Abstract
Soil salinization is a world-wide phenomenon that threatens ecological environment and agricultural production. Modeling soil salt content (SSC) is a big challenge because of its huge spatiotemporal variation and the interference of soil water content (SWC) and soil salt types. Prior studies showed more interest in the use of hyperspectral reflectance, while few studies focused on thermal infrared band domain. In this study, we arranged samples with three salt types and several levels of SWC and measured the soil emissivity for each sample at each level of SWC. We employed both original and derivate emissivity to figure out the relationship between SSC and soil thermal infrared spectra, then used partial least squares regression to estimate SSC. Finally, the optimal model was determined with the evaluation criteria, RPD (ratio of performance to deviation) for predictive ability and AICc (corrected Akaike Information Criterion) for simplicity. The models were applied to estimate SSC and coefficient of determination (R2) of 0.67, 0.71, 0.69 and 0.7, and root mean relative error of 4.03, 3.78, 3.92, 3.86 (g/100 g) was obtained, respectively, for NaCl, Na2SO4, Na2CO3 and all salt types. The study provided a comparison result of three salt types for soil salinity estimation and a criterion for modeling effectively and succinctly and should have potential applications in the future.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akaike, H. (1992). Information Theory and an Extension of the Maximum Likelihood Principle. In S. Kotz, & N. Johnson (Eds.), Breakthroughs in Statistics (pp. 610–624, Springer Series in Statistics): Springer New York.
Allbed, A., & Kumar, L. (2013). Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review. Advances in Remote Sensing, 02(04), 373–385. https://doi.org/10.4236/ars.2013.24040.
Antonucci, F., Pallottino, F., Costa, C., Rimatori, V., Giorgi, S., Papetti, P., et al. (2011). Development of a Rapid Soil Water Content Detection Technique Using Active Infrared Thermal Methods for In-Field Applications. Sensors (Basel), 11(11), 10114–10128. https://doi.org/10.3390/S111110114.
Artz, R. R. E., Chapman, S. J., Jean Robertson, A. H., Potts, J. M., Laggoun-Défarge, F., Gogo, S., et al. (2008). FTIR spectroscopy can be used as a screening tool for organic matter quality in regenerating cutover peatlands. Soil Biology and Biochemistry, 40(2), 515–527. https://doi.org/10.1016/j.soilbio.2007.09.019.
Ben-Dor, E. (2002). Quantitative remote sensing of soil properties. Advances in Agronomy, 75(75), 173–243. https://doi.org/10.1016/S0065-2113(02)75005-0.
Brunner, P., Li, H. T., Kinzelbach, W., & Li, W. P. (2007). Generating soil electrical conductivity maps at regional level by integrating measurements on the ground and remote sensing data. International Journal of Remote Sensing, 28(15), 3341–3361.
Burnham, K. P., Anderson, D. R., & Huyvaert, K. P. (2011). AIC model selection and multimodel inference in behavioral ecology: some background, observations and comparisons. Behavioral Ecology and Sociobiology, 65(1), 23–35. https://doi.org/10.1007/s00265-010-1029-6.
Chang, C. W., Laird, D. A., Mausbach, M. J., & Hurburgh, C. R. (2001). Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Science Society Of America Journal, 65(2), 480–490.
Chen, S., Hong, X., Harris, C. J., & Sharkey, P. M. (2004). Sparse mzodeling using orthogonal forward regression with PRESS statistic and regularization. Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics, 34(2), 898–911. https://doi.org/10.1109/Tsmcb.2003.817107.
Corwin, D. L., & Lesch, S. M. (2003). Application of soil electrical conductivity to precision agriculture: Theory, principles and guidelines. Agronomy Journal, 95(3), 455–471.
Crowley, J. K. (1991). Visible and near-infrared reflectance spectra of playa evaporite minerals. J Geophys Res-Solid Earth., 96(B10), 16231–16240. https://doi.org/10.1029/91jb01714.
Csillag, F., Pasztor, L., & Biehl, L. L. (1993). Spectral Band Selection for the Characterization of Salinity Status of Soils. Remote Sensing of Environment, 43(3), 231–242. https://doi.org/10.1016/0034-4257(93)90068-9.
Dale, P. E. R., Hulsman, K., & Chandica, A. L. (1986). Classification of Reflectance on Color Infrared Aerial Photographs and Subtropical Salt-Marsh Vegetation Types. International Journal of Remote Sensing, 7(12), 1783–1788.
