基于生长度日的冬小麦植株氮浓度监测
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摘要
光谱饱和现象是作物光谱监测中广泛存在的问题。基于连续3 a田间试验,对拔节期、挑旗期和开花期的植被指数(VI)和当季估计指数(INSEI)分别对植株氮浓度(PNC)进行监测,并利用独立生长季数据对模型验证。结果表明,植被指数在低PNC条件下发生饱和现象,且受作物生长阶段的影响;基于INSEI的光谱监测模型有效降低了作物生长阶段对于PNC监测的影响,其中,INSEINDVI的PNC监测模型精度最佳,建模集R~2和RMSE分别为0.75和0.36%,验证集R~2和RMSE分别为0.72和0.52%。基于生长度日的植株氮浓度监测在一定程度上克服了光谱饱和现象,为冬小麦长势监测提供了理论和技术支持。
The spectral saturation problem is a widespread problem in crop spectral monitoring. Based on three consecutive years of field experiments, plant nitrogen content(PNC)was monitored by vegetation index(VI)and in-season estimated index(INSEI)at jointing stage, flag-picking stage and flowering stage, respectively, and the model was validated by independent validated data. The results showed that the saturation phenomenon occurred at low PNC conditions and was affected by crop growth stages. INSEI effectively reduced the impact of crop growth stages on PNC monitoring. The PNC monitoring model based on INSEINDVIhad better performance with R~2 and RMSE for calibrating 0.75 and 0.36% and for validating 0.72 and 0.52%, respectively. Based on growing degree days, PNC monitoring overcomes the phenomenon of "spectral saturation" to a certain extent, which provides theoretical and technical support for the monitoring of winter wheat growth.
The spectral saturation problem is a widespread problem in crop spectral monitoring. Based on three consecutive years of field experiments, plant nitrogen content(PNC)was monitored by vegetation index(VI)and in-season estimated index(INSEI)at jointing stage, flag-picking stage and flowering stage, respectively, and the model was validated by independent validated data. The results showed that the saturation phenomenon occurred at low PNC conditions and was affected by crop growth stages. INSEI effectively reduced the impact of crop growth stages on PNC monitoring. The PNC monitoring model based on INSEINDVIhad better performance with R~2 and RMSE for calibrating 0.75 and 0.36% and for validating 0.72 and 0.52%, respectively. Based on growing degree days, PNC monitoring overcomes the phenomenon of "spectral saturation" to a certain extent, which provides theoretical and technical support for the monitoring of winter wheat growth.
引文
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[19]程晟,刘晋荣.简析氮素营养对超高产小麦的调控[J].山西农业科学,2011,39(3):291-294.
[20]宋红燕,胡克林,彭希.基于高光谱技术的覆膜旱作水稻植株氮含量及籽粒产量估算[J].中国农业大学学报,2016,21(8):27-34.
[2]邓穗生,洪彩香.靛酚蓝比色法测定植物全氮含量方法的改进[J].热带农业科学,2013,33(4):5-7.
[3]张玲,陈新平,贾良良,等.基于无人机可见光遥感的夏玉米氮素营养动态诊断参数研究[J].植物营养与肥料学报,2018,24(1):261-269.
[4]孙慧,冯美臣,杨武德,等.水旱地冬小麦植株氮素含量的高光谱监测[J].山西农业科学,2015,43(3):273-276.
[5]LU L,LIU S,WANG S,et al. Assessing plant nitrogen concentration in winter oilseed rape using hyperspectral measurements[J]. Journal of Applied Remote Sensing,2016,10(3):26-36.
[6]屈莎,李振海,邱春霞,等.基于开花期氮素营养指标的冬小麦籽粒蛋白质含量遥感预测[J].农业工程学报,2017,33(12):186-193.
[7]WANG C,FENG M C,YANG W D,et al. Impact of spectral saturation on leaf area index and aboveground biomass estimation of winter wheat[J]. Spectroscopy Letters,2016,49(4):8.
[8] CAO Z,TAO C,XUE M,et al. A new three-band spectral index for mitigating the saturation in the estimation of leaf area index in wheat[J]. International Journal of Remote Sensing,2017,38(13):3865-3885.
[9]曹伟,张娜.基于Green Seeker对小麦植被指数与产量形成模型的研究[J].华北农学报,2015,30(Z):383-389.
[10] CHATTERJEE A,SUBEDI K,FRANZEN D W,et al. Nitrogen fertilizer optimization for sugarbeet in the Red river valley of north Dakota and Minnesota[J]. Agronomy Journal,2018,110(4):1554-1560.
[11] TUBANA B S. Adjusting mid-season nitrogen using a sensor-based optimization algorithm to increase use efficiency in corn(Zea mays L.)[J]. Journal of Plant Nutrition,2008,31(8):1393-1419.
[12] CHEN Q C,TIAN Y C,YAO X,et al. Comparison of five nitrogen dressing methods to optimize rice growth[J]. Plant Production Science,2014,17(1):66-80.
[13]牛哓颖,钱东平,王秀,等.基于归一化植被指数的变量施肥控制系统研究[J].河北农业大学学报,2005,28(2):94-98.
[14] HURCOM S J,HARRISON A R. The NDVI and spectral decomposition for semi-arid vegetation abundance estimation[J]. International Journal of Remote Sensing,1998,19(16):3109-3125.
[15] ZARCO-TEJADA P J,BERJN A,LPEZ-LOZANO R,et al.Assessing vineyard condition with hyperspectral indices:leaf and canopy reflectance simulation in a row-structured discontinuous canopy[J]. Remote Sensing of Environment,2005,99(3):271-287.
[16]GITELSON A,MERZLYAK M N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L.and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation[J]. Journal of Plant Physiology,1994,143(3):286-292.
[17] MUTANGA O,SKIDMORE A K. Narrow band vegetation indices overcome the saturation problem in biomass estimation[J]. International Journal of Remote Sensing,2004,25(19):3999-4014.
[18] ZOU X,TAMMEORG P,TORRES C L,et al. Retrieval of leaf chlorophyll content in field crops using narrow-band indices:effects of leaf area index and leaf mean tilt angle[J]. International Journal of Remote Sensing,2015,36(24):6031-6055.
[19]程晟,刘晋荣.简析氮素营养对超高产小麦的调控[J].山西农业科学,2011,39(3):291-294.
[20]宋红燕,胡克林,彭希.基于高光谱技术的覆膜旱作水稻植株氮含量及籽粒产量估算[J].中国农业大学学报,2016,21(8):27-34.