Sentinel-1 Imagery Incorporating Machine Learning for Dryland Salinity Monitoring A Case Study in Esperance, Western Australia
| Autor: | Qianqian Zhang, Li Li, Zheng-Shu Zhou, Peter Caccetta, John Simons |
|---|---|
| Jazyk: | němčina |
| Rok vydání: | 2020 |
| Předmět: |
Synthetic aperture radar
Soil salinity 010504 meteorology & atmospheric sciences Mean squared error Correlation coefficient business.industry 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences law.invention Random forest law Soil water Environmental science Dryland salinity Artificial intelligence Radar business computer 0105 earth and related environmental sciences |
| Zdroj: | IGARSS |
| DOI: | 10.13140/rg.2.2.16206.89923 |
| Popis: | 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 ( $\text{RMSE}=2.89\ S/m,\ \text{MAE}=1.90 S/m$ , and $\mathrm{r}=0.81$ ) 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. |
| Databáze: | OpenAIRE |
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