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Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice
摘要： Spectra of re?ectance (Sr) and ?uorescence (Sf) are signi?cant for crop monitoring and ecological environment research, and can be used to indicate the leaf nitrogen content (LNC) of crops indirectly. The aim of this work is to use the Sr-Sf features obtained with hyperspectral and laser-induced ?uorescence LiDAR (HSL, LIFL) systems to construct novel combined spectral indices (NCIH-F) for multi-year rice LNC estimation. The NCIH-F is in a form of FWs*Φ + GSIs*Φ, where Φ is the Sr-Sf features, and FWs and GSIs are the feature weights and global sensitive indices for each characteristic band. In this study, the characteristic bands were chosen in di?erent ways. Firstly, the Sr-Sf characteristics which can be the intensity or derivative variables of spectra in 685 and 740 nm, have been assigned as the Φ value in NCIH-F formula. Simultaneously, the photochemical re?ectance index (PRI) formed with 531 and 570 nm was modi?ed based on a variant spectral index, called PRIfraction, with the Sf intensity in 740 nm, and then compared its potential with NCIH-F on LNC estimation. During the above analysis, both NCIH-F and PRIfraction values were utilized to model rice LNC based on the arti?cial neural networks (ANNs) method. Subsequently, four prior bands were selected, respectively, with high FW and GSI values as the ANNs inputs for rice LNC estimation. Results show that FW- and GSI-based NCIH-F are closely related to rice LNC, and the performance of previous spectral indices used for LNC estimation can be greatly improved by multiplying their FWs and GSIs. Thus, it can be included that the FW- and GSI-based NCIH-F constitutes an e?cient and reliable constructed form combining HSL (Sr) and LIFL (Sf) data together for rice LNC estimation.
关键词： hyperspectral LiDAR,combined spectral index,leaf nitrogen content,laser-induced ?uorescence LiDAR
更新于2025-09-23 15:19:57
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Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics
摘要： BACKGROUND: Rapid and accurate diagnosis of nitrogen (N) status in ?eld crops is of great signi?cance for site-speci?c N fertilizer management. This study aimed to evaluate the potential of hyperspectral imaging coupled with chemometrics for the qualitative and quantitative diagnosis of N status in tea plants under ?eld conditions. RESULTS: Hyperspectral data from mature leaves of tea plants with di?erent N application rates were preprocessed by standard normal variate (SNV). Partial least squares discriminative analysis (PLS-DA) and least squares–support vector machines (LS-SVM) were used for the classi?cation of di?erent N status. Furthermore, partial least squares regression (PLSR) was used for the prediction of N content. The results showed that the LS-SVM model yielded better performance with correct classi?cation rates of 82% and 92% in prediction sets for the diagnosis of di?erent N application rates and N status, respectively. The PLSR model for leaf N content (LNC) showed excellent performance, with correlation coe?cients of 0.924, root mean square error of 0.209, and residual predictive deviation of 2.686 in the prediction set. In addition, the important wavebands of the PLSR model were interpreted based on regression coe?cients. CONCLUSION: Overall, our results suggest that the hyperspectral imaging technique can be an e?ective and accurate tool for qualitative and quantitative diagnosis of N status in tea plants.
关键词： nitrogen status,hyperspectral imaging,leaf nitrogen content,tea plant
更新于2025-09-11 14:15:04