## 5 Conclusions  
Our results support the hypothesis that the integration of Sentinel-1 and 2 time-series data using a phenology-based method in the GEE cloud computing platform can accurately produce maps of rice extent, cropping intensity, and cropping calendars. Furthermore, the proposed method produced rice extent and growth-stage maps with a high spatial resolution (10 m) over a large region. The results show that the proposed method had a high overall map accuracy of 95.95%, with a kappa coefficient of 0.92. The method distinguished rice growing stages and established that most of the rice fields in Peninsula Malaysia had two cropping seasons a year. The proposed methodology had several advantages compared to past studies:  
* It produces not only high-resolution maps of rice extent but also of intensity and cropping calendars in tropical regions due to the use of both Sentinel-1 and 2 time series data;
* It produces high-accuracy maps rapidly without extensive field survey data due to the use of an unsupervised K-Means method in the GEE;  
Figure 9: Sources of error in rice field classification. The green colour shows regions classified as rice field areas, while the rest were non-rice areas in Kedah State (6\({}^{\circ}\)6\({}^{\prime}\)41.10\({}^{\prime\prime}\)N, 100\({}^{\circ}\)22\({}^{\prime}\)58.91\({}^{\prime\prime}\)E). * It reduces salt and pepper effects due to the use of targeted regions of interest;
* It eliminates the need for masking clouds and cloud shadows due to the use of monthly greenest images or maximum NDVI value composites of Sentinel-2 data;
* It is able to identify the signatures of rice phenology stages, including soil tillage and planting, vegetative, reproductive, and ripening stages due to the fusion of Sentinel-1 and 2 time series data. The results presented in this paper could be used as a complementary source of information to field survey data. This information is necessary to achieve the United Nations 2030 Agenda for the Sustainable Development Goals (SDGs) [80]--particularly Goal 2, Zero Hunger. As paddy rice consumes a large amount of water, this data can be used to address water security management (SDG Goal 6). Rice fields are also an important source of methane, and the spatial information can be used for greenhouse gas accounting (SDG Goal 6). Finally, the data can help form government policies to address national food security to reduce poverty (Goal 1). The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14081875/s1](https://www.mdpi.com/article/10.3390/rs14081875/s1)]([https://www.mdpi.com/article/10.3390/rs14081875/s1](https://www.mdpi.com/article/10.3390/rs14081875/s1)), Table S1: Growth stages from generalized phenological parameters for rice clusters in rice fields in the Peninsula Malaysia, Malaysia. Table S1 corresponds to Figures 5-7 and Table 4. T = Tillage, V = Vegetative-1, V2 = Vegetative-2, R = reproductive, M = Maturity, F (blank) = Fallow. The code built for the mapping is available at [[https://code.eartheengine.google.com/61207916864F520a56b9d43](https://code.eartheengine.google.com/61207916864F520a56b9d43) eff10d60]([https://code.eartheengine.google.com/61207916864F520a56b9d43](https://code.eartheengine.google.com/61207916864F520a56b9d43) eff10d60), (accessed on 10 April 2022). Comparison between the rice extent map product from this study and NESEA-Rice10 is available at [[https://rudiyanto.users.eartheengine.app/view/malaysiarice](https://rudiyanto.users.eartheengine.app/view/malaysiarice)]([https://rudiyanto.users.eartheengine.app/view/malaysiarice](https://rudiyanto.users.eartheengine.app/view/malaysiarice)), (accessed on 10 April 2022). Conceptualization, R., B.I.S., B.M., R.M.S., N.C.S. and S.G.E.G. ; methodology, R. and B.M. ; software, F. and R.; validation, F. and R.; formal analysis, F. and R.; investigation, F. and R.; resources, F. and R.; data curation, F. and R.; writing--original draft preparation, F. and R.; writing--review and editing, B.M.