# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 1 Introduction
One of the first products was published by [PERSON] _et al._[31], who combined agricultural census data with a Landsat-derived land cover map to generate a rice map for mainland China with a 0.5\({}^{\circ}\) resolution. [PERSON] _et al._[23] used MODIS data to generate a paddy rice map for southern China at 500-m resolution based on a method that detects flooding of the paddies with a temporary inversion of vegetation and water index values. [PERSON] _et al._[32] employed a similar method with the addition of regionalized crop calendars to produce a 500-m rice map based on the MOD09A1 dataset for China. [PERSON] _et al._[33] developed a method comparing the amplitudes of a water and vegetation index and found that rice areas have lower change ratios of these amplitudes from the tillering to the heading stage. Applying this method to southeast China, they reported a reduction in rice-planted areas of 30% from 2001-2013 with double-cropped rice areas showing a 49% decrease [34]. For the Yangtze Delta region, [PERSON] and [PERSON] [35] mapped rice at 500-m resolution based on a spectral index using the green and mid-infrared MODIS bands and reported a stable total planting area of rice with conversion from double to single cropping in some regions. [PERSON]'s rice mapping approach has also been demonstrated to work with Landsat data at 30-m resolution with case studies in the Yangtze-Huaihe Plain [22] and the Sanjiang Plain [36]. [PERSON] _et al._[37] showed that data from the Chinese Huanjing-1 (HJ-1) A/B satellites can also be used to map rice in the fragmented agricultural areas of the Hangjiahu Plain. During the last two centuries, rice production in northern China continually increased due to food demand and the effects of changing climate. This emerging rice region has been examined in a number of studies. The well-known vegetation-water-index-inversion approach was applied on MODIS data to map rice in northeast China at 500-m resolution for the years 2001-2009 [38]. [PERSON] _et al._[39] integrated Landsat 8 data with MODIS Land-surface temperature data to map rice in the Panjin Plain for 2013 at 30-m resolution. FORMOSAT-2 images and a maximum-likelihood classifier were used by [PERSON] _et al._[40] to map rice in Qixing Farm County at 8-m resolution for 2009. Recent studies employ time series analysis of Landsat images to map rice at the regional scale [41]. [PERSON] _et al._[42] developed a Landsat-based rice classification and land cover change detection system, which they showed to work by classifying rice planting area at the Chinese-Russian border for five epochs between 1986 and 2010 and reported a twenty-fold increase in paddy rice area. This approach was also shown to work at a larger scale by classifying rice areas in northeast China, North and South Korea and Japan [43]. Except for approaches focusing on rice seasonality mapping, classifying rice can be regarded as a binary classification task with the two classes _rice_ and _no_rice_. The distribution of both classes is inherently imbalanced due to the large size and diversity of the _no_rice_ class. The imbalance of the training set is an issue that not all supervised machine learning algorithms handle well [44]. Most large-scale rice classification studies to-date used classification techniques based on the phenological development of the rice plant and the resulting spectral responses. Support Vector Machines (SVMs) offer a framework that works well with limited amounts of training data and uneven class distribution and, as such, are a viable and well-studied tool in land cover classifications based on remotely-sensed data [45]. SVMs sparked a number of methods for one-class classification and outlier detection, such as support vector data description [46], biased SVM [47] and one-class SVM [48; 49]. In this study, we employed a one-class classification approach using MODIS-derived time series data to map all rice paddies in mainland China.