Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Study Area
2.2. Data Collection and Soil Sample Analyses
2.3. Collection of Auxiliary Data
2.3.1. Remote Sensing and Selection of Spectral Index
2.3.2. Proximal Sensing Data
2.3.3. Topographic Attributes
2.3.4. Geology and Geomorphology Maps
2.4. Preprocessing and Feature Selection
2.5. Modeling Approaches
2.6. Assessment of Model Performance
3. Results
3.1. Descriptive Statistics of Field Measurements (ECe, ECa, and pXRF)
3.2. Correlations between ECe and Environmental Covariates
3.3. Covariate Importance
3.4. Modeling Performance and Evaluation
3.5. Spatial Distribution of Soil Salinity
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- El Harti, A.; Lhissou, R.; Chokmani, K.; Ouzemou, J.; Hassouna, M.; Bachaoui, E.M.; El Ghmari, A. Spatiotemporal Monitoring of Soil Salinization in Irrigated Tadla Plain (Morocco) Using Satellite Spectral Indices. Int. J. Appl. Earth Obs. Geoinf. 2016, 50, 64–73. [Google Scholar] [CrossRef]
- Zaman, M.; Shahid, S.A.; Heng, L. Irrigation Systems and Zones of Salinity Development. In Guideline for Salinity Assessment, Mitigation and Adaptation Using Nuclear and Related Techniques; Springer International Publishing: Cham, Switzerland, 2018; pp. 91–111. ISBN 978-3-319-96189-7. [Google Scholar]
- Farahmand, N.; Sadeghi, V. Estimating Soil Salinity in the Dried Lake Bed of Urmia Lake Using Optical Sentinel-2 Images and Nonlinear Regression Models. J. Indian Soc. Remote Sens. 2020, 48, 675–687. [Google Scholar] [CrossRef]
- Taghizadeh-Mehrjardi, R.; Minasny, B.; Sarmadian, F.; Malone, B.P. Digital Mapping of Soil Salinity in Ardakan Region, Central Iran. Geoderma 2014, 213, 15–28. [Google Scholar] [CrossRef]
- Wang, J.; Peng, J.; Li, H.; Yin, C.; Liu, W.; Wang, T.; Zhang, H. Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China. Remote Sens. 2021, 13, 305. [Google Scholar] [CrossRef]
- Li, J.; Pu, L.; Zhu, M.; Dai, X.; Xu, Y.; Chen, X.; Zhang, L.; Zhang, R. Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China. Chin. Geogr. Sci. 2015, 25, 213–223. [Google Scholar] [CrossRef]
- McBratney, A.B.; Mendonça Santos, M.L.; Minasny, B. On Digital Soil Mapping. Geoderma 2003, 117, 3–52. [Google Scholar] [CrossRef]
- McBratney, A.; Field, D.; Morgan, C.L.S.; Huang, J. On Soil Capability, Capacity, and Condition. Sustainability 2019, 11, 3350. [Google Scholar] [CrossRef] [Green Version]
- Viscarra Rossel, R.A.; Behrens, T.; Ben-Dor, E.; Brown, D.J.; Demattê, J.A.M.; Shepherd, K.D.; Shi, Z.; Stenberg, B.; Stevens, A.; Adamchuk, V.; et al. A Global Spectral Library to Characterize the World’s Soil. Earth-Sci. Rev. 2016, 155, 198–230. [Google Scholar] [CrossRef] [Green Version]
- Hengl, T.; Mendes de Jesus, J.; Heuvelink, G.B.M.; Ruiperez Gonzalez, M.; Kilibarda, M.; Blagotić, A.; Wei, S.; Wright, M.N.; Geng, X.; Bauer-Marschallinger, B.; et al. SoilGrids250m: Global Gridded Soil Information Based on Machine Learning. PLoS ONE 2017, 12, e0169748. [Google Scholar] [CrossRef] [Green Version]
- Goydaragh, M.G.; Taghizadeh-Mehrjardi, R.; Jafarzadeh, A.A.; Triantafilis, J.; Lado, M. Using Environmental Variables and Fourier Transform Infrared Spectroscopy to Predict Soil Organic Carbon. CATENA 2021, 202, 105280. [Google Scholar] [CrossRef]
- Jafari, A.; Finke, P.A.; Vande Wauw, J.; Ayoubi, S.; Khademi, H. Spatial Prediction of USDA- Great Soil Groups in the Arid Zarand Region, Iran: Comparing Logistic Regression Approaches to Predict Diagnostic Horizons and Soil Types. Eur. J. Soil Sci. 2012, 63, 284–298. [Google Scholar] [CrossRef]
- Zeraatpisheh, M.; Jafari, A.; Bagheri Bodaghabadi, M.; Ayoubi, S.; Taghizadeh-Mehrjardi, R.; Toomanian, N.; Kerry, R.; Xu, M. Conventional and Digital Soil Mapping in Iran: Past, Present, and Future. CATENA 2020, 188, 104424. [Google Scholar] [CrossRef]
