Yang Jian, Du Lin, Gong Wei, Shi Shuo, Sun Jia, Chen Biwu
Faculty of Information Engineering, China University of Geosciences, Wuhan, Hubei, China.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China.
PLoS One. 2018 Jan 17;13(1):e0191068. doi: 10.1371/journal.pone.0191068. eCollection 2018.
Nitrogen (N) is important for the growth of crops. Leaf nitrogen content (LNC) serves as a crucial indicator of the growth status of crops and can help determine the dose of N fertilizer. Laser-induced fluorescence (LIF) technology and the reflectance spectra of crops are widely used to detect the biochemical content of leaves. Many vegetation indices (VIs) and fluorescence parameters have been developed to estimate LNC. However, the comparison among VIs and between fluorescence parameters and VIs has been rarely studied in the estimation of LNC. In this study, the performances of several published empirical VIs and fluorescence parameters for the estimation of paddy rice LNC were analyzed using the support vector machine (SVM) algorithm. Then, the optimal VIs (TVI, MTVI1, MTVI2, and MSAVI) and fluorescence parameters (F735/F460 and F685/F460), which were suitable for LNC monitoring in this study, were chosen. In addition, the combination of the VIs and fluorescence parameters was proposed as the input variables in the SVM model and used to estimate the LNC. Experimental results exhibited the promising potential of the LIF technology combined with reflectance for the accurate estimation of LNC, which provided guidance for monitoring the LNC.
氮（N）对作物生长至关重要。叶片氮含量（LNC）是作物生长状况的关键指标，有助于确定氮肥施用量。激光诱导荧光（LIF）技术和作物反射光谱被广泛用于检测叶片的生化含量。许多植被指数（VIs）和荧光参数已被开发用于估算LNC。然而，在LNC估算中，VIs之间以及荧光参数与VIs之间的比较很少被研究。在本研究中，使用支持向量机（SVM）算法分析了几种已发表的经验VIs和荧光参数对水稻LNC估算的性能。然后，选择了适合本研究中LNC监测的最佳VIs（TVI、MTVI1、MTVI2和MSAVI）和荧光参数（F735/F460和F685/F460）。此外，提出将VIs和荧光参数的组合作为SVM模型的输入变量，用于估算LNC。实验结果表明，LIF技术与反射率相结合在准确估算LNC方面具有广阔的潜力，为LNC监测提供了指导。
