  A novel algorithm is developed to downscale soil moisture (SM), obtained at
satellite scales of 10-40 km by utilizing its temporal correlations to
historical auxiliary data at finer scales. Including such correlations
drastically reduces the size of the training set needed, accounts for
time-lagged relationships, and enables downscaling even in the presence of
short gaps in the auxiliary data. The algorithm is based upon bagged regression
trees (BRT) and uses correlations between high-resolution remote sensing
products and SM observations. The algorithm trains multiple regression trees
and automatically chooses the trees that generate the best downscaled
estimates. The algorithm was evaluated using a multi-scale synthetic dataset in
north central Florida for two years, including two growing seasons of corn and
one growing season of cotton per year. The time-averaged error across the
region was found to be 0.01 $\mathrm{m}^3/\mathrm{m}^3$, with a standard
deviation of 0.012 $\mathrm{m}^3/\mathrm{m}^3$ when 0.02% of the data were used
for training in addition to temporal correlations from the past seven days, and
all available data from the past year. The maximum spatially averaged errors
obtained using this algorithm in downscaled SM were 0.005
$\mathrm{m}^3/\mathrm{m}^3$, for pixels with cotton land-cover. When land
surface temperature~(LST) on the day of downscaling was not included in the
algorithm to simulate "data gaps", the spatially averaged error increased
minimally by 0.015 $\mathrm{m}^3/\mathrm{m}^3$ when LST is unavailable on the
day of downscaling. The results indicate that the BRT-based algorithm provides
high accuracy for downscaling SM using complex non-linear spatio-temporal
correlations, under heterogeneous micro meteorological conditions.

Computer Vision and Pattern Recognition