Analysis of Riverine Flood Dynamics by using Sentinel-1 Satellite Imagery in An Giang, Vietnam

Availability of reliable information on extent and impact of flood becomes critical for faster and efficient disaster management planning. Access to near realtime satellite data from optical and microwave sensors on Google Earth Engine (GEE) platform helps in rapid flood inundation mapping. Also, integration of Optical and Synthetic Aperture Radar (SAR) data from Sentinel series of satellites helps in detection of landuse/landcover features like Built-up, Agriculture Lands and Water bodies under flooding. The GEE platform provides extensive library tools for simultaneous pre-processing of SAR images and cloud free optical images from multiple satellites. In this research, Differential Thresholding and Differential Smoothening algorithms developed in GEE have been applied on Pre and Post SAR images for automatic identification of flood inundation areas. The inundated areas thus delineated were validated with inundation extents provided by the National Database for Emergency Management (NDEM). The methodology of automation developed in this study is applied to severe floods occurred in Kerala, India during August 2018. The sensitivity of polarization on mapping accuracy is estimated using Vertical- Horizontal (VH) and Vertical-Vertical (VV) modes available with Sentinel-1 data. The result indicates that VV mode of polarization provides better accuracy of 96% than VH mode. The coding is implemented in GEE environment with automation to provide extent of flood inundation for efficient management and mitigation planning during flood disasters.

Floods are an intense and frequent disaster happening in numerous portions of the world. Flood is a excess of water that submerges surroundings that is normally dry. It is the sever problem in Ganga Sub Basin - Ghaghra Confluence to Gomti Confluence of Uttar Pradesh. Near real time mapping of inundated areas is very important for figuring out the flood extent, deployment of emergency reaction teams, and evaluation of damages and casualties. In this thesis work, a real time flood mapping and monitoring online WEB based application using Sentinel-1 time-series data has been developed on Google Earth Engine (GEE) platform. The SAR Data has Capability to see through the cloud and during the flood the major problem with optical data is, it can not see through the cloud. In this thesis work the SAR data has been used to identify the inundated pixels before and during flood to identify the extent and calamities due to flood. Flood is a hazardous natural phenomenon which severely affect lives, goods and services. Monitoring of flood affected area becomes compulsory for emergency responses but due to damage of network and risk for lives in going such areas it becomes a very typical task. Google Earth Engine (GEE) is an open-source cloud based online platform which reduces this problem. In this thesis work the GEE platform has been used for Near-Real time flood mapping in the study area. The GEE based app “FLOOD MONITORING SYSTEM: GANGA SUB BASIN – GHAGHRA CONFLUENCE TO GOMTI CONFLUENCE” has been developed for Near-Real time flood Mapping.

In mountainous countries like Nepal, floods are a major challenge due to complex topography, intense snowmelt, and highly variable monsoon rainfall that drive frequent flooding events. This study focuses on the Hilly and Himalayan regions of Nepal, where flood monitoring and risk management are increasingly important for safeguarding vulnerable communities and infrastructure.This study presents a high-resolution, time-series flood extent dataset derived from the Copernicus Sentinel-2 Level-2A imagery at a 10-meter spatial resolution, covering the years 2019 to 2023. Flood mapping was performed using the Normalized Difference Vegetation Index (NDVI) combined with region-specific thresholding. NDVI values below 0 represent open water, while values between 0 and 0.1 often indicate mud, bare soil. A threshold of NDVI <0.019 was applied to identify flood-affected areas in the hilly region to capture the debris flow type flood, whereas NDVI <0 was used for the Himalayan region, because of the presence of snow and water that complicated classification due to their spectral similarity with other features. Snow-covered areas were masked using the Copernicus Global Land Cover dataset to improve accuracy in the high altitude zones.Data processing was performed on the Google Earth Engine (GEE) platform. Monsoon-season image composites were generated after applying cloud masking using the Scene Classification Layer (SCL), and temporal cloud gaps were filled using post-monsoon imagery to ensure continuous temporal data. The resulting flood extent maps reveal consistent spatial patterns and provide critical data for flood forecasting, risk-sensitive land use planning, and interdisciplinary studies. Despite challenges with cloud interference and complex terrain, this dataset offers valuable insights into flood dynamics across Nepal’s mountainous landscape.

