Reference Time Series Research Articles

Validating improved-MODIS products from spectral mixture-Landsat snow cover maps in a mountain region in southern Spain

Abstract. Remote sensing is the only feasible data source for distributed modelling of snow in mountain regions on medium to large scales, due to the limited access to these areas together with the lack of dense ground monitoring stations for snow variables. Observations worldwide identify snow cover persistence together with snowfall occurrence as the most affected variables by global warming. In Mediterranean regions, the spatiotemporal evolution of the snow cover can experiment quick changes that result in different accumulation-ablation cycles during the cold season. High frequency sensors are required to adequately monitor such shifts; however, for trend analyses, the Landsat time series constitute the only available source of data, being their frequency low for this regime, especially when cloudy conditions limit the available images. On the other hand, the MODIS daily series provide more than 15 years of continuous snow maps, despite the spatial resolution may pose a constraint in areas with abrupt topography; several approaches have been done to improve their spatial resolution from combining different information. This work presents a methodological approach to validate the improved MODIS daily snow cover maps from Notarnicola et al. (2013a, b), with 250 m spatial resolution, in Sierra Nevada (southern Spain), from a reference data set obtained by spectral mixture analyses of Landsat TM data by Pimentel et al. (2017b). This reference time series of fractional snow maps, with 30 m spatial resolution, were validated from high resolution local time series of snow maps obtained by terrestrial time-lapse cameras. The results show a significantly high correlation between the two snow map products both on a global and basin scales in the Sierra Nevada area. Selected areas and time periods are shown to address the convergence and divergence between both products and assess the development of a fusion algorithm to retrieve daily Landsat-resolution snow maps on a long term basis.

Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms

The characterization of plant disease symptoms by hyperspectral imaging is often limited by the missing ability to investigate early, still invisible states. Automatically tracing the symptom position on the leaf back in time could be a promising approach to overcome this limitation. Therefore we present a method to spatially reference time series of close range hyperspectral images. Based on reference points, a robust method is presented to derive a suitable transformation model for each observation within a time series experiment. A non-linear 2D polynomial transformation model has been selected to cope with the specific structure and growth processes of wheat leaves. The potential of the method is outlined by an improved labeling procedure for very early symptoms and by extracting spectral characteristics of single symptoms represented by Vegetation Indices over time. The characteristics are extracted for brown rust and septoria tritici blotch on wheat, based on time series observations using a VISNIR (400–1000 nm) hyperspectral camera.

The IERS EOP 14C04 solution for Earth orientation parameters consistent with ITRF 2014

The Earth Orientation Center of the International Earth Rotation and Reference Systems Service (IERS) has the task to provide the scientific community with the international reference time series of Earth orientation parameters (EOP), referred to as IERS EOP C04 or C04. These series result from a combination of operational EOP series derived from VLBI, GNSS, SLR, and DORIS. The C04 series were updated to provide EOP series consistent with the set of station coordinates of the ITRF 2014. The new C04, referred to as IERS EOP 14C04, is aligned onto the most recent versions of the conventional reference frames (ITRF 2014 and ICRF2). Additionally, the combination algorithm was revised to include an improved weighting of the intra-technique solutions. Over the period 2010–2015, differences to the IVS combination exhibit standard deviations of 40 $$\upmu $$ as for nutation and 10 $$\upmu $$ s for UT1. Differences to the IGS combination reveal a standard deviation of 30 $$\upmu $$ as for polar motion. The IERS EOP 14C04 was adopted by the IERS directing board as the IERS reference series by February 1, 2017.

