High Temporal Resolution Data Research Articles

Assessing the spatiotemporal precipitation trends from ERA5-Land over Indonesia region

Abstract The availability and accuracy of high-resolution spatial and temporal climate data are required for various climate-related analyses, especially in areas with varying topography and climate. The long-term ERA5-Land reanalysis data from the European Centre for Medium-Range Weather Forecast (ECMWF) combines climate model and observation data from around the world into a complete and consistent collection with a spatial resolution of 0.1°. This high-resolution climate data is considered to be a reliable gridded dataset spanning various regions. Due to the scarcity of ground-based data in Indonesia, ERA5-Land is expected as an alternative to represent the climate condition. However, a comprehensive assessment prior to usage should be conducted. This study evaluates the proficiency of ERA5-Land based on observation data from the Climate Hazard Group InfraRed Precipitation with Station data (CHIRPS) to represent the precipitation variability over Indonesia during 1990-2022. Comprehensive investigations on the spatial-temporal dynamics of precipitation are presented. The ERA5-Land precipitation trend shows a similar pattern in annual total accumulated precipitation and precipitation cycle to CHIRPS observations. In areas with monsoon patterns like south Sumatera, Java, southern Kalimantan, Sulawesi, and part of Papua, low bias is observed often during the June-July-August (JJA) dry season, while high bias is observed often during the December-January-February (DJF) and September-October-November (SON). The similar observational trends and acceptable bias of ERA5-Land precipitation data convince the use of the data in simulating precipitation variability in the Indonesian region, which is essential for further climate studies.

Temporal Anomaly Detection in Attention-Deficit/Hyperactivity Disorder Using Recurrent Neural Networks.

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition marked by movement hyperactivity, often persisting into adulthood. Understanding the movement patterns associated with ADHD is crucial for improving diagnostic precision and tailoring interventions. This study leverages the HYPERAKTIV dataset, which includes high-resolution temporal data on motor activity from people diagnosed with ADHD. We used the isolation forest algorithm to detect anomalies in activity data, followed by the development of a recurrent neural network (RNN) model to predict these anomalies over time. The RNN model demonstrated high predictive accuracy, with a mean accuracy of 0.953 and a mean loss of 0.124 for participants with ADHD. These findings suggest that machine learning techniques, particularly RNNs, can effectively identify and predict anomalies in temporal motor activity data, offering objective insights into ADHD-related movement behaviors. This approach is promising for informing personalized interventions and improving clinical decision-making in the management of ADHD.

Cross-Attention-Based High Spatial-Temporal Resolution Fusion of Sentinel-2 and Sentinel-3 Data for Ocean Water Quality Assessment

Current marine research that leverages remote sensing data urgently requires gridded data of high spatial and temporal resolution. However, such high-quality data is often lacking due to the inherent physical and technical constraints of sensors. A necessary trade-off therefore exists between spatial, temporal, and spectral resolution in satellite remote sensing technology: increasing spatial resolution often reduces the coverage area, thereby diminishing temporal resolution. This manuscript introduces an innovative remote sensing image fusion algorithm that combines Sentinel-2 (high spatial resolution) and Sentinel-3 (relatively high spectral and temporal resolution) satellite data. The algorithm, based on a cross-attention mechanism and referred to as the Cross-Attention Spatio-Temporal Spectral Fusion (CASTSF) model, accounts for variations in spectral channels, spatial resolution, and temporal phase among different sensor images. The proposed method enables the fusion of atmospherically corrected ocean remote sensing reflectance products (Level 2 OSR), yielding high-resolution spatial data at 10 m resolution with a temporal frequency of 1–2 days. Subsequently, the algorithm generates chlorophyll-a concentration remote sensing products characterized by enhanced spatial and temporal fidelity. A comparative analysis against existing chlorophyll-a concentration products demonstrates the robustness and effectiveness of the proposed approach, highlighting its potential for advancing remote sensing applications.

