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As a major component of water cycle, the accuracy quantification of different precipitation products is critical for evaluating climate change and ecosystem functions. However, a lack of evidence is available to choose a precise precipitation product in relative applications. Here, to solve this limit, we analyze the spatiotemporal pattern and accuracy of four precipitation products, including CHIRPS V2.0, PERSIANN-CDR, ECMWF ERA5-Land, and GLDAS_NOAH025_3H, over China during the period of 1981–2020, based on the five precipitation traits (i.e., spatial pattern of multi-year average, annual trend, seasonality, frequency, and intensity), and meteorological gauge observations are taken as the benchmark. Our results show that, compared to other products, CHIRPS data has the strongest ability to present spatial pattern of multi-year average precipitation, especially in most parts of northeastern and southern China, and ERA5 has the weakest ability to simulate the multi-year average precipitation. All four precipitation products can accurately depict the spatial pattern of seasonality, among which CHIRPS and ERA5 have the highest and lowest fitting ability, respectively, but four products poorly describe the spatial pattern of precipitation intensity and frequency at a daily scale. These products only correctly predict the interannual precipitation trend in some local areas. Our findings provide evidences to select high-quality precipitation data, and could help to improve the accuracy of relative geophysical models.

In the wide scenario of heritage documentation and conservation, the multi-scale nature of digital models is able to twin the real object, as well as to store information and record investigation results, in order to detect and analyse deformation and materials deterioration, especially from a structural point of view. The contribution proposes an integrated approach for the generation of an n-D enriched model, also called a digital twin, able to support the interdisciplinary investigation process conducted on the site and following the processing of the collected data. Particularly for 20th Century concrete heritage, an integrated approach is required in order to adapt the more consolidated approaches to a new conception of the spaces, where structure and architecture are often coincident. The research plans to present the documentation process for the halls of Torino Esposizioni (Turin, Italy), built in the mid-twentieth century and designed by Pier Luigi Nervi. The HBIM paradigm is explored and expanded in order to fulfil the multi-source data requirements and adapt the consolidated reverse modelling processes based on scan-to-BIM solutions. The most relevant contributions of the research reside in the study of the chances of using and adapting the characteristics of the IFC (Industry Foundation Classes) standard to the archiving needs of the diagnostic investigations results so that the digital twin model can meet the requirements of replicability in the context of the architectural heritage and interoperability with respect to the subsequent intervention phases envisaged by the conservation plan. Another crucial innovation is a proposal of a scan-to-BIM process improved by an automated approach performed by VPL (Visual Programming Languages) contribution. Finally, an online visualisation tool enables the HBIM cognitive system to be accessible and shareable by stakeholders involved in the general conservation process.

Accurate extraction of cropland distribution information using remote sensing technology is a key step in the monitoring, protection, and sustainable development of black soil. To obtain precise spatial distribution of cropland, an information extraction method is developed based on a fractal algorithm integrating temporal and spatial features. The method extracts multi-seasonal fractal features from the Landsat 8 OLI remote sensing data. Its efficiency is demonstrated using black soil in Lishu County, Northeast China. First, each pixel’s upper and lower fractal signals are calculated using a blanket covering method based on the Landsat 8 OLI remote sensing data in the spring, summer, and autumn seasons. The fractal characteristics of the cropland and other land-cover types are analyzed and compared. Second, the ninth lower fractal scale is selected as the feature scale to extract the spatial distribution of cropland in Lishu County. The cropland vector data, the European Space Agency (ESA) WorldCover data, and the statistical yearbook from the same period are used to assess accuracy. Finally, a comparative analysis of this study and existing products at different scales is carried out, and the point matching degree and area matching degree are evaluated. The results show that the point matching degree and the area matching degree of cropland extraction using the multi-seasonal fractal features are 90.66% and 96.21%, and 95.33% and 83.52%, respectively, which are highly consistent with the statistical data provided by the local government. The extracted accuracy of cropland is much better than that of existing products at different scales due to the contribution of the multi-seasonal fractal features. This method can be used to accurately extract cropland information to monitor changes in black soil, and it can be used to support the conservation and development of black soil in China.

The use of unmanned aerial vehicles (UAV) in monitoring applications is constantly increasing due to the improvement in sensor technology and the associated higher accuracy that can be achieved. As a result, UAV-based laser scanning is already being used in various deformation monitoring applications such as the monitoring of landslides or land deformations. The main challenges, which also limit the accuracy of the resulting georeferenced point cloud are given by the trajectory estimation, the measurement environment and the flight planning. Difficult conditions and high accuracy demands are especially given for the monitoring of a water dam. While the use of area-based measurements such as terrestrial laser scanning (TLS) is an already established approach for such monitoring tasks, the use of a similar technology on a platform such as a UAV is promising and investigated in this study by acquiring a single measurement epoch at a water dam. In addition to the proposal of a flight pattern for the measurements, the trajectory estimation results are evaluated in detail. Due to critical GNSS conditions, positioning errors lead to systematic shifts between single flight strips. Subsequent optimization with known control points allows the point cloud to be compared to a TLS reference. The difference between the two is shown to have a mean difference of 5 mm with a 9.2 mm standard deviation. This can be considered a highly promising result, especially as the potential for further improvement by using additional targets and sensors (e.g. camera) has been identified.

The three-river headwaters region (TRHR) is the birthplace of the Yangtze River, the Yellow River and the Lantsang River in China. Based on the grid surface precipitation data released by China Meteorological Administration (CMA), this paper evaluated the accuracy and error components of four near-real-time satellite precipitation products (GSMaP-NRT, GSMaP-MVK, IMERG-Early and IMERG-Late) in the era of a GPM (Global Precipitation Measurement) in TRHR. The conclusions are as follows: (1) The precipitation in TRHR is concentrated in the east and south, and the precipitation in the west is very low. IMERG (Early and Late) has a good spatial distribution of precipitation, while GSMaP has an obvious spatial smoothing of precipitation distribution, and does not better highlight the local precipitation characteristics. (2) The inversion accuracy of the satellite products is the best in the source region of the Lantsang River, followed by the source region of the Yellow River. The satellite products all show the lower correlation coefficient and serious underestimation of precipitation in the west of the TRHR. In addition, the closer to the west of the TRHR, the lower hit rate and the higher false alarm rate of the satellite products, especially the NRT and MVK products. In the eastern margin of the Yellow River headwater region and the Lantsang River headwater Region, RMSE and overestimated precipitation were higher in NRT and MVK, and FAR was higher in spite of higher POD and CSI. (3) The errors of GSMaP in the source region of the Yellow River and the Lantsang River are mainly caused by misreporting precipitation and overestimating the precipitation level, while the errors of GSMaP in the west of the TRHR are mainly caused by missing measurements of precipitation events. The underestimated precipitation of IMERG mainly comes from the missed measurement of precipitation and the underestimate of precipitation level, and there is no large false precipitation. (4) In addition, we found that the satellite products in the lake distribution area of the TRHR have serious missed precipitation errors, indicating that the GPM satellite products have the poor detection ability of precipitation near plateau lakes. On the whole, the precipitation inversion accuracy of IMERG (Early and Late) products is higher, which can better detect the occurrence of precipitation events, but the estimation of precipitation level is still not accurate. The precision of precipitation of satellite products near inland lakes on the plateau is poor, so the algorithm improvement of new products needs to be further solved in the future. HIGHLIGHTS The error performance of IMERG and GSMaP products over the three-river headwaters region in China.; IMERG has a good spatial distribution of precipitation.; The satellite products all show the lower correlation coefficient in the west of the study area.; The estimation of precipitation level of IMERG is still not accurate.; GPM satellite products have the poor detection ability of precipitation near plateau lakes.

