The Coexistence of Trees, Shrubs, and Grasses Creates a Complex Picture of Land Surface Phenology in Dry Tropical Ecosystems
The use of digital cameras to monitor vegetation phenology (phenocams) has become increasingly common as a means of ground truthing estimates of land surface phenology from Earth observation data. Whilst the relationship between phenocam and Earth Observation-derived indices of land surface phenology has been examined across many temperate land cover types, our understanding of these relationships across the seasonally dry tropics is limited. Here we examined phenological time series derived from coarse-scale MODIS and fine-scale phenocam data across four seasonally dry tropical sites in Brazil to determine their correlation and how phenological metrics derived from these time series differed. While MODIS-derived vegetation indices showed seasonal patterns, we found a poor correlation with vegetation indices from phenocams at sites with a high proportion of evergreen vegetation and a poor correlation of MODIS indices with specific vegetation components. The high spatial and temporal resolution of phenocams allowed us to demonstrate differences in phenological metrics among different components of the vegetation which were obscured in the coarser MODIS data. This study highlights the potential of phenocam data to improve our understanding of complex vegetation leaf phenology and its drivers within mixed tree–shrub–grass systems in the seasonally dry tropics. This could help improve the representation of the savanna, grass, and shrubland biomes within terrestrial biosphere models, and lead to better predictions of the impact of climate change on carbon dynamics via shifting vegetation phenology.
Authors
- John L. Godlee (ORCID: https://orcid.org/0000-0001-5595-255X)
- Leonor Patrícia Cerdeira Morellato (ORCID: https://orcid.org/0000-0001-5265-8988)
- M. S. B. de Moura (ORCID: https://orcid.org/0000-0002-2844-1399)
- Desirée M. Ramos (ORCID: https://orcid.org/0000-0002-3342-6708)
- Bruna de Costa Alberton (ORCID: https://orcid.org/0000-0003-4835-8389)
- Kyle Graham Dexter (ORCID: https://orcid.org/0000-0001-9232-5221)
- Stephanie P. Koolen
Institutions
- Royal Botanic Garden Edinburgh (GB)
- Brazilian Agricultural Research Corporation (BR)
- Institute for Biodiversity (DE)
- Czech Academy of Sciences, Oriental Institute (CZ)
- Vale Technological Institute (BR)
- University of Turin (IT)
- University of Edinburgh (GB)
Publication Details
- Journal
- Remote Sensing
- Published
- 2025-08-19
- DOI
- https://doi.org/10.3390/rs17162883
- Citations
- 1
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco
- Natural Environment Research Council