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Assessment of the vegetation indices on Sentinel-2A images for predicting the soil productivity potential in Bursa, Turkey

  • Mert DedeoğluEmail author
  • Levent Başayiğit
  • Mahmut Yüksel
  • Fuat Kaya
Article
  • 80 Downloads

Abstract

Although field surveys represent an essential method for determining soil productivity, the use of remote sensing techniques has become a popular option over recent years due to its economic and practical applications. The fundamental basis of this approach is the estimation of soil productivity by using the vegetation indices as an indicator, with reference to the yield. In this study, it is aimed to estimate the productivity potential of the agriculture areas from biomass density in case of limited pedological and parcel-based data. For this purpose, relationships between the FAO Soil Productivity Rating (SPR) and different vegetation indices were investigated. The indices NDVI, RE-OSAVI, and REMCARI were used with Sentinel-2A images. Wheat was selected as an indicator plant to estimate the yield because it is the most occupied (27.47%) cultigen in the field. The study was conducted at the Karacabey State Farm with an area of 87 km2 and is located in Bursa province, Turkey. The research showed a positive relationship between SPR and 2018 yield values (r2 = 0.616). During the tillering period, the r2 for RE-OSAVI was 0.629. In the heading stage, the r2 for NDVI was 0.577. The index REMCARI provided yield estimations with low accuracy coefficient (0.216 ≤ r2 ≤ 0.258) during all vegetation periods. These findings can be interpreted as the monitoring of the land quality with multispectral satellite images via NDVI and RE-OSAVI. In this way, we could decide the time to re-definition of soil properties with land surveys for determination of soil productivity when the detection of a decrease using the indices during some vegetation periods. However, further investigations are needed in controlled trial patterns with differential reference plants, although the findings obtained from the study are promising for the use of spectral vegetation indices to prediction and/or monitoring of soil productivity. Thus, the possibilities of using spectral indices in different ecologies and different plant species can be evaluated from a broad perspective. It was also suggested that Sentinel-2A images may be used for similar studies due to their spectral capabilities with the ESA-SNAP tool.

Keywords

Canopy reflectance Multispectral images Productivity rating Growth stage 

Notes

Funding information

This study is a part of the TOVAG - 1120487 project and was supported by TUBITAK (Scientific and Technological Research Council of Turkey).

