Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
出版年份 2015 全文链接
标题
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
作者
关键词
-
出版物
Remote Sensing
Volume 7, Issue 12, Pages 16398-16421
出版商
MDPI AG
发表日期
2015-12-09
DOI
10.3390/rs71215841
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band
- (2015) Luca Pasolli et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- A support vector machine to identify irrigated crop types using time-series Landsat NDVI data
- (2015) Baojuan Zheng et al. International Journal of Applied Earth Observation and Geoinformation
- Forest Biomass and Carbon Stock Quantification Using Airborne LiDAR Data: A Case Study Over Huntington Wildlife Forest in the Adirondack Park
- (2014) Manqi Li et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2
- (2014) H. McNairn et al. International Journal of Applied Earth Observation and Geoinformation
- Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling
- (2014) İnci Güneralp et al. International Journal of Applied Earth Observation and Geoinformation
- Characterization of aboveground biomass in an unmanaged boreal forest using Landsat temporal segmentation metrics
- (2014) Ryan J. Frazier et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- A multiple criteria active learning method for support vector regression
- (2014) Begüm Demir et al. PATTERN RECOGNITION
- Green area index from an unmanned aerial system over wheat and rapeseed crops
- (2014) Aleixandre Verger et al. REMOTE SENSING OF ENVIRONMENT
- Translating criteria of international forest definitions into remote sensing image analysis
- (2014) Paul Magdon et al. REMOTE SENSING OF ENVIRONMENT
- Airborne multi-temporal L-band polarimetric SAR data for biomass estimation in semi-arid forests
- (2014) Mihai A. Tanase et al. REMOTE SENSING OF ENVIRONMENT
- Intra-and-Inter Species Biomass Prediction in a Plantation Forest: Testing the Utility of High Spatial Resolution Spaceborne Multispectral RapidEye Sensor and Advanced Machine Learning Algorithms
- (2014) Timothy Dube et al. SENSORS
- Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS
- (2013) Zhe Yuan et al. ADVANCES IN ENGINEERING SOFTWARE
- Forest Biomass Mapping of Northeastern China Using GLAS and MODIS Data
- (2013) Yuzhen Zhang et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models
- (2013) Felix Kogan et al. International Journal of Applied Earth Observation and Geoinformation
- Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines
- (2013) F. Löw et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Rice biomass retrieval from multitemporal ground-based scatterometer data and RADARSAT-2 images using neural networks
- (2013) Mingquan Jia et al. Journal of Applied Remote Sensing
- ANFIS Based Land Cover/Land Use Mapping of LISS IV Imagery Using Optimized Wavelet Packet Features
- (2013) S. Rajesh et al. Journal of the Indian Society of Remote Sensing
- Designing and modeling of ultra low voltage and ultra low power LNA using ANN and ANFIS for Bluetooth applications
- (2013) Gholamreza Karimi et al. NEUROCOMPUTING
- Extreme learning machines for soybean classification in remote sensing hyperspectral images
- (2013) Ramón Moreno et al. NEUROCOMPUTING
- Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation
- (2013) S. Paloscia et al. REMOTE SENSING OF ENVIRONMENT
- An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States
- (2013) David M. Johnson REMOTE SENSING OF ENVIRONMENT
- Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application
- (2013) Prashant K. Srivastava et al. WATER RESOURCES MANAGEMENT
- Estimating the Above-Ground Biomass in Miombo Savanna Woodlands (Mozambique, East Africa) Using L-Band Synthetic Aperture Radar Data
- (2013) João Carreiras et al. Remote Sensing
- Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: A comparison of different transfer functions
- (2012) Rajendra Prasad et al. ADVANCES IN SPACE RESEARCH
- Polarimetric RADARSAT-2 imagery for soil moisture retrieval in alpine areas
- (2012) L. Pasolli et al. CANADIAN JOURNAL OF REMOTE SENSING
- Mapping field-scale yield gaps for maize: An example from Bangladesh
- (2012) U. Schulthess et al. FIELD CROPS RESEARCH
- The SMOS Soil Moisture Retrieval Algorithm
- (2012) Yann H. Kerr et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Assessing Performance of L- and P-Band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-Ground Biomass
- (2012) Maxim Neumann et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm
- (2012) Onisimo Mutanga et al. International Journal of Applied Earth Observation and Geoinformation
- Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data
- (2012) Elfatih M. Abdel-Rahman et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Fusion of remotely sensed data for soil moisture estimation using relevance vector and support vector machines
