Estimation of Soil Surface Moisture in Agricultural Lands Using Satellite Images and Remote Sensing Indicators (Case Study: Shushtar County)

Document Type : Research Paper

Authors

1 MSc in Natural Resources Engineering- Environment, Islamic Azad University, Science and Research Branch of Tehran (Khuzestan), Ahvaz, Iran

2 PhD Student in Geography and Rural Planning, Faculty of Geographical Sciences and Planning, Isfahan University, Isfahan, Iran

Abstract

Estimation of soil moisture is essential for optimal management of water and soil resources. Landsat images with appropriate spatial and temporal resolution are good tools for these studies. The purpose of this study is to estimate and zoning the soil surface moisture in agricultural lands of Shushtar County in Khuzestan province using remote sensing indicators. To do this, first 25 soil samples of agricultural lands were taken from a depth of 0-15 cm and their moisture was measured. Then, normalized vegetation difference index (NDVI), soil reflection adjustment index (SAVI), surface temperature (LST), normalized moisture difference index (NDMI), normalized agricultural differential index (NDTI) and soil moisture index Infrared short band (SMSWIR) were applied to the Landsat 8 Image. In the next step, the values ​​of these indices were transferred to SPSS software for statistical regression and the soil moisture estimation functions were obtained by multivariate linear regression. The results showed; Due to the high coefficient of determination (0.73) and the low root mean square error (1.31) in the simultaneous method (Enter Method), this model was considered suitable for estimating and zoning the surface moisture of agricultural lands in the region. According to the research results, soil surface moisture was directly related to NDVI, SAVI, NDMI, NDTI and SMSWIR indices and inversely related to LST index. Also, LST index has a better estimate of soil moisture, which indicates a significant effect of this factor on the amount of soil surface moisture.

