Automated Rain Detection by Dual-Polarization Sentinel-1 Data
2.2. Weather Radar
2.3. Match-Up Database with Sentinel-1 and Weather Radar Data
3. Co-Analysis of Rain Rate and C-Band SAR Backscattering
3.1. A First Qualitative Assessment
- Case a (Top in Figure 2): This is a typical example of storm cell footprint as discussed by Atlas . Both VV- and VH-polarized images show two rain areas with concurrent bright and dark patches within the cell, the downdraft area and the wind gust. Here the surrounding wind speed is about 3 m/s. Collocated NEXRAD measurements presented in Figure 2(a3) indicate two separate rain cells. Bright patches are located at the same location in VV and VH images and correspond to NEXRAD base reflectivity measurements higher than 40 dBZ. This suggests an increase of C-band radar backscattering at high rain rates. As also noted by Melsheimer et al. , the extent of base reflectivity is less than the storm cell signature in the SAR image as rain radar is not sensitive to the wind acceleration due to downdraft or its associated wind gust. In this case, the relationship between NRCS and base reflectivity confirms the NRCS increases with base reflectivity for values larger than 40 dBZ.
- Case b (Middle in Figure 2): This case acquired in the Gulf of Mexico corresponds to a stationary front with stratiform precipitation without intense vertical convection. As indicated by Figure 2(b3), wind speed is significantly different on each side of the atmospheric front with ambient background wind speed lower than 2 m/s in the western area and higher than 6 m/s in the eastern area. In this case, there is also a good match between bright patches observed on both co- and cross-polarization channels and the highest values of base reflectivity (see Figure 2(b3)). The relationship between NRCS and base reflectivity also confirms that for values larger than 25 dBZ, the NRCS increases with base reflectivity.
- Case c (Bottom in Figure 2): Here, the background ambient wind is stronger than in the previous cases, about 13 m/s from the model. As observed, the distribution, size and intensity of the bright patches are not the same in co- and cross-polarization. More bright patches are observed in VH than in VV. In particular, dark areas are observed in the VV image where the VH image exhibits bright patches associated with high base reflectivity. As a result, in this case, the VV NRCS shows decreases when the base reflectivity increases whereas the VH NRCS stays almost constant.
3.2. Timeliness of the Data
3.3. Scattering by the Melting Layer
3.4. Statistical Analysis
4. Rain Detection
4.1. Optimization of the Heterogeneity Filter(s) for Rain Detection
- : The ratio between the standard deviation and the average computed for each pixel using a sliding bounding box of the smoothed/reduced image. It is computed using a convolutive averaging bounding box over the amplitude (square root of NRCS) and its second moment. This parameter is particularly useful to decipher open water surface from other extended areas (land, tidal zones, sea ice, etc.)
- : The second parameter is built on a Laplace pyramid filter with the difference between images at adjacent levels in the pyramid. It is based on the squared ratio of the high-pass-filtered image and its local average. It has the capability of detecting narrow image features, as slicks, internal waves, or fronts.
- : The third parameter is the ratio of the magnitude of the squared local Sobel-based gradient and its local average. A Sobel operator is basically involved to do spatial gradient measurement on 2-dimensional images. It is generally adapted to detect edges and point targets.
- : The ratio between the reduced/smoothed version of the squared local gradient and its absolute squared gradient: it can be considered to be a measure of directional coherence. It should detect the edges such as ”slicks, internal waves, or fronts” .
4.2. Validation of the Performance of the Filters
4.3. Application of Dual-Pol Filter on Different Rain Types
5. Conclusions and Perspectives
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Zhao, Y.; Longépé, N.; Mouche, A.; Husson, R. Automated Rain Detection by Dual-Polarization Sentinel-1 Data. Remote Sens. 2021, 13, 3155. https://doi.org/10.3390/rs13163155
Zhao Y, Longépé N, Mouche A, Husson R. Automated Rain Detection by Dual-Polarization Sentinel-1 Data. Remote Sensing. 2021; 13(16):3155. https://doi.org/10.3390/rs13163155Chicago/Turabian Style
Zhao, Yuan, Nicolas Longépé, Alexis Mouche, and Romain Husson. 2021. "Automated Rain Detection by Dual-Polarization Sentinel-1 Data" Remote Sensing 13, no. 16: 3155. https://doi.org/10.3390/rs13163155