Integrating Sentinel-2 Remotely Sensed Data into Center-Pivot Irrigation Management for Sustainable Water Resource Use: Evidence from the Nasho Irrigation Scheme, Rwanda
Year: 2026
Type: article
Abstract: The Nasho Irrigation Scheme, one of Rwanda's largest centre-pivot irrigation schemes, faces challenges in aligning irrigation management with spatial and temporal variations in crop conditions. This study evaluated how Sentinel-2 remotely sensed data can support sustainable water resource management and crop productivity by assessing spatial and temporal variation in crop health using the Normalised Difference Vegetation Index (NDVI) and Normalised Difference Moisture Index (NDMI), examining the relationship between NDVI and crop yield, and identifying barriers to the adoption of geospatial tools. A mixed-methods case study design combined Sentinel-2 geospatial analysis with structured surveys of 137 smallholder farmers and key informant interviews across eight centre pivots. Results showed substantial seasonal and spatial variation in crop conditions, with mean NDVI declining from 0.629 in the 2025B season to 0.382 in 2026A, while mean NDMI changed from 0.101 to -0.108. An initial ordinary least squares model indicated a significant relationship between NDVI and yield; however, after accounting for clustering among farmers within pivots using a linear mixed-effects model, NDVI was no longer statistically significant, while crop type and season remained significant predictors of yield. Technology Acceptance Model results revealed high farmer trust in satellite-based recommendations (mean score 3.87) but low willingness to adopt (2.39) and pay for such services (1.24). The findings indicate that Sentinel-2 indices are valuable for crop monitoring and identifying spatial variability relevant to irrigation management, but should not be used as standalone predictors of yield or irrigation requirements. Sustainable water resource management at Nasho would benefit from a hybrid approach that combines remotely sensed indicators with crop, seasonal, agronomic and pivot-level management information, supported by affordable and institutionally mediated advisory services.
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7225165147