Exploring the combined use of SMAP and Sentinel-1 data for downscaling soil moisture beyond the 1 km scale
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Exploring the combined use of SMAP and Sentinel-1 data for downscaling soil moisture beyond the 1 km scale. / Meyer, Rena; Zhang, Wenmin; Kragh, Søren Julsgaard; Andreasen, Mie; Jensen, Karsten Høgh; Fensholt, Rasmus; Stisen, Simon; Looms, Majken C.
I: Hydrology and Earth System Sciences, Bind 26, Nr. 13, 2022, s. 3337-3357.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Exploring the combined use of SMAP and Sentinel-1 data for downscaling soil moisture beyond the 1 km scale
AU - Meyer, Rena
AU - Zhang, Wenmin
AU - Kragh, Søren Julsgaard
AU - Andreasen, Mie
AU - Jensen, Karsten Høgh
AU - Fensholt, Rasmus
AU - Stisen, Simon
AU - Looms, Majken C.
PY - 2022
Y1 - 2022
N2 - Soil moisture estimates at high spatial and temporal resolution are of great value for optimizing water and agricultural management. To fill the gap between local ground observations and coarse spatial resolution remote sensing products, we use Soil Moisture Active Passive (SMAP) and Sentinel-1 data together with a unique data set of ground-based soil moisture estimates by cosmic ray neutron sensors (CRNS) and capacitance probes to test the possibility of downscaling soil moisture to the sub-kilometre resolution. For a high-latitude study area within a highly heterogeneous landscape and diverse land use in Denmark, we first show that SMAP soil moisture and Sentinel-1 backscatter time series correlate well with in situ CRNS observations. Sentinel-1 backscatter in both VV and VH polarizations shows a strong correlation with CRNS soil moisture at higher spatial resolutions (20-400 m) and exhibits distinct and meaningful signals at different land cover types. Satisfactory statistical correlations with CRNS soil moisture time series and capacitance probes are obtained using the SMAP Sentinel-1 downscaling algorithm. Accounting for different land use in the downscaling algorithm additionally improved the spatial distribution. However, the downscaling algorithm investigated here does not fully account for the vegetation dependency at sub-kilometre resolution. The study suggests that future research focussing on further modifying the downscaling algorithm could improve representative soil moisture patterns at a fine scale since backscatter signals are clearly informative.
AB - Soil moisture estimates at high spatial and temporal resolution are of great value for optimizing water and agricultural management. To fill the gap between local ground observations and coarse spatial resolution remote sensing products, we use Soil Moisture Active Passive (SMAP) and Sentinel-1 data together with a unique data set of ground-based soil moisture estimates by cosmic ray neutron sensors (CRNS) and capacitance probes to test the possibility of downscaling soil moisture to the sub-kilometre resolution. For a high-latitude study area within a highly heterogeneous landscape and diverse land use in Denmark, we first show that SMAP soil moisture and Sentinel-1 backscatter time series correlate well with in situ CRNS observations. Sentinel-1 backscatter in both VV and VH polarizations shows a strong correlation with CRNS soil moisture at higher spatial resolutions (20-400 m) and exhibits distinct and meaningful signals at different land cover types. Satisfactory statistical correlations with CRNS soil moisture time series and capacitance probes are obtained using the SMAP Sentinel-1 downscaling algorithm. Accounting for different land use in the downscaling algorithm additionally improved the spatial distribution. However, the downscaling algorithm investigated here does not fully account for the vegetation dependency at sub-kilometre resolution. The study suggests that future research focussing on further modifying the downscaling algorithm could improve representative soil moisture patterns at a fine scale since backscatter signals are clearly informative.
KW - VALIDATION
KW - NETWORK
KW - CALIBRATION
KW - PRODUCTS
KW - DYNAMICS
KW - MODELS
KW - RADAR
U2 - 10.5194/hess-26-3337-2022
DO - 10.5194/hess-26-3337-2022
M3 - Journal article
VL - 26
SP - 3337
EP - 3357
JO - Hydrology and Earth System Sciences
JF - Hydrology and Earth System Sciences
SN - 1027-5606
IS - 13
ER -
ID: 313054716