Glacier velocity and surge detection in the Karakoram region, Pakistan: using remotely sensed data with cross-correlation feature tracking

ABSTRACT This research addresses the knowledge gap regarding glacier surging detection (GSD) and their potential downstream implications for Glacier Lake Outburst Floods (GLOFs) risk. This study investigates GSD across the Karakoram region, Pakistan, focusing on 30 glaciers from 2013 to 2023. The increasing occurrence of irregular and rapid glacial surges poses significant threats to downstream communities, the potential formation of ice dam lakes, and subsequent outburst floods. High-resolution satellite imagery, DEM, temperature, and precipitation data quantified glacier velocity rate (GVR) using feature-tracking, surge detection, and correlations. Findings indicate substantial movement ≥42 m/y during 2015–19 and 2021–22, peaking at 56.98 m/y in 2018–19; with significant Z-Scores, surges were recorded in KG-01, KG-06, KG-07, KG-19, KG-29, and KG-30. The region witnessed significant elevation changes, with a positive change at the terminus of KG-01, KG-06, KG-07, and KG-29 and a negative at the glacier’s accumulation zones. Furthermore, climatic factors, including rising average temperatures (17°C to 20°C) and precipitation (14 to 30 mm/y), significantly influenced glacier velocity, affecting glaciers by 20-43% and 38-59%, respectively. These findings underscore the pivotal role of surge velocities in shaping glacier outlets and influencing lake drainage, potentially leading to GLOFs.

Preserving friendships in school contacts: An algorithm to construct synthetic temporal networks for epidemic modelling.

High-resolution temporal data on contacts between hosts provide crucial information on the mixing patterns underlying infectious disease transmission. Publicly available data sets of contact data are however typically recorded over short time windows with respect to the duration of an epidemic. To inform models of disease transmission, data are thus often repeated several times, yielding synthetic data covering long enough timescales. Looping over short term data to approximate contact patterns on longer timescales can lead to unrealistic transmission chains because of the deterministic repetition of all contacts, without any renewal of the contact partners of each individual between successive periods. Real contacts indeed include a combination of regularly repeated contacts (e.g., due to friendship relations) and of more casual ones. In this paper, we propose an algorithm to longitudinally extend contact data recorded in a school setting, taking into account this dual aspect of contacts and in particular the presence of repeated contacts due to friendships. To illustrate the interest of such an algorithm, we then simulate the spread of SARS-CoV-2 on our synthetic contacts using an agent-based model specific to the school setting. We compare the results with simulations performed on synthetic data extended with simpler algorithms to determine the impact of preserving friendships in the data extension method. Notably, the preservation of friendships does not strongly affect transmission routes between classes in the school but leads to different infection pathways between individual students. Our results moreover indicate that gathering contact data during two days in a population is sufficient to generate realistic synthetic contact sequences between individuals in that population on longer timescales. The proposed tool will allow modellers to leverage existing contact data, and contributes to the design of optimal future field data collection.

Influence of flow nonuniformities and real gas effects on cylindrical shock wave convergence

Convergence of cylindrical shock in argon is studied both experimentally and numerically. Shock tube experiments are conducted, where a planar shock is first transformed to a cylindrical shape and then converged to its focal axis. Numerical simulations of the converging shock using equations of state for an ideal gas and a real gas (SESAME 5173 model) are conducted and compared. High temporal resolution data of cylindrical shock convergence is presented. When comparing the trajectories of the converging shock of initial shock Mach number (MS) of 4.63, the convergence exponent (α) in experiments is found to be 0.833. This α value in experiments is higher than the value obtained from computations with argon treated as an ideal gas but agrees well with the real gas computations. It is revealed that the form of convergence varies with different MS. An asymptotic approach of α toward the self-similar solution for high MS is attributed to an earlier transition of shock motion to self-similarity, while a significantly higher α observed at lower MS is attributed to the negative influence of upstream nonuniformities and weaker initiation of the shock. It is found that even before the shock reflection, real gas effects are significant enough to affect the convergence of the shock and limit the extreme conditions predicted by the ideal gas computations. For an MS of 4.63, the maximum temperature reached is 9250 K before reflection, leading to 0.12% of the argon gas undergoing the first stage of ionization.

