Index Time Series Research Articles

Mapping the Normalized Difference Vegetation Index for the Contiguous U.S. Since 1850 Using 391 Tree-Ring Plots

The forests and grasslands in the U.S. are vulnerable to global warming and extreme weather events. Current satellites do not provide historical vegetation density images over the long term (more than 50 years), which has restricted the documentation of key ecological processes and their resultant responses over decades due to the absence of large-scale and long-term monitoring studies. We performed point-by-point regression and collected data from 391 tree-ring plots to reconstruct the annual normalized difference vegetation index (NDVI) time-series maps for the contiguous U.S. from 1850 to 2010. Among three machine learning approaches for regressions—Support Vector Machine (SVM), General Regression Neural Network (GRNN), and Random Forest (RF)—we chose GRNN regression to simulate the annual NDVI with lowest Root Mean Square Error (RMSE) and highest adjusted R2. From the Little Ice Age to the present, the NDVI increased by 6.73% across the contiguous U.S., except during some extreme events such as the Dust Bowl drought, during which the averaged NDVI decreased, particularly in New Mexico. The NDVI trend was positive in the Northern Forest, Tropical Humid Forest, Northern West Forest Mountains, Marin West Coast Forests, and Mediterranean California, while other ecoregions showed a negative trend. At the state level, Washington and Louisiana had significantly positive correlations with temperature (p < 0.05). Washington had a significantly negative correlation with precipitation (p < 0.05), whereas Oklahoma had a significantly positive correlation (p < 0.05) with precipitation. This study provides insights into the spatial distribution of paleo-vegetation and its climate drivers. This study is the first to attempt a national-scale reconstruction of the NDVI over such a long period (151 years) using tree rings and machine learning.

Revealing Cropping Intensity Dynamics Using High-Resolution Imagery: A Case Study in Shaanxi Province, China

Reliable and continuous information on cropping intensity is crucial for assessing cropland utilization and formulating policies regarding cropland protection and management. However, there is still a lack of high-resolution cropping intensity maps for recent years, particularly in fragmented agricultural regions. In this study, we combined Landsat-8 and Sentinel-2 imagery to generate cropping intensity maps from 2019 to 2023 at a 10 m resolution for Shaanxi Province, China. First, the satellite imagery was harmonized to construct 10-day composite enhanced vegetation index (EVI) time series. Then, the cropping intensity was determined by counting the number of valid EVI peaks within a year. Assessment based on 578 sample points showed a high level of accuracy, with overall accuracy and Kappa coefficient values exceeding 0.96 and 0.93, respectively. We further analyzed the spatiotemporal patterns of cropping intensity and generated a map of abandoned cropland in Shaanxi. The results indicated that cropland in Shaanxi Province was mainly utilized for single-cropping (52.9% of area), followed by double-cropping (35.2%), with non-cropping accounting for 11.9%. Cropping intensity tended to be lower in the north and higher in the south. Temporally, the average cropping intensity of Shaanxi increased from 1.1 to over 1.3 from 2019 to 2023. Despite this upward trend, large areas of cropland were abandoned in northern Shaanxi. These results demonstrate the potential of utilizing Landsat-8 and Sentinel-2 imagery to identify cropping intensity dynamics in fragmented agricultural regions and to guide more efficient cropland management.

Temporal Variability of Hydroclimatic Extremes: A Case Study of Vhembe, uMgungundlovu, and Lejweleputswa District Municipalities in South Africa

The current study investigated hydroclimatic extremes in Vhembe, Lejweleputswa, and uMgungundlovu District Municipalities based on streamflow data from 21 river gauge stations distributed across the study site for the period spanning 1985–2023. Statistical metrics such as the annual mean and maximum streamflow, as well as trends in annual, maximum, seasonal, and high/low flow, were used to evaluate the historical features of streamflow in each of the three district municipalities. Moreover, the Standardized Streamflow Index (SSI) time series computed from streamflow observations at 3- and 6-month accumulation periods were used to assess hydroclimatic extremes, including drought episodes, proportion of wet/dry years and trends in SSI, drought duration (DD), and drought severity (DS). The results indicate that the three district municipalities have experienced localized and varying degrees of streamflow levels and drought conditions. The uMgungundlovu District Municipality in particular has experienced a significant decline in annual and seasonal streamflow as well as an increase in drought conditions during the 38-year period of analysis. This is supported by the negative trends observed in most of the assessed metrics (e.g., annual, maximum, seasonal, low/high flow, and SSI), whereas DD and DS showed positive trends in all the stations, suggesting an increase in prolonged duration and severity of drought. The Lejweleputswa District Municipality depicted positive trends in most of the assessed metrics, suggesting that streamflow increased, whereas drought decreased in the region over the 38-year period of study. Moreover, the Vhembe District Municipality experienced both negative and positive trends, suggesting localized variations in dry and wet conditions. The results presented in this study contribute towards drought monitoring and management efforts in support of policy- and decision-making that aim to uplift water resources management and planning at local and district municipality levels.

RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension

Abstract. The reduction of in situ observations over the last few decades poses a potential risk of losing important information in regions where local effects dominate the climatology. Reanalyses face challenges in representing climatologies with highly localized effects, especially in regions with complex orography. Empirical downscaling methods offer a cost-effective and easier-to-implement alternative to dynamic downscaling methods and can partially overcome the aforementioned limitations of reanalyses by taking into account the local effects through statistical relationships. This article introduces RASCAL (Reconstruction by AnalogS of ClimatologicAL time series), an open-source Python tool designed to extend time series and fill gaps in observational climate data, especially in regions with limited long-term data and significant local effects, such as mountainous areas. Employing an object-oriented programming style, RASCAL's methodology effectively links large-scale circulation patterns with local atmospheric features using the analog method in combination with principal component analysis (PCA). The package contains routines for preprocessing observations and reanalysis data, generating reconstructions using various methods, and evaluating the reconstruction's performance in reproducing the time series of observations, statistical properties, and relevant climatic indices. Its high modularity and flexibility allow fast and reproducible downscaling. The evaluations carried out in central Spain, in mountainous and urbanized areas, demonstrate that RASCAL performs better than the ERA20C and ERA20CM reanalysis, as expected, in terms of R2, standard deviation, and bias. When analyzing reconstructions against observations, RASCAL generates series with statistical properties, such as seasonality and daily distributions, that closely resemble observations. This confirms the potential of this method for conducting robust climate research. The adaptability of RASCAL to diverse scientific objectives is also highlighted. However, as with any other method based on empirical training, this method requires the availability of sufficiently long-term data series. Furthermore, it is susceptible to disruption caused by changes in land use or urbanization processes that might compromise the homogeneity of the training data. Despite these limitations, RASCAL's positive outcomes offer opportunities for comprehensive climate variability analyses and potential applications in downscaling short-term forecasts, seasonal predictions, and climate change scenarios. The Python code and the Jupyter Notebook for the reconstruction validation are publicly available as an open project.

Enhancing Acoustic Data Mobilization: A User-Friendly Data Platform for the Asian Soundscape Monitoring Network

Passive Acoustic Monitoring (PAM) has emerged as a crucial tool in monitoring efforts to track environmental changes and evaluate conservation measures in response to the biodiversity crisis (Sugai et al. 2018). PAM now offers long-term and continuous insights into biodiversity, using acoustic indices correlating with biodiversity and deep-learning tools to aid in the detection and identification of animal sounds (Sueur et al. 2014, Kahl et al. 2021, Wu et al. 2022). However, managing and mobilizing original recordings remains challenging due to their large size and lack of structure, hindering broader applications of these valuable data. Established in 2014 by Academia Sinica and Taiwan Forestry Research Institute, the Asian Soundscape Monitoring Network has amassed over 20 million minutes of audible and ultrasonic recordings from diverse landscapes, including forests, wetlands, urban parks and farmlands across Malaysia, Thailand, Taiwan and Vietnam. To enhance data mobilization and utilization, we developed a user-friendly data platform enabling browsing, searching, visualization, exploration and retrieval of the original recordings (Fig. 1), as well as derived data such as acoustic indices and species occurrences, and associated weather records (Fig. 2) The platform also allows users to visualize the temporal dynamics of the soundscape and identify acoustic events at monitoring sites by examining long-term spectrograms and time series of acoustic indices (Fig. 3). Detailed information about the monitoring sites and recorder deployment is provided. Each recording is tagged with a Creative Commons license, and a unique Archival Resource Key is assigned to every data retrieval for persistent identification, facilitating data reuse. Additionally, recordings containing human voices are identified and restricted to protect privacy. Our aim with this data platform is to streamline the mobilization of acoustic data, foster diverse applications, and enhance the overall value of the data.

