Advancing the mapping of vegetation structure in savannas using Sentinel‐1 imagery

Abstract Vegetation structure monitoring is important for the understanding and conservation of savanna ecosystems. Optical satellite imagery can be used to estimate canopy cover, but provides limited information about the structure of savannas, and is restricted to daytime and clear‐sky captures. Active remote sensing can potentially overcome this. We explore the utility of C‐band synthetic aperture radar imagery for mapping both grassland and woody vegetation structure in savannas. We calibrated Sentinel‐1 VH () and VV () backscatter coefficients and their ratio () to ground‐based estimates of grass biomass, woody canopy volume (<50 000 m 3 /ha) and tree basal area (<15 m 2 /ha) in the Greater Serengeti‐Mara Ecosystem, and simultaneously explored their sensitivity to soil moisture. We show that in particular can be used to estimate grass biomass ( R 2 = 0.54, RMSE = 630 kg/ha, %range = 20.6), woody canopy volume ( R 2 = 0.69, RMSE = 4188 m 3 /ha, %range = 11.8) and tree basal area ( R 2 = 0.44, RMSE = 2.03 m 2 /ha, %range = 18.6) in the dry season, allowing for the extrapolation to regional scale vegetation structure maps. We also introduce new proxies for soil moisture as an option for extending this approach to the wet season using the 90‐day preceding bounded running averages of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and the Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG) datasets. We discuss the potential of Sentinel‐1 imagery for better understanding of the spatio‐temporal dynamics of vegetation structure in savannas.

Authors

Institutions

Publication Details

Journal
Remote Sensing in Ecology and Conservation
Published
2025-04-22
DOI
https://doi.org/10.1002/rse2.70006
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

Funders

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

Advancing the mapping of vegetation structure in savannas using Sentinel‐1 imagery

Ruth A. Howison, Han Olff, Marianthi Tangili, Vera Thijssen
Remote Sensing in Ecology and Conservation
Remote Sensing in Agriculture
article

Advancing the mapping of vegetation structure in savannas using Sentinel‐1 imagery

Ruth A. Howison, Han Olff, Marianthi Tangili, Vera Thijssen
article en

Abstract

Abstract Vegetation structure monitoring is important for the understanding and conservation of savanna ecosystems. Optical satellite imagery can be used to estimate canopy cover, but provides limited information about the structure of savannas, and is restricted to daytime and clear‐sky captures. Active remote sensing can potentially overcome this. We explore the utility of C‐band synthetic aperture radar imagery for mapping both grassland and woody vegetation structure in savannas. We calibrated Sentinel‐1 VH () and VV () backscatter coefficients and their ratio () to ground‐based estimates of grass biomass, woody canopy volume (<50 000 m 3 /ha) and tree basal area (<15 m 2 /ha) in the Greater Serengeti‐Mara Ecosystem, and simultaneously explored their sensitivity to soil moisture. We show that in particular can be used to estimate grass biomass ( R 2 = 0.54, RMSE = 630 kg/ha, %range = 20.6), woody canopy volume ( R 2 = 0.69, RMSE = 4188 m 3 /ha, %range = 11.8) and tree basal area ( R 2 = 0.44, RMSE = 2.03 m 2 /ha, %range = 18.6) in the dry season, allowing for the extrapolation to regional scale vegetation structure maps. We also introduce new proxies for soil moisture as an option for extending this approach to the wet season using the 90‐day preceding bounded running averages of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and the Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG) datasets. We discuss the potential of Sentinel‐1 imagery for better understanding of the spatio‐temporal dynamics of vegetation structure in savannas.

Remote Sensing in Ecology and ConservationVol. 11(5)
University of Groningen (NL), Fryske Akademy (NL)
Rijksuniversiteit Groningen, Tanzania Commission for Science and Technology, Tanzania Wildlife Research Institute
Openalex Percentile: Top 92%
Remote Sensing in Agriculture
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.