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基于PROSAIL模型的青貯玉米葉面積指數(shù)反演
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甘肅農業(yè)大學青年研究生指導教師扶持基金項目(GAU-QDFC-2022-18)、甘肅省教育廳產業(yè)支撐計劃項目(2022CYZC-41)和中央引導地方科技發(fā)展專項(24ZYQA023)


Inversion of Leaf Area Index of Silage Corn Based on PROSAIL Model
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    摘要:

    精準且高效地估算區(qū)域內的玉米葉面積指數(shù)(LAI),對于田間管理決策、地物產量預測以及實施精準農業(yè)具有至關重要的意義。針對多尺度、大范圍遙感反演中存在的尺度效應、精度低、普適性差等問題,本文以張掖市民樂縣青貯玉米實驗田為研究區(qū),選取青貯玉米為研究對象,基于Landsat-8高光譜和Modis多光譜遙感影像,并結合地面實測數(shù)據(jù)。通過對PROSAIL模型的輸入?yún)?shù)進行局部和全局敏感性分析,構建出青貯玉米在多個生育期內的冠層反射率-LAI的查找表和最小尋優(yōu)代價函數(shù)的反演策略,確定研究區(qū)域的最佳LAI反演模型,并利用青貯玉米不同生育期內的實測值完成了反演結果的精度驗證及線性擬合。結果表明:LAI反演結果總體較好,擬合精度較高,與實測值之間有較強的相關性,拔節(jié)期、抽雄期、成熟期最優(yōu)決定系數(shù)R2分別為0.85、0.91、0.90;均方根誤差(RMSE)分別為0.35、0.58、0.51。因此,基于多源高光譜遙感數(shù)據(jù)結合PROSAIL模型的反演策略可為作物參數(shù)反演提供新的科學依據(jù)和方法。

    Abstract:

    Accurately and efficiently estimating corn LAI data within a region is of crucial importance for field management decisions, predicting land yield, and implementing precision agriculture. In response to the problems of scale effect, low accuracy, and poor universality in multi-scale and large-scale remote sensing inversion, taking the silage corn experimental field in Minle County, Zhangye City as the research area, silage corn was selected as the research object, based on Landsat-8 hyperspectral and Modis multispectral remote sensing images, combined with ground measured data. Through local and global sensitivity analysis of the input parameters of the PROSAIL model,the lookup table of canopy reflectance-LAI of silage corn in multiple growth periods and the inversion strategy of the minimum optimization cost function were constructed, and the optimal LAI inversion model for the study area was determined. The accuracy verification and linear fitting of the inversion results were completed by using the measured values in different growth periods of silage corn. The results showed that the inversion results of LAI were generally good, with high fitting accuracy and strong correlation with the measured values. The optimal determination coefficients R2 for the jointing stage, tasseling stage, and maturity stage were 0.85, 0.91, and 0.90, respectively. The root mean square error (RMSE) were 0.35, 0.58, and 0.51, respectively. Therefore, the inversion strategy based on multi-source hyperspectral remote sensing data combined with the PROSAIL model can provide scientific basis and methods for crop parameter inversion.

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汪彥龍,王鈞,崔婷.基于PROSAIL模型的青貯玉米葉面積指數(shù)反演[J].農業(yè)機械學報,2024,55(8):205-213. WANG Yanlong, WANG Jun, CUI Ting. Inversion of Leaf Area Index of Silage Corn Based on PROSAIL Model[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(8):205-213.

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  • 收稿日期:2023-11-13
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  • 在線發(fā)布日期: 2024-08-10
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