Sentinel-1 Imagery Incorporating Machine Learning for Dryland Salinity Monitoring: A Case Study in Esperance, Western Australia

Document Type

Article

Publication Date

17-2-2021

Conference Title

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Place of Publication

Waikoloa, Hawaii, USA

ISSN

Electronic ISSN: 2153-7003 Print on Demand(PoD) ISSN: 2153-6996

Keywords

Soil, Salinity (geophysical), Radar polarimetry, Monitoring, Spaceborne radar, Backscatter, Synthetic aperture radar

Disciplines

Agricultural Science | Agriculture | Natural Resources and Conservation | Natural Resources Management and Policy | Numerical Analysis and Scientific Computing | Soil Science

Abstract

Due to the lack of a suitable theoretical model for simulating radar backscatter of soil based on salt content, we investigated a new method to exploit Sentinel-1 radar backscatters and polarimetric decomposition information for dryland soil salinity monitoring. Soil electrical conductivity (EC) was estimated using Sentinel-1 SAR imagery and field survey data combined with five machine learning models in Esperance, located in the southwest of Western Australia (SWWA). The performance of the five machine learning models was assessed and compared using the root-mean-square error (RMSE), the mean absolute error (MAE), and the correlation coefficient ( r ). The results revealed that the Random Forest Regression model (RFR) yielded the highest prediction performance ( RMSE=2.89 S/m, MAE=1.90S/m , and r=0.81 ) and outperformed the other models. It can be concluded that the intensity images of VV and VH polarization of SAR imagery have the potential to predict EC of soils in SWWA.

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Digital Object Identifier (DOI)

https://doi.org/10.1109/IGARSS39084.2020.9323426