  Filling missing data in cloud-covered areas of satellite imaging is an
important task to improve data quantity and quality for enhanced earth
observation. Traditional cloud filling studies focused on continuous numerical
data such as temperature and cyanobacterial concentration in the open ocean.
Cloud data filling issues in coastal imaging is far less studied because of the
complex landscape. Inspired by the success of data imputation methods in
recommender systems that are designed for online shopping, the present study
explored their application to satellite cloud data filling tasks. A numerical
experiment was designed and conducted for a LandSat dataset with a range of
synthetic cloud covers to examine the performance of different data filling
schemes. The recommender system-inspired matrix factorization algorithm called
Funk-SVD showed superior performance in computational accuracy and efficiency
for the task of recovering landscape types in a complex coastal area than the
traditional data filling scheme of DINEOF (Data Interpolating Empirical
Orthogonal Functions) and the deep learning method of Datawig. The new method
achieved the best filling accuracy and reached a speed comparable to DINEOF and
much faster than deep learning. A theoretical framework was created to analyze
the error propagation in DINEOF and found the algorithm needs to be modified to
converge to the ground truth. The present study showed that Funk-SVD has great
potential to enhance cloud data filling performance and connects the fields of
recommender systems and cloud filling to promote the improvement and sharing of
useful algorithms.
