Water Extent Research Articles

Satellite observations of surface water dynamics and channel migration in the Yellow River since the 1980s

Study regionthe Yellow River (YR) in China. Study focusDue to climate change and human activities, YR channel morphology has undergone significant spatiotemporal variations. Yet, a comprehensive understanding of channel migration in YR and its driving factors remains unclear. Here, we developed a multi-index water extraction method to track the changes in surface water and river channel migration of YR based on Landsat imagery since the 1980s. New hydrological insights for the regionWe find that the average surface water area of YR over the past four decades is 4013 km2, with 73.5 % of permanent surface water. Notably, the surface water extent has experienced a 9 % increase since the 1980s, while the river channel has undergone a 12.2 % decrease. The YR channel’s centerline exhibits diverse change patterns across the entire basin, which can be broadly categorized into six types ranging from unchanged to reverse migration. We identify that climate, particularly temperature and precipitation, contributed 71 % of channel changes in the upper reaches, while 65 % of changes in the lower reaches are from human activities, including reservoir operations and water management policies. Our results unveil the variations in water extent and channel migration of the YR, offering new insights into the interactions between channel migration and climate change and human activities in the YR over the past four decades.

Purification Resistance Index: A new water quality assessment method toward drinking water production

Water quality assessment plays a significant role in ensuring the availability of clean and safe water. The Water Quality Index (WQI) model method has been developed to provide a basis for assessing water quality by integrating various water quality parameters. However, existing WQIs do not “actively” consider the difficulty of water treatment from raw water to specific water use scenarios. This study proposes a novel model framework, named as Purification Resistance Index (PRI), quantitatively evaluating not only the exceedance of pollutants but also how difficult they can be removed in the water treatment process. The framework is built based on the conventional drinking water treatment processes, with sub-indices for coagulation-sedimentation (rc), filtration (rf), disinfection (rd), and advanced treatment (ra). The model considers appropriate weights assigned to each sub-index to calculate the purification resistance, resulting in a comprehensive index for water quality evaluation. Case studies on nationwide and citywide water source reservoirs demonstrated the applicability of PRI approach. PRI breakthrough the traditional water quality risk assessment paradigm and extents to engineering region and provide useful tools for water source supervision, drinking water treatment plant planning and updating, operation control, and other purposes. Water authority, water utility and municipal design institute will all benefit. It is open for more localized practices validation and discussion.

The feasibility of using national‐scale datasets for classifying wetlands in Arizona with machine learning

AbstractThe advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national‐scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland‐specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non‐wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Abstract. Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery (Planet Team, 2017). Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World (Brown et al., 2022), based on Sentinel-2, and the NASA IMPACT model (Paul and Ganju, 2021), based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via https://doi.org/10.25739/03nt-4f29 (Mukherjee et al., 2024).

Super-resolution water body mapping with a feature collaborative CNN model by fusing Sentinel-1 and Sentinel-2 images

Mapping water bodies from remotely sensed imagery is crucial for understanding hydrological and biogeochemical processes. The identification of water extent is mainly dependent on optical and synthetic aperture radar (SAR) images. However, the use of remote sensing for water body mapping is often undermined by the mixed pixel dilemma inherent to traditional hard classification approaches. At the same time, the presence of clouds in optical imagery and speckle noise in SAR imagery, coupled with the difficulty in differentiating between water-like surfaces and actual water bodies, significantly compromise the accuracy of water body identification. This paper proposes a DEEP feature collaborative convolutional neural network (CNN) for Water Super-Resolution Mapping based on Optical and SAR images (DeepOSWSRM), which collaboratively leverages Sentinel-1 and Sentinel-2 imagery to address the challenges of missing data and mixed pixels. The Sentinel-1 image provides complementary water distribution information for the cloudy areas of the Sentinel-2 image, while the Sentinel-2 image enhances the perception capabilities for small water bodies in the Sentinel-1 image. Using PlanetScope imagery as the true reference data, the effectiveness of the proposed method was assessed through two experimental scenarios: one utilizing synthetic coarse-resolution imagery degraded from Sentinel-1 and Sentinel-2 data and another using actual Sentinel-1 and Sentinel-2 data, encompassing both simulated and real cloud conditions. A comparative analysis was conducted against three state-of-the-art CNN-based water mapping methods and two CNN SRM methods. The findings demonstrate that the proposed DeepOSWSRM method successfully produces accurate, fine-resolution water body maps, with its performance mainly benefiting from the fusion of SAR and optical images.

HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service which was co-developed with in-region partners to provide year-round, low-latency weather hazard information across the HKH. This paper describes the end user-focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring the surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which provides the capability to scale automatically with the event size. A performance assessment of our quantitative algorithms is described, demonstrating the capabilities to map the flood extent and water depth with an accuracy of >90% and <1 m, respectively. An application of the HydroSAR service to the 2023 South Asia monsoon seasons showed that monsoon floods peaked near 6 August 2023 and covered 11.6% of Bangladesh in water. At the peak of the flood season, nearly 13.5% of Bangladesh’s agriculture areas were affected.

Flood inundation monitoring using multi-source satellite imagery: a knowledge transfer strategy for heterogeneous image change detection

Flood emergency mapping is essential for flood management, often requiring near real-time extraction of large-scale flood extents by combining pre- and post-event multi-source remote sensing images. Pre-event optical imagery delineates the normal water extent, providing a benchmark for estimation of post-flood changes. Synthetic aperture radar (SAR) imagery provides rapid and accurate interpretation of flood inundation extent during the rainy period. Post-classification comparison is one of the fundamental methods for flood mapping of multi-source imagery, as the image characteristics of SAR and optical imagery are inherently different. However, the accuracy of flood mapping depends on the accuracy of water body delineation from single temporal imagery, which can lead to error propagation in flood extent determination. Change detection methods can extract flood extent directly from pre- and post-event imagery, but the models need to learn a consistent flood feature description between optical and SAR imagery from a limited flood dataset. Here, we propose an automatic method for flood inundation extent extraction using pre- and post-event multi-source imagery that requires only a small number of pseudo-change labels. The methodology adopts a pseudo-Siamese network framework as a Heterogeneous - Flood Inundation Extraction Network (H-FIENet), to detect flood extent between the pre‐ and post-event heterogeneous images. Spatially inconsistent multi-source pre- and post-event imagery was used to create a pseudo flood dataset, and this dataset was used to transfer water body feature knowledge from the pre-trained model to the flood extraction model using a cross-task knowledge transfer strategy. Pre- and post-event images from different satellite sources and different flood scenarios were used to evaluate the performance of H-FIENet. We found that: (1) The methodology produced an overall accuracy of over 0.94 for flood inundation extraction from multi-source heterogeneous pre- and post-event imagery. (2) H-FIENet can detect both expansion and recession of the flood extent using any dual-temporal imagery. Our work makes it possible to automate time-series flood monitoring from multi-source remote sensing imagery.

Surface water dynamics of Lake Chad Basin (Sahelian Africa) based on daily temporal resolution Earth observation time series

ABSTRACT Water availability is vital for the sustenance of livelihoods in the Lake Chad Basin. However, the daily and seasonal dynamics of open water bodies are not well understood. This study aims to (1) analyze the daily and seasonal dynamics of water bodies, (2) estimate changes in surface water area extent including trends and change points, and (3) assess the connection between surface water extent and seasonal rainfall variation. To achieve this, we used the Global WaterPack and ERA5-Land daily aggregated datasets. We employed time series decomposition, trends analysis, and temporal lag correlation in our analysis. The results showed strong seasonal patterns of natural lakes compared to reservoirs/dams. Between 2003 and 2022, Lake Chad averaged 2,475.64 km2. The Northern pool of Lake Chad exhibited significant fluctuations, remaining below 600 km² between 2005 and 2012, from 2016 to 2019), with less than 350 km2 lasting only for a few days annually. The Southern pool averaged between 2,200 and 2,400 km2, except during drought years (2006–2007), specifically between the days of the year to approximately 66, and days 301–365/6. In Lake Fitri, the yearly maximum and minimum water extents were observed between days 1–59 and 305–365/6, and between days 60 and 304, respectively.