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

The completion of missing values is a prevalent problem in many domains of pattern recognition and signal processing. Analyzing data with incompleteness may lead to a loss of power and unreliable results, especially for large missing subsequence(s). Therefore, this paper aims to introduce a new approach for filling successive missing values in low/uncorrelated multivariate time series which allows managing a high level of uncertainty. In this way, we propose using a novel fuzzy weighting-based similarity measure. The proposed method involves three main steps. Firstly, for each incomplete signal, the data before a gap and the data after this gap are considered as two separated reference time series with their respective query windowsQbandQa. We then find the most similar subsequence (Qbs) to the subsequence before this gapQband the most similar one (Qas) to the subsequence after the gapQa. To find these similar windows, we build a new similarity measure based on fuzzy grades of basic similarity measures and on fuzzy logic rules. Finally, we fill in the gap with average values of the window followingQbsand the one precedingQas. The experimental results have demonstrated that the proposed approach outperforms the state-of-the-art methods in case of multivariate time series having low/noncorrelated data but effective information on each signal.

TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods

<p>This paper introduces the Time Series Simulation for Benchmarking of Reconstruction Techniques (TISSBERT) dataset, intended to provide a benchmark for the validation and comparison of time series reconstruction methods. Such methods are routinely used to estimate vegetation characteristics from optical remotely sensed data, where the presence of clouds decreases the usefulness of the data. As for their validation, these methods have been compared with previously published ones, although with different approaches, which sometimes lead to contradictory results. We designed the TISSBERT dataset to be generic so that it could simulate realistic reference and cloud-contaminated time series at global scale. To that end, we estimated both cloud-free and cloud-contaminated Normalized Difference Vegetation Index (NDVI) statistics for randomly selected control points and each day of the year from the Long Term Data Record Version 4 (LTDR-V4) dataset by assuming different statistical distributions. The best approach was then applied to the whole dataset, and validity of the results were estimated through the Kolmogorov-Smirnov statistic. The dataset elaboration is described thoroughly along with how to use it. The advantages and drawbacks of this dataset are then discussed, which emphasize the realistic simulation of the cloud-contaminated and reference time series. This dataset can be obtained from the authors upon demand. It will be used in a next paper to compare widely used NDVI time series reconstruction methods.</p>

Preliminary Data Validation and Reconstruction of Temperature and Precipitation in Central Italy

This study provides a unique procedure for validating and reconstructing temperature and precipitation data. Although developed from data in Middle Italy, the validation method is intended to be universal, subject to appropriate calibration according to the climate zones analysed. This research is an attempt to create shared applicative procedures that are most of the time only theorized or included in some software without a clear definition of the methods. The purpose is to detect most types of errors according to the procedures for data validation prescribed by the World Meteorological Organization, defining practical operations for each of the five types of data controls: gross error checking, internal consistency check, tolerance test, temporal consistency, and spatial consistency. Temperature and precipitation data over the period 1931–2014 were investigated. The outcomes of this process have led to the removal of 375 records (0.02%) of temperature data from 40 weather stations and 1286 records (1.67%) of precipitation data from 118 weather stations, and 171 data points reconstructed. In conclusion, this work contributes to the development of standardized methodologies to validate climate data and provides an innovative procedure to reconstruct missing data in the absence of reliable reference time series.

Mountain agriculture extraction from time-series MODIS NDVI using dynamic time warping technique

ABSTRACTThe study attempts to extract Mountain Agriculture using an optimized Dynamic Time Warping (DTW) algorithm having endpoint constraints. The DTW was applied over a time-series annual stack of Normalized Differential Vegetation Index (NDVI) using a set of reference time series profiles for three agriculture classes (i.e. double cropping, single cropping, and horticulture) and the pixel-wise similarity is examined to identify the agriculture classes. In addition, Euclidean Distance (ED) was used to compare DTW-based result. The detection accuracy of each class was assessed using Google Earth-based agriculture sample, and the spatial agreement of resultant map was assessed with high-resolution reference data using Pareto boundary technique. The sample based accuracy evaluation reveals that DTW algorithm performed better for double and single cropping agriculture detection in compared to the horticulture. Overall, DTW-based agriculture map (0.81 ± 0.01) yielded higher overall accuracy in comparison with ED-based agriculture map (0.75 ± 0.01). The Pareto boundary-based spatial agreement analysis using high-resolution reference data also shows the dominant performance of DTW based agriculture map than an ED-based map. DTW performed better than ED, in terms of optimal distance (OD), in ten out of eleven districts. However, reliable spatial matching (OD less than 0.23) between DTW-based map and reference agriculture map was observed in lower elevation region, especially in Hamirpur (OD = 0.06), Bilaspur (OD = 0.09), Shimla (OD = 0.19) and Una (OD = 0.20) district.

