Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta, China

YANG Lin; HUANG Chong; LIU Gaohuan; LIU Jing; ZHU A-Xing

doi:10.1007/s11769-015-0740-7

Volume 25 Issue 3

Turn off MathJax

Article Contents

Article Navigation > Chinese Geographical Science > 2015 > 25(3): 283-294

YANG Lin, HUANG Chong, LIU Gaohuan, LIU Jing, ZHU A-Xing. Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta, China[J]. Chinese Geographical Science, 2015, 25(3): 283-294. doi: 10.1007/s11769-015-0740-7

Citation:

YANG Lin, HUANG Chong, LIU Gaohuan, LIU Jing, ZHU A-Xing. Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta, China[J]. Chinese Geographical Science, 2015, 25(3): 283-294. doi: 10.1007/s11769-015-0740-7

Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta, China

doi: 10.1007/s11769-015-0740-7

1.
State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
2.
Department of Geography, University of Wisconsin-Madison, Madison WI 53706, USA;
3.
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application and School of Geography, Nanjing Normal University, Nanjing 210023, China

Funds: Under the auspices of Special Fund for Ocean Public Welfare Profession Scientific Research (No. 201105020), National Natural Science Foundation of China (No. 41471178, 41023010, 41431177), National Key Technology Innovation Project for Water Pollution Control and Remediation (No. 2013ZX07103006)

More Information

Corresponding author: HUANG Chong. E-mail:huangch@lreis.ac.cn
Received Date: 2014-06-03
Rev Recd Date: 2014-08-29
Publish Date: 2015-03-27

Abstract

Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe (Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30-40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Indices (NDVIs) and land surface reflectance data from Landsat Thematic Mapper (TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient (CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30-40 cm depth in the study area (with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.
- soil salinization,
- similarity-based prediction approach,
- digital soil mapping,
- Huanghe (Yellow) River Delta,
- environmental factor