A dynamic surface water extent service for Africa developed through continental-scale collaboration

Spatially explicit, near real time information on surface water dynamics is critical for understanding changes in water resources, and for long-term water security planning. The distribution of surface water across the African continent since 1984 and updated as every new Landsat scene becomes available is presented here, and validated for the continent for the first time. We applied the Water Observations from Space (WOfS) algorithm, developed and well-tested in Australia, to every Landsat scene acquired over Africa since the mid 1980s to provide spatial information on surface water dynamics over the past 30+ years. We assessed the accuracy of WOfS using aerial and satellite imagery. Four regional geospatial organisations, coordinated through the Digital Earth Africa Product Development Task Team, conducted the validation campaign and provided both the regional expertise and experience required for a continental-scale validation effort. We assessed whether the point was wet, dry, or cloud covered, for each of the 12 months in 2018, resulting in 34,800 labelled observations. As waterbodies larger than 100 km2 are easy to identify with Landsat resolution data and can thus boost accuracy, these were masked out. The resulting overall accuracy of the water classification was 82%. WOfS in Africa is expected to be used by ministries and departments of agriculture and water across the continent, by international organisations, academia, and the private sector. A large-scale collaborative effort, which included regional and technical skills spanning two continents was required to create a service that is regionally accurate and is both hosted on, and implemented operationally from, the African continent.

Causal feedback loops modify lake chlorophyll a–nutrient relationships over two decades of nutrient reductions and climate warming

AbstractUnderstanding how the causal feedback between phytoplankton and environmental drivers controlling the chlorophyll a (Chl a, as a proxy of phytoplankton biomass)–nutrient relationships are modulated under different ecosystem conditions is a major challenge in aquatic ecology. Using an empirical dynamic model (convergent cross mapping) on a 20‐yr dataset on 20 Danish lakes, we quantified hypothesized causal feedback networks for each lake and related them to lake system properties (e.g., mean water depth, nutrient concentrations and extent of reduction, climate warming) vs. the Chl a–nutrient relationship (estimated from generalized least square models). The results showed prevalent causal feedback across the studied lakes, which demonstrated clear patterns for the tested ecosystem variations. Weaker causal feedbacks were found in deeper lakes and lakes with larger warming trends, while stronger causal feedbacks appeared in lakes experiencing greater reductions of TP (total phosphorus) and TN (total nitrogen). Moreover, these causal feedbacks showed a strong and positive coupled pattern. Most of the causal feedbacks worked as enhancement loops, which promote the sensitivity of phytoplankton to TP, not least in shallow lakes with a high TP reduction, and as regulatory loops, which force a shift in the Chl a–TN relationship from a more negative slope in lakes experiencing a high nutrient reduction and weak warming to a positive slope in lakes with low nutrient reduction and stronger warming. Our findings suggest a mechanistic explanation of how internal feedbacks regulate the Chl a–nutrient relationships across a broad gradient of nutrient reductions, climate warming, and lake morphologies.

Comprehensive assessment of cow manure hydrothermal treatment products for land application and energy recovery

In this study, cow manure was hydrothermally treated in a 2-litre reactor for 1 h at temperatures between 100 °C and 260 °C. Both the raw manure and the solid and liquid products of the hydrothermal treatment were characterized to understand the fate of the inorganic elements and to assess the suitability of the products for land applications and energy recovery. Satisfactory elemental balances were obtained for the organic and most inorganic elements and indicated that most inorganic elements were incorporated into the solids with lower solubility, with the exception of potassium and sodium, which were mostly solubilized in the process water; calcium and chlorine were also solubilized to a lesser extent in the process water. Elemental composition and surface functional groups showed that hydrochar produced within the hydrothermal carbonization range (180–260 °C) seemed better suited for utilization as a soil amendment than raw cow manure. The potential for energy recovery lies in the anaerobic digestion of the process water, from which higher methane yields can be obtained than from raw cow manure. Lower temperatures in hydrothermal carbonization are considered a compromise for the safe land applications of cow manure, energy recovery from the process water, and enhanced dewaterability. These findings can help to eliminate bottlenecks in the upscaling of cow manure hydrothermal treatment and promote the circular bio-economy.

