- Alhajj Ali, S., Tallou, A., Vivaldi, G.A., Camposeo, S., Ferrara, G., & Sanesi, G. (2024). Revitalization potential of marginal areas for sustainable rural development in the Puglia region, Southern Italy: Part I: A Review. Agronomy, 14(3), 431. https://doi.org/10.3390/agronomy14030431
- Baroudy, A.A. E., Ali, A.M., Mohamed, E.S., Moghanm, F.S., Shokr, M.S., Savin, I., & Lasaponara, R. (2020). Modeling land suitability for rice crop using remote sensing and soil quality indicators: The case study of the nile delta. Sustainability, 12(22), 9653. https://doi.org/10.3390/su12229653
- Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31. https://doi.org/10.1016/j.isprsjprs. 2016.01.011
- Dang, K.B., Burkhard, B., Windhorst, W., & Müller, F. (2019). Application of a hybrid neural-fuzzy inference system for mapping crop suitability areas and predicting rice yields. Environmental Modelling & Software, 114, 166-180. https://doi.org/10.1016/j.envsoft.2019.01.015
- Gasmi, A., Gomez, C., Chehbouni, A., Dhiba, D., & Elfil, H. (2022). Satellite multi-sensor data fusion for soil clay mapping based on the spectral index and spectral bands approaches. Remote Sensing, 14(5), 1103. https://doi.org/ 10.3390/rs14051103
- Guo, Z., Adhikari, K., Chellasamy, M., Greve, M.B., Owens, P.R., & Greve, M.H. (2019). Selection of terrain attributes and its scale dependency on soil organic carbon prediction. Geoderma, 340, 303-312. https://doi.org/ 10.1016/j.geoderma.2019.01.023
- Heung, B., Hodúl, M., & Schmidt, M.G., (2017). Comparing the use of training data derived from legacy soil pits and soil survey polygons for mapping soil classes. Geoderma, 290, 51-68. https://doi.org/10.1016/j.geoderma. 2016.12.001
- Khamoshi, S.E., Sarmadian, F., & Keshavarzi, A. (2018). Digital soil mapping using random forests model in Abyek, Qazvin province. Iranian Journal of Soil Research, 32(3), 393-402. (In Persian). https://doi.org/10.22092/ ijsr.2018.117828
- Khan, N.M., Rastoskuev, V.V., Sato, Y., & Shiozawa, S. (2005). Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 77(1-3), 96-109. https://doi.org/10.1016/j.agwat.2004.09.038
- Kidd, D., Webb, M., Malone, B., Minasny, B., & McBratney, A. (2015). Digital soil assessment of agricultural suitability, versatility and capital in Tasmania, Australia. Geoderma Regional, 6, 7-21. https://doi.org/10.1016/ j.geodrs.2015.08.005
- Kılıc, O.M., Ersayın, K., Gunal, H., Khalofah, A., & Alsubeie, M.S. (2022). Combination of fuzzy-AHP and GIS techniques in land suitability assessment for wheat (Triticum aestivum) cultivation. Saudi Journal of Biological Sciences, 29(4), 2634-2644. https://doi.org/10.1016/j.sjbs.2021.12.050
- Kim, Y.J., Nam, B.H., & Youn, H. (2019). Sinkhole detection and characterization using LiDAR-derived DEM with logistic regression. Remote Sensing, 11(13), 1592. https://doi.org/10.3390/rs11131592
- Landis, J.R., & Koch, G.G. (1977). A one-way components of variance model for categorical data. Biometrics, 671-679. https://doi.org/10.2307/2529465
- Martinez Martinez, L.J., & Muñoz, N.C. (2016). Digital elevation models to improve soil mapping in mountainous areas: case study in Colombia. Geopedology: An Integration of Geomorphology and Pedology for Soil and Landscape Studies, 377-388. https://doi.org/10.1007/978-3-319-19159-1_22
- McBratney, A.B., Santos, M.M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1-2), 3-52. https://doi.org/10.1016/S0016-7061(03)00223-4
- Mosleh, Z., Salehi, M.H., Jafari, A., Borujeni, I.E., & Mehnatkesh, A. (2016). The effectiveness of digital soil mapping to predict soil properties over low-relief areas. Environmental Monitoring and Assessment, 188, 1-13. https://doi.org/10.1007/s10661-016-5204-8
- Mousavi, S.R., Sarmadian, F., & Rahmani, A. (2020). Modelling and prediction of soil classes using boosting regression tree and random forests machine learning algorithms in some part of Qazvin plain. Iranian Journal of Soil and Water Research, 50(10), 2525-2538. (In Persian). https://doi.org/10.22059/ijswr.2019.280905.668198
- Mousavi, S.R., Sarmadian, F., Omid, M., & Bogaert, P. (2022). Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran. Measurement, 201, 111706. https://doi.org/10.1016/j.measurement.2022.111706
- Pu, R., Gong, P., & Yu, Q. (2008). Comparative analysis of EO-1 ALI and Hyperion, and Landsat ETM+ data for mapping forest crown closure and leaf area index. Sensors, 8(6), 3744-3766. https://doi.org/10.3390/s8063744
- Roell, Y.E., Beucher, A., Møller, P.G., Greve, M.B., & Greve, M.H. (2020). Comparing a random forest based prediction of winter wheat yield to historical yield potential. Agronomy, 10(3), 395. https://doi.org/10.3390/ agronomy10030395
- Roy, D.P., Kovalskyy, V., Zhang, H.K., Vermote, E.F., Yan, L., Kumar, S.S., & Egorov, A. (2016). Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote sensing of Environment, 185, 57-70. https://doi.org/10.1016/j.rse.2015.12.024
- Sarmadian, F., Teimuri Bardiani, S., Rahmani Siyalarz, S., & Sayadi, N. (2022). GIS-based land capability and suitability evaluation for irrigated agriculture (Case study: Karaj-Qazvin). Water and Soil, 36(4), 459-475. (In Persian). https://doi.org/10.22067/jsw.2022.76330.1159
- Soil Survey Staff. (2022). Keys to Soil Taxonomy, 13th edition. USDA Natural Resources Conservation Service.
- Taghizadeh-Mehrjardi, R., Nabiollahi, K., Rasoli, L., Kerry, R., & Scholten, T. (2020). Land suitability assessment and agricultural production sustainability using machine learning models. Agronomy, 10(4), 573. https://doi.org/ 10.3390/agronomy10040573
- Teng, H., Rossel, R.A.V., Shi, Z., & Behrens, T. (2018). Updating a national soil classification with spectroscopic predictions and digital soil mapping. Catena, 164, 125-134. https://doi.org/10.1016/j.catena.2018.01.015
- Waruru, B.K., Shepherd, K.D., Ndegwa, G.M., & Sila, A.M. (2016). Estimation of wet aggregation indices using soil properties and diffuse reflectance near infrared spectroscopy: An application of classification and regression tree analysis. Biosystems Engineering, 152, 148-164. https://doi.org/10.1016/j.biosystemseng.2016.08.003
|
