Comparison Of Time Series Research Articles

ТРЕНДЫ ДАВЛЕНИЯ НА УРОВНЕ МОРЯ В ЮЖНОМ ОКЕАНЕ И АНТАРКТИКЕ ПО РЕЗУЛЬТАТАМ РЕАНАЛИЗА И НАЗЕМНЫМ ДАННЫМ

Trends in the mean sea-level pressure (SLP) in Antarctica in the last four decades (1980– 2020) have been examined using in situ observations and reanalysis data. The analysis involved time series of monthly mean, season-mean and yearly-mean values of the SLP derived from four reanalysis datasets, NCEP/NCAR, ERA5, JRA55, MERRA2, and from surface observations acquired from the Reference Antarctic Data for Environmental Research (READER) dataset. With this data we have evaluated the trends, characterized their seasonal peculiarities and variation across the high-latitude region of the Southern Hemisphere. The results of the analysis confirmed the dominance of decreasing trends in the annual mean SLP in Antarctica. Larger negative trends were found in the Western Antarctica with the most pronounced pressure drop in the South Pacific. The long-term decrease in the annual mean SLP in Antarctica was due to strong negative pressure trends in the austral summer and fall season whereas in winter and in spring the trends turn to mixed and mostly positive. The comparison of multiyear time series of SLP reanalysis data with in situ observations at Antarctic stations revealed a considerable overestimate of negative SLP trends in the NCEP/NCAR dataset. Among the four examined reanalysis datasets, ERA5 provided the best agreement with the station data on the annual mean and monthly mean SLP trend values.

PERBANDINGAN METODE EXPONENTIAL SMOOTHING DAN ARIMA PADA PERAMALAN GARIS KEMISKINAN PROVINSI JAWA TENGAH

Forecasting is a technique that uses past data or historical data to determine something in the future. Forecasting methods with time series models consist of several methods, such as Double Exponential Smoothing (Holt method) and ARIMA. DES (Holt method) is a method that is used to predict time series data that has a trend pattern. ARIMA model combines AR and MA models with differencing order d. The poverty line is calculated by finding the total cost of all the essential resources that an average human adult consumes in one year. The lack of poverty reduction in an area is the lack of information about poverty. The selection of the forecasting method was made by considering several things. The Exponential Smoothing method was chosen because this method was able to predict time series financial data well and revise prediction errors. While the ARIMA method is better for short-term prediction, it can predict values that are difficult to explain by economic theory and are efficient in predicting time series financial data. There is still little research on comparing time series data for forecasting methods. Researchers are interested in comparing the Exponential Smoothing and ARIMA methods in implementing poverty line forecasting in Central Java. The two methods are compared by determining the best method for forecasting the poverty line in Central Java. The best forecasting method can be seen from the MAPE value of each method

Deep learning models comparable assessment and uncertainty analysis for diurnal temperature range (DTR) predictions over Indian urban cities

For monthly time spans of Indian cities (Ahmedabad, Bengaluru, Guwahati, Kolkata, and New Delhi), this article used daily (max and min) temperature data from 1951 to 2020 and approximated diurnal temperature range (DTR). RClimDex (a user interface for extreme computing indices) was used to do statistical analysis and comparisons of climatological characteristics such time series, means, extremes, and trends. During these years, the DTR trend in the researched area was more variable. This research examines the appropriateness of three deep neural networks [recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)] for one-step-ahead DTR time series (DTRTS) prediction. To test the efficiency of models in the testing set, six statistical error metrics were used [Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of correlation (R), Percent Bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd). The forecasting inaccuracy in predicting the outcome The Wilson score approach was used to do a quantitative uncertainty analysis on DTR. The results show that the LSTM outperforms the other two models in terms of forgetting, remembering, and updating information. It is more accurate on datasets with longer sequences and exhibits substantially higher volatility throughout its gradient descent. It got concluded with LSTM being well adapted to learning from experience to categorize, analyze, and predict time series, and it may be employed as a new dependable artificial intelligence technique for DTRTS forecasting.

