Development of the grass LAI and CCC remote sensing-based models and their transferability using sentinel-2 data in heterogeneous grasslands

ABSTRACT

Estimation of biophysical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) at high spatiotemporal resolution is important for managing natural and heterogeneous environments. However, accurate estimation of biophysical variables particularly over heterogeneous environments remains a challenge. The objective of the study was to develop locally parameterized grass LAI and CCC empirical models using the Sentinel-2 variables combined with the Stepwise multiple linear regression (SMLR) and Random forest (RF) at the Golden Gate Highlands National Park (GGHNP) and Marakele National Park (MNP) in South Africa. Results showed that in MNP, SMLR yielded better LAI estimation with root mean squared error (RMSE) of 0.67 m².m⁻² and mean adjusted error (MAE) of 0.54, explaining 48% of LAI variability, when bands and indices are combined. In contrast, RF gave better CCC estimation i.e. RMSE and MAE of 17.08 µg.cm⁻² and 13.18 respectively, explaining about 40% of CCC variability with Sentinel-2 bands only. In GGHNP, the RF models provided the best estimates of both LAI and CCC compared to SMLR models. Furthermore, the CCC and LAI estimation models of GGHNP showed improved model accuracies when 50% and 75% of the MNP field samples were transferred to the GGHNP models. In contrast, the CCC and LAI estimation models of MNP showed a decline in model performance across all scenarios where the GGHNP field samples were transferred to the MNP models. These findings have significant implications for the development of locally parameterized types of models that can provide improved and consistent site-specific accurate estimates of grass biophysical parameters over heterogeneous environments.

KEYWORDS:

• Leaf area index (LAI)
• canopy chlorophyll content (CCC)
• sentinel-2 imagery
• indices

Acknowledgements

The Sentinel-2 data used in this study were downloaded from the European Space Agency Copernicus Open Access Hub. We sincerely thank the field assistants (namely Phomolo Seriba, Katlego Mashiane, Steven Khosa and Brian Mabunda) in the Golden Gate Highlands National Park and Marakele National Park for their collaborative effort in collecting grass LAI, FVC and LCC ground measurements. We also thank the anonymous reviewers for their valuable and insightful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contributions

Conceptualisation, P.T. and A.R.; methodology, P.T. and A.R.; Formal analysis, P.T. and M.Q; validation, P.T., A.R, and M.Q.; writing – original draft preparation, P.T.; writing – review and editing, A.R.; project administration, P.T.

Data availability statement

We understand that the publication of the data is becoming a good practice in research.

Additional information

Funding

This research was funded by Research development programme of the University of Pretoria, as well as the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) of the National Research Foundation (NRF) of South Africa, grant number 118590.