Different Approaches on Digital Mapping of Soil-Landscape Parameters

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


In soil-landscape parameters mapping, the implementation of geomatics-GIS, GPS, remote sensing, and DEM, suggests new alternatives. Different approaches have been applied for retrieval of soil-landscape parameters. In recent years, machine learning algorithms have received increasing attention for digital mapping.


  1. Ahmad, S., Kalra, A., & Stephen, H. (2010). Estimating soil moisture using remote sensing data: A machine learning approach. Advances in Water Resources, 33(1), 69–80.Google Scholar
  2. An, Q., & Yang, B. (2007). A multicrop identification model based on stepwise removal learning-support vector machine using remote sensing images. New Zealand Journal of Agricultural Research, 50(5), 1013–1019.CrossRefGoogle Scholar
  3. Andrade, O., Kappas, M., & Erasmi, S. (2010). Assessment of erosion hazard in Torres municipality of Laras tate (Venezuela) based on GIS. Interciencia, 35(5), 348–356.Google Scholar
  4. Bagheri Bodaghabadi, M., Martínez-Casasnovas, J., Salehi, M. H., Mohammadi, J., Esfandiarpoor Borujeni, I., Toomanian, N., et al. (2015). Digital soil mapping using artificial neural networks and terrain related attributes. Pedosphere, 25(4), 580–591.CrossRefGoogle Scholar
  5. Balkovič, J., Čemanová, G., & Kollár, J. (2007). Mapping soils using the fuzzy approach and regression-kriging case study from the Považský Inovec Mountains, Slovakia. Soil and Water Research, 2007(4), 123–134.CrossRefGoogle Scholar
  6. Bansal, S., Srivastav, S. K., Roy, P. S., & Krishnamuthy, Y. V. N. (2016). An analysis of land use and land cover dynamics and causative drivers in a thickly populated Yamuna river basin of India. Applied Ecology and Environmental Research, 14(3), 773–792.CrossRefGoogle Scholar
  7. Bauböck, R., Karpenstein-Machan, M., and Kappas, M., 2014. Computing the biomass potentials for maize and two alternative energy crops, triticale and cup plant (Silphium perfoliatum L.), with the crop model BioSTAR in the region of Hannover (Germany). Environmental Sciences Europe, 26(1), 1–12.Google Scholar
  8. Beg, M. K., Srivastav, S. K., Carranza, E. J. M., & de Smeth, J. B. (2011). High fluoride incidence in groundwater and its potential health effects in parts of Raigarh District, Chhattisgarh, India. Current Science, 100(5), 750–754.Google Scholar
  9. Bell, J. C., Grigal, D. F., & Bates, P. C. (2000). A soil terrain model for estimating spatial patterns of soil organic carbon. In I. Gallant (Ed.), Terrain analysis-principles and applications (pp. 295–310). John Wiley & Sons, New York.Google Scholar
  10. Bhakar, R., Srivastav, S. K., Garg, R. D., & Jetten, V. G. (2012). Upscaling soil-hydrologic parameters in sandy desert landscape—An input for distributed hydrological modelling. Asian Journal of Geoinformatics, 12(1), 1–10.Google Scholar
  11. Bishop, T. F. A., & McBratney, A. B. (2001). A comparison of prediction methods for the creation of field-extent soil property maps. Geoderma, 103(1–2), 149–160.Google Scholar
  12. Blahwar, B., Srivastav, S. K., & de Smeth, J. B. (2012). Use of high-resolution satellite imagery for investigating acid mine drainage from artisanal coal mining in North-Eastern India. Geocarto International, 27(3), 231–247.CrossRefGoogle Scholar
  13. 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
  14. Boloorani, A. D., Erasmi, S., & Kappas, M. (2008). Multi-source remotely sensed data combination: Projection transformation gap-fill procedure. Sensors, 8(7), 4429–4440.CrossRefGoogle Scholar
  15. Bourennane, H., King, D., & Couturier, A. (2000). Comparison of kriging with external drift and simple linear regression for predicting soil horizon thickness with different sample densities. Geoderma, 97(3–4), 255–271.Google Scholar
  16. Breiman, L. (2001). Random forest. Machine Learning, 45(1), 5–32.zbMATHCrossRefGoogle Scholar
  17. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Cole Publishing, Monterey. California, USA: Wadsworth and Brooks/Cole.Google Scholar
  18. Bui, E. N., & Moran, C. J. (2001). Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data. Geoderma, 103(1–2), 79–94.Google Scholar
  19. Bui, E. N., & Moran, C. J. (2003). A strategy to fill gaps in soil survey over large spatial extents: An example from the Murray-Darling Basin of Australia. Geoderma, 111(1–2), 21–44.CrossRefGoogle Scholar
  20. Bui, E. N., Loughhead, A., & Corner, R. (1999). Extracting soil-landscape rules from previous soil surveys. Australian Journal of Soil Research, 37(3), 495.CrossRefGoogle Scholar
  21. Burrough, P. A., Van Gaans, P. F. M., & Hootsmans, R. (1997). Continuous classification in soil survey: Spatial correlation, confusion and boundaries. Geoderma, 77(2–4), 115–135.CrossRefGoogle Scholar
  22. Campling, P., Gobin, A., & Feyen, J. (2002). Logistic modeling to spatially predict the probability of soil drainage classes. Soil Science Society of America Journal, 66(4), 1390–1401.CrossRefGoogle Scholar
  23. Carré, F., & Girard, M. C. (2002). Quantitative mapping of soil types based on regression kriging of taxonomic distances with landform and land cover attributes. Geoderma, 110(3–4), 241–263.Google Scholar
