A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data
Published 2015 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data
Authors
Keywords
-
Journal
Remote Sensing
Volume 7, Issue 4, Pages 3633-3650
Publisher
MDPI AG
Online
2015-03-27
DOI
10.3390/rs70403633
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Derivation of temporal windows for accurate crop discrimination in heterogeneous croplands of Uzbekistan using multitemporal RapidEye images
- (2014) Christopher Conrad et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Corn Area Extraction by the Integration of MODIS-EVI Time Series Data and China’s Environment Satellite (HJ-1) Data
- (2014) Fengmei Yao et al. Journal of the Indian Society of Remote Sensing
- Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa
- (2014) Gerald Forkuor et al. Remote Sensing
- Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data
- (2014) Kun Jia et al. Remote Sensing
- Defining the Spatial Resolution Requirements for Crop Identification Using Optical Remote Sensing
- (2014) Fabian Löw et al. Remote Sensing
- The harmonised data model for assessing Land Parcel Identification Systems compliance with requirements of direct aid and agri-environmental schemes of the CAP
- (2013) Valentina Sagris et al. JOURNAL OF ENVIRONMENTAL MANAGEMENT
- Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery
- (2013) Liheng Zhong et al. REMOTE SENSING OF ENVIRONMENT
- Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets
- (2013) Clement Atzberger et al. Remote Sensing
- Hidden Markov Models for Real-Time Estimation of Corn Progress Stages Using MODIS and Meteorological Data
- (2013) Yonglin Shen et al. Remote Sensing
- Crop type mapping using spectral–temporal profiles and phenological information
- (2012) Saskia Foerster et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
- (2012) Peng Gong et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Evaluating high resolution SPOT 5 satellite imagery for crop identification
- (2011) Chenghai Yang et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- A phenology-based approach to map crop types in the San Joaquin Valley, California
- (2011) Liheng Zhong et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil
- (2011) Damien Arvor et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study
- (2011) Mustafa Turker et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data
- (2010) Ross S. Lunetta et al. International Journal of Applied Earth Observation and Geoinformation
- Hidden Markov Models for crop recognition in remote sensing image sequences
- (2010) Paula Beatriz Cerqueira Leite et al. PATTERN RECOGNITION LETTERS
- Remote Sensing of Irrigated Agriculture: Opportunities and Challenges
- (2010) Mutlu Ozdogan et al. Remote Sensing
- Global Croplands and their Importance for Water and Food Security in the Twenty-first Century: Towards an Ever Green Revolution that Combines a Second Green Revolution with a Blue Revolution
- (2010) Prasad S. Thenkabail Remote Sensing
- Spectral and Spatial-Based Classification for Broad-Scale Land Cover Mapping Based on Logistic Regression
- (2008) Georgios Mallinis et al. SENSORS
- Contribution of multispectral and multitemporal information from MODIS images to land cover classification
- (2007) Hugo Carrão et al. REMOTE SENSING OF ENVIRONMENT
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started