Characterizing soil infiltration parameters using field/laboratory measured and remotely-sensed data

Document Type : Research Paper

Authors

1 Associate Professor, Department of Soil Science and Engineering, Faculty of Agriculture, University of Maragheh, Maragheh, Iran.

2 Professor, Department of Soil Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

3 Professor, Mining Engineering Faculty, Sahand University of Technology, Tabriz, Iran

4 Professor, Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

5 Assistant Professor, Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

Abstract

Characterizing soil infiltration parameters is time consuming and costly. We carried out the current research to predict different parameters of soil infiltration using field/laboratory measured and remotely-sensed data. The investigated parameters included infiltration rates at different time intervals and the parameters of the three well-known infiltration models. We employed soil sampling and field measurements on late spring 2012 and acquired ETM+ data for the correspondent dates. We measured several soil properties as well as infiltration. Then, we developed several pedo-transfer functions (PTFs) from the collected field/laboratory measured and remotely sensed data to predict the intended infiltration parameters. Results showed that field/laboratory measured data were able to predict soil infiltration rates and parameters of the investigated models with reasonably high accuracies (E value up to 0.961). The results also revealed that, although there was no significant and robust relationship between soil surface reflectance and the investigated parameters, the developed PTFs had reasonable accuracies (E value up to 0.634) in estimating the intended infiltration parameters using soil characteristics (moisture content, soil separates, and organic carbon) which are predictable from remotely sensed data.
 

Keywords

References

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Environmental Resources Research
Volume 8, Issue 2
July 2020
Pages 129-146

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