Dotto, A. C., Dalmolin, R. S. D., Grunwald, S., ten Caten, A., & Pereira Filho, W. (2017). Two preprocessing techniques to reduce model covariables in soil property predictions by Vis-NIR spectroscopy. Soil and Tillage Research, 172, 59–68. https://doi.org/10.1016/j.still.2017.05.008.
Farifteh, J. (2011). Interference of salt and moisture on soil reflectance spectra. International Journal of Remote Sensing, 32(23), 8711–8724. https://doi.org/10.1080/01431161.2010.549522.
Farifteh, J., Tolpekin, V., Van Der Meer, F., & Sukchan, S. (2010). Salinity modelling by inverted Gaussian parameters of soil reflectance spectra. International Journal of Remote Sensing, 31(12), 3195–3210. https://doi.org/10.1080/01431160903156536.
Farifteh, J., van der Meer, F., van der Meijde, M., & Atzberger, C. (2008). Spectral characteristics of salt-affected soils: A laboratory experiment. Geoderma, 145(3–4), 196–206. https://doi.org/10.1016/j.geoderma.2008.03.011.
Gaffey, S. J. (1987). Spectral Reflectance of Carbonate Minerals in the Visible and near-Infrared (0.35–2.55 Um) - Anhydrous Carbonate Minerals. J Geophys Res-Solid Earth Planets., 92(B2), 1429–1440. https://doi.org/10.1029/JB092iB02p01429.
Ghosh, G., Kumar, S., & Saha, S. K. (2012). Hyperspectral Satellite Data in Mapping Salt-Affected Soils Using Linear Spectral Unmixing Analysis. Journal of the Indian Society of Remote Sensing, 40(1), 129–136. https://doi.org/10.1007/s12524-011-0143-x.
Haaland, D. M., & Thomas, E. V. (1988). Partial Least-Squares Methods for Spectral Analyses .1. Relation to Other Quantitative Calibration Methods and the Extraction of Qualitative Information. Analytical Chem, 60(11), 1193–1202. https://doi.org/10.1021/Ac00162a020.
Hewson, R. D., Cudahy, T. J., Jones, M., & Thomas, M. (2012). Investigations into Soil Composition and Texture Using Infrared Spectroscopy (2–14 μm). Applied and Environmental Soil Science, 2012, 1–12. https://doi.org/10.1155/2012/535646.
Howari, F. M., Goodell, P. C., & Miyamoto, S. (2002). Spectral properties of salt crusts formed on saline soils. Journal of Environmental Quality, 31(5), 1453–1461.
Huang, J., Mokhtari, A. R., Cohen, D. R., Monteiro Santos, F. A., & Triantafilis, J. (2015). Modelling soil salinity across a gilgai landscape by inversion of EM38 and EM31 data. European Journal of Soil Science, 66(5), 951–960. https://doi.org/10.1111/ejss.12278.
Hubert, M., & Vanden Branden, K. (2003). Robust methods for partial least squares regression. Journal of Chemometrics, 17(10), 537–549. https://doi.org/10.1002/Cem.822.
Ji, W., Li, S., Chen, S., Shi, Z., Rossel, R. A. V., & Mouazen, A. M. (2016). Prediction of soil attributes using the Chinese soil spectral library and standardized spectra recorded at field conditions. Soil & Tillage Research, 155, 492–500.
Jin, P., Li, P., Wang, Q., & Pu, Z. (2015). Developing and applying novel spectral feature parameters for classifying soil salt types in arid land. Ecological Indicators, 54, 116–123. https://doi.org/10.1016/j.ecolind.2015.02.028.
Koohafkan, P., & Stewart, B. A. (2012). Water and Cereals in Drylands. Rome: The Food and Agriculture Organization of the United Nations and Earthscan.
Kuang, B., Tekin, Y., & Mouazen, A. M. (2015). Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content. Soil and Tillage Research, 146, 243–252. https://doi.org/10.1016/j.still.2014.11.002.
Lakhankar, T., Jones, A. S., Combs, C. L., Sengupta, M., Haar, T. H. V., & Khanbilvardi, R. (2010). Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method. Sensors (Basel), 10(1), 913–932. https://doi.org/10.3390/S100100913.
Leone, A. P., & Sommer, S. (2000). Multivariate analysis of laboratory spectra for the assessment of soil development and soil degradation in the southern Apennines (Italy). Remote Sensing of Environment, 72(3), 346–359. https://doi.org/10.1016/S0034-4257(99)00110-8.
