آمار
| تعداد نشریات | 13 |
| تعداد شمارهها | 656 |
| تعداد مقالات | 6,849 |
| تعداد مشاهده مقاله | 9,873,469 |
| تعداد دریافت فایل اصل مقاله | 9,238,311 |
Characterizing soil infiltration parameters using field/laboratory measured and remotely-sensed data | ||
| Environmental Resources Research | ||
| دوره 8، شماره 2، مهر 2020، صفحه 129-146 اصل مقاله (778.64 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22069/ijerr.2020.5277 | ||
| نویسندگان | ||
| M Rahmati* 1؛ M.R. Neyshabouri2؛ M. Mohammadi-Oskooei3؛ A. Fakheri-Fard4؛ A. Ahmadi5 | ||
| 1Associate Professor, Department of Soil Science and Engineering, Faculty of Agriculture, University of Maragheh, Maragheh, Iran. | ||
| 2Professor, Department of Soil Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran | ||
| 3Professor, Mining Engineering Faculty, Sahand University of Technology, Tabriz, Iran | ||
| 4Professor, Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran | ||
| 5Assistant Professor, Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran | ||
| چکیده | ||
| Characterizing soil infiltration parameters is time consuming and costly. We carried out the current research to predict different parameters of soil infiltration using field/laboratory measured and remotely-sensed data. The investigated parameters included infiltration rates at different time intervals and the parameters of the three well-known infiltration models. We employed soil sampling and field measurements on late spring 2012 and acquired ETM+ data for the correspondent dates. We measured several soil properties as well as infiltration. Then, we developed several pedo-transfer functions (PTFs) from the collected field/laboratory measured and remotely sensed data to predict the intended infiltration parameters. Results showed that field/laboratory measured data were able to predict soil infiltration rates and parameters of the investigated models with reasonably high accuracies (E value up to 0.961). The results also revealed that, although there was no significant and robust relationship between soil surface reflectance and the investigated parameters, the developed PTFs had reasonable accuracies (E value up to 0.634) in estimating the intended infiltration parameters using soil characteristics (moisture content, soil separates, and organic carbon) which are predictable from remotely sensed data. | ||
| کلیدواژهها | ||
| Lighvan watershed؛ Remote sensing؛ Soil infiltration؛ Pedo-transfer functions | ||
| مراجع | ||
|
Arshad, R., Sayad, G., Mazlum, M., Jafarnejadi, A. and Mohammadi Safarzadeh, V. 2010. Pedo-transfer functions application to estimate the infiltration rate of the soil using neural network and linear regression methods. Journal of Crop Improvement. 2(5), 55-62. Ayoubi, S., Shahri, A. P., Karchegani, P. M. and Sahrawat, K.L. 2011. Application of artificial neural network (ANN) to predict soil organic matter using remote sensing data in two ecosystems, InTech Open Access. Bastiaanssen, W., Menenti, M., Feddes, R. and Holtslag, A. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of hydrology. 212, 198-212. Bouma, J., 1989. Using soil survey data for quantitative land evaluation. Advances in soil science, Springer. 177-213. Clemmens, A. 1983. Infiltration equations for border irrigation models. Conference on Advances in Infiltration. Chicago, USA. Dane, J. and Hopmans, J.W. 2002. Water Retention and Storage. Methods of Soil Analysis: Part 4 Physical Methods. Methods of Soilan. 4, 671-797. Dashtaki, S., Araghi, F. and Mirlatifi, M. 2010. Soil cumulative infiltration prediction using PTFs in calcareous soil. Journal of Water and Soil Conservation. 17(3), 25-44. De Roo, A., Wesseling, C. and Ritsema, C. 1996. LISEM: A single‐event physically based hydrological and soil erosion model for drainage basins. I: theory, input and output. Hydrological Processes. 10(8), 1107-1117. Gee, G.W. and Or, D. 2002. 2.4 Particle-size analysis. Methods of soil analysis. Part 4: 255-293. Gorji, T., Tanik, A. and Sertel, E. 2015. Soil salinity prediction, monitoring and mapping using modern technologies. Procedia Earth and Planetary Science. 15, 507-512. Grossman, R. and Reinsch, T. 2002. 2.1 Bulk density and linear extensibility. Methods of Soil Analysis: Part 4 Physical Methods. Methods of Soilan.4, 201-228. Guo, J., Guo, S. and Li, T. 2011. Daily runoff simulation in Poyang Lake Intervening Basin based on remote sensing data. Procedia Environmental Sciences. 10, 2740-2747. Haghighi, F., Gorji, M., Shorafa, M., Sarmadian, F. and Mohammadi, M. 2010. Evaluation of some infiltration models and hydraulic parameters. Spanish Journal of Agricultural Research. 8(1), 210-217. He, L., Panciera, R., Tanase, M.A., Walker, J.P. and Qin, Q. 2016. Soil Moisture Retrieval in Agricultural Fields Using Adaptive Model-Based Polarimetric Decomposition of SAR Data. IEEE Transactions on Geoscience and Remote Sensing. 