@article{ClaussYanKuenzer2016,
  author    = {Clauss, Kersten and Yan, Huimin and Kuenzer, Claudia},
  title     = {Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series},
  series = {Remote Sensing},
  volume    = {8},
  journal   = {Remote Sensing},
  number    = {5},
  doi       = {10.3390/rs8050434},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180557},
  year      = {2016},
  abstract  = {Rice is an important food crop and a large producer of green-house relevant methane. Accurate and timely maps of paddy fields are most important in the context of food security and greenhouse gas emission modelling. During their life-cycle, rice plants undergo a phenological development that influences their interaction with waves in the visible light and infrared spectrum. Rice growth has a distinctive signature in time series of remotely-sensed data. We used time series of MODIS (Moderate Resolution Imaging Spectroradiometer) products MOD13Q1 and MYD13Q1 and a one-class support vector machine to detect these signatures and classify paddy rice areas in continental China. Based on these classifications, we present a novel product for continental China that shows rice areas for the years 2002, 2005, 2010 and 2014 at 250-m resolution. Our classification has an overall accuracy of 0.90 and a kappa coefficient of 0.77 compared to our own reference dataset for 2014 and correlates highly with rice area statistics from China's Statistical Yearbooks (R2 of 0.92 for 2010, 0.92 for 2005 and 0.90 for 2002). Moderate resolution time series analysis allows accurate and timely mapping of rice paddies over large areas with diverse cropping schemes.},
  language  = {en}
}
@article{NguyenKerstenSenmaoetal.2015,
  author    = {Nguyen, Duy Ba and Kersten, Clauss and Senmao, Cao and Vahid, Naeimi and Kuenzer, Claudia and Wagner, Wolfgang},
  title     = {Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data},
  series = {Remote Sensing},
  volume    = {7},
  journal   = {Remote Sensing},
  number    = {12},
  doi       = {10.3390/rs71215808},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-137554},
  pages     = {15868-15893},
  year      = {2015},
  abstract  = {Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double- and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3\% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R-2 of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions.},
  language  = {en}
}