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

  • Murodjon Sultanov
  • Mirzakhayot Ibrakhimov
  • Akmal Akramkhanov
  • Christian Bauer
  • Christopher ConradEmail author
Original Article
  • 11 Downloads

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.

Keywords

Soil salinity Electromagnetic induction Irrigated agriculture Multi-temporal Environmental parameters Landsat 

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.

Notes

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).

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Copyright information

© Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2019

Authors and Affiliations

  1. 1.Khorezm Rural Advisory Support Service (KRASS)UrgenchUzbekistan
  2. 2.International Center for Agricultural Research in the Dry Areas (ICARDA)TashkentUzbekistan
  3. 3.Remote Sensing DepartmentUniversity of Würzburg, Institute of Geography and GeologyWurzburgGermany
  4. 4.University of Halle-Wittenberg, Institute of Geosciences and GeographyHalleGermany

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