نقشه برداری رقومی کربن آلی خاک با استفاده از داده‌های کمکی و مدل‌های یادگیری ماشین در حوضه آبخیز بدر، جنوب شهرستان قروه استان کردستان

 1.McBratney, A. B., Santos, M. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1-2), 3-52. doi.org/10.1016/S0016-7061(03)00223-4.

2.Minasny, B., McBratney, A. B., Malone, B. P., & Wheeler, I. (2013). Digital mapping of soil carbon. Advances in agronomy, 118, 1-47. doi.org/10.1016/ B978-0-12-405942-9.00001-3.

3.Hengl, T., Heuvelink, G. B., Kempen, B., Leenaars, J. G., Walsh, M. G., Shepherd, K. D., ... & Tondoh, J. E. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PloS one, 10(6), e0125814. doi.org/ 10.1371/journal.pone.0125814.

4.Powers, J. S., Corre, M. D., Twine, T. E., & Veldkamp, E. (2011). Geographic bias of field observations of soil carbon stocks with tropical land-use changes precludes spatial extrapolation. Proceedings of the National Academy of Sciences, 108(15), 6318-6322. doi.org/10.1073/ pnas.1016774108.

5.Grüneberg, E., Schöning, I., Hessenmöller, D., Schulze, E. D., & Weisser, W. W. (2013). Organic layer and clay content control soil organic carbon stocks in density fractions of differently managed German beech forests. Forest Ecology and Management, 303, 1-10. DOI:10.1016/J.FORECO.2013.03.014.

6.Mayer, L. M. (1994). Relationships between mineral surfaces and organic carbon concentrations in soils and sediments. Chemical Geology, 114(3-4), 347-363. doi.org/10.1016/0009-2541(94)90063-9.

7.Bui, E., Henderson, B., & Viergever, K. (2009). Using knowledge discovery with data mining from the Australian Soil Resource Information System database to inform soil carbon mapping in Australia. Global biogeochemical cycles, 23(4). doi.org/10.1029/2009GB003506.

8.Yang, R. M., Zhang, G. L., Yang, F., Zhi, J. J., Yang, F., Liu, F., ... & Li, D. C. (2016). Precise estimation of soil organic carbon stocks in the northeast Tibetan Plateau. Scientific reports, 6(1), 21842. DOI: 10.1038/srep21842.

9.Fissore, C., Dalzell, B. J., Berhe, A. A., Voegtle, M., Evans, M., & Wu, A. (2017). Influence of topography on soil organic carbon dynamics in a Southern California grassland. Catena, 149. 140-149. DOI:10.1016/j.catena.2016.09.016.

10.Nyssen, J., Temesgen, H., Lemenih, M., Zenebe, A., Haregeweyn, N., & Haile, M. (2008). Spatial and temporal variation of soil organic carbon stocks in a lake retreat area of the Ethiopian Rift Valley. Geoderma, 146(1-2), 261-268. DOI:10.1016/j.geoderma.2008.06.007.

11.Oueslati, I., Allamano, P., Bonifacio, E., & Claps, P. (2013). Vegetation and topographic control on spatial variability of soil organic carbon. Pedosphere, 23(1), 48-58. DOI:10.1016/S1002-0160 (12)60079-4.

12.Yang, R. M., Zhang, G. L., Liu, F., Lu, Y. Y., Yang, F., Yang, F., ... & Li, D. C. (2016). Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecological indicators, 60, 870-878. DOI:10.1016/j.ecolind. 2015.08.036.

13.Hounkpatin, O. K., de Hipt, F. O., Bossa, A. Y., Welp, G., & Amelung, W. (2018). Soil organic carbon stocks and their determining factors in the Dano catchment (Southwest Burkina Faso). Catena, 166, 298-309. DOI:10.1016/ j.catena.2018.04.013.

14.Ließ, M., Schmidt, J., & Glaser, B. (2016). Improving the spatial prediction of soil organic carbon stocks in a complex tropical mountain landscape by methodological specifications in machine learning approaches. PLoS One,
11(4), e0153673. doi.org/10.1371/ journal.pone.0153673.

