Modeling the Influence of Biophysical Properties and Surface Topography on the Spatial Distribution of Soil Moisture in the Summer: A Case Study of Balikhli-Chay Watershed

Document Type : Research Article


1 PhD Student, Department of Soil Science, Faculty of Agriculture, University of Zanjan, Iran Expert, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardebili, Ardabil, Iran

2 Professor, Department of Soil Science, Faculty of Agriculture, University of Zanjan, Iran

3 Professor, Department of Remote Sensing & GIS, Faculty of Geography, University of Tehran, Iran

4 Professor, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardebili, Ardabil, Iran


The use of satellite data for rapid estimation of soil moisture (SM) and determination of environmental factors affecting it has been developed in recent years. The aim of this study was to investigate the effect of biophysical and topographic characteristics on spatial distribution of SM in the summer. For this purpose, SM was measured at 148 points in Balikhli-Chay watershed in Ardabil province and triangular method based on ASTER digital elevation model, land cover map and climatic data was applied for SM modeling. Surface biophysical properties including wetness, greenness, brightness, and land surface temperature and topographic variables (solar local incidence angle, elevation, slope, and aspect) were calculated. Model error in different months was determined using error statistics. According to the results, the average SM content in the region in July, August and September were 4.67, 6.22 and 4.66%, respectively. The lowest coefficient of determination (R2) and root mean square error (RMSE) of estimated and measured SM were related to September (0.78 and 1.44, respectively). The strongest linear relationship between SM and biophysical variables (topography) was related to July (with R2 and RMSE equal to 0.53 and 0.29, respectively). SM decreased with increasing land surface temperature and brightness, however increasing greenness, wetness, elevation and solar local incidence angle increased SM content. This study showed that the triangular model can be used to investigate the spatial distribution of SM using biophysical and surface topographic properties. Using the results of the present study can be very useful in improving the accuracy of SM modeling for various applications such as irrigation management, run-off prediction and precision agriculture.