- Ma, Y.; Minasny, B.; Welivitiya, W.D.D.P.; Malone, B.P.; Willgoose, G.R.; McBratney, A.B. The Feasibility of Predicting the Spatial Pattern of Soil Particle-Size Distribution Using a Pedogenesis Model. Geoderma 2019, 341, 195–205. [Google Scholar] [CrossRef]
- Naimi, S.; Ayoubi, S.; Demattê, J.A.M.; Zeraatpisheh, M.; Amorim, M.T.A.; de Oliveira Mello, F.A. Spatial Prediction of Soil Surface Properties in an Arid Region Using Synthetic Soil Image and Machine Learning. Geocarto Int. 2021. [Google Scholar] [CrossRef]
- Hosseini, M.; Rajabi Agereh, S.; Khaledian, Y.; Jafarzadeh Zoghalchali, H.; Brevik, E.C.; Movahedi Naeini, S.A.R. Comparison of Multiple Statistical Techniques to Predict Soil Phosphorus. Appl. Soil Ecol. 2017, 114, 123–131. [Google Scholar] [CrossRef]
- Emadi, M.; Taghizadeh-Mehrjardi, R.; Cherati, A.; Danesh, M.; Mosavi, A.; Scholten, T. Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran. Remote Sens. 2020, 12, 2234. [Google Scholar] [CrossRef]
- Aldabaa, A.A.A.; Weindorf, D.C.; Chakraborty, S.; Sharma, A.; Li, B. Combination of Proximal and Remote Sensing Methods for Rapid Soil Salinity Quantification. Geoderma 2015, 239–240, 34–46. [Google Scholar] [CrossRef] [Green Version]
- Tajik, S.; Ayoubi, S.; Zeraatpisheh, M. Digital Mapping of Soil Organic Carbon Using Ensemble Learning Model in Mollisols of Hyrcanian Forests, Northern Iran. Geoderma Reg. 2020, 20, e00256. [Google Scholar] [CrossRef]
- Mondal, A.; Khare, D.; Kundu, S.; Mondal, S.; Mukherjee, S.; Mukhopadhyay, A. Spatial Soil Organic Carbon (SOC) Prediction by Regression Kriging Using Remote Sensing Data. Egypt. J. Remote Sens. Space Sci. 2017, 20, 61–70. [Google Scholar] [CrossRef] [Green Version]
- Shen, Q.; Wang, Y.; Wang, X.; Liu, X.; Zhang, X.; Zhang, S. Comparing Interpolation Methods to Predict Soil Total Phosphorus in the Mollisol Area of Northeast China. CATENA 2019, 174, 59–72. [Google Scholar] [CrossRef]
- Forkuor, G.; Hounkpatin, O.K.L.; Welp, G.; Thiel, M. High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models. PLoS ONE 2017, 12, e0170478. [Google Scholar] [CrossRef]
- Amirian-Chakan, A.; Minasny, B.; Taghizadeh-Mehrjardi, R.; Akbarifazli, R.; Darvishpasand, Z.; Khordehbin, S. Some Practical Aspects of Predicting Texture Data in Digital Soil Mapping. Soil Tillage Res. 2019, 194, 104289. [Google Scholar] [CrossRef]
- Poppiel, R.R.; Demattê, J.A.M.; Rosin, N.A.; Campos, L.R.; Tayebi, M.; Bonfatti, B.R.; Ayoubi, S.; Tajik, S.; Afshar, F.A.; Jafari, A.; et al. High Resolution Middle Eastern Soil Attributes Mapping via Open Data and Cloud Computing. Geoderma 2021, 385, 114890. [Google Scholar] [CrossRef]
- Mohammed, S.; Al-Ebraheem, A.; Holb, I.J.; Alsafadi, K.; Dikkeh, M.; Pham, Q.B.; Linh, N.T.T.; Szabo, S. Soil Management Effects on Soil Water Erosion and Runoff in Central Syria—A Comparative Evaluation of General Linear Model and Random Forest Regression. Water 2020, 12, 2529. [Google Scholar] [CrossRef]
- Taghizadeh-Mehrjardi, R.; Minasny, B.; Toomanian, N.; Zeraatpisheh, M.; Amirian-Chakan, A.; Triantafilis, J. Digital Mapping of Soil Classes Using Ensemble of Models in Isfahan Region, Iran. Soil Syst. 2019, 3, 37. [Google Scholar] [CrossRef] [Green Version]
- Bannari, A.; El-Battay, A.; Bannari, R.; Rhinane, H. Sentinel-MSI VNIR and SWIR Bands Sensitivity Analysis for Soil Salinity Discrimination in an Arid Landscape. Remote Sens. 2018, 10, 855. [Google Scholar] [CrossRef] [Green Version]
- Taghadosi, M.M.; Hasanlou, M.; Eftekhari, K. Retrieval of Soil Salinity from Sentinel-2 Multispectral Imagery. Eur. J. Remote Sens. 2019, 52, 138–154. [Google Scholar] [CrossRef] [Green Version]
