Cloud Cover and Structural Observation Gaps in African Agricultural Earth Observation: Evidence from Six Agroecological Zones

Optical Earth observation underpins a growing number of agricultural-monitoring and agri-fintech services across Africa, all of which need a usable view of a field often enough to track its condition. Cloud gaps in that record are usually treated as random missing data; we test whether they are instead structurally biased. Using three growing seasons (2022–2024) of Sentinel-2 scene-classification data over cropland in six administrative zones, one per African sub-region and together spanning the climate gradient from hyper-arid to humid-equatorial, we measure the weekly frequency with which each zone’s cropland yields a usable optical observation (a pixel passing the Sentinel-2 quality mask). This ranges from 55% over humid Central Africa to 81% over the arid Nile Delta; “blind-spot weeks”, when more than half a zone’s cropland is unobservable, run from 12% to 42%. Within every zone, usable-observation frequency falls as weekly rainfall rises (Spearman 𝜌 from −0.34 to −0.70, p < 0.001 after adjusting for temporal auto-correlation); this is the study’s one firmly powered result, and it reappears in an independent MODIS cloud product. With only six spatial units, the cross-regional climate gradient and the concentration of the deficit in the rain-fed growing season are reported as indicative patterns consistent with that rainfall mechanism, not as powered tests; Nile-irrigated Egypt inverts, as the mechanism predicts. Optical blind spots in African agricultural monitoring track rainfall and fall hardest on the humid, smallholder, rain-fed systems during their growing season, which motivates evaluating SAR-optical fusion for those zones. The study measures the gap, not the benefit of any fix.