Harmonic Time Series Research Articles

Use of HANTS algorithm in Sentinel-2 images for interpolation of missing data: a case-study for soy agricultural areas in periods of growth and harvesting

Abstract. Information about the current state of an agricultural culture can be fundamental in decision making, varying from quantity of fertilizers to be used in an area to estimating the productivity of a harvest in a period to establish credits payment. Satellite images with reasonable spatial resolution like Sentinel-2 are a powerful tool to visualize agricultural areas production, especially with the application of multispectral indexes like the Normalized Difference Vegetation Index (NDVI). Yet, due to the limitation of temporal resolution of satellites and periods with large cloudy weather can make the analysis difficult. This study proposed the use of the harmonic analysis time series algorithm (HANTS) to aid the incompleteness of time series obtained by the available satellite images in a case study of soy culture in Brazil. By applying the algorithm, a harmonic interpolation is obtained to produce a full daily NDVI time series from the beginning to end of the study period, facilitating further analysis to obtain metrics of interest. In this study, time series with gaps on specified periods are created based on full time series, then interpolated and compared with the real ones based on root mean squared error (RMSE) to access its accuracy.

All Deforestation Matters: Deforestation Alert System for the Caatinga Biome in South America’s Tropical Dry Forest

This study provides a comprehensive overview of Phase I of the deforestation dryland alert system. It focuses on its operation and outcomes from 2020 to 2022 in the Caatinga biome, a unique Brazilian dryland ecosystem. The primary objectives were to analyze deforestation dynamics, identify areas with highest deforestation rates, and determine regions that require prioritization for anti-deforestation efforts and conservation actions. The research methodology involved utilizing remote sensing data, including Landsat imagery, processed through the Google Earth Engine platform. The data were analyzed using spectral unmixing, adjusted Normalized Difference Fraction Index, and harmonic time series models to generate monthly deforestation alerts. The findings reveal a significant increase in deforestation alerts and deforested areas over the study period, with a 148% rise in alerts from 2020 to 2022. The Caatinga biome was identified as the second highest in detected deforestation alerts in Brazil in 2022, accounting for 18.4% of total alerts. Hexagonal assessments illustrate diverse vegetation cover and alert distribution, enabling targeted conservation efforts. The Bivariate Choropleth Map demonstrates the nuanced relationship between alert and vegetation cover, guiding prioritization for deforestation control and native vegetation restoration. The analysis also highlighted the spatial heterogeneity of deforestation, with most deforestation events occurring in small patches, averaging 10.9 ha. The study concludes that while the dryland alert system (SAD-Caatinga—Phase I) has effectively detected deforestation, ongoing challenges such as cloud cover, seasonality, and more frequent and precise monitoring persist. The implementation of DDAS plays a critical role in sustainable forestry by enabling the prompt detection of deforestation, which supports targeted interventions, helps contain the process, and provides decision makers with early insights to distinguish between legal and illegal practices. These capabilities inform decision-making processes and promote sustainable forest management in dryland ecosystems. Future improvements, including using higher-resolution imagery and artificial intelligence for validation, are essential to detect smaller deforestation alerts, reduce manual efforts, and support sustainable dryland management in the Caatinga biome.

Twenty Years of Land Accounts in Europe

Land use and its change impact food security, carbon cycling, biodiversity, and, hence, the condition of ecosystems to mitigate and adapt to climate change, support economic prosperity, and human well-being. To support and guide policy actions between the economy and the environment, harmonized time series datasets, transparent methodologies, and easily interpretable statistics are needed. Therefore, monitoring of the function and condition of lands and their change, along with properly agreed methodologies and freely accessible data, are essential. The Copernicus Land Monitoring Service has produced over 20 years of Corine Land Cover datasets for 39 countries in Europe, which allows continental-wide harmonized and comparable monitoring and accounting of land cover and land use change at a high thematic resolution and in a long time series (2000–2018). With the upcoming 2024 update, the time series will reach a unique product worldwide in terms of time series length, spatial resolution, extent, and thematic detail, enabling policymakers and the scientific community to address the main anthropogenic drivers of land and ecosystem degradation. This paper describes a unified approach for producing continental-wide land accounts that aligns with internationally agreed-upon standards for measuring the environment and its relationship with the economy. Furthermore, the study provides a harmonized time series of geospatial data for deriving land accounts and provides statistics of land cover and land use status and changes for a twenty-year period. All geospatial data and statistics presented in this paper are freely accessible and downloadable to serve other studies.