References

  1. Ahmed, G. B., Shariff, A. R. M., Balasundram, S. K., & Fikri bin Abdullah, A. (2016). Agriculture land suitability analysis evaluation based multi criteria and GIS approach. IOP Conference Series: Earth and Environmental Science, 37, 012044.  https://doi.org/10.1088/1755-1315/37/1/012044.CrossRefGoogle Scholar
  2. Al-doski, J., Mansor, S. B., & Shafri, H. Z. M. (2013). NDVI differencing and postclassification to detect vegetation changes in Halabja city, Iraq. IOSR Journal of Applied Geology and Geophysics, 1(2), 01–10.CrossRefGoogle Scholar
  3. Andrews, S. S., Karlen, D. L., & Cambardella, C. A. (2004). The soil management assessment framework. Soil Science Society of America Journal, 68(6), 1945–1962.CrossRefGoogle Scholar
  4. Askari, M. S., Cui, J., O’Rourke, S. M., & Holden, N. M. (2015). Evaluation of soil structural quality using VIS–NIR spectra. Soil and Tillage Research, 146, 108–117.  https://doi.org/10.1016/j.still.2014.03.006.CrossRefGoogle Scholar
  5. Bagheri, N., Ahmadi, H., Alavipanah, S., & Omid, M. (2012). Soil-line vegetation indices for corn nitrogen content prediction. International Agrophysics, 26(2), 103–108.CrossRefGoogle Scholar
  6. Baisden, W. T. (2006). Agricultural and forest productivity for modelling policy scenarios: evaluating approaches for New Zealand greenhouse gas mitigation. Journal of the Royal Society of New Zealand, 36(1), 1–15.  https://doi.org/10.1080/03014223.2006.9517795.CrossRefGoogle Scholar
  7. Bajracharya, R. M., Sitaula, B. K., & Sharma, S. (2013). Seasonal dynamics, slope aspect and land use effects on soil mesofauna density in the mid-hills of Nepal AU - Begum, Farida. International Journal of Biodiversity Science, Ecosystem Services & Management, 2016(9), 290–297.Google Scholar
  8. Barnes, E., Clarke, T., Richards, S., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., Thompson, T., Lascano, R. J., Li, H., & Moran, M. S. (2000). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA, 2000 (Vol. 1619).Google Scholar
  9. Bellakanji, C. A., Zribi, M., Lili-Chabaane, Z., & Mougenot, B. (2018). Forecasting of cereal yields in a semi-arid area using the simple algorithm for yield estimation (SAFY) agro-meteorological model combined with optical SPOT/HRV images. Sensors, 18(7), 2138.CrossRefGoogle Scholar
  10. Bremner, J. M., & Mulvaney, C. S. (1982). Nitrogen—Total 1. Methods of soil analysis. Part 2. Chemical and Microbiological Properties, 595-624.Google Scholar
  11. Daughtry, C. S. T., Walthall, C. L., Kim, M. S., de Colstoun, E. B., & McMurtrey, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229–239.  https://doi.org/10.1016/S0034-4257(00)00113-9.CrossRefGoogle Scholar
  12. DeFries, R., Hansen, M., & Townshend, J. (1995). Global discrimination of land cover types from metrics derived from AVHRR pathfinder data. Remote Sensing of Environment, 54(3), 209–222.  https://doi.org/10.1016/0034-4257(95)00142-5.CrossRefGoogle Scholar
  13. Dengiz, O., & Sağlam, M. (2012). Determination of land productivity index based on parametric approach using GIS technique. Eurasian Journal of Soil Science, 1, 51–57.Google Scholar
  14. Dong, T., Liu, J., Qian, B., Zhao, T., Jing, Q., Geng, X., Wang, J., Huffman, T., & Shang, J. (2016). Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data. International Journal of Applied Earth Observation and Geoinformation, 49, 63–74.  https://doi.org/10.1016/j.jag.2016.02.001.CrossRefGoogle Scholar
  15. El-Nady, M. A. (2015). Evaluation of the productivity of two soils using productivity index. Egyptian Journal of Soil Science, 55(2), 171–184.CrossRefGoogle Scholar
  16. Elsheikh, R. F. A., & Abdalla, R. (2016). Physical land suitability assessment based on FAO framework. IOSR Journal of Engineering, 12(6), 36–44.Google Scholar
  17. ESRI. (2010). ArcGIS user’s guide, http://www.esri.com.
  18. FAO. (1976). A framework for land evaluation. Food and Agriculture Organization Soils Bulletin 32, Rome. ISBN 92-5-1001 11–1.Google Scholar
