Discriminating the Multi-Frequency Microwave Brightness Temperature Anomalies Relating to 2017 Mw 7.3 Sarpol Zahab (Iran-Iraq Border) Earthquake

A Mw 7.3 earthquake occurred near the Iran-Iraq border on November 12, 2017, as the result of oblique-thrust squeezing of the Eurasian plate and the Arabian plate. By employing the spatio-temporally weighted two-step method (STW-TSM) and microwave brightness temperature (MBT) data from AMSR-2 instrument on board Aqua satellite, this paper investigates carefully the spatiotemporal features of multi-frequency MBT anomalies relating to the earthquake. Soil moisture (SM), satellite cloud image, regional geological map and surface landcover data are utilized to discriminate the potential MBT anomalies revealed from STW-TSM. The low-frequency MBT residual images shows that positive anomalies mainly occurred in the mountainous Urmia lake and the plain region, which were 300 km north and 200 km southwest about to the epicenter, respectively. The north MBT anomaly firstly appeared 51 days before the mainshock and its magnitude increased over time with a maximum of about +40K. Then the anomaly disappeared 3 days before, reappeared 1d after and diminished completely 10 days after the mainshock. Meanwhile, the southwest MBT anomaly firstly occurred 18 days before and peaked 3 days before the mainshock with a maximum of about +20K, and then diminished gradually with aftershocks. It is speculated that the positive MBT anomaly in the Urmia lake was caused by microwave dielectric property change of water body due to gas bubbles leaking from the bottom of the lake disturbed by local crust stress alteration, while the southwest MBT positive anomaly was caused by microwave dielectric constant change of shallow surface due to accumulation of seismically-activated positive charges originated at deep crust. Besides, some accidental abnormal residual stripes existed in line with satellite orbit, which turned out to be periodic data errors of the satellite sensor. High-frequency MBT residual images exhibit some significant negative anomalies, including a narrow stripe pointing to the forthcoming epicenter, which were confirmed to be caused by synchronous altostratus clouds. This study is of guidance meaning for distinguishing non-seismic disturbances and identifying seismic MBT anomaly before, during and after some large earthquakes.

Comparing Greenland Ice Sheet Melt Variability From Different Satellite Passive Microwave Remote Sensing Products Over a Common 5-year Record

Satellite microwave brightness temperature (Tb) observations over the Greenland Ice Sheet permit determination of melted/frozen snow conditions at spatial and temporal scales that are uniquely suited for climate model validation and metrics of ice sheet change. Strong microwave sensitivity to the presence of liquid water in the snowpack is clear. Yet, a host of unique microwave-derived melt products covering the ice sheet are available, each based on different methodology, and with unknown inter-product agreement. Here, we compared five different published microwave melt products over a common 5-year (2003–2007) record to establish compatibility between products and agreement with in situ observations from a network of on-ice weather stations (AWS) spanning the ice sheet. A sixth product, leveraging both Tb seasonal trends and diurnal variability, was also introduced and included in the comparison. We found variable agreement between products and observations, with melt estimates based on microwave emissions modeling and the newly presented Adaptive Threshold (ADT) algorithm showing the best performance for AWS sites with more than 1-day average annual melt period (e.g., 68.9% of ADT melt days consistent with AWS observations; 31.1% of ADT frozen days contrasting with AWS observed melt). Spatial patterns of melting also varied between products. The different products showed substantial spread in melt occurrence even for products with the best AWS agreement. Product differences were generally larger under higher melt conditions; whereby, the fraction of the ice sheet experiencing ≥25 days of melting each year ranged from 4 to 25% for different products. While long-term satellite records have consistently shown increasing decadal trends in melt extent, our results imply that the melt frequency at any given location, particularly in the ice sheet interior where melting is less prevalent, is still subject to significant uncertainty.

Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

The dynamic characteristics of seasonal snow cover are critical for hydrology management, the climate system, and the ecosystem functions. Optical satellite remote sensing has proven to be an effective tool for monitoring global and regional variations in snow cover. However, accurately capturing the characteristics of snow dynamics at a finer spatiotemporal resolution continues to be problematic as observations from optical satellite sensors are greatly impacted by clouds and solar illumination. Traditional methods of mapping snow cover from passive microwave data only provide binary information at a spatial resolution of 25 km. This innovative study applies the random forest regression technique to enhanced-resolution passive microwave brightness temperature data (6.25 km) to estimate fractional snow cover over North America in winter months (January and February). Many influential factors, including land cover, topography, and location information, were incorporated into the retrieval models. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2008 and 2017 were used to create the reference fractional snow cover data as the “true” observations in this study. Although overestimating and underestimating around two extreme values of fractional snow cover, the proposed retrieval algorithm outperformed the other three approaches (linear regression, artificial neural networks, and multivariate adaptive regression splines) using independent test data for all land cover classes with higher accuracy and no out-of-range estimated values. The method enabled the evaluation of the estimated fractional snow cover using independent datasets, in which the root mean square error of evaluation results ranged from 0.189 to 0.221. The snow cover detection capability of the proposed algorithm was validated using meteorological station observations with more than 310 000 records. We found that binary snow cover obtained from the estimated fractional snow cover was in good agreement with ground measurements (kappa: 0.67). There was significant improvement in the accuracy of snow cover identification using our algorithm; the overall accuracy increased by 18 % (from 0.71 to 0.84), and the omission error was reduced by 71 % (from 0.48 to 0.14) when the threshold of fractional snow cover was 0.3. The experimental results show that passive microwave brightness temperature data may potentially be used to estimate fractional snow cover directly in that this retrieval strategy offers a competitive advantage in snow cover detection.