# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 1 Introduction  
China is the world's largest producer and importer of rice with a national rice production of 208 million tonnes, 28% of the global production, and a rice import surplus of 2.2 million tonnes [1; 2]. Rice is the most important food crop in China, consumes vast amounts of irrigation water and fertilizer and produces green-house relevant methane emissions. Knowledge about the amount and location of rice paddy fields is therefore of dual significance: for food security and policy making in China, as well as for global climate change. Rice is mostly cultivated on small paddy fields with crop cycles that can differ every year, which, combined with the vast amount of fields, makes the collection of current data based on agricultural census a labor-intensive and slow process. Remote sensing methods can help to deliver rice maps that are accurate, cost-effective to produce and more timely than agricultural census data. Remote sensing of rice with optical satellite sensors exploits the phenological development of the rice plant throughout its life-cycle. It is commonly divided into the vegetative, the reproductive and the ripening phases. The vegetative phase consists of the germination, seedling, tillering and stem elongation stages. The second phase includes panicle initiation, heading and flowering. Ripening is the last phase and includes the milk, dough and mature grain stages. Before transplanting of the riceseedlings, paddies are usually flooded with water. After transplanting, the rice seedlings undergo a rapid growth and greening during the reproductive phase, which culminates at the heading stage. During the last phase, the plants' leaf density decreases until harvest [3]. All of these changes in plant morphology throughout rice plants' life-cycle influence their interaction with waves in the visible light, infrared and microwave spectra and, therefore, produce discernible changes in time series of remotely-sensed data [4; 5]. In order to distinguish rice from other land cover, especially other crops, time series analysis of multi- or hyper-temporal data has been the most prolific technique. Rice is dominantly grown in precipitation-rich environments with regular cloud cover. Authors of remote sensing-based rice area studies therefore emphasize the need for dense time series and employ data from microwave sensors [6; 7; 8; 9; 10; 11; 12], which are only sparsely affected by clouds, use optical sensors with high revisit times to gather a sufficient amount of cloud-free observations [13; 14; 15; 16; 17; 18; 19] or a combination of both [20; 21; 22]. Two optical sensors that have been widely used for rice area mapping are MODIS (Moderate Resolution Imaging Spectroradiometer) and SPOT-VGT (Satellite Pour l'Observation de la Terre-Vegetation) due to their long standing archives, data acquisition in the visible light, as well as infrared ranges and high revisit times. Most studies using these sensors detected flooding events followed by rapid vegetation growth, which is regarded as a distinctive temporal feature of paddy rice. Remote sensing studies exploiting this temporal feature have mostly been conducted with decision-tree classifications that employed the combination of different vegetation and water indices and auxiliary data, such as crop calendars, land surface temperature or precipitation [13; 23; 24]. Studies that focused on the complete temporal signal of rice classified rice areas using wavelet filters [25; 26], empirical mode decomposition [27; 28] or dynamic time warping [29; 30] have also been undertaken. A number of studies have been performed in China using optical satellite sensors to map rice paddies. A small amount of remote sensing studies have been published that mapped rice areas at the national scale with the majority of studies focusing on mapping at the regional scale in different key regions in northern or southern China.