Harmonized Landsat and Sentinel-2 Data with Google Earth Engine

Continuous and dense time series of satellite remote sensing data are needed for several land monitoring applications, including vegetation phenology, in-season crop assessments, and improving land use and land cover classification. Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an ‘all-in-one’ Google Earth Engine (GEE) web-based workflow to produce harmonized surface reflectance data from Landsat-7 (L7) ETM+, Landsat-8 (L8) OLI, and Sentinel-2 (S2) MSI top of atmosphere (TOA) reflectance data. Six major processing steps to generate a new source of near-daily Harmonized Landsat and Sentinel (HLS) reflectance observations at 30 m spatial resolution are proposed and described: band adjustment, atmospheric correction, cloud and cloud shadow masking, view and illumination angle adjustment, co-registration, and reprojection and resampling. The HLS is applied to six equivalent spectral bands, resulting in a surface nadir BRDF-adjusted reflectance (NBAR) time series gridded to a common pixel resolution, map projection, and spatial extent. The spectrally corresponding bands and derived Normalized Difference Vegetation Index (NDVI) were compared, and their sensor differences were quantified by regression analyses. Examples of HLS time series are presented for two potential applications: agricultural and forest phenology. The HLS product is also validated against ground measurements of NDVI, achieving very similar temporal trajectories and magnitude of values (R2 = 0.98). The workflow and script presented in this work may be useful for the scientific community aiming at taking advantage of multi-sensor harmonized time series of optical data.

Microwave radiometer observations of the ozone diurnal cycle and its short-term variability over Switzerland

Abstract. In Switzerland, two ground-based ozone microwave radiometers are operated in the vicinity of each other (ca. 40 km): the GROund-based Millimeter-wave Ozone Spectrometer (GROMOS) in Bern (Institute of Applied Physics) and the Stratospheric Ozone MOnitoring RAdiometer (SOMORA) in Payerne (MeteoSwiss). Recently, their calibration and retrieval algorithms have been fully harmonized, and updated time series are now available since 2009. Using these harmonized ozone time series, we investigate and cross-validate the strato–mesospheric ozone diurnal cycle derived from the two instruments and compare it with various model-based datasets: the dedicated GEOS-GMI Diurnal Ozone Climatology (GDOC) based on the Goddard Earth Observing System (GEOS-5) general circulation model, the Belgian Assimilation System for Chemical ObsErvations (BASCOE) – a chemical transport model driven by ERA5 dynamics, and a set of free-running simulations from the Whole Atmosphere Community Climate Model (WACCM). Overall, the two instruments show very similar ozone diurnal cycles at all seasons and pressure levels, and the models compare well with each other. There is a good agreement between the models and the measurements at most seasons and pressure levels, and the largest discrepancies can be explained by the limited vertical resolution of the microwave radiometers. However, as in a similar study over Mauna Loa, some discrepancies remain near the stratopause, at the transition region between ozone daytime accumulation and depletion. We report similar delays in the onset of the modelled ozone diurnal depletion in the lower mesosphere. Using the newly harmonized time series of GROMOS and SOMORA radiometers, we present the first observations of short-term (sub-monthly) ozone diurnal cycle variability at mid-latitudes. The short-term variability is observed in the upper stratosphere during wintertime, when the mean monthly cycle has a small amplitude and when the dynamics are more important. This is shown in the form of strong enhancements of the diurnal cycle, reaching up to 4–5 times the amplitude of the mean monthly cycle. We show that BASCOE is able to capture some of these events, and we present a case study of one such event following the minor sudden stratospheric warming of January 2015. Our analysis of this event supports the conclusions of a previous modelling study, attributing regional variability of the ozone diurnal cycle to regional anomalies in nitrogen oxide (NOx) concentrations. However, we also find periods with an enhanced diurnal cycle that do not show much change in NOx and where other processes might be dominant (e.g. atmospheric tides). Given its importance, we believe that the short-term variability of the ozone diurnal cycle should be further investigated over the globe, for instance using the BASCOE model.

