Evaluation of selected Sentinel-2 remotely sensed vegetation indices and MODIS GPP in representing productivity in semi-arid South African ecosystems

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Authors

Maluleke, Amukelani
Feig, Gregor Timothy
Brummer, Christian
Rybchak, Oksana
Midgley, Guy

Journal Title

Journal ISSN

Volume Title

Publisher

Wiley

Abstract

The ability to validate satellite observations with ground‐based data sets is vital for the spatiotemporal assessment of productivity trends in semi‐arid ecosystems. Modeling ecosystem scale parameters such as gross primary production (GPP) with the combination of satellite and ground‐based data however requires a comprehensive understanding of the associated drivers of how the carbon balance of these ecosystems is impacted under climate change. We used GPP estimates from the partitioning of net ecosystem measurements (net ecosystem exchange) from three Eddy Covariance (EC) flux tower sites and applied linear regressions to evaluate the ability of Sentinel‐2 vegetation indices (VIs) retrieved from Google Earth Engine to estimate GPP in semi‐arid ecosystems. The Sentinel‐2 normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and the land surface water index (LSWI) were each assessed separately, and also in combination with selected meteorological variables (incoming radiation, soil water content, air temperature, vapor pressure deficit) using a bi‐directional stepwise linear regression to test whether this can improve GPP estimates. The performance of the MOD17AH2 8‐day GPP was also tested across the sites. NDVI, EVI and LSWI were able to track the phase and amplitude patterns of EC estimated gross primary production (GPPEC) across all sites, albeit with phase delays observed especially at the Benfontein Savanna site (Ben_Sav). In all cases, the VI estimates improved with the addition of meteorological variables except for LSWI at Middleburg Karoo (Mid_Kar). The least improvement in R2 was observed in all EVI‐based estimates —indicating the suitability of EVI as a single VI to estimate GPP. Our results suggest that while productivity assessments using a single VI may be more favorable, the inclusion of meteorological variables can be applied to improve single VIs estimates to accurately detect and characterize changes in GPP. In addition, we found that standard MODIS products better represent the phase than amplitude of productivity in semi‐arid ecosystems, explaining between 68% and 83% of GPP variability.

Description

DATA AVAILABILITY STATEMENT : Eddy covariance data for the Benfontein Savanna and Nama‐Karoo sites was used for the analysis of Sentinel‐2 VIs and MODIS GPP product. This EC data (Maluleke & Feig, 2024) is available on SAEON data portal at https://catalogue.saeon.ac.za/records/10.15493/EFTEON.15012024. Eddy covariance data used for the Middleburg Karoo site (Brümmer et al., 2024) is under the custodianship of the Thünen Institute Climate‐Smart Agriculture and is available at https://zenodo.org/records/10670256. The MODIS Gross Primary Productivity data (Running et al., 2021) is provided by NASA LP DAAC at the USGS EROS Center at https://doi.org/10. 5067/MODIS/MOD17A2H.061. Copernicus Sentinel Data (2023) for Sentinel data was accessed at https:// sentinel.esa.int/web/sentinel/user‐guides/sentinel‐2‐msi/processing‐levels/level‐2. The data analysis workflow (Maluleke, 2024) for the retrieval of MODIS GPP and Sentinel‐2 data sets on Google Earth Engine, as well as the notebooks for the analysis and plotting of figures in R software version 4.2.3 are available at https://zenodo. org/records/10650562.

Keywords

Sentinel‐2, Semi‐arid ecosystems, Gross primary production (GPP), Vegetation indices (VIs), SDG-13: Climate action, Normalized difference vegetation index (NDVI), Land surface water index (LSWI), Enhanced vegetation index (EVI), SDG-15: Life on land

Sustainable Development Goals

SDG-13:Climate action
SDG-15:Life on land

Citation

Maluleke, A., Feig, G., Brümmer, C., Rybchak, O., & Midgley, G. (2024). Evaluation of selected Sentinel‐2 remotely sensed vegetation indices and MODIS GPP in representing productivity in semiarid South African ecosystems. Journal of Geophysical Research: Biogeosciences, 129, e2023JG007728. https://DOI.org/10.1029/2023JG007728.