PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Rice is a vital staple food in Malaysia, with consumption of 2.7 million MT in 2016, forecasted to rise to 3.5 million MT by 2026. To address food security, the Malaysian government targets a 70% self-sufficiency level (SSL) in rice production, requiring precise spatiotemporal information on rice cultivation. Remote sensing has been widely used to map rice extent in Malaysia, particularly in granary areas (facilitated by irrigation system), the main production zones. However, studies on non-granary areas (without irrigation systems) remain limited. This study addresses this gap by employing the Phenology-Expert Based Unsupervised Classification Method (PEB-UC) with Sentinel-2 time series data on the Google Earth Engine platform to map rice extent and cropping patterns at the sub-district level across Terengganu State, Malaysia, covering both granary and non-granary areas. The results revealed scattered rice parcels totalling 8,184 ha, with 4,377 ha in the IADA KETARA granary area (Besut District) and 3,807 ha in non-granary areas. The maps showed a relative discrepancy of -29.15% with agricultural statistics but demonstrated a strong correlation at the district level (R2 = 0.99; RMSE = 632 ha). Validation of PEB-UC achieved an overall accuracy of 0.979 and a kappa coefficient of 0.957, outperforming Random Forest (RF) and Support Vector Machine (SVM) models. The PEB-UC rice map displayed denser, clearer separability between rice and non-rice compared to supervised models, as shown in the comparison map at https://rudiyanto.users.earthengine.app/view/riceterengganu. This study provides valuable insights into rice cultivation in Terengganu State and supports efforts to enhance food security.

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