## 5 Discussion  
### Crop Type/LC Classification Assessment  
The joint use of optimized RF classification models (involving single bands and VIs) and auxiliary geo-data presented results of agricultural fields/LC classifications. As reported in Figure 5, the constructed RF classification models achieved excellent results in all five time periods analyzed. Specifically, the highest accuracy of the crop type/LC classification has been observed in 2021, followed by 2001, 2016 and 2006, respectively. Specifically, 2021 accuracy is always higher than in other years, which can certainly be related to the number of images available for the year in question (134 scenes available in the 2021 compared to 40/90 available in other years). The efficient result of 2021 that  
Figure 7: Crop type/ land cover (LC) changes over 20 years in the Chi River Basin, Thailand: (**a**) 2001–2006; (**b**) 2006–2011; (**c**) 2011–2016; and (**d**) 2016–2021. The chord diagrams (**a–d**) are created from all available Landsat data and a random forest (RF) classifier. used the high spectral and temporal information of the combined analysis of Landsat data sets (i.e., L8 and L9) as well as the availability of high-quality ground-truth data were used. Recent studies [11; 27] also highlighted the multi-temporal Landsat data support LC and crop type classifications. Furthermore, implementing important RF feature rankings makes it possible to select the input variables when applying the classification to large-scale mapping. Similar results were reported by [PERSON], et al. [38], who highlighted the efficiency for use in automated mapping of crop type/LC in the large region using Landsat spectral-temporal metrics. In our study, Landsat spectral-temporal compositing, together with the powerful RF classifier, showed very good results for crop type/LC mapping when dealing with a wide scale and cloudy region. In 2006, 2011 and 2016, high PAs and UAs were obtained in almost all specific classes. Conversely, the highest commission was obtained in 2006 between rice and cassava and also sugarcane and cassava due to the similar spectral information based on crop phenology, leading to misclassification. In 2011, the highest misclassification occurred between sugarcane and cassava, as well as rice and cassava, probably due to spectral signatures overlapping. Meanwhile, in 2016, the most significant crop confusion was between rice and cassava and also cassava and sugarcane because several sugarcane and cassava fields were recently cultivated during this time, leading to similar signatures, as seen in previous findings [13]. Combining both Landsat sensor datasets for 2001 (L5 and L7) and 2021 (L8 and L9) was a good strategy to support crop separation, which consequently led to better classification results for nearly all classes involved in this work. The aggregated Landsat sensor datasets for 2001 demonstrated high efficiency for mapping crop types and LC. The confusion was shown between rice and cassava and also between rice and sugarcane due to rice crop phenology in the reproduction phase being similar in some cassava and sugarcane fields, leading to the loss of accuracy in the results. For 2021, the map result was smoothed well and reduced noises in crop type areas. Nevertheless, the commission between sugarcane and cassava still persists because sugarcane crop phenology in tillering and grand growth stages are similar to cassava canopy, leading to mixed spectral signatures, as reported by [PERSON], et al. [50]. In addition, cassava within the Chi River Basin is usually planted in all seasons; therefore, the canopy and greenness are similar compared to the vigor of sugarcane. In our study, this suggests increasing ground truth data for crop type classes across the study region. The multi-temporal Landsat data and the RF classifier based on GEE produced very excellent results (OA above 85%) in this research. This is consistent with studies that show the utility of spectral and temporal data in monitoring crop types and land change [27; 34; 38; 51].