- Wang, J.; Ding, J.; Yu, D.; Teng, D.; He, B.; Chen, X.; Ge, X.; Zhang, Z.; Wang, Y.; Yang, X.; et al. Machine Learning-Based Detection of Soil Salinity in an Arid Desert Region, Northwest China: A Comparison between Landsat-8 OLI and Sentinel-2 MSI. Sci. Total Environ. 2020, 707, 136092. [Google Scholar] [CrossRef]
- Wang, J.; Ding, J.; Li, G.; Liang, J.; Yu, D.; Aishan, T.; Zhang, F.; Yang, J.; Abulimiti, A.; Liu, J. Dynamic Detection of Water Surface Area of Ebinur Lake Using Multi-Source Satellite Data (Landsat and Sentinel-1A) and Its Responses to Changing Environment. CATENA 2019, 177, 189–201. [Google Scholar] [CrossRef]
- Gorji, T.; Yildirim, A.; Hamzehpour, N.; Tanik, A.; Sertel, E. Soil Salinity Analysis of Urmia Lake Basin Using Landsat-8 OLI and Sentinel-2A Based Spectral Indices and Electrical Conductivity Measurements. Ecol. Indic. 2020, 112, 106173. [Google Scholar] [CrossRef]
- Viscarra Rossel, R.A.; Adamchuk, V.I.; Sudduth, K.A.; McKenzie, N.J.; Lobsey, C. Proximal Soil Sensing: An Effective Approach for Soil Measurements in Space and Time. In Advances in Agronomy; Elsevier: Amsterdam, The Netherlands, 2011; Volume 113, pp. 243–291. ISBN 978-0-12-386473-4. [Google Scholar]
- Grunwald, S.; Vasques, G.M.; Rivero, R.G. Fusion of Soil and Remote Sensing Data to Model Soil Properties. In Advances in Agronomy; Elsevier: Amsterdam, The Netherlands, 2015; Volume 131, pp. 1–109. ISBN 978-0-12-802136-1. [Google Scholar]
- Guo, Y.; Huang, J.; Shi, Z.; Li, H. Mapping Spatial Variability of Soil Salinity in a Coastal Paddy Field Based on Electromagnetic Sensors. PLoS ONE 2015, 10, e0127996. [Google Scholar] [CrossRef]
- Yao, R.; Yang, J.; Wu, D.; Xie, W.; Gao, P.; Jin, W. Digital Mapping of Soil Salinity and Crop Yield across a Coastal Agricultural Landscape Using Repeated Electromagnetic Induction (EMI) Surveys. PLoS ONE 2016, 11, e0153377. [Google Scholar] [CrossRef]
- Nouri, H.; Chavoshi Borujeni, S.; Alaghmand, S.; Anderson, S.; Sutton, P.; Parvazian, S.; Beecham, S. Soil Salinity Mapping of Urban Greenery Using Remote Sensing and Proximal Sensing Techniques; The Case of Veale Gardens within the Adelaide Parklands. Sustainability 2018, 10, 2826. [Google Scholar] [CrossRef] [Green Version]
- Ding, J.; Yang, S.; Shi, Q.; Wei, Y.; Wang, F. Using Apparent Electrical Conductivity as Indicator for Investigating Potential Spatial Variation of Soil Salinity across Seven Oases along Tarim River in Southern Xinjiang, China. Remote Sens. 2020, 12, 2601. [Google Scholar] [CrossRef]
- Swanhart, S.; Weindorf, D.C.; Chakraborty, S.; Bakr, N.; Zhu, Y.; Nelson, C.; Shook, K.; Acree, A. Soil Salinity Measurement Via Portable X-Ray Fluorescence Spectrometry. Soil Sci. 2014, 179, 417–423. [Google Scholar] [CrossRef]
- Vasques, G.M.; Rodrigues, H.M.; Coelho, M.R.; Baca, J.F.M.; Dart, R.O.; Oliveira, R.P.; Teixeira, W.G.; Ceddia, M.B. Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps. Soil Syst. 2020, 4, 52. [Google Scholar] [CrossRef]
- Silva, S.H.G.; dos Santos Teixeira, A.F.; de Menezes, M.D.; Guilherme, L.R.G.; de Souza Moreira, F.M.; Curi, N. Multiple Linear Regression and Random Forest to Predict and Map Soil Properties Using Data from Portable X-Ray Fluorescence Spectrometer (PXRF). Ciênc. Agrotec. 2017, 41, 648–664. [Google Scholar] [CrossRef]
- Silva, S.H.G.; Weindorf, D.C.; Faria, W.M.; Pinto, L.C.; Menezes, M.D.; Guilherme, L.R.G.; Curi, N. Proximal Sensor-Enhanced Soil Mapping in Complex Soil-Landscape Areas of Brazil. Pedosphere 2021, 31, 615–626. [Google Scholar] [CrossRef]
- Bilgili, A.V.; Cullu, M.A.; van Es, H.; Aydemir, A.; Aydemir, S. The Use of Hyperspectral Visible and Near Infrared Reflectance Spectroscopy for the Characterization of Salt-Affected Soils in the Harran Plain, Turkey. Arid Land Res. Manag. 2011, 25, 19–37. [Google Scholar] [CrossRef]
- Islamic Republic of Iran Meteorological Organization|GFCS. Available online: https://gfcs.wmo.int/node/65 (accessed on 23 November 2021).