References

[1]	Abbas A, Khan S, Hussain N et al., 2013. Characterizing soil salinity in irrigated agriculture using a remote sensing approach. Physics and Chemistry of the Earth, 55-57:43-52. doi: 10.1016/j.pce.2010.12.004
[2]	Akramkhanov A, Martius C, Park S J et al., 2011. Environmental factors of spatial distribution of soil salinity on flat irrigated terrain. Geoderma, 163(1-2):55-62. doi:10.1016/j.geoderma. 2011.04.001
[3]	Cui B S, Tang N, Zhao X S et al., 2009. A management-oriented valuation method to determine ecological water requirement for wetlands in the Yellow River Delta of China. Journal for Nature Conservation, 17(3):129-141. doi:10.1016/j.jnc.2009. 01.003
[4]	Fan X, Pedroli B, Liu G H et al., 2012. Soil salinity development in the yellow river delta in relation to groundwater dynamics. Land Degradation and Development, 23(2):175-189. doi: 10.1002/ldr.1071
[5]	Fang H L, Liu G H, Kearney M, 2005. Georelational analysis of soil type, soil salt content, landform, and land use in the Yellow River Delta, China. Environment Management, 35(1):72-83. doi: 10.1007/s00267-004-3066-2
[6]	Farifteh J, Farshad A, George R J, 2006. Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma, 130(3-4):191-206. doi:10.1016/j.geoderma.2005. 02.003
[7]	Ghassemi F, Jakerman A J, Nix H A, 1995. Salinization of Land Water Resources. Wallingford:CAB International.
[8]	Gower J C, 1971. A general coefficient of similarity and some of its properties. Biometrics, 27(4):857-871. doi: 10.2307/2528823
[9]	Hengl T, Heuvelink G B M, Rossiter D G, 2007. About regres-sion-kriging:from equations to case studies. Computers & Geosciences, 33(10):1301-1315. doi:10.1016/j.cageo.2007. 05.001
[10]	Jafari A, Finke P A, de Wauw J V et al., 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. doi: 10.1111/j.1365-2389.2012.01425.x
[11]	Li S N, Wang G X, Deng W et al., 2009. Influence of hydrology process on wetland landscape pattern:a case study in the Yel-low River Delta. Ecological Engineering, 35(12):1719-1726. doi: 10.1016/j.ecoleng.2009.07.009
[12]	Liu Jing, 2010. Mapping Soil Properties Using Individual Repre-sentativeness of Samples over Large Area. Beijing:Beijing Normal University. (in Chinese)
[13]	Ma Yulei, Wang De, Liu Junmin et al., 2013. Relationships be-tween typical vegetations, soil salinity, and groundwater depth in the Yellow River Delta of China. Chinese Journal of Applied Ecology, 24(9):2423-2430. (in Chinese)
[14]	Metternicht G I, Zinck J A, 2003. Remote sensing of soil salinity:potentials and constraints. Remote Sensing of Environment, 85(1):1-20. doi: 10.1016/S0034-4257(02)00188-8
[15]	Minasny B, McBratney A B, Mendonca-Santos M L et al., 2006. Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley. Australian Journal of Soil Research, 44(3):233-244. doi: 10.1071/SR05136
[16]	Mougenot B, Pouget M, Epema G, 1993. Remote sensing of salt-affected soils. Remote Sensing Reviews, 7(3-4):241-259. doi: 10.1080/02757259309532180
[17]	Qin C Z, Zhu A X, Pei T et al., 2007. An adaptive approach to selecting a flow-partition exponent for a multiple-flow-direction algorithm. International Journal of Geographical Information Science, 21(4):443-458. doi: 10.1080/13658810601073240
[18]	Qin C Z, Lu Y J, Li B L et al., 2009. Simple digital terrain analy-sis software (SimDTA 1.0) and its application in fuzzy classi-fication of slope positions. Journal of Geo-Information Science, 11(6):737-743. (in Chinese)
[19]	Quinn P, Beven K J, Lamb R, 1995. The ln(a/tanb) index:how to calculate it and how to use it within the TOPMODEL frame-work. Hydrological Processes, 9(2):161-182. doi: 10.1002/hyp.3360090204
[20]	Rodgers J L, Nicewander W A, 1988. Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1):59-66. doi: 10.1080/00031305.1988.10475524