Linker, R., Shmulevich, I., Kenny, A., & Shaviv, A. (2005). Soil identification and chemometrics for direct determination of nitrate in soils using FTIR-ATR mid-infrared spectroscopy. Chemosphere, 61(5), 652–658. https://doi.org/10.1016/j.chemosphere.2005.03.034.
Liu, Y., Pan, X. Z., Wang, C. K., Li, Y. L., & Shi, R. J. (2016). Can subsurface soil salinity be predicted from surface spectral information? - From the perspective of structural equation modelling. Biosystems Engineering, 152, 138–147. https://doi.org/10.1016/j.biosystemseng.2016.06.008.
Masoud, A. A. (2014). Predicting salt abundance in slightly saline soils from Landsat ETM+ imagery using Spectral Mixture Analysis and soil spectrometry. Geoderma, 217–218, 45–56. https://doi.org/10.1016/j.geoderma.2013.10.027.
Melendez-Pastor, I., Navarro-Pedreno, J., Koch, M., & Gomez, I. (2010). Applying imaging spectroscopy techniques to map saline soils with ASTER images. Geoderma, 158(1–2), 55–65. https://doi.org/10.1016/j.geoderma2010.02.015.
Metternicht, G. I., & Zinck, J. A. (2003). Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment, 85(1), 1–20. https://doi.org/10.1016/S0034-4257(02)00188-8.
Nanni, M. R., & Dematte, J. A. M. (2006). Spectral reflectance methodology in comparison to traditional soil analysis. Soil Science Society Of America Journal, 70(2), 393–407. https://doi.org/10.2136/sssaj2003.0285.
Nawar, S., Buddenbaum, H., & Hill, J. (2015). Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region. Remote Sensing, 7(2), 1181–1205. https://doi.org/10.3390/Rs70201181.
Nawar, S., Buddenbaum, H., Hill, J., & Kozak, J. (2014). Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS). Remote Sensing, 6(11), 10813–10834. https://doi.org/10.3390/Rs61110813.
Peng, J., Ji, W. J., Ma, Z. Q., Li, S., Chen, S. C., Zhou, L. Q., et al. (2016). Predicting total dissolved salts and soluble ion concentrations in agricultural soils using portable visible near-infrared and mid-infrared spectrometers. Biosystems Engineering, 152, 94–103.
Pimentel, D. (2006). Soil Erosion: A Food and Environmental Threat. Environment, Development and Sustainability, 8(1), 119–137.
Rao, B. R. M., Sharma, R. C., Ravi Sankar, T., Das, S. N., Dwivedi, R. S., Thammappa, S. S., et al. (1995). Spectral behaviour of salt-affected soils. International Journal of Remote Sensing, 16(12), 2125–2136. https://doi.org/10.1080/01431169508954546.
Rossel, R. A. V., & Behrens, T. (2010). Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 158(1–2), 46–54. https://doi.org/10.1016/j.geoderma.2009.12.025.
Salisbury, J. W., & Daria, D. M. (1992). Infrared (8–14 Mu-M) Remote-Sensing of Soil Particle-Size. Remote Sensing of Environment, 42(2), 157–165. https://doi.org/10.1016/0034-4257(92)90099-6.
Sanchez, J. M., French, A. N., Mira, M., Hunsaker, D. J., Thorp, K. R., Valor, E., et al. (2011). Thermal Infrared Emissivity Dependence on Soil Moisture in Field Conditions. Ieee Transactions on Geoscience and Remote Sensing, 49(11), 4652–4659. https://doi.org/10.1109/Tgrs.2011.2142000.
Shaviv, A., Kenny, A., Shmulevitch, I., Singher, L., Raichlin, Y., & Katzir, A. (2003). Direct monitoring of soil and water nitrate by FTIR based FEWS or membrane systems. Environmental Science & Technology, 37(12), 2807–2812. https://doi.org/10.1021/Es020885+.
Sidike, A., Zhao, S. H., & Wen, Y. M. (2014). Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra. International Journal of Applied Earth Observation and Geoinformation, 26, 156–175. https://doi.org/10.1016/j.jag.2013.06.002.
