High‐Resolution Soil Moisture Evolution in Hyper‐Arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the Southern Arabian Peninsula

Abstract

and the growing stresses placed on water resources worldwide (Jones, 1999;Rosegrant et al., 2009) motivate efforts to monitor soil moisture and better characterize its variability on a global scale. Satellite-based remote sensing observations support these monitoring efforts, and have the advantages of regular satellite repeat intervals and extensive spatial coverage. However, soil moisture retrievals are sensitive to the presence of vegetation, cloud cover, soil type, and the vertical distribution of soil moisture to a degree that varies between approaches. We present analysis of phase coherence within a Synthetic Aperture Radar (SAR) time series spanning three years, and compare it with soil moisture estimates based on other remote sensing datasets (microwave and visible) and data assimilation systems. We present a focused comparison between the Interferometric Synthetic Aperture Radar (InSAR) coherence results and published estimates of soil moisture from the Soil Moisture Active-Passive satellite, (SMAP, passive microwave), the Advanced SCATterometer (ASCAT, Real Aperture Radar), and Global Land Data Assimilation System (GLDAS). The InSAR

Liquefaction Phenomenon Identification from post-Pohang Earthquake 2017, South Korea based Integrated SAR and optical imagery data using a machine-learning approach

“…In addition to using the interferogram to analyze the liquefaction observations, we also used Sentinel-1 data to establish the coherence conditions in Pohang during 2017-2020. Changes in incoherence can shed light on changes in surface conditions, especially in areas with particular land cover types 19 . There was a relationship between the precipitation amounts and coherence conditions in the study area, especially in agricultural areas and those covered by paddy elds.…”

“…Also, the radar satellite imagery provided by the Sentinel-1 C data has been integrated as the input for the susceptibility analysis. Coherence from the complex image and the time-series surface deformation map which provide the information has been evaluated to assess the liquefaction susceptibility in the paddy area near the epicenter 19,40 . All the remote sensing imagery has been managed and handled using the overlay analysis of all data.…”

Soil moisture effects on InSAR - A correction approach and example from a hyper-arid region

“…In this paper we do not go so far as to convert the metric for soil moisture, , into a more 𝑚 𝑘 meaningful constraint on volumetric soil moisture. Unfortunately, there are no in situ observations of soil moisture in the region examined here, although we have previously compared the temporal behavior of coherence magnitude changes and its recovery time to other independent models and observations (Bürgi and Lohman, 2021). The requirement for long SAR time series and the generation of all possible pairs also presents some challenges for operational use of this approach.…”

Water Vapor Transport Across an Arid Sand Surface—Non‐Linear Thermal Coupling, Wind‐Driven Pore Advection, Subsurface Waves, and Exchange With the Atmospheric Boundary Layer

“…In industry, they can be deployed, for example, to predict vapor contamination through pharmaceutical powders (Louge et al., 2021). Because the probe can reliably detect very low moisture, it could also be used as “ground‐truth” for satellite remote sensing over sand seas (Bürgi & Lohman, 2021; Myeni et al., 2019; Zribi et al., 2014), or to look for scant water in future space applications (Davidsson & Hosseini, 2021; Honniball et al., 2020).…”