Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks

Remote Sensing

Volumn 7, Issue 3, 2015, Pages 2627-2646

  Hassan Esfahani, Leila ^a   Torres Rua, Alfonso ^a   Jensen, Austin ^a   McKee, Mac ^a  

^a UTAH STATE UNIVERSITY   (United States)

Author keywords

Artificial neural network; High resolution; Multi spectral; Remote sensing; Soil moisture; UAV

Indexed keywords

AERIAL PHOTOGRAPHY; CROPS; CULTIVATION; INFRARED DEVICES; MEAN SQUARE ERROR; MOISTURE; MOISTURE DETERMINATION; NEURAL NETWORKS; REMOTE SENSING; SOIL MOISTURE; SOILS; SPECTROSCOPY; SPRINKLER SYSTEMS (IRRIGATION); UNMANNED AERIAL VEHICLES (UAV); UNMANNED VEHICLES;

ARTIFICIAL NEURAL NETWORK MODELS; COEFFICIENT OF DETERMINATION; COEFFICIENT OF PERFORMANCE; HIGH RESOLUTION; HIGH RESOLUTION MULTI-SPECTRAL IMAGERY; HIGH-RESOLUTION AERIAL IMAGES; MULTI-SPECTRAL; SPATIAL AND TEMPORAL RESOLUTIONS;

SOIL SURVEYS;

EID: 84923544573     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70302627     Document Type: Article

References (58)

