Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis
Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis
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DOI：
10.3390/rs13245141
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
R. Dong;Y. Miao;Xinbing Wang;Fei Yuan;K. Kusnierek
中科院分区：
文献类型：
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作者：
R. Dong;Y. Miao;Xinbing Wang;Fei Yuan;K. Kusnierek
准确评估作物氮素状况和了解氮素需求被认为是精准氮素管理的关键。叶绿素荧光不受下层裸土干扰信号的影响，与植物的光合作用活性密切相关。因此，荧光传感被认为是一种很有前途的监测作物氮素状况的技术，即使在作物生长的早期阶段也是如此。本研究的目的是评估利用近冠层荧光传感器Multiplex®3检测玉米氮素状况变异的潜力，并定量估计玉米四个关键生育期的氮素状况指标。在不同的生育期进行了传感器测量，并比较了三种不同的回归方法来估计植物氮素浓度(PNC)、植物氮素吸收(PNU)和氮素营养指数(NNI)。结果表明，玉米植株氮素状况的诱导差异早在V6生育期就可以检测到。第一种方法基于简单回归(SR)和以生长度日(GDD)或氮素充足指数(NSI)归一化的多重传感器指数，获得了可接受的估计精度(R2=0.73-0.87)，表明冠层荧光遥感在氮素状况估计中具有良好的潜力。多元线性回归(MLR)、荧光指数和GDDS的建模精度最低(R2=0.46~0.79)。第三种测试方法采用基于多传感器指数和GDDS的随机森林回归(RFR)形式的非线性回归方法。该方法获得了最好的估计精度(R2=0.84~0.93)和最准确的诊断结果。
Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84–0.93) and the most accurate diagnostic result.