Abstract
Plant nitrogen (N) information has widely been estimated through empirical techniques using hyperspectral data. However, the physical model inversion approach on N spectral response has seldom developed and remains a challenge. In this study, an N-PROSAIL model based on the N-based PROSPECT model and the SAIL model canopy model was constructed and used for retrieving crop N status both at leaf and canopy scales. The results show that the third parameter (3rd-par) retrieving strategy (leaf area index (LAI) and leaf N density (LND) optimized where other parameters in the N-PROSAIL model are set at different values at each growth stage) exhibited the highest accuracy for LAI and LND estimation, which resulted in R2 and RMSE values of 0.80 and 0.69, and 0.46 and 21.18 µg·cm−2, respectively. It also showed good results with R2 and RMSE values of 0.75 and 0.38% for leaf N concentration (LNC) and 0.82 and 0.95 g·m−2 for canopy N density (CND), respectively. The N-PROSAIL model retrieving method performed better than the vegetation index regression model (LNC: RMSE = 0.48 − 0.64%; CND: RMSE = 1.26 − 1.78 g·m−2). This study indicates the potential of using the N-PROSAIL model for crop N diagnosis on leaf and canopy scales in wheat.
Highlights
Nitrogen (N) is a critical nutrient element for maintaining photosynthesis, enhancing production, and improving grain quality in crops, but the excess use of N fertilizer results in a series of plant and environmental problems [1,2]
leaf N density (LND) calculated from leaf N concentration (LNC) showed a strong correlation (r = 0.73) with LNC, while LAI demonstrated a high correlation with LNC (r = 0.66) the two variables were acquired separately
LAI, Cm, and LND were retrieved from the N-PROSAIL model, and LNC and canopy N density (CND) were calculated according to these relationships
Summary
Nitrogen (N) is a critical nutrient element for maintaining photosynthesis, enhancing production, and improving grain quality in crops, but the excess use of N fertilizer results in a series of plant and environmental problems (e.g., vigorous growth, and eutrophication) [1,2]. Various sensitive spectral features and vegetation indices have been analyzed for crop N estimation. Proposed that leaf N concentration (LNC, %) in rice can be estimated by two reflectance absorptions at 2054 nm and 2172 nm [5]. Normalized Difference N Index (NDNI = [log(1/R1510) − log(1/R1680)]/[log(1/R1510) + log(1/R1680)] was used to estimate LNC in native shrub vegetation [9]. Chen et al developed a new index named Double-peak Canopy N Index [DCNI = (R720 − R700)/(R700 − R670)/(R720 − R670 + 0.03)] to estimate plant N concentration (PNC) in corn and wheat [13]. Feng et al created a Water Resistance N Index [WRNI = (R735 − R720) × R900/Rmin(930-980)/(R735 + R720)] to improve the accuracy of the LNC model by minimizing water effects at different growth stages [16]
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