Bloom9ja/Rice-Farm-Satellite-Analysis

Multi-temporal NDVI and NDMI analysis of a commercial rice farm in Nassarawa State, Nigeria Using Sentinel-2 imagery (2023-2025) # Satellite-Based Monitoring of Vegetation Health and Moisture Dynamics ## A Commercial Rice Farm, Nassarawa State, Nigeria (2023–2025) ## Overview This project presents a multi-temporal satellite-based analysis of vegetation health and canopy moisture dynamics at a commercial rice farm in Nassarawa State, Nigeria. Using freely available Sentinel-2 imagery processed through Google Earth Engine, the study computes and analyses the Normalised Difference Vegetation Index (NDVI) and Normalised Difference Moisture Index (NDMI) across three consecutive growing seasons (2023, 2024, and 2025). The farm covers approximately 61.3 hectares and is located at 7°53'N, 8°20'E within the Guinea Savanna agro-ecological zone of North-Central Nigeria. --- ## Objectives - Map the spatial distribution of vegetation health and moisture conditions across the farm at seasonal scale - Track temporal dynamics of crop development through full-season time series analysis - Assess inter-annual trends in farm performance using satellite-derived evidence --- ## Study Area **Location:** Akpetche, Nassarawa State, Nigeria **Coordinates:** 7°53'N, 8°20'E **Farm Size:** 61.3 hectares **Crop:** Rice (single annual season) **Season Window:** June – November --- ## Data and Tools | Item | Details | |------|---------| | Satellite Data | Sentinel-2 SR Harmonised (Copernicus) | | Platform | Google Earth Engine (JavaScript API) | | Indices | NDVI, NDMI | | Spatial Resolution | 10 metres | | CRS | EPSG:32632 — WGS84 UTM Zone 32N | | Cartography | QGIS 3.40 Bratislava | | Soil Data | iSDAsoil Africa v1 (30m) | | Analysis Period | 2023, 2024, 2025 growing seasons | --- ## Methodology 1. Sentinel-2 SR imagery filtered to farm boundary and season window 2. Cloud masking using Scene Classification Layer (SCL) at pixel level 3. NDVI and NDMI computed per image 4. Seasonal median composites generated for spatial mapping 5. Full-season time series extracted for temporal analysis 6. Spatial statistics ( …