Investigating the Relationship between Satellite Remote Sensing Indicators (GIS) and Soil Salinity of Agricultural Lands of Abarkooh-Mehrabad Plain
محورهای موضوعی : فصلنامه علمی پژوهشی سنجش از دور راداری و نوری و سیستم اطلاعات جغرافیاییReza Sourilaki 1 , Mohammad Hassan Rahimian 2
1 - Iran
2 - Assistant Professor, Faculty Member of the National Salinity Research Center, Yazd Agricultural Research, Education and Extension Organization, Iran
کلید واژه: Soil salinity, Salinity index, Agricultural lands,
چکیده مقاله :
In this study, using linear multivariate regression, the relationship between different remote sensing indices (obtained from Landsat satellite images) and surface soil salinity in the study area in 2014 was determined. One of the notable points in the present study is that agricultural areas and rangelands are separated from each other and soil salinity classification has been done only for rangelands. It is inferred that in agricultural lands, soil salinity is a function of farm management, especially irrigation, and it is not possible to determine and model soil salinity without considering this important. Therefore, the soil salinity classification map in this study can be cited in rangeland areas. One of the most important issues that has led to a lack of significant relationship between satellite remote sensing indices and soil salinity of agricultural areas is the type of agricultural cover, which in the study area are mainly perennial pistachio trees. Soil salinity of pistachio orchards at the time of sampling, can not immediately affect the reflections made from the tree surface and computational indicators by these reflections, and this issue creates a significant relationship between soil salinity and remote sensing indicators
In this study, using linear multivariate regression, the relationship between different remote sensing indices (obtained from Landsat satellite images) and surface soil salinity in the study area in 2014 was determined. One of the notable points in the present study is that agricultural areas and rangelands are separated from each other and soil salinity classification has been done only for rangelands. It is inferred that in agricultural lands, soil salinity is a function of farm management, especially irrigation, and it is not possible to determine and model soil salinity without considering this important. Therefore, the soil salinity classification map in this study can be cited in rangeland areas. One of the most important issues that has led to a lack of significant relationship between satellite remote sensing indices and soil salinity of agricultural areas is the type of agricultural cover, which in the study area are mainly perennial pistachio trees. Soil salinity of pistachio orchards at the time of sampling, can not immediately affect the reflections made from the tree surface and computational indicators by these reflections, and this issue creates a significant relationship between soil salinity and remote sensing indicators
Abdelfattah, M., Shahid, Sh., & Othman, Y. (2009). Soil salinity mapping model developed using RS and GIS: A case study from Abu Dhabi, United Arab Emirates. European Journal of Scientific Research, 26(3), 342-351.
Ahmadi, S., & Matinfar, A. (2014). Evaluation of salinity index resulting from Landsat 8 data in identifying salinity levels. The Second National Conference on Desert with the Approach of Management of Dry and Desert Areas, Faculty of Desertology, Semnan University, November 11-20, 2014.
Amini, D., Tavakoli, M., & Rostaminya, M. (2018). Mapping spatial variability of soil salinity using remote sensing data and geostatistical analysis: A case of Shadegan, Khuzestan. Environmental Erosion Research Journal, 7(4), 24-43.
Browning, D. M., & Steele, C. M. (2013). Vegetation index differencing for broad-scale assessment of productivity under prolonged drought and sequential high rainfall conditions. Remote Sensing, 5(1), 327-341.
Fang, K., & Shen, C. (2020). Near-real-time forecast of satellite-based soil moisture using long short-term memory with an adaptive data integration kernel. Journal of Hydrometeorology, 21(3), 399-413.
Gouveia, C., Trigo, R. M., & Dacamra, C. C. (2009). Drought and vegetation stress Monitoring in Portugal using satellite data. Natural Hazards and Earth System Sciences, 9(1), 185–195.
Hadian, F., Hosseini, S. Z., & Seydhasani, M. (2014). Monitoring vegetation changes using precipitation data and satellite images in northwest of Iran. Iranian Journal of Range and Desert Research, 21(4).
Hosseini, S. Z., Kappas, M., & Propastin, P. (2011). Estimating Relationship Between Vegetation Dynamic and Precipitation in Central Iran. Toledo, Spain.
Hasanvandi, N., Landi, A., Matinfar, H. R., Taherzadeh, M. H. 2014. Preparation of Soil Surface Salinity Map Using Landsat ETM+ Satellite Digital Data in a Region South of Ahvaz. Agricultural Engineering (Scientific Journal of Agriculture), 37(1), 23-33.
Jahantigh, M. (2016). Investigation of the relationship between soil and vegetation in river banks. Journal of Ecological Conservation, 4(9).
Metternich, G. I., & Zinck, J. A. (2002). Remote Sensing of soil salinity: potentials and constraints. Remote sensing of Environment, 85,1-20. doi:10.3390/su10082826.
Mirjalili, A., Musaei Sanjeraei, M., & Zarezadeh Mehrizi, S. (2013). Changes of Vegetation Cover under Drought and Wet Periods in the Flood Spreading Site and Control Rangeland of HeratYazd Province. Journal of Range and Watershed Management, 66(1), 145-156.
Morgan, H., Hussien, H. M., Madani, A., & Nassar, T. (2022). Delineating Groundwater Potential Zones in Hyper-Arid Regions Using the Applications of Remote Sensing and GIS Modeling in the Eastern Desert, Egypt. Sustainability, 14(24), 16942.
Nouri, H., Chavoshi Borujeni, S., Alaghmand, S., Anderson, S. J., Sutton, P. C., Parvazian, S., & Beecham, S. (2018). Soil salinity mapping of urban greenery using remote sensing and proximal sensing techniques; the case of Veale Gardens within the Adelaide Parklands. Sustainability, 10(8), 2826.
Padhi, J., & Misra, R. K. (2011). Sensitivity of EM38 in determining soil water distribution in an irrigated wheat field. Soil and Tillage Research, 117, 93-102.
Rivera-Marin, D., Dash, J., & Ogutu, B. (2022). The use of remote sensing for desertification studies: A review. Journal of Arid Environments, 206, 104829.
Sharifi, J., & Akbarzadeh, M. 2012. Investigation of vegetation changes under the influence of rainfall changes in semi-steppe rangelands of Ardabil province (Case study: Arshagh rangeland research site). Journal of Rangeland and Watershed Management. Iranian Journal of Natural Resources, Winter, 65(4), 507-516
Taghizadeh Mehrjardi, R., Mahmoodi, S., Taze, M., & Sahebjalal, E. (2008). Accuracy assessment of soil salinity map in Yazd-Ardakan Plain, Central Iran, based on Landsat ETM+.
Tran, P. H., Nguyen, A. K., Liou, Y. A., Hoang, P. P., & Nguyen, H. T. (2020). Estimation of Salinity Intrusion by Using Landsat 8 OLI Data in The Mekong Delta, Vietnam.