Forecasting day-ahead electricity prices: A comparison of time series and neural network models taking external regressors into account

The amount of renewable energies in electricity production has increased significantly in the last decade, resulting in more variability of the day-ahead electricity spot price. The Electricity Price Forecast (EPF) has to adapt to the new situation by applying flexible models. However, the numerous available forecasting methods differ widely, with no distinct candidate offering the best solution. Against this background, we conduct a comparative study of four different approaches to forecasting the German day-ahead electricity spot price. In addition to the prominent Seasonal Integrated Auto-Regressive Moving Average model ((S)ARIMA(X)) and the Long-Short Term Memory (LSTM) neural network models, we employ a Convolutional Neural Network LSTM (CNN-LSTM) and an extended two-stage multivariate Vector Auto-Regressive model (VAR) approach as hybrid models. For better performance, we include common external influences such as the consumer load, fuel and CO2 emission prices, average solar radiation and wind speed in our analysis. We analyse hourly data for twelve samples from October 2017 to September 2018. Each model is implemented to deliver price forecasts at three horizons, i.e., one day, seven days and thirty days ahead. While the LSTM model achieves the best forecasting performance on average, the two-stage VAR follows closely behind and performs exceedingly well for shorter prediction horizons. Further, we provide evidence that a combination of both forecasting methods outperforms each of the single models. This indicates that combining advanced methods could lead to further improvements in electricity spot price forecasts.

Evaluating time series similarity using concept-based models

Time series similarity evaluation is a crucial processing task performed either as a stand-alone action or as a part of extensive data analysis schemes. Among essential procedures that rely on measuring time series similarity, we find time series clustering and classification. While the similarity of regular (not temporal) data frames is studied extensively, there are not many methods that account for the time flow. In particular, there is a need for methods that are easy to interpret by a human being. In this paper, we present a concept-based approach for time series similarity evaluation. Firstly, a global model describing a given dataset of time series (made of two or more time series) is built. Then, for each time series in the dataset, we create the corresponding local model. Comparing time series is performed with the aid of their local models instead of raw time series values. In the paper, the described processing scheme is implemented using fuzzy sets representing concepts. The proposed approach has been applied to the task of time series classification, yielding highly satisfactory results.

Impact of Medicaid expansion and state-level racial diversity on breast cancer endocrine therapy prescriptions: A quasi-experimental, comparative interrupted time series study

AimsTo determine whether Medicaid expansion impacted racially more diverse states similarly as racially less diverse states in endocrine therapy (ET) prescriptions. MethodsA quasi-experimental, comparative interrupted time series study of Medicaid-financed ET prescriptions from 2011 to 2018 Medicaid State Drug Utilization Database. The exposures were state’s Medicaid expansion and racial diversity status. The outcome was state’s quarterly number ET prescriptions per 100,000 non-elderly adult females (NAFs). ResultsDuring the year of expansion, ET prescriptions increased sharply in expansion states but remained flat in nonexpansion states (slope: 11.96 vs. 0.43 prescriptions per 100,000 NAFs per quarter, p < 0.001). After that, the slopes were similar between expansion and nonexpansion states (1.75 vs. 0.24, p = 0.057) but the level of prescriptions in expansion states maintained at a higher level. When stratified by state’s racial diversity status, the slope of increase in the first year was sharper for raciallymore diverse expansion states (16.49, p = 0.008) than racially less diverse expansion states (8.46, p < 0.001), resulting in significant differences in ET prescriptions between racially more diverse expansion and nonexpansion states but largely nonsignificant differences between racially less diverse expansion and nonexpansion states. ConclusionsAlthough Medicaid expansion significantly increased ET prescriptions in expansion vs. nonexpansion states, this difference was only observed among raciallymore diverse states. Racially more diverse nonexpansion states had the lowest rates of ET prescriptions and the gaps from racially more diverse expansion states significantly widened after expansion. Policy summaryOur study shows that, before expansion, racially more diverse nonexpansion states had the lowest rates of ET prescriptions. After expansion, the gaps between these states and racially more diverse expansion states significantly widened. These results highlighted the importance of continuing to examine the health impacts of states not expanding Medicaid, including the health equity impacts for low income racial/ethnic minority populations with cancer and other life-threatening diseases.

Did States’ Adoption of More Rigorous Standards Lead to Improved Student Achievement? Evidence From a Comparative Interrupted Time Series Study of Standards-Based Reform

This study was designed to assess the effects of states’ adoption of more rigorous standards as part of the current wave of standards-based reform on student achievement using comparative interrupted time series analyses based on state-level NAEP data from 1990 to 2017. Results show that the effects of adopting more rigorous standards on students’ mathematics achievement were generally small and not significant. The effects on students’ reading achievement were also generally small, but negative and statistically significant for Grade 4. The study also revealed that the effects of states’ adoption of more rigorous standards varied across NAEP subscales and student subgroups.

