## 2 Study Area and Datasets
### Dataset and Preprocessing  
This paper uses the microwave data acquired by Sentinel-1 and the optical data acquired by Sentinel-2 (properties summarized in Table 1). The Sentinel-1 is the first of the Copernicus Programme satellite constellations launched by the European Space Agency. Its mission is composed of a two-satellite constellation, Sentinel-1A and Sentinel-1B, which share the same orbital plane. They carry C-band SAR instrumentation to provide a collection of all-weather data both day and night. They operate in four acquisition modes: interferometric wide-swath (IW), strip map, extra wide-swath, and wave. The IW is the default acquisition mode over land. The Sentinel-2 is an Earth observation mission from the Copernicus Programme that systematically acquires optical imagery at high spatial resolutions (10-60 m) over land and coastal waters. The mission is a twin-satellite constellation, Sentinel-2A and Sentinel-2B, to achieve a revisit time of five days. The orbit is sun-synchronous at a 786-km altitude with 14.3 revolutions per day and a 10:30 a.m. descending node local overpass time. In this study, only VH polarization of the IW ground-range-detected (GRD) Sentinel-1A imagery was used because this is more sensitive to flooding signals than VV polarization [21]. The year of interest was 2019. The images obtained from the Sentinel-1 in the Google Earth Engine (GEE) were preprocessed using the Sentinel-1 toolbox. The preprocessing steps are: (1) thermal noise removal, (2) radiometric calibration, (3) terrain correction using SRTM 30 or ASTER DEM for areas greater than 60\({}^{\circ}\) latitude when the SRTM is unavailable, and (4) final terrain-corrected value conversion to decibels using log scaling [10\({}^{\circ}\)log10(x)]. The Sentinel-2 images were atmospherically corrected using the sen2 cor program. The three quality \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Sentinel-1** & **Sentinel-2** \\ \hline Frequency/Wavelength & 5.405 GHz/5.5 cm & - \\ Polarization/Band & VH & 13 spectral bands \\ Spatial Resolution (m) & 10 & 10, 20, 60 \\ Temporal Resolution (d) & 6 & 5 \\ Incidence Angle & 20–47\({}^{\circ}\) & - \\ Mode/Format & IW\_GRD & Level-1C \\ Time Span & 1 January 2019 to 1 January 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sentinel-1/2 data properties. assessment (QA) bands present in the Sentinel-2 images were used to indicate the presence of clouds. The MGRS with 10 km grids were used to split each Level-1C Sentinel-2 tile (100 km \(\times\) 100 km) into one hundred grids. The cloud-free Sentinel-2 grids with cloudy pixel percentages less than 5% were selected. By splitting the original tile into smaller grids, more observations on the grid basis can be preserved compared with that on the tile basis. For example, one tile with a cloudy pixel percentage of 50% will be discarded in traditional usable image selection, which is in the unit of an image, but there may be several clear grids with cloudy pixel percentages less than 5% in this image. Though selecting observations on a pixel basis can preserve even more observations, voids due to cloud coverage in images will cause trouble for edge detection. The usage of grids is the trade-off between preserving more observations and suitability for edge detection. The distribution of clear grids counts in 2019 over the study area is shown in Figure 1.  
Besides the Sentinel-1/2 datasets, other datasets used in our study include the 250-m 8-day composite vegetation index products (MOD13Q1 [29] and MYD13Q1 [30]) and land cover-type product (MCD12Q1 [31]) as well as auxiliary data for validation, including VHR images from Google Earth, cadastral data, geo-referenced filed photos, points of interest (POIs), and street views. The cadastral data provided by Bureau of Land Management in Jiashan County, containing information such as plot boundaries, land use type and ownership, is used to validate segmentation and classification.