An examination of the traceability and dependability of the virtualisation properties is prompted by the widespread use of three-dimensional models. The challenge of obtaining accuracy indicators directly from the photogrammetric method when a reference model is missing is widely acknowledged. In this study, a robust method based on a statistical analysis of the uncertainty associated with Tie Points (TPs) is presented to provide a strict framework for the informed processing of photogrammetric survey data. In the phases of Structure estimation, Structure optimisation, and Dense Cloud generation, the key steps and variables affecting data processing are described. The workflow is then applied to a specific bronze museum finding smaller than 20 cm in size. All tie points that overcome the filtering phase are included in the procedure and for their coordinates the covariance matrix is examined. The error ellipsoid is calculated and the distribution of the lengths of the major semi-axes is analysed to calculate an appropriate tolerance interval which can be used as an indicator of the accuracy of the entire photogrammetric process. Indeed, using the tolerance intervals tool allows for the derivation of a representative indicator that can be compared with the outcomes of other photogrammetric processes while overcoming the ambiguity of statistical indicators that are not representative in the case of a non-normal distribution.

Background Fractional vegetation cover (FVC) is an important basic parameter for the quantitative monitoring of the alpine grassland ecosystem on the Qinghai-Tibetan Plateau. Based on unmanned aerial vehicle (UAV) acquisition of measured data and matching it with satellite remote sensing images at the pixel scale, the proper selection of driving data and inversion algorithms can be determined and is crucial for generating high-precision alpine grassland FVC products. Methods This study presents estimations of alpine grassland FVC using optimized algorithms and multi-dimensional features. The multi-dimensional feature set (using original spectral bands, 22 vegetation indices, and topographical factors) was constructed from many sources of information, then the optimal feature subset was determined based on different feature selection algorithms as the driving data for optimized machine learning algorithms. Finally, the inversion accuracy, sensitivity to sample size, and computational efficiency of the four machine learning algorithms were evaluated. Results (1) The random forest (RF) algorithm (R2: 0.861, RMSE: 9.5%) performed the best for FVC inversion among the four machine learning algorithms driven by the four typical vegetation indices. (2) Compared with the four typical vegetation indices, using multi-dimensional feature sets as driving data obviously improved the FVC inversion accuracy of the four machine learning algorithms (R2 of the RF algorithm increased to 0.890). (3) Among the three variable selection algorithms (Boruta, sequential forward selection [SFS], and permutation importance-recursive feature elimination [PI-RFE]), the constructed PI-RFE feature selection algorithm had the best dimensionality reduction effect on the multi-dimensional feature set. (4) The hyper-parameter optimization of the machine learning algorithms and feature selection of the multi-dimensional feature set further improved FVC inversion accuracy (R2: 0.917 and RMSE: 7.9% in the optimized RF algorithm). Conclusion This study provides a highly precise, optimized algorithm with an optimal multi-dimensional feature set for FVC inversion, which is vital for the quantitative monitoring of the ecological environment of alpine grassland.

Uncontrolled refraction of optical rays in underwater photogrammetry is known to reduce its accuracy potential. Several strategies have been proposed aiming at restoring the accuracy to levels comparable with photogrammetry applied in air. These methods are mainly based on rigours modelling of the refraction phenomenon or empirical iterative refraction corrections. The authors of this contribution have proposed two mitigation strategies of image residuals systematic patterns in the image plane: (i) empirical weighting of image observations as function of their radial position; (ii) iterative look-up table corrections computed in a squared grid. Here, a novel approach is developed. It explicitly takes into account the object point-to-camera distance dependent error introduced by refraction in multimedia photogrammetry. A polynomial correction function is iteratively computed to correct the image residuals clustered in radial slices in the image plane as function of the point-to-camera distance. The effectiveness of the proposed method is demonstrated by simulations that allow to: (i) separate the geometric error under investigation from other effects not easily modellable and (ii) have reliable reference data against which to assess the accuracy of the result., International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2021, ISSN:1682-1750, ISSN:2194-9034, ISSN:1682-1777

Spectral estimation is a critical signal processing step in speed-over-ground (SoG) radar. It is argued that, for accurate speed estimation, spectral estimation should use low bias and variance estimator. However, there is no evaluation on spectral estimation techniques in terms of estimating mean Doppler frequency to date. In this paper, we evaluate two common spectral estimation techniques, namely periodogram based on Fourier transformation and the autoregressive (AR) based on burg algorithm. These spectral estimators are evaluated in terms of their bias and variance in estimating a mean frequency. For this purpose, the spectral estimators are evaluated with different Doppler signals that varied in mean frequency and signal-to-noise ratio (SNR). Results in this study indicates that the periodogram method performs well in most of the tests while the AR method did not perform as well as these but offered a slight improvement over the periodogram in terms of variance.

Background: Robotic-based guidance systems are becoming increasingly capable of assisting in needle placement during interventional procedures. Despite these technical advances, less sophisticated low-cost guidance devices promise to enhance puncture accuracy compared with the traditional freehand technique. Purpose: To compare the in vitro accuracy and feasibility of two different aiming devices for computed-tomography (CT)-guided punctures. Methods: A total of 560 CT-guided punctures were performed by using either a robotic (Perfint Healthcare: Maxio) or a novel low-cost patient-mounted system (Medical Templates AG: Puncture Cube System [PCS]) for the placement of Kirschner wires in a plexiglass phantom with different slice thicknesses. Needle placement accuracy as well as procedural time were assessed. The Euclidean (ED) and normal distances (ND) were calculated at the entry and target point. Results: Using the robotic device, the ND at the target for 1.25 mm, 2.5 mm, 3.75 mm and 5 mm slice thickness were 1.28 mm (SD ± 0.79), 1.25 mm (SD ± 0.81), 1.35 mm (SD ± 1.00) and 1.35 mm (SD ± 1.03). Using the PCS, the ND at the target for 1 mm, 3 mm and 5 mm slices were 3.84 mm (SD ± 1.75), 4.41 mm (SD ± 2.31) and 4.41 mm (SD ± 2.11), respectively. With all comparable slice thicknesses, the robotic device was significantly more accurate compared to the low-cost device (p < 0.001). Needle placement with the PCS resulted in lower intervention time (mean, 158.83 s [SD ± 23.38] vs. 225.67 s [SD ± 17.2]). Conclusion: Although the robotic device provided more accurate results, both guidance systems showed acceptable results and may be helpful for interventions in difficult anatomical regions and for those requiring complex multi-angle trajectories.

