Snow-corrected vegetation indices for improved gross primary productivity assessment in North American evergreen forests

Ran Wang, David R. Bowling, John A. Gamon, Kenneth R. Smith, Rong Yu, Gabriel Hmimina, Masahito Ueyama, Asko Noormets, Thomas E. Kolb, Andrew D. Richardson, Charles P.A. Bourque, Rosvel Bracho, Peter D. Blanken, T. Andrew Black, M. Altaf Arain

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number109600
JournalAgricultural and Forest Meteorology
Volume340
DOIs
StatePublished - Sep 15 2023

Keywords

  • Chlorophyll-carotenoid index (CCI)
  • Evergreen
  • GPP
  • MODIS
  • NIRv
  • Snow

ASJC Scopus subject areas

  • Forestry
  • Global and Planetary Change
  • Agronomy and Crop Science
  • Atmospheric Science

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