| dc.description.abstract | Soil moisture serves as one of the important variables in hydrological
processes or climate change. The availability of water in agricultural fields is also
a critical information for agriculture, where drought events can affect plant
production. Tackling with the large areas covering agricultural fields, spatial
information, usually derived from remote sensing, needs to be timely developed.
Techniques for retrieving soil moisture have been established using multispectral
imageries. Nevertheless, weather condition often constraints information retrievals.
In that sense, developing the estimation based on Synthetic Aperture Radar (SAR)
data, including Sentinel-1, is a necessity for agricultural planning. The main goals
of this research were to model soil moisture using only dual-polarized SAR data
and to investigate its potential in transfer modeling using subsequent data
acquisition with the presence of vegetation. Soil moisture was modeled using
random forest in a set of data and was validated using ground data utilizing soil
moisture meter. The result showed that random forest yielded overall accuracy
about 0.64 with RMSE 1.54. However, the model was inadequate to perform well
when exposed to new data, with averaged accuracy about 0.0045 during transfer
modelling. This suggested that the model could be overfit into single date data and
was insufficient for a more diverse data range. The research concluded that random
forest modelling can be used to estimate soil moisture using remote sensing data
but may not easily be transferable. The study, however, suggests a further
examination into the characteristics of crop canopy above the soil. | id |
