. We also found that correlations between GPP and vegetation indices varied among ecoregions and climate classes. In general, regions with pronounced seasonal GPP patterns had stronger correlations between GPP and greenness-based indices than regions with weaker seasonal GPP patterns. These biome differences were less pronounced for CCI. The snow artifacts and complementary vegetation index effects reported here should be considered in any large-scale studies of GPP using reflectance-based indices from optical satellites.