Exploring the combined use of SMAP and Sentinel-1 data for downscaling soil moisture beyond the 1 km scale
- 1Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
- 2Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
- anow at: Hydrogeology and Landscape Hydrology Group, Institute for Biology and Environmental Sciences, Carl von Ossietzky University of Oldenburg, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany
- 1Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
- 2Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
- anow at: Hydrogeology and Landscape Hydrology Group, Institute for Biology and Environmental Sciences, Carl von Ossietzky University of Oldenburg, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany
Abstract. 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 SMAP and Sentinel-1 data together with a unique dataset 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 study area with diverse land use in Denmark we first show that SMAP soil moisture and Sentinel-1 backscatter time series correlate well with in situ observations of CRNS. Sentinel-1 backscatter in VV and VH polarization both show a strong correlation with CRNS soil moisture at higher spatial resolutions (20 m–400 m) and exhibit distinct and meaningful signals at different land cover types. At a first glance satisfying statistical correlations with CRNS soil moisture time series and capacitance probes are obtained using the SMAP Sentinel-1 downscaling algorithm. However, the spatial distribution of soil moisture pattern is inconclusive. Accounting for different land use in the downscaling algorithm improved the spatial distribution slightly. However, the investigated downscaling algorithm cannot fully account for the vegetation dependency at sub-kilometre resolution. The study implies that further research is needed in the modification of the downscaling algorithm to produce representative soil moisture pattern at fine scale, however backscatter signals appear informative.
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Rena Meyer et al.
Status: closed
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RC1: 'Comment on hess-2021-508', Anonymous Referee #1, 18 Nov 2021
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2021-508/hess-2021-508-RC1-supplement.pdf
- AC1: 'Reply on RC1', Rena Meyer, 20 Jan 2022
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RC2: 'Comment on hess-2021-508', Anonymous Referee #2, 06 Dec 2021
The manuscript by Meyer et al., 2021 presented an interesting study in estimating high-resolution soil moisture via the combination of SMAP L3 soil moisture and Sentinel 1 backscatter data. I recommend that the authors address the following comments before considering the paper for publication.
1. Soil moisture at sub-kilometre is indeed in high demand by many regional and local applications. Combination of radiometer and SAR data is definitely valuable and provides a promising way to improve spatial resolution. One major concern is the strong combined effects of incidence angle, biomass and surface roughness on the backscatter. The studies applied a simple methods to calibrate the incidence angle and made a key assumption that ð½ is invariant in time and space. What impacts of such assumption can influence on the downscaled soil moisture? Furthermore, is that possible to conduct a sensitivity analysis to investigate such impacts?
2. CRNS data was used as a reference to evaluate satellite-based soil moisture. Since CRNS neutron is also influenced by vegetation water content, did you calibrate such impacts in deriving volumetric soil moisture? The CRNS also has variable spatial and vertical footprints. Not sure if the direct comparison with satellite surface soil moisture is appropriate. Is that possible to consider such representative errors in your evaluation?
3. Another comment is regarding the validation of your downscaled soil moisture. As authors described, small modifications to the downscaling approach can induce significant changes in spatial patterns, it is therefore challenging to identify the best approach. I agree with such statement, but also want to ask how to distinguish noise and real soil moisture patterns? Direct comparison with CRNS might not sufficient due to the scale mismatch and high diversity of soil properties. In addition, can you give some practical advice or outlook on how to generate sub-kilometre soil moisture products, which can be used for fine-scale applications?
4. In cluster analysis, 20m, 100m, 1000m were selected and analysed. What is the criterial to choose these scales? For the downscaled soil moisture, 100 m is presented as “the downscaled sub-kilometre” product. Does it mean it is the tradeoff between quality and resolution?
5. Remote the comma in the title.
6. Caption figure 4: c is backscatter and d is db.
- AC2: 'Reply on RC2', Rena Meyer, 20 Jan 2022
Status: closed
-
RC1: 'Comment on hess-2021-508', Anonymous Referee #1, 18 Nov 2021
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2021-508/hess-2021-508-RC1-supplement.pdf
- AC1: 'Reply on RC1', Rena Meyer, 20 Jan 2022
-
RC2: 'Comment on hess-2021-508', Anonymous Referee #2, 06 Dec 2021
The manuscript by Meyer et al., 2021 presented an interesting study in estimating high-resolution soil moisture via the combination of SMAP L3 soil moisture and Sentinel 1 backscatter data. I recommend that the authors address the following comments before considering the paper for publication.
1. Soil moisture at sub-kilometre is indeed in high demand by many regional and local applications. Combination of radiometer and SAR data is definitely valuable and provides a promising way to improve spatial resolution. One major concern is the strong combined effects of incidence angle, biomass and surface roughness on the backscatter. The studies applied a simple methods to calibrate the incidence angle and made a key assumption that ð½ is invariant in time and space. What impacts of such assumption can influence on the downscaled soil moisture? Furthermore, is that possible to conduct a sensitivity analysis to investigate such impacts?
2. CRNS data was used as a reference to evaluate satellite-based soil moisture. Since CRNS neutron is also influenced by vegetation water content, did you calibrate such impacts in deriving volumetric soil moisture? The CRNS also has variable spatial and vertical footprints. Not sure if the direct comparison with satellite surface soil moisture is appropriate. Is that possible to consider such representative errors in your evaluation?
3. Another comment is regarding the validation of your downscaled soil moisture. As authors described, small modifications to the downscaling approach can induce significant changes in spatial patterns, it is therefore challenging to identify the best approach. I agree with such statement, but also want to ask how to distinguish noise and real soil moisture patterns? Direct comparison with CRNS might not sufficient due to the scale mismatch and high diversity of soil properties. In addition, can you give some practical advice or outlook on how to generate sub-kilometre soil moisture products, which can be used for fine-scale applications?
4. In cluster analysis, 20m, 100m, 1000m were selected and analysed. What is the criterial to choose these scales? For the downscaled soil moisture, 100 m is presented as “the downscaled sub-kilometre” product. Does it mean it is the tradeoff between quality and resolution?
5. Remote the comma in the title.
6. Caption figure 4: c is backscatter and d is db.
- AC2: 'Reply on RC2', Rena Meyer, 20 Jan 2022
Rena Meyer et al.
Rena Meyer et al.
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