Accurate Observations Research Articles

AIRS: A QGIS plugin for time series forecasting using deep learning models

Time series forecasting, particularly when applied to geospatial data, serves as an essential tool for accurate observation and prediction of environmental and spatial occurrences. Recently, the integration of deep learning models into forecasting processes has garnered increased importance. Deep learning methods offer enhanced capabilities for identifying complex patterns within geospatial datasets, leading to more accurate forecasts. Nevertheless, there is a critical need for analyzing and identifying effective deep learning models in order to assure the accuracy of forecasting outcomes. This study presents an open-source QGIS plugin named AIRS (Artificial Intelligence forecasting Remote Sensing). This plugin allows time series forecasting using five deep learning models (i.e., FFNN, single LSTM, stacked LSTM, BiLSTM, and Conv-LSTM) and provides a user-friendly tool permitting data processing, model building and training, future prediction, accuracy analysis, and results visualization and saving. AIRS is written in Python using QGIS internal and external packages, with an easy-to-use GUI interface.

The contributions of Indigenous People's earth observations to water quality monitoring

Indigenous Knowledge, observations and understandings of Earth processes are not sufficiently included in global Earth Observations. Drawing on the results obtained during a 3-day hackathon event, we present evidence, best practices and recommendations to water quality organizations seeking to engage and share information with Indigenous communities. The hackathon event revealed three key findings: First, Indigenous Peoples report precise and accurate observations of changes in various Earth systems, particularly the hydrological cycle. Second, this information can significantly enhance global Outreach and Engagement efforts, aiding in the understanding of hydrological cycle components, water quality, mapping water courses, and monitoring and mitigating the effects of climate change (i.e., floods, droughts, etc.). Third, enabling Indigenous Peoples to contribute their scientific knowledge and utilize Earth Observations is crucial for the protection of other vital components of the water cycle. We addressed two crucial questions: What opportunities exist to include Indigenous Knowledge into Earth Observations, and what are the main challenges in doing so?

System for Analysis of Wind Collocations (SAWC): A Novel Archive and Collocation Software Application for the Intercomparison of Winds from Multiple Observing Platforms

Accurate atmospheric 3D wind observations are one of the top priorities for the global scientific community. To address this requirement, and to support researchers’ needs to acquire and analyze wind data from multiple sources, the System for Analysis of Wind Collocations (SAWC) was jointly developed by NOAA/NESDIS/STAR, UMD/ESSIC/CISESS, and UW-Madison/CIMSS. SAWC encompasses the following: a multi-year archive of global 3D winds observed by Aeolus, sondes, aircraft, stratospheric superpressure balloons, and satellite-derived atmospheric motion vectors, archived and uniformly formatted in netCDF for public consumption; identified pairings between select datasets collocated in space and time; and a downloadable software application developed for users to interactively collocate and statistically compare wind observations based on their research needs. The utility of SAWC is demonstrated by conducting a one-year (September 2019–August 2020) evaluation of Aeolus level-2B (L2B) winds (Baseline 11 L2B processor version). Observations from four archived conventional wind datasets are collocated with Aeolus. The recommended quality controls are applied. Wind comparisons are assessed using the SAWC collocation application. Comparison statistics are stratified by season, geographic region, and Aeolus observing mode. The results highlight the value of SAWC’s capabilities, from product validation through intercomparison studies to the evaluation of data usage in applications and advances in the global Earth observing architecture.