  24. Carre, F., McBratney, A. B., Mayr, T., & Montanarella, L. (2007). Digital soil assessments: Beyond DSM. Geoderma, 142(1–2), 69–79.CrossRefGoogle Scholar
  25. Chai, S.-S., Walker, J., Makarynskyy, O., Kuhn, M., Veenendaal, B., & West, G. (2010). Use of soil moisture variability in artificial neural network retrieval of soil moisture. Remote Sensing, 2(1), 166–190.Google Scholar
  26. Chanasyk, D. S., & Naeth, M. A. (1996). Field measurement of soil moisture using neutron probes. Canadian Journal of Soil Science, 76(3), 317–323.CrossRefGoogle Scholar
  27. Chaplot, V., Walter, C., & Curmi, P. (2000). Improving soil hydromorphy prediction according to DEM resolution and available pedological data. Geoderma, 97(3–4), 405–422.Google Scholar
  28. Chaudhary, S. K., Kumar, D., & Jain, M. K. (2016a). Multi-classifier fusion for land use land cover mapping in Jharia Coal Field. In Geostatistical and geospatial approaches for the characterization of natural resources in the environment (pp. 773–777). Cham: Springer International Publishing.Google Scholar
  29. Chaudhary, S. K., Kumar, D., & Jain, M. K. (2016b). Performance analysis of hyperspherical colour sharpening method for IRS satellite images. Imaging Science Journal, 64(6), 305–312.CrossRefGoogle Scholar
  30. Chen, J., Wan, S., Henebry, G., Qi, J., Sun, G., Kappas, M., et al. (2013). Dryland East Asia: Land dynamics amid social and climate change. Berlin, Boston: De Gruyter: Together with Higher Education Press.Google Scholar
  31. ChenChi, F., ChenCheng, R., & Son Nguyen, T. (2012). Investigating rice cropping practices and growing areas from MODIS data using empirical mode decomposition and support vector machines. GIScience & Remote Sensing, 49(1), 117–138.CrossRefGoogle Scholar
  32. Cialella, A., Dubayah, R., Lawrence, W., & Levine, E. (1997). Predicting soil drainage class using remotely sensed and digital elevation data. Photogrammetric Engineering and Remote Sensing, 63(2), 171–177.Google Scholar
  33. Connolly, J., & Holden, N. M. (2009). Mapping peat soils in Ireland: updating the derived Irish peat map. Irish Geography, 42(3), 343–352.Google Scholar
  34. Coopersmith, E. J., Cosh, M. H., Bell, J. E., & Boyles, R. (2016). Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation. Advances in Water Resources, 98, 122–131.CrossRefGoogle Scholar
  35. Cui, Y., Long, D., Hong, Y., Zeng, C., Zhou, J., Han, Z., et al. (2016). Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau. Journal of Hydrology, 543, 242–254.CrossRefGoogle Scholar
  36. Dashora, A., Sreenivas, B., Lohani, B., Malik, J. N., & Shah, A. A. (2006). GCP collection for CORONA satellite photographs: Issues and methodology. Journal of the Indian Society of Remote Sensing, 34(2), 153–160.CrossRefGoogle Scholar
  37. Dashora, A., Lohani, B., & Deb, K. (2013). Two-step procedure of optimisation for flight planning problem for airborne LiDAR data acquisition. International Journal of Mathematical Modelling and Numerical Optimisation, 4(4), 323.zbMATHCrossRefGoogle Scholar
  38. Deng, J., Chen, X., Du, Z., & Zhang, Y. (2011). Soil water simulation and predication using stochastic models based on LS-SVM for red soil region of China. Water Resources Management, 25(11), 2823–2836.CrossRefGoogle Scholar
  39. Deschamps, B., McNairn, H., Shang, J., & Jiao, X. (2012). Towards operational radar-only crop type classification: Comparison of a traditional decision tree with a random forest classifier. Canadian Journal of Remote Sensing, 38(1), 60–68.CrossRefGoogle Scholar
  40. Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees. Machine Learning, 40, 139–157.CrossRefGoogle Scholar
  41. Dobos, E., Micheli, E., Baumgardner, M. F., Biehl, L., & Helt, T. (2000). Use of combined digital elevation model and satellite radiometric data for regional soil mapping. Geoderma, 97(3–4), 367–391.Google Scholar
  42. Dobriyal, P., Qureshi, A., Badola, R., & Hussain, S. A. (2012). A review of the methods available for estimating soil moisture and its implications for water resource management. Journal of Hydrology, 458–459, 110–117.CrossRefGoogle Scholar
  43. 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
  44. Dobson, M. C., Ulaby, F. T., Hallikainen, M. T., & El-Rayes, M. A. (1985). Microwave dielectric behavior of wet soil-part II: Dielectric mixing models. IEEE Transactions on Geoscience and Remote Sensing, GE-23(1), 35–46.Google Scholar
  45. 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
  46. Dukes, M. D., Zotarelli, L., & Morgan, K. T. (2010). Use of irrigation technologies for vegetable crops in Florida. Horttechnology, 20(1), 133–142.CrossRefGoogle Scholar
  47. Erasmi, S., Twele, A., Ardiansyah, M., Malik, A., & Kappas, M. (2004). Mapping deforestation and land cover conversion at the rainforest margin in central Sulawesi, Indonesia. EARSeL eProceedings, 3(3), 388–397.Google Scholar
  48. Erlingsson, S., Baltzer, S., Baena, J., & Bjarnason, G. (2009). Measurement techniques for water flow. In Water in road structures (pp. 45–67). Dordrecht: Springer.Google Scholar
