Remote Sensing Prescription for Rice Nitrogen Fertilizer Recommendation Based on Improved NFOA Model
文献类型： 外文期刊
作者： Yang, Min 1 ; Xu, Xingang 1 ; Li, Zhongyuan 3 ; Meng, Yang 1 ; Yang, Xaiodong 1 ; Song, Xiaoyu 1 ; Yang, Guijun 1 ; Xu, Sizhe 1 ; Zhu, Qilei 1 ; Xue, Hanyu 1 ;
作者机构： 1.Minist Agr & Rural Affairs, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China
3.Hubei Univ, Fac Resources & Environm Sci, Wuhan 430062, Peoples R China
关键词： NFOA; rice; Random Forest; UAV; Sentinel-2; prescription map
期刊名称：AGRONOMY-BASEL （ 影响因子：3.949； 五年影响因子：4.117 ）
ISSN：
年卷期： 2022 年 12 卷 8 期
页码：
收录情况： SCI
摘要： Precise fertilization of rice depends on the timely and effective acquisition of fertilizer application recommended by prescription maps in large-scale cropland, which can provide fertilization spatial information reference. In this paper, the prescription map was discussed based on the improved nitrogen fertilizer optimization algorithm (NFOA), using satellite and unmanned aerial vehicle (UAV) imagery, and supplemented by meteorological data. Based on the principles of NFOA, firstly, remote sensing data and meteorological data were collected from 2019 to 2021 to construct a prediction model for the potential yield of rice based on the in-season estimated yield index (INSEY). Secondly, based on remote sensing vegetation indices (VIs) and spectral features of bands, the grain nitrogen content (GNC) prediction model constructed using the Random Forest (RF) algorithm was used to improve the values of GNC taken in the NFOA. The nitrogen demand for rice was calculated according to the improved NFOA. Finally, the nitrogen fertilizer application recommended prescription map of rice in large-scale cropland was generated based on UAV multispectral images, and the economic cost-effectiveness of the prescription map was analyzed. The analysis results showed that the potential yield prediction model of rice based on the improved INSEY had a high fitting accuracy (R-2 = 0.62). The accuracy of GNC estimated with the RF algorithm reached 96.3% (RMSE = 0.07). The study shows that, compared with the non-directional and non-quantitative conventional tracking of N fertilizer, the recommended prescription map based on the improved NFOA algorithm in large-scale cropland can provide accurate information for crop N fertilizer variable tracking and provide effective positive references for the economic benefits of rice and ecological benefits of the field environment.
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