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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Coexistence of Trees, Shrubs, and Grasses Creates a Complex Picture of Land Surface Phenology in Dry Tropical Ecosystems

John L. Godlee, Leonor Patrícia Cerdeira Morellato, M. S. B. de Moura, Desirée M. Ramos et al.
1 citations
Remote Sensing
Remote Sensing in Agriculture
1.72
article

The Coexistence of Trees, Shrubs, and Grasses Creates a Complex Picture of Land Surface Phenology in Dry Tropical Ecosystems

John L. Godlee, Leonor Patrícia Cerdeira Morellato, M. S. B. de Moura, Desirée M. Ramos, Bruna de Costa Alberton, Kyle Graham Dexter, Stephanie P. Koolen
article en
1 citations

Abstract

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.

Remote SensingVol. 17(16)
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)
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
Openalex Percentile: Top 14%
Remote Sensing in Agriculture
1.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.