# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 5 Discussion
The comparison to data from China's Statistical Yearbooks on the province level has the limitation that the area measured by our classification and the sown areas published are not directly comparable. In the statistics, the _Sown Area per Season_ is published, and from that, it is not possible to deduce the total area of paddies with at least one rice crop per year, which is what our classification method measured. A direct comparison is only possible in provinces where only one rice season is cultivated, which resulted in R\({}^{2}\) of 0.780, 0.820 and 0.911 for 2002, 2005 and 2010, respectively. Comparison of our results to the _Sown Area_ with the largest extent yielded higher R\({}^{2}\), but likely included uncertainties due to underestimation of the total area of paddies due to the effects of multi-cropping. In Sichuan, and to some extent in other provinces, our classification shows a paddy area increase by 30% followed by a 30% decrease from 2002, through 2005-2010, which is not in line with the published _Sown Areas_. We suspect that these fluctuations are, on the one hand, caused by the hilly terrain present in Sichuan and other southern (southwestern) provinces, which increases omission and commission errors due to mixed pixels. On the other hand, we are also not able to rule out that this error is partly caused by our classification process. We trained the model with reference data from a single year and classified multiple years with it. It is possible that in Sichuan, our model overestimated rice area for 2005 or underestimated it for 2002 and 2010. For most regions and years, we could not identify a systematic over- or under-estimation of paddy area. In the northeastern provinces, Heilongjiang, Liaoning and Jilin, the comparison showed that our classification constantly underestimates rice area in these regions. We were not able to find a systematic error in our classification approach that causes this, but suspect it might be caused by the rice cropping schemes employed in these regions. A comparison of sown areas of rice also includes errors introduced by mixed pixel effects inherent in moderate resolution satellite images. Most rice paddies are smaller than the 250-m pixel size of our MODIS data, and as such, a pixel classified as _rice_ also contains _no_rice_ land cover, such as streets, buildings, trees, ponds and other small-scale agriculture. The inverse is also true in that a pixel classified as _no_rice_ can contain small rice paddies that are undetected by our classification. The error introduced by mixed pixel effects is highly dependent on the uniformity of the land cover [26; 27; 35] and, therefore, varies by region. Uniform, large paddy field areas in Heilongjiang likely have little commission errors, while rice terraces in the hilly regions of south China will introduce a commission error in the classification. Recent advances in rice mapping at 30-m spatial resolution using the Landsat archives might enable future studies using MODIS data to quantify the error introduced by mixed pixels. Our reference datasets do not allow for a quantization of the error introduced by the mixed pixel effect, but the agreement with the published _Sown Areas_ indicates that the omission and commission errors introduced by mixed pixels cancel each other out. In the provinces of south China, errors are likely introduced by the hilly terrain and rice terraces. The resolution of MODIS data limits the ability of our approach to accurately map these small-scale paddy field terraces. The good area agreement between our classification and the statistical data suggests that these sources of uncertainty cancel each other out. Regarding the comparison of AMS points and OCSVM\({}_{2002}\), uncertainties are introduced by the different mapping units. Our point data refer to the location of a single station within a rice field and contain no metadata about the exact location inside the field and the field size.