Operationalising weather surveillance radar data for use in ecological research

Global biodiversity declines require a step change in monitoring frameworks to properly track and diagnose population trends. National weather surveillance radar (WSR) networks offer high spatial (ca. 1-10 km) and temporal (5–10 min) resolution data collected over regional and decadal scales, with well-supported infrastructure that holds great promise for the study of biodiversity. However, WSR datasets pose new challenges for ecologists due to their format, volume, and three-dimensional spatial structure. Here, we define a novel approach to the processing of WSR data to produce a product that can be used to interrogate trends in aerial biodiversity (abundance or diversity) at and across individual ground-level sites. From the full volume of WSR data collected approximately every six minutes we extract vertical columns of WSR observations above sites to compare against standardised nocturnal macro-moth monitoring data at ground level. The results show that there is strong agreement between the WSR-derived proxy of biodiversity in the air column and ground-level measurements of abundance and diversity in nocturnal moth communities. The columnar product operates on a biologically relevant scale with a diameter of 5 km, although column dimensions can easily be customised, and can be deployed at any site within a WSR's observable range. These findings have the potential to unlock past and present WSR observations for widespread application to existing and novel ecological questions and can be applied to weather radar networks around the world.

Beyond threats: Extreme heatwaves and economic resilience in China

This study demonstrates extreme heatwaves have a positive impact on economic resilience in China at the provincial level, utilizing high spatial resolution temperature data. This effect may be attributed to heightened climate policy uncertainty and shifts in public climate perception. Furthermore, our findings reveal that these effects are particularly pronounced in the eastern regions of China and in provinces with large economic scales but relatively small agriculture output values.

General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data

In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity.

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits.

Plant height can be an indicator of plant health across environments and used to identify superior genotypes. Typically plant height is measured at a single timepoint when plants reach terminal height. Evaluating plant height using unoccupied aerial vehicles allows for measurements throughout the growing season, facilitating a better understanding of plant-environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected from planting until terminal height at anthesis (14 flights 2018, 27 in 2019, 12 in 2020, and 11 in 2021) for a panel of ~500 diverse maize inbred lines. The percent variance explained in plant height throughout the season was significantly explained by genotype (9-48%), year (4-52%), and genotype-by-year interactions (14-36%) to varying extents throughout development. Genome-wide association studies revealed 717 significant single nucleotide polymorphisms associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth. When plant height growth curves were compared to growth curves estimated from canopy cover, greater Fréchet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits.

Evaluating the Patterns of Maize Development in the Hetao Irrigation Region Using the Sentinel-1 GRD SAR Bipolar Descriptor.

Assessing maize yield is critical, as it is directly influenced by the crop's growth conditions. Therefore, real-time monitoring of maize growth is necessary. Regular monitoring of maize growth indicators is essential for optimizing irrigation management and evaluating agricultural yield. However, quantifying the physical aspects of regional crop development using time-series data is a challenging task. This research was conducted at the Dengkou Experimental Station in the Hetao irrigation area, Northwest China, to develop a monitoring tool for regional maize growth parameters. The tool aimed to establish a correlation between satellite-based physical data and actual crop growth on the ground. This study utilized dual-polarization Sentinel-1A GRD SAR data, accessible via the Google Earth Engine (GEE) cloud platform. Three polarization descriptors were introduced: θc (pseudo-scattering type parameter), Hc (pseudo-scattering entropy parameter), and mc (co-polar purity parameter). Using an unsupervised clustering framework, the maize-growing area was classified into several scattering mechanism groups, and the growth characteristics of the maize crop were analyzed. The results showed that throughout the maize development cycle, the parameters θc, Hc, and mc varied within the ranges of 26.82° to 42.13°, 0.48 to 0.89, and 0.32 to 0.85, respectively. During the leaf development stage, approximately 80% of the maize sampling points were concentrated in the low-to-moderate entropy scattering zone. As the plants reached the big trumpet stage, the entire cluster shifted to the high-entropy vegetation scattering zone. Finally, at maturity, over 60% of the sampling points were located in the high-entropy distribution scattering zone. This study presents an advanced analytical tool for crop management and yield estimation by utilizing precise and high-resolution spatial and temporal data on crop growth dynamics. The tool enhances the accuracy of crop growth management across different spatial and temporal conditions.