Marine Compass Data Quality Control Method Based on Robust Surface Fitting

Towed streamer positioning is a key step in offshore seismic exploration, and its positioning accuracy directly affects the imaging quality of subsequent seismic data. Accurate and clean magnetic compass observations are an important guarantee for high-precision towed streamer positioning. However, the existing compass data quality control methods have the problem of insufficient utilization of spatial distribution prior information, which heavily relies on mathematical statistical characteristics of the data while neglecting the crucial factors of streamer shape, bringing about an inadequate elimination of gross errors. In this work, a compass data quality control algorithm based on robust surface fitting is proposed to improve the accuracy of positioning. Firstly, the towed streamer positioning algorithm and the prior information of the spatial structure distribution of the compass are introduced. Secondly, the robust surface fitting to control the quality of compass data is proposed, and the specific process of the algorithm is given. Lastly, the effectiveness of the algorithm is verified by simulation and measured data. The results of simulation experiments indicate that compared with the traditional single-streamer threshold robust algorithm, the new algorithm has a significant effect on the quality control of compass data, and the positioning accuracy of the towed streamer is significantly improved. The positioning precision of along-line direction is improved by 52.1%, and that of the across-line direction by 52.4%. These demonstrate that the proposed new algorithm is effective for detecting and eliminating gross errors in compass observations in real-time operation scenarios, improving the stability of towed streamer shape calculation, which can better ensure the smooth progress of offshore towed streamer seismic exploration operations.

Accurate semi‐direct lidar‐inertial odometry based on distance and normal direction

AbstractTo improve the autonomous navigation capability of unmanned platforms, an accurate semi‐direct lidar‐inertial odometry algorithm with a new point cloud alignment evaluation metric is proposed. The lidar scan points are corrected through IMU (Inertial Measurement Unit) backward propagation, and the navigation states are predicted through IMU forward propagation. In the update of the navigation states, the point cloud alignment is evaluated based on the distances and the minimum normal direction deviations between the lidar scan points and the local fitted planes of the built map, achieving accurate observations for the navigation states. The proposed algorithm updates navigation states based on the iterative extended Kalman filter. Experiments indicate that the proposed algorithm has higher positioning accuracy than the state‐of‐the‐art method, which gets smaller positioning error (7.868 m) and 52.3% improvement on average.

A spatio-temporal fusion deep learning network with application to lightning nowcasting

Lightning is a rapidly evolving phenomenon, exhibiting both mesoscale and microscale characteristics. Its prediction significantly relies on timely and accurate data observation. With the implementation of new generation weather radar systems and lightning detection networks, radar reflectivity image products, and lightning observation data are becoming increasingly abundant. Research focus has shifted towards lightning nowcasting (prediction of imminent events), utilizing deep learning (DL) methods to extract lightning features from very large data sets. In this paper, we propose a novel spatio-temporal fusion deep learning lightning nowcasting network (STF-LightNet) for lightning nowcasting. The network is based on a 3-dimensional U-Net architecture with encoder-decoder blocks and adopts a structure of multiple branches as well as the main path for the encoder block. To address the challenges of feature extraction and fusion of multi-source data, multiple branches are used to extract different data features independently, and the main path fuses these features. Additionally, a spatial attention (SA) module is added to each branch and the main path to automatically identify lightning areas and enhance their features. The main path fusion is conducted in two steps: the first step fuses features from the branches, and the second fuses features from the previous and current levels of the main path using two different methodsthe weighted summation fusion method and the attention gate fusion method. To overcome the sparsity of lightning observations, we employ an inverse frequency weighted cross-entropy loss function. Finally, STF-LightNet is trained using observations from the previous half hour to predict lightning in the next hour. The outcomes illustrate that the fusion of both the multi-branch and main path structures enhances the network’s ability to effectively integrate features from diverse data sources. Attention mechanisms and fusion modules allow the network to capture more detailed features in the images.

A low-cost and open-source approach for supraglacial debris thickness mapping using UAV-based infrared thermography