  19. FAO. (1977). A framework for land evaluation. International Institute for Land Reclamation and Improvement, 22, 87.Google Scholar
  20. FAO. (1990). Macronutrient, assessment at the country level: An international study. Rome: FAO Soil Bulletin by Mikko Sillanpaa.Google Scholar
  21. Feng, W., Guo, B.-B., Wang, Z. J., He, L., Song, X., Wang, Y. H., & Guo, T. C. (2014). Measuring leaf nitrogen concentration in winter wheat using double-peak spectral reflection remote sensing data. Field Crops Research, 159, 43–52.  https://doi.org/10.1016/j.fcr.2014.01.010.CrossRefGoogle Scholar
  22. Fitzgerald, G., Rodriguez, D., & O’Leary, G. (2010). Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—the canopy chlorophyll content index (CCCI). Field Crops Research, 116(3), 318–324.  https://doi.org/10.1016/j.fcr.2010.01.010.CrossRefGoogle Scholar
  23. Garrigues, S., Allard, D., & Baret, F. (2007). Using first-and second-order variograms for characterizing landscape spatial structures from remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1823–1834.CrossRefGoogle Scholar
  24. GDAE. (1988). Survey and mapping of the Karacabey agricultural enterprise soils. General Directorate of Agricultural Enterprises Published, Ankara, 1988(8).Google Scholar
  25. González-Quiñones, V., de la Torre, A., García, M., Polo, A., & Jiménez-Ballesta, R. (2007). Application of the FAO quantitative and SINDI methods to assess the quality of different soils in Castilla-La Mancha (Spain). Environmental Geology, 53(3), 527–531.CrossRefGoogle Scholar
  26. Govaerts, B., Verhulst, N., Sayre, K. D., De Corte, P., Goudeseune, B., Lichter, K., Crossa, J. D., & Dendooven, L. (2007). Evaluating spatial within plot crop variability for different management practices with an optical sensor? Plant and Soil, 299(1), 29–42.  https://doi.org/10.1007/s11104-007-9358-6.CrossRefGoogle Scholar
  27. Gupta, M., & Sharma, S. (2008). Effect of tree plantation on soil properties, profile morphology and productivity index I. Poplar in Uttarakhand. Annals of Forestry, 16(2), 209–224.Google Scholar
  28. Gupta, R. K., Vijayan, D., & Prasad, T. S. (2003). Comparative analysis of red-edge hyperspectral indices. Advances in Space Research, 32(11), 2217–2222.  https://doi.org/10.1016/S0273-1177(03)90545-X.CrossRefGoogle Scholar
  29. Huang, S., Miao, Y., Yuan, F., Gnyp, M., Yao, Y., Cao, Q., et al. (2017). Potential of RapidEye and WorldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages. Remote Sensing, 9(3), 227.CrossRefGoogle Scholar
  30. Jackson, R. D. (1986). Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology, 24(1), 265–287.CrossRefGoogle Scholar
  31. Jay, S., Maupas, F., Bendoula, R., & Gorretta, N. (2017). Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Research, 210, 33–46.  https://doi.org/10.1016/j.fcr.2017.05.005.CrossRefGoogle Scholar
  32. Jia, L., Yu, Z., Li, F., Gnyp, M., Koppe, W., Bareth, G., et al. (2012). Nitrogen status estimation of winter wheat by using an IKONOS satellite image in the North China Plain. In D. Li & Y. Chen (Eds.), Computer and Computing Technologies in Agriculture V, Berlin, Heidelberg, 2012// 2012 (pp. 174–184). Berlin Heidelberg: Springer.Google Scholar
  33. Joshua, J. K., Anyanwu, N. C., & Ahmed, A. J. (2013). Land suitability analysis for agricultural planning using GIS and multi criteria decision analysis approach in Greater Karu Urban Area, Nasarawa State, Nigeria. African Journal of Agricultural Science Technology, 1(1), 14–23.Google Scholar
  34. Kacar, B. (2009). Soil analysis. Nobel Publication (Extended Edition II) No, 1387, 467.Google Scholar
  35. Kacar, B., & Katkat, A. (2007). Plant nutrition. Nobel Publication No. 849.Google Scholar
  36. Karlen, D., Andrews, S., & Doran, J. (2001). Soil quality: current concepts and applications. Advances in Agronomy, 74, 1–40.CrossRefGoogle Scholar
  37. Karlen, D. L., Andrews, S. S., Wienhold, B. J., & Zobeck, T. M. (2008). Soil quality assessment: past, present and future. Publications from USDA-ARS / UNL Faculty, 1203.Google Scholar
