Sensitivity to soil moisture of Capella X-band high-resolution SAR data over forests

Sensitivity to soil moisture of X-band high-resolution Synthetic Aperture Radar (SAR) data over the openings between the trees in forests is evaluated. The VV backscattering coefficient is extracted from a time-series of Capella images collected over two different regions across the United States. In-situ soil moisture measurements recorded at fixed locations from the National Ecological Observatory Network (NEON) are used as reference for the analysis. The geolocation uncertainty of the original ground range detected Capella images at 0.8 m resolution was evaluated at 5–14 m. Ground control points were used to improve geolocation accuracy to 1–3 m. Radar backscattering and soil moisture exhibit linear correlation values up to 0.95, confirming the strong response of X-band Capella to soil moisture changes when trees are not interfering with the scattering from the thinly-vegetated soil over the openings. These findings reveal the potential for forest soil moisture monitoring by micro-satellites offering meter-level high-resolution and agile tasking. Accordingly, applications such as wildfire precursors, landslide prediction, and forest management may benefit as long as the moisture conditions at the openings represent the soil moisture over larger areas. • Strong response of X-band data to soil moisture changes over 2 m tree gaps. •Correlation of in situ soil moisture and Capella data up to 0.95. •Geolocation accuracy is improved using ground control points from 14 m to 1 m. •Capella data enable soil moisture mapping in sparse arid forest for wildfire application. •Agile and frequent imaging offers advantage over infrequent mainframe SAR satellites.

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Publication Details

Journal
Science of Remote Sensing
Published
2025-09-23
DOI
https://doi.org/10.1016/j.srs.2025.100289
Citations
1
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.80
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article

Sensitivity to soil moisture of Capella X-band high-resolution SAR data over forests

Giovanni Anconitano, Seung-Bum Kim
1 citations
Science of Remote Sensing
Soil Moisture and Remote Sensing
0.80
article

Sensitivity to soil moisture of Capella X-band high-resolution SAR data over forests

Giovanni Anconitano, Seung-Bum Kim
article en
1 citations

Abstract

Sensitivity to soil moisture of X-band high-resolution Synthetic Aperture Radar (SAR) data over the openings between the trees in forests is evaluated. The VV backscattering coefficient is extracted from a time-series of Capella images collected over two different regions across the United States. In-situ soil moisture measurements recorded at fixed locations from the National Ecological Observatory Network (NEON) are used as reference for the analysis. The geolocation uncertainty of the original ground range detected Capella images at 0.8 m resolution was evaluated at 5–14 m. Ground control points were used to improve geolocation accuracy to 1–3 m. Radar backscattering and soil moisture exhibit linear correlation values up to 0.95, confirming the strong response of X-band Capella to soil moisture changes when trees are not interfering with the scattering from the thinly-vegetated soil over the openings. These findings reveal the potential for forest soil moisture monitoring by micro-satellites offering meter-level high-resolution and agile tasking. Accordingly, applications such as wildfire precursors, landslide prediction, and forest management may benefit as long as the moisture conditions at the openings represent the soil moisture over larger areas. • Strong response of X-band data to soil moisture changes over 2 m tree gaps. •Correlation of in situ soil moisture and Capella data up to 0.95. •Geolocation accuracy is improved using ground control points from 14 m to 1 m. •Capella data enable soil moisture mapping in sparse arid forest for wildfire application. •Agile and frequent imaging offers advantage over infrequent mainframe SAR satellites.

Science of Remote SensingVol. 12
Jet Propulsion Laboratory (US), Istituto Nazionale di Geofisica e Vulcanologia (IT)
Openalex Percentile: Top 24%
Soil Moisture and Remote Sensing
0.80
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Sensitivity to soil moisture of Capella X-band high-resolution SAR data over forests — Giovanni Anconitano, Seung-Bum Kim · Science of Remote Sensing (2025) | TGRS Research Map | TGRS