Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study

• Published in: Global change biology Vol. 29; no. 21; pp. 6120 - 6138
• Main Authors: Wang, Ran, Springer, Kyle R., Gamon, John A.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.11.2023
• Subjects: Albedo; Alberta; altitude; Biological Sciences; Boreal forests; Carotenoids; Chlorophyll; chlorophyll/carotenoid index (CCI); Chlorophylls; climate; Climate change; Coniferous forests; deciduous; Deciduous forests; Deciduous trees; ecosystems; Environmental changes; Environmental conditions; evergreen; Evergreen trees; Forests; Functional groups; global change; Latitude; leaf reflectance; Monitoring; NDVI; normalized difference vegetation index; Normalized difference vegetative index; Optical communication; photochemical reflectance index (PRI); Photochemicals; Photochemistry; Photosynthesis; Plant species; Productivity; Reflectance; Remote observing; Remote sensing; Satellites; Snow; Snow cover; snowpack; Spectral reflectance; Taiga; temperature; Tracking; Trees; Vegetation; Vegetation type; vegetation types; Alberta
• ISSN: 1354-1013; 1365-2486; 1365-2486
• DOI: 10.1111/gcb.16916

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. Optical remote sensing at northern latitude or high‐altitude regions is readily confounded by snow coverage, hampering applications of satellite‐based vegetation indices in tracking vegetation productivity. Unraveling the effects of snow can be challenging from satellite data. We established an experimental system including six boreal tree species to evaluate the snow effects on three vegetation indices: the normalized difference vegetation index, the photochemical reflectance index, and the chlorophyll/carotenoid index, all used in tracking vegetation productivity. Snow effects varied among species and functional groups (evergreen and deciduous) and among different vegetation indices.