CAJ | 학술논문

该研究利用高光谱遥感技术分析水稻和小麦两种作物不同生育期的冠层光谱及其叶面积指数和叶绿素密度的变化,比较高光谱植被指数与两种作物的叶面积指数和叶绿素密度之间的关系,最后确定估算两种作物的叶面积指数和叶绿素密度最佳植被指数.结果表明:水稻和小麦两作物的叶面积指数和叶绿素密度在整个生育期内的变化规律基本一致,即先升高后下降的趋势,但两作物叶绿素密度与叶面积指数最大值出现的时期不同:稻麦两作物在整个生育期内的光谱反射率曲线,在可见光区域(400～700nm)变化无明显规律,在近红外区域(700～1 000nm),生育前期反射率由低到高,到生育后期则由高到低,其中最大值分别出现在抽穗期和灌浆期左右;通过14种植被指数与两作物的叶面积指数和叶绿素密度相关性比较分析得知,二次修正土壤调节植被指数(MSAVI2)与水稻农学参数相关性最好,相关系数r>0.91,而小麦在800 nm处的光谱反射率(R_(800))与其农学参数相关性最好,相关系数r>0.92:并利用线性回归的方法,建立了估算两作物叶面积指数和叶绿素密度的模型,决定系数R~2>0.85.这样为不同环境条件下(水作和旱作)农作物的动态监测和科学管理及决策提供了技术支持.
해연구이용고광보요감기술분석수도화소맥량충작물불동생육기적관층광보급기협면적지수화협록소밀도적변화,비교고광보식피지수여량충작물적협면적지수화협록소밀도지간적관계,최후학정고산량충작물적협면적지수화협록소밀도최가식피지수.결과표명:수도화소맥량작물적협면적지수화협록소밀도재정개생육기내적변화규률기본일치,즉선승고후하강적추세,단량작물협록소밀도여협면적지수최대치출현적시기불동:도맥량작물재정개생육기내적광보반사솔곡선,재가견광구역(400～700nm)변화무명현규률,재근홍외구역(700～1 000nm),생육전기반사솔유저도고,도생육후기칙유고도저,기중최대치분별출현재추수기화관장기좌우;통과14충식피지수여량작물적협면적지수화협록소밀도상관성비교분석득지,이차수정토양조절식피지수(MSAVI2)여수도농학삼수상관성최호,상관계수r>0.91,이소맥재800 nm처적광보반사솔(R_(800))여기농학삼수상관성최호,상관계수r>0.92:병이용선성회귀적방법,건립료고산량작물협면적지수화협록소밀도적모형,결정계수R~2>0.85.저양위불동배경조건하(수작화한작)농작물적동태감측화과학관리급결책제공료기술지지.
The aim of this study was to measure the changes of the canopy spectral reflectance, LAI and CCD of rice and wheat in different growth period, to analyze the correlation between hyperspectral vegetation indices, LAI and CCD, and to confirm the optimum vegetation indices for estimating LAI and CCD of rice and wheat. The result showed the change trend of LAI was similar with CCD, that is, the values increased at first and then decreased, but the time for maximum value of CCD and that of LAI in rice and wheat appeared at different growth stage; In near infrared region, the canopy spectral reflectance gradually increased at rice and wheat earlier growing stage, and then decreased gradually in late growth stage. The maximum value appeared around heading stage and filling stage, respectively. Comparison with the correlations among 14 vegetation indices, LAI and CCD, the modified soil adjusted vegetation index (MSA VI2) was significantly correlated with LAI and CCD in rice, and the correlation coefficients were more than 0.91. For wheat the spectral reflectance at 800 nm (R_(800)) was highly correlated with LAI and CCD, the correlation coefficients were higher than 0.92. Linear regression models were built for estimating LAI and CCD of rice and wheat using the AISAVI2 and R_(800), the determination coefficients were more than 0.85 (R~2>0.85). These results provided an insight for monitoring the dynamics of crop and scientific management of agricultural production under different growth environment (irrigation-and rain-fed forming).