  38. Kim, M. S., Daughtry, C., Chappelle, E., McMurtrey, J., & Walthall, C. (1994). The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (A par). CNES, Proceedings of 6th International Symposium on Physical Measurements and Signatures in Remote Sensing; p. p 299-306.Google Scholar
  39. Klute, A. (1986). Water retention: laboratory methods. Methods of soil analysis: Part 1—Physical and mineralogical methods, 635–662.Google Scholar
  40. Kokaly, R. F., & Clark, R. N. (1999). Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67(3), 267–287.  https://doi.org/10.1016/S0034-4257(98)00084-4.CrossRefGoogle Scholar
  41. Korobov, R. M., & Railyan, V. Y. (1993). Canonical correlation relationships among spectral and phytometric variables for twenty winter wheat fields. Remote Sensing of Environment, 43(1), 1–10.  https://doi.org/10.1016/0034-4257(93)90059-7.CrossRefGoogle Scholar
  42. Kostrzewski, M., Waller, P., Guertin, P., Haberland, J., Colaizzi, P., Barnes, E., et al. (2003). Ground–based remote sensing of water and nitrogen stress. Transactions of the ASAE, 46(1), 29.CrossRefGoogle Scholar
  43. Lambert, M.-J., Traoré, P. C. S., Blaes, X., Baret, P., & Defourny, P. (2018). Estimating smallholder crops production at village level from Sentinel-2 time series in Mali’s cotton belt. Remote Sensing of Environment, 216, 647–657.  https://doi.org/10.1016/j.rse.2018.06.036.CrossRefGoogle Scholar
  44. Li, Z., Jin, X., Wang, J., Yang, G., Nie, C., Xu, X., & Feng, H. (2015). Estimating winter wheat (Triticum aestivum) LAI and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the PROSAIL model. International Journal of Remote Sensing, 36(10), 2634–2653.CrossRefGoogle Scholar
  45. Liaqat, M. U., Cheema, M. J. M., Huang, W., Mahmood, T., Zaman, M., & Khan, M. M. (2017). Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin. Computers and Electronics in Agriculture, 138, 39–47.  https://doi.org/10.1016/j.compag.2017.04.006.CrossRefGoogle Scholar
  46. Malczewski, J. (2006). Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. International Journal of Applied Earth Observation and Geoinformation, 8(4), 270–277.  https://doi.org/10.1016/j.jag.2006.01.003.CrossRefGoogle Scholar
  47. Me, C., Balasundram, S. K., & Hanif, A. H. M. (2017). Detecting and monitoring plant nutrient stress using remote sensing approaches: a review. Asian J Plant Science, 16, 1–8.CrossRefGoogle Scholar
  48. Mezera, J., Lukas, V., & Elbl, J. (2017). Evaluation of crop yield spatial variability in relation to variable rate application of fertilizers. MendelNet, 24(1), 2017.000.Google Scholar
  49. Min, M., & Lee, W. (2005). Determination of significant wavelengths and prediction of nitrogen content for citrus. Transactions of the ASAE, 48(2), 455–461.CrossRefGoogle Scholar
  50. Moran, M. S., Rahman, A. F., Washburne, J. C., Goodrich, D. C., Weltz, M. A., & Kustas, W. P. (1996). Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland. Agricultural and Forest Meteorology, 80(2), 87–109.  https://doi.org/10.1016/0168-1923(95)02292-9.CrossRefGoogle Scholar
  51. Mueller, L., Schindler, U., Mirschel, W., Shepherd, T. G., Ball, B. C., Helming, K., et al. (2010). Assessing the productivity function of soils. In Sustainable Agriculture, 2 (pp. 743–760). Dordrecht: Springer.Google Scholar
  52. Mueller, L., Schindler, U., Shepherd, T. G., Ball, B. C., Smolentseva, E., Hu, C., et al. (2012). A framework for assessing agricultural soil quality on a global scale. Archives of Agronomy and Soil Science, 58(1), 76–82.  https://doi.org/10.1080/03650340.2012.692877.CrossRefGoogle Scholar
  53. Nelson, D. W., & Sommers, L. (1982). Total carbon, organic carbon, and organic matter 1. Methods of soil analysis. Part 2. Chemical and microbiological properties, (methodsofsoilan2), 539-579.Google Scholar
  54. Olsen, S. R. (1982). LE: Sommers: Phosphorus. Methods of soil analysis (Eds.: AL Page, RH Miller and DR Keeney), 403.Google Scholar