Mapping understory plant communities in deciduous forests from Sentinel-2 time series

Understory plant communities are an integral component of deciduous forests, playing a vital role in the overall health of the ecosystem. However, remote sensing of understory plant communities is challenging due to the obstruction by the forest canopy. In this study, we proposed an automated dense Sentinel-2 time series-based approach for understory plant communities and created maps of four understory classes (i.e., native shrubs of greenbrier and mountain laurel, and invasive shrubs of barberry and the assemblage of mixed invasive) at 10 m resolution in Connecticut's deciduous forests in 2020. A harmonic time series model and three years of Sentinel-2 time series from 2019 to 2021 were used to classify understory species based on their unique, intra-annual phenology characteristics. The time series model coefficients captured the subtle phenology differences and created synthetic cloud-free images within a short temporal window in the spring prior to canopy leaf-on (hereafter called “observation window”). During the observation window, Sentinel-2 data penetrated the deciduous overstory canopy and observed the unique trajectories of different understory species due to their phenology differences. We also calculated spatial texture features (i.e., mean, second moment, and contrast from gray level co-occurrence matrix) based on the synthetic images created within the observation window to capture the different conditions of leaf growth and distinct spatial patterns within deciduous forests. By using the spectral, temporal, and spatial features as input variables from dense Sentinel-2 data, auxiliary data (i.e., LiDAR and soil drainage layer), a random forest classifier, and a new strategy to iteratively select representative sample (namely ISRS), understory species maps were created with an overall accuracy of approximately 93%, and the user's and producer's accuracies varied from 39% to 99% for the three mapped understory species and one assemblage of species. The proposed method created an accurate binary map of understory presence with an overall accuracy of 95%, a producer's accuracy of 84%, and a user's accuracy of 68%. Additionally, we separated the invasive (i.e. barberry and mixed invasive of multi-flora rose, oriental bittersweet, honeysuckle, winged euonymus, and autumn olive) and native (greenbrier and mountain laurel) species with an overall accuracy of 94%. We estimated that the invasive species cover an area of 649.33 ± 140.59 km2, which occupied a large proportion (∼53%) of the shrub understory in Connecticut's deciduous forests.

Teleconnection between the Low Index Phase of Southern Oscillation and Precipitation Patterns over the Southeastern United States

The purpose of this analysis is to examine the spatiotemporal extent to which the extreme phase of Southern Oscillation affects precipitation patterns over the southeastern region of the United States. Composite and harmonic analyses using monthly precipitation data were conducted based on the 2-year composite resulting in harmonic dials, which represent the spatial coherence of the candidate regions. For the candidate regions identified in the harmonic dial maps, aggregate composite and index time series of precipitation were analyzed to determine the core regions which represent the temporal consistency in relation to the low index phase of Southern Oscillation (LISO) events. As a result, the precipitation patterns of the core regions, North Region (UR), Middle Region (MR), and South Region (LR), were influenced by the occurrence of the extreme phase of SO events, with SR having the greatest response. During the LISO cycle, the harmonic dial tends to face southeast in UR, northeast in MR, and east in LR. Furthermore, the spatial coherence rates of the three core regions range from 0.94 to 0.99, and the temporal consistency rates are 0.76 to 0.83. In addition, from the cross-correlation and annual cycle analyses, the seasonal and annual effect of LISO forcing on the precipitation variability over the three core regions can be recognizable. In conclusion, Southern Oscillation phenomena have a great influence on the precipitation patterns over the southeastern region of the United States.

Overall negative trends for snow cover extent and duration in global mountain regions over 1982–2020

Notwithstanding the large availability of data and models, a consistent picture of the snow cover extent and duration changes in global mountain areas is lacking for long-term trends. Here, model data and satellite images are combined by using Artificial Neural Networks to generate a consistent time series from 1982 to 2020 over global mountain areas. The analysis of the harmonized time series over 38 years indicates an overall negative trend of − 3.6% ± 2.7% for yearly snow cover extent and of − 15.1 days ± 11.6 days for snow cover duration. The most affected season by negative trends is winter with an average reduction in snow cover extent of − 11.5% ± 6.9%, and the most affected season by positive changes is spring with an average increase of 10% ± 5.9%, the latter mainly located in High Mountain Asia. The results indicated a shift in the snow regime located between the 80 s and 90 s of the previous century, where the period from 1982 to 1999 is characterized by a higher number of areas with significant changes and a higher rate of changes with respect to the period 2000–2020. This quantification can lead to a more accurate evaluation of the impact on water resources for mountainous communities.

