Myanmar Monsoon Rice Sown Area: 2023 Sentinel-1 and Sentinel-2 Estimations

The Myanmar Information Management Unit (MIMU) produced these rice sown area estimations for the 2023 monsoon season using a fusion of Sentinel-1 SAR and Sentinel-2 optical imagery. The dataset employs a Random Forest machine learning model to classify agricultural land cover across Myanmar's states and regions.

Use Cases

• Spatial analysis of rice cultivation patterns using SHP boundaries
• Assessing monsoon season agricultural footprints for livelihood impact studies
• Integrating SAR-derived land cover data into regional food security models

Strengths

• Utilizes multi-sensor fusion of radar (SAR) and optical satellite data
• Employs Random Forest machine learning for land cover classification
• Provided in interoperable SHP and GEOJSON formats

Limitations

• Estimations are model-derived and may contain classification errors inherent to automated remote sensing
• Attribute metadata and specific column definitions are not provided in the source documentation

Provenance

Source

Myanmar Information Management Unit (MIMU)

Collection Method

Remote sensing analysis of Sentinel-1 and Sentinel-2 imagery using Random Forest classification

Time Range

2021-2023

Freshness

Last updated March 2026; specifically covers the 2023 monsoon season.

Geography

Myanmar