An introduction to semiparametric function-on-scalar regression

Abstract: Function-on-scalar regression models feature a function over some domain as the response while the regressors are scalars. Collections of time series as well as 2D or 3D images can be considered as functional responses. We provide a hands-on introduction for a flexible semiparametric approach for function-on-scalar regression, using spatially referenced time series of ground velocity measurements from large-scale simulated earthquake data as a running example. We discuss important practical considerations and challenges in the modelling process and outline best practices. The outline of our approach is complemented by comprehensive R code, freely available in the online appendix. This text is aimed at analysts with a working knowledge of generalized regression models and penalized splines.

Selección de parcelas control para estudios de la dinámica post-incendio: desempeño de rutinas no paramétricas y autorregresivas

<p>Natural fire regimes have been modified; therefore robust post-fire monitoring tools are needed to understand the post-fire recovery process. Satellites with high temporal resolution allow us to build time series of vegetation indices for monitoring post-fire vegetation recovery. One of the techniques used is to compare the time series of a burned plot with that of an unburned control plot. However, for its implementation it is necessary to select control plots in which the vegetation has the same structure and functioning than the plot burned before the fire. Previous study defined biological criteria to detect burned and unburned control plots with identical pre-fire vegetation functioning. Moreover, a non-parametric test routine of low statistical power was proposed to test them, this was based on the analysis of the QVI (Quotient Vegetation Index), calculated between NDVI (Normalized Difference Vegetation Index) time series of the burned and control site. However, currently there are autoregressive analysis techniques with greater statistical power. Therefore the aims were to propose six new statistical routines based on autoregressive test, and compare the performance of these with the non-parametric routine. We selected 13,700 forest plots and extracted the NDVI MODIS time series between 2002 and 2005. We randomly selected 43 reference plots, and through each routine, we compared each reference time series with the other 13,657 time series. We estimated the performance of the routines measuring the euclidian distance between the time series of the reference plot and the time series of the plots accepted for each routine. We also measured the quality and the amount of the QVI time series selected by each routine. Autoregressive routines showed better performance than the non-parametric routine, since they selected control plots with NDVI time series with greatest similarity with respect to the reference plots and QVI series with highest quality.</p>

Using rule-based updating procedures to improve the performance of composite indicators

Ideally, the set of variables underlying composite indicators is checked and updated when needed on a regular basis. In practise, the timing and procedures of these updates are usually chosen ad hoc. We suggest a rule-based indicator selection updating procedure, performed at regular intervals, which reduces the arbitrariness of this process. We apply this procedure to one of the most prominent targeted composite leading indicator for Switzerland, which is based on bivariate associations of potential variables with a reference series reflecting the Swiss growth rate cycle. We show that in a simulated real-time analysis the targeted indicator selection procedure outperforms the widely used approach to combine as many potential variables as possible. Furthermore, the regular updating procedure preserves the leading properties of the composite indicator with respect to the reference time series, as compared to the same composite indicator without such updates.

Early detection of anomalies in dam performance: A methodology based on boosted regression trees

Summary The advances in information and communication technologies led to a general trend towards the availability of more detailed information on dam behaviour. This allows applying advanced data-based algorithms in its analysis, which has been reflected in an increasing interest in the field. However, most of the related literature is limited to the evaluation of model prediction accuracy, whereas the ulterior objective of data analysis is dam safety assessment. In this work, a machine-learning algorithm (boosted regression trees) is the core of a methodology for early detection of anomalies. It also includes a criterion to determine whether certain discrepancy between predictions and observations is normal, a procedure to compute a realistic estimate of the model accuracy, and an original approach to identify extraordinary load combinations. The performance of causal and noncausal models is assessed in terms of their ability to detect different types of anomalies, which were artificially introduced on reference time series generated with a numerical model of a 100-m-high arch dam. The final approach was implemented in an online application to visualise the results in an intuitive way to support decision making.