[1]. Tóth B, Szatmári G, Takács K, Laborczi A, Makó A, Rajkai K, et al. Mapping soil hydraulic properties using random forest based pedotransfer functions and geostatistics. HYDROLOGY AND EARTH SYSTEM SCIENCES. 2019;23(6):2615-35.
[2].   McNeill S, Lilburne L, Carrick S, Webb T, Cuthill T. Pedotransfer functions for the soil water characteristics of New Zealand soils using S-map information. Geoderma. 2018;326:96-110.
[3].  Rao K, Chandra G, RAO PN. The relationship between brightness temperature and soil moisture Selection of frequency range for microwave remote sensing. International Journal of Remote Sensing. 1987;8(10):1531-45.
[4].  Qiu Y, Fu B, Wang J, Chen L. Spatial variability of soil moisture content and its relation to environmental indices in a semi-arid gully catchment of the Loess Plateau, China. Journal of Arid Environments. 2001;49(4):723-50.
[5].  Vivoni ER, Rodríguez JC, Watts CJ. On the spatiotemporal variability of soil moisture and evapotranspiration in a mountainous basin within the North American monsoon region. Water Resources Research. 2010;46(2).
[6].  Firozjaei MK, Kiavarz M, Nematollahi O, Karimpour Reihan M, Alavipanah SK. An evaluation of energy balance parameters, and the relations between topographical and biophysical characteristics using the mountainous surface energy balance algorithm for land (sebal). International Journal of Remote Sensing. 2019:1-31.
[7].  Seneviratne SI, Corti T, Davin EL, Hirschi M, Jaeger EB, Lehner I, et al. Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews. 2010;99(3-4):125-61.
[8].  Western AW, Zhou S-L, Grayson RB, McMahon TA, Blöschl G, Wilson DJJJoH. Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. 2004;286(1-4):113-34.
[9].  Vereecken H, Huisman J, Bogena H, Vanderborght J, Vrugt J, Hopmans JW. On the value of soil moisture measurements in vadose zone hydrology: A review. Water resources research. 2008;44(4).
[10].            Srivastava HS, Patel P, Sharma Y, Navalgund RR. Large-area soil moisture estimation using multi-incidence-angle RADARSAT-1 SAR data. IEEE Transactions on Geoscience and Remote Sensing. 2009;47(8):2528-35.
[11].            Silva BM, Silva SHG, Oliveira GCd, Peters PHCR, Santos WJRd, Curi N. Soil moisture assessed by digital mapping techniques and its field validation. Ciência e Agrotecnologia. 2014;38(2):140-8.
[12].            Niu C, Musa A, Liu Y. Analysis of soil moisture condition under different land uses in the arid region of Horqin sandy land, northern China. Solid Earth. 2015;6(4):1157-67.
[13].            Zucco G, Brocca L, Moramarco T, Morbidelli R. Influence of land use on soil moisture spatial–temporal variability and monitoring. Journal of hydrology. 2014;516:193-9.
[14].            Saxton KE, Rawls WJ. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil science society of America Journal. 2006;70(5):1569-78.
[15].            Sugathan N, Biju V, Renuka G. Influence of soil moisture content on surface albedo and soil thermal parameters at a tropical station. Journal of earth system science. 2014;123(5):1115-28.
[16].            Gao H, Zhang W, Chen H. An Improved Algorithm for Discriminating Soil Freezing and Thawing Using AMSR-E and AMSR2 Soil Moisture Products. Remote Sensing. 2018;10(11):1697.
[17].            Zwieback S, Colliander A, Cosh MH, Martínez-Fernándezj J, McNairn H, Starks PJ, et al. Estimating time-dependent vegetation biases in the SMAP soil moisture product. Hydrology and Earth System Sciences. 2018;22(8):4473-89.
[18].            Fathololoumi S, Vaezi AR, Alavipanah SK, Ghorbani A, Biswas A. Comparison of spectral and spatial-based approaches for mapping the local variation of soil moisture in a semi-arid mountainous area. Science of The Total Environment. 2020:138319.
[19].            Barsi JA, Schott JR, Hook SJ, Raqueno NG, Markham BL, Radocinski RG. Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing. 2014;6(11):11607-26.
[20].            Elkhrachy I. Vertical accuracy assessment for SRTM and ASTER Digital Elevation Models: A case study of Najran city, Saudi Arabia. Ain Shams Engineering Journal. 2017.
[21].            Mukherjee S, Joshi PK, Mukherjee S, Ghosh A, Garg R, Mukhopadhyay A. Evaluation of vertical accuracy of open source Digital Elevation Model (DEM). International Journal of Applied Earth Observation and Geoinformation. 2013;21:205-17.