Seasonally flooded landscape connectivity and implications for fish in the Napo Moist Forest: A high-resolution mapping approach

The Amazon River Basin's fish diversity is shaped by its dynamic flood-pulse system, critical for hydrological and ecological connectivity. This study examines the Napo Moist Forest (NMF) ecoregion, mapping permanent and seasonally flooded areas from 2018 to 2021 using remote sensing and deep learning models. We aimed to map these areas at a high spatial resolution (10-m pixels) and analyse their role in maintaining lateral connectivity essential for fish diversity. Using synthetic aperture radar data from Sentinel-1 combined with deep learning algorithms, we produced high-accuracy flood maps to assess landscape connectivity between rivers and floodplains. Our methodology included creating a ground truth dataset with the Normalized Difference Water Index and integrating high-resolution optical data for model training, overcoming challenges of cloud coverage and dense vegetation. Our predictive model achieved high accuracy (mean pixel accuracy = 97 %) and consistently predicted 4801 km² of surface water, with only 3 % (130 km²) being seasonally flooded areas over four years. The Caquetá, Bajo Marañón, Napo, and Pastaza watersheds accounted for nearly 60 % of the flooded areas, highlighting their ecological importance. Connectivity analysis in three areas of interest within the NMF ecoregion revealed important seasonal and interannual fluctuations in hydrological connectivity due to changes in flooded patch characteristics. Reductions in the number and flooded patch area during low water seasons increased the distance between patches, leading to a disconnection between flooded areas, channels and rivers. Despite hydrological fluctuations, certain patches maintained consistent flooding, critical for lateral connectivity and sustaining aquatic biodiversity. These seasonally flooded areas act as connectors, influencing patch dynamics and connectivity with tributaries. Seasonal and interannual variations in hydrological connectivity are crucial for sustaining fish diversity. Conserving dynamic floodplains supports migratory fish life cycles and biodiversity. This study underscores the importance of high-resolution temporal and spatial data in conservation planning for aquatic ecosystems.

Presenting a transdisciplinary robust approach for comprehensive assessment of large-scale underground water resources in western Indo-Gangetic Basin

Overexploitation, pollution, and anthropogenic activities threaten the sustainability of groundwater resources in the western Indo-Gangetic Basin. Meanwhile, distinguishing regions prone to contamination and understanding the natural and anthropogenic factors affecting groundwater quality is challenging due to the heterogeneous nature of aquifer systems and the lack of high-resolution spatial and temporal data on aquifer protective hydrogeological layers. This study presents a transdisciplinary robust approach combining regional electrical resistivity surveys, hydrogeological data, physicochemical analyses, and geospatial datasets to identify regions prone to contamination and understand the impacts of natural and anthropogenic factors on groundwater resources. This approach involves three key steps: evaluating the geohydraulic nature of aquifer protective hydrogeological layers, mapping the aquifer vulnerability index (AVI), and conducting comparative analyses of potentially vulnerable areas with groundwater quality index (GWQI) and hydrological factors. Firstly, model-based inversion of ID Vertical Electrical Sounding (VES) data provides insights into geoelectrical indices such as depth, thickness, apparent resistivity, longitudinal conductance, transverse resistance, and longitudinal resistivity of aquifer protective hydrogeological layers. Second, the Artificial Neural Network (ANN) model is used as a multilayer perceptron network to simulate hydraulic conductivity (K) using geoelectrical indices of aquifer protective hydrogeological layers. Subsequently, by considering ANN-derived K and VES-derived h of aquifer protective hydrogeological layers, the dynamic hydraulic resistance to the vertical flow of wastewater through the protective hydrogeological layers evaluated the index of the potentially vulnerable areas. Comparative analyses of potentially vulnerable areas with GWQI and hydrological factors (e.g., digital elevation model, soil, drainage density, lineament density, slope) enhance understanding regions prone to contaminants and land surface stress. Findings show that the ANN approach to simulate K, reducing effort with costs associated with slug testing is significant for AVI assessment. Furthermore, the geohydraulic characteristics, vulnerability indexing, and comparative analyses assist in identifying contamination-prone areas, improving groundwater resource protection and exploration activities.

Accurate solar radiation site adaptation: Harnessing satellite data and in situ measurements

Accurate solar radiation data are essential to optimize solar energy systems and assess their feasibility. In this study, we propose a site-adaptation procedure based on a machine learning model trained to enhance the accuracy of solar radiation data using a combination of the National Solar Radiation Database (NSRDB) and in situ data collected in southern Colombia. The NSRDB provides high temporal and spatial resolution data, while in situ data offer accurate localized measurements specific to the study area. Our machine learning models were trained to learn the relationships between NSRDB data and in situ meteorological station data. The results demonstrate promising predictive capabilities, with the extreme grading boosting model effectively reducing mean absolute error, while a neural network model trained with the triplet loss function proved effective in minimizing mean bias error (MBE) and improving correlation between model-adjusted and in situ collected data. These findings make significant contributions to the field of solar radiation prediction, highlighting the effectiveness of amalgamating NSRDB and in situ data for precise solar radiation estimation, and promote the advancement of solar energy system design and decision-making processes.