Creating a harmonized time series of environmentally-extended input-output tables to assess the evolution of the US bioeconomy - A retrospective analysis of corn ethanol and soybean biodiesel

Expanding the domestic bioeconomy can help diversify the use of national resources and reduce emissions. Evaluating the sustainability of a growing bioeconomy, however, is inherently complex since it spans several sectors and supply chains. It requires a comprehensive, integrated analysis framework to assess the developments across the traditional sustainability dimensions. Further, the assessment of bioeconomy developments requires a robust baseline of historic data and trends. In this paper, we analyze the evolution of the biofuel portion of the US bioeconomy, focusing on two fuels that had an exponential growth in the last two decades: corn ethanol and soybean biodiesel. For this purpose, we created a novel time series of harmonized environmentally-extended input-output (EEIO) tables based on a publicly available model from the US Environmental Protection Agency and expanded its disaggregation to reflect the main supply chains of the biofuels sectors. The EEIO time series provides the historical evolution of these biofuels relative to the rest of the economy as well as on an energy-unit basis. We find that, except for energy use, the broader US economy declined in both resource intensity and most environmental impacts when normalized per one million dollars of gross domestic product. Deviating from this trend are freshwater ecotoxicity and human toxicity, mainly attributable to the expansion of commodity crops and the increase of domestic oil and gas extraction respectively. We also find that the biofuel industry's total socioeconomic, resource use and environmental impacts grew with their production increases over time. However, the industry's maturation and scale-up, combined with higher feedstock yields, contributed to a reduction of most impacts on an energy-unit basis over time.

Tracking and Estimation of Frequency, Amplitude, and Form Factor of a Harmonic Time Series [Lecture Notes

Formulas for estimating and tracking the (time-dependent) frequency, form factor, and amplitude of harmonic time series are presented in this lecture note; in particular, sine-dominant signals, where the harmonics follow roughly the dominant first harmonic, such as photoplethysmography (PPG) and breathing signals. Special attention is paid to the convergence behavior of the algorithm for stationary signals and the dynamic behavior in case of a transition to another stationary state. The latter issue is considered to be important for assessing the tracking abilities for nonstationary signals. We will discuss special cases of sine-dominant signals that will converge to an approximate or the same value as for a single sinusoid. The presented formulas are recursive in nature and use only the instantaneous values of the signal, in a low-cost and low-complexity manner. In particular, there is no need to use square roots or trigonometric or matrix operations; therefore, these formulas are suitable for low-power ambulatory measurements and computationally demanding machine learning algorithms. Furthermore, there appears to be an interesting connection between the form factor and Geary's test of normality.

Monitoring of dynamic wetland changes using NDVI and NDWI based landsat imagery

Recent initiatives using the GIS tools for mapping and analysis of wetland dynamics bring the hope for an effective wetland management. Degraded or modified wetlands are more sensitive and less resilient to the climate change. This paper focusses on a study of water bodies in Sirmaur District, Uttarakhand, India with the special focus on Renuka wetland. The Renuka wetland, a Ramsar site is affected by global warming, floods and storms and changes due to precipitation. The updated or accurate maps and spatial and temporal changes of Renuka wetland are not available. Only the details about the flora and fauna have been recorded in this region. Traditional methods in mapping and monitoring, like field survey and sampling, are usually labour intensive, time consuming, and expensive and often fail to detect the changes over the regions of wetland zones. The present study is carried using the online interactive cloud-based planetary processing open source platform, Google Earth Engine (GEE). The wetland maps considered are Landsat 7 and 8 images with cloud cover less than 30% during the summer (May) and winter (September) seasons. The suitability index is also calculated based on the area and perimeter of the water body in study region to demonstrate the characteristic change of Renuka wetland over the selected time period. The missing data of NDVI and NDWI is fitted using the harmonic time series and the resultant map with the supervised classification Random Forest shows a high overall accuracy of 90% with a reasonable Kappa coefficient of 0.8. Using the Global Water Occurrence Explorer, the water changes intensity and the seasonality water change over the wetland for the period of 1984–2019 are studied. The results showed the significant insight into the changes over the study region in terms of pixel range with the presence of water and the change in locations in terms of seasonality and persistence.

