Abstract：
Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.
摘要：
位于北纬地区，季节性气温波动较大，北方森林对气候变化很敏感,有证据表明生产率的提高和降低，取决于条件。基于光谱反射率的植被指数光学遥感提供了监测植被光合活动的手段，并为观察北方森林如何应对不断变化的环境条件提供了有力的工具。北纬或高海拔地区的基于反射的遥感光学信号容易被雪覆盖混淆，阻碍了卫星植被指数在大规模跟踪植被生产力中的应用。从卫星数据中解开雪的影响可能具有挑战性，特别是当缺乏验证数据时。在这项研究中,我们在艾伯塔省建立了一个实验系统,加拿大包括六个北方树种，常绿和落叶，评价降雪对三个植被指数的混杂效应:归一化植被指数(NDVI),光化学反射指数(PRI)，和叶绿素/类胡萝卜素指数(CCI)，全部用于跟踪北方森林的植被生产力。我们的结果揭示了雪对冠层反射率和植被指数的重大影响，表示为反照率增加，NDVI值降低，PRI和CCI值升高。这些影响在物种和功能组（常绿和落叶）之间有所不同，并且不同的植被指数受到不同的影响。表明矛盾，降雪对这些指数的混杂影响。除了雪的影响，我们评估了常绿和落叶物种混合林分中落叶乔木对植被指数的贡献，这有助于观察到基于绿色的指数与许多含有落叶成分的常绿为主的森林的生态系统生产力之间的关系。我们的结果表明了雪和植被类型对植被指数的混杂和相互作用的影响，并说明了在使用遥感植被指数的任何全球尺度光合作用监测工作中明确考虑雪效应的重要性。
