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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (2) : 193-201     DOI: 10.6046/zrzyyg.2022108
Artificial neural network-based estimation of root zone soil moisture in the western Liaohe river basin
GUO Xiaomeng(), FANG Xiuqin(), YANG Lulu, CAO Yu
College of Hydrology and Water Resources, Hohai University, Nanjing 211100, China
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Soil moisture is the core of water conversion and circulation that connects the atmosphere, surface, soil, and subsurface. As a basic climate variable of the global climate observing system, soil moisture plays a vital role in regional-scale water and energy exchange. The estimation of root zone soil moisture (RZSM) and the analysis of its spatio-temporal variations are of great significance for crop yield assessment, flood and drought prediction, and soil and water conservation. Based on the artificial neural network (ANN), this study estimated the daily RZSM in the Western Liaohe River basin during 2019—2020 with remote sensing image-based surface soil moisture, cumulative precipitation, cumulative daily maximum and minimum temperatures, relative humidity, sunshine duration, cloud coverage, wind speed, soil attributes, normalized difference vegetation index, and actual evapotranspiration as explanatory variables, the in-situ measured RZSM as the target variable, and the 2013—2018 data used for model training. The estimated results show that the average RMSE and average R between the RZSM estimated based on ANN and the in-situ measured RZSM were 0.056 7 m3/m3 and 0.611 7, respectively. Therefore, the ANN can effectively estimate the RZSM in the Western Liaohe River basin. In addition, this study shows that the variation in the soil moisture is closely related to precipitation.

Keywords root zone soil moisture      artificial neural network      Western Liaohe River basin      remotely sensed soil moisture     
ZTFLH:  TP79  
Issue Date: 07 July 2023
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Xiaomeng GUO
Xiuqin FANG
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Xiaomeng GUO,Xiuqin FANG,Lulu YANG, et al. Artificial neural network-based estimation of root zone soil moisture in the western Liaohe river basin[J]. Remote Sensing for Natural Resources, 2023, 35(2): 193-201.
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Fig.1  Location, topography and river of the study area
Fig.2  MLP model structure diagram
站点名称 R RMSE
巴雅尔吐胡硕 0.735 5 0.051 5
富河 0.696 1 0.030 0
扎鲁特 0.535 8 0.057 7
巴林左 0.614 1 0.041 6
舍伯吐 0.319 7 0.026 0
科左中 0.830 7 0.031 3
巴林右 0.634 3 0.090 3
林西 0.715 1 0.025 4
克什克腾 0.806 5 0.051 2
阿鲁科尔沁 0.841 1 0.037 2
开鲁 0.210 5 0.198 7
通辽 0.438 1 0.084 2
翁牛特 0.597 7 0.021 4
岗子 0.815 3 0.028 9
赤峰 0.330 7 0.033 6
奈曼 0.319 7 0.082 4
敖汉 0.707 2 0.027 7
喀喇沁 0.702 2 0.052 9
八里罕 0.772 6 0.085 7
Tab.1  RMSE and R between the estimated RZSM based on ANN and the in-situ measured RZSM
Fig.3  Time series of the estimated RZSM based on ANN and the in-situ measured RZSM of 2019—2020 at Keshiketeng
Fig.4  Time series of the estimated RZSM based on ANN and the in-situ measured RZSM of 2019—2020 at Gangzi
Fig.5  Time series of the estimated RZSM based on ANN and the in-situ measured RZSM of 2019—2020 at Aohan
Fig.6  The estimated RZSM in the Xiliaohe River Basin on June 1, 2019
Fig.7  The estimated RZSM in the Xiliaohe River Basin of on June 1, 2020
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