- Fars Meteorological Bureau. Available online: https://www.farsmet.ir/ (accessed on 23 November 2021).
- Geological Map of Iran 1:100,000 Series [Cartographic Material]. Available online: https://nla.gov.au/nla.obj-233247255 (accessed on 23 November 2021).
- Soil Survey Staff Keys to Soil Taxonomy, 12th ed.; USDA-Natural Resources Conservation Service: Washington, DC, USA, 2014.
- Minasny, B.; McBratney, A.B. A Conditioned Latin Hypercube Method for Sampling in the Presence of Ancillary Information. Comput. Geosci. 2006, 32, 1378–1388. [Google Scholar] [CrossRef]
- Richards, L. Determination of the Properties of Saline and Alkali Soils. In Diagnosis and Improvement of Saline and Alkali Soils, Agriculture Handbook; No. 60; US Regional Salinity Laboratory: Riverside, CA, USA, 1954; Volume 60, pp. 7–33. [Google Scholar]
- Sparks, D.L. Methods of Soil Analysis. Part. 3: Chemical Methods; Soil Science Society of America, American Society of Agronomy: Madison, WI, USA, 1996; ISBN 978-0-89118-825-4. [Google Scholar]
- Sun, Y.; Cheng, Q.; Lin, J.; Schellberg, J.; Schulze Lammers, P. Investigating Soil Physical Properties and Yield Response in a Grassland Field Using a Dual-Sensor Penetrometer and EM38. Z. Pflanzenernähr. Bodenk. 2013, 176, 209–216. [Google Scholar] [CrossRef]
- Brevik, E.C.; Fenton, T.E.; Horton, R. Effect of Daily Soil Temperature Fluctuations on Soil Electrical Conductivity as Measured with the GeonicsÒ EM-38. Precis. Agric. 2004, 5, 145–152. [Google Scholar] [CrossRef]
- U.S. Salinity Laboratory Staff. Diagnosis and Improvement of Saline and Alcaly Soils; Handbook 60; USDA: Washington, DC, USA, 1954.
- Demattê, J.A.M.; Safanelli, J.L.; Poppiel, R.R.; Rizzo, R.; Silvero, N.E.Q.; de Sousa Mendes, W.; Bonfatti, B.R.; Dotto, A.C.; Salazar, D.F.U.; de Oliveira Mello, F.A.; et al. Bare Earth’s Surface Spectra as a Proxy for Soil Resource Monitoring. Sci. Rep. 2020, 10, 4461. [Google Scholar] [CrossRef] [PubMed]
- Douaoui, A.E.K.; Nicolas, H.; Walter, C. Detecting Salinity Hazards within a Semiarid Context by Means of Combining Soil and Remote-Sensing Data. Geoderma 2006, 134, 217–230. [Google Scholar] [CrossRef]
- Rahmati, M.; Mohammadi-Oskooei, M.; Neyshabouri, M.; Fakheri-Fard, A.; Ahmadi, A.; Walker, J. ETM+ Data Applicability for Remote Sensing of Soil Salinity in Lighvan Watershed, Northwest of Iran. Curr. Opin. Agric. 2014, 3, 10–13. [Google Scholar]
- Allbed, A.; Kumar, L. Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review. ARS 2013, 02, 373–385. [Google Scholar] [CrossRef] [Green Version]
- Allbed, A.; Kumar, L.; Aldakheel, Y.Y. Assessing Soil Salinity Using Soil Salinity and Vegetation Indices Derived from IKONOS High-Spatial Resolution Imageries: Applications in a Date Palm Dominated Region. Geoderma 2014, 230–231, 1–8. [Google Scholar] [CrossRef]
- Cho, K.H.; Beon, M.-S.; Jeong, J.-C. Dynamics of Soil Salinity and Vegetation in a Reclaimed Area in Saemangeum, Republic of Korea. Geoderma 2018, 321, 42–51. [Google Scholar] [CrossRef]
- Qiu, Y.; Chen, C.; Han, J.; Wang, X.; Wei, S.; Zhang, Z. Satellite Remote Sensing Estimation Model of Soil Salinity in Jiefangzha Irrigation under Vegetation Coverage. Water Sav. Irrig. 2019, 44, 108–112. (In Chinese) [Google Scholar]
- Fan, X.; Pedroli, B.; Liu, G.; Liu, Q.; Liu, H.; Shu, L. Soil Salinity Development in the Yellow River Delta in Relation to Groundwater Dynamics. Land Degrad. Dev. 2012, 23, 175–189. [Google Scholar] [CrossRef]
- Toomanian, N.; Jalalian, A.; Khademi, H.; Eghbal, M.K.; Papritz, A. Pedodiversity and Pedogenesis in Zayandeh-Rud Valley, Central Iran. Geomorphology 2006, 81, 376–393. [Google Scholar] [CrossRef]
- Copernicus Open Access Hub. Available online: https://scihub.copernicus.eu/ (accessed on 23 November 2021).
