# Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand
## 4 Discussion  
### Effectiveness of Fine-Scale Image Time Series in Tropical Crop Type Mapping  
Satellite image time series have gained increasing popularity in mapping Asian cropping patterns over the past decade. This trend has benefited from the availability of openly accessible satellite datasets (e.g., MODIS, Landsat, and Sentinel imagery), the maturity of remote sensing algorithms simulating plant phenological cycles, and fast-growing computing capabilities. In Asia, particularly Southeast Asia, the majority of crop mapping activities have employed medium- or low-resolution Landsat or MODIS image time series (e.g., [16; 39; 40; 41; 42]). With the recent availability of Sentinel-2's 10-m resolution imagery, it has become easier to address the spectral mixing issue over smallholder farmlands. To date, most studies have focused on identifying individual crop types, such as sugarcane [43], mung bean [44], and paddy rice [45], with reported accuracies typically higher than 90%. Some other studies have attempted to map multiple, but limited numbers of, crop types (equal to or fewer than five) in one modeling framework (e.g., [46; 47; 48]). The reported accuracies were typically lower than mapping one single crop type, due to the similarity in phenology among the studied crops, making inter-class variation challenging to capture. However, their accuracies were mostly higher than 70%. Figure 9: Change of acreage of 10 main crop types in Eastern Thailand from summer 2017 to summer 2023. In our endeavor to map a diverse array of crop types across smallholder farmlands, we observed significant variation in model performance, ranging from less than 50% to 100%. Notably, rice, as the predominant crop in the region, achieved a relatively high accuracy. Rice paddies, characterized by partial flooding, allow for up to three harvests annually in Thailand. Despite governmental advisories urging farmers to forgo one crop due to recent drought events [49], the distinct phenology of crops within paddy fields facilitates differentiation from those with lower water requirements. For high-value cash crops such as rubber, pineapple, eucalyptus, coconut, durian, and oil palm, phenology proved effective in mapping mature plantations using high-resolution image time series. However, we observed overestimation in the majority of these cash crops, primarily attributed to young plantations with open tree canopies, where the ground surface is covered by grass and shrubs, leading to spectral confusion. This phenomenon also contributed to the high underestimation of the non-crop land cover grass/shrub. While object-based image analysis demonstrated effectiveness in mapping individual crop types, such as rubber plantations [18], determining the optimal object size becomes challenging in regions with a diverse mix of crop types at various growth stages. Deep learning presents another potential solution (e.g., [48; 50]), though logistical challenges often hinder the collection of large volumes of field samples for model training. In contrast to urban mapping, where human photo interpretation can efficiently provide large sample volumes, accurate crop type identification often necessitates on-site assessments. Despite these challenges, our study achieved an overall mapping accuracy of 85.6%, with some crop types surpassing 90%, which was comparable to recent studies on multi-crop mapping. Furthermore, the use of high-resolution imagery demonstrated effectiveness in situations involving intercropping, such as durian or rubber plantations mixed with pineapple or cassava. Compared to the use of medium- or low-resolution data, young plantations in smallholder farmlands can be more accurately identified, providing crucial insights for effective policymaking. While it is possible to consider combined classes in crop mapping owing to mixed cultivation, they do not align with the popular classification schemes employed in Thailand or many other countries in Southeast Asia.