Soil Moisture Estimation Using Sentinel-1/-2 Imagery Coupled With CycleGAN for Time-Series Gap Filing

Natalia Efremova, Mohamed El Amine Seddik, Esra Erten

Abstract:

Fast soil moisture content (SMC) mapping is necessary
to support water resource management and to understand
crop growth, quality, and yield. Therefore, earth observation (EO)
plays a key role due to its ability of almost real-time monitoring
of large areas at a low cost. This study aimed to explore the
possibility of taking advantage of freely available Sentinel-1 (S1)
and Sentinel-2 (S2) EO data for the simultaneous prediction of
SMC with cycle-consistent adversarial network (CycleGAN) for
time-series gap filling. The proposed methodology, first, learns
latent low-dimensional representation of the satellite images,
then learns a simple machine learning (ML) model on top of
these representations. To evaluate the methodology, a series of
vineyards, located in South Australia’s Eden valley are chosen.
Specifically, we presented an efficient framework for extracting
latent features from S1 and S2 imagery. We showed how one
could use S1 to S2 feature translation based on CycleGAN using
S1 and S2 time series when there are missing images acquired
over an area of interest. The resulting data in our study is
then used to fill gaps in time-series data. We used the resulting
latent representations to predict SMC with various ML tools.
In the experiments, CycleGAN and the autoencoders were trained
with data randomly chosen around the site of interest, so we
could augment the existing dataset. The best performance was
demonstrated with random forest (RF) algorithm, whereas linear
regression model demonstrated significant overfitting. The experiments
demonstrate that the proposed methodology outperforms
the compared state-of-the-art methods if there are missing optical
and synthetic-aperture radar (SAR) images.