@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{KnauerGessnerFensholtetal.2016,
  author    = {Knauer, Kim and Gessner, Ursula and Fensholt, Rasmus and Kuenzer, Claudia},
  title     = {An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes},
  series = {Remote Sensing},
  volume    = {8},
  journal   = {Remote Sensing},
  number    = {5},
  doi       = {10.3390/rs8050425},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180712},
  pages     = {425},
  year      = {2016},
  abstract  = {Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series of high temporal and spatial resolution for such areas. In order to overcome this problem, data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) have been established and frequently used in recent years to generate high-resolution time series. In order to make it applicable to larger scales and to increase the input data availability especially in cloud-prone areas, an ESTARFM framework was developed in this study introducing several enhancements. An automatic filling of cloud gaps was included in the framework to make best use of available, even partly cloud-covered Landsat images. Furthermore, the ESTARFM algorithm was enhanced to automatically account for regional differences in the heterogeneity of the study area. The generation of time series was automated and the processing speed was accelerated significantly by parallelization. To test the performance of the developed ESTARFM framework, MODIS and Landsat-8 data were fused for generating an 8-day NDVI time series for a study area of approximately 98,000 km\(^{2}\) in West Africa. The results show that the ESTARFM framework can accurately produce high temporal resolution time series (average MAE (mean absolute error) of 0.02 for the dry season and 0.05 for the vegetative season) while keeping the spatial detail in such a heterogeneous, cloud-prone region. The developments introduced within the ESTARFM framework establish the basis for large-scale research on various geoscientific questions related to land degradation, changes in land surface phenology or agriculture},
  language  = {en}
}