Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt

Abstract

The warming climate is threatening to alter inland water resources on a global scale. Within all waterbody types, lake and river systems are vital not only for natural ecosystems but, also, for human society. Snowmelt phenology is also altered by global warming, and snowmelt is the primary water supply source for many river and lake systems around the globe. Hence, (1) monitoring snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced river and lake systems, and (3) quantifying the causal effect of snowmelt conditions on these waterbodies are critical to understand the cryo-hydrosphere interactions under climate change. Previous studies utilized in-situ or multispectral sensors to track either the surface areas or water levels of waterbodies, which are constrained to small-scale regions and limited by cloud cover, respectively. On the contrary, in the present study, we employed the latest Sentinel-1 synthetic aperture radar (SAR) and Sentinel-3 altimetry data to grant a high-resolution, cloud-free, and illumination-independent comprehensive inland water dynamics monitoring strategy. Moreover, in contrast to previous studies utilizing in-house algorithms, we employed freely available cloud-based services to ensure a broad applicability with high efficiency. Based on altimetry and SAR data, the water level and the water-covered extent (WCE) (surface area of lakes and the flooded area of rivers) can be successfully measured. Furthermore, by fusing the water level and surface area information, for Lake Urmia, we can estimate the hypsometry and derive the water volume change. Additionally, for the Brahmaputra River, the variations of both the water level and the flooded area can be tracked. Last, but not least, together with the wet snow cover extent (WSCE) mapped with SAR imagery, we can analyze the influence of snowmelt conditions on water resource variations. The distributed lag model (DLM) initially developed in the econometrics discipline was employed, and the lagged causal effect of snowmelt conditions on inland water resources was eventually assessed.

Automatic Monitoring of Oil Tank 3D Geometry and Storage Changes With Interferometric Coherence and SAR Intensity Information

“…Although ( 1) and ( 2) are structurally similar, their values are changed with different mechanisms: the interferometric coherence measures the preservation of the phase between paired images, which is not only affected by objects' changes but also SAR sensors' sensing geometry (e.g., spatial baseline) and environment (e.g., atmospheric delay) [32], while the intensity coherence maps the changes in objects' characteristics, e.g., geometry, dielectric properties, and surface roughness [7]. Due to these differences, they are suitable for mapping changes of different magnitudes (the former is more sensitive to subtle deformation, while the latter is suitable for detecting large-scale changes [33,34]).…”

“…Based on the chosen coherence (either γ 𝑐 or γ 𝐼 ), an optimized threshold value 𝛾 𝑡ℎ𝑟𝑒𝑠 is determined based on Otsu's algorithm [38], which is preferred for its simplicity and robustness for binary classifications [7]. Then, together with each oil tank's bounding box extracted in stage one (Section II-B), we compute the average coherence value of each oil tank 𝛾 𝑎𝑣𝑔 .…”

“…In contrast, synthetic aperture radar (SAR), a longwavelength and active sensing technique, provides all-weather and day-any-night sensing opportunities [7]. However, SAR data have much more complex characteristics than optical data:…”

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

“…Long time series that use data of higher temporal resolution start in the early 2000s and use MODIS data from Terra, Aqua, or often both. With the launch of the Sentinel fleet, more and more studies include Sentinel-1-especially-for shorter, but mostly weather independent, high-resolution time series of surface water [4,25,39,74,[102][103][104][105][106][107][108][109][110][111]. Additionally, Sentinel-2 is often used for high spatial and temporal resolution time series, or as validation data [9,24,39,100,110,[112][113][114][115][116][117][118][119].…”