بررسی ویژگی‌های جذبی کربن آلی خاک با روش طیف‌سنجی آزمایشگاهی در مناطق مستعد تولید ریزگرد استان خوزستان

1.Bartholomeus, H., Schaepman, M., Kooistra, L., Stevens, A., Hoogmoed, W., and Spaargaren, O. 2008. Spectral reflectance based indices for soilorganic carbon quantification. Geoderma, 145: 1-2. 28-36.

2.Baumgardner, M.F., Silva, L.F., Biehl, L.L., and Stoner, E.R. 1986. Reflectance properties of soils Advances in Agronomy, 38: 1-44.

3.CAMO, A. 1998. The Unscrambler User Manual. CAMO ASA Norway. Pp: 72-82.

4.Caudill, M. 1987. Neural networks primer, part I. AI expert, 2: 12. 46-52.5.Chang, C.W., Laird, D.A., Mausbach, M.J., and Hurburgh, C.R. 2001.
Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Sci. Soc. Amer. J. 65: 2. 480-490.

6.Clark, R.N. 1999. Spectroscopy of rocks and minerals, and principles of spectroscopy. Manual of Remote Sensing, 3: 3-

7.Clark, R.N., and Roush, T.L. 1984. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. J. Geophys. Res. Solid Earth. 89: B7. 6329-6340.

8.Crowley, J.K. 1991. Visible and near‐infrared (0.4-2.5 μm) reflectance spectra of playa evaporite minerals. J. Geophys. Res. Solid Earth. 96: B10. 16231-16240.

9.Curran, P.J., Dungan, J.L., and Peterson, D.L. 2001. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies. Remote Sensing of Environment, 76: 3. 349-359.

10.Demuth, H., and Beale, M. 1998.Neural network toolbox: For use with MATLAB, Natick, MA: The Math Works. Inc, 14-

11.Dotto, A.C., Dalmolin, R.S.D., ten Caten, A., and Grunwald, S. 2018. A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra. Geoderma, 314: 262-274.

12.Farifteh, J., Van der Meer, F., Van der Meijde, M., and Atzberger, C. 2008. Spectral characteristics of salt-affected soils: A laboratory experiment. Geoderma, 145: 3-4. 196-206.

13.Fystro, G. 2002. The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis–NIR spectroscopy and comparative methods. Plant and soil, 246: 2. 139-149.

14.Gaffey, S., McFadden, L., Nash, D., and Pieters, C. 1993. Ultraviolet, visible and near-infrared reflectance spectroscopy: Laboratory spectra of geologic materials. Remote geochemical analysis: Elemental and Mineralogical Composition, 151: 43-77.

15.He, T., Wang, J., Lin, Z., and Cheng, Y. 2009. Spectral features of soil organic matter. Geo-spatial Information Science, 12: 1. 33-40.

16.Heidarian, P., Azhdari, A., Joudaki, M., Khatooni, J.D., and Firoozjaei, S.F. 2018. Integrating Remote Sensing, GIS, and Sedimentology Techniques for Identifying Dust Storm Sources: A Case Study in Khuzestan, Iran. J. Ind. Soc. Rem. Sens. 46: 7. 1113-1124.

17.Hong, Y., Chen, S., Liu, Y., Zhang, Y., Yu, L., Chen, Y., Liu, Y., Cheng, H., and Liu, Y. 2019. Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy. Catena, 174: 104-116.

18.Huang, Z., Turner, B.J., Dury,S.J., Wallis, I.R., and Foley, W.J.2004. Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sensing of Environment, 93: 1-2. 18-29.

19.Ji, W., Adamchuk, V.I., Biswas, A., Dhawale, N.M., Sudarsan, B., Zhang, Y., Rossel, R.A.V., and Shi, Z. 2016. Assessment of soil properties in situ using a prototype portable MIR spectrometer in two agricultural fields. Biosystems Engineering, 152: 14-27.

20.Ji, W., Shi, Z., Huang, J., and Li, S. 2016. Correction: In Situ Measurement of Some Soil Properties in Paddy
Soil Using Visible and Near-Infrared Spectroscopy. PloS one,11: 7. P. e0159785.

21.Khayamim, F., Khademi, H., Stenberg, B., and Wetterlind, J. 2015. Capability of vis-NIR Spectroscopy to Predict Selected Chemical Soil Properties in Isfahan Province. JWSS-Isfahan University of Technology, 19: 72. 81-92.

22.Kokaly, R.F. 2011. PRISM: Processing routines in IDL for spectroscopic measurements (installation manual
and user's guide, version 1.0): N0: 193. P. 432.

23.Kuang, B., and Mouazen, A. 2011. Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms. Europ. J. Soil Sci. 62: 4. 629-636.

24.Le Guillou, F., Wetterlind, W., Rossel, R.V., Hicks, W., Grundy, M., and Tuomi, S. 2015. How does grinding affect the mid-infrared spectra of soil and their multivariate calibrations to texture and organic carbon? Soil Research, 53: 8. 913-921.

25.Lobell, D.B., and Asner, G.P. 2002. Moisture effects on soil reflectance. Soil Sci. Soc. Amer. J. 66: 3. 722-727.

