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Recent decline in the global land evapotranspiration trend due to limited moisture supply

Martin Jung, Markus Reichstein, Philippe Ciais, Sonia I. Seneviratne, Justin Sheffield, Michael L. Goulden, Gordon Bonan, Alessandro Cescatti, Jiquan Chen, Richard de Jeu, A. Johannes Dolman, Werner Eugster (+21 others)
2010 Nature  
It integrates point-wise ET measurements at the FLUXNET observing sites with geospatial information from satellite remote sensing and surface meteorological data in a machine-learning algorithm (the model  ...  Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network 3 , meteorological and remotesensing observations, and a machine-learning  ...  It integrates point-wise ET measurements at the FLUXNET observing sites with geospatial information from satellite remote sensing and surface meteorological data in a machine-learning algorithm (the model  ... 
doi:10.1038/nature09396 pmid:20935626 fatcat:sxwhtnikpfex7grpkfrphkdsvm


M. Menenti, X. Li, J. Wang, H. Vereecken, J. Li, M. Mancini, Q. Liu, L. Jia, J. Li, C. Kuenzer, S. Huang, H. Yesou (+10 others)
2015 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
At the 1st Dragon 3 Progress Symposium in 2013 a significant potential for a better and deeper integration appeared very clearly and we worked out an overview of the ten projects identifying specific issues  ...  ET Monitor with optical and microwave (SMOS, FY – 3) data; Hydrological data products used both for forcing and evaluation of Qinghai – Tibet Plateau hydrological model; Wetlands vulnerability assessed  ...  and machine learning approach were assessed.  ... 
doi:10.5194/isprsarchives-xl-7-w3-1101-2015 fatcat:cazpqqpivjcmjhgzth4tx44rki

Use Remote Sensing and Machine Learning to Study the Changes of Broad-Leaved Forest Biomass and Their Climate Driving Forces in Nature Reserves of Northern Subtropics

Zhibin Sun, Wenqi Qian, Qingfeng Huang, Haiyan Lv, Dagui Yu, Qiangxin Ou, Haomiao Lu, Xuehai Tang
2022 Remote Sensing  
Based on ground survey data and high-resolution remote sensing images, three machine learning models were used to identify the best remote sensing quantitative inversion model of forest biomass.  ...  With the estimated biomass, multiple leading factors to cause biomass temporal change were then identified from dozens of remote sensing factors by investigating their nonlinear correlations.  ...  Acknowledgments: The authors are thankful to Meiqin Xie, Peng Fan, Jun Ye, and Genshen Fu for surveying and data processing in the study.  ... 
doi:10.3390/rs14051066 fatcat:ql4fdqemzra5jjw2f3ifhs2buq

Assessment and Comparison of Six Machine Learning Models in Estimating Evapotranspiration over Croplands Using Remote Sensing and Meteorological Factors

Yan Liu, Sha Zhang, Jiahua Zhang, Lili Tang, Yun Bai
2021 Remote Sensing  
Accurate estimates of evapotranspiration (ET) over croplands on a regional scale can provide useful information for agricultural management.  ...  The results showed that all hybrid models can reasonably reproduce ET of cropland with the models using two or more remote sensing (RS) factors.  ...  Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2021, 13, 3838  ... 
doi:10.3390/rs13193838 fatcat:ccdbqajhnjclzlewyoq5n4xh3e

Actual Evapotranspiration Estimates in Arid Cold Regions Using Machine Learning Algorithms with In Situ and Remote Sensing Data

Josefina Mosre, Francisco Suárez
2021 Water  
Incorporation of remote-sensing information results in better ETa estimates compared to when only meteorological data are considered.  ...  Meteorological data alone and then combined with remote sensing vegetation indices (VIs) were used as input in ETa estimations.  ...  Acknowledgments: The authors thank the Centro de Desarrollo Urbano Sustentable (CEDEUS-ANID/FONDAP/15110020) and the Centro de Excelencia en Geotermia de los Andes (CEGA-ANID/FONDAP/15090013) for supporting  ... 
doi:10.3390/w13060870 fatcat:3mz6eif7dbftpdzhz5luefatii

A Bayesian Three-Cornered Hat (BTCH) Method: Improving the Terrestrial Evapotranspiration Estimation

Xinlei He, Tongren Xu, Youlong Xia, Sayed M. Bateni, Zhixia Guo, Shaomin Liu, Kebiao Mao, Yuan Zhang, Huaize Feng, Jingxue Zhao
2020 Remote Sensing  
In this study, a Bayesian-based three-cornered hat (BTCH) method is developed to improve the estimation of terrestrial evapotranspiration (ET) by integrating multisource ET products without using any a  ...  Ten long-term (30 years) gridded ET datasets from statistical or empirical, remotely-sensed, and land surface models over contiguous United States (CONUS) are integrated by the BTCH and ensemble mean (  ...  Acknowledgments: This work was funded by the National Natural Science Foundation of China (41531174 and 41671335). Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12050878 fatcat:v2zasm4huvbmtflnztzlqw3tbi

Earth Observation for agricultural drought monitoring in the Pannonian Basin (southeastern Europe): current state and future directions