- European Space Agency. Available online: https://www.esa.int/ (accessed on 23 November 2021).
- Demattê, J.A.M.; Fongaro, C.T.; Rizzo, R.; Safanelli, J.L. Geospatial Soil Sensing System (GEOS3): A Powerful Data Mining Procedure to Retrieve Soil Spectral Reflectance from Satellite Images. Remote Sens. Environ. 2018, 212, 161–175. [Google Scholar] [CrossRef]
- Main-Knorn, M.; Pflug, B.; Louis, J.; Debaecker, V.; Müller-Wilm, U.; Gascon, F. Sen2Cor for Sentinel-2. In Proceedings of the Image and Signal Processing for Remote Sensing XXIII, Warsaw, Poland, 4 October 2017; Bruzzone, L., Bovolo, F., Benediktsson, J.A., Eds.; SPIE: Warsaw, Poland, 2017; p. 3. [Google Scholar]
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2019. [Google Scholar]
- Gorji, T.; Sertel, E.; Tanik, A. Monitoring Soil Salinity via Remote Sensing Technology under Data Scarce Conditions: A Case Study from Turkey. Ecol. Indic. 2017, 74, 384–391. [Google Scholar] [CrossRef]
- Peng, J.; Biswas, A.; Jiang, Q.; Zhao, R.; Hu, J.; Hu, B.; Shi, Z. Estimating Soil Salinity from Remote Sensing and Terrain Data in Southern Xinjiang Province, China. Geoderma 2019, 337, 1309–1319. [Google Scholar] [CrossRef]
- Ministry of Economy, Trade and Industry of Japan, National Aeronautics and Space Administration. Available online: http://www.gdem.aster.ersdac.or.jp (accessed on 3 March 2021).
- Scull, P.; Franklin, J.; Chadwick, O.A. The Application of Classification Tree Analysis to Soil Type Prediction in a Desert Landscape. Ecol. Model. 2005, 181, 1–15. [Google Scholar] [CrossRef]
- Ließ, M.; Schmidt, J.; Glaser, B. Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches. PLoS ONE 2016, 11, e0153673. [Google Scholar] [CrossRef] [Green Version]
- Kuhn, M. Variable Selection Using the Caret Package. Available online: http//cran.cermin.lipi.go.id/web/packages/caret/vignettes/caretSelection.pdf (accessed on 3 April 2021).
- Guo, L.; Fu, P.; Shi, T.; Chen, Y.; Zhang, H.; Meng, R.; Wang, S. Mapping Field-Scale Soil Organic Carbon with Unmanned Aircraft System-Acquired Time Series Multispectral Images. Soil Tillage Res. 2020, 196, 104477. [Google Scholar] [CrossRef]
- Mansuy, N.; Thiffault, E.; Paré, D.; Bernier, P.; Guindon, L.; Villemaire, P.; Poirier, V.; Beaudoin, A. Digital Mapping of Soil Properties in Canadian Managed Forests at 250m of Resolution Using the K-Nearest Neighbor Method. Geoderma 2014, 235–236, 59–73. [Google Scholar] [CrossRef]
- Wei, T.; Simko, V.; Levy, M.; Xie, Y.; Jin, Y.; Zemla, J.; Freidank, M.; Cai, J.; Protivinsky, T. Corrplot: Visualization of a Correlation Matrix. 2021. Available online: https://CRAN.R-project.org/package=corrplot/ (accessed on 23 November 2021).