Assessment of probabilistic distributed factors influencing renewable energy supply for hotels using Monte-Carlo methods

This paper investigates the use of renewable energies to supply hotels in island regions. The aim is to evaluate the effect of weather and occupancy fluctuations on the sensitivity of investment criteria. The sensitivity of the chosen energy system is examined using a Monte Carlo simulation considering stochastic weather data, occupancy rates and energy needs. For this purpose, algorithms based on measured data are developed and applied to a case study on the Canary Islands.The results underline that electricity use in hotels is by far the largest contributor to their overall energy cost. For the invested hotel on the Canary Islands, the optimal share of renewable electricity generation is found to be 63%, split into 67% photovoltaic and 33% wind power. Furthermore, a battery is used to balance the differences between day and night. It is found, that the results are sensitive to weather fluctuations as well as economic parameters to about the same degree. The results underline the risk caused by using reference time series for designing energy systems. The Monte Carlo method helps to define the mean of the annuity more precisely and to rate the risk of fluctuating weather and occupancy better.

Improving the modeling of the atmospheric delay in the data analysis of the Intensive VLBI sessions and the impact on the UT1 estimates

The very long baseline interferometry (VLBI) Intensive sessions are typically 1-h and single-baseline VLBI sessions, specifically designed to yield low-latency estimates of UT1-UTC. In this work, we investigate what accuracy is obtained from these sessions and how it can be improved. In particular, we study the modeling of the troposphere in the data analysis. The impact of including external information on the zenith wet delays (ZWD) and tropospheric gradients from GPS or numerical weather prediction models is studied. Additionally, we test estimating tropospheric gradients in the data analysis, which is normally not done. To evaluate the results, we compared the UT1-UTC values from the Intensives to those from simultaneous 24-h VLBI session. Furthermore, we calculated length of day (LOD) estimates using the UT1-UTC values from consecutive Intensives and compared these to the LOD estimated by GPS. We find that there is not much benefit in using external ZWD; however, including external information on the gradients improves the agreement with the reference data. If gradients are estimated in the data analysis, and appropriate constraints are applied, the WRMS difference w.r.t. UT1-UTC from 24-h sessions is reduced by 5% and the WRMS difference w.r.t. the LOD from GPS by up to 12%. The best agreement between Intensives and the reference time series is obtained when using both external gradients from GPS and additionally estimating gradients in the data analysis.

Implementation and verification of an enhanced algorithm for the automatic computation of RR-interval series derived from 24 h 12-lead ECGs

An important tool in early diagnosis of cardiac dysfunctions is the analysis of electrocardiograms (ECGs) obtained from ambulatory long-term recordings. Heart rate variability (HRV) analysis became a significant tool for assessing the cardiac health. The usefulness of HRV assessment for the prediction of cardiovascular events in end-stage renal disease patients was previously reported. The aim of this work is to verify an enhanced algorithm to obtain an RR-interval time series in a fully automated manner. The multi-lead corrected R-peaks of each ECG lead are used for RR-series computation and the algorithm is verified by a comparison with manually reviewed reference RR-time series. Twenty-four hour 12-lead ECG recordings of 339 end-stage renal disease patients from the ISAR (rISk strAtification in end-stage Renal disease) study were used. Seven universal indicators were calculated to allow for a generalization of the comparison results. The median score of the indicator of synchronization, i.e. intraclass correlation coefficient, was 96.4% and the median of the root mean square error of the difference time series was 7.5 ms. The negligible error and high synchronization rate indicate high similarity and verified the agreement between the fully automated RR-interval series calculated with the AIT Multi-Lead ECGsolver and the reference time series. As a future perspective, HRV parameters calculated on this RR-time series can be evaluated in longitudinal studies to ensure clinical benefit.

