## 1 Introduction
The information acquired by EO can subsequently serve as input data for numerous models that monitor changes in many natural resources, as well as predict significant scenarios such as natural disasters and crop type dynamics, including the changes of LC/LU consequences [17]. Several studies have highlighted that multi-temporal dataset combinations (i.e., Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 (LS)) from multiple sensor sources, coupled with powerful machine learning approaches and time periods, make it possible to obtain crop classifications with high overall accuracy (OA > 85%) [11; 24; 25; 26]. In particular, [PERSON], et al. [11] classified 13 different crop type classes in the Udon Thani province (Thailand)using the random forest (RF) algorithm, with OA > 87%, whereas [PERSON], et al. [24] developed a procedure to map different crop shifts in France and Kenya using the RF classifier, achieving OA > 85%. [PERSON], et al. [25] identified three crop classes in the Yangzi River (China) using support vector machine (SVM), artificial neural network (ANN) and RF classifiers and showed up to 93% OA results. [PERSON], et al. [26] classified five crop types in the Gujranwala District of Punjab Province, Pakistan from Decision-Tree-Classifier (DTC) and RF algorithms, and indicated OA values of > 85%. As highlighted in the previously reported studies, the efficiency of using multi-temporal data from multi-sensor data for classifying agricultural land/LC was proved. Moreover, they have shown that the RF classifier has the greatest efficiency and excellent results in terms of crop type/LC classification. However, a multi-temporal Landsat dataset for mapping several crop types for small field sizes--such as in Thailand--based on the RF algorithm is still a challenge. Monitoring agricultural land dynamics using EO data over time makes it possible to offer valuable information on food management, related stockholders and environmental conditions [11; 27]. Consequently, reliable and accurate information of cropland/LC maps at the national or international levels can be obtained. Such information can be subsequently involved in many applicable problems in agricultural lands and LC change monitoring. However, mapping, monitoring and evaluating crop/LC types in small areas (i.e., field < 1 ha sizes) and complicated landscapes is still a real problem, especially when areas are regularly characterized by high cloud cover, such as in Thailand. Within this framework, in this work, proper mapping and monitoring of temporal and spatial dynamics related to LU/LC was performed in the northeast Thailand area within five different years from 2001 to 2021 (2001, 2006, 2011, 2016, and 2021). In particular, all available Landsat archives were involved inside the Google Earth Engine (GEE) platform, making it possible to produce several LU/LC maps for each period by adopting an RF classifier. Therefore, different sophisticated classification approaches without even downloading the satellite dataset were adopted in order to achieve the highest map accuracy of our result. It is important to highlight that in this work, cutting-edge methods and technology, such as remote sensing and a machine learning technique, were adopted. In particular, in this work, the main aims were to: (i) produce LC maps focusing on the main local crops (i.e., rice, cassava, para rubber, and sugarcane crops) within the Chi River Basin in (NE-Thailand); (ii) monitor long-term transitions in agricultural land and LC transformations within four 5-year periods (2001-2006, 2006-2011, 2011-2016, and 2016-2021). The main advantages of this work are to use reliable historical agricultural land and LC to substitute ground truth and training samples in satellite images. Resulting map accuracy and reliability can be attributed to two distinct key drivers: agricultural policy and staple prices. Therefore, all this information can be useful to support sustainable agriculture land organization, food management, decisions of government and many agricultural industries.