Effects of Climate Changes and Crop Phenological Responses on Soil Organic Carbon of Cultivated Land in Fujian Province

Research on the mechanism of how climate change affects cultivated soil organic carbon is the basis for the management of cultivated land quality in the context of climate change. Crop phenological responses to climate change have an important effect on cultivated soil organic carbon as well. However, previous research primarily focused on the independent effects of climate change or crop phenological responses on the changes in soil organic carbon, and few studies have analyzed the changes in cultivated soil organic carbon under the combined influence of both factors or quantified their contribution rates to the changes in cultivated soil organic carbon. Based on topsoil samples in 2008 and 2021, annual pre-season and mid-season climate data from 2008 to 2021, and the phenological parameters extracted from the enhanced vegetation index （EVI） time series from 2007 to 2022, a soil organic carbon predictive model was constructed using the random forest algorithm. The total change in soil organic carbon from 2008 to 2021, the change in soil organic carbon under climate change alone, and the change in soil organic carbon under the synergistic influence of climate change and crop phenological responses were simulated. Furthermore, the contributions of climate change and crop phenological responses to the changes in cultivated soil organic carbon were distinguished and quantified. Moreover, the dominant influencing factors of soil organic carbon changes and their spatial distributions were identified and analyzed. The results were as follows： ① Under the synergistic influence of climate change and crop phenological responses, a decrease was observed in soil organic carbon in 74.15% of the cultivated land area in Fujian Province during the years 2008-2021, with an average decrease of 2.20 g·kg-1. Additionally, there was an increase in soil organic carbon in 25.85% of the cultivated area, with an average increase of 1.48 g·kg-1. ②The average contribution rates of pre-season climate, crop phenological responses to climate change, mid-season climate, and phenological changes resulting from cultivars shifts or other adjustments of agricultural measures to soil organic carbon changes were 34.08%, 28.56%, 22.75%, and 14.61%, respectively. Overall, climate change had a greater impact on the changes in cultivated soil organic carbon in Fujian Province than the crop phenological response to climate change. ③ The regions where climate change and phenological response jointly acted as dominant influencing factors held the largest area, accounting for 47.06% of the total cultivated land area in Fujian Province, and the regions where climate change was the dominant influencing factor alone held the second-largest area, accounting for 28.64% of the total cultivated land area. ④ Higher contribution rates of pre-season climate factors and phenological changes resulting from cultivar shifts or other adjustments of agricultural measures tended to be distributed in higher-altitude areas, whereas higher contribution rates of mid-season climate factors and phenological responses to climate change tended to be distributed in lower-altitude areas. These research findings can provide a theoretical basis for decision making regarding the management of cultivated land quality and the safeguarding of food security in the context of climate change.

A new deep learning-based model for reconstructing high-quality NDVI time-series data in heavily cloudy areas: fusion of Sentinel 1 and 2 data

ABSTRACT Reconstructing high-quality Normalized Difference Vegetation Index time series data is essential for ecological and agricultural applications but remains challenging in heavily cloudy areas. Fusing Sentinel SAR and optical data with deep learning could be helpful but is also challenging for stable models due to unstable SAR-NDVI relationships caused by imaging mechanism differences and environmental complexities. In this study, we developed a new Bidirectional Recurrent Imputation for Optical-SAR fusion (BRIOS) model to reconstruct high-quality Sentinel-2 NDVI time series data. BRIOS designs a two-layer recurrent architecture that integrates the autocorrelation of discrete, cloud-free NDVI observations into the model for a more stable SAR-NDVI relationship. Evaluating BRIOS against three baseline methods (GF-SG spatiotemporal fusion, Harmonic regression interpolation, and MCNN-Seq deep learning) across three full Sentinel-2 tiles in reconstructing 8-day NDVI time series, BRIOS consistently outperformed in scenarios of either random or continuously missing data, as evidenced by lower RMSE values (e.g. 0.075 for BRIOS vs. 0.108 for GF-SG vs. 0.143 for Harmonic regression vs. 0.303 for MCNN-Seq), better Edge index, and high linear correlation coefficients (R values up to 0.97). Further ablation experiments revealed that deep integration of NDVI autocorrelation features and SAR temporal change patterns has improved the stability and generalization of BRIOS. Discussions on the model's scalability across various cloud sizes and training dataset sizes affirm its practicality for broad-scale application in vegetation monitoring under challenging cloudy conditions.

