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 -