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大田玉米作物系數(shù)無(wú)人機(jī)多光譜遙感估算方法
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國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2017YFC0403203),、新疆自治區(qū)科技支疆項(xiàng)目(2016E02105),、西北農(nóng)林科技大學(xué)學(xué)科重點(diǎn)建設(shè)項(xiàng)目(2017-C03)和陜西省水利科技項(xiàng)目(2017SLKJ-7)


Estimating Method of Crop Coefficient of Maize Based on UAV Multispectral Remote Sensing
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    摘要:

    作物系數(shù)Kc快速獲取是大田作物蒸散量(Evapotranspiration,,ET)估算的關(guān)鍵,為研究無(wú)人機(jī)多光譜遙感估算玉米作物系數(shù)的可行性和適用性,,以2017年內(nèi)蒙古達(dá)拉特旗昭君鎮(zhèn)實(shí)驗(yàn)站大田玉米,、土壤,、氣象等數(shù)據(jù)為基礎(chǔ),,采用經(jīng)氣象因子和作物覆蓋度校正后的雙作物系數(shù)法計(jì)算不同生長(zhǎng)時(shí)期與不同水分脅迫玉米的作物系數(shù),并使用自主研發(fā)的無(wú)人機(jī)多光譜系統(tǒng)航拍玉米的冠層多光譜(藍(lán),、綠,、紅、紅邊,、近紅外, 475~840nm)影像,,研究了不同生長(zhǎng)時(shí)期(快速生長(zhǎng)期、生長(zhǎng)中期和生長(zhǎng)后期)玉米的6種常用植被指數(shù)(Vegetation indices,,VIs):歸一化差值植被指數(shù)(NDVI),、土壤調(diào)節(jié)植被指數(shù)(SAVI)、增強(qiáng)型植被指數(shù)(EVI),、比值植被指數(shù)(SR),、綠度歸一化植被指數(shù)(GNDVI)和抗大氣指數(shù)(VARI),與作物系數(shù)Kc的關(guān)系模型及水分脅迫對(duì)其的影響,。結(jié)果表明:玉米生長(zhǎng)時(shí)期和水分脅迫是影響玉米VIs-Kc模型相關(guān)性的兩個(gè)重要因素,。不同生長(zhǎng)時(shí)期玉米植被指數(shù)和Kc相關(guān)性不同:充分灌溉情況下,,快速生長(zhǎng)期玉米VIs-Kc模型的相關(guān)性(R2為0.7312~0.9401,,p<0.05,n=25)與生長(zhǎng)中期至生長(zhǎng)后期VIs-Kc模型的相關(guān)性(R2為0.2765~0.3732,,p<0.05,,n=40)不同;水分脅迫情況下,,快速生長(zhǎng)期玉米VIs-Kc模型的相關(guān)性(R2為0.0002~0.0830,,p<0.05,n=25)與生長(zhǎng)中期至生長(zhǎng)后期VIs-K模型的相關(guān)性(R2為0.3662~0.8487,,p<0.05,,n=40)不同。水分脅迫對(duì)VIs-Kc模型的相關(guān)性影響較大:快速生長(zhǎng)期,,充分灌溉玉米VIs-Kc模型的相關(guān)性(R2最大為0.9401)比水分脅迫玉米VIs-Kc模型的相關(guān)性(R2最大為0.0830)強(qiáng),;生長(zhǎng)中期至生長(zhǎng)后期,充分灌溉玉米VIs-Kc模型的相關(guān)性(R2最大為0.3732)比水分脅迫玉米VIs-Kc模型的相關(guān)性(R2最大為0.8487)弱,。部分植被指數(shù)和作物系數(shù)相關(guān)性較強(qiáng),;快速生長(zhǎng)期充分灌溉玉米的VIs-Kc模型的相關(guān)性由大到小依次為:SR,、EVI、VARI ,、GNDVI,、SAVI、NDVI,;生長(zhǎng)中期至生長(zhǎng)后期水分脅迫玉米的VIs-Kc模型的相關(guān)性由大到小依次為:SR,、GNDVI、VARI,、NDVI,、SAVI、EVI,;其中比值植被指數(shù)SR與作物系數(shù)Kc的相關(guān)性最好,。結(jié)果表明采用無(wú)人機(jī)多光譜技術(shù)估算Kc具有一定的可行性。

    Abstract:

    Rapid acquisition of crop coefficient Kc is the key to estimation of field evapotranspiration (ET), in order to study the feasibility and applicability of unmanned aerial vehicle (UAV) multispectral remote sensing in estimation of maize crop coefficient, based on the data of field maize in experimental station, soil and meteorology in Zhaojun Town, Dalate Qi, Inner Mongolia in 2017, by using meteorological factors and crop canopy cover to correct dual crop coefficient method at different growth stages and different water stresses. The multispectral (blue, green, red, red edge, near IR, 475~840nm) images from UAV were used to calculate vegetation indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), simple ratio (SR) and green normalized difference vegetation index (GNDVI), visible atmospherically resistant index (VARI)) of maize in different growth stages (rapid growth stage, mid-growth stage and late growth stage). Thus the model relation of VIs and crop coefficient Kc could be established, and the effect of water stress on it was studied. Results demonstrated that maize growth period and water stress were two important factors influencing the VIs-Kc model. The correlation between VIs and Kc in different growth stages was different: under full irrigation condition, the correlation of VIs-Kc model in the rapid growth stage (R2 was 0.7312~0.9401, p<0.05, n=25) was different with the correlation of VIs-Kc model from mid to late growth stage (R2 was 0.2765~0.3732,,p<0.05,,n=40);under water stress condition, the correlation of VIs-Kc model in the rapid growth stage (R2 was 0.0002~0.0830, p<0.05, n=25) was different with the correlation of VIs-Kc model from mid to late growth stage (R2 was 0.3362~0.8487,p<0.05,,n=40). Water stress had a significant effect on the correlation of VIs-Kc model: in the rapid growth stage, the correlation of VIs-Kc model for full irrigation maize (the maximum value of R2 was 0.9401) was better than the correlation for water stress maize (the maximum value of R2 was 0.0830);from mid to late growth stage, the correlation of VIs-Kc model for full irrigation maize (the maximum value of R2 was 0.3732) was worse than the correlation for water stress maize (the maximum value of R2 was 0.8487). The correlation of part of VIs and crop coefficient Kc was good;the descending order of correlation of VIs-Kc model for full irrigated maize in the rapid growth stage was SR, EVI, VARI, GNDVI and SAVI;the descending order of correlation of the VIs-Kc model for water stress maize from mid to late growth stage was SR, GNDVI, VARI, NDVI, SAVI and EVI;the correlation of SR and crop coefficient Kc was the best. Estimation of Kc based on UAV multispectral technology was feasible.

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韓文霆,邵國(guó)敏,馬代健,ZHANG Huihui,王毅,牛亞曉.大田玉米作物系數(shù)無(wú)人機(jī)多光譜遙感估算方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2018,49(7):134-143. HAN Wenting, SHAO Guomin, MA Daijian, ZHANG Huihui, WANG Yi, NIU Yaxiao. Estimating Method of Crop Coefficient of Maize Based on UAV Multispectral Remote Sensing[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(7):134-143.

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  • 收稿日期:2018-01-16
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  • 在線發(fā)布日期: 2018-07-10
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