## 6 Conclusions  
Crop type transition mapping in small field sizes (<1 hectare) over a 20-year period can be effectively automated and delineated in the Google Earth Engine (GEE) platform utilizing multi-temporal Landsat data (i.e., L5, L7, L8 and L9). A random forest (RF) classifier and the multi-temporal Landsat datasets provided high OA for robust crop types or land cover classifications. For crop type classification for the years 2001, 2006, 2011 and 2021, the mapping results were highly satisfactory. Market prices and governmental support for reducing unsuitable rice production areas were certainly the main reasons that encouraged farmers to transform their rice paddies into sugarcane, cassava or para rubber.  
The young farmers and the authorities benefit from accurate and up-to-date information on crop type/LU classification. Our study can offer highly performant and reliable crop type patterns and long-term changes over four five-year periods and promote crop land services by encouraging guidelines.  
Our important findings and further suggestions can be concluded as follows:  
* The derived crop type/LC transitions are highly significant for food security planting and other agricultural land management, especially in developing countries;* Evaluations of biomass, carbon stock, and yield highly rely on accurate information on crop type/LC classification maps over a large region;
* Using multi-temporal Landsat data (i.e., L5, L7, L8 and L9) based on GEE properly enables crop type mapping in small fields and complex landscapes. A dense time series analysis of earth observation data should be adopted for higher accuracy and satisfaction. This improves persistent cloud and compositing methods for crop type classification across the vast region;
* While our study demonstrated that the RF classifier method was highly efficient, additional research towards using the method in small field sizes and combining with multiple EO data sets is recommended;
* The joint use of the GEE computing platform and multi-temporal Landsat dataset make it possible to map crop types on cloudy and rainy days in Northeast Thailand, and ongoing research should be applied to this cloud platform to monitor the crop types throughout all of Thailand;
* The estimated areas of the derived crop type/LC results in this study should be compared to the official land use and land cover statistics from the government, exploring the potential biases in crop transitions and external validation using independent datasets;
* Our results offer up-to-date and reliable information for sustainable agricultural land management in Thailand, as well as for dealing with policymakers, decision-making and production planning.  
Conceptualization, S.R.S. and J.S.-a.; methodology, S.R.S. and J.S.-a.; software, S.K., M.P., S.R.S. and J.S.-a.; validation, N.H., S.R.S. and J.S.-a.; formal analysis, S.K., N.H., S.R.S. and J.S.-a.; writing--original draft preparation, S.R.S., S.K., M.P., N.H., A.P., E.B.-M., F.S. and J.S.-a.; writing--review and editing, S.R.S., S.K., M.P., N.H., A.P., E.B.-M., F.S. and J.S.-a.; visualization, F.S., S.K., M.P., S.R.S. and J.S.-a.; supervision, S.R.S. and J.S.-a.; project administration, S.R.S. and J.S.-a.; funding acquisition, S.R.S. and J.S.-a. All authors have read and agreed to the published version of the manuscript.  
The project is funded by Fast-Track Mahasarakham University.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.