Seasonally inundated area extraction based on long time-series surface water dynamics for improved flood mapping

Accurate extraction of Seasonally Inundated Area (SIA) is pivotal for precise delineation of Flood Inundation Area (FIA). Current methods predominantly rely on Water Inundation Frequency (WIF) to extract SIA, which, due to the lack of analysis of dynamic surface water changes, often yields less accurate and robust results. This significantly hampers the rapid and precise mapping of FIA. In the study, based on the Harmonic Models constructed from Long Time-series Surface Water (LTSW) dynamics, an SIA extraction approach (SHM) was introduced to enhance their accuracy and robustness, thereby improving flood mapping. The experiments were conducted in Poyang Lake, a region characterized by active hydrological phenomena. Sentinel-1/2 remote sensing data were utilized to extract LTSW. Harmonic analysis was applied to the LTSW dataset, using the amplitude terms in the harmonic model to characterise the frequency of variation between land and water for the surface units, thus extracting the SIAs. The results reveal that the harmonic model parameters are capable of portraying SIA. In comparison to the commonly used WIF thresholding method for SIA extraction, the SHM approach demonstrates superior accuracy and robustness. Leveraging the SIA extracted through SHM, a higher level of accuracy in FIA extraction is achieved. Overall, the SHM offers notable advantages, including high accuracy, automation, and robustness. It offers reliable reference water extents for flood mapping, especially in areas with active and complex hydrological dynamics. SHM can play a crucial role in emergency response to flood disasters, providing essential technical support for natural disaster management and related departments.

Sharp decline in surface water resources for agriculture and fisheries in the Lower Mekong Basin over 2000-2020

Water resources play a crucial role in the global water cycle and are affected by human activities and climate change. However, the impacts of hydropower infrastructures on the surface water extent and volume cycle are not well known. We used a multi-satellite approach to quantify the surface water storage variations over the 2000–2020 period and relate these variations to climate-induced and anthropogenic factors over the whole basin. Our results highlight that dam operations have strongly modified the water regime of the Mekong River, exhibiting a 55 % decrease in the seasonal cycle amplitude of inundation extent (from 3178 km2 to 1414 km2) and a 70 % decrease in surface water volume (from 1109 km3 to 327 km3) over 2000–2020. In the floodplains of the Lower Mekong Basin, where rice is cultivated, there has been a decline in water residence time by 30 to 50 days. The recent commissioning of big dams (2010 and 2014) has allowed us to choose 2015 as a turning point year. Results show a trend inversion in rice production, from a rise of 40 % between 2000 and 2014 to a decline of 10 % between 2015 and 2020, and a strong reduction in aquaculture growth, from +730 % between 2000 and 2014, to +53 % between 2015 and 2020. All these results show the negative impact of dams on the Mekong basin, causing a 70 % decline in surface water volumes, with major repercussions for agriculture and fisheries over the period 2000–2020. Therefore, new future projects such as the Funan Techo canal in Cambodia, scheduled to start construction at the end of 2024, will particularly affect 1300 km2 of floodplains in the lower Mekong basin, with a reduction in the amount of water received, and other areas will be subjected to flooding. The human, material and economic damage could be catastrophic.

