本文選題：多源遙感數(shù)據(jù) + 無人機(jī)�。� 參考：《應(yīng)用生態(tài)學(xué)報(bào)》2016年01期

【摘要】：近年來遙感技術(shù)的革新促使遙感源越來越豐富.為分析多源遙感數(shù)據(jù)的葉面積指數(shù)(LAI)估測精度,本文以大豆為研究對象,利用比值植被指數(shù)(RVI)、歸一化植被指數(shù)(NDVI)、土壤調(diào)整植被指數(shù)(SAVI)、差值植被指數(shù)(DVI)、三角植被指數(shù)(TVI)5種植被指數(shù),結(jié)合地面實(shí)測LAI構(gòu)建經(jīng)驗(yàn)回歸模型,比較3類遙感數(shù)據(jù)(地面高光譜數(shù)據(jù)、無人機(jī)多光譜影像以及高分一號WFV影像)對大豆LAI的估測能力,并從傳感器幾何位置和光譜響應(yīng)特性以及像元空間分辨率三方面分析討論了3類遙感數(shù)據(jù)的LAI反演差異.結(jié)果表明:地面高光譜數(shù)據(jù)模型和無人機(jī)多光譜數(shù)據(jù)模型都可以準(zhǔn)確預(yù)測大豆LAI(在α=0.01顯著水平下,R~2均0.69,RMSE均0.40);地面高光譜RVI對數(shù)模型的LAI預(yù)測能力優(yōu)于無人機(jī)多光譜NDVI線性模型,但兩者差異不大(E_A相差0.3%,R~2相差0.04,RMSE相差0.006);高分一號WFV數(shù)據(jù)模型對研究區(qū)內(nèi)大豆LAI的預(yù)測效果不理想(R~20.30,RMSE0.70).針對星、機(jī)、地三類遙感信息源,地面高光譜數(shù)據(jù)在反演LAI方面較傳統(tǒng)多光譜數(shù)據(jù)有優(yōu)勢但不突出;16 m空間分辨率的高分一號WFV影像無法滿足田塊尺度作物長勢監(jiān)測的需求;在保證獲得高精度大豆LAI預(yù)測值和高工作效率的前提條件下,基于無人機(jī)遙感的農(nóng)情信息獲取技術(shù)不失為一種最佳試驗(yàn)方案.在當(dāng)今可用遙感信息源越來越多的情況下,農(nóng)業(yè)無人機(jī)遙感信息可成為指導(dǎo)田塊精細(xì)尺度作物管理的重要依據(jù),為精準(zhǔn)農(nóng)業(yè)研究提供更科學(xué)準(zhǔn)確的信息.
[Abstract]:In recent years, the innovation of remote sensing technology makes remote sensing sources more and more abundant. In order to analyze the estimation accuracy of leaf area index (Lai) of multi-source remote sensing data, soybean was studied in this paper. Using ratio vegetation index (RVI), normalized vegetation index (NDV), soil adjusted vegetation index (LAI), difference vegetation index (DVI), triangular vegetation index (TVB) 5, and LAI measured on the ground, an empirical regression model was established. The ability of estimating soybean LAI from three kinds of remote sensing data (ground hyperspectral data, UAV multispectral image and high-fractionated 1 WFV image) was compared. The LAI inversion differences of three kinds of remote sensing data are analyzed and discussed from three aspects: geometric position, spectral response characteristics and pixel spatial resolution. The results showed that both the ground hyperspectral data model and the UAV multispectral data model could accurately predict soybean Lai (0. 40 渭 g / L, 0. 69% RMSE), and the LAI prediction ability of ground hyperspectral RVI logarithm model was better than that of UAV multispectral NDVI linear model. However, there was no significant difference between the two groups. The difference between the two groups was 0.04% and 0.04%, and the prediction effect of the WFV data model No. 1 on soybean LAI in the study area was not satisfactory (0.70%). For satellite, computer and ground remote sensing information sources, the ground hyperspectral data is superior to the traditional multi-spectral data in retrieving LAI data, but it can not meet the needs of crop growth monitoring in field scale. Under the precondition of obtaining high precision soybean LAI prediction value and high working efficiency, the technology of agricultural information acquisition based on UAV remote sensing is the best test scheme. With more and more remote sensing information sources available, the remote sensing information of agricultural UAV can become an important basis for guiding crop management in fine scale, and provide more scientific and accurate information for precision agriculture research.
【作者單位】： 河南理工大學(xué)測繪與國土信息工程學(xué)院;國家農(nóng)業(yè)信息化工程技術(shù)研究中心;農(nóng)業(yè)部農(nóng)業(yè)信息技術(shù)重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金項(xiàng)目(41271345) 北京市自然科學(xué)基金項(xiàng)目(4141001)資助~~
【分類號】：S127;S565.1

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