Kira (2026) A scalable framework for monitoring urban vegetation function for planning and management - data

Identification

• Journal: Mendeley Data
• Year: 2026
• Date: 2026-03-31
• Authors: Oz Kira
• DOI: 10.17632/cxj8ck6hfs

Research Groups

• Ben-Gurion University of the Negev

Short Summary

This dataset provides processed results to evaluate the seasonal behavior and cross-sensor consistency of satellite-derived vegetation productivity proxies across six diverse urban environments. It aims to support scalable monitoring of urban vegetation function for planning and management.

Objective

• To evaluate the seasonal behavior of satellite-derived vegetation productivity proxies and their consistency across different sensors in heterogeneous urban environments.

Study Configuration

• Spatial Scale: Six global urban environments (Tel Aviv, Rome, Washington DC, Beijing, Johannesburg, Buenos Aires), aggregated at the city scale and for vegetation-only areas.
• Temporal Scale: January 2019 to October 2020 (22 months).

Methodology and Data

• Models used: Not applicable; the study utilizes satellite-derived vegetation productivity proxies including Solar-Induced Chlorophyll Fluorescence (SIF), Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), radiation-scaled NIRvP, and Gross Primary Production (GPP).
• Data sources: TROPOMI satellite (SIF), Sentinel-2 satellite (NDVI, NIRv), MODIS satellite (GPP), and ESA WorldCover dataset (for vegetation area definition).

Main Results

• The study generated a comprehensive dataset containing time series of TROPOMI SIF, Sentinel-2 NDVI and NIRv, NIRvP, and MODIS GPP for six major urban centers.
• All variables are aggregated at the city scale and for vegetation-only areas, spanning January 2019 to October 2020.
• This dataset facilitates cross-sensor comparisons of seasonal dynamics and consistency among various vegetation productivity proxies in complex urban settings.

Contributions

• Provides a unique, processed dataset for multi-sensor and multi-proxy monitoring of urban vegetation function, addressing the need for scalable solutions in urban planning.
• Enables reproducibility of analyses presented in an associated manuscript and supports further research on urban vegetation dynamics.
• Offers a valuable resource for understanding vegetation productivity in heterogeneous urban environments globally.

Funding

• Israel Science Foundation, Grant ID: 2782/24

Citation

@article{Kira2026scalable,
  author = {Kira, Oz},
  title = {A scalable framework for monitoring urban vegetation function for planning and management - data},
  journal = {Mendeley Data},
  year = {2026},
  doi = {10.17632/cxj8ck6hfs},
  url = {https://doi.org/10.17632/cxj8ck6hfs}
}