[21]	Scull P, Franklin J, Chadwick O A, 2005. The application of clas-sification of tree analysis to soil type prediction in a desert landscape. Ecological Modelling, 181(1):1-15. doi:10.1016/j. ecolmodel.2004.06.036
[22]	Sheng J D, Ma L C, Jiang P A et al., 2010. Digital soil mapping to enable classification of the salt-affected soils in desert agro-ecological zones. Agricultural Water Management, 97:1944-1951. doi: 10.1016/j.agwat.2009.04.011
[23]	Shi X, Zhu A X, Burt J E et al., 2004. A case-based reasoning approach to fuzzy soil mapping. Soil Science Society of America Journal, 68(3):885-894. doi:10.2136/sssaj2004. 8850
[24]	Song Chuangye, Huang Chong, Liu Huimin, 2013. Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models. Chinese Geographical Science, 23(3):331-343. doi: 10.1007/s11769-013-0590-0
[25]	Taghizadeh-Mehrjardi R, Minasny B, Sarmadian F et al., 2014. Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213:15-28. doi:10.1016/j.geoderma.2013. 07.020
[26]	Triantafilis J, Odeh I O A, Mcbratney A B, 2001. Five geostatis-tical models to predict soil salinity from electromagnetic in-duction data across irrigated cotton. Soil Science Society of America Journal, 65(3):869-878. doi:10.2136/sssaj2001. 653869x
[27]	Wang X G, Lian Y, Huang C et al., 2011. Environmental flows and its evaluation of restoration effect based on LEDESS model in Yellow River Delta wetlands. Mitigation and Adaptation Strategies for Global Change, 17(4):357-367. doi: 10.1007/s11027-011-9330-x
[28]	Webster R, Oliver M A, 2001. Geostatistics for Environmental Science. Toronto, Canada:John Wiley and Sons, LTD.
[29]	Xie T, Liu X H, Sun T, 2011. The effects of groundwater table and flood irrigation strategies on soil water and salt dynamics and reed water use in the Yellow River Delta, China. Ecological Modelling, 222(2):241-252. doi:10.1016/j.ecolmodel. 2010.01.012
[30]	Xu Xuegong, 1997. An analysis on the land structure in the Yellow River Delta. Acta Geographical Sinica, 64(1):18-26. (in Chinese)
[31]	Yang L, Zhu A X, Qi F et al., 2013. An integrative hierarchical stepwise sampling strategy and its application in digital soil mapping. International Journal of Geographical Information Science, 27(1):1-23. doi: 10.1080/13658816.2012.658053
[32]	Yao R J, Yang J S, 2010. Quantitative evaluation of soil salinity and its spatial distribution using electromagnetic induction method. Agricultural Water Management, 97(12):1961-1970. doi: 10.1016/j.agwat.2010.02.001
[33]	Ye Q H, Liu G H, Tian G L, 2004. Geospatial-temporal analysis of land-use changes in the Yellow River Delta in the last 40 years. Science in China Series D Earth Sciences, 47(11):1008-1024. doi: 10.1360/03yd0151
[34]	Zhang T T, Zeng S L, Gao Y et al., 2011. Assessing impact of land uses on land salinization in the Yellow River Delta, China using an integrated and spatial statistical model. Land Use Policy, 28(4):857-866. doi: 10.1016/j.landusepol.2011.03.002
[35]	Zhao X S, Cui B S, Sun T et al., 2010. The relationship between the spatial distribution of vegetation and soil environmental factors in the tidal creek areas of the Yellow River Delta. Ecology and Environmental Sciences, 19(8):1855-1861. (in Chinese)
[36]	Zhou W Z, Tian Y Z, Zhu L F, 2007. Land use/land cover change in Yellow River Delta China during fast development period. Conference on Remote Sensing and Modelling of Ecosystems for Sustainability IV, San Antonio, CA. doi:10.1117/12. 734015
[37]	Zhu A X, 1997. A similarity model for representing soil spatial information. Geoderma, 77:217-242. doi:10.1016/S0016- 7061(97)00023-2
[38]	Zhu A X, Band L E, 1994. A knowledge-based approach to data integration for soil mapping. Canadian Journal of Remote Sensing, 20:408-418. doi: 10.1080/07038992.1994.10874583

