Downscaling satellite soil moisture using geomorphometry and machine learning

Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwav...

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Bibliographic Details
Published in:PloS one 2019-09, Vol.14 (9), p.e0219639
Main Authors: Guevara, Mario, Vargas, Rodrigo
Format: Article
Language:English
Subjects:
Algorithms
Atmospheric models
Biogeochemistry
Climate change
Climate system
Computer and Information Sciences
Data mining
Dielectric devices
Digital Elevation Models
Earth Sciences
Ecology and Environmental Sciences
Ecosystems
Engineering and Technology
Environmental conditions
Environmental Monitoring
Estimates
Geographic information systems
Geomorphology
Hydrology
Learning algorithms
Machine Learning
Model accuracy
Parameters
Physical Sciences
Rain
Remote sensing
Remote Sensing Technology - methods
Reproducibility of Results
Research and Analysis Methods
Satellite Imagery
Satellite soil moisture estimates
Satellites
Soil - chemistry
Soil moisture
Soil sciences
Soil water
Soils
Spatial data
Spatial resolution
Statistical analysis
Terrain
Topography
Trends
Water
Water - analysis
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Summary:Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25-50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991-2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991-2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0219639