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Selection of Suitable Variables and Their Development

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Part of the Studies in Big Data book series (SBD, volume 72)

Abstract

Model performance depends on the variables used to represent soil–landscape relationships.

References

  1. Allbed, A., Kumar, L., & Sinha, P. (2017). Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia. Geocarto International, 6049, 1–17.Google Scholar
  2. Alrababah, M. A., & Alhamad, M. N. (2006). Land use/cover classification of arid and semi-arid Mediterranean landscapes using Landsat ETM. International Journal of Remote Sensing, 27(13), 2703–2718.CrossRefGoogle Scholar
  3. ASF, D. (2016). Alaska satellite facility distributed active archive centers Copernicus Sentinel-1A data center [online]. Alaska Satellite Facility. Retrieved November 29, 2016, from https://vertex.daac.asf.alaska.edu/#.
  4. Beltran, C., & Belmonte, A. (2001). Irrigated crop area estimation using Landsat TM imagery in La Mancha Spain. Photogrammetric Engineering and Remote Sensing, 67(10), 1177–1184.Google Scholar
  5. Bocco, M., Sayago, S., & Willington, E. (2014). Neural network and crop residue index multiband models for estimating crop residue cover from Landsat TM and ETM+ images. International Journal of Remote Sensing, 35(10), 3651–3663.CrossRefGoogle Scholar
  6. Bodily, J. (2005). Developing a digital soil survey update protocol at the golden spike national historic site. Utah State University Logan.Google Scholar
  7. Boerner, R. E. J., Morris, S. J., Sutherland, E. K., & Hutchinson, T. F. (2000). Spatial variability in soil nitrogen dynamics after prescribed burning in Ohio mixed-oak forests. Landscape Ecology, 15(5), 425–439.CrossRefGoogle Scholar
  8. Chávez, P. (1996). Image-based atmospheric corrections—revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025–1036.Google Scholar
  9. Costa-Cabral, M. C., & Burges, J. (1994). Digital Elevation Model Networks (DEMON): A model of flow over hillslopes for computation and dispersal areas specific contributing area. Water Resources Research, 30(6), 1681–1692.CrossRefGoogle Scholar
  10. Dewitte, O., Jones, A., Elbelrhiti, H., Horion, S., & Montanarella, L. (2012). Satellite remote sensing for soil mapping in Africa: An overview. Progress in Physical Geography, 36(4), 514–538.CrossRefGoogle Scholar
  11. Dobson, M. C., & Ulaby, F. T. (1981). Microwave backscatter dependence on surface roughness, soil moisture and soil texture: Part III—Soil tension. IEEE Transactions on Geoscience and Remote Sensing, 19(1), 51–61.CrossRefGoogle Scholar
  12. Du, Y., Ulaby, F. T., & Dobson, M. C. (2000). Sensitivity to soil moisture by active and passive microwave sensors. IEEE Transactions on Geoscience and Remote Sensing, 38(1), 105–114.CrossRefGoogle Scholar
  13. Emery, X. (2005). Simple and ordinary multigaussian kriging for estimating recoverable reserves. Mathematical Geology, 37(3), 295–319.zbMATHCrossRefGoogle Scholar
  14. Fairfield, J., & Leymarie, P. (1991). Drainage network from grid digital elevation models. Water Resources Research, 27(5), 709–717.CrossRefGoogle Scholar
  15. Fan, C., Zheng, B., Myint, S. W., & Aggarwal, R. (2014). Characterizing changes in cropping patterns using sequential Landsat imagery: An adaptive threshold approach and application to Phoenix Arizona. International Journal of Remote Sensing, 35(20), 7263–7278.CrossRefGoogle Scholar
  16. Finke, P. A. (2012). On digital soil assessment with models and the Pedometrics agenda. Geoderma, 171–172, 3–15.CrossRefGoogle Scholar
  17. Gessler, P. E., Moore, I. D., Mckenzie, N. J., & Ryan, P. J. (1995). Soil-landscape modelling and spatial prediction of soil attributes. International Journal of Geographical Information Systems, 9(4), 421–432.CrossRefGoogle Scholar
  18. GOI/OGDPI. (2015). Government of India/Open Government Data Platform India, transport, socio-economics, climate, agricultural, education and national statics database [online]. GOI/OGDPI. Retrieved September 20, 2015, from https://data.gov.in.