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Get Citation

PDF

XML

Article Metrics

Article views(311) PDF downloads(1427) Cited by()

Proportional views

Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta, China

1. State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

2. Department of Geography, University of Wisconsin-Madison, Madison WI 53706, USA;

3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application and School of Geography, Nanjing Normal University, Nanjing 210023, China

Corresponding author: HUANG Chong. E-mail:huangch@lreis.ac.cn

Received Date: 2014-06-03

Rev Recd Date: 2014-08-29

Publish Date: 2015-03-27

Key words:

Abstract: Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe (Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30-40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Indices (NDVIs) and land surface reflectance data from Landsat Thematic Mapper (TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient (CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30-40 cm depth in the study area (with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.

HTML

Reference (38)

[1]	Abbas A, Khan S, Hussain N et al., 2013. Characterizing soil salinity in irrigated agriculture using a remote sensing approach. Physics and Chemistry of the Earth, 55-57:43-52. doi:10.1016/j.pce.2010.12.004
[2]	Akramkhanov A, Martius C, Park S J et al., 2011. Environmental factors of spatial distribution of soil salinity on flat irrigated terrain. Geoderma, 163(1-2):55-62. doi:10.1016/j.geoderma. 2011.04.001
[3]	Cui B S, Tang N, Zhao X S et al., 2009. A management-oriented valuation method to determine ecological water requirement for wetlands in the Yellow River Delta of China. Journal for Nature Conservation, 17(3):129-141. doi:10.1016/j.jnc.2009. 01.003
[4]	Fan X, Pedroli B, Liu G H et al., 2012. Soil salinity development in the yellow river delta in relation to groundwater dynamics. Land Degradation and Development, 23(2):175-189. doi:10.1002/ldr.1071
[5]	Fang H L, Liu G H, Kearney M, 2005. Georelational analysis of soil type, soil salt content, landform, and land use in the Yellow River Delta, China. Environment Management, 35(1):72-83. doi:10.1007/s00267-004-3066-2
[6]	Farifteh J, Farshad A, George R J, 2006. Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma, 130(3-4):191-206. doi:10.1016/j.geoderma.2005. 02.003
[7]	Ghassemi F, Jakerman A J, Nix H A, 1995. Salinization of Land Water Resources. Wallingford:CAB International.
[8]	Gower J C, 1971. A general coefficient of similarity and some of its properties. Biometrics, 27(4):857-871. doi:10.2307/2528823
[9]	Hengl T, Heuvelink G B M, Rossiter D G, 2007. About regres-sion-kriging:from equations to case studies. Computers & Geosciences, 33(10):1301-1315. doi:10.1016/j.cageo.2007. 05.001
[10]	Jafari A, Finke P A, de Wauw J V et al., 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. doi:10.1111/j.1365-2389.2012.01425.x
[11]	Li S N, Wang G X, Deng W et al., 2009. Influence of hydrology process on wetland landscape pattern:a case study in the Yel-low River Delta. Ecological Engineering, 35(12):1719-1726. doi:10.1016/j.ecoleng.2009.07.009
[12]	Liu Jing, 2010. Mapping Soil Properties Using Individual Repre-sentativeness of Samples over Large Area. Beijing:Beijing Normal University. (in Chinese)
[13]	Ma Yulei, Wang De, Liu Junmin et al., 2013. Relationships be-tween typical vegetations, soil salinity, and groundwater depth in the Yellow River Delta of China. Chinese Journal of Applied Ecology, 24(9):2423-2430. (in Chinese)
[14]	Metternicht G I, Zinck J A, 2003. Remote sensing of soil salinity:potentials and constraints. Remote Sensing of Environment, 85(1):1-20. doi:10.1016/S0034-4257(02)00188-8
[15]	Minasny B, McBratney A B, Mendonca-Santos M L et al., 2006. Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley. Australian Journal of Soil Research, 44(3):233-244. doi:10.1071/SR05136
[16]	Mougenot B, Pouget M, Epema G, 1993. Remote sensing of salt-affected soils. Remote Sensing Reviews, 7(3-4):241-259. doi:10.1080/02757259309532180
[17]	Qin C Z, Zhu A X, Pei T et al., 2007. An adaptive approach to selecting a flow-partition exponent for a multiple-flow-direction algorithm. International Journal of Geographical Information Science, 21(4):443-458. doi:10.1080/13658810601073240
