Skip to main content

Advertisement

SpringerLink
Log in

Modelling End-of-Season Soil Salinity in Irrigated Agriculture Through Multi-temporal Optical Remote Sensing, Environmental Parameters, and In Situ Information

  • Original Article
  • Published:
PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Aims and scope Submit manuscript

Abstract

Accurate information of soil salinity levels enables for remediation actions in long-term operating irrigation systems with malfunctioning drainage and shallow groundwater (GW), as they are widespread throughout the Aral Sea Basin (ASB). Multi-temporal Landsat 5 data combined with GW levels and potentials, elevation and relative topographic position, and soil (clay content) parameters, were used for modelling bulk electromagnetic induction (EMI) at the end of the irrigation season. Random forest (RF) regressionwas applied to predict in situ observations of 2008–2011 which originated from a cotton research station in Uzbekistan. Validation, i.e. median statistics from 100 RF runs with a holdout of each 20% of the samples, revealed that mono-temporal (R2: 0.1–0.18, RMSE: 16.7–24.9 mSm−1) underperformed multi-temporal RS data (R2: 0.29–0.45; RMSE: 15.1–20.9 mSm−1). Combinations of multi-temporal RS data with environmental parameters achieved highest accuracies (R2: 0.36–0.50, RMSE: 13.2–19.9 mSm−1). Beside RS data recorded at the initial peaks of the major irrigation phases, terrain and GW parameters turned out to be important variables for the model. RF preferred neither raw data nor spectral indices known to be suitable for detecting soil salinity. Unexplained variance components result from missing environmental variables, but also from processes not considered in the data. A calibration of the EMI for electrical conductivity and the standard soil salinity classification returned an overall accuracy of 76–83% for the period 2008–2011. The presented indirect approach together with the in situ calibration of the EMI data can support an accurate mapping of soil salinity at the end of the season, at least in the type of irrigation systems found in the ASB.

Zusammenfassung

Modellierung der Bodensalinität am Ende einer Bewässerungssaison durch multi-temporale optische Fernerkundungsdaten, Umweltvariablen und in situ Informationen. Genaue Informationen über den Salzgehalt des Bodens ermöglichen Sanierungsmaßnahmen in etablierten Bewässerungssystemen mit mangelhafter Entwässerung und flachem Grundwasser (GW), wie sie etwa im gesamten Aralseebecken (ASB) verbreitet sind. Landsat-5-Daten aus mehreren Zeiträumen wurden mit GW-Werten und -Potentialen, Höhe und relativer topographischer Position sowie Bodeninformation (Tongehalt) kombiniert, um die elektromagnetische Induktion (EMI) am Ende der Bewässerungssaison zu modellieren. Random Forest (RF) Regression wurde angewendet, um in situ Beobachtungen von 2008 – 2011 vorherzusagen, die von einer Baumwollforschungsstation in Usbekistan stammen. Die Medianstatistik der Validierung von 100 RF-Läufen mit einem Holdout von jeweils 20% der Proben zeigte, dass mono-temporale (R2: 0,1 – 0,18; RMSE: 16,7 mSm−1 – 24,9 mSm−1) multi-temporalen Fernerkundungsdaten unterlegen waren (R2: 0,29 – 0,45; RMSE: 15,1 mSm−1 – 20,9 mSm−1). Optimale Ergebnisse wurden aber durch Kombinationen von multi-temporalen Fernerkundungsdaten und Umweltvariablen erzielt (R2: 0,36 – 0,50, RMSE: 13,2 mSm−1 – 19,9 mSm−1). Neben den Fernerkundungsdaten, die zu Beginn der Hauptbewässerungsphasen aufgezeichnet wurden, erwiesen sich die Gelände- und GW-Parameter als wichtige Variablen für das Modell. RF bevorzugte weder Rohdaten noch Spektralindizes, die vorwiegend zum Nachweis der Salzgehalte im Boden geeignet sind. Unerklärte Varianzanteile resultieren aus fehlenden Umweltvariablen, aber auch aus in den Daten nicht berücksichtigten Prozessen. Eine Kalibrierung der EMI auf die elektrische Leitfähigkeit und die Klassifizierung nach Standard-Bodensalzgehalt ergab eine Gesamtgenauigkeit von 76% bis 83% für den Zeitraum 2008 – 2011. Der vorgestellte indirekte Ansatz zusammen mit der in situ Kalibrierung der EMI-Daten kann eine genaue Kartierung des Bodensalzgehaltes am Ende der Saison unterstützen, zumindest in der Art von Bewässerungssystemen, wie sie im ASB vorkommen.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Abbas A, Khan S (2007) Using remote sensing techniques for appraisal of irrigated soil salinity. In: Oxley L, Kulasiri D (eds) Advances and applications for management and decision making land, water and environmental management: integrated systems for sustainability MODSIM07, pp 2632–2638

