Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing

International Journal of Applied Earth Observation and Geoinformation

Volumn 32, Issue 1, 2014, Pages 54-66

  Gao, Shengguo ^a   Zhu, Zhongli ^a   Liu, Shaomin ^a   Jin, Rui ^b   Yang, Guangchao ^a   Tan, Lei ^a  

^a BEIJING NORMAL UNIVERSITY   (China)

^b Chinese Academy of Sciences   (China)

Author keywords

Bayesian maximum entropy (BME); Remote sensing; Soft data; Soil moisture; Wireless sensor network (WSN)

Indexed keywords

ACCURACY ASSESSMENT; ASTER; ATMOSPHERE-BIOSPHERE INTERACTION; BAYESIAN ANALYSIS; ESTIMATION METHOD; KRIGING; REMOTE SENSING; SENSOR; SOIL MOISTURE; SPATIAL DATA; SPATIAL DISTRIBUTION; SURFACE TEMPERATURE;

EID: 84904500663     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2014.03.003     Document Type: Article

References (71)

1. Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E., 2002. Wireless sensornetworks: a survey. Comput. Netw. 38, 393-422.
2. Asli, M., Marcotte, D., 1995. Comparison of approaches to spatial estimation in abivariate context. Math. Geol. 27, 641-658.
3. Bartlett, J.E., Kotrlik, J.W., Higgins, C.C., 2001. Organizational research: Determining appropriate sample size in survey research appropriate sample size in survey research. Inf. Technol. Learn. Perform. J. 19 (1), 43-50.
4. Bogaert, P., D'Or, D., 2002. Estimating soil properties from thematic soil maps. Soil Sci. Soc.Am.J. 66, 1492-1500.
5. Bogaert, P., Christakos, G., Jerrett, M., Yu, H.L., 2009. Spatiotemporal modelling of ozone distribution in the State of California. Atmos. Environ. 43, 2471-2480.
6. Bogena, H.R., Herbst, M., Huisman, J.A., Rosenbaum, U., Weuthen, A., Vereecken, H., 2010. Potential of wireless sensor networks for measuring soil water content variability. Vadose Zone J. 9,1002-1013.
7. Boyer, D.G., Wright, R.J., Feldhake, CM., Bligh, D.P., 1996. Soil spatial variability relationships in a steeply sloping acid soil environment. Soil Sci. 161, 278-287.
8. Burgess, T., Webster, R., 1980. Optimal interpolation and isarithmic mapping of soil properties. J. Soil Sci. 31,333-341.
9. Cheng, G.D., 2009. Integrated Management of the Water-Ecology-Economy System in the Heihe River Basin. Science Press, Beijing (in Chinese).
10. Christakos, G., 1990a. A Bayesian/maximum-entropy view to the spatial estimation problem. Math. Geol. 22, 763-776.
11. Christakos, G., 1990b. ModernSpatiotemporal Geostatistics. Oxford University Press, New York.
12. Christakos, G., 1991. Some Applications of the Bayesian, Maximum-Entropy Concept in Geostatistics. Maximum Entropy and Bayesian Methods. Springer, Netherlands.
13. Christakos, G., Li, X., 1998. Bayesian maximum entropy analysis and mapping: a farewell to kriging estimators? Math. Geol. 30, 435-462.
14. Christakos, G., 2000. Modern Spatiotemporal Geostatistics. Oxford University Press, New York.
15. Christakos, G., Serre, MX., 2000. BME analysis of spatiotemporal paniculate matter distributions in North Carolina. Atmos. Environ. 34,3393-3406.
16. Christakos, G., Serre, M.L., Kovitz.J.L., 2001. BME representation of particulate matter distributions in the state of California on the basis of uncertain measurements.J. Geophys. Res. Atmos. 106, 9717-9731.
17. Christakos, G., Bogaert, P., Serre, M.L., 2002. Temporal GIS. Springer-Verlag, New York.
18. Christakos, G., Kolovos, A., Serre, MX., Vukovich, F., 2004. Total ozone mapping by integrating data bases from remote sensing instruments and empirical models.IEEE Trans. Geosci. Remote 42,991-1008.
19. Cressie, N., 1990. The origins of kriging. Math. Geol. 22, 239-252.
20. D'Or, D., (Ph.D. dissertation) 2003. Spatial Prediction of Soil Properties, the Bayesian Maximum Entropy Approach. Universite catholique de Louvain, Louvain, Belgium.
21. D'Or, D., Bogaert, P., Christakos, G., 2001. Application of the BME approach to soil texture mapping. Stoch. Environ. Res. Risk Assess. 15, 87-100.
22. Douaik, A., Meirvenne, M., Toth, T., 2004. Space-time mapping of soil salinity using probabilistic Bayesian maximum entropy. Stoch. Environ. Res. Risk Assess. 18, 219-227.