  49. Fang, B., Lakshmi, V., Bindlish, R., Jackson, T. J., Cosh, M., & Basara, J. (2013). Passive microwave soil moisture downscaling using vegetation index and skin surface temperature. Vadose Zone Journal, 12(3), 1–19.CrossRefGoogle Scholar
  50. Fayne, J. V., Bolten, J. D., Doyle, C. S., Fuhrmann, S., Rice, M. T., Houser, P. R., et al. (2017). Flood mapping in the lower Mekong River Basin using daily MODIS observations. International Journal of Remote Sensing, 38(6), 1737–1757.CrossRefGoogle Scholar
  51. Finke, P. A. (2012). On digital soil assessment with models and the Pedometrics agenda. Geoderma, 171–172, 3–15.CrossRefGoogle Scholar
  52. Gao, Z., Xu, X., Wang, J., Yang, H., Huang, W., & Feng, H. (2013). A method of estimating soil moisture based on the linear decomposition of mixture pixels. Mathematical and Computer Modelling, 58(3–4), 606–613.CrossRefGoogle Scholar
  53. Gens, R. (2000). The influence of input parameters on SAR interferometric processing and its implication on the calibration of SAR interferometric data. International Journal of Remote Sensing, 21(8), 1767–1771.CrossRefGoogle Scholar
  54. Gens, R. (2003). Two-dimensional phase unwrapping for radar interferometry: Developments and new challenges. International Journal of Remote Sensing, 24(4), 703–710.CrossRefGoogle Scholar
  55. Gens, R. (2008). Oceanographic applications of SAR remote sensing. GIScience & Remote Sensing, 45(3), 275–305.CrossRefGoogle Scholar
  56. 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
  57. Ghosh, S., & Lohani, B. (2013). Mining lidar data with spatial clustering algorithms. International Journal of Remote Sensing, 34(14), 5119–5135.CrossRefGoogle Scholar
  58. Giasson, E., Clarke, R. T., Inda Junior, A. V., Merten, G. H., & Tornquist, C. G. (2006). Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil. Scientia Agricola, 63(3), 262–268.CrossRefGoogle Scholar
  59. Giraldo, M. A., Bosch, D., Madden, M., Usery, L., & Kvien, C. (2008). Landscape complexity and soil moisture variation in south Georgia, USA, for remote sensing applications. Journal of Hydrology, 357(3–4), 405–420.CrossRefGoogle Scholar
  60. Grimm, R., Behrens, T., Märker, M., & Elsenbeer, H. (2008). Soil organic carbon concentrations and stocks on Barro Colorado Island—Digital soil mapping using random forests analysis. Geoderma, 146(1–2), 102–113.CrossRefGoogle Scholar
  61. Hallikainen, M. T., Ulaby, F. T., Dobson, M. C., El-Rayes, M. A., & Wu, L.-K. (1985). Microwave dielectric behavior of wet soil-part I: Empirical models and experimental observations. IEEE Transactions on Geoscience and Remote Sensing, GE-23(1), 25–34.Google Scholar
  62. Hashemi, H., Nordin, M., Lakshmi, V., Huffman, G. J., & Knight, R. (2017). Bias correction of long-term satellite monthly precipitation product (TRMM 3B43) over the conterminous United States. Journal of Hydrometeorology, 18(9), 2491–2509.CrossRefGoogle Scholar
  63. Hengl, T., Toomanian, N., Reuter, H. I., & Malakouti, M. J. (2007). Methods to interpolate soil categorical variables from profile observations: Lessons from Iran. Geoderma, 140(4), 417–427.CrossRefGoogle Scholar
  64. Hong, S., Lakshmi, V., Small, E. E., Chen, F., Tewari, M., & Manning, K. W. (2009). Effects of vegetation and soil moisture on the simulated land surface processes from the coupled WRF/Noah model. Journal of Geophysical Research, 114(D18), D18118.CrossRefGoogle Scholar
  65. Hong, S., Lakshmi, V., Small, E. E., & Chen, F. (2011). The influence of the land surface on hydrometeorology and ecology : new advances from modeling and satellite remote sensing. Hydrology Research, 42.2(3), 95–113.Google Scholar
  66. Hsieh, C.-Y. (2001). Microwave backscattering model for a bare soil field. Electromagnetics, 21(3), 259–273.CrossRefGoogle Scholar
  67. Hulley, G. C., Hook, S. J., & Baldridge, A. M. (2010). Investigating the effects of soil moisture on thermal infrared land surface temperature and emissivity using satellite retrievals and laboratory measurements. Remote Sensing of Environment, 114(7), 1480–1493.CrossRefGoogle Scholar
  68. 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
  69. 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
  70. Jackson, T. J. (1993). Measuring surface soil moisture using passive microwave remote sensing. Hydrological Processes, 7(2), 139–152.CrossRefGoogle Scholar
  71. Jafari, A., Ayoubi, S., Khademi, H., Finke, P. A., & Toomanian, N. (2013). Selection of a taxonomic level for soil mapping using diversity and map purity indices: A case study from an Iranian arid region. Geomorphology, 201, 86–97.CrossRefGoogle Scholar
  72. Jafari, A., Finke, P. A., Vande Wauw, J., Ayoubi, S., & Khademi, H. (2012). Spatial prediction of USDA-great soil groups in the arid Zarand region, Iran: Comparing logistic regression approaches to predict diagnostic horizons and soil types. European Journal of Soil Science, 63(2), 284–298.CrossRefGoogle Scholar
  73. Jenny, H. (1941). Factors of soil formation. A system of quantitative pedology. McGraw-Hill Book Company. New York.Google Scholar
  74. Jeyaseelan, A. T., Roy, P. S., & Young, S. S. (2007). Persistent changes in NDVI between 1982 and 2003 over India using AVHRR GIMMS (Global Inventory Modeling and Mapping Studies) data. International Journal of Remote Sensing, 28(21), 4927–4946.CrossRefGoogle Scholar