The New Sunspot Number: Assembling All Corrections

The Sunspot Number, created by R.Wolf in 1849, provides a direct long-term record of solar activity from 1700 to the present. In spite of its central role in multiple studies of the solar dynamo and of the past Sun-Earth relations, it was never submitted to a global critical revision. However, various discrepancies with other solar indices recently motivated a full re-calibration of this series. Based on various diagnostics and corrections established in the framework of several Sunspot Number Workshops and described in Clette et al. 2014, we assembled all corrections in order to produce a new standard version of this reference time series. In this paper, we explain the three main corrections and the criteria used to choose a final optimal version of each correction factor or function, given the available information and published analyses. We then discuss the good agreement obtained with the Group sunspot Number derived from a recent reconstruction. Among the implications emerging from this re-calibrated series, we also discuss the absence of a rising secular trend in the newly-determined solar cycle amplitudes, also in relation with contradictory indications derived from cosmogenic radionuclides. As conclusion, we introduce the new version management scheme now implemented at the World Data Center - SILSO, which reflects a major conceptual transition: beyond the re-scaled numbers, this first revision of the Sunspot Number also transforms the former locked data archive into a living observational series open to future improvements.

A Simple Harmonic Model for FAPAR Temporal Dynamics in the Wetlands of the Volga-Akhtuba Floodplain

The paper reports a technique used to construct a reference time series for the fraction of absorbed photosynthetically-active radiation (FAPAR) based on remotely-sensed data in the largest Russian arid wetland territory. For the arid Volga-Akhtuba wetlands, FAPAR appears to be an informative spectral index for estimating plant cover health and its seasonal and annual dynamics. Since FAPAR algorithms are developed for multiple satellite sensors, all FAPAR-based models are suggested to be universal and useful for future studies and long-term monitoring of plant cover, particularly in wetlands. The model developed in the present work for FAPAR temporal dynamics clearly reflects the field-observed seasonal and annual changes of plant cover in the Volga-Akhtuba floodplain wetlands. Various types of wetland plant communities were categorized by the specific parameters of the model seasonal vegetation curve. In addition, the values derived from the model function allow quantitative estimation of wetland plant cover health. This information is particularly important for the Volga-Akhtuba floodplain, because its hydrological regime is regulated by the Volzhskaya hydropower plant. The ecosystem is extremely fragile and sensitive to human impact, and wetland plant cover health is a key indicator of regulatory efficiency. The present study is another step towards developing a methodology focused on arid wetland vegetation monitoring and conservation of its biodiversity and natural conditions.

Modelling of Vortex-Induced Loading on a Single-Blade Installation Setup

Vortex-induced integral loading fluctuations on a single suspended blade at various inflow angles were modeled in the presents work by means of stochastic modelling methods. The reference time series were obtained by 3D DES CFD computations carried out on the DTU 10MW reference wind turbine blade. In the reference time series, the flapwise force component, Fx, showed both higher absolute values and variation than the chordwise force component, Fz, for every inflow angle considered. For this reason, the present paper focused on modelling of the Fx and not the Fz whereas Fz would be modelled using exactly the same procedure. The reference time series were significantly different, depending on the inflow angle. This made the modelling of all the time series with a single and relatively simple engineering model challenging. In order to find model parameters, optimizations were carried out, based on the root-mean-square error between the Single-Sided Amplitude Spectra of the reference and modelled time series. In order to model well defined frequency peaks present at certain inflow angles, optimized sine functions were superposed on the stochastically modelled time series. The results showed that the modelling accuracy varied depending on the inflow angle. None the less, the modelled and reference time series showed a satisfactory general agreement in terms of their visual and frequency characteristics. This indicated that the proposed method is suitable to model loading fluctuations on suspended blades.

