Abstract
Spectral indices rarely show consistency in estimating crop traits across growth stages; thus, it is critical to simultaneously evaluate a group of spectral variables and select the most informative spectral indices for retrieving crop traits. The objective of this study was to explore the optimal spectral predictors for above-ground biomass (AGB) by applying Random Forest (RF) on three types of spectral predictors: the full spectrum, published spectral indices (Pub-SIs), and optimized spectral indices (Opt-SIs). Canopy hyperspectral reflectance of potato plants, treated with seven nitrogen (N) rates, was obtained during the tuber formation and tuber bulking from 2015 to 2016. Twelve Pub-SIs were selected, and their spectral bands were optimized using band optimization algorithms. Results showed that the Opt-SIs were the best input variables of RF models. Compared to the best empirical model based on Opt-SIs, the Opt-SIs based RF model improved the prediction of AGB, with R2 increased by 6%, 10%, and 16% at the tuber formation, tuber bulking, and for across the two growth stages, respectively. The Opt-SIs can significantly reduce the number of input variables. The optimized Blue nitrogen index (Opt-BNI) and Modified red-edge normalized difference vegetation index (Opt-mND705) combined with an RF model showed the best performance in estimating potato AGB at the tuber formation stage (R2 = 0.88). In the tuber bulking stage, only using optimized Nitrogen planar domain index (Opt-NPDI) as the input variable of the RF model produced satisfactory accuracy in training and testing datasets, with the R2, RMSE, and RE being 0.92, 208.6 kg/ha, and 10.3%, respectively. The Opt-BNI and Double-peak nitrogen index (Opt-NDDA) coupling with an RF model explained 86% of the variations in potato AGB, with the lowest RMSE (262.9 kg/ha) and RE (14.8%) across two growth stages. This study shows that combining the Opt-SIs and RF can greatly enhance the prediction accuracy for crop AGB while significantly reduces collinearity and redundancies of spectral data.
Highlights
The above-ground biomass (AGB) in the training dataset ranged from 492.8 kg/ha to 3881.0 kg/ha with a CV value of 48.1%, while it varied from 497.2 kg/ha to 3353.5 kg/ha with a CV value of 44.9% for the testing dataset during the tuber formation and tuber bulking stage
Using the optimized spectral indices (Opt-spectral indices (SIs)) as the input variables of the Random Forest (RF) model significantly improved the initial number of decision trees is adequate to explain the variation of AGB in the training dataset
The results showed that the RF models coupled with Opt-SIs had the best
Summary
Above-ground biomass (AGB) as an insightful indicator of crop production is essential to guide agricultural management practices [1,2,3]. The AGB is the most important indicator for the calculation of the nitrogen nutrition index, which has been proposed to diagnose the plant nitrogen status for precise nitrogen fertilizer management in crops [4,5]). Traditional manual measurements of the AGB are time-consuming and labor-intensive. These point-sampling-based methods are infeasible in regional crop management [6,7,8]. It is imperative to develop effective technologies to rapidly and accurately monitor crops AGB on a regional scale for precision crop nitrogen management [9,10]
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