  75. Kale, M. P., Chavan, M., Pardeshi, S., Joshi, C., Verma, P. A., Roy, P. S., et al. (2016). Land-use and land-cover change in Western Ghats of India. Environmental Monitoring and Assessment, 188(7).Google Scholar
  76. Kappas, M. (2013). Estimation of global bioenergy potentials and their contribution to the world’s future energy demand—a short review. In H. Ruppert, M. Kappas, & J. Ibendorf (Eds.), Sustainable bioenergy production—An integrated approach (pp. 75–95). Dordrecht: Springer Netherlands.Google Scholar
  77. Kempen, B., Brus, D. J., & Heuvelink, G. B. M. (2012). Soil type mapping using the generalised linear geostatistical model: A case study in a Dutch cultivated peatland. Geoderma, 189–190, 540–553.CrossRefGoogle Scholar
  78. Khanna, S., Palacios-Orueta, A., Whiting, M. L., Ustin, S. L., Riaño, D., & Litago, J. (2007). Development of angle indexes for soil moisture estimation, dry matter detection and land-cover discrimination. Remote Sensing of Environment, 109(2), 154–165.CrossRefGoogle Scholar
  79. Kim, J., Grunwald, S., Rivero, R. G., & Robbins, R. (2012). Multi-scale modeling of soil series using remote sensing in a wetland ecosystem. Soil Science Society of America Journal, 76(6), 2327.CrossRefGoogle Scholar
  80. Korolyuk, T. V., & Shcherbenko, H. V. (2007). Compiling soil maps on the basis of remotely-sensed data digital processing: soil interpretation. International Journal of Remote Sensing, 15(7), 1379–1400.Google Scholar
  81. Kothapalli Venkata, R., Poloju, S., Mullapudi Venkata Rama, S. S., Gogineni, A., Prabir Kumar, D., Allakki Venkata, R., et al. (2017). Multi-incidence angle RISAT-1 Hybrid Polarimetric SAR data for large area mapping of maize crop—A case study in Khagaria district, Bihar, India. International Journal of Remote Sensing, 38(20), 5487–5501.CrossRefGoogle Scholar
  82. Kovačević, M., Bajat, B., & Gajić, B. (2010). Soil type classification and estimation of soil properties using support vector machines. Geoderma, 154(3–4), 340–347.Google Scholar
  83. Kravchenko, A. N. (2008). Mapping of soil drainage classes using topographical data and soil electrical conductivity. Handbook of Agricultural Geophysics (1), 255–261.Google Scholar
  84. Krishan, G., Srivastav, S. K., Kumar, S., Saha, S. K., & Dadhwal, V. K. (2009). Quantifying the underestimation of soil organic carbon by the Walkley and Black technique—Examples from Himalayan and Central Indian soils. Current Science, 96(8), 1133–1136.Google Scholar
  85. Kumar, D. (2015a). Fair allocation of multi-resources for multi-class users in cloud computing. In International Conference on Computing, Communication and Automation (ICCCA2015) (pp. 661–663). Greater Noida, India: IEEE.Google Scholar
  86. A survey on resource allocation techniques in cloud computing. In International Conference on Computing, Communication and Automation (ICCCA2015) (pp. 655–660). Greater Noida, India: IEEE.Google Scholar
  87. Kumar, D., Deb, D., & Mamgain, R. (2015a). Analysis of different multiprocessor architectures for radar signal processing with performance metrics. In IEEE Radar Conference (pp. 289–294). Johannesburg, South Africa: IEEE.Google Scholar
  88. Kumar, P., Gupta, D. K., Mishra, V. N., & Prasad, R. (2015b). Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data. International Journal of Remote Sensing, 36(6), 1604–1617.CrossRefGoogle Scholar
  89. Kumar, P., Prasad, R., Choudhary, A., Mishra, V. N., Gupta, D. K., & Srivastava, P. K. (2016). A statistical significance of differences in classification accuracy of crop types using different classification algorithms. Geocarto International, 6049, 1–19.CrossRefGoogle Scholar
  90. Kumar, B., Patra, K. C., & Lakshmi, V. (2017). Error in digital network and basin area delineation using D8 method: A case study in a sub-basin of the Ganga. Journal of Geological Society of India, 89, 65–70.Google Scholar
  91. Lagacherie, P., Legros, J. P., & Burfough, P. A. (1995). A soil survey procedure using the knowledge of soil pattern established on a previously mapped reference area. Geoderma, 65(3–4), 283–301.CrossRefGoogle Scholar
  92. Lakshmi, V. (2013). Remote sensing of soil moisture. ISRN Soil Science, 1–33.CrossRefGoogle Scholar
  93. Liang, X., Lettenmaier, D. P., Wood, E. F., & Burges, S. J. (1994). A simple hydrologically based model of land surface water and energy fluxes for general circulation models. Journal of Geophysical Research, 99(D7), 14415.CrossRefGoogle Scholar
  94. Liu, H., Xie, D., & Wu, W. (2008). Soil water content forecasting by ANN and SVM hybrid architecture. Environmental Monitoring and Assessment, 143(1–3), 187–193.CrossRefGoogle Scholar
  95. Liu, D., Yu, Z., & Lü, H. (2010). Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction. Water Science and Engineering, 3(4), 361–377.Google Scholar
  96. Loew, A. (2008). Impact of surface heterogeneity on surface soil moisture retrievals from passive microwave data at the regional scale: The Upper Danube case. Remote Sensing of Environment, 112(1), 231–248.CrossRefGoogle Scholar