FORECASTING CBOE OPENING VALUES: AN EFFECTIVE ARIMA APPROACH WITH TRANSFORMATION AND MODEL DIAGNOSTICS

This study investigates the use of an ARIMA model, coupled with Monte Carlo simulation, to forecast the opening value of a Volatility Index (VIX) time series. The data obtained from the Chicago Board Options Exchange (CBOE) for the years 1992–2019 have been transformed into stationary data using a detrend method and first-order difference. The Augmented Dickey-Fuller (ADF) test is used to ensure the data are adequately transformed. The autocorrelation function (ACF) and partial ACF (PACF) are then used to identify series with serial correlation and determine whether an autoregressive (AR) model is appropriate. Significant moving average (MA) lags are also determined for model identification. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) are used to find the best-fit ARIMA model. Among the evaluated ARIMA models, which ranged from a random walk ARIMA(0, 1, 0) to ARIMA(2, 1, 2), the ARIMA(2, 1, 2) model was found to be the most optimal, exhibiting the lowest AIC and BIC values. This model was then used to forecast the opening value for the year 2014, using 2013 data as the real data. The generated ARIMA(2, 1, 2) model demonstrates reasonable alignment with the actual 2014 data, suggesting its potential for forecasting in this context. Additionally, Monte Carlo simulations are employed to assess the model’s robustness by generating a range of potential outcomes, providing a more comprehensive understanding of the uncertainty associated with the forecasts.

Remote sensing-based green and blue agricultural water footprint estimation at the river basin scale

Since the development of Water Footprint environmental indicator, significant research on blue and green crop water use and the respective water footprint estimations has been published. Such research is commonly approached using different methodologies that leverage tabulated values for crop development characterisation, while studies based on remote sensing data are less abundant, despite crop monitoring using remote sensing-based vegetation indices having demonstrated great capabilities and operability. To help fill this gap, we present a methodology that uses a remote sensing vegetation index time series from Sentinel-2 satellite near infra-red and red spectral bands data to derive basal crop coefficient time series to subsequently be used under the Remote Sensing-based Soil Water Balance approach that follows the globally operative FAO56 procedure. It provides pixel-based temporal and spatially distributed estimations of net irrigation requirements and adjusted crop evapotranspiration, with the aim being to divide up the latter and estimate the remote sensing-based green and blue crop water use and the subsequent green and blue water footprint. This is all done under the Agricultural Water Footprint Assessment framework for a growing crop or tree. This methodology was applied over a large, crop-diverse Spanish river basin district (Júcar) and across two different climatological years (humid vs. dry). Its feasibility was demonstrated by the acceptable behaviour of the remote sensing-based blue crop water use estimation for different herbaceous and woody crops, against the official dataset for irrigation water accounting at two water management scales (of a relative mean absolute error of 15.4 % in the case of the largest water user association and of 17.1 % in the case of the river basin water authorities’ own estimations). The proposed approach, which we call Remote Sensing-based Agricultural Water Accounting and Footprint, aims to provide reliable and accurate spatially and temporally distributed thematic cartography about the remote sensing-based blue and green crop water use and water footprint. This information is essential for water managers with the goal of generating transparent and complementary information to incorporate into their own working scales.

Multi-scale contrast approach for stock index prediction with adaptive stock fusion

The stock index summarizes the overall movement of a group of stocks, typically calculated as a weighted average of constituent stock prices. Stock Index Prediction (SIP) aids investors in assessing economic performance and making informed decisions. Traditional SIP methods rely heavily on historical index data, neglecting valuable insights from macro-financial indicators and stock prices. However, challenges arise when incorporating these data: (1) Including numerous unfiltered macro-financial indicators introduces noise. (2) Using non-correlated stock prices can be counterproductive. (3) Supervised training on stochastic time series can lead to overfitting. To overcome these issues, we propose a multi-scale contrast approach for stock index prediction with adaptive stock fusion (MCSIP). This method identifies highly correlated macro-financial indicators and stocks, optimizing the model through self-supervised multi-scale contrastive learning. MCSIP relies on contrastive learning in the time series domain to extract robust contextual information from stock index time series, mitigating stochastic data backpropagation and ensuring reliable representation. Experiments on US and Chinese stock datasets demonstrate superior performance compared to existing methods.