15.Wang, B., Waters, C., Orgill, S., Gray, J., Cowie, A., Clark, A., & Li Liu, D. (2018). High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia. Science of the Total Environment, 630, 367-378. doi.org/10.1016/j.scitotenv.2018.02.204.

16.Adhikari, K., Hartemink, A. E., Minasny, B., Bou Kheir, R., Greve, M. B., & Greve, M. H. (2014). Digital mapping of soil organic carbon contents and stocks in Denmark. PloS one,
9(8), e105519. doi.org/10.1371/journal. pone.0105519.

17.Gray, J. M., Bishop, T. F., & Yang, X. (2015). Pragmatic models for the prediction and digital mapping of soil properties in eastern Australia. Soil Research, 53(1), 24-42. DOI:10.1071/ SR13306.

18.Rossel, R. V., Brus, D. J., Lobsey, C., Shi, Z., & McLachlan, G. (2016). Baseline estimates of soil organic carbon by proximal sensing: Comparing design-based, model-assisted and model-based inference. Geoderma, 265, 152-163. DOI:10.1016/j.geoderma.2015.11.016.

19.Ottoy, S., De Vos, B., Sindayihebura, A., Hermy, M., & Van Orshoven, J. (2017). Assessing soil organic carbon stocks under current and potential forest cover using digital soil mapping
and spatial generalisation. Ecological indicators, 77, 139-150. DOI:10.1016/ j.ecolind.2017.02.010.

20.Minasny, B., Setiawan, B. I., Saptomo, S. K., & McBratney, A. B. (2018). Open digital mapping as a cost-effective method for mapping peat thickness and assessing the carbon stock of tropical peatlands. Geoderma, 313, 25-40. DOI:10.1016/j.geoderma.2017.10.018.

21.Bonfatti, B. R., Hartemink, A. E., Giasson, E., Tornquist, C. G., & Adhikari, K. (2016). Digital mapping of soil carbon in a viticultural region of Southern Brazil. Geoderma, 261, 204-221. DOI:10.1016/j.geoderma.2015.07.016.

22.Gamble, J. D., Feyereisen, G. W., Papiernik, S. K., Wente, C., & Baker, J. (2017). Regression‐Kriged Soil Organic Carbon Stock Changes in Manured Corn Silage–Alfalfa Production Systems. Soil Science Society of America Journal, 81(6), 1557-1566. DOI:10.2136/sssaj 2017.04.0138.

23.Batty, M., & Torrens, P. M. (2001). Modelling complexity: the limits to prediction. Cybergeo: European Journal of Geography. https://doi.org/10.4000/ cybergeo.1035.

24.Brungard, C. W., Boettinger, J. L., Duniway, M. C., Wills, S. A., & Edwards Jr, T. C. (2015). Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma, 239, 68-83. DOI:10.1016/j.geoderma. 2014.09.019.

25.Ballabio, C. (2009). Spatial prediction of soil properties in temperate mountain regions using support vector regression. Geoderma, 151(3-4), 338-350. doi.org/ 10.1016/j.geoderma.2009.04.022.

26.Hounkpatin, O. K., de Hipt, F. O., Bossa, A. Y., Welp, G., & Amelung, W. (2018). Soil organic carbon stocks and their determining factors in the Dano catchment (Southwest Burkina Faso). Catena, 166, 298-309. DOI:10.1016/ j.catena.2018.04.013.

27.Stevens, A., Nocita, M., Tóth, G., Montanarella, L., & van Wesemael, B. (2013). Prediction of soil organic carbon at the European scale by visible and near infrared reflectance spectroscopy. PloS one, 8(6), e66409. doi.org/10.1371/ journal.pone.0066409.

28.Vašát, R., Kodešová, R., Borůvka, L., Jakšík, O., Klement, A., & Brodský, L. (2017). Combining reflectance spectroscopy and the digital elevation model for soil oxidizable carbon estimation. Geoderma, 303, 133-142. doi.org/10.1016/j.geoderma.2017.05.018.

29.Minasny, B., McBratney, A. B., Mendonça-Santos, M. D. L., Odeh, I. O. A., & Guyon, B. (2006). Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley. Soil Research, 44(3), 233-244. DOI:10.1071/SR05136.