- Hengl, T.; Heuvelink, G.B.M.; Kempen, B.; Leenaars, J.G.B.; Walsh, M.G.; Shepherd, K.D.; Sila, A.; MacMillan, R.A.; Mendes de Jesus, J.; Tamene, L.; et al. Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS ONE 2015, 10, e0125814. [Google Scholar] [CrossRef]
- Esfandiarpoor Borujeni, I.; Mohammadi, J.; Salehi, M.H.; Toomanian, N.; Poch, R.M. Assessing Geopedological Soil Mapping Approach by Statistical and Geostatistical Methods: A Case Study in the Borujen Region, Central Iran. CATENA 2010, 82, 1–14. [Google Scholar] [CrossRef]
- Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J.C.; Sheridan, R.P.; Feuston, B.P. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. J. Chem. Inf. Comput. Sci. 2003, 43, 1947–1958. [Google Scholar] [CrossRef] [PubMed]
- Wang, F.; Shi, Z.; Biswas, A.; Yang, S.; Ding, J. Multi-Algorithm Comparison for Predicting Soil Salinity. Geoderma 2020, 365, 114211. [Google Scholar] [CrossRef]
- Clay, D.E.; Chang, J.; Malo, D.D.; Carlson, C.G.; Reese, C.; Clay, S.A.; Ellsbury, M.; Berg, B. Factors Influencing Spatial Variability of Soil Apparent Electrical Conductivity. Commun. Soil Sci. Plant. Anal. 2001, 32, 2993–3008. [Google Scholar] [CrossRef] [Green Version]
- Pozdnyakova, L.; Zhang, R. Geostatistical Analyses of Soil Salinity in a Large Field. Precis. Agric. 1999, 1, 153–165. [Google Scholar] [CrossRef]
- Yang, L.; Huang, C.; Liu, G.; Liu, J.; Zhu, A.-X. Mapping Soil Salinity Using a Similarity-Based Prediction Approach: A Case Study in Huanghe River Delta, China. Chin. Geogr. Sci. 2015, 25, 283–294. [Google Scholar] [CrossRef]
- Akramkhanov, A.; Martius, C.; Park, S.J.; Hendrickx, J.M.H. Environmental Factors of Spatial Distribution of Soil Salinity on Flat Irrigated Terrain. Geoderma 2011, 163, 55–62. [Google Scholar] [CrossRef]
- Sugimori, Y.; Funakawa, S.; Pachikin, K.M.; Ishida, N.; Kosaki, T. Soil Salinity Dynamics in Irrigated Fields and Its Effects on Paddy-Based Rotation Systems in Southern Kazakhstan. Land Degrad. Dev. 2008, 19, 305–320. [Google Scholar] [CrossRef]
- Ma, G.; Ding, J.; Han, L.; Zhang, Z.; Ran, S. Digital Mapping of Soil Salinization Based on Sentinel-1 and Sentinel-2 Data Combined with Machine Learning Algorithms. Reg. Sustain. 2021, 2, 177–188. [Google Scholar] [CrossRef]
- Sidike, A.; Zhao, S.; Wen, Y. Estimating Soil Salinity in Pingluo County of China Using QuickBird Data and Soil Reflectance Spectra. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 156–175. [Google Scholar] [CrossRef]
- Han, L.; Liu, D.; Cheng, G.; Zhang, G.; Wang, L. Spatial Distribution and Genesis of Salt on the Saline Playa at Qehan Lake, Inner Mongolia, China. CATENA 2019, 177, 22–30. [Google Scholar] [CrossRef]
- Wang, J.; Ding, J.; Yu, D.; Ma, X.; Zhang, Z.; Ge, X.; Teng, D.; Li, X.; Liang, J.; Lizaga, I.; et al. Capability of Sentinel-2 MSI Data for Monitoring and Mapping of Soil Salinity in Dry and Wet Seasons in the Ebinur Lake Region, Xinjiang, China. Geoderma 2019, 353, 172–187. [Google Scholar] [CrossRef]
- Zeraatpisheh, M.; Garosi, Y.; Reza Owliaie, H.; Ayoubi, S.; Taghizadeh-Mehrjardi, R.; Scholten, T.; Xu, M. Improving the Spatial Prediction of Soil Organic Carbon Using Environmental Covariates Selection: A Comparison of a Group of Environmental Covariates. CATENA 2022, 208, 105723. [Google Scholar] [CrossRef]
- Peng, J.; Liu, H.; Shi, Z.; Xiang, H.; Chi, C. Regional Heterogeneity of Hyperspectral Characteristics of Salt-Affected Soil and Salinity Inversion. Trans. Chin. Soc. Agric. Eng. 2014, 30, 167–174. [Google Scholar]