Chinese corporate biodiversity exposure

We present time series indices of corporate biodiversity risk exposure for listed Chinese companies. Utilizing recent advancements in text-as-data techniques, we analyze the annual reports of more than 4000 companies spanning a 15-year period. Our study introduces two indices: an exposure index, inspired by the work of Giglio et al., 2023, and an extensiveness index, which draws from policy uncertainty indices like Lucey et al., 2022. Our findings reveal substantial exposure to biodiversity risks among Chinese companies, notably exceeding levels observed in the USA.Corporate-level data are available as an Internet appendix to this note.

Long-term changes of mangrove distribution and its response to anthropogenic impacts in the Vietnamese Southern Coastal Region

Human activities related to land use and land cover (LULC) conversion have been the primary factor driving changes to mangrove distribution over recent decades. In order to quantify the anthropogenic influences associated with LULC changes on mangroves in the Vietnamese Southern Coast (VSC), we investigated the variations and trends in mangrove distribution between 1988 and 2023. We used a time-series of Landsat spectral indices from Google Earth Engine and applied hot spot analysis and machine learning algorithms to analyse mangrove variations and LULC classification, respectively. Our findings revealed that over the past 36 years, approximately half of the mangrove area has been lost due to LULC conversions. The most significant losses in mangrove cover occurred during the 1998–2011 period, with a decline of 46.79% in total area (an average of 3.6% per annum). The rate of mangrove deforestation more than halved to 17.49% (1.5% per annum) in the period between from 2011 to 2023. We attribute the reduction in mangrove loss to conservation efforts and natural regeneration processes. The emerging hot spot analysis indicated that the most significant restoration of new mangrove areas occurred between 1988 and 1998, totalling 1795 ha (1.4%), while the highest rate of mangrove deforestation was observed between 1998 and 2011, amounting to 2249 ha (2.0%). The primary causes of these variations in mangrove distribution were the conversion of mangrove areas to shrimp farming (38.91%), followed by other agricultural land use (5.82%) and the expansion of impervious surfaces (3.34%). In contrast, a result of enhanced conservation efforts and natural regeneration was associated with a 17.91% of mangrove area gain in the 2011–2023 period. Despite the regeneration potential of mangroves, our study highlighted the ongoing need to manage and protect mangrove forests to facilitate their expansion in the VSC. The analytical approach adopted in this study is applicable to other coastal areas when assessing changes in mangroves and land use practices.

Crop cover identification based on different vegetation indices by using machine learning algorithms

In this article, three different indices NDVI (Normalized Difference Vegetation Index), BNDVI (Blue Normalized Difference Vegetation Index) and GNDVI (Green Normalized Difference Vegetation Index) are used for the identification of wheat, mustard and sugarcane crop of Saharanpur district’s region of Uttar Pradesh. Sentinel 2B satellite images are collected from October 02, 2018 to April 15, 2019. These images are processed using Google Earth Engine. These sentinel images are used to generate NDVI, BNDVI and GNDVI images using GEE. These three different indices images are further processed using SNAP software and particular indices values for 210 different locations are calculated. The same process is used for calculating BNDVI and GNDVI values. ARIMA, LSTM and Prophet models are used to train the time series indices values (NDVI, BNDVI and GNDVI) of wheat, mustard and sugarcane crop. these models are used to analyse MSE (mean absolute percentage error) and RMSE values by considering various parameters. Using ARIMA Model, for wheat crop GNDVI indices shows minimum RMSE 0.020, For Sugarcane crop NDVI indices shows minimum RMSE 0.053, For Mustard crop GNDVI indices shows minimum RMSE 0.024. Using LSTM model, for wheat crop NDVI indices shows minimum RMSE 0.036, For Sugarcane crop BNDVI indices shows minimum RMSE 0.054, For Mustard crop GNDVI indices shows minimum RMSE 0.026. Using Prophet model, for wheat crop GNDVI indices shows minimum RMSE 0.055, For Sugarcane crop NDVI indices shows minimum RMSE 0.088, For Mustard crop GNDVI indices using Prophet model shows minimum RMSE 0.101.