30.Mishra, U., Lal, R., Slater, B., Calhoun, F., Liu, D., & Van Meirvenne, M. (2009). Predicting soil organic carbon stock using profile depth distribution functions and ordinary kriging. Soil Science Society of America Journal, 73(2), 614-621. doi.org/10.2136/sssaj 2007.0410.

31.Bishop, T. F. A., McBratney, A. B., & Laslett, G. M. (1999). Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma, 91(1-2), 27-45. DOI:10.1016/S0016-7061(99)00003-8.

32.Adhikari, K., Kheir, R. B., Greve, M. B., Bøcher, P. K., Malone, B. P., Minasny, B., ... & Greve, M. H. (2013). High‐resolution 3‐D mapping of soil texture in Denmark. Soil Science Society of America Journal, 77(3), 860-876. DOI:10.2136/sssaj2012.0275.

33.Mulder, V. L., Lacoste, M., Richer-de-Forges, A. C., Martin, M. P., & Arrouays, D. (2016). National versus global modelling the 3D distribution of soil organic carbon in mainland France. Geoderma, 263, 16-34. DOI: 10.1016/ j.geoderma.2015.08.035.

34.Fontaine, S., Barot, S., Barré, P., Bdioui, N., Mary, B., & Rumpel, C. (2007). Stability of organic carbon in deep soil layers controlled by fresh carbon supply. Nature, 450(7167), 277-280. DOI:10. 1038/nature06275.

35.Meersmans, J., van Wesemael, B., De Ridder, F. A., & Van Molle, M. (2009). Modelling the three-dimensional spatial distribution of soil organic carbon (SOC) at the regional scale (Flanders, Belgium). Geoderma, 152(1-2), 43-52. DOI:10.1016/j.geoderma.2009.05.015.

36.Pan, L., & Politis, D. N. (2016). Bootstrap prediction intervals for linear, nonlinear and nonparametric autoregressions. Journal of Statistical Planning and Inference, 177, 1-27. doi.org/10.1016/j.jspi.2014.10.003.

37.Shrestha, D. L., & Solomatine, D. P. (2006). Machine learning approaches for estimation of prediction interval for the model output. Neural networks, 19(2), 225-235. DOI:10.1016/j.neunet. 2006.01.012.

38.Solomatine, D. P., & Shrestha, D. L. (2009). A novel method to estimate model uncertainty using machine learning techniques. Water Resources Research, 45(12). doi.org/10.1029/ 2008WR006839.

39.Malone, B. P., McBratney, A. B., & Minasny, B. (2011). Empirical estimates of uncertainty for mapping continuous depth functions of soil attributes. Geoderma, 160(3-4), 614-626. DOI:10.1016/j.geoderma.2010.11.013.

40.Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B., Hong, S. Y., ... & Zhang, G. L. (2014). GlobalSoilMap: Toward a fine-resolution global grid of soil properties. Advances in agronomy, 125, 93-134. DOI: 10.1016/B978-0-12-800137-0.00003-0.

41.Hariri, A. (1995). An attitude on the origin of a group of different rocks in the Qorve area. Master's thesis, Shahid Beheshti University, Tehran. [In Persian]

42.Hosseini, M. (1996). Description of Geological Map 1:100000 Quarter Corners (Map Attachment), Geological and Mineral Exploration Organization of the country. [In Persian]

43.Zinck, J. A. (1989). Physiography and soils. Lecture notes for soil students. Soil Science Division. Soil survey courses subject matter: K6 ITC, Enschede, The Netherlands.

44.Schoeneberger, P. J., Wysocki, D. A., & Benham, E. C. (Eds.). (2012). Field book for describing and sampling soils. Government Printing Office.

45.Walkley, A., & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil science, 37(1), 29-38. doi.org/10.1097/ 00010694-193401000-00003.

46.Givi, J. (1997). Qualitative assessment of land suitability for agricultural and garden plants, Soil and Water Research Institute. Technical magazine 1015, 100 p. [In Persian]

47.Valavi, R., Elith, J., Lahoz-Monfort, J. J., & Guillera-Arroita, G. (2018). blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Biorxiv, 357798. DOI:10.1111/2041-210X.13107.