  19. Henderson, F.M., & Lewis, J. (1998). Principles and applications of imaging radar. In Manual of remote sensing (pp. 461–465). New York: Wiley.Google Scholar
  20. Heung, B., Knudby, A., & Bulmer, C. (2016, December). An overview and comparison of machine- learning techniques for classification purposes in digital soil mapping. Geoderma, 265, 62–77.Google Scholar
  21. Hijmans, R.J., Phillips, S., Leathwick, J., & Elith, J. (2016). The comprehensive R archive network package- dismo, species distribution modeling R package.Google Scholar
  22. Hirosawa, H., Komiyama, S., & Matsuzaka, Y. (1978). Cross-polarized radar backscatter from moist soil. Remote Sensing of Environment, 7(3), 211–217.CrossRefGoogle Scholar
  23. Holah, N., Baghdadi, N., Zribi, M., Bruand, A., & King, C. (2005). Potential of ASAR/ENVISAT for the characterisation of soil surface parameters over bare agricultural fields. Remote Sensing of Environment, 96, 78–86.CrossRefGoogle Scholar
  24. Hunt, G. R. (1977). Spectral signatures of particulate minerals in the visible and near infrared. Geophysics, 42(3), 501–513.CrossRefGoogle Scholar
  25. Hutchinson, T. F., Boerner, R. E. J., Iverson, L. R., Sutherland, S., & Sutherland, E. K. (1999). Landscape patterns of understory composition and richness across a moisture and nitrogen mineralization gradient in Ohio (U.S.A.) Quercus forests. Plant Ecology, 144(2), 177–189.Google Scholar
  26. Ippoliti-Ramilo, G. A., Epiphanio, J. C. N., & Shimabukuro, Y. E. (2003). Landsat-5 Thematic Mapper data for pre-planting crop area evaluation in tropical countries. International Journal of Remote Sensing, 24(7), 1521–1534.CrossRefGoogle Scholar
  27. Iverson, L. R., Dale, M. E., Scott, C. T., & Prasad, A. (1997). A GIS-derived integrated moisture index to predict forest composition and productivity of Ohio forests (U.S.A.). Landscape Ecology, 12(5), 331–348.Google Scholar
  28. Jia, K., Tian, Y., & Zhang, F. (2012). Crop classification using multi-configuration SAR data in the North China plain. International Journal of Remote Sensing, 33(1), 170–183.CrossRefGoogle Scholar
  29. Kahle, A. B., & Rowan, L. C. (1980). Evaluation of multispectral middle infrared aircraft images for lithologic mapping in the East Tintic Mountains, Utah. Geology, 8(5), 234.CrossRefGoogle Scholar
  30. Kobayashi, T., & Hirosawa, H. (1985). Measurement of radar backscatter from rough soil surfaces using linear and circular polarizations†. International Journal of Remote Sensing, 6(2), 345–352.CrossRefGoogle Scholar
  31. Liu, Y., Zeng, J., Chen, K.-S., & Li, Z. (2016). Parameter sensitivity analysis for bistatic scattering of rough surface. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 4335–4338). Beijing, China: IEEE.Google Scholar
  32. McBratney, A. B., Mendonça Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1–2), 3–52.CrossRefGoogle Scholar
  33. McKenzie, N. J., & Ryan, P. J. (1999). Spatial prediction of soil properties using environmental correlation. Geoderma, 89(1–2), 67–94.CrossRefGoogle Scholar
  34. Mellor, A., Haywood, A., Stone, C., & Jones, S. (2013). The performance of random forests in an operational setting for large area Sclerophyll forest classification. Remote Sensing, 5(6), 2838–2856.CrossRefGoogle Scholar
  35. Mishra, M. D., Patel, P., Srivastava, H. S., Patel, P. R., Shukla, A., & Shukla, A. K. (2014). Absolute radiometric calibration of FRS-1 and MRS mode of RISAT-1 Synthetic Aperture Radar (SAR) data using corner reflectors. International Journal of Advanced Engineering Research and Science, 1(6), 78–89.Google Scholar
