## 1 Introduction
In temperate regions where the temperature is the primary factor limiting rice growth, a signal detection time window during the transplanting phase of rice can be determined from the time-series temperature data to filter false signals from outside the window [11]. Apart from temperature constraints, the choice of when to establish a crop is driven by several other factors, such as water availability in water-limited environments, temporal variability in water salinity, risk of submergence, or combinations therein [12]. Therefore, it is possible to map the potential growing season for rice based on climatic suitability parameters. However, in South, Southeast, and East Asia, where 90% of the world's rice is produced, the subtropical and tropical temperatures, the abundance of water during the monsoon season, and the increasing reliance on irrigation in the dry season have allowed rice to be grown at nearly any time of year across the region [13]. The MODIS satellite is characterized by its hyper-temporal resolution and has become the primary data source for vegetation phenology retrieval. Several algorithms have been proposed based on this system. For example, [PERSON] et al. [14; 15] used wavelet analysis to extract seasonality information from multi-temporal MODIS imagery for rice crops in Japan and along the Mekong delta. [PERSON] et al. [16] and [PERSON] et al. [17] used labeling and grouping of unsupervised classifications with VGT data in the Mekong delta and MODIS data in the Philippines, respectively, to generate rice crop calendars. [PERSON] et al. [18] studied the temporal characteristics of rice cropping systems in Spain and demonstrated that time-series data of vegetation indices can help analyze changes in flooding periods(hydroperiod). [PERSON] [19] presented a rule-based algorithm called PhenoRice for the automated extraction of temporal information on rice crops using MODIS data. PhenoRice has been applied extensively in research [4; 20], and the results showed that this could effectively estimate temporal information. In addition to complex cropping systems, many regions in South and Southeast Asia are characterized as fragmented landscapes due to their complex terrain and frequent cloud coverage during the rice-growing season as induced by monsoons. Current applications of such phenology-based approaches generally use coarse-resolution MODIS data, which involves mixed pixel issues in Asia where smallholders constitute the majority of paddy rice agriculture [3]. For fragmented agro-landscapes, low-resolution satellite imagery may significantly over-estimate or under-estimate cropland areas [21]. The free release of Landsat archival data and the launch of Landsat 8 and Sentinel-2 provide unprecedented opportunities to map paddy rice in fragmented landscapes with a higher spatial resolution; however, the salt-and-pepper effect from the enhanced spatial resolution is another issue in pixel-based classification. Classifying crops on a per-field (object-based) approach is known to produce better results than per-pixel methods. Several remote sensing approaches to determine the extent of cropland have been explored. It has been proved that the use of multi-temporal images with higher spatial resolution generally achieves better performance compared with the use of mono-temporal images or images of lower resolution [22; 23]. [PERSON] [24] evaluated several Earth observation methodologies to automatically delineate crop field boundaries from multi-temporal Sentinel-2 imagery, which uses edge detection as applied to multiple images acquired during the growing season along with image segmentation to delimit agricultural fields, orchards, and vineyards. A promising result was achieved by applying the Canny edge detector to multiple Sentinel-2 images [24]. However, the usage of multi-temporal high spatial resolution data causes an explosive increase in data volume, meaning the size of study area is limited by data storage and processing capability of the local general-purpose computer.