Pluvial floods caused by extreme overland flow inland account for half of all flood damage claims each year along with fluvial floods. In order to increase confidence in pluvial flood susceptibility mapping, overland flow models need to be intensively evaluated using observations from past events. However, most remote-sensing-based flood detection techniques only focus on the identification of degradations and/or water pixels in the close vicinity of overflowing streams after heavy rainfall. Many occurrences of pluvial-flood-induced damages such as soil erosion, gullies, landslides and mudflows located further away from the stream are thus often unrevealed. To fill this gap, a transferable remote sensing fusion method called FuSVIPR, for Fusion of Sentinel-2 & Very high resolution Imagery for Pluvial Runoff, is developed to produce damage-detection maps. Based on very high spatial resolution optical imagery (from Pléiades satellites or airborne sensors) combined with 10 m change images from Sentinel-2 satellites, the Random Forest and U-net machine/deep learning techniques are separately trained and compared to locate pluvial flood footprints on the ground at 0.5 m spatial resolution following heavy weather events. In this work, three flash flood events in the Aude and Alpes-Maritimes departments in the South of France are investigated, covering over more than 160 km² of rural and periurban areas between 2018 and 2020. Pluvial-flood-detection accuracies hover around 75% (with a minimum area detection ratio for annotated ground truths of 25%), and false-positive rates mostly below 2% are achieved on all three distinct events using a cross-site validation framework. FuSVIPR is then further evaluated on the latest devastating flash floods of April 2022 in the Durban area (South Africa), without additional training. Very good agreement with the impact maps produced in the context of the International Charter “Space and Major Disasters” are reached with similar performance figures. These results emphasize the high generalization capability of this method to locate pluvial floods at any time of the year and over diverse regions worldwide using a very high spatial resolution visible product and two Sentinel-2 images. The resulting impact maps have high potential for helping thorough evaluation and improvement of surface water inundation models and boosting extreme precipitation downscaling at a very high spatial resolution.

Keywords: Image Quality, Radiometry, Calibration The first Pleiades-HR satellite, part of a constellation of two, has been launched on December 17, 2011. This satellite produces high resolution optical images. In order to achieve good image quality, Pleiades-HR should first undergo an important 6 month commissioning phase period. This phase consists in calibrating and assessing the radiometric and geometric image quality to offer the best images to end users. This new satellite has benefited from technology improvements in various fields which make it stand out from other Earth observation satellites. In particular, its best-in-class agility performance enables new calibration and assessment techniques. This paper is dedicated to presenting these innovative techniques that have been tested for the first time for the Pleiades- HR radiometric commissioning. Radiometric activities concern compression, absolute calibration, detector normalization, and refocusing operations, MTF (Modulation Transfer Function) assessment, signal-to-noise ratio (SNR) estimation, and tuning of the ground processing parameters. The radiometric performances of each activity are summarized in this paper.

The accurate on-orbit radiometric calibration of optical sensors has become a challenge for space agencies who combine their efforts through international working groups such as CEOS/WGCV or WMO/GSICS with the objective of ensuring the consistency of space measurements and to reach the absolute accuracy required by increasingly demanding scientific requirements. Different targets are generally used for calibration, depending on the specific features of the sensor or spacecraft: from on-board calibration systems to ground targets, they can all profitably be characterized and modeled. Thanks to their agility, some satellites have the capability to view extra-terrestrial targets such as stars. Although the spectral irradiance of some stars is known with very high accuracy, very little work has been done on their use as an absolute reference for the calibration of remote sensors. The reason is their low irradiance, requiring a small instantaneous field of view to observe them. Moreover, their use as a calibration reference also requires a good knowledge of the instrument's modulation transfer function (MTF). The MTF which drives the spatial quality is a major parameter for high-resolution (HR) optical remote sensors as it indicates how spatial frequencies are transmitted and weakened by the imaging system. On the other hand, stars provide ideal targets for MTF measurement as they can be assimilated to point sources. This paper proposes a method for simultaneously computing the absolute radiometric calibration of PLEIADES' HR optical sensors and their MTF, using stars. The radiometric model is solved in Fourier space for point sources whose irradiance can be easily controlled. Results are provided for both PLEIADES satellites and compared to the official radiometric calibration and MTF. The quality of the results and the main error contributors are discussed.

Very high resolution optical remote sensing images (RSI) are often corrupted by noise. Among popular denoising methods in the state of the art, nonlocal Bayes (NLB) has led to successful results on real datasets, with high quality and reasonable computation time. However, its computation time remains prohibitive with respect to requirements of operational RSI pipelines, such as Pléiades one. In this paper, we tackle such an issue and introduce several optimizations aiming to significantly reduce the computation time required by NLB while keeping the best denoising quality (i.e., preserving edges, textures, and homogeneous areas). More precisely, our improvements consist of reducing multiple estimations of a same pixel with a masking technique and modifying the spatial extent of the similar patch search area (i.e., one of the main parts of nonlocal algorithms, such as NLB). We report several experiments and discuss optimal settings for these parameters, allowing a gain in computation time of 50% (resp. 15%) with optimized masking strategy (resp. spatial extent of the search area). When both contributions are combined, we achieve the same denoising quality as standard NLB while doubling the computation efficiency, the latter being increased fivefold if we accept a very small (lower than 0.1%) loss in quality.

Along with fluvial floods (FFs), surface water floods (SWFs) caused by extreme overland flow are one of the main flood hazards occurring after heavy rainfall. Using physics-based distributed hydrological models, surface runoff can be simulated from precipitation inputs to investigate regions prone to soil erosion, mudflows or landslides. Geomatics approaches have also been developed to map susceptibility towards intense surface runoff without explicit hydrological modeling or event-based rainfall forcing. However, in order for these methods to be applicable for prevention purposes, they need to be comprehensively evaluated using proxy data of runoff-related impacts following a given event. Here, the IRIP geomatics mapping model, or “Indicator of Intense Pluvial Runoff”, is faced with rainfall radar measurements and damage maps derived from satellite imagery and supervised classification algorithms. Six watersheds in the Aude and Alpes-Maritimes departments in the South of France are investigated over more than 2000 km² of rural areas during two flash-flood events. The results of this study show that the greater the IRIP susceptibility scores, the more SWFs are detected by the remote sensing-based detection algorithm. Proportions of damaged plots become even larger when considering areas which experienced heavier precipitations. A negative relationship between the mean IRIP accumulation scores and the intensity of rainfall is found among damaged plots, confirming that SWFs preferably occur over potentially riskier areas where rainfall is lower. Land use and soil hydraulic conductivity are identified as the most relevant indicators for IRIP to define production areas responsible for downslope deteriorations. Multivariate logistic regression is also used to determine the relative weights of upstream and local topography, uphill production areas and rainfall intensity for explaining SWF occurrence. This work overall confirms the relevance of IRIP methodology while suggesting improvements to its core framework to implement better prevention strategies against SWF-related hazards.