Tatzber, M., Stemmer, M., Spiegel, H., Katzlberger, C., Haberhauer, G., & Gerzabek, M. H. (2007). An alternative method to measure carbonate in soils by FT-IR spectroscopy. Environmental Chemistry Letters, 5(1), 9–12. https://doi.org/10.1007/s10311-006-0079-5.
van der Meijde, M., Knox, N. M., Cundill, S. L., Noomen, M. F., van der Werff, H. M. A., & Hecker, C. (2013). Detection of hydrocarbons in clay soils: A laboratory experiment using spectroscopy in the mid- and thermal infrared. International Journal of Applied Earth Observation and Geoinformation, 23, 384–388. https://doi.org/10.1016/j.jag.2012.11.001.
Wang, Q., Li, P., & Chen, X. (2012a). Modeling salinity effects on soil reflectance under various moisture conditions and its inverse application: A laboratory experiment. Geoderma, 170, 103–111. https://doi.org/10.1016/j.geoderma.2011.10.015.
Wang, Q., Li, P., & Chen, X. (2012b). Retrieval of Soil Salt Content From an Integrated Approach of Combining Inversed Reflectance Model and Regressions: An Experimental Study. Ieee Transactions on Geoscience and Remote Sensing, 50(10), 3950–3957. https://doi.org/10.1109/Tgrs.2012.2187790.
Wang, Q., Li, P., Pu, Z., & Chen, X. (2011). Calibration and validation of salt-resistant hyperspectral indices for estimating soil moisture in arid land. Journal of Hydrology, 408(3–4), 276–285. https://doi.org/10.1016/j.jhydrol.2011.08.012.
Weng, Y. L., Gong, P., & Zhu, Z. L. (2008). Soil salt content estimation in the Yellow River delta with satellite hyperspectral data. Canadian Journal of Remote Sensing, 34(3), 259–270.
Wold, S., Sjostrom, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. https://doi.org/10.1016/S0169-7439(01)00155-1.
Wu, C.-Y., Jacobson, A. R., Laba, M., & Baveye, P. C. (2009). Accounting for surface roughness effects in the near-infrared reflectance sensing of soils. Geoderma, 152(1–2), 171–180. https://doi.org/10.1016/j.geoderma.2009.06.002.
Wu, Y. Z., Chen, J., Wu, X. M., Tian, Q. J., Ji, J. F., & Qin, Z. H. (2005). Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils. Applied Geochemistry, 20(6), 1051–1059. https://doi.org/10.1016/j.apgeochem.2005.01.009.
Xia, J., Tashpolat, T., Mamat, S., Zhang, F., & Han, G. H. (2012). Application Study of the Thermal Infrared Emissivity Spectra in the Estimation of Salt Content of Saline Soil. Spectroscopy and Spectral Analysis, 32(11), 2956–2961. https://doi.org/10.3964/j.issn.1000-0593(2012)11-2956-06.
Xu, H., & Li, Y. (2006). Water-use strategy of three central Asian desert shrubs and their responses to rain pulse events. [journal article]. Plant and Soil, 285(1), 5–17. https://doi.org/10.1007/s11104-005-5108-9.
Xu, L., & Wang, Q. (2015). Retrieval of Soil Water Content in Saline Soils from Emitted Thermal Infrared Spectra Using Partial Linear Squares Regression. Remote Sensing, 7(11), 14646–14662. https://doi.org/10.3390/rs71114646.
Xu, L., Wang, Z., & Nyongesah, J. M. (2019). Soil Column Sample Height Influences Soil Spectral Reflectance in Laboratory Experiment. [journal article]. Journal of the Indian Society of Remote Sensing, 47(7), 1187–1196. https://doi.org/10.1007/s12524-019-00982-y.
Zhang, T.-T., Qi, J.-G., Gao, Y., Ouyang, Z.-T., Zeng, S.-L., & Zhao, B. (2015). Detecting soil salinity with MODIS time series VI data. Ecological Indicators, 52, 480–489. https://doi.org/10.1016/j.ecolind.2015.01.004.
Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant No. 41807001, 41671395, 41971305) and Natural Science Foundation of Jiangsu Province (Grant No. BK20171165). We thank the staff at Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences and Wulanwusu Agricultural Meteorological Station for their facilities and support.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Xu, L., Wang, Z., Hu, J. et al. Estimation of Soil Salinity Under Various Soil Moisture Conditions Using Laboratory Based Thermal Infrared Spectra. J Indian Soc Remote Sens 49, 959–969 (2021). https://doi.org/10.1007/s12524-020-01271-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-020-01271-9