Abstract. Debris-covered glaciers exist in many mountain ranges and play an important role in the regional water cycle. However, modelling the surface mass balance, runoff contribution and future evolution of debris-covered glaciers is fraught with uncertainty as accurate observations on small-scale variations in debris thickness and sub-debris ice melt rates are only available for a few locations worldwide. Here we describe a customised low-cost unoccupied aerial vehicle (UAV) for high-resolution thermal imaging of mountain glaciers and present a complete open-source pipeline that facilitates the generation of accurate surface temperature and debris thickness maps from radiometric images. First, a radiometric orthophoto is computed from individual radiometric UAV images using structure-from-motion and multi-view-stereo techniques. User-specific calibration and correction procedures can then be applied to the radiometric orthophoto to account for atmospheric and environmental influences that affect the radiometric measurement. The thermal orthophoto reveals distinct spatial variations in surface temperature across the surveyed debris-covered area. Finally, a high-resolution debris thickness map is derived from the corrected thermal orthophoto using an empirical or inverse surface energy balance model that relates surface temperature to debris thickness and is calibrated against in situ measurements. Our results from a small-scale experiment on the Kanderfirn (also known as Kander Neve) in the Swiss Alps show that the surface temperature and thickness of a relatively thin debris layer (ca. 0–15 cm) can be mapped with high accuracy using an empirical or physical model. On snow and ice surfaces, the mean deviation of the mapped surface temperature from the melting point (∼ 0 ∘C) was 0.6 ± 2.0 ∘C. The root-mean-square error of the modelled debris thickness was 1.3 cm. Through the detailed mapping, typical small-scale debris features and debris thickness patterns become visible, which are not spatially resolved by the thermal infrared sensors of current-generation satellites. The presented approach paves the way for comprehensive high-resolution supraglacial debris thickness mapping and opens up new opportunities for more accurate monitoring and modelling of debris-covered glaciers.

Sliding mode control of electro-hydraulic servo system based on double observers

Abstract. In this paper, in order to solve the real-time state value acquisition and external-disturbance problems faced during the working process of an electro-hydraulic servo system, a sliding mode controller based on dual observers is designed, which enables the system to effectively acquire the state value and realize better control accuracy. The method uses a high-gain observer to obtain the system state in real time and then adds a perturbation observer to provide more accurate state and perturbation observations for the sliding mode controller. The dual observer observes the obtained states and external perturbations and feeds these back to the sliding mode controller to control the system accurately. Finally, the observation performance of the observers is verified by comparative simulation, and the proposed control method can improve the control accuracy.

The Impact of Diurnal Variability of Sea Surface Temperature on Air–Sea Heat Flux Estimation over the Northwest Pacific Ocean

Accurate and consistent observations of diurnal variability of sea surface temperature (SST DV) and its impact on air–sea heat fluxes over large areas for extended periods are challenging due to their short time scale and wide coverage. The hourly gap-free SSTs generated from Japan Aerospace Exploration Agency-Japan Agency for Marine–Earth Science and Technology (JAXA-JAMSTEC) are input to the COARE3.5 bulk flux algorithm to investigate the impact of SST DV on air–sea heat fluxes over the Northwest Pacific Ocean (NWPO). The main results are as follows. (1) The JAXA-JAMSTEC SSTs were found to be in good agreement with the buoy observations on SST DV with a very slight negative bias of −0.007 °C and a root mean square error of 0.018 °C. (2) The case study conducted on 26 June 2020 showed that the fluxes’ diurnal amplitudes were about 30–50 W m−2, and evolution was in agreement with SST DV. (3) The average impact of SST DV on heat fluxes was 2.93 W m−2 over the subtropical NWPO, decreasing from southeast to northwest and from low to high latitudes, and showing a clear seasonal cycle during 2019–2022. This research highlights the need to consider SST DV for accurate estimation of heat fluxes, which is crucial for climate and atmospheric studies.

A Method for Retrieving Cloud Microphysical Properties Using Combined Measurement of Millimeter-Wave Radar and Lidar

Clouds are an important component of weather systems and are difficult to effectively characterize using current climate models and estimation of radiative forcing. Due to the limitations in observational capabilities, it remains difficult to obtain high-spatiotemporal-resolution, continuous, and accurate observations of clouds. To overcome this issue, we propose a novel and practical combined retrieval method using millimeter-wave radar and lidar, which enables the microphysical properties of thin liquid water clouds, such as cloud droplet effective radius, number concentration, and liquid water content, to be retrieved. This method was utilized to analyze the clouds observed at the Shanghai World Expo Park and was validated through synchronous observations with a microwave radiometer. Furthermore, the most suitable extinction backscatter ratio was determined through sensitivity analysis. This study provides vertical distributions of cloud microphysical properties with a time resolution of 1 min and a spatial resolution of 30 m, demonstrating the scientific potential of this combined retrieval method.