  97. Lohani, B., & Singh, R. (2008). Effect of data density, scan angle, and flying height on the accuracy of building extraction using LiDAR data. Geocarto International, 23(2), 81–94.CrossRefGoogle Scholar
  98. Lohani, B., Mason, D. C., Scott, T. R., & Sreenivas, B. (2006). Extraction of tidal channel networks from aerial photographs alone and combined with laser altimetry. International Journal of Remote Sensing, 27(1), 5–25.CrossRefGoogle Scholar
  99. Lunt, I. A., Hubbard, S. S., & Rubin, Y. (2005). Soil moisture content estimation using ground-penetrating radar reflection data. Journal of Hydrology, 307(1–4), 254–269.CrossRefGoogle Scholar
  100. Mallick, K., Bhattacharya, B. K., & Patel, N. K. (2009). Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI. Agricultural and Forest Meteorology, 149(8), 1327–1342.CrossRefGoogle Scholar
  101. Marchetti, A., Piccini, C., Santucci, S., Chiuchiarelli, I., & Francaviglia, R. (2011). Simulation of soil types in Teramo province (Central Italy) with terrain parameters and remote sensing data. CATENA, 85(3), 267–273.CrossRefGoogle Scholar
  102. Mathur, A., & Foody, G. M. (2008). Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, 29(8), 2227–2240.CrossRefGoogle Scholar
  103. McBratney, A. B., Mendonça Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1–2), 3–52.CrossRefGoogle Scholar
  104. McKenzie, N. J., & Ryan, P. J. (1999). Spatial prediction of soil properties using environmental correlation. Geoderma, 89(1–2), 67–94.CrossRefGoogle Scholar
  105. Meesters, A. G. C. A., DeJeu, R. A. M., & Owe, M. (2005). Analytical derivation of the vegetation optical depth from the microwave polarization difference index. IEEE Geoscience and Remote Sensing Letters, 2(2), 121–123.CrossRefGoogle Scholar
  106. 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
  107. Mladenova, I., Lakshmi, V., Walker, J. P., Panciera, R., Wagner, W., & Doubkova, M. (2010). Validation of the ASAR global monitoring mode soil moisture product using the NAFE ’ 05 data set. IEEE Transactions on Geoscience and Remote Sensing, 48(6), 2498–2508.Google Scholar
  108. Mondal, M. S., Sharma, N., Garg, P. K., & Kappas, M. (2016). Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. Egyptian Journal of Remote Sensing and Space Science, 19(2), 259–272.CrossRefGoogle Scholar
  109. Moonjun, R., Farshad, A., Shrestha, D. P., & Vaiphasa, C. (2010). Artificial neural network and decision tree in predictive soil mapping of Hoi Num Rin sub-watershed, Thailand. In Digital soil mapping (pp. 151–164). Dordrecht: Springer Netherlands.Google Scholar
  110. 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
  111. 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
  112. Muñoz-Carpena, R. (2015). Field devices for monitoring soil water content [online]. EDIS Publication BUL343. Retrieved November 21, 2015 from
  113. Murthy, C. S., Raju, P. V., & Badrinath, K. V. S. (2003). Classification of wheat crop with multi-temporal images: Performance of maximum likelihood and artificial neural networks. International Journal of Remote Sensing, 24(23), 4871–4890.CrossRefGoogle Scholar
  114. Noi, P., Kappas, M., & Degener, J. (2016). Estimating daily maximum and minimum land air surface temperature using MODIS land surface temperature data and ground truth data in Northern Vietnam. Remote Sensing, 8(12), 1002.CrossRefGoogle Scholar
  115. Odeh, I. O. A., & McBratney, A. B. (2000). Using AVHRR images for spatial prediction of clay content in the lower Namoi Valley of eastern Australia. Geoderma, 97(3–4), 237–254.Google Scholar
  116. Ohlmacher, G. C., & Davis, J. C. (2003). Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology, 69(3–4), 331–343.CrossRefGoogle Scholar
  117. Owe, M., De Jeu, R., & Walker, J. (2001). A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Transactions on Geoscience and Remote Sensing, 39(8), 1643–1654.CrossRefGoogle Scholar
  118. Pahlavan Rad, M. R., Toomanian, N., Khormali, F., Brungard, C. W., Komaki, C. B., & Bogaert, P. (2014). Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran. Geoderma, 232–234, 97–106.CrossRefGoogle Scholar
  119. Pal, M. (2008). Ensemble of support vector machines for land cover classification. International Journal of Remote Sensing, 29(10), 3043–3049.CrossRefGoogle Scholar
  120. Pal, M., & Mather, P. M. (2003a). Support vector classifiers for land cover classification. In Map India Conference (pp. 1–11). Aligarh, Uttar Pradesh.Google Scholar
  121. Pal, M., & Mather, P. M. (2003b). An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86(4), 554–565.CrossRefGoogle Scholar
  122. Pal, M., & Mather, P. M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5), 1007–1011.CrossRefGoogle Scholar
  123. Palacios-Orueta, A., Khanna, S., & Litago, J. (2005). Assessment of NDVI and NDWI spectral indices using MODIS time series analysis and development of a new spectral index based on MODIS shortwave infrared bands. In 1st International Conference of Remote Sensing and Geoinformation Processing. Trier, Germany.Google Scholar
  124. Paloscia, S., Pampaloni, P., Pettinato, S., & Santi, E. (2008). A comparison of algorithms for retrieving soil moisture from ENVIS AT/AS AR images. IEEE Transactions on Geoscience and Remote Sensing, 46(10), 3274–3284.CrossRefGoogle Scholar
