Discriminating the Mediterranean Pinus spp. using the land surface phenology extracted from the whole MODIS NDVI time series and machine learning algorithms

2017 ◽  
Author(s):  
Victor F. Rodriguez-Galiano ◽  
David Aragones ◽  
Jose A. Caparros-Santiago ◽  
Rafael M. Navarro-Cerrillo
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Land Surface ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Land Surface Phenology ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
The Mediterranean ◽  
Pinus Spp

Multi-year monitoring land surface phenology in relation to climatic variables using MODIS-NDVI time-series in Mediterranean forest, Northeast Tunisia

2022 ◽  
Vol 114 ◽  
pp. 103804
Author(s):  
Issam Touhami ◽  
Hassane Moutahir ◽  
Dorsaf Assoul ◽  
Kaouther Bergaoui ◽  
Hamdi Aouinti ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Climatic Variables ◽  
Mediterranean Forest ◽  
Land Surface Phenology ◽  
Modis Ndvi ◽  
Ndvi Time Series

Jump detection in financial time series using machine learning algorithms

2019 ◽  
Vol 24 (3) ◽  
pp. 1789-1801 ◽  
Author(s):  
Jay F. K. Au Yeung ◽  
Zi-kai Wei ◽  
Kit Yan Chan ◽  
Henry Y. K. Lau ◽  
Ka-Fai Cedric Yiu
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Financial Time Series ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Jump Detection ◽  
Financial Time

scholarly journals Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant

2017 ◽  
Author(s):  
Donald D. Lucas ◽  
Matthew D. Simpson ◽  
Philip Cameron-Smith ◽  
Ronald L. Baskett
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Inverse Modeling ◽  
Land Surface ◽  
Nuclear Power ◽  
Learning Algorithms ◽  
Distribution Functions ◽  
Machine Learning Algorithms ◽  
Surface Model

Abstract. Probability distribution functions (PDFs) of model inputs that affect the transport and dispersion of a trace gas released from a coastal California nuclear power plant are quantified using ensemble simulations, machine learning algorithms, and Bayesian inversion. The PDFs are constrained by observations of tracer concentrations and account for uncertainty in meteorology, transport, diffusion, and emissions. Meteorological uncertainty is calculated using an ensemble of simulations of the Weather Research and Forecasting (WRF) model that samples five categories of model inputs (initialization time, boundary layer physics, land surface model, nudging options, and reanalysis data). The WRF output is used to drive tens of thousands of FLEXPART dispersion simulations that sample a uniform distribution of six emissions inputs. Machine learning algorithms are trained on the ensemble data, and used to quantify the sources of ensemble variability and to infer, via inverse modeling, the values of the 11 model inputs most consistent with tracer measurements. We find a substantial ensemble spread in tracer concentrations (factors of 10 to 103), most of which is due to changing emissions inputs (about 80 %), though the cumulative effects of meteorological variations are not negligible. The performance of the inverse method is verified using synthetic observations generated from arbitrarily selected simulations. When applied to measurements from a controlled tracer release experiment, the most likely inversion results are within about 200 meters of the known release location, 5 and 50 minutes of the release start and duration times, respectively, and 22 % of the release amount. The inversion also estimates probabilities of different combinations of WRF inputs of matching the tracer observations.

scholarly journals Evaluation of global terrestrial evapotranspiration by state-of-the-art approaches in remote sensing, machine learning, and land surface models

2019 ◽  
Author(s):  
Shufen Pan ◽  
Naiqing Pan ◽  
Hanqin Tian ◽  
Pierre Friedlingstein ◽  
Stephen Sitch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Surface ◽  
Amazon Basin ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Tropical Regions ◽  
Surface Models

Abstract. Evapotranspiration (ET) is a critical component in global water cycle and links terrestrial water, carbon and energy cycles. Accurate estimate of terrestrial ET is important for hydrological, meteorological, and agricultural research and applications, such as quantifying surface energy and water budgets, weather forecasting, and scheduling of irrigation. However, direct measurement of global terrestrial ET is not feasible. Here, we first gave a retrospective introduction to the basic theory and recent developments of state-of-the-art approaches for estimating global terrestrial ET, including remote sensing-based physical models, machine learning algorithms and land surface models (LSMs). Then, we utilized six remote sensing-based models (including four physical models and two machine learning algorithms) and fourteen LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the mean annual global terrestrial ET ranged from 50.7 × 103 km3 yr−1（454 mm yr−1）to 75.7 × 103 km3 yr−1 (6977 mm yr−1), with the average being 65.5 × 103 km3 yr−1 (588 mm yr−1), during 1982–2011. LSMs had significant uncertainty in the ET magnitude in tropical regions especially the Amazon Basin, while remote sensing-based ET products showed larger inter-model range in arid and semi-arid regions than LSMs. LSMs and remote sensing-based physical models presented much larger inter-annual variability (IAV) of ET than machine learning algorithms in southwestern U.S. and the Southern Hemisphere, particularly in Australia. LSMs suggested stronger control of precipitation on ET IAV than remote sensing-based models. The ensemble remote sensing-based physical models and machine-learning algorithm suggested significant increasing trends in global terrestrial ET at the rate of 0.62 mm yr−2 (p  0.05), even though most of the individual LSMs reproduced the increasing trend. Moreover, all models suggested a positive effect of vegetation greening on ET intensification. Spatially, all methods showed that ET significantly increased in western and southern Africa, western India and northeastern Australia, but decreased severely in southwestern U.S., southern South America and Mongolia. Discrepancies in ET trend mainly appeared in tropical regions like the Amazon Basin. The ensemble means of the three ET categories showed generally good consistency, however, considerable uncertainties still exist in both the temporal and spatial variations in global ET estimates. The uncertainties were induced by multiple factors, including parameterization of land processes, meteorological forcing, lack of in situ measurements, remote sensing acquisition and scaling effects. Improvements in the representation of water stress and canopy dynamics are essentially needed to reduce uncertainty in LSM-simulated ET. Utilization of latest satellite sensors and deep learning methods, theoretical advancements in nonequilibrium thermodynamics, and application of integrated methods that fuse different ET estimates or relevant key biophysical variables will improve the accuracy of remote sensing-based models.

Sign in / Sign up

Export Citation Format

Share Document