# Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand
These free and dense image time series have demonstrated the potential to capture crop phenology, i.e., the physiological development stages of plant growth from planting to harvest, and hence map crop types [15; 16]. This helps address the classic challenge of high spectral similarities among diverse crop types when using single-date imagery. Moderate success has been reported at both local and regional scales (e.g., [17; 18]). However, medium- and low-resolution sensors (e.g., Landsat and MODIS, respectively) have been utilized in the majority of studies for crop type mapping. This has posed challenges in regions like Southeast Asia, especially Thailand, which has a long-term tradition of family farming, and the vast majority of farmland is run by smallholder farmers. Half of Thai farming households owned below 10 rai (1.6 ha) of farmland, with an average size of 14.3 rai (2.3 ha; [19]). At a medium- or coarse-resolution, an image pixel may contain one or more farming patches resulting in strong mixing spectral signatures. Such a mixing effect is not only caused by the size of the land. To maximize economic returns from smallholdings, Thai farmers often practice mixed cultivation or cropping (also known as intercropping), which involves growing two or more plant species simultaneously in the same patch of land. For example, pineapples have a shallow root system and start to produce fruits in two years in Eastern Thailand. Thus, in a young rubber plantation, a mixed cultivation of pineapples and rubber trees (Figure 1) allows farmers to generate income before rubber trees reach maturity and can be tapped in 6-7 years [18]. While dense image time series can carefully capture crop growing stages, there is a lack of investigation into the effect of mixed cultivation on crop mapping performance. We note that relatively high-resolution data have been recently used in tropical crop mapping; however, previous studies have been restricted to small geographic regions or to extracting single or a limited number of crop types (e.g., [20; 21; 22; 23]). Based on the above considerations, the main objective of this study was to map and analyze the spatiotemporal change patterns of tropical crop cultivation in Eastern Thailand through (i.e., before, during, and after) the COVID-19 pandemic. We focused on Eastern Thailand because it is particularly illustrative for its diversity of smallholder crop production (including high-value export crops such as rubber, rice, durian, and rambutans) and the rapidly evolving land use change due to the planned urbanization of land near beaches and industrial corridors. The region also includes the Eastern Economic Corridor (EEC), which has been promised strong government support to accelerate the country's economic growth in the new era of Thailand 4.0 [24]. Specifically, we ask two questions in the study: (i) How accurate is it to use high-resolution (10 m), dense image time series to map diverse types of tropical crops on smallholder farmlands? (ii) How did the crop cultivation pattern change (i.e., shift in crop type and areal size) through the pandemic in Eastern Thailand, and why?