The Qilian Mountains are a climate-sensitive area in northwest China, and extreme precipitation events have an important impact on its ecological environment. Therefore, considering the global warming scenario, it is highly important to project the extreme precipitation indices over the Qilian Mountains in the future. This study is based on three CMIP6 models (CESM2, EC-Earth3, and KACE-1-0-G). A bias correction algorithm (QDM) was used to correct the precipitation outputs of the models. The eight extreme precipitation indices over the Qilian Mountains during the historical period and in the future were calculated using meteorological software (ClimPACT2), and the performance of the CMIP6 models to simulate the extreme precipitation indices of the Qilian Mountains in the historical period was evaluated. Results revealed that: (1) The corrected CMIP6 models could simulate the changes in extreme precipitation indices over the Qilian Mountains in the historical period relatively well, and the corrected CESM2 displayed better simulation as compared to the other two CMIP6 models. The CMIP6 models performed well while simulating R10mm (CC is higher than 0.71) and PRCPTOT (CC is higher than 0.84). (2) The changes in the eight extreme precipitation indices were greater with the enhancement of the SSP scenario. The growth rate of precipitation in the Qilian Mountains during the 21st century under SSP585 is significantly higher than the other two SSP scenarios. The increment of precipitation in the Qilian Mountains mainly comes from the increase in heavy precipitation. (3) The Qilian Mountains will become wetter in the 21st century, especially in the central and eastern regions. The largest increase in precipitation intensity will be observed in the western Qilian Mountains. Additionally, total precipitation will also increase in the middle and end of the 21st century under SSP585. Furthermore, the precipitation increment of the Qilian Mountains will increase with the altitude in the middle and end of the 21st century. This study aims to provide a reference for the changes in extreme precipitation events, glacier mass balance, and water resources in the Qilian Mountains during the 21st century.

Sea surface temperature (SST) is a key factor for both climate and weather. Most studies focus on evaluation of satellite SSTs in different oceanic regions, but the performance of ERA5 reanalysis SST datasets (ERA5 SSTs) has not been systematically evaluated. In this article, buoy measurements from 46 National Data Buoy Center (NDBC) sites around the Pacific and the Atlantic are used to make an analysis of the deviations of the ERA5 SSTs on an hourly basis and its variations on a daily basis. Overall, ERA5 SSTs have a global coverage with a $- 0.04\,\,^{\circ }\text{C}$ bias and 4.60 °C standard deviation in the study area. It is worthwhile to notice that ERA5 SSTs exhibit significant differences in the amplitude and the phasing of their spatial patterns. The products are found to meet the accuracy criterion, except in near coastal areas, which may result from the near-coastal mixture effect and the warm SST. The results reveal that ERA5 SSTs completely capture the diurnal variability of SST under all wind speed conditions, especially in the situation when wind speed is above 6 m/s. There is a slight cold bias when wind speed is below 6m/s, indicating that ERA5 SSTs are not affected by solar radiation heating. These findings contribute to the sensible use of ERA5 SSTs around the Pacific and the Atlantic.

The rehabilitation of the skull’s bones is a difficult process that poses a challenge to the surgical team. Due to the range of design methods and the availability of materials, the main concerns are the implant design and material selection. Mirror-image reconstruction is one of the widely used implant reconstruction techniques, but it is not a feasible option in asymmetrical regions. The ideal design approach and material should result in an implant outcome that is compact, easy to fit, resilient, and provides the perfect aesthetic and functional outcomes irrespective of the location. The design technique for the making of the personalized implant must be easy to use and independent of the defect’s position on the skull. As a result, this article proposes a hybrid system that incorporates computer tomography acquisition, an adaptive design (or modeling) scheme, computational analysis, and accuracy assessment. The newly developed hybrid approach aims to obtain ideal cranial implants that are unique to each patient and defect. Polyetheretherketone (PEEK) is chosen to fabricate the implant because it is a viable alternative to titanium implants for personalized implants, and because it is simpler to use, lighter, and sturdy enough to shield the brain. The aesthetic result or the fitting accuracy is adequate, with a maximum deviation of 0.59 mm in the outside direction. The results of the biomechanical analysis demonstrate that the maximum Von Mises stress (8.15 MPa), Von Mises strain (0.002), and deformation (0.18 mm) are all extremely low, and the factor of safety is reasonably high, highlighting the implant’s load resistance potential and safety under high loading. Moreover, the time it takes to develop an implant model for any cranial defect using the proposed modeling scheme is very fast, at around one hour. This study illustrates that the utilized 3D reconstruction method and PEEK material would minimize time-consuming alterations while also improving the implant’s fit, stability, and strength.

The ecological and natural conditions in drought regions are harsh. Water shortages and land desertification are prominent features of these regions. The land cover in these regions has a large impact on global climate change, as well as on ecological protection and construction. To make rational and sustainable use of land resources, it is crucial to quickly grasp the accuracy and spatial distribution differences of multi-source remote sensing land cover products in drought regions. Therefore, taking northwestern China as the study area, in this study, the accuracy and spatial pattern distribution differences of three high-resolution (10 m) land cover products, namely, the Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC), European Space Agency (ESA), and Environmental Systems Research Institute (ESRI) products, were compared and analyzed via area composition similarity, spatial pattern consistency, and absolute accuracy assessment for three validation samples. The results show that the FROM-GLC product had the highest overall accuracy, ranging from 53.81% to 73.45%. The ESRI product had the lowest overall accuracy, ranging from 35.90% to 64.16%. The spatial consistencies of the three products were low, accounting for 46.26% of the total area, and they were mostly distributed in a single area (mainly bare land and forest). The low accuracy for grassland, bare land, shrubland, and other vegetation types was the primary reason for the large differences between the three products. Future research should focus on improving the mapping accuracy for these vegetation types. Accuracies for water and cropland of the three products were consistent, and, thus, the FROM-GLC, ESA, and ESRI products can be used as auxiliary data in research related to water resources and cropland resources in drought regions.

BDS-3 is a global satellite navigation system independently built by China. Its performance level and performance comparison with other satellite navigation systems have an important impact on the follow-up promotion and application. In this paper, the signal in space range error (SISRE) is used as a key performance index of the system. Taking the multi-system precise orbit and clock offset provided by GFZ as the standard, the comparison and evaluation method of satellite orbit, satellite clock offset and SISRE is given. Based on the measured data of three months from January to March 2020, the accuracy improvement of BDS-3 relative to BDS-2 is verified, and the performance comparison between BDS-3, GPS, Galileo and GLONASS is analyzed emphatically. The results show that the accuracy level of BDS-3 is significantly higher than that of BDS-2 both in satellite orbit and in satellite clock offset. The orbit accuracy of BDS-3 in the R, T and N direction is 0.07 m, 0.30 m and 0.26 m respectively, which is at the optimal level among the four global systems. The satellite clock offset accuracy is 1.83 ns, which is basically the same as that of GPS, superior to GLONASS, but slightly worse than Galileo. In terms of the signal in space range error, if only orbit error is considered, BDS-3 SISRE(orb) is averagely 0.08 m. Next, Galileo SISRE(orb) is 0.26 m, GPS SISRE(orb) is 0.57 m, and GLONASS SISRE(orb) is 0.98 m. If the orbit and clock error are considered comprehensively, the average SISRE of BDS-3 is 0.50 m, which is slightly lower than 0.38 m of Galileo, better than 0.58 m of GPS, and significantly better than 2.35 m of GLONASS.

Ro-Ro and Ro-Pax ships works on basis of regular schedule and try keep this schedule as accurate as possible. In same time difficulties during Ro-Ro and Ro-Pax ships voyages like bad weather conditions, waiting time to entry in to port, ship's failures and others, sometime request more time as it was planned initially. Ro-Ro and Ro-Pax ships arrivals in to port evaluation is important not only just for the Ro-Ro and Ro-Pax shipping lines planning but as well it is very important for passengers, transport units (trucks) and port terminals. In this Article are analyzed delay times of Ro-Ro and Ro-Pax ships arrival to port on basis of statistical data and used mathematical models for calculation of the arrival time pass.

