首页> 外文会议>International Conference on Agro-Geoinformatics >Evaluating different active crop canopy sensors for estimating rice yield potential
【24h】

Evaluating different active crop canopy sensors for estimating rice yield potential

机译:评估不同的有源作物冠层传感器,用于估算水稻产量潜力

获取原文

摘要

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主动作物冠层传感器已被广泛报道在季节氮气(n)管理中。已经开发出N施肥优化算法来使用该传感器进行追踪N推荐。该算法的一个重要组成部分是在追踪N应用之前使用作物传感器来估计产量电位。除了格林赛,另一个商业作物传感器,裁判圈ACS-470最近已开发出6种可能的光谱频段选择(蓝色,绿色,两个红色频带,红色和NIR)。已经进行了很少的研究以比较这两个传感器来估算水稻产量潜力。与Greenseeker传感器相比,该研究的目的是确定作物圈ACS 470在干伸长型阶段估算水稻产量电位的改进。 2008年和2009年,在中国东北地区的Jiansanjiang江江省建筑公江省河南江省采用了四个N次净实验。用于收集树冠反射率的Fieldspec 3高光谱冠层传感器,用于模拟绿塞克尔和裁判圈传感器的光谱带。结果表明,用Greenseeker NDVI和RVI计算的产量(insey)的季节性估计可以解释42%和62%的产量潜在变异性。使用九个植被指数计算使用模拟的作物圆谱带比Greenseeker索引更好地计算,R 2 为0.64-0.75。 insey(MTCari)具有最佳性能,解释了75%的产量潜在变异性,其次是insey(Mcari1)(R 2 = 0.73)。初步结果表明,作物圈ACS 470传感器可以在干伸长级的水稻产量电位中解释13%的变化。需要更多的研究来更系统地评估可以用作物圆光谱带计算的所有可能的植被索引,以估计不同生长阶段的水稻产量潜力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号