- Xu, C.; Zeng, W.; Huang, J.; Wu, J.; van Leeuwen, W. Prediction of Soil Moisture Content and Soil Salt Concentration from Hyperspectral Laboratory and Field Data. Remote Sens. 2016, 8, 42. [Google Scholar] [CrossRef] [Green Version]
- Heung, B.; Ho, H.C.; Zhang, J.; Knudby, A.; Bulmer, C.E.; Schmidt, M.G. An Overview and Comparison of Machine-Learning Techniques for Classification Purposes in Digital Soil Mapping. Geoderma 2016, 265, 62–77. [Google Scholar] [CrossRef]
- Pakparvar, M.; Gabriels, D.; Aarabi, K.; Edraki, M.; Raes, D.; Cornelis, W. Incorporating Legacy Soil Data to Minimize Errors in Salinity Change Detection: A Case Study of Darab Plain, Iran. Int. J. Remote Sens. 2012, 33, 6215–6238. [Google Scholar] [CrossRef]
- Ding, J.; Yu, D. Monitoring and Evaluating Spatial Variability of Soil Salinity in Dry and Wet Seasons in the Werigan–Kuqa Oasis, China, Using Remote Sensing and Electromagnetic Induction Instruments. Geoderma 2014, 235–236, 316–322. [Google Scholar] [CrossRef]
- Metternicht, G.I.; Zinck, J.A. Remote Sensing of Soil Salinity: Potentials and Constraints. Remote Sens. Environ. 2003, 85, 1–20. [Google Scholar] [CrossRef]
- Bui, E.N. Soil Salinity: A Neglected Factor in Plant Ecology and Biogeography. J. Arid Environ. 2013, 92, 14–25. [Google Scholar] [CrossRef]
Landscape | Landform | Lithology | Geomorphological Surface | Code |
---|---|---|---|---|
Mountain | Eroded rock outcrop | Rock surfaces | Mo111 | |
Rock surfaces | Mo121 | |||
Hill | Eroded outcrop | Rock outcrops with braided stream | Hi111 | |
Piedmont | Alluvial fan | Active fan, upper section, high slope | Pi111 | |
Active fan, lower section, low slope | Pi112 | |||
Active fan, lower section, low slope | Pi121 | |||
Bajada | Upper section, high slope, dense drainage system | Pi211 | ||
Lower section, low slope | Pi212 | |||
Pediment | High slope, rocky | Pi311 | ||
Low slope, cultivated | Pi312 | |||
High slope, rocky | Pi321 | |||
Low slope, cultivated | Pi322 | |||
Low slope, cultivated | Pi331 | |||
High slope, rocky | Pi341 | |||
Low slope, cultivated | Pi342 | |||
Alluvial plain | Cultivated, saline | Pi411 | ||
Cultivated | Pi412 | |||
Cultivated | Pi421 | |||
Cultivated | Pi431 | |||
River | River sediments | Channel sediments | Ri111 | |
Playa | Clay flat | Cultivated clay flat | Pl111 | |
Salty and cultivated | Pl121 | |||
Gypsic, salty, and wetness | Pl131 | |||
Soft clay flat, salty, and cultivated | Pl132 | |||
Gypsic, salty with low drainage | Pl133 |
Auxiliary Data | Covariate | Definition | Reference/Source |
---|---|---|---|
Remote sensing data | SySI bands | Synthetic Soil Image | [53] |
Sentinel-2A bands | |||
Salinity Index 1 (SI) | [54,55] | ||
Salinity index 2 (SI1) | [54,55] | ||
Salinity Index 3 (SI2) | [54,55] | ||
Salinity Index 4 (SI3) | [54,55] | ||
Salinity Index 5 (S) | [56] | ||
Salinity Index (S1) | [57] | ||
Salinity Index (S2) | [57] | ||
Salinity Index (S3) | [57] | ||
Salinity Index (S5) | [57] | ||
Salinity Index (S6) | [57] | ||
Salinity Index-T (SI-T) | [57] | ||
Brightness Index (BI) | [57] | ||
Normalized Difference Salinity Index (NDSI) | [57] | ||
Normalized Difference Vegetation Index (NDVI) | [58] | ||
Enhanced Vegetation Index (EVI) | [59] | ||
Normalized Difference Vegetation Index red-edge 1 (NDVIre1) | [59] | ||
Normalized Difference Vegetation Index red-edge 2 (NDVIre2) | [59] | ||
Ratio Vegetation Index (RVI) | [60] | ||
Renormalized Difference Vegetation Index (RDVI) | [27] | ||