Study region: The Akaki catchment is found in the Upper Awash River Basin in Ethiopia. Study focus: Understanding the accuracy of rainfall forecasts in the data-scarce urban catchment has a multitude of benefits given the increased urban flood risk caused by climate change and urbanization. In this study, accuracy of the weather research and forecasting (WRF) model rainfall forecast was evaluated using citizen science data. Categorical and continuous accuracy evaluation metrics were used beside gauge representativeness effect. New hydrological insights for the region: The rainfall forecasts performance accuracy is high for 1–3-days lead-time but deteriorates for 4–5-days lead-time. The WRF model captured the temporal dynamics and the rainfall amount according to the estimated KGE values. The model has relatively higher detection performance for no rain and light rain events (< 6 mm/day), but it has lower performance for moderate and heavy rain events (> 6 mm/day). Use of data from a single rain gauge misrepresents the accuracy level of the rainfall forecast in the study area. The gauge representativeness error contributed a variance of 28.08–83.33 % to the variance of WRF-gauge rainfall difference. Thus, the use of citizen science rainfall monitoring program is an essential alternative source of information where in-situ rainfall monitoring is limited that can be used to understand the “true” accuracy of WRF rainfall forecasts.

This research proposes a detailed analysis of the potential of MMS (Mobile Mapping Systems), supported by SLAM (Simultaneous Localisation And Mapping) algorithms, performed on a multiscale test field in order to make a concrete contribution to the morphological study of cities. These systems, developed with the aim of acquiring a large number of points in a short time, are able to map the surrounding area and automatically localise themselves in real time in relation to a determined reference system. The analysed area, located in Venice, was divided into three different test fields characterised by typical elements potentially comparable to those of other urban realities. The data were acquired using the LiBackPack C50, Kaarta Stencil and Heron Lite systems and compared quantitatively and qualitatively with data obtained from more traditional surveying techniques. Specifically, the data obtained from TLS (Terrestrial Laser Scanning) surveys, supported by topographic measurements, were the most accurate basis on which to evaluate the accuracy and completeness of the three different MMS devices. The standard deviation values were initially analysed in the final 3D global models using the C2C (Cloud to Cloud) and C2M (Cloud to Mesh) distance calculation methods. Subsequently, the geometric differences were investigated through the extraction of horizontal profiles, and two more specific 2D analyses were carried out: the first inspecting the residual parameters calculated after the Helmert transformation from two sets of control points obtained from the profiles, followed by a local strain analysis. The study of the local deformation parameters allowed us to validate the results obtained and to identify the real limits of these survey instruments. The aim was to make a concrete contribution to the formalisation of an operative protocol for the morphological study of the city, exploiting the potential of these technologies to overcome the differences in scale and the gap between outdoor and indoor spaces.

The purpose of this paper is to analyze the influence of satellite altimetry data accuracy on the marine gravity anomaly accuracy. The data of 12 altimetry satellites in the research area (5°N–23°N, 105°E–118°E) were selected. These data were classified into three groups: A, B, and C, according to the track density, the accuracy of the altimetry satellites, and the differences of self-crossover. Group A contains CryoSat-2, group B includes Geosat, ERS-1, ERS-2, and Envisat, and group C comprises T/P, Jason-1/2/3, HY-2A, SARAL, and Sentinel-3A. In Experiment I, the 5′×5′ marine gravity anomalies were obtained based on the data of groups A, B, and C, respectively. Compared with the shipborne gravity data, the root mean square error (RMSE) of groups A, B, and C was 4.59 mGal, 4.61 mGal, and 4.51 mGal, respectively. The results show that high-precision satellite altimetry data can improve the calculation accuracy of gravity anomaly, and the single satellite CryoSat-2 enables achieving the same effect of multi-satellite joint processing. In Experiment II, the 2′×2′ marine gravity anomalies were acquired based on the data of groups A, A + B, and A + C, respectively. The root mean square error of the above three groups was, respectively, 4.29 mGal, 4.30 mGal, and 4.21 mGal, and the outcomes show that when the spatial resolution is satisfied, adding redundant low-precision altimetry data will add pressure to the calculation of marine gravity anomalies and will not improve the accuracy. An effective combination of multi-satellite data can improve the accuracy and spatial resolution of the marine gravity anomaly inversion.

High-definition maps (HDM) for autonomous driving (AD) are an important component of AD systems. HDMs accurately provide a priori information, including lane lines, and road signs, for AD systems. It is an important task to make a reasonable accuracy assessment of the HDM. The current methods for relative accuracy evaluation of general maps in the field of mapping are not fully applicable to HDMs. In this study, a method based on point set alignment and resampling is used to evaluate the relative accuracy of lane lines, and experiments are conducted based on relevant real HDM data. The results show that the relative accuracy of the lane lines is more detailed and relevant than the traditional method. This has implications for the quality control of HDM production.

Ground-based multi-channel microwave radiometers (MWRs) can continuously detect atmospheric profiles in the tropospheric atmosphere. This makes MWR an ideal tool to supplement radiosonde and satellite observations in monitoring the thermodynamic evolution of the atmosphere and improving numerical weather prediction (NWP) through data assimilation. The analysis of product characteristics of MWR is the basis for applying its data to real-time monitoring and assimilation. In this paper, observations from the latest generation of ground-based multi-channel MWR RPG-HATPRO-G5 installed in Shanghai, China, are compared with the radiosonde observations (RAOB) observed in the same location. The detection performance, characteristics of various channels, and the accuracy of the retrieval profile products of the MWR RPG are comprehensively evaluated during various weather conditions. The results show that the brightness temperatures (BTs) observed by the ground-based MWR RPG during precipitation conditions were high, which affected its detection performance. The bias and the standard deviation (SD) between the BT observed by MWR RPG and the simulated BT during clear and cloudy sky conditions were slight and large, respectively, and the coefficient of determination (R2) was high and low, respectively. However, when the cloud liquid water (CLW) information was added when simulating BT, the bias and the SD of the observed BT and the simulated BT during cloudy days were reduced and the R2 value improved, which indicated that CLW information should be taken into account when simulating BT during cloudy conditions. The temperature profiles of the MWR retrieval had the same accuracy of RMSEs (root-mean-square error) with heights during both clear-sky and cloudy sky conditions, where the RMSEs were below 2 K when the heights were below 4 km. In addition, the MWR RPG has the potential ability to retrieve the temperature inversion in the boundary layer, which has important application value for fog and air pollution monitoring.

The use of moving devices equipped with range- and image-based sensor, generically defined Mobile Mapping systems (MMS), have been quite a disruptive innovation in the development of Geomatics techniques for 3D surveying large indoor-outdoor spaces and offer multiple solutions. The recent expansion of portable devices in the form of trolleys, backpacks, handheld tools largely implements SLAM (Simultaneous Localization and Mapping) algorithms and technology based on both Lidar and/or visual solutions for answering to the positioning and the 3D reconstruction problems. The research on MMS is directed to improve both multi-sensor integration implementation and usability of systems in diversified use contexts and application fields. The aim of the presented research is the evaluation of the potential of the Swift system recently developed by FARO Technologies, that has been fine-tuned for regular and large extent interiors mapping (such as factories, hospitals, airports, offices). The work tries to preliminary investigate the data delivery and usability of the integrated system. This is based on three elements mounted on a sliding trolley moved by the operator walking: the ScanPlan profilometer working for the 2D SLAM mapping, the static TLS Focus S-series, and the smartphone managing the sensors operation and the acquisition progress. The evaluation strategy undertaken will be based on the global and local performance analysis related to the trajectory, the data accuracy, the metric content and consistency. Two test studies belonging to the 20th century. architecture are presented in a preliminary framework of evaluation and validation: a Liberty-style cinema and the Torino Esposizioni Hall B designed in ferrocement by pier Luigi Nervi.