  • Abrol IP, Yadav JSP, Massoud FI (1988) Salt-affected soils and their management. FAO Soils Bulletin 39. Food and Agriculture Organization of the United Nations, Rome. http://www.fao.org/docrep/x5871e/x5871e00.htm#Contents. Accessed 22 Oct 2018

  • AghaKouchak A, Farahmand A (2015) Remote sensing of drought: progress, challenges and opportunities. Rev Geophys 53:452–480. https://doi.org/10.1002/2014rg000456

    Article  Google Scholar 

  • Akramkhanov A, Vlek PLG (2012) The assessment of spatial distribution of soil salinity risk using neural network. Environ Monit Assess 184(4):2475–2485. https://doi.org/10.1007/s10661-011-2132-5

    Article  Google Scholar 

  • Akramkhanov A, Sommer R, Martius C, Hendrickx JMH, Vlek PLG (2008) Comparison and sensitivity of measurement techniques for spatial distribution of soil salinity. Irrig Drain Syst 22(1):115–126. https://doi.org/10.1007/s10795-008-9043-9

    Article  Google Scholar 

  • Akramkhanov A, Martius C, Park SJ, Hendrickx JMH (2011) Environmental factors of spatial distribution of soil salinity on flat irrigated terrain. Geoderma 163(1–2):55–62. https://doi.org/10.1016/j.geoderma.2011.04.001

    Article  Google Scholar 

  • Akramkhanov A, Kuziev R, Sommer R, Martius C, Forkutsa O, Massucati L (2012) Soils and soil ecology in Khorezm. In: Martius C, Rudenko I, Lamers JPA, Vlek PLG (eds) Cotton, water, salts and soums: economic and ecological restructuring in Khorezm, Uzbekistan. Springer Netherlands, Dordrecht, pp 37–58. https://doi.org/10.1007/978-94-007-1963-7_3

  • Akramkhanov A, Brus DJ, Walvoort DJJ (2014) Geostatistical monitoring of soil salinity in Uzbekistan by repeated EMI surveys. Geoderma 213:600–607

    Article  Google Scholar 

  • Allbed A, Kumar L (2013) Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Adv Remote Sens 2:373–385

    Article  Google Scholar 

  • Asfaw E, Suryabhagavan KV, Argaw M (2018) Soil salinity modeling and mapping using remote sensing and GIS : the case of Wonji Sugar Cane Irrigation Farm. Ethiopia. J Saudi Soc Agric Sci 17(3):250–258. https://doi.org/10.1016/j.jssas.2016.05.003

    Article  Google Scholar 

  • Azabdaftari A, Sunar F (2016) Soil salinity mapping using multitemporal landsat data. Int Arch Photogramm Remote Sens Spat Inf Sci ISPRS Arch 41:3–9. https://doi.org/10.5194/isprsarchives-xli-b7-3-2016

    Article  Google Scholar 

  • Bastiaanssen WGM, Allen RG, Droogers P, D’Urso G, Steduto P (2007) Twenty-five years modeling irrigated and drained soils: state of the art. Agric Water Manag 92(3):111–125. https://doi.org/10.1016/j.agwat.2007.05.013

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Brower C, Goffeau A, Heibloem M (1985) Irrigation water management: Training Manual No. 1—introduction to irrigation. FAO—Food and Agriculture Organization of the United Nations (Rome), Rome

  • Congalton RG, Green K (2008) Assessing the accuracy of remotely sensed data: principles and practices, vol 2. CRC Press, Boca Raton

    Book  Google Scholar 

  • Conrad C, Schorcht G, Tischbein B, Davletov S, Sultonov M, Lamers JPA (2012) Agro-meteorological trends of recent climate development in Khorezm and implications for crop production. In: Martius C, Rudenko I, Lamers JPA, Vlek PLG (eds) Cotton, water, salts and soums: economic and ecological restructuring in Khorezm, Uzbekistan, vol 9789400719. https://doi.org/10.1007/978-94-007-1963-7_2

