Institute of Dryland Farming Engineer, Shanxi Agricultural University, Taigu, People's Republic of China.
Department of Chemistry, Northern State University, Aberdeen, South Dakota, United States of America.
PLoS One. 2014 Jan 3;9(1):e80989. doi: 10.1371/journal.pone.0080989. eCollection 2014.
In this study, relationships between normalized difference vegetation index (NDVI) and plant (winter wheat) nitrogen content (PNC) and between PNC and grain protein content (GPC) were investigated using multi-temporal moderate-resolution imaging spectroradiometer (MODIS) data at the different stages of winter wheat in Linfen (Shanxi, P. R. China). The anticipating model for GPC of winter wheat was also established by the approach of NDVI at the different stages of winter wheat. The results showed that the spectrum models of PNC passed F test. The NDVI4.14 regression effect of PNC model of irrigated winter wheat was the best, and that in dry land was NDVI4.30. The PNC of irrigated and dry land winter wheat were significantly (P<0.01) and positively correlated to GPC. Both of protein spectral anticipating model of irrigated and dry land winter wheat passed a significance test (P<0.01). Multiple anticipating models (MAM) were established by NDVI from two periods of irrigated and dry land winter wheat and PNC to link GPC anticipating model. The coefficient of determination R(2) (R) of MAM was greater than that of the other two single-factor models. The relative root mean square error (RRMSE) and relative error (RE) of MAM were lower than those of the other two single-factor models. Therefore, test effects of multiple proteins anticipating model were better than those of single-factor models. The application of multiple anticipating models for predication of protein content (PC) of irrigated and dry land winter wheat was more accurate and reliable. The regionalization analysis of GPC was performed using inverse distance weighted function of GIS, which is likely to provide the scientific basis for the reasonable winter wheat planting in Linfen city, China.
本研究利用多时相中等分辨率成像光谱仪（MODIS）数据，在山西省临汾市冬小麦不同生育期，探讨归一化植被指数（NDVI）与植物（冬小麦）氮含量（PNC）之间以及 PNC 与籽粒蛋白质含量（GPC）之间的关系。采用冬小麦不同生育期 NDVI 建立了冬小麦 GPC 的预测模型。结果表明，PNC 的光谱模型通过了 F 检验。灌溉冬小麦 PNC 模型的 NDVI4.14 回归效果最好，旱地为 NDVI4.30。灌溉和旱地冬小麦的 PNC 与 GPC 呈显著（P<0.01）正相关。灌溉和旱地冬小麦的蛋白质光谱预测模型均通过了显著性检验（P<0.01）。通过灌溉和旱地冬小麦的 NDVI 以及 PNC 建立了多因素预测模型（MAM），以建立与 GPC 预测模型的联系。MAM 的决定系数 R2（R）大于其他两个单因素模型。MAM 的相对均方根误差（RRMSE）和相对误差（RE）均低于其他两个单因素模型。因此，多因素蛋白质预测模型的试验效果优于单因素模型。多因素预测模型在预测灌溉和旱地冬小麦蛋白质含量（PC）方面的应用更为准确可靠。利用 GIS 的反距离加权函数进行 GPC 的区域化分析，为中国临汾市合理种植冬小麦提供了科学依据。