HiPhase: jointly phasing small, structural, and tandem repeat variants from HiFi sequencing.

In diploid organisms, phasing is the problem of assigning the alleles at heterozygous variants to one of two haplotypes. Reads from PacBio HiFi sequencing provide long, accurate observations that can be used as the basis for both calling and phasing variants. HiFi reads also excel at calling larger classes of variation, such as structural or tandem repeat variants. However, current phasing tools typically only phase small variants, leaving larger variants unphased. We developed HiPhase, a tool that jointly phases SNVs, indels, structural, and tandem repeat variants. The main benefits of HiPhase are (i) dual mode allele assignment for detecting large variants, (ii) a novel application of the A*-algorithm to phasing, and (iii) logic allowing phase blocks to span breaks caused by alignment issues around reference gaps and homozygous deletions. In our assessment, HiPhase produced an average phase block NG50 of 480 kb with 929 switchflip errors and fully phased 93.8% of genes, improving over the current state of the art. Additionally, HiPhase jointly phases SNVs, indels, structural, and tandem repeat variants and includes innate multi-threading, statistics gathering, and concurrent phased alignment output generation. HiPhase is available as source code and a pre-compiled Linux binary with a user guide at https://github.com/PacificBiosciences/HiPhase.

Comprehensive evaluation of dam seepage safety combining deep learning with Dempster-Shafer evidence theory

Seepage state is one of the most important factors affecting the safety and durability of dams, which calls for accurate observation and evaluation. Traditional dam seepage evaluation mainly focusses on single monitoring points, and ignores the uncertainty and subjectivity with multi-sensor fusion. This paper presents an evaluation method of dam seepage based on Dempster-Shafer (D-S) evidence theory and deep neural network (DNN). In view of the nonlinearity of the monitoring data and the difference of seepage influencing factors, attention mechanism (AM) and long short-term memory (LSTM) model are combined to evaluate dam seepage safety of single monitoring points, which provides the basis for the comprehensive evaluation. The case study demonstrates that the average RMSE, MAE and MAPE values of AM-LSTM are of minimal losses and higher fitting accuracy. The forecast results of the proposed comprehensive evaluation approach are consistent with the actual dam seepage state, demonstrating its reasonability and reliability.

Feasibility of monitoring floodplain on-farm water storages by integrating airborne and satellite LiDAR altimetry with optical remote sensing

Small water storages (≤ 500 ha surface area) enhance water supply for agricultural, human and livestock consumption. Their oftentimes large numbers and wide geographic spread, plus inaccurate or absent in situ metering, make their inclusion in water resource management difficult. This research assessed the capabilities of satellite optical remote sensing and LiDAR altimetry to characterise small water storages used for irrigation, so they can be included in water resources assessments. Landsat and Sentinel-2 water maps were integrated with airborne and satellite LiDAR altimetry to assess: (i) locations; (ii) elevation-area-volume relations; (iii) dates of construction/decommissioning; (iv) actual evaporation losses; and (v) storage volume changes of floodplain on-farm water storages (FoFWS) used primarily for cotton irrigation in the semi-arid Barwon-Darling region (142,173 km2), Australia. These remotely sensed characteristics would enable the representation of FoFWS in river system models and in regional water management plans, thus enhancing socioeconomic and environmental outcomes. Results from Landsat water maps showcased the growth in FoFWS surface area. In January 1988 there were 17 FoFWS with a total maximum surface area of 101.89 ha. By December 2021, the then 105 FoFWS maximum surface area was 5587.68 ha; a 55-times increase. For the first time in a regional context, mean annual (2000/01–2020/21) standing water evaporation losses were estimated for all FoFWS. These accounted to 52.47 MCM/year, which is a substantial ∼27% of the estimated long-term mean regional irrigation diversion limit (189 MCM/year). Elevation measurements from satellite LiDAR altimetry data, specifically GEDI and ICESat-2, were evaluated to assess if water volumes in FoFWS can be estimated through elevation-area-volume relations from a 1 m digital elevation model derived from airborne LiDAR data. Although GEDI's spatially variable footprint and ICESat-2's 91-day repeat cycle render them insufficient to monitor changes in water volumes in FoFWS, both satellite LiDAR altimeters provided accurate water level observations, with root mean squared differences (RMSD) of 0.38 m for GEDI and 0.12 m for ICESat-2, when compared to in situ water level measurements. ICESat-2's 0.7 m along-track sampling interval also detected changes of FoFWS structural features, such as increases in wall heights in recent years. This study demonstrated the value of integrating high-resolution airborne LiDAR data with satellite altimetry (LiDAR and/or radar interferometry), along with satellite optical water maps and rates of actual evaporation, to monitor FoFWS and other similar individually small yet collectively large reservoirs. Where similar data are available globally, then accurate information about small and dispersed water storages across poorly metered irrigated agricultural landscapes can be retrieved. Doing so will improve water resource management in the Barwon-Darling region, and in similar semi-arid and arid areas globally where irrigated agriculture is present.

