【摘要】：精確地估測干旱區(qū)土壤水分含量,對該區(qū)域的農(nóng)業(yè)發(fā)展與水土保持具有重要意義。該文以MODIS與Landsat TM數(shù)據(jù)為數(shù)據(jù)源,利用其反演獲得的條件溫度植被指數(shù)(temperature-vegetation drought Index,TVDI)作為觀測算子,將集合卡爾曼濾波(ensemble Kalman filter,En-KF)同化方法應用于水文模型(HYDRUS-1D),進行干旱區(qū)表層土壤水分的模擬。結果表明:遙感數(shù)據(jù)反演土壤水分所構建的二維特征空間TVDI與表層土壤水分有較好的一致性;En-KF同化方法對模型變量與觀測算子的更新,與單純使用HYDRUS模型相比,獲得的表層土壤水分含量精度有了明顯提高,其均方根誤差縮小了1個百分點,平均誤差縮小了5個百分點。可見,基于多源遙感數(shù)據(jù)對表層土壤水分的En-KF同化模擬在干旱區(qū)具有較大的潛力,是提高干旱區(qū)土壤水分含水量監(jiān)測精度的有效手段。
[Abstract]:Accurate estimation of soil moisture content in arid area is of great significance for agricultural development and soil and water conservation in this region. In this paper, the MODIS and Landsat TM data are used as data sources, and the conditional temperature vegetation index (temperature-vegetation drought Index,TVDI) obtained from the inversion is used as the observation operator. The set Kalman filter (ensemble Kalman filter, is used in this paper. En-KF) assimilation method was applied to the hydrological model (HYDRUS-1D) to simulate the surface soil moisture in arid areas. The results show that the two-dimensional characteristic space TVDI constructed by retrieving soil moisture from remote sensing data is in good agreement with that of surface soil moisture. Compared with the simple use of HYDRUS model, the accuracy of surface soil moisture content obtained by En-KF assimilation method is obviously improved, and its root mean square error is reduced by 1 percentage point, compared with the model variables and observation operators, and the root mean square error (RMS) is reduced by 1 percentage point. The average error was reduced by 5 percentage points. Therefore, the En-KF assimilation simulation of surface soil moisture based on multi-source remote sensing data has great potential in arid areas, and it is an effective means to improve the monitoring accuracy of soil moisture content in arid areas.
【作者單位】： 新疆大學資源與環(huán)境科學學院;綠洲生態(tài)教育部重點實驗室;
【基金】：國家自然科學基金(U1303381、41261090) 自治區(qū)重點實驗室專項基金(2016D03001) 自治區(qū)科技支疆項目(201591101) 教育部促進與美大地區(qū)科研合作與高層次人才培養(yǎng)項目 新疆大學優(yōu)秀博士生科技創(chuàng)新項目(XJUBSCX-2016014)
【分類號】：S152.7

【參考文獻】

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