Daily Variations of Plasma Density in the Solar Streamer Belt

Abstract Improved space weather diagnostics depend critically on improving our understanding of the evolution of the slow solar wind in the streamer belts near the Sun. Recent innovations in tomography techniques are opening a new window on this complex environment. In this work, a new time-dependent technique is applied to COR2A/Solar Terrestrial Relations Observatory observations from a period near solar minimum (2018 November 11) for heliocentric distances of 4–8 R ⊙. For the first time, we find density variations of large amplitude throughout the quiescent streamer belt, ranging between 50% and 150% of the mean density, on timescales of tens of hours to days. Good agreement is found with Parker Solar Probe measurements at perihelion; thus, the variations revealed by tomography must form a major component of the slow solar wind variability, distinct from coronal mass ejections or smaller transients. A comparison of time series at different heights reveals a consistent time lag, so that changes at 4 R ⊙ occur later at increasing height, corresponding to an outward propagation speed of around 100 km s−1. This speed may correspond to either the plasma sound speed or the bulk outflow speed depending on an important question: are the density variations caused by the spatial movement of a narrow streamer belt (moving magnetic field, constant plasma density), or changes in plasma density within a nonmoving streamer belt (rigid magnetic field, variable density), or a combination of both?

Comparative Interrupted Time Series Analysis of Long-term Direct Medical Costs in Patients With Hip Fractures and a Matched Cohort: A Large-database Study.

Comparative Interrupted Time Series Analysis of Long-term Direct Medical Costs in Patients With Hip Fractures and a Matched Cohort: A Large-database Study.

Sales Forecast Optimization: Ensemble and Time Series Comparison

To ensure competitive advantage, companies seek solutions that allow them to optimize the management of their resources. Sales forecasting is the process of organizing and analyzing information in a way that allows estimating how sales will be. In this context, decision support systems can be allies to explore scenarios based on historical data. This is an essential and inexpensive way for every company to increase its profits, decrease its costs, and achieve greater flexibility to change. The objective of this study is to analyze a sales database to predict the amount of sales. Thus, this research aims to compare the Ensemble and Time Series (ARIMA) forecasting methods in order to find the most optimized model for the proposed problem. The preliminary results of this study showed inconclusive results with 5% significance that there was a change in performance between the two approaches.

Medicaid Expansion and Cancer Mortality by Race and Sex in Louisiana

The purpose of this study is to determine the association between Medicaid expansion in Louisiana and cancer mortality by race and sex. Data from the National Vital Statistics System mortality files were used to quantify deaths from cancer between 2010 and 2019 for Louisiana and a sample of states that had yet to adopt the Affordable Care Act's Medicaid expansion as of December 2019. A series of population-weighted comparative interrupted time series models were estimated to determine whether Louisiana's Medicaid expansion was associated with reduced cancer mortality. Analyses were conducted in May 2021-August 2021. Medicaid expansion was associated with an average of 3.3 (95% CI= -6.4, -0.1; p=0.045) fewer quarterly cancer deaths per 100,000 Black female Louisiana residents and an average of 5.8 (95% CI= -10.4, -1.1; p=0.015) fewer quarterly cancer deaths per 100,000 Black male residents. There were no statistically significant changes in cancer mortality for White people in Louisiana associated with Medicaid expansion. Following expansion, the Black-White mortality gap in cancer deaths declined by approximately 57% for female individuals (4.6-2.0) and 49% for male individuals (10.1-5.2). Medicaid expansion in Louisiana was associated with a reduction in cancer mortality for Black female and male adults. Estimates of the association between Medicaid expansion and cancer mortality in Louisiana directly relate to the potential impacts for states that have yet to adopt Medicaid expansion under the Affordable Care Act, which are primarily located in the Southern U.S.

The Spectral Diagram as a new tool for model assessment in the frequency domain: Application to a global ocean general circulation model with tides

Here we introduce a new tool, the Spectral Diagram (SD), for the comparison of time series in the frequency domain. The SD provides a novel way to display the coherence function, power, amplitude, phase, and skill score of discrete frequencies of two time series. Each SD summarises these quantities in a single plot for multiple targeted frequencies. The versatility of SDs is demonstrated through a series of sea-level comparisons between observations from tide gauges and the model results from a global eddy-permitting ocean general circulation model (MOM5) with explicit tidal forcing. Phase information for the eight principal lunisolar constituents (M2, S2, N2, K2, K1, O1, P1, Q1) is added to the default configuration of MOM5. Inaccurate estimation of phase information is an important source of barotropic errors in ocean modelling, thereby compromising the skill scores in regions where amplitudes are close to the tidal gauge datasets. The greatest contribution of SD analysis is the indication that some diurnal estimates can be improved by adjusting the phase lag in the model as severe underestimation of semidiurnal amplitudes is the main reason for lower skill scores despite higher coherence. Although the SD has been designed for tidal analysis, it is a powerful tool for detecting co-oscillating patterns in multi-scale analyses, and this approach might provide guidance in devising skill scores for inter-comparing model results.

