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Integration of methods based on satellite remote sensing into current maritime monitoring strategies could help tackle the problem of global overfishing. Operational software is now available to perform vessel detection on satellite imagery, but research on vessel classification has mainly focused on bulk carriers, container ships, and oil tankers, using high-resolution commercial Synthetic Aperture Radar (SAR) imagery. Here, we present a method based on Random Forest (RF) to distinguish fishing and non-fishing vessels, and apply it to an area in the North Sea. The RF classifier takes as input the vessel's length, longitude, and latitude, its distance to the nearest shore, and the time of the measurement (am or pm). The classifier is trained and tested on data from the Automatic Identification System (AIS). The overall classification accuracy is 91%, but the precision for the fishing class is only 58% because of specific regions in the study area where activities of fishing and non-fishing vessels overlap. We then apply the classifier to a collection of vessel detections obtained by applying the Search for Unidentified Maritime Objects (SUMO) vessel detector to the 2017 Sentinel-1 SAR images of the North Sea. The trend in our monthly fishing-vessel count agrees with data from Global Fishing Watch on fishing-vessel presence. These initial results suggest that our approach could help monitor intensification or reduction of fishing activity, which is critical in the context of the global overfishing problem. [ABSTRACT FROM AUTHOR]

Geosynchronous synthetic aperture radar (GEO SAR) has been studied for several decades but has not yet been implemented. This paper provides an overview of mission design, describing significant constraints (atmosphere, orbit, temporal stability of the surface and atmosphere, measurement physics, and radar performance) and then uses these to propose an approach to initial system design. The methodology encompasses all GEO SAR mission concepts proposed to date. Important classifications of missions are: 1) those that require atmospheric phase compensation to achieve their design spatial resolution; and 2) those that achieve full spatial resolution without phase compensation. Means of estimating the atmospheric phase screen are noted, including a novel measurement of the mean rate of change of the atmospheric phase delay, which GEO SAR enables. Candidate mission concepts are described. It seems likely that GEO SAR will be feasible in a wide range of situations, although extreme weather and unstable surfaces (e.g., water, tall vegetation) prevent 100% coverage. GEO SAR offers an exciting imaging capability that powerfully complements existing systems. [ABSTRACT FROM PUBLISHER]

Regularly updated soil moisture maps are needed that combine wide scale for regional and national studies, whilst capturing the field-scale variability that is needed by many applications in agriculture, hydrology and meteorology. C-band SAR satellites, such as Sentinel-1, offer the high spatial and temporal resolution required, but the estimation of soil moisture from SAR requires correction for many contributing factors including vegetation, soil roughness, soil texture and temperature. This paper reviews and predicts the significance of soil texture and organic matter content to the errors that may be present in any estimation that is made using default assumptions. We show that each factor may contribute to a 10% error if an incorrect assumption is made. Soil moisture retrieval over agricultural fields in northern latitudes requires any algorithm to account for rapid and large changes in SAR backscatter due to crop growth and harvesting, tillage operations and freezing of the soil surface. This has particular significance for the extending the use of change detection approaches into arable farming areas. We discuss the prospect for developing a model to guide the setting of soil roughness parameters based on land use, soil texture and tillage, and for automatic correction for frozen soil. Successful implementation will improve the accuracy and validity of estimating soil moisture from C-band SAR satellite data, at the field scale. [ABSTRACT FROM AUTHOR]