0.68)明顯優(yōu)于陸氣溫差(R2<0.6),;旬間尺度下100%充分灌溉時(shí),,WDI,、陸氣溫差與土壤含水率,、氣孔導(dǎo)度均無顯著相關(guān)性(R2<0.12),;在不同水分脅迫下,WDI與氣孔導(dǎo)度,、土壤含水率均顯著相關(guān)(R2為0.7283~0.82),,而陸氣溫差與氣孔導(dǎo)度、土壤含水率的相關(guān)性則出現(xiàn)較大差異(R2為0.3566~0.8074),;與陸氣溫差相比,,采用WDI實(shí)時(shí)監(jiān)測(cè)青貯夏玉米旱情更為穩(wěn)定。研究結(jié)果可為大田作物干旱信息的實(shí)時(shí)監(jiān)測(cè)提供參考,。"/>

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基于無人機(jī)遙感的青貯夏玉米水分虧缺指數(shù)反演研究
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陜西省水利科技項(xiàng)目(2017slk-7)


Silage Summer Maize Water Deficit Index Inversion Based on UAV Remote Sensing
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    為了研究不同水分脅迫和不同時(shí)間尺度對(duì)拔節(jié)期青貯夏玉米水分虧缺指數(shù)(WDI)和陸氣溫差監(jiān)測(cè)效果的影響,,利用地面數(shù)據(jù)結(jié)合無人機(jī)遙感數(shù)據(jù)建立植被指數(shù)-溫度梯形空間,計(jì)算WDI干旱指數(shù),,并生成WDI分布圖和陸氣溫差分布圖,。在不同的時(shí)間尺度和水分脅迫梯度下分析WDI、陸氣溫差與土壤含水率,、氣孔導(dǎo)度的相關(guān)性,。結(jié)果表明,植被指數(shù)-溫度梯形空間和WDI分布圖對(duì)短期降雨事件反應(yīng)敏感,;日間尺度下WDI,、陸氣溫差與土壤含水率、氣孔導(dǎo)度均表現(xiàn)了較好的相關(guān)性(R2為0.4~0.85),;旬間尺度下WDI與土壤含水率,、氣孔導(dǎo)度的相關(guān)性(R2>0.68)明顯優(yōu)于陸氣溫差(R2<0.6);旬間尺度下100%充分灌溉時(shí),,WDI,、陸氣溫差與土壤含水率、氣孔導(dǎo)度均無顯著相關(guān)性(R2<0.12),;在不同水分脅迫下,,WDI與氣孔導(dǎo)度、土壤含水率均顯著相關(guān)(R2為0.7283~0.82),,而陸氣溫差與氣孔導(dǎo)度,、土壤含水率的相關(guān)性則出現(xiàn)較大差異(R2為0.3566~0.8074);與陸氣溫差相比,,采用WDI實(shí)時(shí)監(jiān)測(cè)青貯夏玉米旱情更為穩(wěn)定,。研究結(jié)果可為大田作物干旱信息的實(shí)時(shí)監(jiān)測(cè)提供參考,。

    Abstract:

    Vegetation index temperature mixed pixels affects the remote sensing monitoring of drought conditions, Water deficit index (WDI) was compared with surface minus air temperature (Ts-Ta) as a water stress indicator which can overcome the difficulty. The experimental field was located in Dalat Banner, Inner Mongolia, and the experimental object was silage summer maize. Normalized difference vegetation index (NDVI) and land surface mixing temperature (Ts) were extracted from UAV-acquired images (multispectral, thermal infrared). Maize physiological parameters and meteorological data were collected to establish WDI model, under three irrigation regimes. WDI model and remote sensing data (NDVI, Ts) were used to generate vegetation indextemperature trapezoidal space, WDI map, and Ts-Ta map. WDI and Ts-Ta were extracted in the sample area. WDI and Ts-Ta were extracted in sample areas. The relationships between WDI or Ts-Ta and soil water content/stomata conductance were analyzed. Results demonstrated that vegetation indextemperature trapezoidal space, WDI and Ts-Ta map were sensitive to shortterm drought response. WDI and Ts-Ta showed similarity, both showed strong correlation with soil water content and stomata conductance (R2=0.4~0.85). At scale of ten days, the correlation between WDI and soil water content/stomata conductance (R2>0.68) was significantly higher than the correlation between Ts-Ta and soil moisture content/stomata conductance (R2<0.6). At the scale of ten days, the effects of different water stress gradients on drought monitoring were analyzed. It was found that under sufficient irrigation, there was no significant correlation between WDI or Ts-Ta and soil water content/stomata conductance (R2<0.12). Under different deficit irrigation, the correlation between WDI and stomata conductance/soil water content was significant (R2=0.7283~0.82), the correlation between Ts-Ta and stomata conductance/soil water content showed large fluctuations (R2=0.3566~0.8074). WDI had greater practicability and stability when monitoring the continuous change of drought compared with the difference in land temperature, at the scale of ten days.

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李星恕,程雙飛,薛志,熊秀芳,韓文霆,張立元.基于無人機(jī)遙感的青貯夏玉米水分虧缺指數(shù)反演研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2019,50(9):177-185. LI Xingshu, CHENG Shuangfei, XUE Zhi, XIONG Xiufang, HAN Wenting, ZHANG Liyuan. Silage Summer Maize Water Deficit Index Inversion Based on UAV Remote Sensing[J]. Transactions of the Chinese Society for Agricultural Machinery,2019,50(9):177-185.

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  • 收稿日期:2019-01-29
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  • 在線發(fā)布日期: 2019-09-10
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