Environmental variability and larval supply to wild and cultured shellfish populations

Coastal upwelling ecosystems support some of the most productive fisheries of the planet together with a large shellfish aquaculture sector that depends on oceanographic processes to deliver planktonic larvae to replenish and feed the farmed stock. Coastal shellfish aquaculture operations in Chile and Perú have experienced large interannual fluctuations in larval supply over the past decade, yet the drivers of such variability remain unidentified. We focused on the effects of environmental variability on larval supply of the farmed Peruvian bay scallop Argopecten purpuratus in a bay in northern Chile (Tongoy Bay, 30∘S) that accounts for over 90% of countrywide landings. We examined the hypothesis that the environmental processes governing larval supply were shared with wild benthic invertebrates with planktonic larval development and compared time series of larval abundance for the scallop with larval supply rates to benthic populations of two well-studied wild intertidal species: the Chthamalid barnacle Jehlius cirratus and the purple mussel Perumytilus purpuratus. To this end, we examined the cross-correlation of larval supply to environmental variability using MODIS satellite fields of sea surface temperature (SST) chlorophyll-a concentration (chl-a) and fluorescence line height (nFLH), together with three climate indices relevant for the south east Pacific sector: the Southern Oscillation index (SOI), the Pacific Decadal Oscillation (PDO) and the Antarctic Oscillation Index (AAO). Our results showed that over the five-year study period (2009–2013), patterns of larval supply to the scallop population were related to inter-annual variability in the environmental processes as captured by their Empirical Orthogonal Functions (EOFs), likely to adult condition before spawning. Surprisingly, larval supply for none of the wild species showed a clear association to the EOFs. In contrast, scallops and wild species showed significant association to lower frequency climate variability as captured by the SOI and the PDO, but not the AAO. Results suggest that larval supply patterns to Tongoy Bay may be modulated by regional patterns of climatic variability, particularly of tropical origin. Thus, changes in coastal oceanography associated with ongoing changes in global climate could have strong and lasting effects on the supply of seedstock for wild and cultivated species across this eastern boundary coastal system and argue for the establishment of long-term ocean observing and early warning systems along the region.

Шляхи підвищення прибутку від експорту української кукурудзи на світовому ринку

The purpose of the article is to carry out a comprehensive analysis of the world corn market and to determine the main directions of development of the domestic corn market. Research methods. In the process of research the following methods were used: abstract-logical - to formulate conclusions; monographic - when detailing the functioning of the corn market; comparative analysis and time series - to compare indicators and identify trends in their change over time; statistical - when analyzing supply and demand in the corn market; graphic - to identify and illustrate the trends of the studied economic phenomena; economic and mathematical methods - in determining the economic indicators that affect the supply and demand of corn. Research results. The study reflects the author's view on the current process of development of foreign trade of corn grain in Ukraine. Author analyzed the potential of Ukrainian corn production in Ukraine. Was determined the list of the most important issues for Ukraine, which should be solved to increase exports of corn. The directions of mutually beneficial cooperation in the future were substantiated, which will ensure economic benefits for Ukrainian producers. Scientific novelty. Directions of development of Ukrainian corn exports and the development of mutually beneficial cooperation in the agricultural sector were proposed. Practical significance. The article is aimed at disseminating the most relevant information among domestic producers and exporters on necessary conditions and differences in the implementation of production and exports of corn. These areas of market development can be used by public authorities to increase production efficiency and maximize profits from trade. Tabl.: 1. Figs.: 2. Refs.: 18.

Exploiting Sentinel-5P TROPOMI and Ground Sensor Data for the Detection of Volcanic SO2 Plumes and Activity in 2018-2021 at Stromboli, Italy.