  125. Panagos, P., Jones, A., Bosco, C., & Senthil Kumar, P. S. (2011). European digital archive on soil maps (EuDASM): preserving important soil data for public free access. International Journal of Digital Earth, 4(5), 434–443.Google Scholar
  126. Pandey, J., Kumar, D., Mishra, R. K., Mohalik, N. K., Khalkho, A., & Singh, V. K. (2013). Application of thermography technique for assessment and monitoring of coal mine fire: A special reference to Jharia Coal Field, Jharkhand, India. International Journal of Advanced Remote Sensing and GIS, 2(1), 138–147.Google Scholar
  127. Pandey, J., Mohalik, N. K., Mishra, R. K., Khalkho, A., Kumar, D., & Singh, V. K. (2015). Investigation of the role of fire retardants in preventing spontaneous heating of coal and controlling coal mine fires. Fire Technology, 51(2), 227–245.CrossRefGoogle Scholar
  128. Pandey, J., Kumar, D., Singh, V. K., & Mohalik, N. K. (2016). Environmental and socio-economic impacts of fire in Jharia coalfield, Jharkhand, India: An appraisal. Current Science, 110(9), 19–23.CrossRefGoogle Scholar
  129. Parinussa, R., Lakshmi, V., Johnson, F., & Sharma, A. (2016a). Comparing and combining remotely sensed land surface temperature products for improved hydrological applications. Remote Sensing, 8(2), 162.CrossRefGoogle Scholar
  130. Parinussa, R. M., Lakshmi, V., Johnson, F. M., & Sharma, A. (2016b). A new framework for monitoring flood inundation using readily available satellite data. Geophysical Research Letters, 43(6), 2599–2605.CrossRefGoogle Scholar
  131. Pásztor, L., Szabó, J., Bakacsi, Z., Matus, J., & Laborczi, A. (2012). Compilation of 1:50,000 scale digital soil maps for Hungary based on the digital Kreybig soil information system. Journal of Maps, 8(3), 215–219.Google Scholar
  132. 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
  133. Pires, L. F., Bacchi, O. O. S., & Reichardt, K. (2005). Soil water retention curve determined by gamma-ray beam attenuation. Soil and Tillage Research, 82(1), 89–97.Google Scholar
  134. Poggio, L., Gimona, A., & Brewer, M. J. (2013). Regional scale mapping of soil properties and their uncertainty with a large number of satellite-derived covariates. Geoderma, 209–210, 1–14.CrossRefGoogle Scholar
  135. Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems, 9(2), 181–199.CrossRefGoogle Scholar
  136. Punera, K., & Ghosh, J. (2008). Consensus-based ensembles of soft clusterings. Applied Artificial Intelligence, 22(7–8), 780–810.CrossRefGoogle Scholar
  137. Ranjan, V., Sen, P., Kumar, D., & Sarsawat, A. (2015). A review on dump slope stabilization by revegetation with reference to indigenous plant. Ecological Processes, 4(1), 14.CrossRefGoogle Scholar
  138. Rätsch, G., Onoda, T., & Müller, K. R. (2001). Soft margins for AdaBoost. Machine Learning, 42(3), 287–320.zbMATHCrossRefGoogle Scholar
  139. Ravan, S., & Roy, P. S. (1997). Satellite remote sensing for the ecological analysis of peatbog areas in Lower Saxony. Plant Ecology, 131, 129–141.CrossRefGoogle Scholar
  140. Ravan, S., Roy, P. S., & Sharma, C. M. (1995). Space remote-sensing for spatial vegetation characterization. Journal of Biosciences, 20(3), 427–438.CrossRefGoogle Scholar
  141. Ravan, S., Kale, M., & Roy, P. S. (2004). Identification of potential sites for in situ conservation of landraces associated with forest ecosystem—Geomatics approach. Current Science, 87(8), 1115–1122.Google Scholar
  142. Reddy, C. S. S., Bhattacharya, A., & Srivastav, S. K. (1993a). Night-time TM short wavelength infrared data analysis of Barren Island volcano, South Andaman, India. International Journal of Remote Sensing, 14(4), 783–787.CrossRefGoogle Scholar
  143. Reddy, C. S. S., Srivastav, S. K., & Bhattacharya, A. (1993b). Application of thematic mapper short wavelength infrared data for the detection and monitoring of high temperature related geoenvironmental features. International Journal of Remote Sensing, 14(17), 3125–3132.CrossRefGoogle Scholar
  144. Reddy, C. S., Singh, S., Dadhwal, V. K., Jha, C. S., Rao, N. R., & Dwakar, P. G. (2017). Predictive modelling of the spatial pattern of past and future forest cover changes in India. Journal of Earth System Science, 126(1), 8.CrossRefGoogle Scholar
  145. Richardson, M. D., Meisner, C. A., Hoveland, C. S., & Karnok, K. J. (1992). Time domain reflectometry in closed container studies. Agronomy Journal, 84(6), 1061–1063.Google Scholar
  146. Rodriguez-galiano, V. F., Ghimire, B., Rogan, J., Chica-olmo, M., & Rigol-sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104.CrossRefGoogle Scholar
  147. Rodriguez-Galiano, V. F., & Chica-Rivas, M. (2014). Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models. International Journal of Digital Earth, 7(6), 492–509.CrossRefGoogle Scholar
  148. Roy, P. S., & Ravan, S. A. (1996). Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest. Journal of Biosciences, 21(4), 535–561.CrossRefGoogle Scholar
  149. Said, S., Kothyari, U. C., & Arora, M. K. (2008). ANN-based soil moisture retrieval over bare and vegetated areas using ERS-2 SAR data. Journal of Hydrologic Engineering, 13(6), 461–475.CrossRefGoogle Scholar
