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Evaluating different active crop canopy sensors for estimating rice yield potential

机译:评估不同的活跃作物冠层传感器以估算水稻单产潜力

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GreenSeeker active crop canopy sensor has been widely reported in literature for in-season nitrogen (N) management. An N fertilization optimization algorithm has been developed to use this sensor for topdressing N recommendation. An important component of this algorithm is to use crop sensor to estimate yield potential before topdressing N application. In addition to GreenSeeker, another commercial crop sensor, Crop Circle ACS - 470, has been recently developed with 6 possible spectral band choices (blue, green, two red bands, red edge and NIR). Little research has been conducted to compare these two sensors for estimating rice yield potential. The objective of this research was to determine how much improvements the Crop Circle ACS 470 would achieve for estimating rice yield potential at stem elongation stage, as compared with GreenSeeker sensor. Four N rate experiments were conducted in Jiansanjiang, Heilongjiang Province, in Northeast China in 2008 and 2009. The FieldSpec 3 hyperspectral canopy sensor was used to collect canopy reflectance, which was used to simulate spectral bands of GreenSeeker and Crop Circle sensors. The results indicated that in-season estimate of yield (INSEY) calculated with GreenSeeker NDVI and RVI could explain 42% and 62% of yield potential variability. The INSEY calculated with nine vegetation indices using simulated Crop Circle spectral bands performed better than GreenSeeker indices, with R2 being 0.64–0.75. INSEY (MTCARI) had the best performance, explaining 75% of the yield potential variability, followed by INSEY (MCARI1) (R2=0.73). The preliminary result indicated that Crop Circle ACS 470 sensor could explain 13% more variability in rice yield potential at stem elongation stage. More studies are needed to more systematically evaluate all possible vegetation indices that can be calculated with Crop Circle spectral bands for estimating rice yield potential at different growth stages.
机译:GreenSeeker活性农作物冠层传感器已在文献中被广泛报道用于季节氮(N)管理。已开发了一种氮肥优化算法,以将该传感器用于追肥氮推荐。该算法的重要组成部分是在追施氮肥之前,使用作物传感器估算单产潜力。除了GreenSeeker之外,最近还开发了另一种商用作物传感器Crop Circle ACS-470,具有6种可能的光谱带选择(蓝色,绿色,两个红色带,红色边缘和NIR)。很少有研究比较这两种传感器来估算稻米的潜在产量。这项研究的目的是确定与GreenSeeker传感器相比,Crop Circle ACS 470在估计茎伸长阶段的水稻产量潜力方面将取得多少改进。 2008年和2009年在中国东北的黑龙江省建三江市进行了四个N速率实验。使用FieldSpec 3高光谱冠层传感器收集冠层反射率,该冠层反射率用于模拟GreenSeeker和Crop Circle传感器的光谱带。结果表明,使用GreenSeeker NDVI和RVI计算的季节内单产估计值(INSEY)可以解释42%和62%的单产潜力变化。用模拟的麦田怪圈光谱带计算的九种植被指数的INSEY表现优于GreenSeeker指数,R 2 为0.64-0.75。 INSEY(MTCARI)表现最好,解释了75%的单产潜力,其次是INSEY(MCARI1)(R 2 = 0.73)。初步结果表明,Crop Circle ACS 470传感器可以解释茎伸长期水稻产量潜力增加13%的变化。需要更多的研究来更系统地评估所有可能的植被指数,这些指数可以通过“作物圆”光谱带计算得出,以估算不同生育阶段的水稻单产潜力。

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