Satellite radar altimetry has made unique contributions to global and coastal gravity field recovery. This paper starts with a general introduction followed by the progress of satellite radar altimetry technology. Then, the methods of marine gravity field recovery and dominating gravity models are described briefly. Finally, typical gravity models are compared with shipboard gravity measurements to evaluate their accuracies in offshore and coastal regions of China. The root mean squares of deviations between gravity models and shipboard gravity are all more than 7 mGal in offshore regions and within the range of 9.5–10.2 mGal in coastal regions. Further analysis in coastal regions indicates that the new gravity models with new satellite missions including Jason-2, SARAL/Altika, and Envisat data have relatively higher accuracy, especially SARAL/Altika data, significantly improving the coastal gravity field. Accuracies are low in areas with strong currents, showing that tide correction is very important for altimetry-derived marine gravity recovery as well as shipboard measurements in coastal gravity field determination. Moreover, as an external check, shipboard gravity data need more operations to improve their precision, such as higher instrument accuracy and finer data processing.

In the past decades, global land cover datasets have been produced but also been criticized for their low accuracies, which have been affecting the applications of these datasets. Producing a new global dataset requires a tremendous amount of efforts; however, it is also possible to improve the accuracy of global land cover mapping by fusing the existing datasets. A decision-fuse method was developed based on fuzzy logic to quantify the consistencies and uncertainties of the existing datasets and then aggregated to provide the most certain estimation. The method was applied to produce a 1-km global land cover map (SYNLCover) by integrating five global land cover datasets and three global datasets of tree cover and croplands. Efforts were carried out to assess the quality: 1) inter-comparison of the datasets revealed that the SYNLCover dataset had higher consistency than these input global land cover datasets, suggesting that the data fusion method reduced the disagreement among the input datasets; 2) quality assessment using the human-interpreted reference dataset reported the highest accuracy in the fused SYNLCover dataset, which had an overall accuracy of 71.1%, in contrast to the overall accuracy between 48.6% and 68.9% for the other global land cover datasets.

In order to ensure the electrical performance of the reflector antenna, the surface accuracy constraint need to be satisfied during the structure design. Different calculating methods can get different accuracy values, then influence the difficulty of antenna structure design. Therefore, the evaluation method for antenna surface accuracy is of vital importance. In this paper, the traditional evaluation procedure is first reviewed. This method has been successfully used in structure design of many large antennas, but based on our analysis, this method is sub-optimal. For this reason, a new accuracy evaluation method is introduced, which can get the optimal worst-case surface accuracy. Both quantitative analysis and numerical example of a 110 m radio telescope show that this method can improve the worst-case accuracy effectively. The structural optimization of an 8 m antenna, as a test problem, is also discussed and the results are given.

Remotely sensed rainfall plays an important role in providing efficient approaches for global or regional rainfall analysis. However, the accuracy of satellite-based products is mainly affected by the errors in sensor observation and retrieval algorithms, particularly with respect to extreme rainfall estimates. The objective of this study is to evaluate the accuracy of satellite-based products in capturing rainfall extremes. The eastern coastal areas of China were chosen as the case study area to compare the accuracy of three mainstream satellite-based products with respect to extreme rainfall estimates during 2003–2015 period. This included the Tropical Rainfall Measurement Mission (TRMM) rainfall product 3B42V7, the Climate Prediction Centre Morphing technique RAW (CMORPH-RAW), and the CMORPH bias-corrected product (CMORPH-CRT). In general, all satellite-based products demonstrated numerous errors in extreme rainfall estimates. Based on three different indices of extreme rainfall, it was observed that the satellite-based products underestimated the amounts of rainfall extremes and their respective average values. It was noted that CMORPH-RAW demonstrated the largest relative bias (RB) and underestimated the average extreme rainfall by −31% to −35%. Additionally, all satellite-based products exhibited poor capabilities in capturing the variations in hourly extreme rainfall processes. Finally, a simple potential flood index was developed to simulate the potential flood areas in the eastern coastal areas of China. We found that the potential flood areas can be simulated by combining the potential flood index with the amounts of rainfall derived by satellite-based products.

Ground control point (GCP) is an important calibration factor when correcting position information during unmanned aerial vehicle (UAV) remote sensing. Studies of the optimal number and distribution shape of GCPs have been conducted worldwide in recent years. However, when conducting surveys at houses, construction sites, farming lands, forests, and some other locations, it is both difficult and destructive to install GCP inside the subject area. In many cases, it is only possible to install GCP at the outer edge around the area. Therefore, this study aims to suggest the optimal GCP distribution pattern, which can provide the highest accuracy, when only the outer edge of a particular area is available. In this research, 88 GCP patterns have been validated and compared at an 18 ha farm. Results show that the patterns with GCPs distributed evenly around the field provided the best calibration (RMSE = 0.15 m). If this kind of pattern is not achievable because of obstructions, patterns with GCPs distributed evenly around half of the field or forming an evenly distributed triangle can provide moderate accuracy (RMSE = 0.18 m and 0.43 m, respectively). Patterns with GCPs forming a straight line yielded the worst accuracy (RMSE = 2.10 m). This shows that GCP distributions of a two-dimensional shape, even if the surrounding area is small, are better calibrated than a long, straight line. These results strongly suggest that appropriate GCP distribution patterns in the study areas will provide satisfactory accuracy for constructing integrated monitoring systems of diverse resources.

Remote sensing precipitation products provide rich data for ungauged basins. Evaluating the accuracy and detection capability of remote sensing precipitation products is crucial before application. In this study, an index system in terms of quantitative differences, capturing capacity and precipitation distribution was constructed to evaluate three precipitation products, TRMM 3B42 V7, GPM IMERGE Final and CMORPH V1.0, at various temporal and spatial scales on the Tibetan Plateau from 2001 to 2016. The results show that the correlations among the three products were larger at the monthly scale than at the annual scale. The lowest correlations between the products and observation data were found in December. GPM performed the best at the monthly and annual scales. Particularly, the GPM product presented the best capability of detection of both precipitation and non-precipitation events among the three products. All three precipitation products overestimated 0.1~1 mm/day precipitation, which occurred most frequently. An underestimation of precipitation at 10~20 mm/day was observed, and this intensity accounted for the majority of the precipitation. All three precipitation products showed an underestimation in terms of the annual maximum daily precipitation. The accuracy of the same product varied in different regions of the Tibetan Plateau, such as the south, the southeast, eastern–central region and the northeast, and there was a certain clustering of the accuracies of neighboring stations. GPM was superior to TRMM and CMORPH in the southern Tibetan Plateau, making it recommended for applications.