48.Balan, B., Mohaghegh, S., & Ameri, S. (1995, September). State-of-the-art in permeability determination from well log data: Part 1-A comparative study, model development. In SPE Eastern Regional Meeting (pp. SPE-30978). SPE. DOI:10.2118/30979-MS.

49.Matinfar, H. R., Mghsodi, Z., Mossavi, S. R., & Jalali, M. (2021). Evaluation of Machine Learning Methods in
Digital Mapping of Soil Organic Carbon (part of Khorramabad Plain). Journal of Water and Soil Science, 24 (4), 327-342. 10.47176/jwss.24.4.8411. (In Farsi)

50.Salehi, M. H., & Khademi, H. (2017). Basics of soil mapping. Isfahan University Jihad Publications. 210 p. [In Persian]

51.Behrens, T., Förster, H., Scholten, T., Steinrücken, U., Spies, E. D., & Goldschmitt, M. (2005). Digital soil mapping using artificial neural networks. Journal of plant nutrition and soil science, 168(1), 21-33. https://doi.org/10.1002/jpln.200421414.

52.Angiulli, F. (2005, August). Fast condensed nearest neighbor rule. In Proceedings of the 22nd international conference on Machine learning (pp. 25-32).

53.Nemes, A., Rawls, W. J., & Pachepsky, Y. A. (2006). Use of the nonparametric nearest neighbor approach to estimate soil hydraulic properties. Soil Science Society of America Journal, 70(2), 327-336. DOI:10.2136/sssaj 2005.0128.

54.Zolfaghari, A. A., Tirgar Soltani, M. T., Afshart, T., & Sarmadian, F. (2013). Comparison of K-nearest neighbor and artificial neural network methods for predicting cation exchange capacity of soil. Journal of Soil Management and Sustainable Production, 3(1), 77-94. https://dor.isc.ac/dor/20.1001.1.23221267.1392.3.1.5.2. [In Persian]

55.Ayoubi, S., Taghizadeh, R., Namazi, Z., Zulfiqari, A., & Rustaee Sadrabadi, F., (2015). Comparison of k-nearest neighbor and artificial neural network methods for digital zoning of soil salinity in Afzal Ardakan well area. Journal of Water and Soil Science. https:// civilica.com/ doc/ 1201298. http://dx.doi.org/10.18869/acadpub.jstnar.20.76.59. [In Persian]

56.Scull, P., Franklin, J., & Chadwick, O. A. (2005). The application of classification tree analysis to soil type prediction in a desert landscape. Ecological modelling, 181(1), 1-15. DOI:10.1016/j.ecolmodel.2004.06.036.

57.Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes, 5(1), 3-30. https://doi.org/10.1002/ hyp.3360050103.

58.Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. http://dx. doi.org/10.1023/A:1010933404324.

59.Heung, B., Bulmer, C. E., & Schmidt, M. G. (2014). Predictive soil parent material mapping at a regional-scale: A Random Forest approach. Geoderma, 214, 141-154. DOI:10.1016/j.geoderma. 2013.09.016.

60.Wilding, L., & Drees, L. R. (1983). Spatial variability and pedology. In Developments in Soil Science
(Vol. 11, pp. 83-116). Elsevier.
ttps:// doi.org/ 10.1016/ S0166-2481 (08)70599-3.

61.Yoo, K., Amundson, R., Heimsath, A. M., & Dietrich, W. E. (2006). Spatial patterns of soil organic carbon on hillslopes: Integrating geomorphic processes and the biological C cycle. Geoderma, 130(1-2), 47-65. https:// doi. org/10.1016/j.geoderma.2005.01.008.

62.Bangroo, S. A., Najar, G. R., & Rasool, A. (2017). Effect of altitude and aspect on soil organic carbon and nitrogen stocks in the Himalayan Mawer Forest Range. Catena, 158, 63-68. DOI:10.1016/j.catena.2017.06.017.

63.Mirzaee, S., Ghorbani-Dashtaki, S., Mohammadi, J., Asadi, H., & Asadzadeh, F. (2016). Spatial variability of soil organic matter using remote sensing data. Catena, 145, 118-127. DOI:10.1016/j.catena.2016.05.023.