Continuous monitoring of eating and sleeping behaviors in the home environments of older adults: a case study demonstration.

Accurate observation of patient functioning is necessary for rigorous clinical research and for improving the quality of patient care. However, clinic or laboratory environments systematically differ from the contexts of everyday life. Further, assessments that are completed in a single institutional session may not be generalizable. Here, we describe a computer vision methodology that measures human functioning continuously in the environments where patients live, sleep, and eat.

Automatic tracking and intelligent observation of tidal bore propagation velocity based on UAV and computer vision

The rapidly developed Unmanned Aerial Vehicles (UAV) and artificial intelligence technology has prompted the real-time and accurate observation measurements of tidal bore, the basis of which is tidal bore propagation velocity. In this article, we construct a tidal observation system framework based on UAV and computer vision in order to obtain the tidal bore propagating velocity datasets. Firstly, we focus on the identification of tidal headlines based on the Sobel edge detection, the improved Otsu image segmentation algorithm and the edge connection algorithm with an accuracy of 91%. And then, the detected tidal headlines could be used to control the flight parameters of UAV in order to stably track tidal bore on the specified route with the deviation range below 0.5, and finally to acquire the tidal bore propagation velocity datasets. Comparing with the propagation velocity of the tidal line measured on site, the error of the results is maintained within 0.1 m/s, which demonstrates the effectiveness of our proposed observation method.

An Improved Super-Twisting Sliding Mode Composite Control for Quadcopter UAV Formation

Aiming at the nonlinear and multiple disturbances in the multi-quadcopter UAV system, this paper proposes a leader–follower composite formation control strategy based on an improved super-twisted sliding mode controller (ISTSMC) and a finite-time extended state observer (FTESO). For the designed sliding mode control algorithm, the integral term’s switching function is replaced with a non-smooth term to reduce the vibration in the control, further improving the overall performance of the system. For external disturbances, the finite-time extended state observer achieves rapid and accurate observation of external disturbances. Finally, through formation control experiments, the reliability and superiority of the proposed composite formation controller (CFC) is validated.

Fabrication of a 3D bioprinting model for posterior capsule opacification using GelMA and PLMA hydrogel-coated resin.