Characterizing, modelling and understanding the climate variability of the deep water formation in the North-Western Mediterranean Sea

Observing, modelling and understanding the climate-scale variability of the deep water formation (DWF) in the North-Western Mediterranean Sea remains today very challenging. In this study, we first characterize the interannual variability of this phenomenon by a thorough reanalysis of observations in order to establish reference time series. These quantitative indicators include 31 observed years for the yearly maximum mixed layer depth over the period 1980–2013 and a detailed multi-indicator description of the period 2007–2013. Then a 1980–2013 hindcast simulation is performed with a fully-coupled regional climate system model including the high-resolution representation of the regional atmosphere, ocean, land-surface and rivers. The simulation reproduces quantitatively well the mean behaviour and the large interannual variability of the DWF phenomenon. The model shows convection deeper than 1000 m in 2/3 of the modelled winters, a mean DWF rate equal to 0.35 Sv with maximum values of 1.7 (resp. 1.6) Sv in 2013 (resp. 2005). Using the model results, the winter-integrated buoyancy loss over the Gulf of Lions is identified as the primary driving factor of the DWF interannual variability and explains, alone, around 50 % of its variance. It is itself explained by the occurrence of few stormy days during winter. At daily scale, the Atlantic ridge weather regime is identified as favourable to strong buoyancy losses and therefore DWF, whereas the positive phase of the North Atlantic oscillation is unfavourable. The driving role of the vertical stratification in autumn, a measure of the water column inhibition to mixing, has also been analyzed. Combining both driving factors allows to explain more than 70 % of the interannual variance of the phenomenon and in particular the occurrence of the five strongest convective years of the model (1981, 1999, 2005, 2009, 2013). The model simulates qualitatively well the trends in the deep waters (warming, saltening, increase in the dense water volume, increase in the bottom water density) despite an underestimation of the salinity and density trends. These deep trends come from a heat and salt accumulation during the 1980s and the 1990s in the surface and intermediate layers of the Gulf of Lions before being transferred stepwise towards the deep layers when very convective years occur in 1999 and later. The salinity increase in the near Atlantic Ocean surface layers seems to be the external forcing that finally leads to these deep trends. In the future, our results may allow to better understand the behaviour of the DWF phenomenon in Mediterranean Sea simulations in hindcast, forecast, reanalysis or future climate change scenario modes. The robustness of the obtained results must be however confirmed in multi-model studies.

Flight Considerations and Hyperspectral Image Classifications for Dryland Vegetation Management from a Fixed-wing UAS

<p class="emsd"><span lang="EN-GB">Unmanned Aerial Systems (UAS)-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Boise Center Aerospace Lab were tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The motivation for this study was to better understand the challenges associated with UAS-based hyperspectral data for distinguishing native grasses such as Sandberg bluegrass (<em>Poa secunda</em>) from invasives such as burr buttercup (<em>Ranunculus testiculatus)</em> in a shrubland environment. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. However, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. In many cases, the supervised classifications accentuated noise or features in the mosaic that were artifacts of color balancing and feathering in areas of flightline overlap. Future UAS flight missions that optimize flight planning; minimize illumination differences between flightlines; and leverage ground reference data and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass from burr buttercup. </span></p>

연속적 결측이 존재하는 기온 자료에 대한 결측복원 기법의 비교

Abstract Consecutive missing values are likely to occur in long climate data due to system error or defective equipment.Furthermore, it is difficult to impute missing values. However, these complicated problems can be overcameby imputing missing values with reference time series. Reference time series must be composed of similartime series to time series that include missing values. We performed a simulation to compare three missingimputation methods (the adjusted normal ratio method, the regression method and the IDW method) tocomplete the missing values of time series. A comparison of the three missing imputation methods for thedaily mean temperatures at 14 climatological stations indicated that the IDW method was better thanxothers at south seaside stations. We also found the regression method was better than others at moststations (except south seaside stations).Keywords: consecutive missing value, missing value imputation, adjusted normal ratio methods, regressionmethod, IDW method