An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat

doi:10.1016/S2095-3119(20)63379-2

摘要/Abstract

摘要：

氮素营养指数（NNI）是作物氮素诊断的可靠指标。然而，目前还没有适用于多生育时期NNI反演的光谱指数。为克服传统NNI直接反演方法（NNI_T1）和通过反演生物量（AGB）和植株氮浓度（PNC）进行NNI间接反演方法（NNI_T2）在多生育期应用的局限性，本文构建了一个新的NNI遥感指数（NNI_RS）。本文基于连续四年（2012–2013（Exp.1），2013–2014（Exp.2），2014–2015（Exp.3）和2015–2016（Exp.4））的冬小麦田间试验，采用交叉验证方法利用氮素相关植被指数和生物量相关植被指数构建了遥感关键氮浓度稀释曲线（N_{c_RS}）和根据NNI构建原理得到的NNI_RS进行综合评价。结果表明：（1）由标准叶面积指数决定指数（sLAIDI）和红边叶绿素指数（CI_{red edge}）构建的NNIR_S模型表达式为NNI_RS=CI_{red edge}/（a×sLAIDI^b），在Exp.1/2/4，Exp.1/2/3，Exp.1/3/4和Exp.2/3/4中参数“a”分别等于2.06，2.10，2.08和2.02，参数“b”分别等于0.66，0.73，0.67和0.62；（2）与NNI_T1和NNI_T2模型相比，NNI_RS模型的精度最高（R²的范围为0.50–0.82，RMSE的范围为0.12–0.14）；（3）NNI_RS在验证数据集中也达到了较好的精度，RMSE分别为0.09，0.18，0.13和0.10。因此，本文认为NNI_RS模型在氮素遥感诊断中具有较大的潜力。

Abstract:

The nitrogen nutrition index (NNI) is a reliable indicator for diagnosing crop nitrogen (N) status. However, there is currently no specific vegetation index for the NNI inversion across multiple growth periods. To overcome the limitations of the traditional direct NNI inversion method (NNI_T1) of the vegetation index and traditional indirect NNI inversion method (NNI_T2) by inverting intermediate variables including the aboveground dry biomass (AGB) and plant N concentration (PNC), this study proposed a new NNI remote sensing index (NNI_RS). A remote-sensing-based critical N dilution curve (N_{c_RS}) was set up directly from two vegetation indices and then used to calculate NNIRS. Field data including AGB, PNC, and canopy hyperspectral data were collected over four growing seasons (2012–2013 (Exp.1), 2013–2014 (Exp. 2), 2014–2015 (Exp. 3), 2015–2016 (Exp. 4)) in Beijing, China. All experimental datasets were cross-validated to each of the NNI models (NNI_T1, NNI_T2 and NNI_RS). The results showed that: (1) the NNI_RS models were represented by the standardized leaf area index determining index (sLAIDI) and the red-edge chlorophyll index (CI_{red edge}) in the form of NNI_RS=CI_{red edge}/(a×sLAIDI^b), where “a” equals 2.06, 2.10, 2.08 and 2.02 and “b” equals 0.66, 0.73, 0.67 and 0.62 when the modeling set data came from Exp.1/2/4, Exp.1/2/3, Exp.1/3/4, and Exp.2/3/4, respectively; (2) the NNI_RS models achieved better performance than the other two NNI revised methods, and the ranges of R² and RMSE were 0.50–0.82 and 0.12–0.14, respectively; (3) when the remaining data were used for verification, the NNI_RS models also showed good stability, with RMSE values of 0.09, 0.18, 0.13 and 0.10, respectively. Therefore, it is concluded that the NNI_RS method is promising for the remote assessment of crop N status.

Key words: nitrogen nutrition index (NNI) , critical nitrogen dilution curve , standardized leaf area index determining index (sLAIDI) , the red-edge chlorophyll index (CI_{red edge})

. [J]. Journal of Integrative Agriculture, 2021, 20(9): 2535-2551.

ZHAO Yu, WANG Jian-wen, CHEN Li-ping, FU Yuan-yuan, ZHU Hong-chun, FENG Hai-kuan, XU Xin-gang, LI Zhen-hai. An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat[J]. Journal of Integrative Agriculture, 2021, 20(9): 2535-2551.

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