Sentinel-1 Surface Soil MoistureComparison against an optimized Metop ASCAT soil moisture product and in situ data in Lower Austria
Isabella Pfeil, Sebastian Hahn, Bernhard Bauer-Marschallinger, Mariette Vreugdenhil,
Simon Hochstöger, Wolfgang Wagner
Outline
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Introduction Study area
• In situ soil moisture network Petzenkirchen ASCAT optimization to local conditions Results
• Sentinel-1 vs. in situ soil moisture• Sentinel-1 vs. satellite soil moisture
Objectives
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Sentinel-1 surface soil moisture (SSM)• Available observations since 2014• TU Wien: development of SSM retrieval• Assessment of the performance of the current retrieval algorithm
Comparison to:• In situ soil moisture from a network in lower Austria• ASCAT SSM – optimized for test region
Study Area
Agricultural catchment (66 ha) In situ soil moisture network „Hydrological Open Air Laboratory“ (HOAL) 21 permanent, 11 temporary soil moisture stations Upscaled to coarse satellite footprint
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Optimization of the ASCAT SSM retrieval
Metop ASCAT
Advanced Scatterometer Data available since 2007 6-36 hours revisit rate 25 km spatial resolution WARP algorithm – soil moisture retrieval:
• Scaling between historically driest and wettest measurements
• Backscatter normalization• Cross-over angles (global: 25°, 40°)
Optimization to local conditions?• Test area: region around HOAL (lower Austria)
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(from Wagner et al., 2013)
Variation of the cross-over angles
Dry conditions: 25°, wet conditions: 40° (globally found) Can retrieval be improved over Petzenkirchen?
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Global parameters: varying bias over year Optimized parameters: larger bias, but constant over year
ASCAT SSM time series – 2015
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ASCAT-HOAL:Spearman‘s R: 0.54RMSD: 0.045
ASCAT-HOAL:Spearman‘s R: 0.72RMSD: 0.052
Retrieval with global parameters
Retrieval with optimized parameters
ASCAT SSM time series – 2016
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ASCAT-HOAL:Spearman‘s R: 0.18RMSD: 0.052
ASCAT-HOAL:Spearman‘s R: 0.30RMSD: 0.048
Retrieval with global parameters
Retrieval with optimized parameters
Global parameters: varying bias over year Optimized parameters: larger bias, rather constant over year
Validation of Sentinel-1 SSM
Sentinel-1 Surface Soil Moisture
Sentinel-1A: part of the ESA Copernicus Programme• Land surface monitoring at a very high spatial resolution
Soil moisture at 1 km spatial scale Temporal revisit time: 3-8 days (depending on location)
SSM retrieval• Adaptation of the TU Wien Change Detection Model• Scaling of the normalized backscatter between the historically driest and wettest
value
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𝑆𝑆𝑆𝑆𝑆𝑆 =𝜎𝜎40𝑜𝑜 − 𝜎𝜎𝑑𝑑𝑑𝑑𝑑𝑑(40)
𝑜𝑜
𝜎𝜎𝑤𝑤𝑤𝑤𝑤𝑤(40)𝑜𝑜 − 𝜎𝜎𝑑𝑑𝑑𝑑𝑑𝑑(40)
𝑜𝑜
(Rama, Cc-by-sa-2.0-fr)
Sentinel-1 Surface Soil Moisture
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20m SAR backscatter in dB 1km Soil Moisture in %
(from Bauer-Marschallinger et al., 2017)
Sentinel-1 SSM over HOAL
96 observations in 2015 and 2016 Averaged for 3x3 pixels over HOAL Masked for frozen air temperatures (in situ data) Absolute units: multiplication with porosity
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Sentinel-1 vs. ASCAT and in situ soil moisture
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Spearman‘s R: S1-ASCAT: 0.58S1-HOAL: 0.39p < 0.01
Spearman‘s R: S1-ASCAT: 0.61S1-HOAL: 0.36p < 0.05
Conclusions
ASCAT SSM retrieval parameters can be optimized• Better representation of regional conditions• A bias remains, but constant over year
SSM retrieval from Sentinel-1A• Correlations between 0.35 and 0.6• Static vegetation correction biases during growing period• Temporal coverage depending on location
Next steps• ASCAT optimization in other regions• Continue improvement of Sentinel-1 retrieval• Include Sentinel-1B
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References
Wagner, Wolfgang, Guido Lemoine, and Helmut Rott. "A method for estimatingsoil moisture from ERS scatterometer and soil data." Remote sensing ofenvironment 70.2 (1999): 191-207.
Pathe, Carsten, et al. "Using ENVISAT ASAR global mode data for surface soilmoisture retrieval over Oklahoma, USA." IEEE Transactions on Geoscience andRemote Sensing 47.2 (2009): 468-480.
Wagner, Wolfgang, et al. "The ASCAT soil moisture product: A review of its specifications, validation results, and emerging applications." MeteorologischeZeitschrift 22.1 (2013): 5-33.
Bauer-Marschallinger, Bernhard, et al. "1km Soil Moisture from DownsampledSentinel-1 SAR Data: Harnessing Assets and Overcoming Obstacles." EGU General Assembly Conference Abstracts. Vol. 19. 2017.
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