# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 3 Data and Methods
### Reference Datasets  
We created a reference dataset for rice paddies based on very high resolution satellite images and our aforementioned EVI and LSWI\({}_{2130}\) time series. Training areas were selected that are distributed over the different rice growing regions of China with the goal to incorporate all major rice growing schemes in regards to crop calendar, length of the growing season, multi-cropping, temperature and precipitation regimes, as well as different paddy types, such as terraces, large uniform farms and small and scattered fields interspersed with other crops and land cover. We selected 25 training areas (Figure 2). We also had access to data from Agricultural Meteorological Stations (AMSs) for the year 2002, which have been operated by the Chinese Meteorological Agency and have previously been used to derive the multiple cropping intensity of China's agriculture [63]. The subset we were able to use includes information about key crop growth phases of rice plants, such as planting, emergence and maturity dates, of 126 rice paddy fields, as well as the location of the AMSs (Figure 2). For each training area, we drew a bounding box, inside which we delineated rice paddies as the class _rice_, as well as all other land cover types and agriculture as the class _no_rice_. The size of the bounding boxes is 12 km by 12 km. An exception are three training areas with small rice terraces in Sichuan, Guangxi and Yunnan provinces where the size is 6 km by 6 km. The delineation of the land cover polygons, meaning areas of equal land cover, was based on very high resolution imagery from the WorldView, Ikonos and Spot satellite constellations for the year 2014 available through virtual globe software Google Earth, as well as MapWorld. When an image from 2014 was not available, we used imagery from 2013 or 2015 on the basis that paddy field boundaries are not usually subject to sudden change. For each paddy polygon, we decided to classify it as _rice_ or _no_rice_ based on the interpretation of the temporal profiles of TS\({}_{\text{EVI}}\) and TS\({}_{\text{LSWI2130}}\), since rice is difficult to distinguish from other crops in mono-temporal imagery. Most paddy fields are flooded prior to transplanting rice seedlings, resulting in a peak of the water index prior to a steep rise in the values of the vegetation index. This creates temporal profiles for these indices that are unique to rice and have been successfully employed for its classification [7; 13; 25; 42]. An early peak of TS\({}_{\text{LSWI2130}}\) just before a steep rise in TS\({}_{\text{EVI}}\), as the distinctive temporal feature of paddy rice, becomes apparent when it is compared to other crops and land cover (Figure 3). The temporal profiles of the water index in Figure 3a, near Lake Xingkai, Heilongjiang, and Figure 3b, near Lu'an, Anhui, show a clear and sudden rise resulting in a peak (a-1, b-2) before the vegetation index starts to rise (a-3, b-4). This is a clear indicator of the flooding of the paddies before rice is transplanted and subsequently grows, resulting in the sharp increase in TS\({}_{\text{EVI}}\). If a paddy in one of our training areas exhibited this behavior, we classified it as _rice_; otherwise, it was classified as _no_rice_. The _rice_ class therefore contains fields with at least one discernible rice crop per year. Rice is hard to distinguish from other crops in a mono-temporal satellite image, but their differences become clear when comparing the temporal profiles of (a) and (b) in Figure 3 to the example (c), a corn field in Tianin province at Bohai Bay, as well as example (d), an assembly of aquaculture ponds at Dongting Lake. With this method, we created a total of 2132 _rice_ polygons and 471 _no_rice_ polygons in our 25 training areas. In order to train and select a model, as well as determining its accuracy on independent data, the reference data have to be split. We followed the recommendations by [PERSON] _et al._[64] and performed a 50/25/25 split on the _rice_ class and a 50/50 split on the _no_rice_ class.