1. Grayson, R.B.; Western, A.W. Towards areal estimation of soil water content from point measurements: Time and space stability of mean response. J. Hydrol. 1998, 207, 68-82.
2. Gill, K.; Kemblowski, M.; McKee, M. Soil moisture data assimilation using support vector machines and ensemble Kalman filter. JAWRA J. Am. Water Resour. Assoc. 2007, 43, 1004-1015.
3. Zaman, B.; McKee, M.; Neale, C.M.U. Fusion of remotely sensed data for soil moisture estimation using relevance vector and support vector machines. Int. J. Remote Sens. 2012, 33, 6516-6552.
4. Liu, W.; Baret, F.; Gu, X.; Zhang, B.; Tong, Q.; Zheng, L. Evaluation of methods for soil surface moisture estimation from reflectance data. Int. J. Remote Sens. 2003, 24, 2069-2083.
5. Idso, S.; Jackson, R.; Reginato, R. Estimating evaporation: A technique adaptable to remote sensing. Science 1975, 189, 991-992.
6. Idso, S.; Jackson, R.; Reginato, R. Compensating for environmental variability in the thermal inertia approach to remote sensing of soil moisture. J. Appl. Meteorol. 1976, 15, 811-817.
7. Reginato, R.; Idso, S.; Jackson, R.; Vedder, J.; Blanchard, M.; Goettelman, R. Soil water content and evaporation determined by thermal parameters obtained from ground-based and remote measurements. J. Geophys. Res. 1976, 81, 1617-1620.
8. Reginato, R.; Jackson, R.; Pinter, P. Evapotranspiration calculated from remote multispectral and ground station meteorological data. Remote Sens. Environ. 1985, 18, 75-89.
9. Jackson, T. Soil water modeling and remote sensing. IEEE Trans. Geosci. Remote Sens. 1986, 24, 37-46.
10. Quattrochi, D.; Luvall, J. Thermal infrared remote sensing for analysis of landscape ecological processes: Methods and applications. Landsc. Ecol. 1999, 14, 577-598.
11. Kaleita, A.; Tian, L.; Hirschi, M. Relationship between soil moisture content and soil surface reflectance. Trans. ASAE 2005, 48, 1979-1986.
12. Humes, K.; Kustas, W.; Jackson, T.; Schmugge, T.; Moran, M. Combined use of optical and microwave remotely sensed data for the estimation of surface energy balance components over a semi-arid watershed. In Proceedings of the IEEE Topical Symposium on Combined Optical-Microwave Earth and Atmosphere Sensing, Albuquerque, NM, USA, 22-25 March 1993; pp. 86-89.
13. Njoku, E.G.; Jackson, T.J.; Lakshimi, V.; Chan, T.K.; Ngheim, S.V. Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sens. 2003, 41, 215-229.
14. Aritola, J.F.; Pepper, I.L.; Brusseau, M.L. Environmental Monitoring and Characterization; Elsevier Academic Press: San Diego, CA, USA, 2004.
15. Das, N.N.; Mohanty, B.P. Root zone soil moisture assessment using remote sensing and vadose zone modeling. Vadose Zone J. 2006, 5, 296-307.
16. Njoku, E.G.; Ashcroft, P.; Chan, T.K.; Li, L. Global survey and statistics of radio-frequency interference in AMSR-E land observations. IEEE Trans. Geosci. Remote Sens. 2005, 43, 938-947.
17. Crosson, W.L.; Limaye, A.S.; Laymon, C.A. Parameter sensitivity of soil moisture retrievals from airborn C-and X-band radiomter measurements in SMEX02. IEEE Trans. Geosci. Remote Sens. 2005, 43, 2842-2853.
18. Crow, W.T.; Chan, T.K.; Entekhabi, D.; Houser, P.R.; Hsu, A.Y.; Jackson, T.J.; Njoku, E.G.; O'Neill, P.E.; Shi, J.; Zhan, X. An observing system simulation experiment for hydros radiomter-only soil moisture products. IEEE Trans. Geosci. Remote Sens. 2005, 43, 1289-1303.
19. Fernandez-Galvez, J. Errors in soil moisture content estimates induced by uncertainties in the effective soil dielectric constant. Int. J. Remote Sens. 2008, 29, 3317-3323.
20. Cai, G.; Xue, Y.; Hu, Y.; Wang, Y.; Guo, J.; Luo, Y.; Wu, C.; Zhong, S.; Qi, S. Soil moisture retrieval from MODIS data in northern China plain using thermal inertia model. Int. J. Remote Sens. 2007, 28, 3567-3581.
21. Lu, S.; Ju, Z.; Ren, T.; Horton, R. A general approach to estimate soil water content from thermal inertia. Agric. For. Meteorol. 2009, 149, 1693-1698.
22. Verstraeten, W.W.; Veroustraete, F.; van der Sande, C.J.; Grootaers, I.; Feyen, J. Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests. Remote Sens. Environ. 2006, 101, 299-314.
23. Gill, M.; Asefa, T.; Kemblowski, M.; McKee, M. Soil moisture prediction using support vector machines. J. Am. Water Resour. Assoc. 2006, 42, 1033-1046.
24. Jiang, H.; Cotton, W. Soil moisture estimation using an artificial neural network: A feasibility study. Can. J. Remote Sens. 2004, 30, 827-839.
25. Hassan-Esfahani, L; Torres-Rua, A.; Jensen, A.; McKee, M. Topsoil moisture estimation for precision agriculture using unmmaned aerial vehicle multispectral imagery. In Proceedings of the 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada, 13-18 July 2014; pp. 3263-3266.
26. Elshorbagy, A.; Parasuraman, K. On the relevance of using artificial neural networks for estimating soil moisture content. J. Hydrol. 2008, 362, 1-18.