Correlation between insulin-like growth factor and complexity of glucose time series index in patients with newly diagnosed acromegaly: a PILOT study.

Acromegaly has a high risk of abnormal glucose metabolism. The complexity of the glucose time series index (CGI) is calculated from refined composite multi-scale entropy analysis of the continuous glucose monitoring (CGM) data. CGI is a new indicator of glucose imbalance based on ambulatory glucose monitoring technology, which allows for earlier response to glucose metabolism imbalance and correlates with patient prognosis. To compare the differences in glucose metabolic profile and CGI between acromegaly with normal glucose tolerance (NGT) and healthy subjects. Eight newly diagnosed patients with acromegaly (GH group) and eight age- and gender-matched healthy subjects (Control group) were included in this study. All participants underwent oral glucose tolerance test (OGTT) and 72-h CGM. A refined composite multi-scale entropy analysis was performed on the CGM data to calculate the CGI and we compare the differences in glycemic profiles and CGI between the two groups. After OGTT, compared with the control group, patients in the GH group had higher 2 h blood glucose (BG) (mmol/L) [GH vs control, 6.7 (6.1, 7.0) vs 5.2 (3.8, 6.3), P = 0.012], 3 h BG [5.1 (3.8, 6.5) vs 4.0 (3.4, 4.2), P = 0.046], mean BG [6.3 (6.1, 6.5) vs 5.5 (5.1, 5.9), P = 0.002], 2 h insulin (mU/L) [112.9 (46.8, 175.5) vs 34.1 (17.1, 55.6), P = 0.009], and 3 h insulin [26.8 (17.1, 55.4) vs 10.4 (4.2, 17.8), P = 0.016]. CGI was lower in the GH group [2.77 (1.92, 3.15) vs 4.2 (3.3, 4.8), P = 0.008]. Spearman's correlation analysis showed insulin-like growth factor (IGF) (r = -0.897, P < 0.001) and mean glucose (r = -0.717, P = 0.003) were significantly negatively correlated with CGI. Multiple linear stepwise regression showed that IGF-1 (r = -0.652, P = 0.028) was independent factor associated with CGI in acromegaly. IGF-1 was significantly associated with CGI, and CGI may serve as a novel marker to evaluate glucose homeostasis in acromegaly with normal glucose tolerance.

Vegetation Phenology Changes and Recovery after an Extreme Rainfall Event: A Case Study in Henan Province, China

Extreme rainfall can severely affect all vegetation types, significantly impacting crop yield and quality. This study aimed to assess the response and recovery of vegetation phenology to an extreme rainfall event (with total weekly rainfall exceeding 500 mm in several cities) in Henan Province, China, in 2021. The analysis utilized multi-sourced data, including remote sensing reflectance, meteorological, and crop yield data. First, the Normalized Difference Vegetation Index (NDVI) time series was calculated from reflectance data on the Google Earth Engine (GEE) platform. Next, the ‘phenofit’ R language package was used to extract the phenology parameters—the start of the growing season (SOS) and the end of the growing season (EOS). Finally, the Statistical Package for the Social Sciences (SPSS, v.26.0.0.0) software was used for Duncan’s analysis, and Matrix Laboratory (MATLAB, v.R2022b) software was used to analyze the effects of rainfall on land surface phenology (LSP) and crop yield. The results showed the following. (1) The extreme rainfall event’s impact on phenology manifested directly as a delay in EOS in the year of the event. In 2021, the EOS of the second growing season was delayed by 4.97 days for cropland, 15.54 days for forest, 13.06 days for grassland, and 12.49 days for shrubland. (2) Resistance was weak in 2021, but recovery reached in most areas by 2022 and slowed in 2023. (3) In each year, SOS was predominantly negatively correlated with total rainfall in July (64% of cropland area in the first growing season, 53% of grassland area, and 71% of shrubland area). In contrast, the EOS was predominantly positively correlated with rainfall (51% and 54% area of cropland in the first and second growing season, respectively, and 76% of shrubland area); however, crop yields were mainly negatively correlated with rainfall (71% for corn, 60% for beans) and decreased during the year of the event, with negative correlation coefficients between rainfall and yield (−0.02 for corn, −0.25 for beans). This work highlights the sensitivity of crops to extreme rainfall and underscores the need for further research on their long-term recovery.