Sulfur dioxide (SO2) degassing at Strombolian volcanoes is directly associated with magmatic activity, thus its monitoring can inform about the style and intensity of eruptions. The Stromboli volcano in southern Italy is used as a test case to demonstrate that the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (Sentinel-5P) satellite has the suitable spatial resolution and sensitivity to carry out local-scale SO2 monitoring of relatively small-size, nearly point-wise volcanic sources, and distinguish periods of different activity intensity. The entire dataset consisting of TROPOMI Level 2 SO2 geophysical products from UV sensor data collected over Stromboli from 6 May 2018 to 31 May 2021 is processed with purposely adapted Python scripts. A methodological workflow is developed to encompass the extraction of total SO2 Vertical Column Density (VCD) at given coordinates (including conditional VCD for three different hypothetical peaks at 0–1, 7 and 15 km), as well as filtering by quality in compliance with the Sentinel-5P Validation Team’s recommendations. The comparison of total SO2 VCD time series for the main crater and across different averaging windows (3 × 3, 5 × 5 and 4 × 2) proves the correctness of the adopted spatial sampling criterion, and practical recommendations are proposed for further implementation in similar volcanic environments. An approach for detecting SO2 VCD peaks at the volcano is trialed, and the detections are compared with the level of SO2 flux measured at ground-based instrumentation. SO2 time series analysis is complemented with information provided by contextual Sentinel-2 multispectral (in the visible, near and short-wave infrared) and Suomi NPP VIIRS observations. The aim is to correctly interpret SO2 total VCD peaks when they either (i) coincide with medium to very high SO2 emissions as measured in situ and known from volcanological observatory bulletins, or (ii) occur outside periods of significant emissions despite signs of activity visible in Sentinel-2 data. Finally, SO2 VCD peaks in the time series are further investigated through daily time lapses during the paroxysms in July–August 2019, major explosions in August 2020 and a more recent period of activity in May 2021. Hourly wind records from ECMWF Reanalysis v5 (ERA5) data are used to identify local wind direction and SO2 plume drift during the time lapses. The proposed analysis approach is successful in showing the SO2 degassing associated with these events, and warning whenever the SO2 VCD at Stromboli may be overestimated due to clustering with the plume of the Mount Etna volcano.

Increasing planning targets is not enough: health workforce management in rheumatology in Germany

Abstract Background Health workforce shortage in German rheumatology and its negative impact in healthcare are increasingly recognised. Health policy has increased staffing targets, but effective intervention strategies are lacking. This paper aims to systematically map the rheumatology workforce and to explore opportunities for intervention, thus contributing to better health workforce management. Methods The WHO National Health Workforce Accounts (NHWA) provided a conceptual framework for the mapping exercise. Four major sets of indicators were selected, comprising staffing levels, health labour market flows, skill-mix and education/training. Comparison of age-groups and time series was applied to explore trends. Public statistics and other secondary sources served the analysis, using descriptive methodology. Results Rheumatologists nearly doubled in absolute numbers since 2000, but this trend shows a problematic demographic bias. Between 2000 and 2019 numbers increased markedly in the groups aged +50 years, but only about 9% in younger groups under 50 years. Most alarming, since 2010 the group 40 to 50 years showed a reverse trend and strong decreases. In 2019, absolute numbers of rheumatologists working in healthcare after retirement-age exceeded those aged 40 and under. Since 2015 an expanding workforce trend overall flattened, but strongest in the hospital sector; annual inflows do not show relevant growths. Conclusions Health workforce assessment reveals negative demographic trends and decreasing appeal of the specialty especially to men, thus highlighting a need for more effective health workforce management. Intervention strategies should target innovation in resident training and task shifting and improve gender equality. Key messages Health workforce trends indicate that new health policy planning targets cannot be met with an available stock of rheumatologist. The WHO NHWA provide a useful toolbox to map health workforce trends and identify weaknesses and gaps.

A 10-gene biosignature of tuberculosis treatment monitoring and treatment outcome prediction

The clinical utility of blood transcriptomic biosignatures for the treatment monitoring and outcome prediction of tuberculosis (TB) remains limited. In this study, we aimed to discover and validate biomarkers for pulmonary TB treatment monitoring and outcome prediction based on kinetic responses of gene expression during treatment. In particular, differentially expressed genes (DEGs) were identified by time-series comparison. Subsequently, DEGs with the monotonic expression alterations during the treatment were selected. Ten consistently down-regulated genes (CD274, KIF1B, IL15, TLR1, TLR5, FCGR1A, GBP1, NOD2, GBP2, EGF) exhibited significant potential in treatment monitoring, demonstrated via biological and technical validation. Additionally, the biosignature showed potential in predicting the cured versus relapsed patients. Furthermore, the biosignature could be utilized for TB diagnosis, latent tuberculosis infection/active TB differential diagnosis, and risk of progression to active TB. Benchmarking analysis of the 10-gene biosignature with other biosignatures showed equivalent performance in tested data sets. In conclusion, we established a 10-gene transcriptomic biosignature that represents the kinetic responses of TB treatment. Subsequent studies are warranted to validate, refine and translate the biosignature into a precise assay to assist clinical decisions in a broad spectrum of TB management.

