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Application of synthetic-aperture radar (SAR) remote sensing observations for flood mapping using a Sentinel-1 image from 29 January 2018. A false-colour RGB map of the Sentinel-2 image is provided together with a map, histogram, binary map, permanent water body map and flood map for the SAR backscatter. The dashed line in the histograms shows the threshold used to generate the binary, water and flood maps.
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Water is an essential natural resource, but increasingly water also forms a threat to the human population, with floods being the most common natural disaster worldwide. Earth Observation has the potential for developing cost-effective methods to monitor risk, with free and open data available at the global scale. In this study, we present the appl...
Citations
... However, relying solely on spectral indices-which primarily focus on water reflectance-can lead to the omission of key urban features such as shape, texture, and spatial relationships [31]. These contextual urban characteristics play a critical role in helping ML models distinguish floodwater from visually similar elements, such as shadows, wet roads, or rooftops [31]. Therefore, incorporating these contextual features alongside spectral data enhances the models' capacity for more accurate flood detection [32,33]. ...
In post-flood disaster analysis, accurate flood mapping in complex riverine urban areas is critical for effective flood risk management. Recent studies have explored the use of water-related spectral indices derived from satellite imagery combined with machine learning (ML) models to achieve this purpose. However, relying solely on spectral indices can lead these models to overlook crucial urban contextual features, making it difficult to distinguish inundated areas from other similar features like shadows or wet roads. To address this, our research explores a novel approach to improve flood segmentation by integrating a row-wise cross attention (CA) module with ML ensemble learning. We apply this method to the analysis of the Brisbane Floods of 2022, utilizing 4-band satellite imagery from PlanetScope and derived spectral indices. Applied as a pre-processing step, the CA module fuses a spectral band index into each band of a peak-flood satellite image using a row-wise operation. This process amplifies subtle differences between floodwater and other urban characteristics while preserving complete landscape information. The CA-fused datasets are then fed into our proposed ensemble model, which is constructed using four classic ML models. A soft voting strategy averages their binary predictions to determine the final classification for each pixel. Our research demonstrates that CA datasets can enhance the sensitivity of individual ML models to floodwater in complex riverine urban areas, generally improving flood mapping accuracy. The experimental results reveal that the ensemble model achieves high accuracy (approaching 100%) on each CA dataset. However, this may be affected by overfitting, which indicates that evaluating the model on additional datasets may lead to reduced accuracy. This study encourages further research to optimize the model and validate its generalizability in various urban contexts.
The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model.