This study examines the impact of land-use (LU) conversions on flood dynamics in An Giang province, a flood-prone headwater region of the Mekong River, from 2000 to 2023. Using Landsat imagery and Google Earth Engine (GEE), we analyzed LU changes and flood extent during peak seasons (June–November). Results reveal that LU shifts—particularly the rise in aquaculture (703 ha to 5,531 ha) and crops which replacing with rice—reduced water storage capacity, altering flood drainage. Before dyke construction (2000–2006), floods vol/frequency 175,719 ha, peaking at 235,290 ha in 2000. Post-2007 dyke expansion decreased flooded areas to 61,658 ha (2007–2013) and 25,502 ha (2014–2023), while autumn-winter rice surged from 45,633 ha to 116,067 ha. Periodic controlled flooding proved essential for ecological balance and dyke stability. In conclusion, 24-year analysis uniquely integrates LU shifts with flood dynamics in An Giang, highlighting the need for flexible LU policies and controlled flooding strategies to ensure sustainable development amid climate change. Periodic flooding in the Long Xuyen Quadrilateral proved vital for dyke stability and ecosystems. Adaptive LU strategies—prioritizing rice-shrimp systems and drought-tolerant crops—combined with ENSO-informed flood releases, are essential for sustainable development amid climate change. Keywords: An Giang region, crops replacing, flood dynamics, land-use change, remote sensing

The Vietnamese Mekong Delta (VMD), a critical agricultural hub, faces recurrent flooding that poses substantial threats to livelihoods and productivity. Ben Tre province, with its low-lying coastal terrain, is particularly vulnerable. Effective risk management and sustainable agricultural development necessitate a thorough understanding of these flood dynamics. This study leveraged the Google Earth Engine (GEE) platform and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to analyze flood inundation patterns and their impacts on diverse agricultural land uses in Ben Tre province from 2015 to 2023. The methodology involved SAR data pre-processing, Otsu thresholding for water body delineation from VH polarization data and change detection using a 2020 land use map to quantify annual flooded areas and their impact on specific agricultural categories. The total inundated area peaked in 2018 at 58,334 ha, a significant increase from 27,934 ha in 2015, before stabilizing around 42,000–44,000 ha in 2021–2023. Flooded agricultural land mirrored this trend, increasing from 18,615 ha (2015) to a peak of 39,514 ha (2018), then decreasing to 28,841 ha (2023). Notably, wet rice cultivation experienced a 37.8% increase in its flooded area over the study period, while other annual crops and perennial crops saw increases of 38.9% and 68.4%, respectively. This research demonstrates the GEE platform's efficacy with Sentinel-1 SAR for robust, long-term flood monitoring and impact assessment, revealing escalating flood pressure on key agricultural systems and an expansion of flooding beyond traditional low-lying zones, providing crucial data for adaptive land use planning.

The traffic congestion in Beijing has become a significantly serious problem and is hindering the development of the city. It also causes inconvenience of traveling, especially commuting for people. Google earth and Google Earth Engine (GEE), can provide much information about the traffic and surrounding environment. However, there are few studies exist to utilize the GEE and traffic data to analyze the effect of the congestion on the city`s development and people`s life. Therefore, to explore and analyze the causes of traffic congestion and ultimately to put forward a viable solution, we propose to model the impact of traffic congestion based on Google Earth and Google Earth engine, taking Zhongguancun Street as an example. The results show that the congestion at the intersection of the main street has a radiation influence of about three kilometers on the main road. And according to the big data, we find that at 8:00 am and 6:00 pm every day, it is the peak time of traffic congestion. Finally, we can conclude that the GEE platform is a profound and potential tool for effectively analyzing traffic problems, and our subsequent research can be continued to be further developed based on this platform.

Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa

Forest fires are major disturbances in forest ecosystems. The rapid detection of the spatial and temporal characteristics of fires is essential for formulating targeted post-fire vegetation restoration measures and assessing fire-induced carbon emissions. We propose an accurate and efficient framework for extracting the spatiotemporal characteristics of fires using vegetation change tracker (VCT) products and the Google Earth Engine (GEE) platform. The VCT was used to extract areas of persistent forest and forest disturbance patches from Landsat images of Xichang and Muli, Liangshan prefecture, Sichuan province in southwestern China and Huma, Heilongjiang province, in northeastern China. All available Landsat images in the GEE platform in a year were normalized using the VCT-derived persisting forest mask to derive three standardized vegetation indices (normalized burn ratio (NBRr), normalized difference moisture index (NDMIr), and normalized difference vegetation index (NDVIr)). Historical forest disturbance events in Xichang were used to train two decision trees using the C4.5 data mining tool. The differenced NBRr, NDMIr, and NDVIr (dNBRr, dNDMIr, and dNDVIr) were obtained by calculating the difference in the index values between two temporally adjacent images. The occurrence time of disturbance events were extracted using the thresholds identified by decision tree 1. The use of all available images in GEE narrowed the disturbance occurrence time down to 16 days. This period was extended if images were not available or had cloud cover. Fire disturbances were distinguished from other disturbances by comparing the dNBRr, dNDMIr, and dNDVIr values with the thresholds identified by decision tree 2. The results showed that the proposed framework performed well in three study areas. The temporal accuracy for detecting disturbances in the three areas was 94.33%, 90.33%, and 89.67%, the classification accuracy of fire and non-fire disturbances was 85.33%, 89.67%, and 83.67%, and the Kappa coefficients were 0.71, 0.74, and 0.67, respectively. The proposed framework enables the efficient and rapid extraction of the spatiotemporal characteristics of forest fire disturbances using frequent Landsat time-series data, GEE, and VCT products. The results can be used in forest fire disturbance databases and to implement targeted post-disturbance vegetation restoration practices.

This study addresses the pressing need for flood extent and exposure information in data-scarce and vulnerable regions, with a specific focus on West Africa, particularly Senegal. Leveraging the Google Earth Engine (GEE) platform and integrating data from the Sentinel-1 SAR, Global Surface Water, HydroSHEDS, the Global Human Settlement Layer, and MODIS land cover type, our primary objective is to delineate the extent of flooding and compare this with flooding for a one-in-a-hundred-year flood event, offering a comprehensive assessment of exposure during the period from July to October 2022 across Senegal’s 14 regions. The findings underscore a total inundation area of 2951 square kilometers, impacting 782,681 people, 238 square kilometers of urbanized area, and 21 square kilometers of farmland. Notably, August witnessed the largest flood extent, reaching 780 square kilometers, accounting for 0.40% of the country’s land area. Other regions, including Saint-Louis, Ziguinchor, Fatick, and Matam, experienced varying extents of flooding, with the data for August showing a 1.34% overlap with flooding for a one-in-a-hundred-year flood event derived from hydrological and hydraulic modeling. This low percentage reveals the distinct purpose and nature of the two approaches (remote sensing and modeling), as well as their complementarity. In terms of flood exposure, October emerges as the most critical month, affecting 281,406 people (1.56% of the population). The Dakar, Diourbel, Thiès, and Saint-Louis regions bore substantial impacts, affecting 437,025; 171,537; 115,552; and 77,501 people, respectively. These findings emphasize the imperative for comprehensive disaster preparation and mitigation efforts. This study provides a crucial national-scale perspective to guide Senegal’s authorities in formulating effective flood management, intervention, and adaptation strategies.