Posterior capsule opacification (PCO) remains the predominant complication following cataract surgery, significantly impairing visual function restoration. In this study, we developed a PCO model that closely mimics the anatomical structure of the crystalline lens capsule post-surgery. The model incorporated a threaded structure for accurate positioning and observation, allowing for opening and closing. Utilizing 3D printing technology, a stable external support system was created using resin material consisting of a rigid, hollow base and cover. To replicate the lens capsule structure, a thin hydrogel coating was applied to the resin scaffold. The biocompatibility and impact on cellular functionality of various hydrogel compositions were assessed through an array of staining techniques, including calcein-AM/PI staining, rhodamine staining, BODIPY-C11 staining and EdU staining in conjunction with transwell assays. Additionally, the PCO model was utilized to investigate the effects of eight drugs with anti-inflammatory and anti-proliferative properties, including 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), THZ1, sorbinil, 4-octyl itaconate (4-OI), xanthohumol, zebularine, rapamycin and caffeic acid phenethyl ester, on human lens epithelial cells (HLECs). Confocal microscopy facilitated comprehensive imaging of the PCO model. The results demonstrated that the GelMA 60 5% + PLMA 2% composite hydrogel exhibited superior biocompatibility and minimal lipid peroxidation levels among the tested hydrogels. Moreover, compared to using hydrogel as the material for 3D printing the entire model, applying surface hydrogel spin coating with parameters of 2000 rpm × 2 on the resin-based 3D printed base yielded a more uniform cell distribution and reduced apoptosis. Furthermore, rapamycin, 4-OI and AICAR demonstrated potent antiproliferative effects in the drug intervention study. Confocal microscopy imaging revealed a uniform distribution of HLECs along the anatomical structure of the crystalline lens capsule within the PCO model, showcasing robust cell viability and regular morphology. In conclusion, the PCO model provides a valuable experimental platform for studying PCO pathogenesis and exploring potential therapeutic interventions.

D. P. Svyatopolk-Mirsky on Slavs

The article highlights the place of the Slavs in the work of D. P. Svyatopolk-Mirsky, first a Eurasianist and then a Marxist. The key role of Russian studies in his Slavic studies is noted. It is argued that the vast majority of Svyatopolk-Mirsky’s thoughts and assessments regarding the history of Russian literature have not yet been fully studied. Significant results on Ukrainian studies were presented in his articles of the 1920s and in an article in the Encyclopedia Britannica; Svyatopolk-Mirsky was one of the first to highlight the problems of Belarusian studies there. His legacy as a (geo)political thinker and researcher of culture and literature of other Slavic peoples, though unevenly represented throughout his body of work, is considered and evaluated: for example, a lot has been written by Svyatopolk-Mirsky about the Poles, and almost nothing about the southern Slavs, but still, when considering them, one can find not only accurate observations and assessments, but also original ideas.

Global Variability of Phytoplankton Carbon and Non‐Algal Particles From Ocean Color Data Based on a Photoacclimation Model

AbstractPhytoplankton is the most important primary producer in the ocean, owing to its pivotal role at the base of the food chain and in the ocean carbon cycle. To better estimate ocean productivity, the retrieval of accurate observations of phytoplankton carbon (Cphy) biomass from ocean color satellites is of the utmost importance. When Cphy is retrieved using the backscattering signal generated by marine particles, the signal due to non‐algal particles—NAP (bbNAP) must be removed. However, algorithms currently used to retrieve bbNAP do not work globally, especially in the large mid‐ocean subtropical gyres. Here, we propose a new model to derive Cphy globally, including all subtropical gyres, that accounts for the estimation of phytoplankton photoacclimation when retrieving bbNAP. The new model shows more valid retrievable bbNAP coefficients than the previously published methods and values mainly around 0.0011 m−1, consistent with coefficients estimated with in situ observations. Moreover, the retrieved Cphy concentrations show a relative error of 7% with respect to in situ measured picophytoplankton carbon data. Our research indicates that the photoacclimation status of phytoplankton must be addressed to confidently retrieve bbNAP and Cphy at the global scale from ocean color measurements.

Application of Different Types of Electrocardiogram Monitoring in Arrhythmias

Abstract Arrhythmias are a common cardiovascular disease with multiple clinical manifestations, of which ventricular arrhythmias may indicate the risk of sudden cardiac death. Although great progress has been made in the treatment of ventricular arrhythmias, there is still no solution for sudden malignant arrhythmic events. Portable electrocardiogram (ECG) monitoring tools play a vital role in the process of monitoring spontaneous arrhythmias, especially in early cardiovascular diseases without clinical symptoms, the use of ECG monitoring tools can provide more timely and accurate observation of the occurrence of occult arrhythmias. This mini-review summarizes the application of ECG monitoring tools in the process of arrhythmias and provides a potential reference for monitoring the occurrence of early arrhythmias.