Assessment on the Exposure of Air, Water and Noise Pollution, and Mental Stress on Preeclamptic Patients of Rajshahi, Bangladesh

Although preeclampsia claims the lives of 70,000 mothers and 500,000 newborns each year worldwide, its origin is still elusive and a number of risk factors such as environmental pollution are not yet addressed properly. In this study, exposures due to the extents of air, water and noise pollution as well as mental stress on preeclamptic patients have been investigated. Using a cross-sectional longitudinal design, 90 women hospitalized with preeclampsia in 7 hospitals of Rajshahi, Bangladesh were considered, of which Rajshahi Medical College Hospital is a tertiary referral hospital. The data were collected by interviewing the patients, physical examinations and merging the patients’ data with British Geological Survey’s groundwater data-sets (n=3,540). For statistical analyses, SPSS software was employed. It was found that most of the patients’ living rooms were within 15 feet from kitchen. Only 10% patients had good room ventilation, while the remaining 90% patients had either moderate or poor room ventilation. Combination of these facts reveals that the preeclamptic patients were subject to moderate CO2 exposure. Since 79% of the preeclamptic patients’ living rooms were below 50 ft from the nearest roads and 84% for 100 ft distance, they would experience noise pollution. Combination of traffic conditions and potential sources of noise pollution revealed that 60% of the preeclamptic patients experienced moderate to intense noise pollution. Groundwater arsenic, calcium, magnesium, iron and sodium concentrations in the patients’ drinking water were higher than WHO guideline values that should favour constipation and mild hypertension. While 70% patients were under high mental stress and 24% under very high mental stress, only 6% patients had moderate mental stress. It is concluded that air, water and noise pollution, and mental stress are potential risk factors of preeclampsia.

Origins of Low‐Oxygen Bottom Water Influenced by Tide and ENSO on the Outer‐Edge Shelf of East China Sea: Multi‐Chemical Tracer Approaches

AbstractThe East China Sea (ECS) is one of the largest marginal seas in the world with high primary productivity and a large area of low dissolved oxygen. This study observed the low‐oxygen bottom water on the outer‐edge shelf of ECS in summer seasons of 2018–2020. The contributions of various water masses to low‐oxygen waters were quantified, and the interaction between low‐oxygen water and Kuroshio water was validated by a mixing model using heavy rare earth elements (HREEs) together with potential temperature, and salinity. For further reliability verification, tracers such as δ34S and 226Ra were additionally utilized to avoid the non‐conservative processes on the shelf area. The low‐oxygen water on the shelf was clarified to be linked to the inner/middle shelf water, and dominated by Kuroshio Subsurface Water (KSSW, 81% ± 3%) and influenced by a non‐negligible extent of pore water (3.0% ± 0.5%). Our findings revealed the transport of low‐oxygen water from the outer‐shelf edge to the Kuroshio area, whose dissolved inorganic nitrogen and dissolved inorganic phosphorus contributions to Kuroshio were 34%–82% and 35%–83%, respectively. The nutrients of low‐oxygen water were regulated primarily by the origins of water and secondarily by organic matter remineralization (DIN: ∼17% and DIP: ∼24%). As control factors, the tide, especially spring tide largely enhanced the water contribution of pore water to low‐oxygen water by 67%, and the El Niño‐Southern Oscillation potentially affected the contributions of KSSW and mid‐shelf water to low‐oxygen water.

The Shrinking Great Salt Lake May Exacerbate Droughts by Reducing Local Precipitation: A Case Study

Abstract The Great Salt Lake (GSL) is a shallow terminal lake located in northern Utah, United States. Over the years, the water extent of the GSL has undergone substantial reduction due to water diversions and a changing climate—in particular rising temperatures. However, the potential impacts of the shrinking GSL water body on the local hydroclimate system are poorly understood. In this study, we utilized the Weather Research and Forecasting Model, version 4.2, coupled with a lake model to simulate a series of high-resolution numerical experiments; these experiments aimed to assess the effect of varying lake areal extents on a storm event that occurred on 6 June 2007. The results revealed a systematic decline in the quantity of precipitation over the GSL and downwind regions with declining areal coverage. In the event of complete disappearance, the regional average precipitation would experience an approximate 50% reduction relative to its 2004 base lake extent; this decrease is principally attributed to a diminished water vapor flux and moist static energy (MSE) above the lake. The research underscores the consequences of a shrinking GSL, not just for precipitation delivery downstream but that of a negative feedback loop within the hydroclimatic system of the GSL basin, i.e., water flow reductions into the basin.

Characterizing the Behavior of Water Interacting with a Nano-Pore Material: A Structural Investigation in Native Environment Using Magnetic Resonance Approaches.