Keywords


Adegoke, J.O., and Carleton, and A.M. (2002). Relations between Soil Moisture and Satellite Vegetation Indices in the U.S. Corn Belt. Journal of Hydrometeorology, 3(4), 395–405.
Bagheri, H., Kashani-Nejad, M., Alami, M., Ziaiifar, A. (2017). The Application of PLS Regression to Study the Relationships Between Sensory and Texture Characteristics. Iranian Food Science and Technology Research Journal, 13(4), 540-552. (In Farsi)
Bagheri, K., Bagheri, M., Hosein-Zadeh, A.A. (2019). Estimation of Soil Moisture Using Optical, Thermal and Radar Remote Sensing (Case Study: South of Tehran). Iranian Journal of Watershed Management Science and Engineering, 13(47), 63-74. (In Farsi)
Bai, X., Zhang, L., He, C., and Zhu, Y. (2020). Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China. Journal of Remote Sensing, 12(15), 2414.
Dahrazma, B., Hafezi Moghaddas, N., Hasanvand, M., and Karami, R. (2014). Investigation on the Geochemistry of Formations of Gotvand-Olya Dam Reservoir and its Influence on the Quality of Water in Reservoir. Journal of Iranian Association of Engineering Geology, 7(1–2), 29–40. (In Farsi)
Eshaghi, A., Motamedvaziri, B., Feiznia, S. (2010). Landslides Hazard Zonation Using Logistic Regression Method (Case Study: Safaroud Watershed). Geographical Journal of Territory, 6(24), 67-77. (In Farsi)
Fabre, S., Briottet, X., and Lesaignoux, A. (2015). Estimation of Soil Moisture Content from the Spectral Reflectance of Bare Soils in the 0.4–2.5 µm Domain. Journal of Sensors, 15(2), 3262–3281.
Fatemeh Pordel, F., Ebrahim, A., Azizi, Z. (2019). The Effect of Atmospheric Correction Methods on the Relationship Between Vegetation Indices and Canopy Cover (Case Study: Marjan Rangelands of Borujen). Journal of Geospatial Information Technology, 7(2), 133-153. (In Farsi)
Gao, Z., Xu, X., Wang, J., Yang, H., Huang, W., and Feng, H. (2013). A Method of Estimating Soil Moisture Based on the Linear Decomposition of Mixture Pixels. Journal of Mathematical and Computer Modelling, 3–4(58), 606–613.
Ghulam, A., Qin, Q., and Zhan, Z. (2007). Designing of the Perpendicular Drought Index. Journal of Environmental Geology, 52(6), 1045–1052.
Goetz, S. (2010). Multi-Sensor Analysis of NDVI, Surface Temperature and Biophysical Variables at a Mixed Grassland Site. Journal of Remote Sensing, 18(1), 71–94.
Hosseini, F., and Farrokhian, A. (2019). Pedotransfer Function (PTF) for Estimation Soil Moisture Using NDVI, Land Surface Temperature (LST) and Normalized Moisture (NDMI) Indices. Journal of Water and Soil Conservation, 26(4), 239–254. (In Farsi)
Jafari, M., Dinpasho, Y. (2017). Evaluation of Multiple Ridge Regression Model to Estimation of Pan Evaporation. Irrigation Sciences and Engineering, 40(1), 83-97. (In Farsi)
Johnson, R.A., Wichern, D.W. (1996). Applied Multivariate Statistical Analysis. Prentice Hall. New Delhi, India.
Khanmohammadi, F., Homaee, M., and Noroozi, A.A. (2015). Soil Moisture Estimating with NDVI and Land Surface Temperature and Normalized Moisture Index Using MODIS Images. Journal of Water and Soil Resources Conservation, 4(2), 37–45. (In Farsi)
Khazaei, S., Sarjaz, M.R., Valizadeh, E., and Ghorbani, K. (2017). Estimation of Surface Soil Moisture from Satellite Images Using Vegetation and Thermal Indices. Iranian Journal of Irrigation and Drainage, 11(2), 151–162. (In Farsi)
Koohbanani, H., and Yazdani, M. (2018). Mapping the Moisture of Surface Soil Using Landsat 8 Imagery, Case Study: Suburb of Semnan City Geography and Environmental Sustainability, 8(3), 65–77. (In Farsi)
Li, B., Ti, C., Zhao, Y., and Yan, X. (2016). Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies. Journal of Remote Sensing, 8(1), 38.
Lin, M.L., Cao, Y., Juan, C.H., Chen, C.W., Hsueh, I.C., Wang, Q.B., and Lee, Y.T. (2008). Monitoring Drought Dynamics in the Ejin Oasis Using drought Indices from Modis Data. International Geoscience and Remote Sensing Symposium, 4(1), 834–837.
Lobell, D., and Asner, G. (2002). Moisture Effects on Soil Reflectance. Soil Science Society, 66(3), 722–727.
Mehrabi, M., Hamzeh, S., Alavipanah, S. K., Kiavarz, M., & Ziaee, R. (2019). Estimating Soil Moisture Using Remotely Sensed Data and Surface Energy Balance System. Journal of Watershed Engineering and Management, 11(3), 759–770. (In Farsi)
Merdasi, G., Yazdanpanah, M., Forouzani, M., and Baradaran, M. (2018). Application of Analytical Hierarchy Process (AHP) in Analysis of Agricultural Systems: A Case Study of Shushtar County of Iran. Journal of Village and Development, 21(1), 1–23. (In Farsi)
Mesgari, E., Tavousi, T., Mahmoudi, P. (2020). Modelling Topo-Climatology and Zoning Frost Statistical Indices in Kurdistan Province. Journal of Geography and Planning, 24(72), 357-383. (In Farsi)
Mombeni, M., and Asgari, H. (2018). Monitoring, Assessment and Prediction of Spatial Changes of Land Use/Cover Using Markov Chain Model, Case Study: Shushtar, Khuzestan. Scientific Research Quarterly of Geographical Data, 27(105), 35–47.
Nasrabadi, M.A., Mokhtari, M.H., and Hakimzadeh, M.A., and Shahmoradi, S. (2020). Estimation of the Soil Moisture Using Thermal Inertia and MODIS Satellite Data Imagery: A Case Study of Mortazieh Area. Journal of Geographical Research on Desert Areas, 8(1), 55–80. (In Farsi)
Nateghi, S., Nohegar, A., Ehsani, A.H., and Bazrafshan, O. (2017). Evaluating the Vegetation Changes upon Vegetation Index by Using Remote Sensing. Range and Desert Research, 24(4), 778–790. (In Farsi)
Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.M., Tucker, C.J., and Stenseth, N.C. (2005). Using the Satellite-Derived NDVI to Assess Ecological Responses to Environmental Change. Journal of Trends in Ecology and Evolution, 20(9), 503–510.
Pourtaheri, M. (2016). Applied Statistical Analysis in Geographical Sciences. Ghoomes Publishing, Tehran. (In Farsi)
Rouse, J., Haas, R., Schell, J., and Deering, D. (1973). Monitoring Vegetation Systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite-1 Symposium, Washington, DC: NASA, 309–317.
Sánchez-Ruiz, S., Piles, M., Sánchez, N., Martínez-Fernández, J., Vall-Llossera, M., and Camps, A. (2014). Combining SMOS with Visible and Near/Shortwave/Thermal Infrared Satellite Data for High Resolution Soil Moisture Estimates. Journal of Hydrology, 516, 273–283.
Shahmoradi, S., Malamiri, H. R. G., and Amini, M. (2021). Extraction of Soil Moisture Index (TVDI) Using a Scatter Diagram Temperature/Vegetation and MODIS Images. Journal of RS and GIS for Natural Resources, 12(1), 38–62. (In Farsi)
Torkizadeh, S., and Eslami, H. (2020). An Investigation on Proper Location of Urban Waste Landfill, Case Study: Shooshtar. Journal of Water Engineering, 7(4), 267–280. (In Farsi)
Van de Griend, A.A., and Engman, E.T. (1985). Partial Area Hydrology and Remote Sensing. Journal of Hydrology, 81(3–4), 211–251.
Wang, L., and Qu, J.J. (2009). Satellite Remote Sensing Applications for Surface Soil Moisture Monitoring: A Review. Frontiers of Earth Science, 3(2), 237–247.
Wang, L., Qu, J.J., Wang, L., and Qu, J.J. (2007). NMDI: A Normalized Multi-Band Drought Index for Monitoring Soil and Vegetation Moisture with Satellite Remote Sensing. Journal of Geophysical Research Letters, 34(20), L20405.
Zhang, J., Zhou, Z., Yao, F., Yang, L., and Hao, C. (2015). Validating the Modified Perpendicular Drought Index in the North China Region Using in Situ Soil Moisture Measurement. Journal of Geoscience and Remote Sensing Letters, 12(3), 542–546.
Zhao, S., Yang, Y., Qiu, G., Qin, Q., Yao, Y., Xiong, Y., and Li, C. (2010). Remote Detection of Bare Soil Moisture Using a Surface Temperature Based Soil Evaporation Transfer Coefficient. International Journal of Applied Earth Observation and Geoinformation, 12(5), 351–358.