In the Ba river basin, the natural forest area has been decreasing and agriculture area has been increasing at an uneven rate for the last 30 years. Objective of this research is analysing the change in the agricultural and forest areas under the impact of socioeconomic activities in this basin. Landsat satellite images with a resolution of 30 m acquired from 1989 to 2019 were used to assess the changes. To enhance the classification speed of data, this research applied the online Google Earth Engine platform and GIS tools to process and edit results. The results showed that the largest decrease in forest area was from 1989-2000 (-159,000 ha) while the agricultural area increased the most in the period 2010-2017 with more than 176,000 ha. The main reasons of conversion forest land into agricultural land such as fruit trees and perennial industrial crops were the increasing of population and agricultural prices. Besides, the demand of electricity caused the deforestation for hydropower plantations.

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.