International audience; All sorts of positioning system exist and there is a growing demand nowadays in order to provide users with additional data in many activities. Horce racing is no exception and the PMU (Paris Mutuel Urbain) is in charge of driving the project concerning the deployment of a tracking system. PMU is Europe's largest betting operator. It is an Economic Interest Grouping (EIG), whose mission is to finance the French horce racing industry. The specifications of the tracking system to be implemented are quite tough : an accuracy of positioning of 25 cm for more than 98 % of the time during races for all the horses. A call for proposals has been issued and a few competitors have been hired in order to demonstrate the real performances on their system in a so-called "pilot" phase. The problem we now have is how to evaluate the real accuracy of the system in real conditions,i.e., during "simulated races" Additional aspects are also of uppermost importance, such as the latency and the way data are displayed, but the present paper will only focus on the evaluation methodology used for the positioning accuracy assessment. An incremental validation approach was set up in order to allow the competitors to gradually improve their solutions, from the "easiest" tests (with cars) to the "most difficult" ones on horses during simutated races. Note that all competitors proposed a GNSS (Global Navigation Satellite System)-based solution using an RTK (Real-Time Kinematic) approach

Based on the high-quality homogenized precipitation data from all of the 2419 national weather stations in China, the climatology and the anomalies percentage fields are derived, then the Digital Elevation Model (DEM) is employed to reduce the influence of elevation on the spatial interpolation accuracy of precipitation due to the unique topography in China. Then, the gradient plus inverse distance squared (GIDS) method and the inverse distance squared method (IDS) method are used to grid the climatology field and the anomaly percentage field, respectively, and the 0.5° × 0.5° gridded data sets during 1961-2018 in China is obtained by combining them together. The evaluation shows that the mean absolute error (MAE) between the analysis value and the observation is 15.8 mm/month. The MAE in South China is generally higher than that in North China, and the MAE is obviously larger in summer than in other seasons. Specifically, 94.6%, 54.4%, 4.6% and 53.8% of the MAE are below 10 mm/month in winter (DJF), spring (MAM), summer (JJA) and autumn (SON), respectively, and 99.5%, 79.9%, 22.8% and 82.1% of them are less than 20 mm/month. The MAE over China in four seasons are 3.8 mm/month, 13.2 mm/month, 33.5 mm/month and 12.7 mm/month, respectively. This dataset has the potentials of broad application prospects in the evaluations of the weather and climate models and the satellite products.

Co-registering the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data of the European Space Agency (ESA) is of great importance for many remote sensing applications. However, we find that there are evident misregistration shifts between the Sentinel-1 SAR and Sentinel-2 optical images that are directly downloaded from the official website. To address that, this paper presents a fast and effective registration method for the two types of images. In the proposed method, a block-based scheme is first designed to extract evenly distributed interest points. Then, the correspondences are detected by using the similarity of structural features between the SAR and optical images, where the three-dimensional (3D) phase correlation (PC) is used as the similarity measure for accelerating image matching. Lastly, the obtained correspondences are employed to measure the misregistration shifts between the images. Moreover, to eliminate the misregistration, we use some representative geometric transformation models such as polynomial models, projective models, and rational function models for the co-registration of the two types of images, and we compare and analyze their registration accuracy under different numbers of control points and different terrains. Six pairs of the Sentinel-1 SAR L1 and Sentinel-2 optical L1C images covering three different terrains are tested in our experiments. Experimental results show that the proposed method can achieve precise correspondences between the images, and the third-order polynomial achieves the most satisfactory registration results. Its registration accuracy of the flat areas is less than 1.0 10 m pixel, that of the hilly areas is about 1.5 10 m pixels, and that of the mountainous areas is between 1.7 and 2.3 10 m pixels, which significantly improves the co-registration accuracy of the Sentinel-1 SAR and Sentinel-2 optical images.

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

Chinese-French Oceanography Satellite (CFOSAT), the first satellite which can observe global ocean wave and wind synchronously, was successfully launched On 29 October 2018. The CFOSAT carries SWIM that can observe ocean wave on a global scale. Based on National Data Buoy Center (NDBC) buoys and Jason-3 altimeter data, this study evaluated the accuracy of L2 level products of CFOSAT SWIM from August 2019 to September 2020. The results show that the accuracy of the nadir Significant Wave Height (SWH) data of the SWIM wave spectrometer is good. Compared with the data of the NDBC buoys and Jason-3 altimeter, the RMSE of the nadir box SWH were 0.39 and 0.21 m, respectively. The variation trend of SWH were first increasing and then decreasing with the increasing of the wave height. The precision of off-nadir wave spectrum SWH is not better than nadir box SWH data. Accuracy was evaluated for off-nadir data from August 2019 to June 2020 and after June 2020, respectively. After linear regression correction, the accuracy of off-nadir wave spectrum SWH was improved. The data accuracy evaluation and comparison of different time period showed that the off-nadir wave spectrum SWH accuracy was improved after the data version was updated in June 2020, especially for 6° and 8° wave spectrum. The precision of off-nadir wave spectrum SWH decreases with the increasing of wave height. The accuracy of the dominant wave direction of each wave spectrum is also not very good, and the accuracy of the dominant wave direction of 10° wave spectrum is slightly better than the others. In general, the accuracy of SWIM nadir beam SWH data reaches the high data accuracy of traditional altimeter, while the accuracy of off-nadir wave spectrum SWH is less than that of nadir beam SWH data. The off-nadir SWH data accuracy after June 2020 has been greatly improved.

This study presents the accuracy evaluation of the recently proposed Bojilova–Mukhtarov model [1] for reconstruction of the ionospheric critical frequencies from the total electron content (TEC) over Bulgaria. For this purpose the mean error (ME) and root mean square error (RMSE) of the critical frequency foF2 and maximum usable frequency of the distance to 3000 km (MUF3000) have been calculated. The errors obtained for the whole period under consideration, 1999–2014, have been used as a base for comparison with particularly considered intervals. Examples of specific months with strong geomagnetic disturbances are presented. The results reveal that the proposed reconstruction model is rather efficient not only in quiet but also in periods with geomagnetic storms and even super-storms.

Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: 1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. 2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. 3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. 4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. 5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone.

SGS-2021-021 Two different approaches for the calculation of the household photovoltaic (PV) installations energy balance are presented and discussed in this paper. The main methodology is based on the Monte Carlo method is proposed and applied to the case study with the effort to increase the annual energy balance calculation accuracy without increasing significantly problem complexity. Results are discussed and compared to the second approach based on direct calculation using only mean values as well as with the real (measured) values of the household energy balance.

Micromodels are important for studying various pore-scale phenomena in hydrogeology. However, the fabrication of a custom micromodel involves complicated steps with cost-prohibitive equipment. The direct fabrication of micromodels with a 3D printer can accelerate the fabrication steps and reduce the cost. A stereolithography (SLA) 3D printer is one of the best options because it has sufficient printing performance for micromodel fabrication and is relatively inexpensive. However, it is not without drawbacks. In this report, we explored the capability of an SLA 3D printer for micromodel fabrication. Various parameters affecting the printing results, such as the effects of geometries, dimensions, printing axis configurations, printing thickness resolutions, and pattern thicknesses were investigated using microtomography for the first time. Eventually, the most optimal printing configuration was then also discussed. In the end, a complete micromodel was printed, assembled, and used for fluid displacement experiments. As a demonstration, viscous and capillary fingerings were successfully performed using this micromodel design.