- (2012) Bushra Zaman et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms
- (2012) Shaban Shataee et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Forest biomass estimation from airborne LiDAR data using machine learning approaches
- (2012) Colin J. Gleason et al. REMOTE SENSING OF ENVIRONMENT
- Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa)
- (2012) João M.B. Carreiras et al. REMOTE SENSING OF ENVIRONMENT
- Assimilation of Remote Sensing and Crop Model for LAI Estimation Based on Ensemble Kaiman Filter
- (2011) Rui LI et al. Agricultural Sciences in China
- Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery
- (2011) George P. Petropoulos et al. COMPUTERS & GEOSCIENCES
- Evaluating high resolution SPOT 5 satellite imagery for crop identification
- (2011) Chenghai Yang et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping
- (2011) George P. Petropoulos et al. EXPERT SYSTEMS WITH APPLICATIONS
- Estimating Soil Moisture With the Support Vector Regression Technique
- (2011) Luca Pasolli et al. IEEE Geoscience and Remote Sensing Letters
- A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: A case study in Quebec, Canada
- (2011) Gang Chen et al. International Journal of Applied Earth Observation and Geoinformation
- Derivation of biomass information for semi-arid areas using remote-sensing data
- (2011) Christina Eisfelder et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- An assessment of the effectiveness of a random forest classifier for land-cover classification
- (2011) V.F. Rodriguez-Galiano et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Mapping forest canopy height globally with spaceborne lidar
- (2011) Marc Simard et al. JOURNAL OF GEOPHYSICAL RESEARCH
- Estimation of flood losses to agricultural crops using remote sensing
- (2011) Felipe-Omar Tapia-Silva et al. PHYSICS AND CHEMISTRY OF THE EARTH
- Characterizing forest canopy structure with lidar composite metrics and machine learning
- (2011) Kaiguang Zhao et al. REMOTE SENSING OF ENVIRONMENT
- Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda
- (2011) Valerio Avitabile et al. REMOTE SENSING OF ENVIRONMENT
- Prediction of plot-level forest variables using TerraSAR-X stereo SAR data
- (2011) Mika Karjalainen et al. REMOTE SENSING OF ENVIRONMENT
- Estimating biophysical parameters of rice with remote sensing data using support vector machines
- (2011) XiaoHua Yang et al. Science China-Life Sciences
- Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance
- (2010) T. Rumpf et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment
- (2010) Barbara Koch ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Recent decline in the global land evapotranspiration trend due to limited moisture supply
- (2010) Martin Jung et al. NATURE
- Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches
- (2010) Scott L. Powell et al. REMOTE SENSING OF ENVIRONMENT
- Estimating soil moisture using remote sensing data: A machine learning approach
- (2009) Sajjad Ahmad et al. ADVANCES IN WATER RESOURCES
- A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China
- (2009) Yichun Xie et al. ECOLOGICAL MODELLING
- Automatic Parameter Optimization for Support Vector Regression for Land and Sea Surface Temperature Estimation From Remote Sensing Data
- (2009) G. Moser et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Empirically Adopted IEM for Retrieval of Soil Moisture From Radar Backscattering Coefficients
- (2009) Kaijun Song et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Mapping paddy rice with multitemporal ALOS/PALSAR imagery in southeast China
- (2009) Yuan Zhang et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data
- (2009) Tarendra Lakhankar et al. Remote Sensing
- Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques
- (2009) Brian Barrett et al. Remote Sensing
- Precision agriculture on grassland: Applications, perspectives and constraints
- (2008) Jürgen Schellberg et al. EUROPEAN JOURNAL OF AGRONOMY
- Soil moisture retrieval from remotely sensed data: Neural network approach versus Bayesian method
- (2008) Claudia Notarnicola et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- A Comparison of Algorithms for Retrieving Soil Moisture from ENVISAT/ASAR Images
- (2008) Simonetta Paloscia et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland
- (2008) Roshanak Darvishzadeh et al. REMOTE SENSING OF ENVIRONMENT
- Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning
- (2008) Kai Liu et al. WETLANDS
- Mapping sunflower yield as affected by Ridolfia segetum patches and elevation by applying evolutionary product unit neural networks to remote sensed data
- (2007) P.A. Gutiérrez et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information
- (2007) J BLACKARD et al. REMOTE SENSING OF ENVIRONMENT
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started