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Supervised machine learning algorithms usually require huge labeled data sets to produce sufficiently good results. For many applications, these data sets are still not available today, and the reasons for this can be manifold. As a solution, the missing training data can be generated by fast simulators. This procedure is well studied and allows filling possible gaps in the training data, which can further improve the results of a machine learning model. For this reason, this article deals with the development of a two-dimensional electromagnetic field simulator for modeling the response of a radar sensor in an imaging system based on the synthetic aperture radar principle. The creation of completely random scenes is essential to achieve data sets with large variance. Therefore, special emphasis is placed on the development of methods that allow creating random objects, which can then be assembled into an entire scene. In the context of this contribution, we focus on humanitarian demining with regard to improvised explosive devices using a ground-penetrating radar system. This is an area where the use of trained classifiers is of great importance, but in practice, there are little to no labeled datasets for the training process. The simulation results show good agreement with the measurement results obtained in a previous contribution, demonstrating the possibility of enhancing sparse training data sets with synthetic data. [ABSTRACT FROM AUTHOR]

Extreme climate events such as storms and severe droughts are becoming more frequent under the warming climate. In the tropics, excess rainfall carried by hurricanes causes massive flooding and threatens ecosystems and human society. We assessed recent major floodings on the tropical island of Puerto Rico after Hurricane Maria in 2017 and Hurricane Fiona in 2022, both of which cost billions of dollars damages to the island. We analyzed the Sentinel-1 synthetic aperture radar (SAR) images right after the hurricanes and detected surface inundation extent by applying a random forest classifier. We further explored hurricane rainfall patterns, flow accumulation, and other possible drivers of surface inundation at watershed scale and discussed the limitations. An independent validation dataset on flooding derived from high-resolution aerial images indicated a high classification accuracy with a Kappa statistic of 0.83. The total detected surface inundation amounted to 10,307 ha after Hurricane Maria and 7949 ha after Hurricane Fiona for areas with SAR images available. The inundation patterns are differentiated by the hurricane paths and associated rainfall patterns. We found that flow accumulation estimated from the interpolated Fiona rainfall highly correlated with the ground-observed stream discharges, with a Pearson's correlation coefficient of 0.98. The detected inundation extent was found to depend strongly on hurricane rainfall and topography in lowlands within watersheds. Normal climate, which connects to mean soil moisture, also contributed to the differentiated flooding extent among watersheds. The higher the accumulated Fiona rain and the lower the mean elevation in the flat lowlands, the larger the detected surface flooding extent at the watershed scale. Additionally, the drier the climate, which might indicate drier soils, the smaller the surface flooding areas. The approach used in this study is limited by the penetration capability of C-band SAR; further application of L-band images would expand the detection to flooding under dense vegetation. Detecting flooding by applying machine learning techniques to SAR satellite images provides an effective, efficient, and reliable approach to flood assessment in coastal regions on a large scale, hence helping to guide emergency responses and policy making and to mitigate flooding disasters. [ABSTRACT FROM AUTHOR]

Remote sensing technologies have been successfully used for deforestation monitoring, and with the wide availability of satellite products from different platforms, forest monitoring applications have grown in recent years. The observed potential in these technologies motivates the development of forest mapping and monitoring tools that could also be used for neighboring applications like agriculture or land-use mapping. A literature review confirmed the research areas of interest in deforestation monitoring using synthetic aperture radar (SAR) and data fusion techniques, which guided the formulation of the method developed in this article consisting of a data preprocessing workflow for SAR (Sentinel-1) and multispectral (Sentinel-2) data and a procedure for the selection of a machine learning model for forest/nonforest segmentation evaluated in different combinations of Sentinel-1 and Sentinel-2 bands. The selected model is a random forest algorithm that uses C-band SAR dual-polarimetric bands, intensity features, and vegetation indices derived from optical/multispectral data. The selected random forest classifier's balanced accuracies were 79–81%, and the f1-scores were 0.72–0.76 for the validation set. The results allow the obtention of yearly forest/nonforest and forest loss maps in the study area of Bajo Cauca in Colombia, a region with a documented high deforestation rate. [ABSTRACT FROM AUTHOR]