Characteristics of Current-Induced Harmonic Tremor Signals in Ocean-Bottom Seismometer Records

Abstract Long-lasting harmonic tremor signals are frequently observed in spectrograms of seismological data. Natural sources, such as volcanoes and icebergs, or artificial sources, such as ships and helicopters, produce very similar harmonic tremor episodes. Ocean-bottom seismometer (OBS) records may additionally be contaminated by tremor induced by ocean-bottom currents acting on the OBS structure. This harmonic tremor noise may severely hinder earthquake detection and can be misinterpreted as volcanic tremor. In a 160-km-long network of 27 OBSs deployed for 1 yr along the Knipovich ridge in the Greenland Sea, harmonic tremor was widely observed away from natural sources such as volcanoes. Based on this network, we present a systematic analysis of the characteristics of hydrodynamically induced harmonic tremor in OBS records to make it distinguishable from natural tremor sources and reveal its generation processes. We apply an algorithm that detects harmonic tremor and extracts time series of its fundamental frequency and spectral amplitude. Tremor episodes typically occur twice per day, starting with fundamental frequencies of 0.5–1.0 Hz, and show three distinct stages that are characterized by frequency-gliding, mode-locking, and large spectral amplitudes, respectively. We propose that ocean-bottom currents larger than ∼5 cm/s cause rhythmical Karman vortex shedding around protruding structures of the OBS and excite eigenvibrations. Head-buoy strumming is the most likely source of the dominant tremor signal, whereas a distinctly different tremor signal with a fundamental frequency ∼6 Hz may be related to eigenvibrations of the radio antenna. Ocean-bottom current velocities reconstructed from the fundamental tremor frequency and from cross correlation of tremor time series between stations match observed average current velocities of 14–20 cm/s in this region. The tremor signal periodicity shows the same tidal constituents as the forcing ocean-bottom currents, which is a further evidence of the hydrodynamic nature of the tremor.

Approach to determine harmonic contributions in distribution networks using underdetermined blind source separation and form similarity distance

This study proposes a new approach to determine harmonic contributions in distribution networks. The approach effectively combines underdetermined blind source separation (UBSS) and form similarity distance (FSD), and it aims to minimise the negative impacts of harmonic source coupling and background harmonic fluctuations. Considering the underdetermined measurement, UBSS is introduced to estimate the harmonic emission current in the light of the sparsity of source signals. Through analysing the form variation features of harmonic time series, FSD is applied to identify the relatively stable background harmonics for utility equivalent harmonic impedance calculation. Furthermore, a complete procedure of harmonic contribution assessment is presented. To verify the high performance of the proposed approach, the simulation study and field test are both conducted, and a detailed comparative analysis of different approaches is carried out. The results show that the proposed approach is able to more accurately determine harmonic contributions than the existing methods, and has better robustness against significant harmonic source coupling and drastic background harmonic fluctuations.

Towards a Harmonized Long‐Term Spaceborne Record of Far‐Red Solar‐Induced Fluorescence

AbstractFar‐red solar‐induced chlorophyll fluorescence (SIF) has been retrieved from multiple satellites with nearly continuous global coverage since 1996. Multiple official and research‐grade retrievals provide a means for cross validation across sensors and algorithms, but produces substantial variation across products due to differences in instrument characteristics and retrieval algorithm. The lack of a consistent, calibrated SIF data set hampers scientific interpretation of planetary photosynthesis. NASA's Orbiting Carbon Observatory 2 (OCO‐2) offers small sampling footprints, high data acquisition, and repeating spatially resolved targets at bioclimatically diverse locations, providing a unique benchmark for spaceborne sensors traceable to ground data. We leverage overlap between the longer running Global Ozone Monitoring Instrument version 2 (GOME‐2) SIF time series, and more recent state‐of‐the‐art OCO‐2 and TROPOspheric Monitoring Instrument (TROPOMI) data, in a first attempt to reconcile inconsistencies in the long‐term record. After screening and correcting for key instrument differences (time of day, wavelength, Sun‐sensor geometry, cloud effects, footprint area), we find that Global Ozone Monitoring Instrument version 2 and TROPOspheric Monitoring Instrument perform exceedingly well in capturing spatial, seasonal, and interannual variability across OCO‐2 targets. However, Global Ozone Monitoring Instrument version 2 retrieval methods differ by up to a factor of 2 in signal‐to‐noise and magnitude. Magnitude differences are largely attributed to retrieval window choice, with wider windows producing higher magnitudes. The assumed SIF spectral shape has negligible effect. Substantial research is needed to understand remaining sensitivities to atmospheric absorption and reflectance. We conclude that OCO‐2 and TROPOspheric Monitoring Instrument have opened up the possibility to produce a multidecadal SIF record with well‐characterized uncertainty and error quantification for overlapping instruments, enabling back‐calibration of previous instruments and production of a consistent, research‐grade, harmonized time series.

Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for remotely monitoring crop production because this could support predictions of food shortages and improve resource allocation. In this study, we demonstrate a new method to map paddy rice using Google Earth Engine (GEE) and the Landsat archive in Bangladesh during the dry (boro) season. Using GEE and Landsat, dry-season rice areas were mapped at 30 m resolution for approximately 90,000 km2 annually between 2014 and 2018. The method first reconstructs spectral vegetation indices (VIs) for individual pixels using a harmonic time series (HTS) model to minimize the effect of any sensor inconsistencies and atmospheric noise, and then combines the time series indices with a rule-based algorithm to identify characteristics of rice phenology to classify rice pixels. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. Findings suggest that the harmonic-time-series-based vegetation indices (HTS-VIs) model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other estimates, but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national and district estimates from official sources at an average R-squared of 0.8. Moreover, accuracy assessment with independent validation locations resulted in an overall accuracy of 91% and a kappa coefficient of 0.83 for the boro/non-boro stable rice map from 2014 to 2018. We conclude with a discussion of potential improvements and future research pathways for this approach to spatiotemporal mapping of rice in heterogeneous landscapes.

A combination approach based on a novel data clustering method and Bayesian recurrent neural network for day-ahead price forecasting of electricity markets

Electricity price forecasting is an ancillary service that plays a key role for market participants in a deregulated structure. Compared to other commodities, electricity price exhibits higher levels of volatility and uncertainty. This imposes more restrictions on the accuracy of the forecast and leads to significant errors. This paper proposes a hybrid electricity price-forecasting framework with a novel time series data mining method to enhance the feature selection. The proposed method includes clustering, preprocessing and training stages. The proposed data clustering method uses both an enhanced game theoretic approach and neural gas in combination with competitive Hebbian learning to provide a better vector quantization. Six strategies are proposed to enable the non-winning neurons to participate in the learning phase and resolve the shortcomings of the original self-organizing map, where the dead neurons are far from the input patterns without having any chance to compete with the winning neurons. The price-load input data are clustered into a proper number of subsets using the proposed data mining method. A novel cluster selection method based on the persistence approach is applied to select the most appropriate cluster as the input to the BRNN. The selected data set is filtered by the harmonic analysis time series, and is time-series processed to provide the proper inputs for training neural networks. Bayesian approach is used to train a recurrent neural network, and forecast the electricity price. The performance of the proposed clustering algorithm is evaluated using different electricity market data. Our results demonstrate the efficiency of the proposed clustering algorithm as compared to K-means, neural gas and self-organizing map clustering methods Our proposed clustering provides 16.7%, 28.6%, and 13% more accurate results than K-means, neural gas, and self-organizing map for the NYISO. CAPITL data. For the NYISO. CENTRL data, the developed clustering outperforms the K-means, neural gas, and self-organizing map by 21.4%, 21.4%, and 8.3%, respectively. The clustering accuracy of the proposed method for NYISO. DUNWOD data is 5.5%, 19%, and 5.5% better than that of the K-means, neural gas, and self-organizing map methods Lastly, for the NYISO. GENESE data, the mean square error value for the proposed clustering is 13.7%, 14.9%, and 12.5% less than that of the K-means, neural gas, and self-organizing map, respectively. The developed forecasting method is also compared with the existing state-of-the-art forecasting algorithms. The comparison results show an improvement in the forecast accuracy of the developed method over other forecasting approaches.

