Artificial neural network-based estimation of root zone soil moisture in the western Liaohe river basin

doi:10.6046/zrzyyg.2022108

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(3779 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

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

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Xiaomeng GUO
	Xiuqin FANG
	Lulu YANG
	Yu CAO

Cite this article:

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.

URL:

https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022108 OR https://www.gtzyyg.com/EN/Y2023/V35/I2/193

Fig.1 Location, topography and river of the study area

Fig.2 MLP model structure diagram

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

[1]	Baldwin D, Manfreda S, Keller K, et al. Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous united states[J]. Journal of Hydrology, 2017, 546:393-404. doi: 10.1016/j.jhydrol.2017.01.020 url: https://linkinghub.elsevier.com/retrieve/pii/S0022169417300215
[2]	Lu H, Shi J. Reconstruction and analysis of temporal and spatial variations in surface soil moisture in China using remote sensing[J]. Chinese Science Bulletin, 2012, 57(22):2824-2834. doi: 10.1007/s11434-012-5011-8 url: http://link.springer.com/10.1007/s11434-012-5011-8
[3]	聂艳, 马泽玥, 周逍峰, 等. 阿克苏河流域土壤湿度反演与监测研究[J]. 生态学报, 2019, 39(14):5138-5148.
[3]	Nie Y, Ma Z Y, Zhou X F, et al. Soil moisture retrieval and monitoring in the Aksu River basin[J]. Acta Ecologica Sinica, 2019, 39(14):5138-5148.
[4]	高露, 张圣微, 赵鸿彬, 等. 退化草原土壤理化性质空间异质性及其对土壤水分的影响[J]. 干旱区研究, 2020, 37(3):607-617.
[4]	Gao L, Zhang S W, Zhao H B, et al. Spatial heterogeneity of soil physical and chemical properties in degraded grassland and their effect on soil moisture[J]. Arid Zone Research, 2020, 37(3):607-617.
[5]	Alfieri L, Claps P, D’odorico P, et al. An analysis of the soil moisture feedback on convective and stratiform precipitation[J]. Journal of Hydrometeorology, 2008, 9(2):280-291. doi: 10.1175/2007JHM863.1 url: http://journals.ametsoc.org/doi/10.1175/2007JHM863.1
[6]	Sriwongsitanon N, Gao H, Savenije H H G, et al. Comparing the normalized difference infrared index (NDII) with root zone storage in a lumped conceptual model[J]. Hydrology and Earth System Sciences, 2016, 20(8):3361-3377. doi: 10.5194/hess-20-3361-2016 url: https://hess.copernicus.org/articles/20/3361/2016/
[7]	Shi C, Xie Z, Qian H, et al. China land soil moisture EnKF data assimilation based on satellite remote sensing data[J]. Science China-Earth Sciences, 2011, 54(9):1430-1440. doi: 10.1007/s11430-010-4160-3 url: http://link.springer.com/10.1007/s11430-010-4160-3
[8]	Dumedah G, Coulibaly P. Evolutionary assimilation of streamflow in distributed hydrologic modeling using in-situ soil moisture data[J]. Advances in Water Resources, 2013, 53:231-241. doi: 10.1016/j.advwatres.2012.07.012 url: https://linkinghub.elsevier.com/retrieve/pii/S030917081200200X
[9]	Albergel C, Ruediger C, Pellarin T, et al. From near-surface to root-zone soil moisture using an exponential filter:An assessment of the method based on in-situ observations and model simulations[J]. Hydrology and Earth System Sciences, 2008, 12(6):1323-1337. doi: 10.5194/hess-12-1323-2008 url: https://hess.copernicus.org/articles/12/1323/2008/
[10]	Ford T W, Harris E, Quiring S M. Estimating root zone soil moisture using near-surface observations from SMOS[J]. Hydrology and Earth System Sciences, 2014, 18(1):139-154. doi: 10.5194/hess-18-139-2014 url: https://hess.copernicus.org/articles/18/139/2014/
[11]	Gao X, Zhao X, Zhang B, et al. Estimation of root-zone soil moisture over gullies using an exponential filter[J]. Advances in Water Science, 2014, 25(5):684-694.
[12]	Faridani F, Farid A, Ansari H, et al. A modified version of the SMAR model for estimating root-zone soil moisture from time-series of surface soil moisture[J]. Water SA, 2017, 43(3):492-498. doi: 10.4314/wsa.v43i3.14 url: https://www.ajol.info/index.php/wsa/article/view/159665
[13]	Manfreda S, Brocca L, Moramarco T, et al. A physically based approach for the estimation of root-zone soil moisture from surface measurements[J]. Hydrology and Earth System Sciences, 2014, 18(3):1199-1212. doi: 10.5194/hess-18-1199-2014 url: https://hess.copernicus.org/articles/18/1199/2014/