  • Conrad C, Lamers JPA, Ibragimov N, Löw F, Martius C (2016a) Analysing irrigated crop rotation patterns in arid Uzbekistan by the means of remote sensing: a case study on post-Soviet agricultural land use. J Arid Environ 124:150–159. https://doi.org/10.1016/j.jaridenv.2015.08.008

    Article  Google Scholar 

  • Conrad C, Schönbrodt-Stitt S, Löw F, Sorokin D, Paeth H (2016b) Cropping intensity in the aral sea basin and its dependency from the runoff formation 2000–2012. Remote Sens 8(8):630. https://doi.org/10.3390/rs8080630

    Article  Google Scholar 

  • Dehaan RL, Taylor GR (2002) Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization. Remote Sens Environ 80(3):406–417. https://doi.org/10.1016/S0034-4257(01)00321-2

    Article  Google Scholar 

  • Eldeiry AA, Garcia LA (2008) Detecting soil salinity in alfalfa fields using spatial modeling and remote sensing. Soil Sci Soc Am J 72(1):201–211. https://doi.org/10.2136/sssaj2007.0013

    Article  Google Scholar 

  • Farifteh J, Van der Meer F, Atzberger C, Carranza EJM (2007) Quantitative analysis of salt-affected soil reflectance spectra: a comparison of two adaptive methods (PLSR and ANN). Remote Sens Env 110(1):59–78. https://doi.org/10.1016/j.rse.2007.02.005

  • Farifteh J, Van der Meer F, van der Meijde M, Atzberger C (2008) Spectral characteristics of salt-affected soils: a laboratory experiment. Geoderma 145:196–206. https://doi.org/10.1016/j.geoderma.2008.03.011

  • Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random forests for land cover classification. Pattern Recognit Lett 27(4):294–300. https://doi.org/10.1016/j.patrec.2005.08.011

    Article  Google Scholar 

  • Hendrickx JMH, Baerends B, Raza ZI, Sadig M, Akram Chaudhry M (1992) Soil salinity assessment by electromagnetic induction of irrigated land. Soil Sci Soc Am J 56(6):1933–1941. https://doi.org/10.2136/sssaj1992.03615995005600060047x

    Article  Google Scholar 

  • Hillel D (2000) Salinity management for sustainable irrigation. Integrating science, environment, and economics. Washington, DC, USA

  • Hothorn T, Hornik K, Zeileis A (2006) Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat 15(July):651–674. https://doi.org/10.1198/106186006X133933

    Article  Google Scholar 

  • Huang J, Prochazka MJ, Triantafilis J (2016) Irrigation salinity hazard assessment and risk mapping in the lower Macintyre Valley, Australia. Sci Total Environ 551–552:460–473. https://doi.org/10.1016/j.scitotenv.2016.01.200

    Article  Google Scholar 

  • Huete AR (1988) A Soil-Adjusted Vegetation Index (SAVI). Remote Sens Environ 25(3):295–309

    Article  Google Scholar 

  • Ibrakhimov M, Khamzina A, Irina Forkutsa G, Paluasheva JPA, Lamers B, Tischbein PLG, Vlek PLG, Martius C (2007) Groundwater table and salinity: spatial and temporal distribution and influence on soil salinization in Khorezm Region (Uzbekistan, Aral Sea Basin). Irrig Drain Syst 21(3–4):219–236. https://doi.org/10.1007/s10795-007-9033-3

    Article  Google Scholar 

  • Ibrakhimov M, Martius C, Lamers JPA, Tischbein B (2011) The dynamics of groundwater table and salinity over 17 years in Khorezm. Agric Water Manag 101(1):52–61. https://doi.org/10.1016/j.agwat.2011.09.002

    Article  Google Scholar 

  • Ittersum MK, Cassman KG, Grassini P, Wolf J, Tittonell P, Hochman Z (2013) Field crops research yield gap analysis with local to global relevance—a review. Field Crops Res 143:4–17. https://doi.org/10.1016/j.fcr.2012.09.009

    Article  Google Scholar 

  • Jabbarov H (1990) The analysis of the indicators of ameliorative conditions in irrigated Areas of Khorezm Region. Ph.D. Thesis, NPO SANIIRI, Tashkent Uzbekistan

  • Keeley JE (2009) Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int J Wildland Fire 18(1):116–126. https://doi.org/10.1071/WF07049

    Article  Google Scholar 

  • Lobell DB, Ivan Ortiz-Monasterio J, Gurrola FC, Valenzuela L (2007) Identification of saline soils with multiyear remote sensing of crop yields. Soil Sci Soc Am J 71(3):777. https://doi.org/10.2136/sssaj2006.0306

