Evaluating the role of solar-induced fluorescence (SIF) and plant physiological traits for leaf nitrogen assessment in almond using airborne hyperspectral imagery

Accurate, spatially extensive, and frequent assessments of plant nitrogen (N) enabled by remote sensing allow growers to optimize fertilizer applications and reduce environmental impacts. Standard remote sensing methods for N assessment typically involve the use of chlorophyll-sensitive vegetation indices calculated from multi -spectral or hyperspectral reflectance data. However, the chlorophyll a + b derived from spectral indices is indirectly related to leaf N and saturates at high leaf N levels, dramatically reducing the sensitivity with leaf N under these conditions. Furthermore, these relationships are heavily influenced by canopy structure, variability in leaf area density, proportion of sunlit-shaded tree-crown components, soil background, and understory. Recent studies in uniform crops have demonstrated that estimation of plant N can be improved by considering leaf biochemical constituents derived from radiative transfer model (RTM) and solar-induced fluorescence (SIF). However, it is unclear whether these methods are transferable to tree crops due to their intrinsic physiological differences, structural complexity, and within-tree crown heterogeneity. We investigated how various hyper -spectrally derived proxies for leaf N, including RTM-based traits and SIF, could be combined to assess N status on a 1200-ha almond orchard across two growing seasons. RTM-based chlorophyll a + b content (C-ab) and SIF were found to be the most important and consistent predictors for leaf N compared to other leaf biochemical and biophysical traits. C-ab alone was a modest predictor of leaf N variability (r(2) = 0.49, RMSE = 0.16%, p-value < 0.001), but when the non-collinear SIF and C-ab traits were coupled together, predictions improved dramatically (r(2) = 0.95, RMSE = 0.05%, p-value < 0.001). Leaf area index (LAI) was poorly associated with leaf N, suggesting that leaf physiological traits may be more important than structural traits in quantifying leaf N in well-managed orchards characterized by high N levels. Consistent results across the 2 years suggests the importance of airborne SIF coupled with Cab for precision agriculture and leaf N status assessment in almond orchards.

Automated summary

This study investigated the combination of different hyperspectrally derived proxies for leaf nitrogen (N) to assess N status in a 1200-ha almond orchard across two growing seasons. The results showed that the RTM-based chlorophyll a + b content and solar-induced fluorescence (SIF) were the most important and consistent predictors for leaf N compared to other leaf biochemical and biophysical traits. The combination of non-collinear SIF and chlorophyll a + b content significantly improved the predictions of leaf N variability.