  36. Moore, I. D., Gessler, P. E., Nielsen, G. A., & Petersen, G. A. (1993). Terrain attributes: Estimation methods and scale effects. In A. J. Jakeman, M. B. Beck, & M. J. MCalee (Eds.), Modelling change in environmental systems (pp. 189–214). Chichester: Wiley.Google Scholar
  37. Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modeling: A review of hydrological geomorphological and biological applications. Hydrological Processes, 5(1), 3–30.CrossRefGoogle Scholar
  38. Moran, C. J., & Bui, E. N. (2002). Spatial data mining for enhanced soil map modelling. International Journal of Geographical Information Science, 16(6), 533–549.CrossRefGoogle Scholar
  39. Morris, S. J., & Boerner, R. E. J. (1998). Landscape patterns of nitrogen mineralization and nitrification in southern Ohio hardwood forests. Landscape Ecology, 13(4), 215–224.CrossRefGoogle Scholar
  40. Mwaniki, M. W., Agutu, N. O., Mbaka, J. G., Ngigi, T. G., & Waithaka, E. H. (2015). Landslide scar/soil erodibility mapping using Landsat TM/ETM+ bands 7 and 3 normalised difference index: A case study of central region of Kenya. Applied Geography, 64, 108–120.CrossRefGoogle Scholar
  41. Newhall, F., & Berdanier, C. R. (1996). Calculation of soil moisture regimes from the climatic record.Google Scholar
  42. Nield, S. J., Boettinger, J. L., & Ramsey, R. D. (2007). Digitally mapping gypsic and natric soil areas using Landsat ETM data. Soil Science Society of America Journal, 71(1), 245.CrossRefGoogle Scholar
  43. Pal, M., Maity, R., Suman, M., Das, S. K., Patel, P., & Srivastava, H. S. (2017). Satellite-based probabilistic assessment of soil moisture using C-band quad-polarized RISAT1 data. IEEE Transactions on Geoscience and Remote Sensing, 55(3), 1351–1362.CrossRefGoogle Scholar
  44. Patel, P., Srivastava, H. S., & Navalgund, R. R. (2009). Use of synthetic aperture radar polarimetry to characterize wetland targets of Keoladeo National Park, Bharatpur India. Current Science, 97(4), 529–537.Google Scholar
  45. Patel, P., Srivastava, H. S., Panigrahy, S., & Parihar, J. S. (2006). Comparative evaluation of the sensitivity of multi-polarized multi-frequency SAR backscatter to plant density. International Journal of Remote Sensing, 27(2), 293–305.CrossRefGoogle Scholar
  46. Quinn, P., Beven, K., Chevallier, P., & Planchon, O. (1991). The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models. Hydrological Processes, 5(1), 59–79.CrossRefGoogle Scholar
  47. El Rakaiby, M. L., Ashmawy, M. H., Yehia, M. A., & Ayoub, A. S. (1994). In situ reflectance measurements and TM data of some sedimentary rocks with emphasis on white sandstone, southwestern Sinai, Egypt. International Journal of Remote Sensing, 15(18), 3785–3797.Google Scholar
  48. Ranson, K. J., Saatchi, S. S., & Sun, G. (1995). Boreal forest ecosystem characterization with SIR-C/XSAR. IEEE Transactions on Geoscience and Remote Sensing, 33(4), 867–876.CrossRefGoogle Scholar
  49. 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.CrossRefGoogle Scholar
  50. Seto, K. C., & Kaufmann, R. K. (2005). Using logit models to classify land cover and land-cover change from Landsat Thematic Mapper. International Journal of Remote Sensing, 26(3), 563–577.CrossRefGoogle Scholar
  51. Srivastava, H. S., Patel, P., Manchanda, M. L., & Adiga, S. (2003). Use of multiincidence angle RADARSAT-1 SAR data to incorporate the effect of surface roughness in soil moisture estimation. IEEE Transactions on Geoscience and Remote Sensing, 41(7 PART I), 1638–1640.Google Scholar
  52. Srivastava, H. S., Patel, P., & Navalgund, R. R. (2006). Incorporating soil texture in soil moisture estimation from extended low-1 beam mode RADARSAT-1 SAR data. International Journal of Remote Sensing, 27(12), 2587–2598.CrossRefGoogle Scholar
