The Effect of Atmospheric Correction Approaches on Leaf Area Index Retrieval Accuracy in Heterogeneous Croplands

Sosdito Mananze; Sebastiao Vilanculos; Mario Cunha

doi:10.48084/etasr.16209

Authors

Sosdito Mananze Laboratory of Geographic Information Systems and Remote Sensing, Department of Rural Sociology, School of Rural Development, Eduardo Mondlane University, Mozambique | Regional Centre of Excellence in Agri-Food Systems and Nutrition, Eduardo Mondlane University, Maputo, Mozambique | United Methodist University of Mozambique, Cambine, Inhambane, Mozambique
Sebastiao Vilanculos Laboratory of Geographic Information Systems and Remote Sensing, Department of Rural Sociology, School of Rural Development, Eduardo Mondlane University, Mozambique | Regional Centre of Excellence in Agri-Food Systems and Nutrition, Eduardo Mondlane University, Maputo, Mozambique
Mario Cunha Faculty of Sciences, University of Porto, Portugal | INESCTEC, Centre for Robotics in Industry and Intelligent Systems, Portugal

Volume: 16 | Issue: 2 | Pages: 33882-33889 | April 2026 | https://doi.org/10.48084/etasr.16209

Received: 13 November 2025 | Revised: 23 December 2025 | Accepted: 3 January 2026 | Online: 4 April 2026

Corresponding author: Sosdito Mananze

Abstract

This study evaluates four atmospheric correction approaches, including Sentinel-2 Correction (Sen2Cor), Rayleigh correction, image correction iCOR, and Dark Object Subtraction (DOS) for Leaf Area Index (LAI) retrieval in heterogeneous croplands in southern Mozambique, using the Sentinel Application Platform (SNAP) biophysical processor with PROSAIL neural network inversion. Field LAI measurements from 270 sample points across multiple vegetation types were used as reference data. The results showed that iCOR delivered the best overall performance, with a Root Mean Square Error (RMSE) of 1.315, a Mean Absolute Error (MAE) of 1.063, and a Bias of -0.684. This was followed by DOS, with an RMSE of 1.423, Sen2Cor with an RMSE of 1.514, and Rayleigh correction with an RMSE of 1.567. All methods exhibited negative coefficient of determination (R²) values from-1.223 to -2.155, indicating systematic LAI underestimation and limited predictive capability. Performance varied substantially across stratification levels. LAI-class analysis revealed that DOS performed the best for low LAI with an RMSE of 0.478, while iCOR excelled at medium LAI with an RMSE of 0.956, and high LAI with an RMSE of 1.929. Strong positive correlations between absolute error and field LAI (Pearson's correlation coefficient (r) = 0.733 to 0.895) indicated increasing retrieval challenges at higher canopy densities. Significant spatial variation was observed, with ESUDER Campus exhibiting lower errors (RMSE = 0.518-0.745) compared to Machengue (RMSE = 1.435-1.741). Vegetation-type specific analysis showed that Miombo Forest had the lowest errors (RMSE = 0.542 for DOS), while Mango orchards presented the greatest challenges (RMSE = 1.754 for iCOR). These findings demonstrate the importance of selecting appropriate atmospheric correction methods based on specific vegetation characteristics, canopy density, and local environmental conditions for operational LAI monitoring in heterogeneous agricultural landscapes.

Keywords:

leaf area index, sentinel-2, atmospheric correction, heterogeneous agricultural landscapes

Downloads

Download data is not yet available.

References

J. M. Chen, C. H. Menges, and S. G. Leblanc, "Global Mapping of Foliage Clumping Index Using Multi-Angular Satellite Data," Remote Sensing of Environment, vol. 97, no. 4, pp. 447–457, Sept. 2005. DOI: https://doi.org/10.1016/j.rse.2005.05.003

"Systematic Observation Requirements for Satellite-based Data Products for Climate, 2011 Update," Global Climate Observing System, Geneva, Switzerland, Technical Report GCOS-154, 2011.

