CAJ | 학술논문

该研究引入温度、降水、太阳辐射和土壤肥力等影响小麦籽粒蛋白质含量的重要生态子,结合感数据进行小麦籽粒蛋白质含量监测.以北京郊区的小麦种植区为实验区,获取多时相的HJ1A/B卫星数据,多个气象站点全生育期气象数据和土壤养分数据,以及收获时小麦籽粒蛋白质含量.分别构建了感光谱蛋白质含量模型、生态子籽粒蛋白质含量模型、光谱生态子蛋白质含量模型.结果表明:北京地区冬小麦以5月11日(开花期左右)NDVIgreen 值与籽粒蛋白质含量相关性最好,达到极显著水平,此该时期为冬小麦籽粒蛋白质含量感监测的最佳时相,并将利用该时期的NDVIgreen参与光谱蛋白质含量模型、光谱生态子蛋白质含量模型的构建.对光谱蛋白质含量模型、生态子籽粒蛋白质含量模型、光谱生态子蛋白质含量模型进行F检验,表明各模型均达到极显著水平,3种模型的决定系数分别为:0.782,0.635,0.843,相对误差分别为:0.151,0.123,0.049.说明综合利用感数据和生态子的监测结果比单独利用感数据或单独利用生态子的精度高.引入生态子的小麦籽粒蛋白质含量感监测有助提高监测精度,并增加监测模型的农业机理.
해연구인입온도、강수、태양복사화토양비력등영향소맥자립단백질함량적중요생태자,결합감수거진행소맥자립단백질함량감측.이북경교구적소맥충식구위실험구,획취다시상적HJ1A/B위성수거,다개기상참점전생육기기상수거화토양양분수거,이급수획시소맥자립단백질함량.분별구건료감광보단백질함량모형、생태자자립단백질함량모형、광보생태자단백질함량모형.결과표명:북경지구동소맥이5월11일(개화기좌우)NDVIgreen 치여자립단백질함량상관성최호,체도겁현저수평,차해시기위동소맥자립단백질함량감감측적최가시상,병장이용해시기적NDVIgreen삼여광보단백질함량모형、광보생태자단백질함량모형적구건.대광보단백질함량모형、생태자자립단백질함량모형、광보생태자단백질함량모형진행F검험,표명각모형균체도겁현저수평,3충모형적결정계수분별위:0.782,0.635,0.843,상대오차분별위:0.151,0.123,0.049.설명종합이용감수거화생태자적감측결과비단독이용감수거혹단독이용생태자적정도고.인입생태자적소맥자립단백질함량감감측유조제고감측정도,병증가감측모형적농업궤리.
Temperature, precipitation, solar radiation and soil fertility are important ecological factors for wheat grain protein content (GPC), which are combined with remote sensing data to monitor GPC in this research. Experiments were carried out in suburban areas in Beijing. Multi-temporal HJ1A/B satellite data, meteorological data for the whole growing season from the corresponding meteorological stations, soil nutrient data and GPC obtained at maturity were acquired. Spectral GPC model, ecological factors GPC model and spectral ecological factors GPC model were constructed respectively. The results show that NDVIgreen corresponding to May 11 (around anthesis stage ) has best correlation with GPC in the research area. The correlation coefficient reaches significant level, thus May 11 was the best time for monitoring GPC by remote sensing. NDVIgreen values on May 11 were used for constructing spectral GPC model and spectral ecological factors GPC model. F-test shows that spectral GPC model, ecological factors GPC model, spectral ecological factors GPC model reach extremely significant levels with determination coefficients of 0.782, 0.635, 0.843, and relative error of 0.151, 0.123, 0.049 respectively. The results indicate that accuracy of spectral ecological factors GPC model combined with remote sensing data and ecological factor is higher than GPC model based on only spectral data or only ecological factors. Introduction of ecological factors into spectral protein GPC model helps to improve monitoring accuracy and agricultural mechanism of monitoring models.