Estimation model of potassium content in cotton leaves based on hyperspectral information of multi-leaf position

Potassium (K) is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution, indirectly leading to variations in the leaf potassium content (LKC, %) at different leaf positions. However, due to the interaction between light and leaf age, leaf sensitivity to this change varies at different positions, including the reflection and absorption of the spectrum. Selecting the optimal leaf position for monitoring is a crucial factor in the rapid and accurate evaluation of cotton LKC using spectral remote sensing technology. Therefore, this study proposes a comprehensive multi-leaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom, aiming to achieve an accurate estimation of cotton LKC and optimize the strategy for selecting the monitored leaf position. Between 2020 and 2021, we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom (Li, i=1, 2, 3, ..., n) during the cotton budding, flowering, and boll-setting stages. Vertical distribution characteristics, sensitivity differences, and spectral correlations of LKC at different leaf positions were investigated. Additionally, the optimal range of the dominant leaf position for monitoring was determined. Partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and the entropy weight method (EWM) were employed to develop LKC estimation models for single- and multi-leaf positions. The results showed a vertical heterogeneous distribution of cotton LKC, with LKC initially increasing and then gradually decreasing from top to bottom; the average LKC of cotton reached its maximum value at the flowering stage. The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum. The selected dominant leaf positions for the three growth stages were L1–L5, L1–L4, and L1–L2, respectively. Based on the dominant leaf position monitoring range, the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4, PLSR-L1, and SVR-L2, with the coefficient of determination of the validation set (R²val) being 0.786, 0.580, and 0.768, and the root-mean-square error of the validation set (RMSEval) being 0.168, 0.197, and 0.191, respectively. The multi-leaf position LKC estimation model was constructed by EWM with R²val being 0.887, 0.728, and 0.703, and RMSEval being 0.134, 0.172, and 0.209, respectively. In contrast, the newly developed multi-leaf position comprehensive estimation model yielded superior results, improving the model’s stability based on high accuracy, especially during the budding and flowering stages. These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.