  150. Sarkar, A., Majumdar, A., Chatterjee, S., Chatterjee, D., Ray, S. S., & Kartikeyan, B. (2008). Study of the potential of alternative crops by integration of multisource data using a neuro-fuzzy technique. International Journal of Remote Sensing, 29(795405157), 5479–5493.CrossRefGoogle Scholar
  151. Schrott, L., & Sass, O. (2008). Application of field geophysics in geomorphology: Advances and limitations exemplified by case studies. Geomorphology, 93(1–2), 55–73.CrossRefGoogle Scholar
  152. 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
  153. Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., et al. (2010). Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Science Reviews, 99(3–4), 125–161.CrossRefGoogle Scholar
  154. Shirong, Z. (2002). GIS-based simulation of regional soil water and nitrogen behavior and analysis of agricultural management. Beijing: China Agriculture University.Google Scholar
  155. Shukla, G., Garg, R. D., Srivastava, H. S., & Garg, P. K. (2018). Performance analysis of different predictive models for crop classification across an aridic to ustic area of Indian states. Geocarto International, 33(3), 240–259.CrossRefGoogle Scholar
  156. Singh, R. P., Kumar, V., & Srivastav, S. K. (1990). Technical note Use of microwave remote sensing in salinity estimation. International Journal of Remote Sensing, 11(2), 321–330.CrossRefGoogle Scholar
  157. Sommer, M., Wehrhan, M., Zipprich, M., Weller, U., Zu Castell, W., Ehrich, S., et al. (2003). Hierarchical data fusion for mapping soil units at field scale. Geoderma, 112(3–4), 179–196.CrossRefGoogle Scholar
  158. Song, X., Zhang, G., Liu, F., Li, D., Zhao, Y., & Yang, J. (2016). Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. Journal of Arid Land, 8(5), 734–748.Google Scholar
  159. Sonobe, R., Tani, H., Wang, X., Kobayashi, N., & Shimamura, H. (2014a). Random Forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data. Remote Sensing Letters, 5(2), 157–164.CrossRefGoogle Scholar
  160. Sonobe, R., Tani, H., Wang, X., Kobayashi, N., & Shimamura, H. (2014b). Parameter tuning in the Support Vector Machine and Random Forest and their performances in cross- and same-year crop classification using TerraSAR-X. International Journal of Remote Sensing, 35(23), 7898–7909.CrossRefGoogle Scholar
  161. Sonobe, R., Tani, H., & Wang, X. (2017). An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data. Geocarto International, 32(2), 128–138.Google Scholar
  162. Srivastava, H. S., Patel, P., Navalgund, R. R., & Sharma, Y. (2008). Retrieval of surface roughness using multi-polarized Envisat-1 ASAR data. Geocarto International, 23(1), 67–77.CrossRefGoogle Scholar
  163. Srivastava, P. K., Han, D., Ramirez, M. R., & Islam, T. (2013). Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resources Management, 27(8), 3127–3144.CrossRefGoogle Scholar
  164. Stisen, S., Sandholt, I., Nørgaard, A., Fensholt, R., & Jensen, K. H. (2008). Combining the triangle method with thermal inertia to estimate regional evapotranspiration—Applied to MSG-SEVIRI data in the Senegal River basin. Remote Sensing of Environment, 112(3), 1242–1255.CrossRefGoogle Scholar
  165. Stum, A. K. (2010). Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah. Utah State University.Google Scholar
  166. Sun, L., Sun, R., Li, X., Liang, S., & Zhang, R. (2012). Monitoring surface soil moisture status based on remotely sensed surface temperature and vegetation index information. Agricultural and Forest Meteorology, 166–167, 175–187.CrossRefGoogle Scholar
  167. Sutton, C. D. (2004). Classification and regression trees, bagging, and boosting. Handbook of Statistics, 24(04), 303–329.Google Scholar
  168. Sutton, J. R. P., & Lakshmi, V. (2017). From space to the rocky intertidal: Using NASA MODIS sea surface temperature and NOAA water temperature to predict intertidal logger temperature. Remote Sensing, 9(162), 1–14.Google Scholar
  169. Taghizadeh-Mehrjardi, R. (2015). Digital mapping of cation exchange capacity using genetic programming and soil depth functions in Baneh region, Iran. Archives of Agronomy and Soil Science, 62(1), 109–126.CrossRefGoogle Scholar
  170. Taghizadeh-Mehrjardi, R., Sarmadian, F., Minasny, B., Triantafilis, J., & Omid, M. (2014). Digital mapping of soil classes using decision tree and auxiliary data in the Ardakan Region, Iran. Arid Land Research and Management, 28(2), 147–168.CrossRefGoogle Scholar
  171. Tan, C. P., Ewe, H. T., & Chuah, H. T. (2011). Agricultural crop-type classification of multi-polarization SAR images using a hybrid entropy decomposition and support vector machine technique. International Journal of Remote Sensing, 32(22), 7057–7071.CrossRefGoogle Scholar
  172. Trenberth, K. E., Fasullo, J. T., & Kiehl, J. (2009). Earth’s global energy budget. Bulletin of the American Meteorological Society, 90(3), 311–323.CrossRefGoogle Scholar
  173. Triantafilis, J., Earl, N., & Gibbs, I. (2012). Digital soil-class mapping across the Edgeroi district using numerical clustering and gamma-ray spectrometry data. In A. B. McBratney (Ed.), Digital soil assessments and beyond (pp. 187–191). Sydney: CRC Press.CrossRefGoogle Scholar
