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<p>Groundwater resources within the Mekong and Red River deltas of Vietnam are experiencing a significant reduction due to excessive extraction for agricultural purposes and the repercussions of climate change. In light of this situation, this research employed a Convolutional Neural Network (CNN) model using Sentinel-2 multispectral imagery at 10&#xa0;m spatial resolution to categorize rice-cultivated areas in the Red River Delta (RRD) and Mekong Delta (MKD), achieving impressive accuracy with overall accuracy (OA) exceeding 86% and F1 values surpassing 0.85. The classification outcomes indicated that the rice cultivation area in MKD totaled 2,094,467&#xa0;ha, predominantly located in the provinces of Kien Giang, Long An, and An Giang, whereas the area in RRD was 675,058&#xa0;ha, chiefly found in Hanoi, Thai Binh, and Nam Dinh. Importantly, the assessment of groundwater potential utilizing the hybrid Genetic Algorithm-eXtreme Gradient Boosting (GA-XGBoost) model demonstrated robust performance with R² for training greater than 0.86, based on nine key conditioning factors including lithology, soil type, lineament density, rainfall, slope, elevation, drainage density, land use, and distance to water sources, thereby ensuring reproducibility of the analysis. The model highlighted distinct differences in principal influencing factors: lithological composition (R²=0.42) and lineament density (R²=0.35) were significant in RRD, while rainfall (R²=0.40) and distance to water (R²=0.31) predominantly influenced MKD. Spatial analysis corroborated the close link between groundwater resources and agricultural activities, revealing that critical rice-producing regions also coincide with areas of medium to high groundwater reserves, representing 72.28% of the RRD and 56.99% of the MKD. These results, derived from Earth Observation (EO) and Artificial Intelligence (AI) data, have established a replicable framework for forthcoming applications and will act as a vital scientific basis for the management and sustainable utilization of groundwater resources for rice production in Vietnam’s two principal deltas.</p>

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Assessing Groundwater Potential Using Artificial Intelligence and Earth Observation to Support Sustainable Agriculture in Vietnam’s Deltas

  • 期刊论文

  • 发表日期：

    2026年3月18日

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  • Anh Ngoc Thi Do,
  • The Van Pham,
  • Tuyet Anh Thi Do,
  • Bui Thi Phuong Thuy,
  • Quyen Vu Thi

摘要

Groundwater resources within the Mekong and Red River deltas of Vietnam are experiencing a significant reduction due to excessive extraction for agricultural purposes and the repercussions of climate change. In light of this situation, this research employed a Convolutional Neural Network (CNN) model using Sentinel-2 multispectral imagery at 10 m spatial resolution to categorize rice-cultivated areas in the Red River Delta (RRD) and Mekong Delta (MKD), achieving impressive accuracy with overall accuracy (OA) exceeding 86% and F1 values surpassing 0.85. The classification outcomes indicated that the rice cultivation area in MKD totaled 2,094,467 ha, predominantly located in the provinces of Kien Giang, Long An, and An Giang, whereas the area in RRD was 675,058 ha, chiefly found in Hanoi, Thai Binh, and Nam Dinh. Importantly, the assessment of groundwater potential utilizing the hybrid Genetic Algorithm-eXtreme Gradient Boosting (GA-XGBoost) model demonstrated robust performance with R² for training greater than 0.86, based on nine key conditioning factors including lithology, soil type, lineament density, rainfall, slope, elevation, drainage density, land use, and distance to water sources, thereby ensuring reproducibility of the analysis. The model highlighted distinct differences in principal influencing factors: lithological composition (R²=0.42) and lineament density (R²=0.35) were significant in RRD, while rainfall (R²=0.40) and distance to water (R²=0.31) predominantly influenced MKD. Spatial analysis corroborated the close link between groundwater resources and agricultural activities, revealing that critical rice-producing regions also coincide with areas of medium to high groundwater reserves, representing 72.28% of the RRD and 56.99% of the MKD. These results, derived from Earth Observation (EO) and Artificial Intelligence (AI) data, have established a replicable framework for forthcoming applications and will act as a vital scientific basis for the management and sustainable utilization of groundwater resources for rice production in Vietnam’s two principal deltas.