[14]	Faridani F, Farid A, Ansari H, et al. Estimation of the root-zone soil moisture using passive microwave remote sensing and SMAR model[J]. Journal of Irrigation and Drainage Engineering, 2017, 143(1):1-9.
[15]	Kornelsen K C, Coulibaly P. Root-zone soil moisture estimation using data- driven methods[J]. Water Resources Research, 2014, 50(4):2946-2962. doi: 10.1002/2013WR014127 url: http://doi.wiley.com/10.1002/2013WR014127
[16]	吴善玉. 基于神经网络算法的多源遥感联合反演土壤湿度研究[D]. 南京: 南京信息工程大学, 2019.
[16]	Wu S Y. Multi-source remote sensing soil moisture retrieval based on neural network algorithm[D]. Nanjing: Nanjing University of Information Science and Technology, 2019.
[17]	杨晓霞, 贾嵩, 张承明, 等. 一种基于神经网络的土壤湿度预测方法[J]. 江苏农业科学, 2018, 46(10):232-236.
[17]	Yang X X, Jia S, Zhang C M, et al. A soil moisture prediction algorithm based on artificial neural network[J]. Jiangsu Agricultural Sciences, 2018, 46(10):232-236.
[18]	朱丽亚, 孙爽, 胡克. 西辽河流域植被NPP时空分布特征及其影响因素研究[J]. 广西植物, 2020, 40(11):1563-1574.
[18]	Zhu L Y, Sun S, Hu K. Spatiotemporal distribution of vegetation net primary productivity(NPP) and its impact factors in the Xiliaohe basin[J]. Guihaia, 2020, 40(11):1563-1574.
[19]	赵子娟, 范蓓蕾, 王玉庭, 等. 2000—2018年西辽河流域植被覆盖度时空变化特征及影响因素研究[J]. 中国农业资源与区划, 2021, 42(12):75-88.
[19]	Zhao Z J, Fan B L, Wang Y T, et al. Analysis on the characteristics of spatial-temporal changes and influencing factors of vagetation coverage in the Xiliao River basin from 2000 to 2018[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2021, 42(12):75-88.
[20]	宫丽娟, 刘丹, 赵慧颖, 等. 西辽河地区植被气候生产潜力及其对气候变化的响应[J]. 生态环境学报, 2020, 29(5):866-875. doi: 10.16258/j.cnki.1674-5906.2020.05.002
[20]	Gong L J, Liu D, Zhao H Y, et al. Evolution of vegetation climatic potential productivity and its response to climate change in west Liao River basin[J]. Ecology and Environmental Sciences, 2020, 29(5):866-875.
[21]	崔一娇, 朱琳, 赵力娟. 基于面向对象及光谱特征的植被信息提取与分析[J]. 生态学报, 2013, 33(3):867-875.
[21]	Cui Y J, Zhu L, Zhao L J. Abstraction and analysis of vegetation information based on object-oriented and spectra features[J]. Acta Ecologica Sinica, 2013, 33(3):867-875. doi: 10.5846/stxb url: http://www.ecologica.cn/
[22]	孙小舟, 封志明, 杨艳昭, 等. 西辽河流域近60年来气候变化趋势分析[J]. 干旱区资源与环境, 2009, 23(9):62-66.
[22]	Sun X Z, Feng Z M, Yang Y Z, et al. The climate change trend in Xiliao River basin in recent 60 years[J]. Journal of Arid Land Resources and Environment, 2009, 23(9):62-66.
[23]	谢冰绮, 吕海深, 朱永华. 基于遥感土壤湿度反演中尺度流域水储量季节性变化[J]. 中国农村水利水电, 2020,(10):170-175.
[23]	Xie B Q, Lyu H S, Zhu Y H. Evaluation of the seasonal water storage changes in medium-scale basins based on remotely-sensed soil moisture retrievals[J]. China Rural Water and Hydropower, 2020,(10):170-175.
[24]	Gruber A, Dorigo W A, Crow W, et al. Triple collocation-based merging of satellite soil moisture retrievals[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(12):6780-6792. doi: 10.1109/TGRS.2017.2734070 url: https://ieeexplore.ieee.org/document/8046118/
[25]	Xiao J B, Sun Z X, Yang J, et al. Effect of subsoiling on soil water and crop yield in semi-arid area[J]. Journal of Soil Science, 2011, 42(3):709-714.
[26]	Paltineanu C, Septar L, Gavat C, et al. Spatial distribution of apricot roots in a semi-arid environment[J]. Agroforestry Systems, 2016, 90(3):469-478. doi: 10.1007/s10457-015-9869-8 url: http://link.springer.com/10.1007/s10457-015-9869-8
[27]	Liu F, Wu H, Zhao Y, et al. Mapping high resolution national soil information grids of China[J]. Science Bulletin, 2022, 67(3):328-340. doi: 10.1016/j.scib.2021.10.013 pmid: 36546081
[28]	Xyu X C. Spatial and temporal change characteristics and influencing factors of NDVI of vegetation in China[D]. Harbin: Harbin Normal University, 2019.
[29]	荀其蕾, 董乙强, 安沙舟, 等. 基于MOD 09GA数据的新疆草地生长状况遥感监测研究[J]. 草业学报, 2018, 27(4):10-26. doi: 10.11686/cyxb2017232
[29]	Xun Q L, Dong Y Q, An S Z, et al. Monitoring of grassland herbage accumulation by remote sensing MOD 09GA data in Xinjiang[J]. Actapra Aculturae Sinica, 2018, 27(4):10-26.
[30]	张仁平, 冯琦胜, 郭靖, 等. 2000—2012年中国北方草地NDVI和气候因子时空变化[J]. 中国沙漠, 2015, 35(5):1403-1412. doi: 10.7522/j.issn.1000-694X.2014.00130
[30]	Zhang R P, Feng Q S, Guo J, et al. Spatio-temporal changes of NDVI and climate factors of grassland in northern China from 2000 to 2012[J]. Journal of Desert Research, 2015, 35(5):1403-1412.
[31]	杨秀芹, 王国杰, 潘欣, 等. 基于GLEAM遥感模型的中国1980—2011年地表蒸散发时空变化[J]. 农业工程学报, 2015, 31(21):132-141.