    Article  Google Scholar 

  • Löw F, Knöfel P, Conrad C (2015) Analysis of uncertainty in multi-temporal object-based classification. ISPRS J Photogramm Remote Sens 105:91–106. https://doi.org/10.1016/j.isprsjprs.2015.03.004

    Article  Google Scholar 

  • Majka D, Jenness J, Beier P (2007) CorridorDesigner: ArcGIS tools for designing and evaluating corridors. http://corridordesign.org. Accessed 22 Oct 2018

  • Masek JG, Vermote EF, Saleous N, Wolfe R, Hall FG, Huemmrich KF, Gao F, Kutler J, Lim TK (2013) LEDAPS calibration, reflectance, atmospheric correction preprocessing code, version 2. ORNL Distributed Active Archive Center. https://doi.org/10.3334/ornldaac/1146

  • Metternicht GI, Zinck JA (2003) Remote sensing of soil salinity: potentials and constraints. Remote Sens Environ 85:1–20. https://doi.org/10.1016/S0034-4257(02)00188-8

    Article  Google Scholar 

  • Qadir M, Noble AD, Qureshi AS, Gupta RK, Yuldashev T, Karimov A (2009) Salt induced land and water degradation in the aral sea basin: a challenge to sustainable agriculture in central Asia. Nat Resour Forum 33(2):134–149. https://doi.org/10.1111/j.1477-8947.2009.01217.x

    Article  Google Scholar 

  • Rhoades JD, Kandiah A, Mashali AM (1992) The use of saline waters for crop production. FAO Irrigation and Drainage Paper, vol 48

  • Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the great plains with ERTS. In: Third earth resources technology satellite-1 symposium, vol I: technical presentations. NASA SP-351, pp 309–317

  • Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 ACM national conference, pp 517–524. https://doi.org/10.1145/800186.810616

  • Shirokova Y, Forkutsa I, Sharafutdinova N (2000) Use of electrical conductivity instead of soluble salts for soil salinity monitoring in Central Asia. Irrig Drain Syst 14(3):199–205. https://doi.org/10.1023/A:1026560204665

    Article  Google Scholar 

  • Stolbovoi V (2000) Soils of Russia: correlated with the revised legend of the FAO soil map of the world and world reference base for soil resources

  • Tischbein B, Manschadi AM, Conrad C, Hornidge A-K, Bhaduri A, Ul Hassan M, Lamers JPA, Awan UK, Vlek PLG (2013) Adapting to water scarcity: constraints and opportunities for improving irrigation management in Khorezm, Uzbekistan. Water Sci Technol Water Supply 13(2):337. https://doi.org/10.2166/ws.2013.028

    Article  Google Scholar 

  • Xiao XM, Boles S, Liu JY, Zhuang DF, Frolking S, Li CS, Salas W, Moore B (2005) Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens Environ 95(4):480–492

    Article  Google Scholar 

Download references

Acknowledgements

This research is a part of the joint project “Assessing Land Value Changes and Developing a Discussion-Support-Tool for Improved Land Use Planning in the Irrigated Lowlands of Central Asia” (LaVaCCA), funded by the Volkswagen Foundation (Az. 88506).

Author information

Authors and Affiliations

  1. Khorezm Rural Advisory Support Service (KRASS), 14, Khamid Olimjan Street, 220100, Urgench, Khorezm, Uzbekistan

    Murodjon Sultanov & Mirzakhayot Ibrakhimov

  2. International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 4375, 100000, Tashkent, Uzbekistan

    Akmal Akramkhanov

  3. Remote Sensing Department, University of Würzburg, Institute of Geography and Geology, Oswald-Külpe-Weg 86, 97074, Wurzburg, Germany

    Christian Bauer & Christopher Conrad

  4. University of Halle-Wittenberg, Institute of Geosciences and Geography, Von-Seckendorff-Platz 4, 06120, Halle, Germany

    Christopher Conrad

Authors
  1. Murodjon Sultanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mirzakhayot Ibrakhimov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Akmal Akramkhanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christian Bauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christopher Conrad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher Conrad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sultanov, M., Ibrakhimov, M., Akramkhanov, A. et al. Modelling End-of-Season Soil Salinity in Irrigated Agriculture Through Multi-temporal Optical Remote Sensing, Environmental Parameters, and In Situ Information. PFG 86, 221–233 (2018). https://doi.org/10.1007/s41064-019-00062-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41064-019-00062-3

Keywords

Search

Navigation