Analysis of underwater radiated noise from ships using distributed acoustic sensing technology

Distributed Acoustic Sensing (DAS) technology enables continuous monitoring of acoustic vibrations along fiber optic cables, providing high-resolution spatial and temporal data. This study explores the application of DAS technology for analyzing underwater radiated noise from ships. In November 2021, four days of DAS data were collected using two cables from the Ocean Observatories Initiative Regional Cabled Array, extending offshore central Oregon. Numerous ship passages occurred over these cables, with information available through the Automatic Identification System (AIS). DAS data were collected using two different interrogators on two fibers in each cable, providing an opportunity to investigate the differences in ship noise detected on different fibers within the same cable and across different DAS systems with varying configurations. Preliminary analysis of two large ships shows that their noise is clearly detected in the DAS measurements. By combining these measurements with AIS data, we can accurately locate the receiving channels on the cable. This talk will expand on these observations to perform a comparative analysis of the measurements across different fibers and interrogators. These findings demonstrate the capability of DAS technology in maritime acoustic monitoring and its potential to enhance our understanding of underwater noise pollution.

Impacts of changing weather patterns on the dynamics of water pollutants in agricultural catchments: Insights from 11-year high temporal resolution data analysis

Widespread and long-term shifts in weather patterns are contributing to further degradation of surface water quality. This challenge caused by the increasing frequency of extreme weather events requires appropriate adaptation of current mitigation strategies. But to confirm the need to redesign such strategies, an understanding of the impacts of increasing weather extremes on pollutant losses in different catchment types is required. With this in view, this study investigated the impact of changing weather patterns on the inter-seasonal and inter-annual dynamics of nutrient losses in six agricultural catchments in Ireland over 11 years. The high temporal resolution data (10-min) from these intensively managed catchments represented different characteristics and management practices. Mann-Kendall Trend Analysis and Generalised Additive Models were used to study nutrient concentration trends, and to investigate the significance of water discharge, precipitation, potential evapotranspiration, soil moisture deficit, air temperature, and soil temperature on the losses of nutrients, respectively. The analysis of historical data revealed changes in the trends of daily average nitrate (NO3-N), phosphorus (P), and suspended sediment (SS) concentrations in association with significant increasing trends in air temperature, soil temperature, and precipitation across the same month over 11 years of monitoring. While discharge was significantly contributing to the concentrations of NO3-N, P, and SS across different catchments, air and soil temperature were significantly correlated to NO3-N losses, and precipitation was the major contributor to regulating P (total P and total reactive P) concentrations. In short, air temperature, soil temperature, soil moisture deficit, and precipitation were the main climatic drivers regulating the nutrient concentrations while the soil chemistry and drainage status were the non-climatically related drivers. The results revealed that the extent of the impact of climatic drivers depends on catchment characteristics. Therefore, expanding the application of this type of study would facilitate better understanding of current and future challenges to water management and provision of climate-resilient mitigation strategies for different catchment typologies.

Agricultural Drought Monitoring: A Comparative Review of Conventional and Satellite-Based Indices

Drought is a natural hazard that causes significant economic and human losses by creating a persistent lack of precipitation that impacts agriculture and hydrology. It has various characteristics, such as delayed effects and variability across dimensions like severity, spatial extent, and duration, making it difficult to characterize. The agricultural sector is especially susceptible to drought, which is a primary cause of crop failures and poses a significant threat to global food security. To address these risks, it is crucial to develop effective methods for identifying, classifying, and monitoring agricultural drought, thereby aiding in planning and mitigation efforts. Researchers have developed various tools, including agricultural drought indices, to quantify severity levels and determine the onset and evolution of droughts. These tools help in early-stage forecasting and ongoing monitoring of drought conditions. The field has been significantly advanced by remote sensing technology, which now offers high-resolution spatial and temporal data, improving our capacity to monitor and assess agricultural drought. Despite these technological advancements, the unpredictable nature of environmental conditions continues to pose challenges in drought assessment. It remains essential to provide an overview of agricultural drought indices, incorporating both conventional methods and modern remote sensing-based indices used in drought monitoring and assessment.

