鄧 超1，蘇 南1，潘曉婷2，馬麗麗2，林 薇1

　　(1.水利部南京水利水文自動化研究所，江蘇 南京 210012;

　　2.揚州市生態環境局，江蘇 揚州 225000)

　　摘 要：土壤含水量作為地表的重要參量之一，對地球能量循環、水循環、碳循環及生態環境都有十分重要的意義。以南京市金川河流域為研究區，融合哨兵2號L2A數據和Landsat 8遙感數據2種數據源，分別采用偏最小二乘法(PLSR)、最小二乘-支持向量機(LS-SVM)、反向傳播神經網絡(BPNN)和隨機森林(RF)等4種建模方法，建立遙感數據與土壤含水量之間的關系，并進行模型的驗證與評價。結果表明：1)土壤含水量與哨兵2號和Landsat 8各波段反射率均呈負相關關系，和海岸帶監測波段(波長為430～450 nm)和近紅外波段(波長為2100～2300 nm)相關性最佳;2)融合后的遙感數據相較于單一遙感數據源，預測土壤含水量的能力更佳，最優模型R2達0.996，均方根誤差僅為0.003g/g;3)4種建模方法中，建模效果從好到差依次為PLSR，RF ，LS-SVM，BPNN。融合哨兵2號L2A和Landsat 8數據，結合PLSR建模方法可進行土壤含水量的精準反演，相較于現有研究反演精度大大提升，對研究該地區地表與地下水循環和生態環境治理有一定參考價值。

　　關鍵詞：土壤含水量;多源遙感數據;融合;反演;PLSR

　　Inverse estimation of soil moisture based on multi-source remote sensing data fusion

　　DENG Chao1，SU Nan1, PAN Xiaoting2,MA Lili2, LIN Wei1

　　(1.Nanjing Research Institute of Hydrology and Water Conservation Automation, Ministry of Water Resources, Nanjing 210012, China;

　　2.Bureau of Ecology and Environment of Yangzhou, Yangzhou 225000,China)

　　Abstract： As one of the important parameters, soil moisture is of great significance to affect the earth’s energy cycle, water cycle, carbon cycle and ecological environment. Taking the Jinchuan River Basin in Nanjing as the case study, the remote sensing data form Sentinel 2 L2A and Landsat 8 are gathered and fused. The relationships between remote sensing data and soil water content are established respectively by using four modeling method including Partial Least Squares Regression(PLSR), Least Square Support Vector Machine (LS-SVM), Back Propagation Neural Network (BPNN) and Random Forest (RF). And moreover, the relationships are verified and evaluated. The results show that:1) There is a negative correlation between soil water content and the reflectance of Sentinel 2 and Landsat 8,but it is best correlated with coastal zone monitoring band(wavelength 430~450 nm) and near infrared band (wavelength 2100~2300 nm); 2) Compared with the single remote sensing data source, the fusion data has a better ability to predict soil water content, and R2 of the optimal model is 0.996, and the RMSE is only 0.003g/g; 3) Among the four methods, the performance from good to bad is PLSR, RF, LS-SVM, BPNN. Combined with PLSR method, precise inversion estimation of soil moisture content can be carried out, and the inversion accuracy is greatly improved. It can provide more information for the study of surface and groundwater circulation and ecological environment management in this area.

　　Key words：soil moisture;multi-source remote sensing data;fusion;inverse estimation;PLSR