27. Khalil, A.; Gill, M.K.; McKee, M. New applications for information fusion and soil moisture forecasting. In Proceedings of the 8th International Conference on Information Fusion, Philadelphia, PA, USA, 25-28 July 2005; pp. 1622-1628.
28. Haykin, S. Neural Networks: A Comprehensive Foundation, 2nd ed.; MacMillan: New York, NY, USA, 1999.
29. Khan, M.; Coulibaly, P. Streamflow forecasting with uncertainty estimate using Bayesian learning For ANN. In Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, Montreal, QC, Canada, 31 July-4 August 2005; Volume 5, pp. 2680-2685.
30. Tokar, S.A.; Johnson, P.A. Rainfall-runoff modeling using artificial neural networks. J. Hydrol. Eng. 1999, 4, 232-239.
31. Rumelhart, D.E.; McClelland, J.L.; PDP Research Group. Parallel Distributed Processing. Volume 1: Foundations; The MIT Press: Cambridge, MA, USA, 1986.
32. Hecht-Nielsen, R. Neurocomputing; Addison-Wesley Publishing Company: Reading, MA, USA, 1990.
33. Wasserman, P.D. Neural Computing Theory and Practice; Van Nostrand Reinhold: New York, NY, USA, 1989.
34. Gillies, R.R.; Carlson, T.N. Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. J. Appl. Meteorol. 1995, 34, 745-756.
35. Wetzel, P.J.; Woodward, R.H. Soil moisture estimation using GOES-VISSR infrared data: A case study with a simple statistical method. J. Clim. Appl. Meteorol. 1987, 26, 107-117.
36. Kogan, F.N. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bull. Am. Meteorol. Soc. 1995, 76, 655-668.
37. Mallick, K.; Bhattacharya, B.K.; Patel, N.K. Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI. Agric. For. Meteorol. 2009, 149, 1327-1342.
38. Liang, S.; Rui, S.; Xiaowen, Li.; Shunlin, L.; Renhua, Z. Monitoring surface soil moisture status based on remotely sensed surface temperature and vegetation index information. Agric. For. Meteorol. 2012, 166, 175-187.
39. Bhuiyan, C.; Singha, R.P.; Koganc, F.N. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. Int. J. Appl. Earth Observ. Geoinf. 2006, 8, 289-302.
40. Jensen, A.M. A Geospatial Real-Time Aerial Image Display for a Low-Cost Autonomous Multispectral Remote Sensing. Master's Thesis, Utah State University, Logan, UT, USA, 2009.
41. MosaicMill EnsoMOSAIC. Available online: http://www.ensomosaic.com (accessed on 6 May 2012).
42. Long, D.; Longuevergne, L.; Scanlon, B.R. Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites. Water Resour. Res. 2014, 50, 1131-1151.
43. Decagon Devices, Inc. Available online: http://www.decagon.com/ (accessed on 20 November 2014).
44. Israelson, O.W.; West, F.L. Water holding capacity of irrigated soils. Utah State Agric. Exp. Station Bull 1922, 183, 1-24.
45. Costa, A.; Albuquerque, J.A.; Costa, A.; Pértile, P.; Silva, F.R. Water retention and availability in soils of the State of Santa Catarina-Brazil: Effect of textural classes, soil classes and lithology. Braz. Soil Sci. Soc. 2013, 37, 1535-1548.
46. Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, QC, Canada, 20-25 August 1995; Volume 12, pp. 1137-1143.
47. Geisser, S. Predictive Inference; Chapman and Hall: New York, NY, USA, 1993.
48. Guyon, I. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3, 1157-1182.
49. Kohavi, R.; John, G.H. Wrappers for feature subset selection. Artif. Intell. 1997, 12, 273-324.
50. Glover, D.M.; Jenkins, W.J; Doney, S.C. Least Squares and Regression Techniques, Goodness of Fit and Tests, Non-Linear Least Squares Techniques; Woods Hole Oceanographic Institute: Woods Hole, MA, USA, 2008.
51. Bowden, G.J.; Maier, H.R.; Dandy, G.C. Optimal division of data for neural network models in water resources applications. Water Resour. Res. 2002, doi:10.1029/2001WR000266.
52. Bowden, G.J.; Maier, H.R.; Dandy, G.C. Input determination for neural network models in water resources applications. Part 2. Case study: Forecasting salinity in a river. J. Hydrol. 2005, 301, 93-107.
53. Anctil, F.; Lauzon, N. Generalisation for neural networks through data sampling and training procedures, with applications to stream flow predictions. Hydrol. Earth Syst. Sci. 2004, 8, 940-958.
54. Jia, Y.; Culver, T.B. Bootstrapped artificial neural networks for synthetic flow generation with a small data sample. J. Hydrol. 2006, 331, 580-590.
55. Lauzon, N.; Anctil, F.; Petrinovic, J. Characterization of soil moisture conditions at temporal scales from a few days to annual. Hydrol. Process. 2004, 18, 3235-3254.
56. Whalley, W.R.; Leeds-Harrison, P.B.; Bowman, G.E. Estimation of soil moisture status using near infrared reflectance. Hydrol. Process. 1991, 5, 321-327.
57. Hassan-Esfahani, L.; Torres-Rua, A.; McKee, M. Assessment of optimal irrigation water allocation for pressurized irrigation system using water balance approach, learning machines, and remotely sensed data. Agric. Water Manage. 2015, 153, 42-50.
58. Yin, Z.; Lei, T.; Yan, Q.; Chen, Z.; Dong, Y. A near-infrared reflectance sensor for soil surface moisture measurement. Comput. Electron. Agric. 2013, 99, 101-107.