[31]	Yang X Q, Wang G J, Pan X, et al. Spatio-temporal variability of terrestrial evapotranspiration in China from 1980 to 2011 based on GLEAM data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(21):132-141.
[32]	Yang X, Wang G, Pan X, et al. Spatio-temporal variability of terrestrial evapotranspiration in China from 1980 to 2011 based on gleam data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(21):132-141.
[33]	Miralles D G, De Jeu R A M, Gash J H, et al. Magnitude and variability of land evaporation and its components at the global scale[J]. Hydrology and Earth System Sciences, 2011, 15(3):967-981. doi: 10.5194/hess-15-967-2011 url: https://hess.copernicus.org/articles/15/967/2011/
[34]	Yang X, Yong B, Ren L, et al. Multi-scale validation of gleam evapotranspiration products over china via chinaflux et measurements[J]. International Journal of Remote Sensing, 2017, 38(20):5688-5709. doi: 10.1080/01431161.2017.1346400 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2017.1346400
[35]	Abrahart R J, Anctil F, Coulibaly P, et al. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting[J]. Progress in Physical Geography-Earth and Environment, 2012, 36(4):480-513. doi: 10.1177/0309133312444943 url: http://journals.sagepub.com/doi/10.1177/0309133312444943
[36]	Taver V, Johannet A, Borrell-Estupina V, et al. Feed-forward vs recurrent neural network models for non-stationarity modelling using data assimilation and adaptivity[J]. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 2015, 60(7-8):1242-1265. doi: 10.1080/02626667.2014.967696 url: http://www.tandfonline.com/doi/full/10.1080/02626667.2014.967696
[37]	Shoaib M, Shamseldin A Y, Melville B W, et al. A comparison between wavelet based static and dynamic neural network approaches for runoff prediction[J]. Journal of Hydrology, 2016, 535:211-225. doi: 10.1016/j.jhydrol.2016.01.076 url: https://linkinghub.elsevier.com/retrieve/pii/S0022169416300166
[38]	王青青, 张珂, 叶金印, 等. 安徽省土壤湿度时空变化规律分析及遥感反演[J]. 河海大学学报(自然科学版), 2019, 47(2):114-118.
[38]	Wang Q Q, Zhang K, Ye J Y, et al. Spatiotemporal analysis and remote sensing retrieval of soil moisture across Anhui Province,China[J]. Journal of Hohai University(Natural Sciences), 2019, 47(2):114-118.
[39]	Golden R M. Neural networks:A comprehensive foundation-Haykin,S[J]. Journal of Mathematical Psychology, 1997, 41(3):287-292. doi: 10.1006/jmps.1997.1164 url: https://linkinghub.elsevier.com/retrieve/pii/S0022249697911640
[40]	Hagan M T, Demuth H B, Beale M H. Neural network design[M]. Boston, PWS Publishing, 1996.
[41]	Yonaba H, Anctil F, Fortin V. Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting[J]. Journal of Hydrologic Engineering, 2010, 15(4):275-283. doi: 10.1061/(ASCE)HE.1943-5584.0000188 url: https://ascelibrary.org/doi/10.1061/%28ASCE%29HE.1943-5584.0000188
[42]	Basara J B, Crawford K C. Linear relationships between root-zone soil moisture and atmospheric processes in the planetary boundary layer[J]. Journal of Geophysical Research-Atmospheres, 2002, 107(D15):
[43]	Entekhabi D, Njoku E G, O’Neill P E, et al. The soil moisture active passive (SMAP) mission[J]. Proceedings of the IEEE, 2010, 98(5):704-716. doi: 10.1109/JPROC.2010.2043918 url: http://ieeexplore.ieee.org/document/5460980/
[44]	Elshorbagy A, El-Baroudy I. Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content[J]. Journal of Hydroinformatics, 2009, 11(3-4):237-251. doi: 10.2166/hydro.2009.032 url: https://iwaponline.com/jh/article/11/3-4/237/41/Investigating-the-capabilities-of-evolutionary
[45]	Elshorbagy A, Parasuraman K. On the relevance of using artificial neural networks for estimating soil moisture content[J]. Journal of Hydrology, 2008, 362(1-2):1-18. doi: 10.1016/j.jhydrol.2008.08.012 url: https://linkinghub.elsevier.com/retrieve/pii/S0022169408004204
[46]	Jiang H L, Cotton W R. Soil moisture estimation using an artificial neural network:A feasibility study[J]. Canadian Journal of Remote Sensing, 2004, 30(5):827-839. doi: 10.5589/m04-041 url: http://www.tandfonline.com/doi/abs/10.5589/m04-041
[47]	Pan X, Kornelsen K C, Coulibaly P. Estimating root zone soil moisture at continental scale using neural networks[J]. Journal of the American Water Resources Association, 2017, 53(1):220-237. doi: 10.1111/jawr.2017.53.issue-1 url: http://doi.wiley.com/10.1111/jawr.2017.53.issue-1