  53. Srivastava, H. S., Patel, P., Sharma, Y., & Navalgund, R. R. (2009). Large-area soil moisture estimation using multi-incidence-angle RADARSAT-1 SAR data. IEEE Transactions on Geoscience and Remote Sensing, 47(8), 2528–2535.CrossRefGoogle Scholar
  54. Tadono, T., Ishida, H., Oda, F., Naito, S., Minakawa, K., & Iwamoto, H. (2014). Precise global DEM generation by ALOS PRISM. In J. Jiang & H. Zhang (Eds.), ISPRS annals of photogrammetry, remote sensing and spatial information sciences (pp. 71–76). Suzhou, China: Copernicus Publications.Google Scholar
  55. Takaku, J., Tadono, T., & Tsutsui, K. (2014). Generation of high resolution global DSM from ALOS PRISM. In J. Jiang & H. Zhang (Eds.), ISPRS—International archives of the photogrammetry, remote sensing and spatial information sciences (pp. 243–248). Suzhou, China: Copernicus Publications.Google Scholar
  56. Tarboton, D. G. (1997). A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research, 33(2), 309–319.CrossRefGoogle Scholar
  57. TauDEM. (1991). Software package developed by Utah State University-Terrain analysis using digital elevation models.Google Scholar
  58. Le Toan, T., Beaudoin, A., Riom, J., & Guyon, D. (1992). Relating forest biomass to SAR data. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 403–411.CrossRefGoogle Scholar
  59. Ulaby, F., & Batlivala, P. (1976). Optimum radar parameters for mapping soil moisture. IEEE Transactions on Geoscience Electronics, 14(2), 81–93.CrossRefGoogle Scholar
  60. Ulaby, F. T., Bradley, G. A., & Obson, M. C. (1979). Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: Part II-vegetation covered soil. IEEE Transactions on Geoscience Electronics, 17(2), 33–40.CrossRefGoogle Scholar
  61. Ulaby, F. T., Razani, M., & Dobson, M. C. (1983). Effects of vegetation cover on the microwave radiometric sensitivity to soil moisture. IEEE Transactions on Geoscience and Remote Sensing, GE-21(1), 51–61.Google Scholar
  62. USGS Landsat-8. (2016). Product guide-using the USGS Landsat 8 Product [online]. USGS. Retrieved January 07, 2016, from https://landsat.usgs.gov/using-usgs-landsat-8-product.
  63. Vasenev, V. I., Stoorvogel, J. J., Vasenev, I. I., & Valentini, R. (2014). How to map soil organic carbon stocks in highly urbanized regions? Geoderma, 226–227(1), 103–115.CrossRefGoogle Scholar
  64. Vasques, G. M., Grunwald, S., & Myers, D. B. (2012). Associations between soil carbon and ecological landscape variables at escalating spatial scales in Florida, USA. Landscape Ecology, 27(3), 355–367.CrossRefGoogle Scholar
  65. WBG/CCKP. (2015). Climate change knowledge portal of the world bank group [online]. WBG/CCKP. Retrieved March 20, 2015, from https://sdwebx.worldbank.org/climateportal/.
  66. Xiong, X., Grunwald, S., Myers, D. B., Kim, J., Harris, W. G., & Comerford, N. B. (2014). Holistic environmental soil-landscape modeling of soil organic carbon. Environmental Modelling and Software, 57, 202–215.CrossRefGoogle Scholar
  67. Yang, S., & Huang, Y. (2002). Application of support vector machine based on time series for soil moisture and nitrate nitrogen content prediction. In D. Li & Z. Chunjiang (Eds.), IFIP advances in information and communication technology (pp. 2037–2045). Boston: Springer.Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Geomatics Section, Civil Engineering DepartmentIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Surveying and Geomatics Section, Civil Engineering DepartmentMaharishi Markandeshwar UniversityAmbalaIndia
  3. 3.Indian Institute of Remote Sensing (IIRS)DehradunIndia

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