This paper provides a comprehensive review of the available literature on the observation and characterization of river ice using remote sensing technologies. Through an analysis of 200 publications spanning from 1919 to June 2024, we reviewed different observation technologies deployed on in situ, aerial and satellite platforms for their utility in monitoring and characterizing river ice covers. River ice information, captured by 51 terms extracted from the literature, holds significant value in enhancing infrastructure resilience in the face of climate change. Satellite technologies, in particular the multispectral optical and multi-polarimetric synthetic aperture radar (SAR), provide a number of advantages, such as ice features discrimination, better ice characterization, and reliable delineation of open water and ice, with both current and upcoming sensors. The review includes data analysis methods employed for the monitoring and characterization of river ice, including ice information retrieval methods and corresponding accuracies. The need for further research on artificial intelligence and, in particular, deep learning (DL) techniques has been recognized as valuable for enhancing the accuracy of automated systems. The growing availability of freely available and commercial satellites, UAVs, and in situ data with improved characteristics suggests significant operational potential for river ice observation in the near future. Our study also identifies gaps in the current capabilities for river ice observation and provides suggestions for improved data analysis and interpretation.

Soil moisture is a key factor in the reclamation of wetland habitats. Understanding the distribution and relative amount of water can be critical in reintroducing trees and grasses to disturbed soils. Soil moisture is also one of the main factors affecting microwave radar backscatter from the ground; while there are other factors determining backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, high resolution, multitemporal synthetic aperture radar (SAR). In this work, relative soil moisture indicators are derived from a time series of Sentinel-1 SAR data over previously mined oil sands in Alberta, Canada. The algorithm provides a relative assessment of soil moisture and requires calibration over wet and dry periods. An evaluation of the soil moisture product is validated using in situ measurements at multiple sites with observations showing agreement from May to August. Comparisons with precipitation records show that SAR derived surface soil moisture is influenced by discreet precipitation events; that is, rainfall that is coincident with the satellite observation reduces the effectiveness of the measurement. The resulting algorithm controls for rain events by including local weather records to adjust estimates based on the known precipitation.

Soil moisture is one of the main factors affecting microwave radar backscatter from the ground. While there are other factors that affect backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, medium resolution, multi-temporal synthetic aperture radar (SAR). Understanding the distribution and identification of water-saturated areas using SAR soil moisture can be important for wetland mapping. The SAR soil moisture retrieval algorithm provides a relative assessment and requires calibration over wet and dry periods. In this work, relative soil moisture indicators are derived from a time series of the RADARSAT Constellation Mission (RCM) SAR compact polarimetric (CP) data over reclaimed areas of an oil sands mine in Alberta, Canada. An evaluation of the soil moisture product is performed using in situ measurements showing agreement from June to September. The surface scattering component of m-chi CP decomposition and the RL SAR products demonstrated a good agreement with the field data (low RMSE values and a perfect alignment with field-identified wetlands).

Spaceborne synthetic aperture radar (SAR) is an important technology for ship detection applications. It can provide timely information on small boat locations, which is important for security and safety applications. This paper describes the advantages of using very-high-resolution TerraSAR-X data acquired in Staring Spotlight mode for detecting small boats. Coincident to the SAR image acquisitions, Electro-Optical satellite imagery was used, together with field photographs of boats. The results demonstrate the ability to distinguish SAR signatures of small wooden and fiberglass vessels with size of up to 4 m in length.

Information on the locations and characteristics of extreme sea ice features (such as, hummocks, ridges, stamukhas and icebergs) is important for various marine applications. Imagery acquired by high resolution optical satellites was previously used for qualitative image interpretation to identify various sea ice features and it is especially valuable when detailed ground validation is not available. Current optical satellites, such as GeoEye-1, are able to acquire images with very high resolution of 0.5m. This work addresses the problem of quantitative retrieval of ice feature parameters from very high resolution optical imagery. The developed algorithms facilitate extraction of ice feature height from shadow and derivation of statistical information on ice deformation parameters. Automated processing of GeoEye-1 image demonstrated capabilities of retrieval of ridge frequency and segmentation of rubble fields.

A multi-category numerical sea ice model CICE was used along with data assimilation to derive sea ice parameters in the region of Baffin Bay and Labrador Sea. The assimilation of ice concentration was performed using the data derived from the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2). The model uses a mixed-layer slab ocean parameterization to compute the sea surface temperature (SST) and thereby to compute the freezing and melting potential of ice. The data from Advanced Very High Resolution Radiometer (AVHRR-only optimum interpolation analysis) were used to assimilate SST. The modelled ice parameters including concentration, ice thickness, freeboard and keel depth were compared with parameters estimated from remote-sensing data. The ice thickness estimated from the model was compared with the measurements derived from Soil Moisture Ocean Salinity – Microwave Imaging Radiometer using Aperture Synthesis (SMOS–MIRAS). The model freeboard estimates were compared with the freeboard measurements derived from CryoSat2. The ice concentration, thickness and freeboard estimates from the model assimilated with both ice concentration and SST were found to be within the uncertainty in the observation except during March. The model-estimated draft was compared with the measurements from an upward-looking sonar (ULS) deployed in the Labrador Sea (near Makkovik Bank). The difference between modelled draft and ULS measurements estimated from the model was found to be within 10 cm. The keel depth measurements from the ULS instruments were compared to the estimates from the model to retrieve a relationship between the ridge height and keel depth.