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基于無人機-衛(wèi)星遙感升尺度的土壤鹽漬化監(jiān)測方法
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國家重點研發(fā)計劃項目(2017YFC0403302)和陜西省自然科學(xué)基礎(chǔ)研究計劃項目(2019JM-066)


Soil Salinization Monitoring Method Based on UAV-Satellite Remote Sensing Scale-up
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    摘要:

    為提高衛(wèi)星遙感對裸土期土壤鹽漬化的監(jiān)測精度,,以河套灌區(qū)沙壕渠灌域為研究區(qū)域,,利用無人機多光譜遙感和GF-1衛(wèi)星遙感分別獲取圖像數(shù)據(jù),并同步采集土壤表層含鹽量,;將實測含鹽量與無人機和GF-1衛(wèi)星兩種數(shù)據(jù)的光譜因子進行相關(guān)性分析,引入多元線性回歸模型(Multivariable linear regression,,MLR),、逐步回歸模型(Stepwise regression,SR)和嶺回歸模型(Ridge regression,,RR),,分別構(gòu)建鹽漬化監(jiān)測模型;采用改進的TsHARP尺度轉(zhuǎn)換方法,,將無人機數(shù)據(jù)建立的趨勢面應(yīng)用到GF-1衛(wèi)星尺度上,,經(jīng)過轉(zhuǎn)換殘差校正,對升尺度結(jié)果進行定性和定量分析,。結(jié)果表明:在兩種遙感數(shù)據(jù)的光譜波段和鹽分指數(shù)中,,藍波段B1、近紅外波段B5,、鹽分指數(shù)SI,、鹽分指數(shù)S5和改進的光譜指數(shù)NDVI-S1與表層土壤鹽分的相關(guān)性較好,相關(guān)系數(shù)均在0.3以上,;在3種回歸模型中,,利用無人機多光譜影像數(shù)據(jù)和GF-1多光譜影像數(shù)據(jù)反演表層土壤含鹽量的最優(yōu)模型分別是SRU模型和MLRS模型;升尺度后土壤含鹽量的反演精度高于直接采用衛(wèi)星遙感數(shù)據(jù)反演的精度,。本研究可為裸土期土壤鹽漬化的大范圍快速精準(zhǔn)監(jiān)測提供參考,。

    Abstract:

    Improving the accuracy of salinization monitoring by satellite remote sensing plays a crucial role in salinization. A synthesized model for assessment of regional soil salinity was established based on UAV and GF-1 satellite remote sensing data. Applying the trend surface of the UAV data creation to the GF-1 satellite scale, through the improved TsHARP scale conversion method, after the conversion residual correction, the upscaling results were quantitatively and qualitatively analyzed. The results showed that the blue band B1, the nearinfrared band B5, the salt index SI, the salt index S5, and the improved spectral index NDVI-S1 had a good correlation with the measured soil salinity data in two remote sensing data. Correlation coefficients were more than 03. In the three regression models, the best model for monitoring soil salinization by UAV data was the SRU model, the optimal model of GF-1 data was the MLRS model. After upscale conversion, the inversion accuracy of soil salinity was much higher than that of direct satellite data inversion. The optimal model after ascending scale was obviously improved with the optimal model by directly using GF-1 data inversion, the former R2c was 0.338 higher than that of the latter, R2v was 0.369 higher, but RMSE was 0.057 percentge points lower. The research results can provide a reference for largescale rapid monitoring of salinization in the bare soil period of irrigation districts. 

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陳俊英,王新濤,張智韜,韓佳,姚志華,魏廣飛.基于無人機-衛(wèi)星遙感升尺度的土壤鹽漬化監(jiān)測方法[J].農(nóng)業(yè)機械學(xué)報,2019,50(12):161-169. CHEN Junying, WANG Xintao, ZHANG Zhitao, HAN Jia, YAO Zhihua, WEI Guangfei. Soil Salinization Monitoring Method Based on UAV-Satellite Remote Sensing Scale-up[J]. Transactions of the Chinese Society for Agricultural Machinery,2019,50(12):161-169.

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  • 收稿日期:2019-07-29
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  • 在線發(fā)布日期: 2019-12-10
  • 出版日期: 2019-12-10
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