Salinity forecasting of saline soil based on ANN and hyperspectral remote sensing

Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering

Volumn 25, Issue 12, 2009, Pages 161-166

  Wang, Jing ^a   Liu, Xiangnan ^b   Huang, Fang ^a   Tang, Jilong ^c   Zhao, Lengbing ^d  

^a NORTHEAST NORMAL UNIVERSITY   (China)

^b CHINA UNIVERSITY OF GEOSCIENCES   (China)

^c CHANGCHUN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^d Academy of Forestry Inventory and Planning of Jilin   (China)

Author keywords

Forecasting; Hyperspectral; Neural network; Remote sensing; Salination

Indexed keywords

AGRICULTURAL AREAS; ARTIFICIAL NEURAL NETWORK; ELECTRIC CONDUCTION; HYPERSPECTRAL; HYPERSPECTRAL DATA; HYPERSPECTRAL REMOTE SENSING; LAND DEGRADATION; LAYER NETWORKS; PREDICTIVE ACCURACY; PREDICTIVE MODELS; REGRESSION MODEL; SALINATION; SALINE SOIL; SEMI ARID; SOIL PARAMETERS; SOIL SALINIZATION; SONGNEN PLAIN; SPECTRAL BAND; SPECTRAL REFLECTANCES;

ELECTRIC LOAD FORECASTING; FORESTRY; GEOLOGIC MODELS; REFLECTION; REGRESSION ANALYSIS; REMOTE SENSING; SALINITY MEASUREMENT; SOILS; STRATEGIC PLANNING;

NEURAL NETWORKS;

EID: 77049116080     PISSN: 10026819     EISSN: None     Source Type: Journal    
DOI: 10.3969/j.issn.1002-6819.2009.12.029     Document Type: Article

References (26)

1. Tan Junli, Kang Yuehu, Jiao Yanping, et al. Characteristics of soil salinity and salt ions distribution in salt-affected field under mulch-drip irrigation in different planting years[J]. Transactions of the Chinese Society of Agricultural Engineering, 2008, 24(6): 59-63. (in Chinese with English abstract)
2. Metternicht G I, Zinck J A. Remote sensing of soil salinity: potentials and constraints[J]. Remote Sensing of Environment, 2003, 85(1): 1-20.
3. Farifteh J, Farshad A, George R J. Assessing salt-affected soils using remote sensing, solute modelling, and geophysics[J]. Geoderma, 2006, 130(3/4): 191-206.
4. Chang C W, Laird D A. Near-infrared reflectance spectroscopic analysis of soil C and N[J]. Soil Science, 2002, 167(2): 110.
5. Shepherd K D, Walsh M G. Development of reflectance spectral libraries for characterization of soil properties[J]. Soil Science Society of America Journal, 2002, 66(3): 988-998.
6. Nanni M R, Dematte J A M. Spectral reflectance methodology in comparison to traditional soil analysis[J]. Soil Science Society of America Journal, 2006, 70(2): 393-407.
7. Lobell D B, Ortiz-Monasterio J I, Gurrola F C, et al. Identification of saline soils with multiyear remote sensing of crop yields[J]. Soil Science Society of America, 2007, 71(3): 777-783.
8. Fernández-Buces N, Siebe C, Cram S, et al. Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico. Journal of Arid Environments, 2006, 65(4): 644-667.
9. Farifteh J, Farshad A, George R J. Assessing salt-affected soils using remote sensing, solute modelling, and geophysics[J]. Geoderma, 2005, 130(3/4): 191-206.
10. Brunner P, Li H T, Kinzelbach W, et al. Generating soil electrical conductivity maps at regional level by integrating measurements on the ground and remote sensing data[J]. International Journal of Remote Sensing, 2007, 28(15): 3341-3361.
11. Metternicht G, Zinck J A. Spatial discrimination of salt-and sodium-affected soil surfaces[J]. International Journal of Remote Sensing, 1997, 18(12): 2571-2586.
12. Weng Y, Gong P, Zhu Z. Soil salt content estimation in the Yellow River delta with satellite hyperspectral data[J]. Canadian Journal of Remote Sensing, 2008, 34(3): 259-270.
13. Rao B R M, Sankar T R, Dwivedi R S, et al. Spectral behaviour of salt-affected soils[J]. International Journal of Remote Sensing, 1995, 16(12): 2125-2136.
14. Coleman T L, Agbu P A, Montgomery O L. Spectral band selection for quantifying selected properties in highly weathered soils[J]. Soil Science, 1991, 151(1): 355-361.
15. Ben-Dor E, Patkin K, Banin A, et al. Mapping of several soil properties using DAIS-7915 hyperspectral scanner data. A case study over soils in Israel[J]. International Journal of Remote Sensing, 2002, 23(1): 1043-1062.
16. Valeriano M M, Epiphanio J C N, Formaggio A R, et al. Bi-directional reflectance factor of 14 soil classes from Brazil[J]. International Journal of Remote Sensing, 1995, 16(1): 113-128.
17. Schaap M G, Leij F J. Using neural networks to predict soil water retention and soil hydraulic conductivity[J]. Soil & Tillage Research, 1998, 47(1/2): 37-42.
18. Walthall C, Dulaney W, Anderson M, et al. A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery[J]. Remote Sensing of Environment, 2004, 92(4): 465-474.
19. Tadjudin S, Landgrebe D A. Covariance estimation with limited training samples[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37 (4): 2113-2118.
20. Behrens T, Forster H, Scholten T, et al. Digital soil mapping using artificial neural networks[J]. Journal of Plant Nutrition and Soil Science, 2005, 168(1): 55-62.
21. Li Xiujun, Li Qusheng, Wang Zhichun, et al. A research on characteristics and rational exploitation of soda saline land in the western Songnen Plain[J]. Research of Agricultural Modernization, 2002, 9(2): 361-364. (in Chinese with English abstract)
22. Zeng J, Guo H, Hu Y. Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission[J]. Journal of Environmental Science, 2007, 7(4): 427-431.
23. Lou Xiulin, Huang Weigen. An artificial neural network method for detecting red tides with NOAA AVHRR imagery[J]. Journal of Remote Sensing, 2003, 3(2): 125-129. (in Chinese with English abstract)
24. Zhang Tinglu, He Mingxia. A method to retrieve the oceanic chlorophyll-a concentrations in case I water based on artificial neural network[J]. Journal of Remote Sensing, 2002, 6(1):40-44. (in Chinese with English abstract)
25. Zhang Y Z, Pullianinen J, Koponen, S, Hallikainen, M. Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data[J]. Remote Sensing of Environment, 2002, 81(2/3): 327-336.
26. Dreyer P. Classification of land cover using optimized neural nets on SPOT data[J]. Photogrammetric Engineering & Remote Sensing, 1993, 59(5): 617-621.