[22].            Cooley T, Anderson G, Felde G, Hoke M, Ratkowski A, Chetwynd J, et al., editors. FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. Geoscience and Remote Sensing Symposium, 2002 IGARSS'02 2002 IEEE International; 2002: IEEE.
[23].            Zhang D, Zhou G. Estimation of soil moisture from optical and thermal remote sensing: A review. Sensors. 2016;16(8):1308.
[24].            Jung C, Lee Y, Cho Y, Kim S. A study of spatial soil moisture estimation using a multiple linear regression model and MODIS Land surface temperature data corrected by conditional merging. Remote Sensing. 2017;9(8):870.
[25].            Xu C, Qu J, Hao X, Cosh M, Prueger J, Zhu Z, et al. Downscaling of surface soil moisture retrieval by combining MODIS/Landsat and in situ measurements. Remote Sensing. 2018;10(2):210.
[26].            Srivastava PK. Satellite soil moisture: Review of theory and applications in water resources. Water Resources Management. 2017;31(10):3161-76.
[27].            Zhao W, Duan S-B, Li A, Yin G. A practical method for reducing terrain effect on land surface temperature using random forest regression. Remote sensing of environment. 2019;221:635-49.
[28].            Mekonnen D. Satellite remote sensing for soil moisture estimation: Gumara catchment. Ethiopia Satellite remote sensing for soil moisture estimation: Gumara catchment, Ethiopia. 2009.
[29].            Rahmati M, Oskouei MM, Neyshabouri MR, Walker J, Fakherifard A, Ahmadi A, et al. Soil moisture derivation using triangle method in the lighvan watershed, north western Iran. Journal of soil science and plant nutrition. 2015;15(1):167-78.
[30].            Sobrino JA, Jimenez-Munoz JC, Paolini L. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of environment. 2004;90(4):434-40.
[31].            Yu X, Guo X, Wu Z. Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing. 2014;6(10):9829-52.
[32].            Jiménez‐Muñoz JC, Sobrino JA. A generalized single‐channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research: Atmospheres. 2003;108(D22).
[33].            Jiménez-Muñoz JC, Sobrino JA, Skoković D, Mattar C, Cristóbal J. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and remote sensing letters. 2014;11(10):1840-3.
[34].            Liu Q, Liu G, Huang C, Liu S, Zhao J, editors. A tasseled cap transformation for Landsat 8 OLI TOA reflectance images. Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International; 2014: IEEE.
[35].            Liu Q, Liu G, Huang C, Xie C. Comparison of tasselled cap transformations based on the selective bands of Landsat 8 OLI TOA reflectance images. International Journal of Remote Sensing. 2015;36(2):417-41.
[36].            Huang C, Wylie B, Yang L, Homer C, Zylstra G. Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance. Int J Remote Sens. 2002;23(8):1741-8.
[37].            Holtgrave A-K, Förster M, Greifeneder F, Notarnicola C, Kleinschmit B. Estimation of Soil Moisture in Vegetation-Covered Floodplains with Sentinel-1 SAR Data Using Support Vector Regression. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 2018;86(2):85-101.
[38].            Weng B, Bi W, Zhao Z, Xu T, Yan D. Spatial and temporal variability of soil moisture based on multifractal analysis. Arabian Journal of Geosciences. 2018;11(16):469.
[39].            Firozjaei MK, Kiavarz M, Alavipanah SK, Lakes T, Qureshi S. Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-Markov chain modelling: A case of Babol city, Iran. Ecological Indicators. 2018;91:155-70.
[40].            Yu Q, Acheampong M, Pu R, Landry SM, Ji W, Dahigamuwa T. Assessing effects of urban vegetation height on land surface temperature in the city of Tampa, Florida, USA. International journal of applied earth observation and geoinformation. 2018;73:712-20.
[41].            Adugna G. A review on impact of compost on soil properties, water use and crop productivity. Academic Research Journal of Agricultural Science and Research. 2016;4(3):93-104.
[42].            Eghdami H, Azhdari G, Lebailly P, Azadi H. Impact of land use changes on soil and vegetation characteristics in Fereydan, Iran. Agriculture. 2019;9(3):58.
[43].            Toohey RC, Boll J, Brooks ES, Jones JR. Effects of land use on soil properties and hydrological processes at the point, plot, and catchment scale in volcanic soils near Turrialba, Costa Rica. Geoderma. 2018;315:138-48.