Real-time, near-real-time, and accurate flood extent information is critical for emergency response during disaster events such as floods. Accurate extents are critical for disaster management and relief efforts. Despite multiple efforts, there are still many challenges in automated processing of Sentinel-1 SAR to generate reliable inundation maps. The major advantage of SAR compared to optical imagery is its data collection capability despite any weather conditions even thick cloud situation. Currently, there is a knowledge gap of employing different polarization combinations of SAR imagery for flooding research. First, ten different combinations of the two original VH and VV polarizations are designed for rapid and accurate urban flood mapping. To examine the significant potentials of the polarization combinations for flood mapping, four flood mapping methods namely threshold, change detection, unsupervised and supervised classification, in combination with a zero-depth flood method, are designed and used to map flood extents. Among different polarization combinations, the multiplication, squared multiplication, addition, and squared addition combinations have resulted in good results for flood extent mapping. In addition, a flood depth estimation approach has been used to address the overestimation of urban flooded areas. In all four methods, the deduction of overestimated flooded areas using the threshold of zero flood depth has improved the overall accuracy on average 7 % for all methods. The results show that all four methods implemented on Google Earth Engine are good using different combinations to identify flooded areas but change detection method requires little user involvement, and this can be applied to new study areas without estimating flooding depth for the affected areas. Whereas the supervised classification will need more user’s involvement to collect sample points. Among all the combinations, squared addition of polarizations has been consistently performed well for all methods. All the analysis has been done on the Google Earth Engine platform, and this strategy can be used to map flood in any urban environment. The finding of this study will enhance local governments and federal agencies rapid assessment of flooding disasters and making accurate decisions.

Nitrogen plays an important role in improving soil productivity and maintaining ecosystem stability. Mapping and monitoring the soil total nitrogen (STN) content is the basis for modern soil management. The Google Earth Engine (GEE) platform covers a wide range of available satellite remote sensing datasets and can process massive data calculations. We collected 6823 soil samples in Shandong Province, China. The random forest (RF) algorithm predicted the STN content in croplands from 2002 to 2016 in Shandong Province, China on the GEE platform. Our results showed that RF had the coefficient of determination (R2) (0.57), which can predict the spatial distribution of the STN and analyze the trend of STN changes. The remote sensing spectral reflectance is more important in model building according to the variable importance analysis. From 2002 to 2016, the STN content of cropland in the province had an upward trend of 35.6%, which increased before 2010 and then decreased slightly. The GEE platform provides an opportunity to map dynamic changes of the STN content effectively, which can be used to evaluate soil properties in the future long-term agricultural management.

Assessing the impact of unplanned settlements on urban renewal projects with GEE

With the growth of cloud computing, the use of the Google Earth Engine (GEE) platform to conduct research on water inversion, natural disaster monitoring, and land use change using long time series of Landsat images has also gradually become mainstream. Landsat images are currently one of the most important image data sources for remote sensing inversion. As a result of changes in time and weather conditions in single-view images, varying image radiances are acquired; hence, using a monthly or annual time scale to mosaic multi-view images results in strip color variation. In this study, the NDWI and MNDWI within 50 km of the coastline of the Yucatán Peninsula from 1993 to 2021 are used as the object of study on GEE platform, and mosaic areas with chromatic aberrations are reconstructed using Landsat TOA (top of atmosphere reflectance) and SR (surface reflectance) images as the study data. The DN (digital number) values and probability distributions of the reference image and the image to be restored are classified and counted independently using the random forest algorithm, and the classification results of the reference image are mapped to the area of the image to be restored in a histogram-matching manner. MODIS and Sentinel-2 NDWI products are used for comparison and validation. The results demonstrate that the restored Landsat NDWI and MNDWI images do not exhibit obvious band chromatic aberration, and the image stacking is smoother; the Landsat TOA images provide improved results for the study of water bodies, and the correlation between the restored Landsat SR and TOA images with the Sentinel-2 data is as high as 0.5358 and 0.5269, respectively. In addition, none of the existing Landsat NDWI products in the GEE platform can effectively eliminate the chromatic aberration of image bands.

Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory (~28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

An entire-process MaxEnt framework for habitat suitability modeling on Google Earth Engine: A case study of the oriental white stork in eastern mainland China.