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基于无人机多光谱遥感的水稻氮营养指数精准估算氮肥追肥研究:以中国西南地区为例

Precision Estimation of Rice Nitrogen Fertilizer Topdressing According to the Nitrogen Nutrition Index Using UAV Multi-Spectral Remote Sensing: A Case Study in Southwest China.

作者信息

Wang Lijuan, Ling Qihan, Liu Zhan, Dai Mingzhu, Zhou Yu, Shi Xiaojun, Wang Jie

机构信息

College of Resources and Environment, Southwest University, Chongqing 400716, China.

National Monitoring Station of Soil Fertility and Fertilizer Efficiency on Purple Soils, Chongqing 400716, China.

出版信息

Plants (Basel). 2025 Apr 11;14(8):1195. doi: 10.3390/plants14081195.

Abstract

The precision estimation of N fertilizer application according to the nitrogen nutrition index (NNI) using unmanned aerial vehicle (UAV) multi-spectral measurements remains to be tested in different rice cultivars and planting areas. Therefore, two field experiments were conducted using varied N rates (0, 60, 120, 160, and 200 kg N ha) on two rice cultivars, Yunjing37 (YJ-37, , the Institute of Food Crops at the Yunnan Academy of Agricultural Sciences, Kunming, China) and Jiyou6135 (JY-6135, , Hunan Longping Gaoke Nongping seed industry Co., Ltd., Changsha, China), in southwest China. The rice canopy spectral images were measured by the UAV's multi-spectral remote sensing at three growing stages. The NNI was calculated based on the critical N (Nc) dilution curve. A random forest model integrating multi-vegetation indices established the NNI inversion, facilitating precise N topdressing through a linear platform of NNI-Relative Yield and the remote sensing NNI-based N balance approaches. The Nc dilution curve calibrated with aboveground dry matter demonstrated the highest accuracy (R = 0.93, 0.97 for shoot components in cultivars YJ-37 and JY-6135), outperforming stem (R = 0.70, 0.76) and leaf (R = 0.80, 0.89) based models. The RF combined with six vegetation index combinations was found to be the best predictor of NNI at each growing period (YJ-37: R is 0.70-0.97, RMSE is 0.020.04; JY-6135: R is 0.71-0.92, RMSE is 0.040.05). The RF surpassed BPNN/PLSR by 6.14-10.10% in R and 13.71-33.65% in error reduction across the critical rice growth stages. The topdressing amounts of YJ-37 and JY-6135 were 111-124 kg ha and 80-133 kg ha, with low errors of 2.508.73 kg ha for YJ-37 and 2.525.53 kg ha for JY-6135 in the jointing (JT) and heading (HD) stages. These results are promising for the precise topdressing of rice using a remote sensing NNI-based N balance method. The combination of UAV multi-spectral imaging with the NNI-nitrogen balance method was tested for the first time in southwest China, demonstrating its feasibility and offering a regional approach for precise rice topdressing.

摘要

利用无人机多光谱测量,依据氮营养指数(NNI)精确估算氮肥施用量,这一方法在不同水稻品种和种植区域仍有待验证。因此,在中国西南部对两个水稻品种进行了两次田间试验,采用了不同的施氮量(0、60、120、160和200千克氮/公顷),这两个品种分别是云粳37(YJ - 37,由中国云南省农业科学院粮食作物研究所提供,昆明,中国)和吉优6135(JY - 6135,由湖南隆平高科农平种业有限公司提供,长沙,中国)。在三个生长阶段,通过无人机多光谱遥感测量水稻冠层光谱图像。基于临界氮(Nc)稀释曲线计算NNI。一个整合多种植被指数的随机森林模型建立了NNI反演,通过NNI - 相对产量线性平台和基于遥感NNI的氮平衡方法实现精确的氮肥追肥。用地上部干物质校准的Nc稀释曲线显示出最高的准确性(YJ - 37和JY - 6135品种地上部组分的R分别为0.93和0.97),优于基于茎(R分别为0.70和0.76)和叶(R分别为0.80和0.89)的模型。发现在每个生长时期,随机森林与六种植被指数组合相结合是NNI的最佳预测指标(YJ - 37:R为0.70 - 0.97,RMSE为0.020.04;JY - 6135:R为0.71 - 0.92,RMSE为0.040.05)。在关键水稻生长阶段,随机森林在R方面比反向传播神经网络/偏最小二乘回归(BPNN/PLSR)高出6.14 - 10.10%,在误差降低方面高出13.71 - 33.65%。YJ - 37和JY - 6135在拔节期(JT)和抽穗期(HD)的追肥量分别为111 - 124千克/公顷和80 - 133千克/公顷,YJ - 37的误差为2.508.73千克/公顷,JY - 6135的误差为2.525.53千克/公顷。这些结果对于采用基于遥感NNI的氮平衡方法精确追肥水稻很有前景。无人机多光谱成像与NNI - 氮平衡方法的结合首次在中国西南部进行了测试,证明了其可行性,并为精确水稻追肥提供了一种区域方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/714e/12030202/c00c931e0408/plants-14-01195-g0A1.jpg

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