Fusion of Sentinel-1 data with Sentinel-2 products to overcome non-favourable atmospheric conditions for the delineation of inundation maps

Abstract

Sentinel-1 data are an alternative for monitoring flooded inland surfaces during cloudy periods. Supervised classification approaches with a single-trained model for the entire image demonstrate poor accuracy due to confusing backscatter conditions of the inundated areas in relation with the prevailing land cover features. This study follows instead a pixel-centric approach, which exploits the varying backscatter values of each pixel through a time series of Sentinel-1 images to train local Random Forest classification models per 3×3 pixels, and classifies each pixel in the target Sentinel-1 image, accordingly. Reference training data is retrieved from the timely close Sentinel-2-derived inundation maps. This study aims to identify the furthest mean day difference between the target Sentinel-1 image and available Sentinel-2 high accurate inundation maps (kappa coefficient-k > 0.9) that allows for the estimation of credible inundation maps for the Sentinel-1 target date. Various combinations of Sentinel-2 and Sentinel-1 training datasets are examined. The evaluation for eight target dates confirms that a Sentinel-1 inundation map with a k of 0.75 on average can be generated, when mean day difference is less than 30 days. The increment of the considered Sentinel-2 maps allows for the estimation of Sentinel-1 inundation maps with higher accuracy.

Automatic Inundation Mapping Using Sentinel-2 Data Applicable to Both Camargue and Doñana Biosphere Reserves

“…Future steps could consider the exploitation of ancillary information, such as digital elevation models to improve water detection under emergent vegetation, by inferring the water presence based on detected adjacent water covered areas having similar elevation, and land cover information to correct areas erroneously classified as water covered, where water presence is not expected. Furthermore, S2 inundation maps of a site generated via the automatic thresholding alternative achieving top accuracy among other alternatives can be fused with S1 data in order to allow for inundation mapping during extended cloudy periods, based on the example of Reference [12].…”

“…The main limitation of using optical data is cloud presence, which prohibits the observation of the earth's surface [10]. Several approaches utilize both optical and radar data to deal with the lack of optical data during extended periods of cloud cover and overcome the limitations of radar data [11,12]. The methodology presented in this work relies on optical data.…”

Semi-supervised Convolutional Neural Networks for Flood Mapping using Multi-modal Remote Sensing Data

“…Meanwhile, the object-based algorithm first segment an image into constituent regions according to a similarity criterion, and then assign a label to the whole region based on spectral, texture, and shape features [18], [19]. Most of the works working on a supervised classification area are based on various conventional machine learning algorithms such as artificial neural network [16], random forest [15], [20], k-Nearest Neighbour [17] or late ensemble of these models [17].…”

“…Unfortunately, annotating flooded areas in optical and SAR images suffers difficulty. As flood events often take place during periods of extended cloud coverage, the exploitation of optical data may become questionable [20]. Meanwhile, SAR data contains a large number of confusing pixels caused by irregular reflections of radar waves, which make it challenging to annotate a pixel as water or non-water.…”

Introduction to the special issue: “Earth observation supporting sustainability research”

“…This issue contains seven additional full papers related to the contributions to the events. They address climate change and natural hazards [Abdel-Hamid et al, 2020;Nikolakopoulos, 2020], use of thermal data [Schultz et al, 2019], photogrammetry [Nikolakopoulos, 2020], humanitarian assistance [Lang et al, 2019], hydrology [Manakos et al, 2019], and image processing techniques [Ablin et al, 2019]. All papers bring forward improved methods and new knowledge on employing Earth Observation data for the promotion of various aspects of sustainability.…”