Rocky desertification is one of the most critical ecological and environmental problems in areas underlain by carbonate rocks globally. Land cover and land use in the region affects large-scale ecosystem processes on a global scale, and many Earth system models rely on accurate land cover information. Therefore, it is important to evaluate current global land cover products and to understand the differences between them, and the findings of these studies can provide guidance to different researchers when using or making land cover products. Whereas there are many studies on the assessment of coarser resolution land cover products, there are few studies on the assessment of higher resolution land cover products (10 m). In order to provide guidance for users of 10 m data, this paper uses the rock deserted southwest region of China as the experimental area. We analyzed the consistency and accuracy of the FROM-GLC, ESA WorldCover 10 and ESRI products using spatial pattern consistency, absolute accuracy assessment of three validation samples, and analyzed their intrinsic relationships among classification systems, classification methods, and validation samples. The results show that (1) the overall accuracy of the FROM-GLC product is the highest, ranging from 49.47 to 62.42%; followed by the overall accuracy of the ESA product, ranging from 45.13 to 64.50%; and the overall accuracy of the ESRI product is the lowest, between 39.03 and 61.94%. (2) The consistency between FROM-GLC and ESA is higher than the consistency between other products, with an area correlation coefficient of 0.94. Analysis of the spatial consistency of the three products shows that the proportion of perfectly consistent areas is low at 44.89%, mainly in areas with low surface heterogeneity and more homogeneous cover types. (3) Across the study area, the main land cover types such as forest and water bodies were the most consistent across the three product species, while the grassland, shrubland, and bareland were lower. All products showed high accuracy in homogeneous areas, with local accuracy varied in other areas, especially at high altitudes in the central and western regions. Therefore, land cover users cannot use these products directly when conducting relevant studies in rocky desertification areas, as their use may introduce serious errors.

Satellite-based and reanalysis products are precipitation data sources with high potential, which may exhibit high uncertainties over areas with a complex climate and terrain. This study aimed to evaluate the accuracy of the latest versions of six precipitation products (i.e., Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) V2.0, gauge-satellite blended (BLD) Climate Prediction Center Morphing technique (CMORPH) V1.0, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) 5-Land, Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) V6 Final, Global Satellite Mapping of Precipitation (GSMaP) near-real-time product (NRT) V6, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-CDR) over the Yellow River Basin, China. The daily precipitation amounts determined by these products were evaluated against gauge observations using continuous and categorical indices to reflect their quantitative accuracy and capability to detect rainfall events, respectively. The evaluation was first performed at different time scales (i.e., daily, monthly, and seasonal scales), and indices were then calculated at different precipitation grades and elevation levels. The results show that CMORPH outperforms the other products in terms of the quantitative accuracy and rainfall detection capability, while CHIRPS performs the worst. The mean absolute error (MAE), root mean square error (RMSE), probability of detection (POD), and equitable threat score (ETS) increase from northwest to southeast, which is similar to the spatial pattern of precipitation amount. The correlation coefficient (CC) exhibits a decreasing trend with increasing precipitation, and the mean error (ME), MAE, RMSE, POD and BIAS reveal an increasing trend. CHIRPS demonstrates the highest capability to detect no-rain events and the lowest capability to detect rain events, while ERA5 has the opposite performance. This study suggests that CMORPH is the most reliable among the six precipitation products over the Yellow River Basin considering both the quantitative accuracy and rainfall detection capability. ME, MAE, RMSE, POD (except for ERA5) and BIAS (except for ERA5) increase with the daily precipitation grade, and CC, RMSE, POD, false alarm ratio (FAR), BIAS, and ETS exhibit a negative correlation with elevation. The results of this study could be beneficial for both developers and users of satellite and reanalysis precipitation products in regions with a complex climate and terrain.

Underwater photogrammetry is increasingly being used by marine ecologists because of its ability to produce accurate, spatially detailed, non-destructive measurements of benthic communities, coupled with affordability and ease of use. However, independent quality control, rigorous imaging system set-up, optimal geometry design and a strict modeling of the imaging process are essential to achieving a high degree of measurable accuracy and resolution. If a proper photogrammetric approach that enables the formal description of the propagation of measurement error and modeling uncertainties is not undertaken, statements regarding the statistical significance of the results are limited. In this paper, we tackle these critical topics, based on the experience gained in the Moorea Island Digital Ecosystem Avatar (IDEA) project, where we have developed a rigorous underwater photogrammetric pipeline for coral reef monitoring and change detection. Here, we discuss the need for a permanent, underwater geodetic network, which serves to define a temporally stable reference datum and a check for the time series of photogrammetrically derived three-dimensional (3D) models of the reef structure. We present a methodology to evaluate the suitability of several underwater camera systems for photogrammetric and multi-temporal monitoring purposes and stress the importance of camera network geometry to minimize the deformations of photogrammetrically derived 3D reef models. Finally, we incorporate the measurement and modeling uncertainties of the full photogrammetric process into a simple and flexible framework for detecting statistically significant changes among a time series of models., Remote Sensing, 12 (18), ISSN:2072-4292

Land cover changes in tropical rainforest climate zones play an important role in global climate change and the functioning of the Earth’s natural system. Existing research on the consistency of different land cover products has mainly focused on administrative divisions (continental or national scales). However, the ongoing production of large regional or global land cover products with higher resolutions requires us to have a better grasp of confusing land types and their geographical locations for different zoning (e.g., geographical zoning) in order to guide the optimization of strategies such as zoning and sample selection in automated land cover classification. Therefore, we selected the GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC2015 global land cover products with a 30-m resolution covering Indonesia, which has a tropical rainforest climate, as a case study, and then analyzed these products in terms of areal consistency, spatial consistency, and accuracy evaluation. The results revealed that (a) all three land cover products revealed that forest is the main land cover type in Indonesia. The area correlation coefficient of any two products is better than 0.89; (b) the areas that are completely consistent among the three products account for 58% of the total area of Indonesia, mainly distributed in the central and northern parts of Kalimantan and Papua, which are dominated by forest land types. The spatial consistency of the three products is low, however, due to the complex surface types and staggered distributions of grassland, shrub, cultivated land, artificial surface, and other land cover types in Java, eastern Sumatra, and the eastern, southern, and northwestern sections of Kalimantan, where the elevation is less than 200 m. Given these results, land cover producers should take heed of the classification accuracy of these areas; (c) the absolute accuracy evaluation demonstrated that the GLC_FCS30-2015 product has the highest overall accuracy (65.59%), followed by the overall accuracy of the GlobeLand30-2010 product (61.65%), while the FROM_GLC2015 exhibits the lowest overall accuracy (57.71%). The mapping accuracy of the three products is higher for forests and artificial surfaces. The cropland mapping accuracy of the GLC_FCS30-2015 product is higher than those of the other two products. The mapping accuracy of all products is low for grassland, shrubland, bareland, and wetland. The classification accuracy of these land cover types requires further improvement and cannot be used directly by land cover users when conducting relevant research in tropical rainforest climate zones, since the utilization of these products could lead to serious errors.

Image registration is crucial in multimodal longitudinal skeletal muscle Magnetic Resonance Imaging (MRI) studies to extract reliable parameters that can be used as indicators for physio/pathological characterization of muscle tissue and for assessing the effectiveness of treatments. This paper aims at proposing a reliable registration protocol and evaluating its accuracy in a longitudinal study. The hips of 6 subjects were scanned, in a multimodal protocol, at 2 different time points by a 3 Tesla scanner, the proposed multi-step registration pipeline is based on rigid and elastic transformations implemented in SimpleITK using a multi-resolution technique. The effects of different image pre-processing (muscle masks, isotropic voxels) and different parameters&rsquo, values (learning rates and mesh sizes) were quantitatively assessed using standard accuracy indexes. Rigid registration alone does not provide satisfactory accuracy for inter-sessions alignment and a further elastic step is needed. The use of isotropic voxels, combined with the muscle masking, provides the best result in terms of accuracy. Learning rates can be increased to speed up the process without affecting the final results. The protocol described in this paper, complemented by open-source software, can be a useful guide for researchers that approach for the first time the issues related to the muscle MR image registration.