[18]	Qin C Z, Lu Y J, Li B L et al., 2009. Simple digital terrain analy-sis software (SimDTA 1.0) and its application in fuzzy classi-fication of slope positions. Journal of Geo-Information Science, 11(6):737-743. (in Chinese)
[19]	Quinn P, Beven K J, Lamb R, 1995. The ln(a/tanb) index:how to calculate it and how to use it within the TOPMODEL frame-work. Hydrological Processes, 9(2):161-182. doi:10.1002/hyp.3360090204
[20]	Rodgers J L, Nicewander W A, 1988. Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1):59-66. doi:10.1080/00031305.1988.10475524
[21]	Scull P, Franklin J, Chadwick O A, 2005. The application of clas-sification of tree analysis to soil type prediction in a desert landscape. Ecological Modelling, 181(1):1-15. doi:10.1016/j. ecolmodel.2004.06.036
[22]	Sheng J D, Ma L C, Jiang P A et al., 2010. Digital soil mapping to enable classification of the salt-affected soils in desert agro-ecological zones. Agricultural Water Management, 97:1944-1951. doi:10.1016/j.agwat.2009.04.011
[23]	Shi X, Zhu A X, Burt J E et al., 2004. A case-based reasoning approach to fuzzy soil mapping. Soil Science Society of America Journal, 68(3):885-894. doi:10.2136/sssaj2004. 8850
[24]	Song Chuangye, Huang Chong, Liu Huimin, 2013. Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models. Chinese Geographical Science, 23(3):331-343. doi:10.1007/s11769-013-0590-0
[25]	Taghizadeh-Mehrjardi R, Minasny B, Sarmadian F et al., 2014. Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213:15-28. doi:10.1016/j.geoderma.2013. 07.020
[26]	Triantafilis J, Odeh I O A, Mcbratney A B, 2001. Five geostatis-tical models to predict soil salinity from electromagnetic in-duction data across irrigated cotton. Soil Science Society of America Journal, 65(3):869-878. doi:10.2136/sssaj2001. 653869x
[27]	Wang X G, Lian Y, Huang C et al., 2011. Environmental flows and its evaluation of restoration effect based on LEDESS model in Yellow River Delta wetlands. Mitigation and Adaptation Strategies for Global Change, 17(4):357-367. doi:10.1007/s11027-011-9330-x
[28]	Webster R, Oliver M A, 2001. Geostatistics for Environmental Science. Toronto, Canada:John Wiley and Sons, LTD.
[29]	Xie T, Liu X H, Sun T, 2011. The effects of groundwater table and flood irrigation strategies on soil water and salt dynamics and reed water use in the Yellow River Delta, China. Ecological Modelling, 222(2):241-252. doi:10.1016/j.ecolmodel. 2010.01.012
[30]	Xu Xuegong, 1997. An analysis on the land structure in the Yellow River Delta. Acta Geographical Sinica, 64(1):18-26. (in Chinese)
[31]	Yang L, Zhu A X, Qi F et al., 2013. An integrative hierarchical stepwise sampling strategy and its application in digital soil mapping. International Journal of Geographical Information Science, 27(1):1-23. doi:10.1080/13658816.2012.658053
[32]	Yao R J, Yang J S, 2010. Quantitative evaluation of soil salinity and its spatial distribution using electromagnetic induction method. Agricultural Water Management, 97(12):1961-1970. doi:10.1016/j.agwat.2010.02.001
[33]	Ye Q H, Liu G H, Tian G L, 2004. Geospatial-temporal analysis of land-use changes in the Yellow River Delta in the last 40 years. Science in China Series D Earth Sciences, 47(11):1008-1024. doi:10.1360/03yd0151
[34]	Zhang T T, Zeng S L, Gao Y et al., 2011. Assessing impact of land uses on land salinization in the Yellow River Delta, China using an integrated and spatial statistical model. Land Use Policy, 28(4):857-866. doi:10.1016/j.landusepol.2011.03.002
[35]	Zhao X S, Cui B S, Sun T et al., 2010. The relationship between the spatial distribution of vegetation and soil environmental factors in the tidal creek areas of the Yellow River Delta. Ecology and Environmental Sciences, 19(8):1855-1861. (in Chinese)
[36]	Zhou W Z, Tian Y Z, Zhu L F, 2007. Land use/land cover change in Yellow River Delta China during fast development period. Conference on Remote Sensing and Modelling of Ecosystems for Sustainability IV, San Antonio, CA. doi:10.1117/12. 734015
[37]	Zhu A X, 1997. A similarity model for representing soil spatial information. Geoderma, 77:217-242. doi:10.1016/S0016- 7061(97)00023-2
[38]	Zhu A X, Band L E, 1994. A knowledge-based approach to data integration for soil mapping. Canadian Journal of Remote Sensing, 20:408-418. doi:10.1080/07038992.1994.10874583

Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta, China

doi: 10.1007/s11769-015-0740-7

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related