[1]	KONG Zhuo, YANG Haitao, ZHENG Fengjie, LI Yang, QI Ji, ZHU Qinyu, YANG Zhonglin. Research advances in atmospheric correction of hyperspectral remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(4): 1-10.
[2]	ZHU Jin-shan, LIANG Shi-ying, SU Xun-bo. A Method to Retrieve the Oceanic Absorption Coefficient Based on Artificial Neural Network[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 6-10.
[3]	ZHANG Chong, CHEN Xiao-Hua, ZOU Le-Jun, WU Wen-Yuan, SU Nan, KONG Fan-Li. The Quantification Method in the Estimation Model for Landslide Danger: a Case Study of Yongjia County[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(3): 16-20.
[4]	LI Xiao-Tao, LI Ji-Ren, HUANG Shi-Feng, SONG Xiao-Ning. A REMOTE SENSING IMAGE CLASSIFICATION METHOD BASED ON GEOSTATISTICS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2006, 18(1): 18-21.
[5]	HE Yong-qiang, YAO Guo-qing . THE STRUCTURES OF ARTIFICIAL NEURAL NETWORKS USED FOR IMAGING SPECTRAL DATA PATTERN RECOGNITION[J]. REMOTE SENSING FOR LAND & RESOURCES, 2004, 16(3): 23-27.
[6]	Jiang Dong, Wang Jianhua . Application and Development of Artificial Neural Network in Remote Sensing[J]. REMOTE SENSING FOR LAND & RESOURCES, 1999, 11(2): 12-18.
[7]	Zhang Baoguang . THE APPLICATION OF ARTIFICIAL NEURAL NETWORK TO CLASSIFICATION PROCESSING OF REMOTE SENSING DIGITAL IMAGES[J]. REMOTE SENSING FOR LAND & RESOURCES, 1998, 10(1): 21-27.

Viewed

Full text

Abstract

Cited

Shared

Discussed