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基于高光譜和機器學習模型的冬小麥土壤含水率監(jiān)測研究
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國家自然科學基金項目（52179045）


Monitoring of Soil Moisture Content of Winter Wheat Based on Hyperspectral and Machine Learning Models
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    摘要:

    為及時獲取大田作物根區(qū)土壤含水率（Soil moisture content, SMC），實現(xiàn)精準灌溉,，運用高光譜技術,，通過連續(xù)2年（2019—2020年）田間試驗采集了冬小麥拔節(jié)期不同土層深度SMC及高光譜數(shù)據(jù)，構建了3類植被指數(shù)（藍,、黃和紅邊面積等三邊光譜參數(shù),，與冬小麥根區(qū)SMC相關性最高的任意兩波段植被指數(shù)和前人研究與作物參數(shù)相關性較好的經(jīng)驗植被指數(shù)）并篩選與各土層深度SMC相關系數(shù)最高的植被指數(shù)，隨后將篩選后的植被指數(shù)作為模型輸入,，分別采用隨機森林（Random forest,，RF）,、反向神經(jīng)網(wǎng)絡（Back propagation neural network，BPNN）和極限學習機（Extreme learning machine,，ELM）構建冬小麥拔節(jié)期不同土層深度SMC估算模型,。結果表明，絕大部分三邊參數(shù),、任意兩波段植被指數(shù)和經(jīng)驗植被指數(shù)在深度0~20cm土層的SMC相關系數(shù)較20~40cm和40~60cm更高,，在深度0~20cm土層兩波段組合構建的光譜指數(shù)與SMC的相關系數(shù)最高，均超過0.8,，其中RI與SMC的相關系數(shù)最高,，為0.851，其波長組合為675nm和695nm,。RF模型是SMC的最佳建模方法,，其中深度0~20cm土層的模型精度最高，估算模型驗證集的決定系數(shù)R2達0.909,，均方根誤差（RMSE）為0.008,，平均相對誤差（MRE）為3.949%。本研究結果可為高光譜監(jiān)測冬小麥根區(qū)SMC提供依據(jù),，為快速評估水分脅迫下的作物生長提供應用參考,。

    Abstract:

    Aiming to promptly obtain soil moisture content (SMC) in the root zone of field crops for precise irrigation, hyperspectral technology was utilized. Over a 2year period spanning from 2019 to 2020, during the winter wheat jointing stage, SMC data at varying soil depths and hyperspectral data were collected. Three categories of vegetation indices were created, comprising ‘trilateral’ spectral parameters related to blue, yellow, and rededge areas, any two-band vegetation indices with the highest correlation to winter wheat root zone SMC, and empirical vegetation indices showing good correlation with crop parameters from previous studies. The vegetation indices exhibited the highest correlation with SMC at different soil depths were selected. Subsequently, random forest (RF), back propagation neural network (BPNN), and extreme learning machine (ELM) were employed to construct SMC estimation models, using the selected vegetation indices as model inputs. The results revealed that a majority of the ‘trilateral’ spectral parameters spectral indices, any two-band vegetation indices, and empirical vegetation indices displayed stronger correlations with SMC in the 0~20cm soil layer in comparison with the 20~40cm and 40~60cm layers. The two-band combinations in the 0~20cm layer exhibited the highest correlations with SMC, all surpassing 0.8. Among which, RI showed the highest correlation with SMC at 0.851, utilizing a wavelength combination of 675nm and 695nm. The RF model emerged as the most effective modeling method for SMC, with the highest accuracy observed in the 0~20cm soil layer. The coefficient of determination (R2) for the validation set of the estimation model in the 0~20cm layer reached 0.909, and the root mean square error (RMSE) was 0.008, while the mean relative error (MRE) was 3.949%. The outcomes can serve as a foundation for hyperspectral monitoring of winter wheat root zone SMC and provide valuable insights for the rapid assessment of crop growth under water stress.

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唐子竣,張威,向友珍,李志軍,張富倉,陳俊英.基于高光譜和機器學習模型的冬小麥土壤含水率監(jiān)測研究[J].農(nóng)業(yè)機械學報,2023,54(12):350-358. TANG Zijun, ZHANG Wei, XIANG Youzhen, LI Zhijun, ZHANG Fucang, CHEN Junying. Monitoring of Soil Moisture Content of Winter Wheat Based on Hyperspectral and Machine Learning Models[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(12):350-358.

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  • 收稿日期:2023-05-31
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  • 在線發(fā)布日期: 2023-06-28
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