Laura Crocetti, Matthias Forkel, Milan Fischer, František Jurečka, Aleš Grlj, Andreas Salentinig, Miroslav Trnka, Martha Anderson, Wai-Tim Ng, Žiga Kokalj, Andreea Bucur, Wouter Dorigo
2020 Regional Environmental Change  
With the increasing availability of high-resolution and long-term Earth Observation (EO) data and recent progress in machine learning and artificial intelligence, further improvements in drought monitoring  ...  Hence, ongoing monitoring of droughts and estimation of their impact on agriculture is necessary to adapt agricultural practices to changing weather and climate extremes.  ...  with novel machine learning methods to forecast drought impacts on vegetation state and crop production.  ... 
doi:10.1007/s10113-020-01710-w fatcat:4uld5orzm5davb4diu4urocsge

Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021

Yang Xu, Yaping Yang, Xiaona Chen, Yangxiaoyue Liu
2022 Remote Sensing  
In future, machine learning methods and cloud computing platforms led by Google Earth Engine will substantially improve the accuracy and production efficiency of NDVI data products for more effective global  ...  As one of the earliest remote sensing indices, the Normalized Difference Vegetation Index (NDVI) has been employed extensively for vegetation research.  ...  Comments and suggestions from anonymous reviewers, the Academic Editor, and the Editor are greatly appreciated. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs14163967 fatcat:bx727nv5hbbypa4xpf7cpiadfa

Improved Spatiotemporal Representativeness and Bias Reduction of Satellite-Based Evapotranspiration Retrievals via Use of In Situ Meteorology and Constrained Canopy Surface Resistance

Ryan C. Sullivan, David R. Cook, Virendra P. Ghate, V. Rao Kotamarthi, Yan Feng
2019 Journal of Geophysical Research - Biogeosciences  
Evapotranspiration (ET) is a key component of the atmospheric and terrestrial water and energy budgets.  ...  remote sensing of vegetation and data-driven simulations (reanalysis) of meteorological conditions.  ...  We thank the FLUXNET PIs for providing their data openly: Billesbach, Bradford, and Torn (US-AR1 10.18140/FLX/  ... 
doi:10.1029/2018jg004744 fatcat:d67rzuvfnrgcbpye6moqmihatu

Data Mining in Earth System Science (DMESS 2011)

Forrest M. Hoffman, J. Walter Larson, Richard Tran Mills, Bjørn-Gustaf J. Brooks, Auroop R. Ganguly, William W. Hargrove, Jian Huang, Jitendra Kumar, Ranga R. Vatsavai
2011 Procedia Computer Science  
From field-scale measurements to global climate simulations and remote sensing, the growing body of very large and long time series Earth science data are increasingly difficult to analyze, visualize,  ...  Data mining, information theoretic, and machine learning techniques-such as cluster analysis, singular value decomposition, block entropy, Fourier and wavelet analysis, phase-space reconstruction, and  ...  Additionally this geospatial data mining method was previously applied to remotely sensed hyperspectral imagery for detection of brine scar disturbances across a regional landscape [21] .  ... 
doi:10.1016/j.procs.2011.04.157 fatcat:rwvpv2pzk5h4vmhiscyndohhim

2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14

2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  -that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  Estimation From Sentinel-2 (MSI) in the Barents Sea Using Machine Learning.  ... 
doi:10.1109/jstars.2022.3143012 fatcat:dnetkulbyvdyne7zxlblmek2qy

Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops

Terenzio Zenone, Luca Vitale, Daniela Famulari, Vincenzo Magliulo
2022 Ecological Processes  
In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms  ...  Conclusion ML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable.  ...  Acknowledgements The authors thank Dr Paul di Tomasi for the technical support of the EC instrumentation, and Dr Andrea Esposito for the informatic support of the data acquisition.  ... 
doi:10.1186/s13717-022-00400-1 fatcat:2dvglyxu7bec7m7dpoxp5w5jsu

Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Haoyu Niu, Derek Hollenbeck, Tiebiao Zhao, Dong Wang, YangQuan Chen
2020 Sensors  
With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements.  ...  Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and  ...  We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.  ... 
doi:10.3390/s20226427 pmid:33182824 pmcid:PMC7697511 fatcat:7yyhv26qb5dgfcjaxwuc4cgdq4

Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach

Florian Ellsäßer, Alexander Röll, Joyson Ahongshangbam, Pierre-André Waite, Hendrayanto, Bernhard Schuldt, Dirk Hölscher
2020 Remote Sensing  
suggest interchangeability of the methods.  ...  Recently emerging approaches based on surface temperatures and a wide range of machine learning techniques offer new possibilities to quantify transpiration.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12244070 fatcat:ob2dce566bb53hvoqzoj3cl5mu

Examining Interactions Between and Among Predictors of Net Ecosystem Exchange: A Machine Learning Approach in a Semi-arid Landscape

Qingtao Zhou, Aaron Fellows, Gerald N. Flerchinger, Alejandro N. Flores
2019 Scientific Reports  
This work provides a demonstration of the potential power of machine learning methods for combining a variety of observational datasets to create spatiotemporally extensive datasets for land model verification  ...  We apply a machine learning approach (Random Forest (RF)) to develop spatiotemporally varying NEE estimates using observations from a flux tower and several variables that can potentially be retrieved  ...  Matlab is sufficient for data analysis and has its own high quality machine learning algorithm for users -CARET. The RF method is one of many machine learning approaches.  ... 
doi:10.1038/s41598-019-38639-y pmid:30778156 pmcid:PMC6379406 fatcat:bmqex5xmurcx3neb5brn2hvhha
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