Extending Crop Type Reference Data Using a Phenology-Based Approach

Object-based crop identification using multiple vegetation indices, textural features and crop phenology

Article history: Received 28 August 2010 Received in revised form 14 January 2011 Accepted 16 January 2011 Available online 25 February 2011

A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data

Vegetation monitoring and mapping based on multi-temporal imagery has recently received much attention due to the plethora of medium-high spatial resolution satellites and the improved classification accuracies attained compared to uni-temporal approaches. Efficient image processing strategies are needed to exploit the phenological information present in temporal image sequences and to limit da...

Detecting Rice Crop Phenology from Time-series Modis Data

Information on rice crop phenology is important for crop management. This study aimed to detect rice crop phenology in the Vietnamese Mekong Delta using time-series MODIS data in 2007. Data processing steps included: (1) constructing time-series MODIS NDVI data, (2) filtering noise from the time-series data using empirical mode decomposition (EMD) and wavelet transform, (3) detecting rice crop ...

Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop pheno...

Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index