The synthetic aperture radar (SAR) imagery has been widely applied for flooding mapping based on change detection approaches. However, errors in the mapping result are expected since not all land-cover changes are flood-induced, and those changes are sensitive to SAR data, such as crop growth or harvest over agricultural lands, clearance of forested areas, and/or modifications on the urban landscape. This study, therefore, incorporated historical SAR images to boost the detection of flood-induced changes during extreme weather events, using the Long Short-Term Memory (LSTM) method. Additionally, to incorporate the spatial signatures for the change detection, we applied a deep learning-based spatiotemporal simulation framework, Convolutional Long Short-Term Memory (ConvLSTM), for simulating a synthetic image using Sentinel One intensity time series. This synthetic image will be prepared in advance of flood events, and then it can be used to detect flood areas using change detection when the post-image is available. Practically, significant divergence between the synthetic image and post-image is expected over inundated zones, which can be mapped by applying thresholds to the Delta image (synthetic image minus post-image). We trained and tested our model on three events from Australia, Brazil, and Mozambique. The generated Flood Proxy Maps were compared against reference data derived from Sentinel Two and Planet Labs optical data. To corroborate the effectiveness of the proposed methods, we also generated Delta products for two baseline models (closest post-image minus pre-image and historical mean minus post-image) and two LSTM architectures: normal LSTM and ConvLSTM. Results show that thresholding of ConvLSTM Delta yielded the highest Cohen’s Kappa coefficients in all study cases: 0.92 for Australia, 0.78 for Mozambique, and 0.68 for Brazil. Lower Kappa values obtained in the Mozambique case can be subject to the topographic effect on SAR imagery. These results still confirm the benefits in terms of classification accuracy that convolutional operations provide in time series analysis of satellite data employing spatially correlated information in a deep learning framework.

Rainfall-induced floods often cause significant loss of life as well as damage to infrastructure and crops. Synthetic Aperture Radar (SAR) Earth Observation Satellites (EOS) can be used to determine the extent of flooding over large geographical areas. Unlike optical sensors, SAR instruments are suitable for cloudy weather conditions, making them suitable for flood detection and mapping during extreme weather events. In this study, we explore the application of the Normalized Difference Sigma-Naught Index (NDSI) and Shannon’s entropy of NDSI (SNDSI) of Sentinel-1 data for open water flooding detection, based on automatic thresholding and Bayesian probability. The proposed methodology was tested using the floods in Sofala province, Mozambique, caused by cyclone Idai on March 14–19 of 2019. Results show that thresholding of the NDSI Vertical Transmit-Horizontal Receive (VH) can produce results with Overall Accuracy above 90%, and Kappa higher than 0.6. Considerable performance improvements were obtained by our thresholding method over the entropy of NDSI, yielding results with Kappa of 0.70–0.77. Additionally, it was found that Weibull distribution can properly describe the properties of flooded pixels within the histogram of SNDSI, which allows us to generate a flood probability raster using a Bayesian approach. The final per-pixel flooding probability is useful to indicate certainty in the classification results. The SNDSI Bayesian model produced an AUC (Area Under the Receiver Operating Characteristic Curve) of 0.93–0.97, with cross-polarized data yielding the most accurate results.

Wildfires are considered one of the most major hazards and environmental issues worldwide. Recently, Earth observation satellite (EOS) sensors have proven to be effective for wildfire detection, although the quality and usefulness of the data are often hindered by cloud presence. One practical workaround is to combine datasets from multiple sensors. This research presents a methodology that utilizes data of the recently-launched Sentinel-3 sea and land surface temperature radiometer (S3-SLSTR) to reflect its applicability for detecting wildfires. In addition, visible infrared imaging radiometer suite day night band (VIIRS-DNB) imagery was introduced to assure day-night tracking capabilities. The wildfire event in the Indio Maiz Biological Reserve, Nicaragua, during 3–13 April 2018, was the study case. Six S3-SLSTR images were processed to compute spectral indices, such as the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized burn ratio (NBR), to perform image segmentation for estimating the burnt area. The results indicate that 5870.7 ha of forest was affected during the wildfire, close to the 5945 ha reported by local authorities. In this study, the fire expansion was delineated and tracked in the Indio Maiz Biological Reserve using a modified fast marching method on nighttime-sensed temporal VIIRS-DNB. This study shows the importance of S3-SLSRT for wildfire monitoring and how it can be complemented with VIIRS-DNB to track burning biomass at daytime and nighttime.