Nghi Dang-Pham-Bao

Accurate monitoring of inundation in rice paddies is essential for optimizing water use efficiency and mitigating methane emissions; yet, detecting water beneath dense rice canopies remains a major challenge. This study proposed a reliable classification approach applied to the Winter–Spring 2025 season in An Giang province, Vietnam, by integrating multi-temporal ALOS-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) SAR data with in situ field surveys. Time-series Sentinel-1 observations were used to estimate rice phenology (rice age), while multi-polarization backscatter from ALOS-2 PALSAR-2 was analyzed to discriminate inundated from non-inundated conditions across different growth stages. Results demonstrated that L-band signals, particularly in VV polarization, penetrated dense vegetation effectively, enabling classification of inundated vs. non-inundated fields with an overall accuracy of 81% and a Kappa coefficient of 0.77. The resulting multi-date inundation maps revealed distinct flooding regimes consistent with local field survey observations. These findings demonstrated the potential of L-band VV SAR data for characterizing sub-canopy inundation conditions under rice canopies. Crucially, the approach provides essential data for greenhouse gas inventories and supports the verification of low-emission water management practices, such as Alternate Wetting and Drying (AWD). Overall, the study demonstrated the value of multi-frequency SAR integration for advancing agricultural monitoring and climate-smart management in rice-growing regions.

The main objective of this research is to study land use/land cover changes in the Mekong Delta, Vietnam, from 1990 to 2018 using remote sensing. By using multi-temporal Landsat data and analysis techniques across the entire region, the image interpretation results show significant changes in land cover and land use. The study employed a supervised classification method, specifically the Maximum Likelihood classifier, combined with the Support Vector Machine algorithm to classify different land cover types. Additionally, the vegetation differentiation index NDVI and the water differentiation index MDWI were utilized to enhance classification and analyze the classification results. Based on the classification results, during the period from 1990 to 2000, approximately 42% of the total area in the Mekong Delta experienced changes in land cover types. From 1990 to 2018, over 52% of the Mekong Delta’s area had changes in spatial distribution and area. Rice exhibited the most significant changes, while aquaculture expanded rapidly, and forest cover decreased by nearly half in terms of area.