Robust Landsat-based crop time series modelling

Reliable satellite monitoring of agriculture is often difficult because surface variations occur rapidly compared to the cloud-free satellite observation frequency. Harmonic time series models, i.e., superimposed sequences of sines and cosines, have an established provenance for fitting satellite vegetation index time series to coarse resolution satellite data, but their application to medium resolution Landsat data for crop monitoring has been limited. Non-linear harmonic models have been shown to perform well over agricultural sites using single-year Moderate Resolution Imaging Spectroradiometer (MODIS) time series, but have not been explored with Landsat data. The 2017 availability of Landsat Analysis Ready Data (ARD) over the United States provides the opportunity to investigate the utility of temporally rich Landsat data for 30 m pixel-level crop monitoring. In this paper, the capability of 5- and 7-parameter linear harmonic models and a 5-parameter non-linear harmonic model applied to a year of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) ARD is investigated. The analysis is undertaken over six sites, each defined by a 5000 × 5000 30 m pixel ARD tile, that together include the major conterminous United States (CONUS) crops identified by inspection of the United States Department of Agriculture (USDA) Cropland Data Layer (CDL). The model fits are evaluated as the root mean square difference (RMSD) between the fitted and the observed Landsat data. Considering locations with at least 21 annual Landsat observations, the 7-parameter linear harmonic model (tile mean crop NDVI RMSD values ranging from 0.052 to 0.072) and the 5-parameter non-linear harmonic model (tile mean crop NDVI RMSD values ranging from 0.054 to 0.074) are shown to be able to fit annual Landsat NDVI time series for most CONUS crops, whereas the 5-parameter linear harmonic model cannot (tile mean crop NDVI RMSD values ranging from 0.072 to 0.099). If there are between 15 and 20 annual Landsat observations, the 5-parameter non-linear harmonic model is recommended for fitting annual NDVI crop time series, and if there are ≥21 observations, then either the 5-parameter non-linear or the 7-parameter linear model can be used. The 7-parameter model had marginally smaller mean NDVI RMSD values but larger standard deviations than the 5-parameter non-linear model, likely due to the relative robustness of the non-linear model to over-fitting and oscillations. None of the models could reliably fit crops with multiple stages, such as alfalfa, that are insufficiently sampled using combined Landsat 5 TM and Landsat 7 ETM+ time series. Given the utility of the growing season peak NDVI for crop yield applications, the date and magnitude of the model fitted peak NDVI are compared to quantify model reporting differences. The differences between the 7-parameter linear and the 5-parameter non-linear harmonic models are not large. For each ARD tile, the mean absolute differences in the estimated peak NDVI days varied from <2 days in the northern ARD tiles, which had short growing seasons and similar crops, to less than a week for the other tiles except for nearly 10 days for the California tile that had longer growing seasons and more diverse crops including crops with multiple stages. The paper concludes with a discussion and recommendations for future research.

USING MULTI-TEMPORAL REMOTE SENSING DATA TO ANALYZE THE SPATIO-TEMPORAL PATTERNS OF DRY SEASON RICE PRODUCTION IN BANGLADESH

Abstract. Remote sensing in the optical domain is widely used in agricultural monitoring; however, such initiatives pose a challenge for developing countries due to a lack of high quality in situ information. Our proposed methodology could help developing countries bridge this gap by demonstrating the potential to quantify patterns of dry season rice production in Bangladesh. To analyze approximately 90,000 km2 of cultivated land in Bangladesh at 30 m spatial resolution, we used two decades of remote sensing data from the Landsat archive and Google Earth Engine (GEE), a cloud-based geospatial data analysis platform built on Google infrastructure and capable of processing petabyte-scale remote sensing data. We reconstructed the seasonal patterns of vegetation indices (VIs) for each pixel using a harmonic time series (HTS) model, which minimizes the effects of missing observations and noise. Next, we combined the seasonality information of VIs with our knowledge of rice cultivation systems in Bangladesh to delineate rice areas in the dry season, which are predominantly hybrid and High Yielding Varieties (HYV). Based on historical Landsat imagery, the harmonic time series of vegetation indices (HTS-VIs) model estimated 4.605 million ha, 3.519 million ha, and 4.021 million ha of rice production for Bangladesh in 2005, 2010, and 2015 respectively. Fine spatial scale information on HYV rice over the last 20 years will greatly improve our understanding of double-cropped rice systems, current status of production, and potential for HYV rice adoption in Bangladesh during the dry season.