  55. Oppelt, N., & Mauser, W. (2004). Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data. International Journal of Remote Sensing, 25(1), 145–159.  https://doi.org/10.1080/0143116031000115300.CrossRefGoogle Scholar
  56. Pandžić, M., Mihajlović, D., Pandžić, J., & Pfeifer, N. (2016). Assessment of the geometric QUALITY of Sentinel-2 data. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41, 489–494.CrossRefGoogle Scholar
  57. Pasqualotto, N., Delegido, J., Van Wittenberghe, S., Rinaldi, M., & Moreno, J. (2019). Multi-crop green LAI estimation with a new simple Sentinel-2 LAI index (SeLI). Sensors, 19(4), 904.  https://doi.org/10.3390/s19040904.CrossRefGoogle Scholar
  58. Pinter, P. J., Kimball, B. A., Mauncy, J. R., Hendrey, G. R., Lewin, K. F., & Nagy, J. (1994). Effects of free-air carbon dioxide enrichment on PAR absorption and conversion efficiency by cotton. Agricultural and Forest Meteorology, 70(1), 209–230.  https://doi.org/10.1016/0168-1923(94)90059-0.CrossRefGoogle Scholar
  59. Riquier, J., Bramao, D. L., & Cornet, J.-P. (1970). A new system of soil appraisal in terms of actual and potential productivity (first approximation). Food and Agriculture Organization of the United Nations, Soil Resources, Development and Conservation Service, Land and Water Development Division.Google Scholar
  60. Rogowski, A., & Wolf, J. (1994). Incorporating variability into soil map unit delineations. Soil Science Society of America Journal, 58(1), 163–174.CrossRefGoogle Scholar
  61. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107.  https://doi.org/10.1016/0034-4257(95)00186-7.CrossRefGoogle Scholar
  62. Salazar, L., Kogan, F., & Roytman, L. (2007). Use of remote sensing data for estimation of winter wheat yield in the United States. International Journal of Remote Sensing, 28(17), 3795–3811.  https://doi.org/10.1080/01431160601050395.CrossRefGoogle Scholar
  63. Sanchez, P. A., Couto, W., & Buol, S. W. (1982). The fertility capability soil classification system: interpretation, applicability and modification. Geoderma, 27(4), 283–309.CrossRefGoogle Scholar
  64. Sauer, T., Havlík, P., Schneider, U. A., Schmid, E., Kindermann, G., & Obersteiner, M. (2010). Agriculture and resource availability in a changing world: the role of irrigation. Water Resources Research, 46(6), 1–12.CrossRefGoogle Scholar
  65. Shepherd, T. (2009). Visual Soil Assessment. Volume 1. Field guide for pastoral grazing and cropping on flat to rolling country. Horizons Regional Council, Palmerston North, New Zealand, 119.Google Scholar
  66. Shou, L., Jia, L., Cui, Z., Chen, X., & Zhang, F. (2007). Using high-resolution satellite imaging to evaluate nitrogen status of winter wheat. Journal of Plant Nutrition, 30(10), 1669–1680.  https://doi.org/10.1080/01904160701615533.CrossRefGoogle Scholar
  67. Singh, R., Singh, R. S., Purohit, H. S., Verma, T. P., & Garhwal, R. S. (2016). Productivity and suitability evaluation of orange (citrus reticulata)-growing soils of hot and semi-arid region of Rajasthan (AESR 5.2). Journal of the Indian Society of Soil Science, 64(1), 46–57.CrossRefGoogle Scholar
  68. Skakun, S., Franch, B., Vermote, E., Roger, J. C., Justice, C., Masek, J., & Murphy, E. (2018). Winter wheat yield assessment using Landsat 8 and Sentinel-2 data. In IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 5964–5967).Google Scholar
  69. Soil Science Division Staff. (2017). Soil survey manual. USDA-NRCS. U.S. Gov. PRInt. Off, Washington, DC., Handbook No 18.Google Scholar
  70. Soil Survey Staff. (2004). Soil survey laboratory methods manual. Off, Washington, DC: USDA-NRCS. U.S. Gov. Print.Google Scholar
  71. Song, R., Cheng, T., Yao, X., Tian, Y., Zhu, Y., & Cao, W. (2016). Evaluation of Landsat 8 time series image stacks for predicitng yield and yield components of winter wheat. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 6300–6303).Google Scholar
  72. Storie, R. (1978). Storie index soil rating (revised). Special Publication. Div of Agricultural Sciences No. 3203. Univ of California, Berkeley, CA.Google Scholar
