Enhanced winter wheat LAI retrieval from Sentinel-2: asoil-informed radiative transfer based approach

Leaf Area Index (LAI) is a key trait related to several agronomic issues such as soil cover, plant health, crop productivity, biomass and yield estimation. Availability of high-resolution LAI information at large scale is crucial for monitoring and managing agricultural landscapes effectively [1], as it can help monitor growth conditions and adapt practices. However, its satellite-based assessment is confounded by several factors such as soil background, vegetation type and noise. Today, the retrieval of LAI through the inversion of a radiative transfer model (RTM) is state-of-the-art. Still, research investigating the performance of crop-type specific models compared to across-biome models such as the ESA’s Sentinel Application Platform (SNAP) and in-situ data is rare. 

In this research we propose to improve the combined leaf and canopy PROSAIL [2] RTM crop-specific reflectance simulations by integrating soil spectra into this model. We specifically sample Sentinel-2 spectra from fields over which we perform LAI retrieval. A neural network is trained to invert the RTM. To scale this strategy to larger areas (i.e. country scale) we exploit Sentinel-2 observations of bare soil and use clustering methods to generate a condensed soil dataset representing varying background conditions across space.

We use Switzerland to test the approach, with in-situ measurements of winter wheat from 2022 and 2023 available for validation. We focus on Sentinel-2 imagery for its high temporal and spatial resoltuions. Preliminary results show that a model trained on a data generated with a Switzerland-wide soil dataset and constrained for winter wheat (CH-LAI-WW model) outperformed predictions (nRMSE: 0.180) obtained from a classic setup without the soil inclusion (nRMSE: 0.201). Furthermore, prediction errors were improved compared to the across-biome SNAP LAI processor (nRMSE: 0.268). The proposed methodology demonstrates a way to improve the crop- and biome-specific prediction of key traits and consequently to improve the reliability for agricultural monitoring and management applications.

[1] B. Brisco, R. Brown, T. Hirose, H. McNairn, and K. Staenz, “Precision agriculture and the roleof remote sensing: A review,” Canadian Journal of Remote Sensing, vol. 24:3, pp. 315–327, 1998

[2] S. Jacquemoud, W. Verhoef, F. Baret, C. Bacour, P. J. Zarco-Tejada, G. P. Asner, C. François, and S. L. Ustin, “PROSPECT+SAIL models: A review of use for vegetation characterization,” Remote Sensing of Environment, vol. 113, pp. S56–S66, Sept. 2009.