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基于Landsat 8的節(jié)水改造背景下鹽漬化土壤含鹽量反演
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國家重點研發(fā)計劃項目(2021YFC3201202-05)和國家自然科學(xué)基金項目(52269014)


Inversion of Salt Content in Salinized Soil under Background of Water-saving Transformation Based on Landsat 8
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    摘要:

    為探明沈烏灌域節(jié)水改造后因渠道襯砌、引排水量減少引起的土壤含鹽量時空分布特征及變化規(guī)律,采用區(qū)域土壤信息定點監(jiān)測,結(jié)合經(jīng)典統(tǒng)計學(xué)、空間插值以及機器學(xué)習(xí)建模反演等技術(shù)手段,利用Landsat 8衛(wèi)星獲取光譜數(shù)據(jù),通過對實測土壤含鹽量、光譜指數(shù)及波段反射率進行處理,運用Adaboost回歸、BP神經(jīng)網(wǎng)絡(luò)回歸、梯度提升樹回歸、KNN回歸、決策樹回歸、隨機森林回歸方法構(gòu)建了沈烏灌域土壤含鹽量空間反演模型。采用最優(yōu)反演模型對沈烏灌域土壤含鹽量空間分布特征進行了遙感反演。結(jié)果表明: 通過全變量單一回歸法篩選出相關(guān)系數(shù)大于0.55的9個光譜因子,使用SPSS PRO軟件構(gòu)建6種機器學(xué)習(xí)反演模型,對比6種反演模型精度,驗證集決定系數(shù)R2由大到小依次為隨機森林回歸、梯度提升樹回歸、Adaboost回歸、KNN回歸、決策樹回歸、BP神經(jīng)網(wǎng)絡(luò)回歸。其中隨機森林回歸模型的擬合精度最佳,訓(xùn)練集與驗證集的決定系數(shù)R2分別為0.834和0.86,說明隨機森林回歸模型的反演效果較好。反演結(jié)果表明:節(jié)水改造后非鹽漬土面積增加391.7km2,占灌域總面積的21%,中度鹽漬土面積、重度鹽漬土面積、鹽土面積分別減少95.61、63.37、45.7km2,分別占灌域總面積的5%、3%、2%。綜上所述,節(jié)水改造工程完成后,沈烏灌域土壤鹽漬化程度減輕,作物生長安全區(qū)面積增加,但由于渠道襯砌以及引排水量減少,土壤鹽分淋洗效果減弱,土壤鹽分在灌域內(nèi)部運移,整體土壤環(huán)境得到改善,局部地區(qū)出現(xiàn)鹽分聚集。

    Abstract:

    In order to explore the spatial-temporal distribution characteristics and variation rules of soil salt content caused by the reduction of channel lining and drainage water after water-saving transformation in Shenwu irrigated district, fixed-point monitoring of regional soil information was adopted, combined with classical statistics, spatial interpolation and machine learning modeling and inversion, and spectral data was obtained by Landsat 8 satellite. By processing the measured soil salt content, spectral index and band reflectance, using Adaboost regression, BP neural network regression, gradient lifting tree regression, KNN regression, decision tree regression and random forest regression, the spatial inversion model of soil salt content in Shenwu irrigated district was constructed. The optimal inversion model was used to invert the spatial distribution characteristics of soil salt content in Shenwu irrigated district. The results showed that the correlation coefficient was screened by the whole variable single regression method. With nine spectral factors of 0.55, six inversion models of machine learning were constructed using SPSS PRO software, and the accuracy of the six inversion models was compared. The verification set R2 from high to low was random forest regression, gradient lifting tree regression, Adaboost regression, KNN regression, decision tree regression, and BP neural network regression. The random forest regression model had the best fitting accuracy, and the R2 of training set and verification set were 0.834 and 0.86, respectively. It was showed that random forest regression model had better inversion effect. The inversion results showed that the non-salinization soil increased by 391.7km2, accounting for 21% of the total irrigation area, while the moderate salinization soil, severe salinization soil and saline soid reduced the square by 95.61km2, 63.37 km2 and 45.7 km2, accounting for 5%, 3% and 2% of the total irrigation area, respectively. In summary, after the completion of the water-saving transformation project, the degree of soil salinization in Shenwu irrigated district was reduced, and the area of crop growth security area was increased. However, due to the reduction of channel lining and drainage water, the effect of soil salt leaching was weakened, and soil salt migrated within the irrigation area, the overall soil environment was improved, and salt accumulation occurred in some areas.

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劉偉,史海濱,苗慶豐,劉曉志,段倢,王禹森.基于Landsat 8的節(jié)水改造背景下鹽漬化土壤含鹽量反演[J].農(nóng)業(yè)機械學(xué)報,2024,55(1):294-304. LIU Wei, SHI Haibin, MIAO Qingfeng, LIU Xiaozhi, DUAN Jie, WANG Yusen. Inversion of Salt Content in Salinized Soil under Background of Water-saving Transformation Based on Landsat 8[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(1):294-304.

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  • 收稿日期:2023-05-12
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