High-resolution temperature, salinity and depth data from southeastern Australian estuaries, 2018–2021

Estuaries are the important interface between the land and sea, providing significant environmental, economic, cultural and social values. However, they face unprecedented pressures including eutrophication, harmful algal blooms, habitat loss, and extreme weather due to climate change. Here we present an open access, quality-controlled water quality dataset collected from twelve diverse estuaries spanning 1000 km along the southeastern Australian coastline. Water depth, temperature and salinity data were collected across two years (2018–2021) capturing drought, wildfire and flood periods, using high accuracy Seabird MicroCAT field sensors located within oyster leases. These fully autonomous instruments collected and transmitted data every 10 minutes before downstream quality checking and uploading onto a public website. Simultaneous, high-resolution, longitudinal environmental data collected across multiple estuaries throughout a range of extreme weather events are exceptionally rare in the Southern Hemisphere, yet provide an invaluable resource for the aquaculture industry, researchers and environmental regulators alike.

Spatiotemporal Facility-Level Patterns of Summer Heat Exposure, Vulnerability, and Risk in United States Prison Landscapes.

Heat is associated with increased risk of morbidity and mortality. People who are incarcerated are especially vulnerable to heat exposure due to demographic characteristics and their conditions of confinement. Evaluating heat exposure in prisons, and the characteristics of exposed populations and prisons, can elucidate prison-level risk to heat exposure. We leveraged a high-resolution air temperature data set to evaluate short and long-term patterns of heat metrics for 1,614 prisons in the United States from 1990 to 2023. We found that the most heat-exposed facilities and states were mostly in the Southwestern United States, while the prisons with the highest temperature anomalies from the historical record were in the Pacific Northwest, the Northeast, Texas, and parts of the Midwest. Prisons in the Pacific Northwest, the Northeast, and upper Midwest had the highest occurrences of days associated with an increased risk of heat-related mortality. We also estimated differences in heat exposure at prisons by facility and individual-level characteristics. We found higher proportions of non-white and Hispanic populations in the prisons with higher heat exposure. Lastly, we found that heat exposure was higher in prisons with any of nine facility-level characteristics that may modify risk to heat. This study brings together distinct measures of exposure, vulnerability, and risk, which would each inform unique strategies for heat-interventions. Community leaders and policymakers should carefully consider which measures they want to apply, and include the voices of directly impacted people, as the differing metrics and perspectives will have implications for who is included in fights for environmental justice.

High-resolution temporal NDVI data reveal contrasting intratidal, spring-neap and seasonal biomass dynamics in euglenoid- and diatom-dominated biofilms

Intertidal microphytobenthos (MPB) are a major contributor to primary production in estuarine ecosystems. While their biomass is highly variable at multiple spatial and temporal scales, the underlying drivers are as yet little understood. Both in situ sampling and remote-sensing techniques often lack the temporal resolution or coverage to simultaneously capture short-term (intratidal to daily) and longer-term (weekly to annual) biomass changes. Our field setup with in-situ NDVI sensors allowed us to study MPB surface biomass variability at high temporal resolution (10 mins) for up to two years in a freshwater euglenoid dominated mudflat, and a brackish and a marine diatom dominated mudflat. MPB biomass showed marked periodicities at multiple temporal scales: seasonal, spring-neap and intratidal. The diatom-dominated MPB community showed a seasonal biomass peak in winter, while the euglenoid-dominated community showed biomass peaks during spring and summer, probably caused by underlying divergent responses to mainly irradiance, temperature and wind-induced resuspension, and macrobenthos grazing. Spring-neap periodicity likely resulted from differential migratory responses of the MPB communities to variation in timing and duration of daylight exposure. In the freshwater community, upward migration only occurred when exposure duration was sufficiently long (≥4 h). In the diatom-dominated community, morning daylight exposure resulted in highest NDVI values. This study highlights the differences in MPB biomass dynamics between MPB communities within estuarine ecosystems, and underscores the great potential of high-resolution temporal NDVI monitoring for more accurate estimates of MPB biomass and primary production.