Sentinel-1数据在西南山区水库变形斜坡InSAR监测中的适用性评价:以溪洛渡水库为例

Sentinel卫星凭借其超高的辐射分辨率、稳定的轨道系统、较大的覆盖能力、较短的重返时间、可免费下载的数据，在斜坡灾害识别监测方向上有广泛的应用。自1963年意大利瓦伊昂特大滑坡发生以来，岸坡地质灾害一直是峡谷区水库关注的主要问题之一。以金沙江上游溪洛渡水库区为例，结合PALSAR-2、TerraSAR-X数据，评价Sentinel-1 SAR数据在西南山区水库变形斜坡InSAR监测中的适用性，以理论结合实际结果分析Sentinel-1数据是否可以在一定条件下替代其他商业数据，为今后相关行业应用提供参考。结果显示:Sentinel-1数据在研究区可解译的变形斜坡约200处，类型有滑坡、危岩体和塌岸；经现场核查，Sentinel-1数据解译的最小变形斜坡投影面积约为2400 m2，约35 m(长)×77 m(宽)大小，共16个变形像元聚集。高山峡谷区叠掩、阴影现象严重，通过对雷达常用观测模式下的SAR数据的比较，在SAR数据交集区域，有效观测面积为Sentinel-1升轨70.3%，Sentinel-1降轨68.9%，PALSAR-2升轨70.4%，PALSAR-2降轨67.6%，TerraSAR-X降轨52.5%，在不考虑分辨率的情况下，在库区Sentinel-1数据与其他两种SAR数据观测能力相比持平或更优秀。6月至11月初是溪洛渡水库的水位上升期，周边植被发育较好，造成数据相干性较差，2017年后Sentinel-1A(1B)双星拍摄获取的SAR数据量增加，高频观测可使相干性提高，利用2017年后该卫星数据可有效识别水库蓄—排水周期内的区域性变形斜坡发育变化情况。当长时间缺失SAR数据时，会造成最近一对SAR数据间的某些像元测量的变形超过其InSAR最大量程，解缠时丢失相位周期。Sentinel-1数据由于连续性较好，监测斜坡的变形趋势较为连续，因此更适合连续小变形的趋势识别。 

The applicability assessment of Sentinel-1 data in InSAR monitoring of the deformed slopes of reservoir in the mountains of southwest China:A case study in the Xiluodu Reservoir

Sentinel satellite is widely used in deformed slope identification and monitoring due to its high resolution, stable orbit system, large coverage capacity, short repetition time, and free data download. Since the 1963 catastrophic landslide of Vaiant in Italy, the geologic hazard on bank slopes has been one of the main problems of the reservoir in the mountainous area. Taking the Xiluodu reservoir in the upper reaches of the Jinsha River as the study area, the applicability of the Sentinel-1 SAR data in InSAR monitoring of deformed slopes of reservoir in mountainous areas was evaluated by combining PALSAR-2 and TerraSAR-X data. The results were used to evaluate whether Sentinel-1 data could replace other commercial SAR data under certain conditions. It provides a reference for future applications in related researches. The results show that:About 200 deformed slopes were interpreted by the Sentinel-1 data in the study area, including landslide, rockfall, and bank collapse. According to field investigations, the minimum projected area of deformed slope based on the Sentinel-1 data is about 2400 m², a size of 35 m (length)×77 m (width), gathered by 16 high-value raster pixels. Overlapping mask shadow phenomenon is severe in the alpine valley area. By comparing SAR data in common satellite radar observation modes, the effective observation area is 70.3% of the ascending Sentinel-1 orbit, 68.9% of the descending Sentinel-1 orbit, 70.4% of the ascending PALSAR-2 orbit, 67.6% of the descending PALSAR-2 orbit, and 52.5% of the descending TerraSAR-X orbit in the intersection area of all SAR data used. Without considering the resolution, it can be concluded that the Sentinel-1 data in the reservoir area has an equal or more excellent observation ability than the other two SAR data. The water level rises from June to early November, and the currounding vegetation develops well, resulting in poor data coherence. Since 2017, the amount of SAR data acquired by Sentinel-1A (1B) has increased, and high-frequency observations can improve the coherence. Therefore, the SAR data can be used to effectively identify the development and change of regional deformed slopes during the water fluctuation cycle. When the SAR data is lacking for a long time, the deformation measured by some pixels between the nearest pair of SAR data could exceed the maximum InSAR measurement range, and the phase period will be lost during the unwrapping. Sentinel-1 SAR data is more suitable for trend identification of continuous small deformations due to its good continuity.