26.Mohamed, E., Saleh, A., Belal, A., and Gad, A.A. 2018. Application of near-infrared reflectance for quantitative assessment of soil properties. The Egypt. J. Rem. Sens. Space Sci. 21: 1. 1-14.

27.Nawar, S., Buddenbaum, H., and Hill, J. 2015. Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy: a case study from Egypt. Arabi. J. Geosci. 8: 7. 5127-5140.

28.Nawar, S., Buddenbaum, H., Hill, J., Kozak, J., and Mouazen, A.M. 2016. Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil and Tillage Research, 155: 510-522.

29.Nelson, D.W., and Sommers, L.E. 1982. Total carbon, organic carbon and organic matter: P 539-579. In: A.L. Page, R.H. Miller and D.R. Keeney. Methods of soil analysis. Part 2 Chemical and Microbiological Properties, Madison, WI.

30.Nocita, M., Stevens, A., Toth, G., Panagos, P., van Wesemael, B.,
and Montanarella, L. 2014. Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biology and Biochemistry,68: 337-347.

31.Ostovari, Y., Ghorbani-Dashtaki, S., Bahrami, H.A., Abbasi, M., Dematte, J.A.M., Arthur, E., and Panagos, P. 2018. Towards prediction of soil erodibility, SOM and CaCO₃ using laboratory Vis-NIR spectra: A case study in a semi-arid region of Iran. Geoderma, 314: 102-112.

32.Powlson, D., Brookes, P., Whitmore, A., Goulding, K., and Hopkins, D. 2011. Soil organic matters. Europ. J. Soil Sci. 62: 1-62.

33.Rossel, R.V., and Behrens, T. 2010. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 158: 1-2. 46-54.

34.Rossel, R.V., Behrens, T., Ben-Dor, E., Brown, D., Demattê, J., Shepherd,K.D., Shi, Z., Stenberg, B., Stevens, A., and Adamchuk, V. 2016. A global spectral library to characterize the world's soil. Earth-Science Reviews, 155: 198-230.

35.Rossel, R.V., Cattle, S.R., Ortega,A., and Fouad, Y. 2009. In situ measurements of soil colour, mineral composition and clay content by vis–NIR spectroscopy. Geoderma,150: 3-4. 253-266.

36.Rossel, R.V., Fouad, Y., and Walter,C. 2008. Using a digital camera to measure soil organic carbon and iron contents. biosystems engineering, 100: 2. 149-159.

37.Rossel, R.V., Walvoort, D., McBratney, A., Janik, L.J., and Skjemstad, J.2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131: 1. 59-75.

38.Shi, T., Chen, Y., Liu, H., Wang, J., and Wu, G. 2014. Soil organic carbon content estimation with laboratory- based visible–near-infrared reflectance spectroscopy: Feature selection. Applied Spectroscopy, 68: 8. 831-837.

39.Smola, A.J., and Schölkopf, B.2004. A tutorial on support vector regression. Statistics and Computing, 14: 3. 199-222.

40.Tian, Y., Zhang, J., Yao, X., Cao,W. and Zhu, Y. 2013. Laboratory assessment of three quantitative methods for estimating the organic matter content of soils in China based on visible/near-infrared reflectance spectra. Geoderma, 202: 161-170.

41.Tong, P., Du, Y., Zheng, K., Wu, T., and Wang, J. 2015. Improvement of NIR model by fractional order Savitzky–Golay derivation (FOSGD) coupled with wavelength selection. Chemometrics and Intelligent Laboratory Systems,143: 40-48.

42.Udelhoven, T., Emmerling, C., and Jarmer, T. 2003. Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study. Plant and soil, 251: 2.  319-329.

43.Vapnik, V., and Vapnik, V.1998. Statistical learning theory Wiley. New York. Pp: 156-160.

44.Wang, J., Ding, J., Abulimiti, A., and Cai, L. 2018. Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China. PeerJ, 6, p.e4703.

45.Wang, J., He, T., Lv, C., Chen, Y., and Jian, W. 2010. Mapping soil organic matter based on land degradation spectral response units using Hyperion images. Inter. J. Appl. Earth Observ. Geoinfor. 12: 171-180.

46.Weng, Y., Gong, P., and Zhu, Z. 2008. Soil salt content estimation in the Yellow River delta with satellite hyperspectral data. Can. J. Rem. Sens. 34: 3. 259-270.

47.Wilding, L. 1985. Spatial variability: its documentation, accommodation and implication to soil surveys. Paper presented at the Soil spatial variability. Las Vegas NV, 30 November-1 December 1984 (pp. 166-194).

48.Xu, C., Zeng, W., Huang, J., Wu, J., and van Leeuwen, W. 2016. Prediction of soil moisture content and soil salt concentration from hyperspectral laboratory and field data. Remote Sensing, 8: 1. p. 42.

49.Xuemei, L., and Jianshe, L. 2013. Measurement of soil properties using visible and short wave-near infrared spectroscopy and multivariate calibration. Measurement, 46: 10. 3808-3814.

50.Zheng, K.Y., Zhang, X., Tong, P.J., Yao, Y., and Du, Y.P. 2015. Pretreating near infrared spectra with fractional order Savitzky–Golay differentiation (FOSGD). Chinese Chemical Letters, 26: 3. 293-296.