Leaf Area Index and Leaf Chlorophyll Content estimation from hyperspectral imaging using SCOPE model inversion

Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) are key vegetation indices for modeling energy and mass exchange between the atmosphere and land surfaces and can therefore be utilized for yield prediction. Consequently, suitable methods have been developed to retrieve LAI and LCC from remotely sensed data. Among these, the inversion of Radiative Transfer Models stands out as a promising approach, as it addresses the issue of limited transferability and minimizes the need for extensive field measurements also accounting for crop variability.

The objective of this study is to assess the applicability of the Soil Canopy Observation of Photochemistry and Energy Fluxes (SCOPE) model for estimating LAI and LCC of potato (Solanum tuberosum L.) using time series of hyperspectral images captured by an uncrewed aerial vehicle. A field experiment was conducted in Belgium from June to October 2024, involving two potato varieties, early and late, subjected to two nitrogen fertilization levels and six different biostimulants. Throughout the crop growth cycle, hyperspectral UAV images were captured biweekly using the Specim AFX10 camera. On the same days, in situ measurements of LAI and LCC were performed. LAI and LCC were estimated using a look-up table (LUT) approach based on the inversion of the SCOPE model. A cost function (norm2 distance) was employed to sort the LUT and identify a set of spectra that minimized the distance between measured reflectance and simulated reflectance in the LUT. The estimated LAI and LCC values were then compared with their corresponding in situ measurements.

Preliminary results indicate that simulated LAI and LCC showed potential for use in designing models to predict measured LAI and LCC (R²=0.26 and R²=0.30, respectively, p<0.001). In conclusion, simulated LAI and LCC correlated well with measured values for the late variety at the beginning of the crop cycle. Future work will focus on extending the analysis to cover the entire season, incorporating remote sensing observations into the parametrization of a crop growth model for yield predictions.