University of Oulu

T. Manninen et al., "Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-17, 2022, Art no. 4405317, doi: 10.1109/TGRS.2021.3109695

Very high spatial resolution soil moisture observation of heterogeneous subarctic catchment using nonlocal averaging and multitemporal SAR data

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Author: Manninen, Terhikki1; Jääskeläinen, Emmihenna1; Lohila, Annalea2,3;
Organizations: 1Finnish Meteorological Institute, FI-00101 Helsinki, Finland
2Climate System Research, Finnish Meteoro- logical Institute, FI-00101 Helsinki, Finland
3Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, FI-00100 Helsinki, Finland
4Ecosystems and Environment Research Program, Faculty of Biological and Environmental Sciences, University of Helsinki, FI-00100 Helsinki, Finland
5Natural Resources Institute Finland (Luke), FI-90570 Oulu, Finland
6Water, Energy and Environmental Engineering Research Unit, University of Oulu, FI-90570 Oulu, Finland
7Energy and Construction Solutions, Geological Survey of Finland, FI-02151 Espoo, Finland
8Environmental Solutions, Geological Survey of Finland, FI-96101 Rovaniemi, Finland
9Energy and Construction Solutions, Geological Survey of Finland, FI-96101 Rovaniemi, Finland
10Water Management Solutions, Geological Survey of Finland, FI-02151 Espoo, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 9.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-03-08


A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination ( R2 ) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.

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Series: IEEE transactions on geoscience and remote sensing
ISSN: 0196-2892
ISSN-E: 1558-0644
ISSN-L: 0196-2892
Volume: 60
Article number: 4405317
DOI: 10.1109/TGRS.2021.3109695
Type of Publication: A1 Journal article – refereed
Field of Science: 218 Environmental engineering
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