A hybrid wind power forecasting model based on data mining and wavelets analysis

Accurate forecasting of wind power plays a key role in energy balancing and wind power integration into the grid. This paper proposes a novel time-series based K-means clustering method, named T.S.B K-means, and a cluster selection algorithm to better extract features of wind time-series data. A hybrid of T.S.B K-means, discrete wavelet transform (DWT) and harmonic analysis time series (HANTS) methods, and a multilayer perceptron neural network (MLPNN) is developed for wind power forecasting. The proposed T.S.B K-means classifies data into separate groups and leads to more appropriate learning for neural networks by identifying anomalies and irregular patterns. This improves the accuracy of the forecast results. A cluster selection method is developed to determine the cluster that provides the best training for the MLPNN. This significantly accelerates the forecast process as the most appropriate portion of the data rather than the whole data is used for the NN training. The wind power data is decomposed by the Daubechies D4 wavelet transform, filtered by the HANTS, and pre-processed to provide the most appropriate inputs for the MLPNN. Time-series analysis is used to pre-process the historical wind-power generation data and structure it into input-output series. Wind power datasets with diverse characteristics, from different wind farms located in the United States, are used to evaluate the accuracy of the hybrid forecasting method through various performance measures and different experiments. A comparative analysis with well-established forecasting models shows the superior performance of the proposed forecasting method.

Contribution of oil and natural gas production to renewed increase in atmospheric methane (2007–2014): top–down estimate from ethane and methane column observations

Abstract. Harmonized time series of column-averaged mole fractions of atmospheric methane and ethane over the period 1999–2014 are derived from solar Fourier transform infrared (FTIR) measurements at the Zugspitze summit (47° N, 11° E; 2964 m a.s.l.) and at Lauder (45° S, 170° E; 370 m a.s.l.). Long-term trend analysis reveals a consistent renewed methane increase since 2007 of 6.2 [5.6, 6.9] ppb yr−1 (parts-per-billion per year) at the Zugspitze and 6.0 [5.3, 6.7] ppb yr−1 at Lauder (95 % confidence intervals). Several recent studies provide pieces of evidence that the renewed methane increase is most likely driven by two main factors: (i) increased methane emissions from tropical wetlands, followed by (ii) increased thermogenic methane emissions due to growing oil and natural gas production. Here, we quantify the magnitude of the second class of sources, using long-term measurements of atmospheric ethane as a tracer for thermogenic methane emissions. In 2007, after years of weak decline, the Zugspitze ethane time series shows the sudden onset of a significant positive trend (2.3 [1.8, 2.8] × 10−2 ppb yr−1 for 2007–2014), while a negative trend persists at Lauder after 2007 (−0.4 [−0.6, −0.1] × 10−2 ppb yr−1). Zugspitze methane and ethane time series are significantly correlated for the period 2007–2014 and can be assigned to thermogenic methane emissions with an ethane-to-methane ratio (EMR) of 12–19 %. We present optimized emission scenarios for 2007–2014 derived from an atmospheric two-box model. From our trend observations we infer a total ethane emission increase over the period 2007–2014 from oil and natural gas sources of 1–11 Tg yr−1 along with an overall methane emission increase of 24–45 Tg yr−1. Based on these results, the oil and natural gas emission contribution (C) to the renewed methane increase is deduced using three different emission scenarios with dedicated EMR ranges. Reference scenario 1 assumes an oil and gas emission combination with EMR = 7.0–16.2 %, which results in a minimum contribution C &gt; 39 % (given as lower bound of 95 % confidence interval). Beside this most plausible scenario 1, we consider two less realistic limiting cases of pure oil-related emissions (scenario 2 with EMR = 16.2–31.4 %) and pure natural gas sources (scenario 3 with EMR = 4.4–7.0 %), which result in C &gt; 18 % and C &gt; 73 %, respectively. Our results suggest that long-term observations of column-averaged ethane provide a valuable constraint on the source attribution of methane emission changes and provide basic knowledge for developing effective climate change mitigation strategies.