SOIL MOISTURE ANALYSIS BY NOVEL IMAGE RECONSTUCTION ALGORITHM
Keywords:
-Abstract
Surface soil moisture is an important environment variable that is dominant in a variety of research
and application areas. Acquiring spatiotemporal continuous soil moisture observations is therefore of great importance.
Weather conditions can contaminate optical remote sensing observations on soil moisture, and the absence of remote
sensors causes gaps in regional soil moisture observation time series. Therefore, reconstruction is highly motivated to
overcome such contamination and to fill in such gaps. In this paper, we propose a novel image reconstruction algorithm
that improved upon the Satellite and In situ sensor Collaborated Reconstruction (SICR) algorithm provided by our
previous publication. Taking artificial neural networks as a model, complex and highly variable relationships between in
situ observations and remote sensing soil moisture is better projected. With historical data for the network training, feed
forward neural networks (FNNs) project in situ soil moisture to remote sensing soil moisture at better performances than
conventional models. Consequently, regional soil moisture observations can be reconstructed under full cloud
contamination or under a total absence of remote sensors. Experiments confirmed better reconstruction accuracy and
precision with this improvement than with SICR. The new algorithm enhances the temporal resolution of high spatial
resolution remote sensing regional soil moisture observations with good quality and can benefit multiple soil moisturebased applications and research.