23. Douaik, A., Van, M.M., Toth, T., 2005. Soil salinity mapping using spatio-temporal kriging and Bayesian maximum entropy with interval soft data. Geoderma 128, 234-248.
24. Emery, X., 2012. Cokriging random fields with means related by known linear combinations.Comput. Geosci. 38,136-144.
25. Entekhabi, D., Njoku, E.G., O'Neill, P.E., Kellogg, K.H., Crow, W.T., Edelstein, W.N., Van, Z.J., 2010. The soil moisture active passive (SMAP) mission. Proc. IEEE 98, 704-716.
26. Goovaerts, P., 1998. Ordinary cokriging revisited. Math. Geol. 30, 21-42.
27. Goovaerts, P., 1999. Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena 34 (3), 227-242.
28. Guswa, A.J., Celia, M.A., Rodriguez-Iturbe, I., 2002. Models of soil moisture dynamics in ecohydrology: a comparative study. Water Resour. Res. 38,5-1-5-15.
29. Hengl, T., Heuvelink, G., Rossiter, D.G., 2007. About regression-kriging: from equations to case studies. Comput. Geosci. 33 (10), 1301-1315.
30. Hernandez-Stefanoni, J.L., Alberto Gallardo-Cruz, J., Meave, J.A., et al., 2011. Combining geostatistical models and remotely sensed data to improve tropical tree richness mapping. Ecol. Ind. 11 (5), 1046-1056.
31. Hu, Y.Q, Gao, Y.X., Wang, J.M.Ji, G.L., Shen, Z.B., Cheng, L.S., Chen, J.Y., Li, S.Q, 1994.Some achievements in scientific research during HEIFE. Plateau Meteorol. 13, 225-236 (in Chinese).
32. Jackson, T., Bindlish, R., Cosh, M., 2009. Validation of AMSR-E soil moisture products using in-situ observations.]. Remote Sens. Soc. Japan 29, 263-270.
33. Jackson, T., Cosh, M., Crow, W., Colliander, A., Walker, J., 2011. In situ validation of the Soil Moisture Active Passive (SMAP) satellite mission. In: 34th International Symposium on Remote Sensing of Environment, Sydney, Australia.
34. Jin, R., Li, X., Yan, B., 2012. Introduction of eco-hydrological wireless sensor network in the Heihe River Basin. Adv. Earth Sci. 27, 993-1005 (in Chinese).
35. Journel, A.G., Huijbregts, C.J., 1978. Mining Geostatistics. Academic Press, London.
36. Kerr, Y.H., Waldteufel, P., Wigneron, J., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M., Font, J., Reul, N., Gruhier, C, Juglea, S.E., Drinkwater, M.R., Hahne, A., Martin-Neira, M., Mecklenburg, S., 2010. The SMOS mission: new tool for monitoring key elements of the global water cycle. Proc. IEEE 98, 666-687.
37. Knotters, M., Brus, D.J., Oude Voshaar, J.H., 1995. A comparison of kriging, cokriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations. Geoderma 67 (3), 227-246.
38. Kravchenko, A.N., 2008. Stochastic simulations of spatial variability based on multifractal characteristics. Vadose Zone J. 7,521-524.
39. Lakshmi, V., Zehrfuhs, D., Jackson, T., 2000. Observations of land surface temperature and its relationship to soil moisture during SGP99. In: Proc. IGARSS. 3, pp.1256-1258.
40. Le Vine, D.M., Lagerloef, G.S., Torrusio, S.E., 2010. Aquarius and remote sensing of sea surface salinity from space. Proc. IEEE 98, 688-703.
41. Lee, S.J., (Ph.D. dissertation) 2005. Models of Soft Data in Geostatistics and their Application in Environmental and Health Mapping. Univ. of N. C. at Chapel Hill.
42. Lee, S., Yeatts, It, Serre, L.M., 2009. A Bayesian maximum entropy approach to address the change of support problem in the spatial analysis of childhood asthma prevalence across North Carolina. Spatial Spatio-temporal Epidemiol 1,49-60.
43. Li, A., Bo, Y., Chen, L., 2012. Bayesian maximum entropy data fusion of field-observed leaf area index (LAI) and Landsat Enhanced Thematic Mapper Plus-derived LAI. Int. J. Remote Sens. 34, 227-246.
44. Li, A., Bo, Y., Zhu, Y., Guo, P., Bi, J., He, Y., 2013a. Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method.Remote Sens. Environ. 135,52-63.
45. Li, X., Li, X.W., Li, Z.Y., Ma, M.G., Wang, J., Xiao, Q, Liu, Q, Ren, H.Z., 2009. Watershed allied telemetry experimental research. J. Geophys. Res. 114, D22.
46. Li, X., Cheng, G.D., Liu, S.M., Xiao, Q, Ma, M.G., Jin, R., Xu, Z.W., 2013b. Heihe Watershed Allied Telemetry Experimental Research (HiWATER): scientific objectives and experimental design. Bull. Am. Meteorol. Soc. 94, BAMS-D-12-00154.