54(8), 4445-4460. Jemsi, S., Sayyad, G., Jafarnezhadi, A. and KashefiPour, S.M. 2013. Development of infiltration rate pedotransfer functions using artificial neural networks and multiple linear regressions for Khuzestan province in south of Iran. International Journal of Agriculture. 3(4), 766-777. Karimi, A. 2010. Determination of some parameters of infiltartion models using physical and chemical characteristics of soil with an emphasis on aggregates stability indicators. Master of Science, University of Tabriz. Kashi, H., Emamgholizadeh, S. and Ghorbani, H. 2014. Estimation of soil infiltration and cation exchange capacity based on multiple regression, ANN (RBF, MLP), and ANFIS models. Communications in Soil Science and Plant Analysis. 45(9), 1195-1213. Mahdian, M., Oskoee, R., Kamali, K., Angoshtari, H. and Kadkhodapoor, M. 2009. Developing Pedo Transfer Functions to predict infiltration rate in flood spreading stations of Iran. Research Journal of Environmental Sciences. 3(6), 697-704. Mao, L., Bralts, V. F., Pan, Y., Liu, H. and Lei, T. 2008. Methods for measuring soil infiltration: State of the art. International Journal of Agricultural and Biological Engineering. 1(1), 22-30. Mao, L., Lei, T., Li, X., Liu, H., Huang, X. and Zhang, Y. 2008. A linear source method for soil infiltrability measurement and model representations. Journal of Hydrology. 353(1), 49-58. Nelson, D. and Sommers, L.E. 1982. Total carbon, organic carbon, and organic matter. Methods of soil analysis. Part 2. Chemical and microbiological properties (methodsofsoilan2): 539-579. Nimmo, J. R. and Perkins, K.S. 2002. 2.6 Aggregate Stability and Size Distribution. Methods of Soil Analysis: Part. 4, 317-328. Parviz, L., Kholghi, M., Valizadeh, K., Araghenezhad, S. and Irannezhad, P. 2010. Performance evaluation of normalized difference vegetation index (NDVI) through monitoring cover condition. Geomatics Conference. Perroux, K. and White, I. 1988. Designs for disc permeameters. Soil Science Society of America Journal. 52(5), 1205-1215. Rahmati, M. 2016. Evaluation of ETM+ data applicability for remote sensing of the soil texture and vegetation effects on accuracy of the predictions. Journal of Water and Soil Conservation (In Persian). 23(1), 187-201. Rahmati, M. 2017. Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: a comparison between GMDH, ANN, and MLR. Journal of Hydrology, 551, 81-91. Rahmati, M. and Hamzehpour, N. 2017. Quantitative remote sensing of soil electrical conductivity using ETM+ and ground measured data. International Journal of Remote Sensing. 38(1), 123-140. Rahmati, M., Mohammadi-Oskooei, M., Neyshabouri, M., Fakheri-Fard, A., Ahmadi, A. and Walker, J., 2014. ETM+ data applicability for remote sensing of soil salinity in Lighvan watershed, Northwest of Iran. Current Opinion in Agriculture. 3(1), 10-20. Rahmati, M., Neyshabouri, M. R., Mohammadi Oskooei, M., Fakheri Fard, A., Ahmadi, A. and Mousavi, S. B., 2016. Evaluating and optimizing the parameters of soil infiltration models in Lighvan watershed (Northwest of Iran). Journal of Water and Soil Conservation (In Persian) 22(6), 167-178. Rahmati, M., Neyshabouri, M.R., Oskouei, M.M., Fakheri, A. and Ahmadi, A. 2016. Soil organic carbon prediction using remotely sensed data at Lighvan watershed, Northwest of Iran. Azarian Journal of Agriculture. 3(2), 45-49 Rahmati, M., Oskouei, M., Neyshabouri, M., Walker, J., Fakherifard, A., Ahmadi, A. and Mousavi, S. 2015. Soil moisture derivation using triangle method in the lighvan watershed, north western Iran. Journal of Soil Sience and Plant Nutrition. 15(1), 167-178. Reynolds, W., Elrick, D., Youngs, E., Amoozegar, A., Booltink, H. and Bouma, J., 2002. 3.4 Saturated and field-saturated water flow parameters. Methods of Soil Analysis. 4, 797-801. Sarmadian, F. and Taghizadeh-Mehrjardi, R. 2014. Estimation of infiltration rate and deep percolation water using feed-forward neural networks in Gorgan Province. Eurasian Journal of Soil Science. 3(1), 1-10. Scudiero, E., Skaggs, T.H. and Corwin, D.L. 2015. Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance. Remote Sensing of Environment. 169, 335-343. Sidike, A., Zhao, S. and Wen, Y., 2014. Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra. International Journal of Applied Earth Observation and Geoinformation. 26: 156-175. Yee, M.S., Walker, J.P., Monerris, A., Rüdiger, C. and Jackson, T.J. 2016. On the identification of representative in situ soil moisture monitoring stations for the validation of SMAP soil moisture products in Australia. Journal of Hydrology. 537, 367-381. | ||
|
آمار تعداد مشاهده مقاله: 378 تعداد دریافت فایل اصل مقاله: 410 |
||
سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب