Synergistic use of multi-sensor satellite data for mapping crop types and land cover dynamics from 2021 to 2023 in Northeast Thailand

Abstract

Accurate and timely information on the spatiotemporal distribution of crops is essential for sustainable agricultural practices and ensuring food security. The significant challenges persist in accurately classifying crop types in highly fragmented cropland regions characterized by small field sizes, complex landscapes, and highly frequent cloud cover. This study presents a novel classification workflow designed to generate archaic/historic and reliable land cover (LC) maps from integrating time series data from multiple EO sources—Sentinel-1, Sentinel-2, and the Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM)—with the Random Forest (RF) classifier and cloud computing technology. To the evaluate the effectiveness of this approach, Northeast (NE) Thailand was selected as a case study region, focusing on the classification of 14 crop types between 2021 and 2023. Different combinations of EO datasets and a RF classifier were evaluated using a substantial dataset of 13,453 reference points. The crop type/LC transitions from 2021 to 2023 were then analysed and a temporal transfer model was employed to map historical crop fields. The combined all EO datasets in this work achieved high overall accuracy and F1 scores (>85 %) with the high spatial consistency of crop fields when compared to the use of combined both datasets. Results demonstrated the high potential and excellent efficiency of the RF, utilising an extensive reference dataset and the continuous temporal monthly information of gap-filled data. The most dominant crops were rice, followed by cassava, sugarcane and rubber trees throughout the three study years. The transfer learning RF model proved effective in mapping historical crop types and LC even when ground data was limited. Transitions of 7,287 km² (∼5%) appeared from 2021 to 2022, with major crop decreases in rice and sugarcane. From 2022 to 2023, cropland changes totaled 8,466 km² (∼6%), primarily as reductions in sugarcane and rubber trees. Our findings highlight the effectiveness of integrating multiple EO datasets in this study for mapping crop types across large areas and confirm the benefit of using monthly temporal data to obtain historic LC maps, providing valuable insights for a large range of stakeholders.