Temporal change and the influence of climate and weather factors on mercury concentrations in Hudson Bay polar bears, caribou, and seabird eggs

Temporal trends of mercury in Arctic wildlife are inconsistent within and between species and are often insignificant, which limits data interpretation. Recent multivariate analyses have shown that weather and climate factors (e.g. temperatures, sea ice conditions) are related to total Hg (THg) concentrations in wildlife tissues, though relatively few studies have explored these relationships. The present study compared time series of THg concentrations in liver of polar bear (Ursus maritimus, 2007/08–2015/16), eggs of thick-billed murres (Uria lomvia, 1993–2015) and kidney of caribou (Rangifer tarandus groenlandicus, 2006–2015) from the Hudson Bay region of Canada and statistically modelled THg over time with available climate and weather data. Significant temporal trends of THg concentrations were not detected in any species. However, in multivariate models that included time-lagged sea ice freeze up dates, THg concentrations increased 4.4% yr−1 in Qamanirjuaq caribou. Sea ice conditions were also related to THg levels in polar bear liver but not those in eggs of murres, though year was not a signifcant factor. Greater precipitation levels one to two years prior to sampling were associated with greater THg concentrations in polar bears and caribou, likely due to greater deposition, flooding and discharge from nearby wetlands and rivers. Time-lagged Arctic and/or North Atlantic Oscillation (AO/NAO) indices also generated significant, inverse models for all three species, agreeing with relationships in other time series of similar length. The magnitude and direction of many relationships were affected by season, duration of time-lags, and the length of the time series. Our findings support recent observations suggesting that temporal studies monitoring Hg in Arctic wildlife should consider including key climatic or weather factors to help identify consistent variables of influence and to improve temporal analyses of THg time series.

Understanding marine life through long-term passive acoustic time series

Sound allows us to study the ocean on a much finer time scales than other methods of study as it is feasible to collect continuous underwater recordings over months or years at a time. My work has been focused on using such long recordings to improve our understanding of the ecology of large whales and marine fishes. However, ecological insights can only be gained from data that are truly comparable across time and space. The initial steps to achieving comparable time series from long-term recordings collected at vastly different locations include: effective detection of signals of interest, full knowledge of the characteristics of those signals and interfering noise, and normalization of those detections by appropriate recording effort across time and space. I will highlight a few things we have learned about baleen whale and fish species through long-term passive acoustic monitoring effort, such as an improved understanding of call function for some baleen whales or a better sense of animal occurrence. This, in turn, will allow us to start coupling the occurrence of these animals into the larger patterns of ecosystem dynamics, ultimately leading to a fuller understanding of their roles in the ocean.

Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery

As the spatial and temporal resolution of remotely sensed imagery has improved over the last four decades, algorithms for monitoring and mapping seasonal changes in surface properties have evolved rapidly. Most recently, the availability of daily PlanetScope imagery has created new opportunities for monitoring the land surface phenology (LSP) of terrestrial ecosystems at high spatial resolution. However, the quality and value of LSP information from PlanetScope imagery have not been systematically examined. In this paper, we evaluate the character and quality of LSP information derived from PlanetScope by comparing time series of vegetation indices and LSP metrics from PlanetScope to corresponding time series and LSP metrics derived from Harmonized Landsat 8 and Sentinel-2 (HLS) imagery and PhenoCams at six sites that span a diverse range of land cover types and climate. Results show that vegetation index time series from all three data sources show high temporal correlation, and LSP metrics derived from HLS, PlanetScope, and PhenoCam show high agreement with negligible bias. Semi-variograms for phenometrics estimated from PlanetScope imagery indicate that the majority of spatial variance captured in PlanetScope phenometrics occurs well below the spatial resolution HLS imagery. At the same time, LSP metrics from HLS are most strongly correlated with the 50–75% quantiles of 3 m LSP metrics from PlanetScope. This indicates that HLS captures the average phenology at sub-pixel scale captured in PlanetScope imagery. Our results represent the first comprehensive comparison of LSP metrics estimated from PlanetScope and publicly available moderate spatial resolution imagery, and provide insights regarding: (1) the quality and character of LSP metrics derived from HLS and PlanetScope; and (2) the relative merits and trade-offs associated with the use of each data source for LSP studies.