[44].            Yu B, Liu G, Liu Q, Wang X, Feng J, Huang C. Soil moisture variations at different topographic domains and land use types in the semi-arid Loess Plateau, China. Catena. 2018;165:125-32.
[45].            Katra I, Blumberg D, Lavee H, Sarah P. Topsoil moisture patterns on arid hillsides–micro-scale mapping by thermal infrared images. Journal of Hydrology. 2007;334(3-4):359-67.
[46].            Mahmoudabadi E, Karimi A, Haghnia GH, Sepehr A. Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran. Environmental monitoring and assessment. 2017;189(10):500.
[47].            Dai A, Trenberth KE, Qian T. A global dataset of Palmer Drought Severity Index for 1870–2002: Relationship with soil moisture and effects of surface warming. Journal of Hydrometeorology. 2004;5(6):1117-1130.
[48].            Zhang R, Kim S, Sharma A. A comprehensive validation of the SMAP Enhanced Level-3 Soil Moisture product using ground measurements over varied climates and landscapes. Remote sensing of environment. 2019;223:82-94.
[49].            Babaeian E, Sadeghi M, Franz TE, Jones S, Tuller M. Mapping soil moisture with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations. Remote Sens Environ. 2018;211:425-40.
[50].            El Harti A, Lhissou R, Chokmani K, Ouzemou J-e, Hassouna M, Bachaoui EM, et al. Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. International Journal of Applied Earth Observation and Geoinformation. 2016;50:64-73.
[51].            Kurc S, Benton L. Digital image-derived greenness links deep soil moisture to carbon uptake in a creosotebush-dominated shrubland. Journal of Arid Environments. 2010;74(5):585-94.
[52].            Franceschini MHD, Demattê JA, da Silva Terra F, Vicente L, Bartholomeus H, de Souza Filho CR. Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy. International Journal of Applied Earth Observation and Geoinformation. 2015;38:358-70.
[53].            Martínez-Murillo J, Hueso-González P, Ruiz-Sinoga J. Topsoil moisture mapping using geostatistical techniques under different Mediterranean climatic conditions. Science of The Total Environment. 2017;595:400-12.
[54].            Eswar R, Sekhar M, Bhattacharya B. Disaggregation of LST over India: comparative analysis of different vegetation indices. International Journal of Remote Sensing. 2016;37(5):1035-54.
[55].            Levine E, Knox R, Lawrence W. Relationships between soil properties and vegetation at the Northern Experimental Forest, Howland, Maine. Remote Sensing of Environment. 1994;47(2):231-241.
[56].            Zhou D, Zhang L, Li D, Huang D, Zhu C. Climate–vegetation control on the diurnal and seasonal variations of surface urban heat islands in China. Environmental Research Letters. 2016;11(7):074009.
[57].            Zhao D, Xu M, Liu G, Ma L, Zhang S, Xiao T, et al. Effect of vegetation type on microstructure of soil aggregates on the Loess Plateau, China. Agriculture, ecosystems & environment. 2017;242:1-8.
[58].            Kattel D, Yao T, Yang K, Tian L, Yang G, Joswiak D. Temperature lapse rate in complex mountain terrain on the southern slope of the central Himalayas. Theoretical and applied climatology. 2013;113(3-4):671-82.
[59].            Firozjaei M, Fathololuomi S, Alavipanah S, Kiavarz M, Vaezi A, Biswas A, et al. Modeling the Impact of Surface Characteristics on the Near Surface Temperature Lapse Rate. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019;42:395-9.
[60].            Jiang Y, Weng Q. Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces. GIScience & Remote Sensing. 2017;54(1):95-117.
[61].            Yang L, Wei W, Chen L, Jia F, Mo B. Spatial variations of shallow and deep soil moisture in the semi-arid Loess Plateau, China. Hydrology & Earth System Sciences. 2012;16(9).
[62].            Firozjaei MK, Kiavarz M, Nematollahi O, Karimpour Reihan M, Alavipanah SK. An evaluation of energy balance parameters, and the relations between topographical and biophysical characteristics using the mountainous surface energy balance algorithm for land (SEBAL). International Journal of Remote Sensing. 2019;40(13):5230-60.
[63].            Alexandridis TK, Cherif I, Bilas G, Almeida WG, Hartanto IM, Van Andel SJ, et al. Spatial and temporal distribution of soil moisture at the catchment scale using remotely-sensed energy fluxes. Water. 2016;8(1):32.
Volume 7, Issue 3
October 2020
Pages 563-581
  • Receive Date: 06 May 2019
  • Revise Date: 12 May 2020
  • Accept Date: 12 May 2020
  • First Publish Date: 22 September 2020