Using ensemble learning to take advantage of high-resolution radar backscatter in conjunction with surface features to disaggregate SMAP soil moisture product

Remote sensing based retrieved of soil moisture from low frequency passive microwave observations is preferred in different aspects such as better spatial coverage and more measurement compared to traditional ground-based measurements. However, due to coarse spatial resolution of the observations, t...

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Bibliographic Details
Published in:International journal of remote sensing 2022-02, Vol.43 (3), p.894-914
Main Authors: Karami, Ayoob, Moradi, Hamid Reza, Mousivand, Alijafar, van Dijk, Albert I. J. M., Renzullo, Luigi
Format: Article
Language:English
Subjects:
Agricultural land
Backscattering
Catchment area
Cloud cover
Cloudiness
disaggregation
Distribution functions
Ensemble learning
Grasslands
Heterogeneity
Land cover
Machine-learning
MODIS
Radar
Radar backscatter
Remote sensing
Resolution
Sentinel-1
SMAP
Soil dynamics
Soil moisture
Soil moisture measurements
Soil properties
Soil surfaces
Spatial resolution
surface soil moisture
Temporal variations
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Summary:Remote sensing based retrieved of soil moisture from low frequency passive microwave observations is preferred in different aspects such as better spatial coverage and more measurement compared to traditional ground-based measurements. However, due to coarse spatial resolution of the observations, their applications are limited in local to regional studies. This paper provides a framework using random forest regression to disaggregate the daily SMAP enhanced soil moisture (SPL3SMP_E) utilizing several ancillary data to overcome the spatial resolution limits and cloudiness effects. Ancillaries were acquired from sentinel-1 radar, MODIS monthly NDVI, land cover, topography, and surface soil properties. To validate the downscaled results with 1-km spatial resolution, the OZNET soil moisture measurements and sparse TDR ground soil moisture measurements were collected from Murrumbidgee catchment (Australia) and Firozabad catchment (Iran), respectively. Downscaled soil moisture product unbiased root-mean-square error (UnbRMSE) of ensemble learning demonstrated a range of 0.023 and -0.07 cm 3 /cm 3 . The produced downscaled soil moisture exhibited better local heterogeneity when compared to the coarse data and tracked the dynamics of temporal changes in soil moisture. Furthermore, cumulative distribution function (CDF) analysis showed good accuracy of downscaled soil moisture in grassland and cropland. Taken together, the findings supported usefulness of the suggested methodology in downscaling the medium- resolution SMAP soil moisture product.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2021.2022239