Proximal sensing data | Apparent Electrical Conductivity (ECa readings (ECah and ECav)) | Calculated the ECa of soil volume down to 1.5 m | Geonics Ltd. EM38 |
Portable X-Ray Fluorescence (pXRF) | elemental concentration (K, Ca, Cl and S) | ||
Topographic attributes | Elevation (m) | El | SAGA GIS |
Analytical Hillshading | AH | ||
Aspect | Aspect | ||
Channel Network Base Level | CNBL | ||
Cross-Sectional Curvature | CSCu | ||
Convergence Index | CI | ||
Closed Depressions | CD | ||
Flow Accumulation | FA | ||
General Curvature | GCu | ||
Longitudinal Curvature | LCu | ||
LS Factor | LSF | ||
Multi-resolution of Ridge Top Flatness Index | MRRTF | ||
Multi-resolution Valley Bottom Flatness Index | MRVBF | ||
Plan Curvature | PlCu | ||
Profile Curvature | PrCu | ||
Relative Slope Position | RSP | ||
Slope | Slope | ||
Topographic Wetness Index | TWI | ||
Total Curvature | TCu | ||
Valley Depth | Val_Dep | ||
Vertical Distance to Channel Network | VDCN | ||
Geology | Geology map | ||
Geomorphology | Geomorphology map | Geomorphology surfaces | [61] |
Soil Properties | Unit | Min | Max | Mean | StDev | CV% | Skew. | Kurt. |
---|---|---|---|---|---|---|---|---|
ECe | dSm−1 | 0.48 | 73.92 | 7.31 | 10.99 | 150.43 | 3.19 | 12.37 |
Log10 ECe | dSm−1 | −0.31 | 1.87 | 0.52 | 0.54 | 102.23 | 0.39 | −0.86 |
ECav | mSm−1 | 7 | 431 | 45.92 | 58.89 | 128.25 | 3.61 | 16.81 |
ECah | mSm−1 | 3 | 198 | 26.64 | 31.41 | 117.90 | 2.58 | 8.23 |
K | mg kg−1 | 914 | 18,410 | 13,485 | 2540.80 | 18.84 | −1.83 | 5.27 |
Ca | mg kg−1 | 116,000 | 454,000 | 285,170 | 53,118.13 | 18.62 | 0.47 | 0.78 |
S | mg kg−1 | 0 | 329,000 | 9816 | 36,159.78 | 368.34 | 5.88 | 38.84 |
Cl | mg kg−1 | 306 | 10,944 | 1265.80 | 1334.51 | 105.42 | 4.03 | 19.01 |
Model | Scenario (1) | Scenario (2) | Scenario (3) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | R2 | MAE | nRMSE | RMSE | R2 | MAE | nRMSE | RMSE | R2 | MAE | nRMSE | |
kNN | 3.38 ± 1.10 | 0.06 ± 0.07 | 2.79 ± 1.08 | 46.21 | 2.86 ± 1.11 | 0.29 ± 0.12 | 2.38 ± 1.09 | 39.13 | 2.92 ± 1.11 | 0.26 ± 0.10 | 2.41 ± 1.09 | 39.97 |
ANN | 3.07 ± 1.12 | 0.20 ± 0.14 | 2.55 ± 1.10 | 42.00 | 2.89 ± 1.13 | 0.28 ± 0.15 | 2.43 ± 1.12 | 39.59 | 2.99 ± 1.13 | 0.25 ± 0.15 | 2.49 ± 1.13 | 40.93 |
PLS | 3.13 ± 1.12 | 0.17 ± 0.12 | 2.59 ± 1.09 | 42.89 | 2.81 ± 1.12 | 0.32 ± 0.15 | 2.36 ± 1.09 | 38.45 | 2.93 ± 1.12 | 0.27 ± 0.14 | 2.41 ± 1.10 | 40.06 |
RF | 3.02 ± 1.13 | 0.22 ± 0.11 | 2.40 ± 1.10 | 41.39 | 2.50 ± 1.10 | 0.47 ± 0.11 | 2.12 ± 1.019 | 34.16 | 2.49 ± 1.11 | 0.48 ± 0.13 | 2.10 ± 1.09 | 34.11 |
SVM | 2.97 ± 1.14 | 0.26 ± 0.12 | 2.33 ± 1.11 | 40.59 | 2.64 ± 1.16 | 0.44 ± 0.12 | 2.15 ± 1.15 | 36.09 | 2.63 ± 1.16 | 0.45 ± 0.13 | 2.15 ± 1.15 | 35.94 |
Ensemble | 2.22 | 0.58 | 1.79 | 30.45 | 1.89 | 0.73 | 1.56 | 25.92 | 1.76 | 0.79 | 1.47 | 24.12 |
RI (%) * | 33.28 | 120.95 | 30.56 | 33.28 | 31.82 | 56.82 | 35.42 | 31.82 | 41.40 | 66.71 | 42.95 | 41.40 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Naimi, S.; Ayoubi, S.; Zeraatpisheh, M.; Dematte, J.A.M. Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach. Remote Sens. 2021, 13, 4825. https://doi.org/10.3390/rs13234825
Naimi S, Ayoubi S, Zeraatpisheh M, Dematte JAM. Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach. Remote Sensing. 2021; 13(23):4825. https://doi.org/10.3390/rs13234825
Chicago/Turabian StyleNaimi, Salman, Shamsollah Ayoubi, Mojtaba Zeraatpisheh, and Jose Alexandre Melo Dematte. 2021. "Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach" Remote Sensing 13, no. 23: 4825. https://doi.org/10.3390/rs13234825