# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 5 Discussion  
A novel rice mapping approach using hyper-temporal MODIS data and a one-class support vector machine was developed and applied to continental China. Our results show that the overall paddy rice area remained largely stable between 2002 and 2014, which is in line with previous studies [23; 31; 34; 53] and the agricultural census data published by the National Bureau of Statistics of China. Our maps are able to show detailed shifts in rice area on the local to county scale, which could not be deduced from the publicly-available agricultural statistics. The biggest observable change during the time period studied is the large increase in rice area in Heilongjiang, which has also been reported by previous studies [36; 38; 40; 42]. From 2005 onward, paddy rice area in Heilongjiang increased at an unprecedented rate. We suspect that this behavior is largely driven by China's increasing demand for rice as a food supply and the compensation of cropland loss in southern China by cropland gains in the north [73]. The increase of paddy rice area and agriculturally-used land is also made possible by rising temperatures, as well as an increase of irrigated areas by means of newly-constructed pump wells and irrigation channels [42; 74]. In 2014, China was the largest producer of rice and a large importer of internationally-traded rice. Since the rice yield per sown area cannot be increased indefinitely and paddy area density lessened in the southern provinces [34], we suspect this increase in area dedicated to rice agriculture will continue in China's northeastern provinces to satisfy the rice demand and compensate for area losses in the southern provinces. We used MODIS data in our method, which have been preprocessed from the raw data into composites with vegetation indices and quality flags included. We chose the MOD13Q1 and MYD13Q1 datasets because their advantages and limitations are well studied and reported; they are continuously improved and are freely available, as well as easy to use for anyone who wants to replicate our approach or transfer it to other rice growing regions. By extracting features that are robust to temporal overlap and the length of the time series, we were able to incorporate the year 2002 in our classification approach. 2002 has less data points than the other years due to the Aqua satellite, from which the MYD13Q1 are derived, being launched in May 2002. Based on the features extracted from the MODIS time series, we classified rice areas with a one-class support vector machine. We chose this classifier for two main reasons: it is not influenced by uneven class distribution, and it works well with a limited amount of training data. Classifying paddy rice areas in a study region as large as China is an inherently imbalanced classification task. Rice is China's second largest crop after corn, according to _Sown Area_[2]. Even though rice paddies make up a large part of the arable land, they only make up a small fraction of China's total land cover. Supportvector machines provide a classification framework that works well in cases of such uneven class distribution. Employing a 'one-against-all' binary classification of _rice_ and _no_rice_ would have been a daunting endeavor, since it would require the collection of training data for all possible land cover in China that is not rice. Our OCSVM model obviates the need for training samples from the outlier class and was trained only with samples from our target class: _rice_. The model was solely trained with samples from the year 2014, and the results of our accuracy assessment show that it can be used to predict rice areas from other years, as well. The accuracy assessment of our classifications show an overall accuracy of 0.90 when compared to our 2014 reference polygons, 0.64 compared to the AMS points and coefficients of determination larger than 0.90 compared to the official agriculture statistics.