## 4 Results  
### Crop Type/LC Classification  
RF classification maps are reported in Figure 3. These maps turn out to be very useful to monitor LC dynamics over time; thus, it can be noted that some classes have changed over the years while others have remained stable in terms of area. For example, on the 2001 map, the main classes are rice and forest; while considering the same classes in the 2021 map, they seem to have less impact. This assumption can actually be made, specifically when considering that the cassava class area within the 2001 map compared to the 2021 map rises from about 5000 km\({}^{2}\) to almost 12,000 km\({}^{2}\), respectively. Moreover, in addition to the cassava increase, para rubber, sugarcane and built-up land also register large surface increments. Specifically, during the observed period, there is an increase from dozens of km\({}^{2}\) to over 3000 km\({}^{2}\) for para rubber, from about 4500 km\({}^{2}\) to almost 6000 km\({}^{2}\) for sugarcane and from about 2000 km\({}^{2}\) to almost 3000 km\({}^{2}\) for built-up land, respectively, in the maps referring to 2001 and 2021. In contrast, rice cultivation, an opposite behavior, can be observed, i.e., a decline in rice fields cultivation over time. Specifically, fields classified as rice in 2001 covered about 25,000 km\({}^{2}\), while there was only 17,000 km\({}^{2}\) in 2021. Incontrast, considering the water classes and bare soil, these have remained constant over time in terms of surface. In order to better understand class variations over time, hexagon examples of crop types/land cover (LC) classifications considering the eight classes for the entire observed period within the Chi River Basin were reported (Figure 4). In particular, a qualitative increase in area is again observed over time in the cassava, para rubber, sugarcane and built-up land classes. Specifically, row d of Figure 4 shows a large change in comparisons of the cassava class between 2016 and 2021. In contrast, with regard to the hexagons representing the para rubber class (row b), a large change in LC can be observed between 2006 and 2011. The hexagons representing the sugarcane class (line b) show a large variation between 2001 and 2011, while the one representing the water class (row f) show no variation between the years. The interesting point that arises is that, according to Figure 4, LC variation does not occur between two specific periods, but can in relation to the class observed that can occur between different years. Following the primary performance parameters that had been calculated (refer to the section in Materials and Methods for more information), a comparison and discussion of each classifier was proposed with reference to the validation set that was presented in Table 1. Results related to the model accuracy and validation were reported in Figure 5 and Tables 4 and 5. Figure 3: Crop types/land cover (LC) classifications with eight classes for 2001, 2006, 2011, 2016 and 2021 in the Chi River Basin, Thailand using all available Landsat data, several vegetation indices (VIs) and geo-data together with a random forest (RF) classifier based on Google Earth Engine (GEE). Figure 4: The hexagon examples of crop types/land cover (LC) classifications with eight classes for 2001, 2006, 2011, 2016 and 2021 in the Chi River Basin, Thailand. (**a**) Built-up; (**b**) para rubber; (**c**) rice; (**d**) cassava; (**e**) sugarcane; (**f**) forest; and (**g**) bare soil. Considering Figure 5, the average values of OA (model accuracy), Kappa (model accuracy), OA (validation) and Kappa (validation) are 86.7%, 83.4%, 90.3% and 88.1%, respectively. Such high average values of OA and Kappa validation confirm the goodness of the proposed classifications. However, considering OA over the years, the 2021 results have the highest values (OA = 92.9%), while 2006 results have the lowest (OA = 88.7%).