F. Baret et al., "GEOV1: LAI and FAPAR Essential Climate Variables and FCOVER Global Time Series Capitalizing Over Existing Products. Part 1: Principles of Development and Production," Remote Sensing of Environment, vol. 137, pp. 299–309, Oct. 2013. DOI: https://doi.org/10.1016/j.rse.2012.12.027

R. B. Myneni et al., "Global Products of Vegetation Leaf Area and Fraction Absorbed PAR from Year One of MODIS Data," Remote Sensing of Environment, vol. 83, no. 1–2, pp. 214–231, Nov. 2002. DOI: https://doi.org/10.1016/S0034-4257(02)00074-3

L. A. Brown et al., "Validation of Baseline and Modified Sentinel-2 Level 2 Prototype Processor Leaf Area Index Retrievals Over the United States," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 175, pp. 71–87, May 2021. DOI: https://doi.org/10.1016/j.isprsjprs.2021.02.020

S. Mananze, I. Pôças, and M. Cunha, "Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data," Remote Sensing, vol. 10, no. 12, Dec. 2018, Art. no. 1942. DOI: https://doi.org/10.3390/rs10121942

J. Verrelst et al., "Optical Remote Sensing and the Retrieval of Terrestrial Vegetation Bio-Geophysical Properties – A Review," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 108, pp. 273–290, Oct. 2015. DOI: https://doi.org/10.1016/j.isprsjprs.2015.05.005

F. J. García-Haro et al., "Derivation of Global Vegetation Biophysical Parameters from EUMETSAT Polar System," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 139, pp. 57–74, May 2018. DOI: https://doi.org/10.1016/j.isprsjprs.2018.03.005

M. Campos-Taberner et al., "A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System," Remote Sensing, vol. 10, no. 5, May 2018, Art. no. 763. DOI: https://doi.org/10.3390/rs10050763

M. Weiss and F. Baret, S2 ToolBox Level 2 Products: LAI, FAPAR, FCOVER Version 1.1. Paris, France: European Space Agency, 2016.

P. S. Nagendram, P. Satyanarayana, and P. Ravi Teja, "Mapping Paddy Cropland in Guntur District Using Machine Learning and Google Earth Engine utilizing Images from Sentinel-1 and Sentinel-2," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12427–12432, Dec. 2023. DOI: https://doi.org/10.48084/etasr.6460

R. Khalil, M. S. Khan, Y. Hasan, N. Nacer, and S. Khan, "Supervised NDVI Composite Thresholding for Arid Region Vegetation Mapping," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14420–14427, Jun. 2024. DOI: https://doi.org/10.48084/etasr.7202

M. Kganyago, P. Mhangara, T. Alexandridis, G. Laneve, G. Ovakoglou, and N. Mashiyi, "Validation of Sentinel-2 Leaf Area Index (LAI) Product Derived from SNAP Toolbox and its Comparison with Global LAI Products in an African Semi-Arid Agricultural Landscape," Remote Sensing Letters, vol. 11, no. 10, pp. 883–892, Oct. 2020. DOI: https://doi.org/10.1080/2150704X.2020.1767823

Z. Bochenek et al., "Validation of the Lai Biophysical Product Derived from Sentinel-2 and Proba-V Images for Winter Wheat in Western Poland," Geoinformation Issues, vol. 9, no. 1, pp. 15–26, 2017.

N. Djamai, R. Fernandes, M. Weiss, H. McNairn, and K. Goïta, "Validation of the Sentinel Simplified Level 2 Product Prototype Processor (SL2P) for mapping cropland biophysical variables Using Sentinel-2/MSI and Landsat-8/OLI data," Remote Sensing of Environment, vol. 225, pp. 416–430, May 2019. DOI: https://doi.org/10.1016/j.rse.2019.03.020

J. Louis et al., "SENTINEL-2 SEN2COR: L2A Processor for Users," in Proceedings Living Planet Symposium, Prague, Czech Republic, May 2016, Art. no. SP-740.

L. De Keukelaere et al., "Atmospheric Correction of Landsat-8/OLI and Sentinel-2/MSI Data Using iCOR Algorithm: Validation for Coastal and Inland Waters," European Journal of Remote Sensing, vol. 51, no. 1, pp. 525–542, Jan. 2018. DOI: https://doi.org/10.1080/22797254.2018.1457937

S. Sterckx, S. Knaeps, S. Kratzer, and K. Ruddick, "SIMilarity Environment Correction (SIMEC) Applied to MERIS Data Over Inland and Coastal Waters," Remote Sensing of Environment, vol. 157, pp. 96–110, Feb. 2015. DOI: https://doi.org/10.1016/j.rse.2014.06.017

A. B. Ruescas and D. Müller, Rayleigh Correction Tutorial. Paris, France: European Space Agency, 2021.

L. Congedo, "Semi-Automatic Classification Plugin: A Python Tool for the Download and Processing of Remote Sensing Images in QGIS," Journal of Open Source Software, vol. 6, no. 64, Aug. 2021, Art. no. 3172. DOI: https://doi.org/10.21105/joss.03172