  73. Tekwa, I., Shehu, H., & Maunde, S. (2011). Soil nutrient status and productivity potentials of lithosols in Mubi Area, Northeastern Nigeria. Agriculture And Biology Journal Of North America, 2(6), 887–896.CrossRefGoogle Scholar
  74. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.  https://doi.org/10.1016/0034-4257(79)90013-0.CrossRefGoogle Scholar
  75. USDA. (1999). Soil Taxonomy. A basic of soil classification for making and interpreting soil survey. United States Department of Agriculture Natural Resources Conservation ServiceHandbook 436.Google Scholar
  76. Usul, M. (2010). Determination of effects of land quality parameters on wheat yield by using remote sensing and geographic information systems, case study; Altinova state farm. Ankara University, Phd Thesis (published).Google Scholar
  77. Verheye, W. H. (Ed.). (2009). Land use, land cover and soil sciences-volume IV: Land use management and case studies. EOLSS Publications.Google Scholar
  78. Verhulst, N., Govaerts, B., Sayre, K. D., Deckers, J., François, I. M., & Dendooven, L. (2009). Using NDVI and soil quality analysis to assess influence of agronomic management on within-plot spatial variability and factors limiting production. Plant and Soil, 317(1), 41–59.  https://doi.org/10.1007/s11104-008-9787-x.CrossRefGoogle Scholar
  79. Vincini, M., Frazzi, E., & D’Alessio, P. (2008). A broad-band leaf chlorophyll vegetation index at the canopy scale. Precision Agriculture, 9(5), 303–319.  https://doi.org/10.1007/s11119-008-9075-z.CrossRefGoogle Scholar
  80. Vohland, M., Ludwig, M., Thiele-Bruhn, S., & Ludwig, B. (2014). Determination of soil properties with visible to near- and mid-infrared spectroscopy: effects of spectral variable selection. Geoderma, 223-225, 88–96.  https://doi.org/10.1016/j.geoderma.2014.01.013.CrossRefGoogle Scholar
  81. Wójtowicz, M., Wójtowicz, A., & Piekarczyk, J. (2016). Application of remote sensing methods in agriculture. Communications in Biometry and Crop Science, 11(1), 31–50.Google Scholar
  82. Wu, C., Niu, Z., Tang, Q., & Huang, W. (2008). Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agricultural and Forest Meteorology, 148(8), 1230–1241.  https://doi.org/10.1016/j.agrformet.2008.03.005.CrossRefGoogle Scholar
  83. Xie, Q., Dash, J., Huang, W., Peng, D., Qin, Q., Mortimer, H., et al. (2018). Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5), 1482–1493.  https://doi.org/10.1109/JSTARS.2018.2813281.CrossRefGoogle Scholar
  84. Xue, R., Wang, C., Liu, M., Zhang, D., Li, K., & Li, N. (2019). A new method for soil health assessment based on analytic hierarchy process and meta-analysis. Science of the Total Environment, 650, 2771–2777.  https://doi.org/10.1016/j.scitotenv.2018.10.049.CrossRefGoogle Scholar
  85. Yüksel, M., Başayiğit, L., & Özaytekin, H. H. (2014). Usability of soil quality testing models in the different region of Turkey. The Scientific and Technical Research Council of Turkey, Agriculture, Forestry & Veterinary Research Grant Committee, Project No: TOVAG 112 O 487.Google Scholar
  86. Zand, F., & Matinfar, H. R. (2012). Winter wheat yield estimation base upon spectral data and ground measurement. Annals of Biological Research, 3(11), 5169–5177.Google Scholar
  87. Zhou, C., Chen, S., Zhang, Y., Zhao, J., Song, D., & Liu, D. (2018). Evaluating metal effects on the reflectance spectra of plant leaves during different seasons in post-mining areas, China. Remote Sensing, 10(8), 1211.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Agriculture Faculty, Department of Soil Science and Plant NutritionSelcuk UniversityKonyaTurkey
  2. 2.Faculty of Agricultural Sciences and Technology, Department of Soil Science and Plant NutritionIsparta University of Applied SciencesIspartaTurkey
  3. 3.Agriculture Faculty, Department of Soil Science and Plant NutritionAnkara UniversityAnkaraTurkey

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