Unmanned aerial vehicle (UAV) systems are heavily adopted nowadays to collect high-resolution imagery with the purpose of documenting and mapping environment and cultural heritage. Such data are currently processed by programs based on the Structure from Motion (SfM) concept, coming from the computer vision community, rather than from classical photogrammetry. It is interesting to check whether some widely accepted rules coming from old-fashioned photogrammetry still holds: the relation between accuracy and ground sampling distance (GSD), the ratio between the vertical and horizontal accuracy, accuracy estimated on ground control points (GCPs) vs. that estimated with check points (CPs) also in relation to their ratio and distribution. To face the envisaged aspects, the paper adopts a comparative approach, as several programs are used and numerous configurations considered. The paper illustrates the dataset adopted, the carefully tuned processing strategies and bundle block adjustment (BBA) results in terms of accuracy for both GCPs and CPs. Finally, a leave-one-out (LOO) cross-validation strategy is proposed to assess the accuracy for one of the proposed configurations. Some of the reported results were previously presented in the 5th GISTAM Conference.

The increasing recognition of minimally invasive thermal treatment of tumors motivate the development of accurate thermometry approaches for guaranteeing the therapeutic efficacy and safety. Magnetic Resonance Thermometry Imaging (MRTI) is nowadays considered the gold-standard in thermometry for tumor thermal therapy, and assessment of its performances is required for clinical applications. This study evaluates the accuracy of fast MRTI on a synthetic phantom, using dense ultra-short Fiber Bragg Grating (FBG) array, as a reference. Fast MRTI is achieved with a multi-slice gradient-echo echo-planar imaging (GRE-EPI) sequence, allowing monitoring the temperature increase induced with a 980 nm laser source. The temperature distributions measured with 1 mm-spatial resolution with both FBGs and MRTI were compared. The root mean squared error (RMSE) value obtained by comparing temperature profiles showed a maximum error of 1.2 &deg, C. The Bland-Altman analysis revealed a mean of difference of 0.1 &deg, C and limits of agreement 1.5/&minus, 1.3 &deg, C. FBG sensors allowed to extensively assess the performances of the GRE-EPI sequence, in addition to the information on the MRTI precision estimated by considering the signal-to-noise ratio of the images (0.4 &deg, C). Overall, the results obtained for the GRE-EPI fully satisfy the accuracy (~2 &deg, C) required for proper temperature monitoring during thermal therapies.

During the last years, the European and the Nordic quasi-geoid models and existing national q-geoid models covered the territory of Latvia. There are many ways for comparison and tests of results achieved. Scientists and professionals can compare models directly at some special geodetic co-location stations or use GNSS/levelling sites. The results of this research can be used by scientists and specialists in the fundamental geodetic observations for independent monitoring of existing q-geoid models and evaluation of accuracy. The research aims at evaluating the transition to the best updated regional q-geoid model. The research objectives are the following: 1) to investigate and analyse the development of q-geoid model LV14; 2) to conduct precision research; 3) to assess the challenges of the European Vertical Reference System; 4) to draw conclusions that allow for further research in this area for development and improvement. Within the framework of the research, the authors have used a variety of research methods. Historical and logical approaches, comparative analysis and synthesis methods, as well as inductive – deductive data analysis methods have been selected for the research. A conclusion for such kind of studies is to implement the most appropriate q-geoid solution and to develop new astrogeodetic methods for unification, monitoring and for reliability of a geodetic reference network.

Passive systems are being implemented in several nuclear plants already constructed, under construction or under design. The purpose of these systems is to increase the inherent safety of the plant in any transient condition, so they can provide a higher safety level in case of an accident. Best estimate thermal-hydraulic system codes, widely adopted in the nuclear field to perform deterministic safety analyses, need to be validated against the phenomena typical of passive systems (e.g. natural circulation, thermal-hydraulic behavior of large pools, low pressure phenomena, etc.). An experimental test facility called PERSEO (in-Pool Energy Removal System for Emergency Operation) was built at SIET laboratory in Piacenza (Italy) modifying the existing PANTHERS IC-PCC facility. It is a full-scale test facility aimed at studying a new passive heat removal system and it can be used to analyze the capability of system codes to simulate the related phenomena. In the present analysis, Test 9, which was performed in the PERSEO facility at around 4.1 MPa to study the activation and the heat removal capability of the proposed system, has been simulated with the RELAP5-3D code to assess its capability to predict the dominant phenomena of the transient. Particular attention has been dedicated to the analysis of condensation heat transfer coefficient computed by the code, since in previous analyses it has been found an evident underestimation of the power exchanged by the passive system. Alternative correlations for the condensation heat transfer coefficient have been identified and a correction factor for the heat transfer coefficient calculated by RELAP5-3D has been computed. After the application of the correction factor, the set composed by Nusselt-Kutateladze-Chen correlations provided the better agreement with the experimental data. Finally, the accuracy of the code calculation has been evaluated qualitatively and quantitatively by adopting the Fast Fourier Transform Based Method.

Current surveying techniques are typically applied to survey the as-is condition of buildings, brownfield sites and infrastructure prior to design. However, within the past decade, these techniques evolved significantly, and their applications can be enhanced by adopting unmanned aerial vehicles (UAVs) for data acquisition, up-to-date software for creating 3D reality mesh, which in turn opens new possibilities for much more efficient construction site surveying and constant updating and process management. In this study the workflows of three UAV-based photogrammetry techniques: Real Time Kinematic (RTK), Post-Processing Kinematic (PPK) and Global Positioning System (GPS) based on control points were analyzed, described, and compared to conventional surveying method with Global Navigation Satellite System (GNSS) receiver. Tests were performed under realistic conditions in 36 ha quarry in Lithuania. The results of the relationship between ground sample distance (GSD) and the comparison of volume measurements under each technique, including conventional method were analyzed. The deviation of data collected on field vs. generated in reality mesh, including ground control points (GCPs) and check points (CHPs) with different configurations, was investigated. The research provides observations on each workflow in the terms of efficiency and reliability for earthwork quantity estimations and explains processing schemes with advanced commercial software tools.

Even more the use of UAV platforms is a standard for images or videos acquisitions from an aerial point of view. According to the enormous growth of requests, we are assisting to an increasing of the production of COTS (Commercial off the Shelf) platforms and systems to answer to the market requirements. In this last years, different platforms have been developed and sell at low-medium cost and nowadays the offer of interesting systems is very large. One of the most important company that produce UAV and other imaging systems is the DJI (Dà-Jiāng Innovations Science and Technology Co., Ltd) founded in 2006 headquartered in Shenzhen – China. The platforms realized by the company range from low cost systems up to professional equipment, tailored for high resolution acquisitions useful for film maker purposes. According to the characteristics of the last developed low cost DJI platforms, the onboard sensors and the performance of the modern photogrammetric software based on Structure from Motion (SfM) algorithms, those systems are nowadays employed for performing 3D surveys starting from the small up to the large scale. The present paper is aimed to test the characteristic in terms of image quality, flight operations, flight planning and accuracy evaluation of the final products of three COTS platforms realized by DJI: the Mavic Pro, the Phantom 4 and the Phantom 4 PRO. The test site chosen was the Chapel of San Giuliano in the municipality of Savigliano (Cuneo-Italy), a small church with two aisles dating back to the early eleventh century.