The study of fluid absorption, particularly that of water, into nanoporous materials has garnered increasing attention in the last decades across a broad range of disciplines. However, most investigation approaches to probe such behaviors are limited by characterization conditions and may lead to misinterpretations. In this study, a combined MRI and MAS NMR method was used to study a nanoporous silica glass to acquire information about its structural framework and interactions with confined water in a native humid environment. Specifically, MRI was used for a quantitative analysis of water extent. While MAS NMR techniques provided structural information of silicate materials, including interactive surface area and framework packing. Analysis of water spin-spin relaxation times (T2) suggested differences in water confinement within the characterized framework. Subsequent unsuccessful delivery of paramagnetic molecule into the pores enabled a quantitative assessment of the dimensions that "bottleneck" the pores. Finally, pore sizes were derived from the paramagnetic molecular size, density function theory (DFT) simulation and characterizations on standard samples. Our result matches with Brunauer-Emmett-Teller (BET) analysis that the pore size is less than 1.3 nm. The use of a paramagnetic probe for pore size determination introduces a new approach of characterization in the liquid phase, offering an alternative to the conventional BET analysis that uses gas molecule as probes.

Sensorweb Systems for Global High-Resolution Monitoring of Environmental Phenomena

Many science phenomena are difficult to observe because they occur infrequently and without warning. Over the course of the last 20 years, the concept of a “sensorweb,” or a network of interdependent instruments that autonomously coordinate to observe phenomena of interest, has been developed and implemented. In one use case, sensorwebs use in situ and low- to moderate-resolution sensors for event detection and pointable sensors with high spatial resolution for subsequent observation of targets, thus accomplishing efficient high-resolution monitoring. These sensorwebs have previously been implemented to study flood extent and volcanic lava flow. In the flood monitoring case, mostly remote sensors have been used for low-resolution flood detection, while more in-situ sensors have been used for volcanic activity detection in the volcano monitoring application. To improve these sensorwebs, we integrate commercial satellite constellations for high-resolution observations, resulting in higher spatial and temporal resolutions than the individual assets used in previous work. We also spatially extend sensorwebs to the global scale, produce a high volume of alerts, and automatically generate useful high-resolution data products. These resulting data products include surface water extent products for our flood sensorweb and volcanic lava classifiers for our volcano sensorweb. Our sensorwebs represent the state of the art in automatic acquisition and processing of data for environmental phenomenon monitoring. We demonstrate the capabilities of these improved sensorwebs in our new flood sensorweb and volcano sensorweb systems and describe ongoing efforts to utilize additional in-situ and spaced-based sensors and generate additional data products.

Drought Monitoring in Burdur Lake Using Sentinel-2 Images

Drought Monitoring in Burdur Lake Using Sentinel-2 Images

Assessing the time variability of GIEMS-2 satellite-derived surface water extent over 30 years

Inland waters, especially wetlands, play a crucial role in biodiversity, water resources and climate, and contribute significantly to global methane emissions. This study investigates the seasonal and inter-annual variability of the 0.25° × 0.25° surface water extent (SWE) from the Global Inundation Extent from Multi-Satellites (GIEMS-2) extended to a 30-year time series (1992–2020). Comparison with MODIS-derived SWE, CYGNSS-derived SWE and the Global Lakes and Wetlands Database (GLWD) shows consistent spatial patterns globally and over 10 different basins, although there are discrepancies in extent, partly due to different resolutions of the initial satellite observations. Strong cross-correlation (>0.8) in seasonal variability is observed when comparing GIEMS-2 with MODIS, CYGNSS and river discharge in most of the basins studied. Encouraging similarities were found in the inter-annual variability in most basins (cross-correlation >0.6) between GIEMS-2 and MODIS over 20 years, and between GIEMS-2 and river discharge over long time series, including over the Amazon and the Congo basins. These results highlight the reliability of GIEMS-2 in detecting changes in SWE in different environments, especially under dense vegetation, making it a valuable resource for calibrating hydrological models and studying global methane emissions.