47. Moral, F.J., 2010. Comparison of different geostatistical approaches to map climate variables: application to precipitation. Int. J. Climatol. 30(4), 620-631.
48. Myers, D.E., 1982. Matrix formulation of cokriging. Math. Geol. 14, 249-257.
49. Nazelle, D.A., Arunachalam, S., Serre, L.M., 2010. Bayesian maximum entropy integration of ozone observations and model predictions: an application for attainment demonstration in North Carolina. Environ. Sci. Technol. 44, 5707-5713.
50. Ottle, C, Stoll, M., 1993. Effect of atmospheric absorption and surface emissivity on the determination of land surface temperature from infrared satellite data. Int.J. Remote Sens. 14 (10), 2025-2037.
51. Pang, W., Christakos, G., Wang, J.F., 2010. Comparative spatiotemporal analysis of fine particulate matter pollution. Environmetrics 21,305-317.
52. Rivero, R.G., Grunwald, S., Bruland, G.L., 2007. Incorporation of spectral data into multivariate geostatistical models to map soil phosphorus variability in a Florida wetland. Geoderma 140 (4), 428-443.
53. Ruiz-Garcia, L, Lunadei, L., Barreiro, P., Robla, I., 2009. A review of wireless sensor technologies and applications in agriculture and food industry: state of the art and current trends. Sensors 9, 4728-4750.
54. Schabenberger, O., Gotway, C.A., 2004. Statistical Methods for Spatial Data Analysis.Chapman and Hall/CRC Press.
55. Serre, MX., Christakos, G., 1999. Modern geostatistics: computational BME in the light of uncertain physical knowledge-The Equus Beds study. Stoch. Environ.Res. Risk Assess. 13 (1/2), 1-26.
56. Shannon, C.E., Weaver, W., 1948. A mathematical theory of communication. Bell Syst. Technol. J. 7,379-423.
57. Stein, A., Corsten, L.C.A., 1991. Universal kriging and cokriging as a regression procedure.Biometrics, 575-587.
58. Stein, A., Van, D.W., Bouma, J., Bregt, A.K., 1988. Cokriging point data on moisture deficit. Soil Sci. Soc. Am. J. 52,1418-1423.
59. Sun, D., Pinker, R.T., 2004. Case study of soil moisture effect on land surface temperature retrieval. IEEE Geosci. Remote Sens. Lett. 1,127-130.
60. Triantafilis, J., Odeh, I.O.A., McBratney, A.B., 2001. Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton.Soil Sci. Soc. Am. J. 65, 869-878.
61. Vereecken, H., Huisman, J.A., Bogena, H.R., Vanderborght, J., Vrugt, J.A., Hopmans, J.W., 2008. On the value of soil moisture measurements in vadose zone hydrology: a review. Water Resour. Res. 44, W00D06.
62. Wang, L., Qu.J.J., 2009. Satellite remote sensing applications forsurface soil moisture monitoring: a review. Front. Earth Sci. China 3, 237-247.
63. Wu, C, Wu, J., Luo, Y., Zhang, L., DeGloria, S.D., 2009. Spatial prediction of soil organic matter content using cokriging with remotely sensed data. Soil Sci. Soc. Am. J.73,1202-1208.
64. Wu, J., Norvell, W.A., Hopkins, D.G., Smith, D.B., Ulmer, M.G., Welch, R.M., 2003.Improved prediction and mapping of soil copper by kriging with auxiliary data for cation-exchange capacity. Soil Sci. Soc. Am. J. 67,919-927.
65. Yates, S.R., Warrick, A.W., 1987. Estimating soil water content using cokriging. Soil Sci. Soc.Am.J. 51,23-30.
66. Younger, M.S., 1985. A First Course in Linear Regression, 2nd ed. Duxbury Press, Boston.
67. Yu, H.L., Chen, J.C., Christakos, G., Jerrett, M., 2009. BME estimation of residential exposure to ambient PM10 and ozone at multiple time scales. Environ. Health Perspect. 117, 537-544.
68. Zhang, R.D., Myers, D.E., Warrick, A.W., 1992. Estimation of the spatial distribution of soil chemicals using pseudo-cross-variograms. Soil Sci. Soc. Am. J. 56, 1444-1452.
69. Zhang, R.D., Shouse, P., Yates, S., 1997. Use of pseudo-crossvariograms and cokriging to improve estimates of soil solute concentrations. Soil Sci. Soc. Am. J. 61, 1342-1347.
70. Zhang, R.D., 2005. Theory and Application of Spatial Variation. Science Press, Beijing (in Chinese).
71. Zhou.J., Li, J., Zhang, L., Hu, D., Zhan, W., 2012. Intercomparison of methods for estimating land surface temperature from a Landsat-5 TM image in an arid region with low water vapour in the atmosphere. Int. J. Remote Sens. 33, 2582-2602.