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Remote Sensing & Environmental Analysis

Ahero Irrigation Scheme — Crop & Soil Health Analysis

A spectral index stack (NDVI, NDMI, SAVI, SOCI) combined with principal component analysis to map crop and soil health variability across the scheme.

Role
Remote Sensing Analyst
Period
2024
Location
Ahero, Kisumu County
  • Google Earth Engine
  • ENVI
  • QGIS
NDVI distribution map of the Ahero irrigation scheme

The problem

Different parts of a large irrigation scheme perform very differently. To guide fertiliser, water and management decisions, the scheme needed a spatial read on where crops and soils are thriving and where they are stressed.

Approach

  • Computed a core stack of spectral indices — NDVI, NDMI, SAVI and SOCI — to capture vegetation vigour, moisture and soil organic signals.
  • Applied principal component analysis (PCA) separately for crop health and soil health to reduce the many correlated bands into interpretable components.
  • Laid the results out as a consistent, comparable series of maps with matching legends and symbology.

Result

A visual diagnostic of variability across the scheme — a foundation for targeted interventions and a repeatable template for seasonal monitoring.

Gallery

NDVI distribution map
Normalised Difference Vegetation Index (NDVI).
NDMI distribution map
Normalised Difference Moisture Index (NDMI).
SAVI distribution map
Soil-Adjusted Vegetation Index (SAVI).
SOCI distribution map
Soil Organic Carbon Index (SOCI).
PCA for crop health
Principal component analysis — crop health.
PCA for soil health
Principal component analysis — soil health.
Soil type map
Soil type distribution across the scheme.

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