Flooding is one of the most frequently occurring meteorological disasters nowadays, and its occurrence can cause significant socio-economic losses. Aiming at the problem that traditional optical remote sensing makes it difficult to monitor floods, this paper designs a scheme to jointly extract the scope of the affected area by using heterogeneous satellite-based SAR images acquired at different times within the flood period. This paper takes the “7-20” rainstorm in Zhengzhou City as an example and uses two kinds of heterogeneous SAR images, Sentinel-1A and GF-3, to extract the flooding damage in the main urban area. In addition, combinations with the vector data of the Gaode road network were performed to further monitor and analyze the “7-20” rainstorm damage in the main urban area of Zhengzhou. The results showed that the main urban area of Zhengzhou City was affected by the “7-20” rainstorm. The roads in the main urban area of Zhengzhou were seriously affected, and the total length of the affected roads reached 1324.63 km. The monitoring scheme for flooding road network damage using Sentinel-1A and GF-3 heterogeneous SAR images has certain feasibility.

Lake ice thickness (LIT) is one of the key climate variables in the lake ice domain, but there are currently large uncertainties in the retrieval of LIT. We present and validate a new LIT retrieval method that utilizes ICESat-2 data to assist CryoSat-2 echo peak selection, aiming to improve the accuracy of LIT retrieval and enable data acquisition without on-site measurements. The method involves screening out similar ICESat-2 and CryoSat-2 tracks based on time and space constraints. It also involves dynamically adjusting the range constraint window of CryoSat-2 waveforms based on the high-precision lake ice surface ellipsoid height obtained from ICESat-2/ATL06 data. Within this range constraint window, the peak selection strategy is used to determine the scattering interfaces between snow-ice and ice-water. By utilizing the distance between the scattering horizons, the thickness of the lake ice can be determined. We performed the ice thickness retrieval experiment for Baker Lake in winter and verified it against the on-site measurement data. The results showed that the accuracy was about 0.143 m. At the same time, we performed the ice thickness retrieval experiment for Great Bear Lake (GBL), which does not have on-site measurement data, and compared it with the climate change trend of GBL. The results showed that the retrieval results were consistent with the climate change trend of GBL, confirming the validity of the proposed method.

The ScanSAR mode image obtained by the Gaofen-3 (GF-3) satellite has an imaging width of up to 130–500 km, which is of great significance in monitoring oceanography, meteorology, water conservancy, and transportation. To address the issues of subswath misalignment and the inability to correct in the processing of GF-3 ScanSAR images in coastal areas using software such as PIE, ENVI, and SNAP, a method for mosaicking and correcting GF-3 ScanSAR images with subswaths that overlap within specified range constraints is proposed. This method involves correlating the coefficients of each subswath thumbnail image in order to determine the extent of the overlap range. Given that the matching points are constrained to the overlap between subswaths, the normalized cross-correlation (NCC) matching algorithm is utilized to calculate the matching points between subswaths. Subsequently, the random sampling consistency (RANSAC) algorithm is employed to eliminate the mismatching points. Subsequently, the subswaths should be mosaicked together with the stitching translation of subswaths, based on the coordinates of the matching points. The image brightness correction coefficient is calculated based on the average grayscale value of pixels in the overlapping region. This is performed in order to correct the grayscale values of adjacent subswaths and thereby reducing the brightness difference at the junction of subswaths. The entire ScanSAR slant range image is produced. By employing the Range–Doppler model for indirect orthorectification, corrected images with geographic information are generated. The experiment utilized three coastal GF-3 ScanSAR images for mosaicking and correction, and the results were contrasted with those attained through PIE software V7.0 processing. This was conducted to substantiate the efficacy and precision of the methodology for mosaicking and correcting coastal GF-3 ScanSAR images.