Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data
Published 2016 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data
Authors
Keywords
-
Journal
Remote Sensing
Volume 8, Issue 8, Pages 682
Publisher
MDPI AG
Online
2016-08-22
DOI
10.3390/rs8080682
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data
- (2016) Kun Jia et al. REMOTE SENSING OF ENVIRONMENT
- GLASS Daytime All-Wave Net Radiation Product: Algorithm Development and Preliminary Validation
- (2016) Bo Jiang et al. Remote Sensing
- Validating GEOV1 Fractional Vegetation Cover Derived From Coarse-Resolution Remote Sensing Images Over Croplands
- (2015) Xihan Mu et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Global Land Surface Fractional Vegetation Cover Estimation Using General Regression Neural Networks From MODIS Surface Reflectance
- (2015) Kun Jia et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Evaluation of Spatiotemporal Variations of Global Fractional Vegetation Cover Based on GIMMS NDVI Data from 1982 to 2011
- (2014) Donghai Wu et al. Remote Sensing
- Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance
- (2013) Zhiqiang Xiao et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products
- (2013) Fernando Camacho et al. REMOTE SENSING OF ENVIRONMENT
- GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production
- (2013) F. Baret et al. REMOTE SENSING OF ENVIRONMENT
- Remote Sensing of Fractional Green Vegetation Cover Using Spatially-Interpolated Endmembers
- (2012) Brian Johnson et al. Remote Sensing
- LIBSVM
- (2012) Chih-Chung Chang et al. ACM Transactions on Intelligent Systems and Technology
- A comparison of methods for estimating fractional vegetation cover in arid regions
- (2011) Guli Jiapaer et al. AGRICULTURAL AND FOREST METEOROLOGY
- Variational retrieval of leaf area index from MODIS time series data: examples from the Heihe river basin, north-west China
- (2011) Zhiqiang Xiao et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Identification of priority areas for controlling soil erosion
- (2010) Xiwang Zhang et al. CATENA
- Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations
- (2010) Aleixandre Verger 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
- Comparison of three vegetation monitoring methods: Their relative utility for ecological assessment and monitoring
- (2009) H. Godínez-Alvarez et al. ECOLOGICAL INDICATORS
- Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area
- (2009) Juan Jiménez-Muñoz et al. SENSORS
- Estimation of leaf area and clumping indexes of crops with hemispherical photographs
- (2008) Valérie Demarez et al. AGRICULTURAL AND FOREST METEOROLOGY
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started