## 1 Introduction  
Agricultural land is a key component of land use and land cover. Nearly 40% of the Earth's land surface is now being used for agriculture [1]. The expansion of agricultural land to increase food production and intensification of its use [2] has altered the structure and function of ecosystems and in some cases has diminished their ability to continue providing valuable resources [1; 3; 4]. Many land management applications require reliable and timely land cover mapping over large heterogeneous landscapes [5]. Therefore, mapping and monitoring of agricultural land use is an important topic of high public interest. The use of satellite remote sensing to monitor the use and management of agricultural land over a large area is an efficient approach (e.g., [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]). In agricultural mapping, vegetation indices are used to capture the unique growth pattern of each crop, and high classification accuracy has been achieved by using multi-temporal imagery [10]. Multi-temporal Landsat data (spatial resolution = 30 m) has been used in phenological analyses of various regions with varied land cover (e.g., [16; 18; 19; 20; 21]). However, Landsat satellites (ex. TM, ETM+) collect data every 16 days, which makes it difficult to capture agricultural events in some regions [22], because clouds often hamper image acquisition. According to [PERSON] et al. (2015) [23], the potential for cloud cover is particularly high and agricultural events are hard to capture using existing optical satellite observation networks in Southeast Asia, Southwest Africa, and Central America, where agriculture relies on rainfall. Consequently, attention has been paid to high-frequency observation satellites (e.g., MODerate resolution Imaging Spectroradiometer (MODIS); spatial resolution = 250 m), and an agricultural monitoring method that uses high-temporal-resolution data has been developed [7; 14; 24; 25; 26; 27]. However, there is a fundamental limit to the spatial scale of the agricultural field that can be monitored due to the effects of mixed pixels resulting from the lower spatial resolution that is a trade-off with higher temporal resolution. In particular, many regions with frequent cloud cover have small agricultural areas (<2 ha) [28; 29], which cannot be reliably visualized using these data. Sugarcane is grown mainly in large-scale production areas such as Brazil, but its cultivation is also possible in small areas on tropical and subtropical islands, where it is a major crop [30; 31]. These areas have high cloud cover potential which makes surface information imaging problematic [28; 29]. In such areas, sugarcane production planning is the main concern from the point of view of employment [32]. In Okinawa, Japan, sugarcane is the main crop, and production planning is a primary concern [33]. Soil runoff from sugarcane fields has a great impact on rivers and coastal ecosystems [34], and cropping practices greatly affect its spatio-temporal fluctuations [35]. Therefore, it is important to understand the spatial distribution of crop types in fields, as well as sugarcane cropping practices, for both agricultural production planning and environmental conservation measures [36]. To achieve this understanding, tracking crop growth patterns and mapping crop types and practices by remote sensing is required. However, the crop types classification of sugarcane area in Japan does not provide satisfactory accuracy in previous study [37]. In addition, sugarcane cropping practices has not been classified. Sugarcane has a relatively long harvest period (approximately 3 months) and multiple management practices, and the spatio-temporal variations are large [38; 39]. Therefore, monitoring sugarcane by using satellite remotely sensed data is limited by spatio-temporal resolution [38]. Heterogeneous agricultural landscapes are characterized by land cover categories that can be difficult to separate spectrally due to low inter-class separability and high intra-class variability in a single image [5].