  174. Tumer, K., & Oza, N. C. (2003). Input decimated ensembles. Pattern Analysis and Applications, 6(1), 65–77.MathSciNetzbMATHCrossRefGoogle Scholar
  175. 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
  176. 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
  177. Vapnik, V. (1998). Statistical learning theory. New York: Wiley-Interscience.Google Scholar
  178. Vapnik, V. N. (2000). The nature of statistical learning theory. In M. Jordan, S. L. Lauritzen, J. F. Lawless, & V. Nair (Eds.), Statistics for engineering and information science (pp. 1564–1564). New York: Springer.Google Scholar
  179. Vaysse, K., & Lagacherie, P. (2015). Evaluating digital soil mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France). Geoderma Regional, 4, 20–30.CrossRefGoogle Scholar
  180. Verhoest, N. E. C., Lievens, H., Wagner, W., Álvarez-Mozos, J., Moran, M. S., & Mattia, F. (2008). On the soil roughness parameterization problem in soil moisture retrieval of bare surfaces from synthetic aperture radar. Sensors, 8(7), 4213–4248.CrossRefGoogle Scholar
  181. Wagner, W., Lemoine, G., & Rott, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70(2), 191–207.CrossRefGoogle Scholar
  182. Wang, L., & Qu, J. J. (2009). Satellite remote sensing applications for surface soil moisture monitoring: A review. Frontiers of Earth Science in China, 3(2), 237–247.CrossRefGoogle Scholar
  183. Wang, L., Young, S. S., Wang, W., Ren, G., Xiao, W., Long, Y., et al. (2016) Conservation priorities of forest ecosystems with evaluations of connectivity and future threats: Implications in the Eastern Himalaya of China. Biological Conservation, 195, 128–135.CrossRefGoogle Scholar
  184. Watham, T., Patel, N. R., Kushwaha, S. P. S., Dadhwal, V. K., & Kumar, A. S. (2017). Evaluation of remote-sensing-based models of gross primary productivity over Indian sal forest using flux tower and MODIS satellite data. International Journal of Remote Sensing, 38(18), 5069–5090.CrossRefGoogle Scholar
  185. Weigend, A. S., Mangeas, M., & Srivastava, A. N. (1995). Nonlinear gated experts for time series: discovering regimes and avoiding overfitting. International Journal of Neural Systems, 6(4), 373–399.CrossRefGoogle Scholar
  186. Weihermüller, L., Huisman, J. A., Lambot, S., Herbst, M., & Vereecken, H. (2007). Mapping the spatial variation of soil water content at the field scale with different ground penetrating radar techniques. Journal of Hydrology, 340(3–4), 205–216.CrossRefGoogle Scholar
  187. Wigneron, J. P., Calvet, J. C., Pellarin, T., Van De Griend, A. A., Berger, M., & Ferrazzoli, P. (2003). Retrieving near-surface soil moisture from microwave radiometric observations: Current status and future plans. Remote Sensing of Environment, 85(4), 489–506.CrossRefGoogle Scholar
  188. Wirth, L., Rosenberger, A., Prakash, A., Gens, R., Margraf, J. F., & Hamazak, T. (2012). A remote-sensing, GIS-based approach to identify, characterize, and model spawning habitat for fall-run chum salmon in a sub-arctic, glacially fed river. Transactions of the American Fisheries Society, 141(5), 1349–1363.CrossRefGoogle Scholar
  189. Xin, Q., Broich, M., Zhu, P., & Gong, P. (2015). Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics. Remote Sensing of Environment, 161, 63–77.CrossRefGoogle Scholar
  190. 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
  191. Young, S. S. (2003). Satellite detected broad-scale vegetation change in China, 1982–1999. Asian Geographer, 22(1–2), 123–142.CrossRefGoogle Scholar
  192. Young, S. S., & Harris, R. (2005). Changing patterns of global-scale vegetation photosynthesis, 1982–1999. International Journal of Remote Sensing, 26(20), 4537–4563.CrossRefGoogle Scholar
  193. Zaman, B., McKee, M., & Neale, C. M. U. (2012). Fusion of remotely sensed data for soil moisture estimation using relevance vector and support vector machines. International Journal of Remote Sensing, 33(20), 6516–6552.CrossRefGoogle Scholar
  194. Zhang, R., Tian, J., Su, H., Sun, X., Chen, S., & Xia, J. (2008). Two improvements of an operational two-layer model for terrestrial surface heat flux retrieval. Sensors, 8(10), 6165–6187.CrossRefGoogle Scholar
  195. Zhang, D., Zhang, W., Huang, W., Hong, Z., & Meng, L. (2017). Upscaling of surface soil moisture using a deep learning model with VIIRS RDR. ISPRS International Journal of Geo-Information, 6(5), 130.CrossRefGoogle Scholar
  196. Zhao, T. J., Zhang, L. X., Shi, J. C., & Jiang, L. M. (2011). A physically based statistical methodology for surface soil moisture retrieval in the Tibet Plateau using microwave vegetation indices. Journal of Geophysical Research, 116(D8), D08116.CrossRefGoogle Scholar
  197. Zhu, A. X. (2000). Mapping soil landscape as spatial continua: The neural network approach. Water Resources Research, 36(3), 663–677.CrossRefGoogle Scholar
  198. Zhu, A. X., Hudson, B., Burt, J., Lubich, K., & Simonson, D. (2001). Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of America Journal, 65(5), 1463.CrossRefGoogle Scholar

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© 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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