Multifractal properties of Covid-19 data

Multifractal detrended moving average analysis (MFDFA) has been performed on time series of three different epidemiological indices, namely daily number of new cases per million population, reproduction rate and positive rate, recorded during the Covid-19 pandemic. We consider India, Japan, USA and UK for this study. For all the series the scaling behavior of MFDMA fluctuation functions has been examined, and the multifractal variables like the generalised Hurst exponent spectra and singularity spectra have been calculated. It is found that the time series of the Covid-19 indices are of multifractal nature. The degree of multifractality of the reproduction rate series is significantly weaker than the other two series for all the countries. To find out the source(s) of multifractality, we generate a set of one hundred shuffled series and Amplitude-Adjusted Fourier Transform surrogate series for each of the original series. Comparing the results obtained from the original series with those from the shuffled and surrogate series we understand that the main source of multifractality in Covid-19 data is long-range temporal correlation among the series values.

Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin

Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin

Assessing the stock status of Euthynnus alletteratus (Rafinesque 1810) exploited by tuna purse seiners in the Northeast Atlantic Ocean: implications of using the length-based Integrated Mixed Effect model

ABSTRACT Euthynnus alletteratus, a crucial commercial resource for West African states in the Northeast Atlantic Ocean (ATNE), is of high research priority in the Atlantic Ocean, but its stock assessment is limited. This study aimed to explore the implications of integrating length, catch and abundance indices on the status of ATNE stock using the LIME model. We innovatively developed time series of abundance indices using tuna purse seine catches and efforts obtained from separate ICCAT datasets, alongside length-frequencies. The scenarios (S) S1 (length-only), S2 (length + catch + abundance), S3 (length + catch), and S4 (length + abundance) were considered. We observed that S1–S3 did not converge in LIME, except for S4. The stock's spawning potential ratio and mean length were below the target (SPR< 40%) and 95% maturity length (M95), respectively. By implication, the lack of convergence in S1–S3 portrayed data conflicts, which were mitigated by the abundance index in S4. A mean length above M95 is recommended when evaluating the stock using the length-only option in LIME. The stock was unsustainable, requiring management measures. Integrating the abundance index with length improved the performance of LIME for the stock, which estimate could be replicated for other species' assessments.

High spatiotemporal resolution vegetation index time series can facilitate enhanced remote sensing monitoring of soil salinization

High spatiotemporal resolution vegetation index time series can facilitate enhanced remote sensing monitoring of soil salinization

Spatio-temporal analysis of Permian-Cretaceous magmatic activities in the Tengchong block: Implications for tectono-magmatic evolution

Understanding the tectono-magmatic evolution history of the Tengchong block is crucial for elucidating the formation of the Eastern Tethys tectonic domain. However, the correlation and evolution of the Tengchong block with the Sibumasu and Lhasa blocks is controversial during the Permian and Cretaceous. This study explores the information contained within magmatic rocks using big data and spatio-temporal analysis, providing quantitative constraints for the discussion of the tectono-magmatic evolution of the Tengchong block. To more accurately assess true magma activities and reduce errors caused by preservation and sampling processes, we utilized local singularity analysis to obtain the singularity index time-series. Correlation analysis of zircon ages and εHf(t) (correlation coefficient ≥ 0.5) values indicates that the Tengchong block is more similar to the Sibumasu block. Results from time-lagged cross-correlation analysis indicate that the Tengchong block and Sibumasu block exhibit a shorter lag in magmatic activities (3 Myr). Wavelet analysis reveals similar periods of collision-related magmatic activities (57 Myr and 43 Myr). Integrating evidence from paleontology and ophiolite belts, we propose that the Tengchong block co-evolved more closely with the Sibumasu block than with the Lhasa block, suggesting similar tectonic processes during the Early Permian to Early Cretaceous. Approximately 250–236 Ma, in the western Tengchong block, partial melting of the lower crust occurs due to crustal thickening. Around 219–213 Ma and 198–180 Ma, after the Tengchong block collided with the Eurasian continent, the subduction of the Meso-Tethys Ocean commenced. Around 130–111 Ma, the overall tectonic feature was a scissor-like closure of the Meso-Tethys Ocean from north to south.