The team, publishing in Frontiers in Horticulture, turned to the European Sentinel-2 satellites, which photograph nearly the entire land surface every few days at a resolution of 10 meters per pixel. From the Copernicus archive they pulled every single cloud-free and cloud-shadow-free image of a large commercial orchard in northern Azerbaijan across eight growing seasons, from 2017 to 2024. That labor of curation yielded exactly 81 usable images. For each of those pictures, the researchers computed NDVI, the normalized difference vegetation index, using the satellite’s red band B4 and near-infrared band B8. NDVI exploits a simple optical fact: healthy, chlorophyll-rich leaves absorb red light for photosynthesis while scattering near-infrared light off their spongy internal cell structure, so the greener and denser the canopy, the higher the index climbs.

The research site itself was no laboratory simplification. Roughly 303 hectares of intensively irrigated apple trees stretch across a plain sloping toward the Caspian Sea, in a hot, dry landscape where summer temperatures routinely exceed 35 degrees Celsius and rainfall of just 250 to 300 millimeters makes irrigation non-negotiable. The orchard contained 53 plots of varying ages; the researchers focused on 30 of them, planted between 2007 and 2017 and ranging from 3 to 10 hectares each. Six varieties grew there, including Gala, Fuji, Golden Delicious, Golden Parsi, Granny Smith and Arian, and nine plots even mixed two or three varieties side by side. Phenology followed a long Mediterranean-style rhythm: buds burst around late March and early April, flowering lasted eight to sixteen days at the end of April depending on cultivar and weather, and harvest staggered from July’s summer Gala crop through October and November for winter varieties like Fuji and Granny Smith.

On the ground, the team did things the hard way: direct metering. For every plot, in every year from 2018 to 2024, all apples were harvested and weighed, and the total tonnage was divided by plot area to give true per-hectare yields. This ground truth is the gold standard that satellite models dream of matching. From the image archive, the researchers derived two NDVI statistics per plot per season: the absolute maximum NDVI reached by any pixel during the growing season, and an area-weighted seasonal average computed across all pixels and all images, which numbered between eight and fourteen per plot. Then they built simple linear regression models linking each NDVI statistic to yield, validated with a repeated cross-validation procedure in which the data were split into three blocks, one held out for testing, seven times over, and scored on the coefficient of determination, the root mean squared prediction error and the ratio of the interquartile range to that error, known as RPIQ.

The first result was a spectacular failure, and a revealing one. When all 210 yield observations, spanning every variety, every age and every plot, were pooled into one giant dataset, no meaningful model emerged at all. The best attempt explained a miserable 14 percent of the variance, with an RPIQ of just 0.3, far below any threshold of usefulness. The reason is buried in the biology: apple trees are perennials that sit in the same spot for decades, accumulating aboveground biomass in a nonlinear, age-dependent way, and different varieties follow different phenological calendars. A mature Granny Smith and a young Gala can have similar canopy greenness yet wildly different productivity. The spectral signature that works so well for annual crops, where NDVI-based yield forecasting for wheat and similar staples is now routine operational practice, simply collapses when varieties and ages are jumbled together.

Grouping by variety alone did not rescue the situation. Whether the team modeled Gala, Fuji, Granny Smith or the mixed plots, the cross-validated R-squared values hovered between essentially zero and 0.39, and RPIQ values mostly stayed below 1.5. The authors attribute this to residual age heterogeneity: each variety group still contained trees spanning a decade of planting years. One subtle but important technical note emerged here, though. Models built on the seasonal average NDVI consistently outperformed those built on the seasonal maximum. The maximum, the researchers explain, can be hijacked by local pockets of unusually vigorous trees, reflecting spatial heterogeneity in soil fertility across a plot, whereas the average integrates the condition of every tree in the stand. For an orchard block, the mean beats the peak.

Grouping by planting age told a more coherent story, and it is where the fruiting biology of apple trees took center stage. The relationship between NDVI and yield appeared only for middle-aged plantings, roughly 13 to 16 years old, with R-squared values reaching 0.62 and 0.64 for the 2011 cohort and RPIQ values around 2.0. Older and younger stands showed essentially nothing. The explanation lies in the well-documented peak of apple fruiting: young trees are still building their canopy, so the soil background and its spatial variability dominate their spectral signal, while in old trees crown greenness becomes nearly constant from year to year and stops tracking the swings in annual crop load. Only in the prime middle years does the vigor of the canopy, as seen from orbit, move in step with the tonnage of fruit.

The decisive breakthrough came when the researchers abandoned all grouping and modeled each of the 30 plots individually. Suddenly, in more than half of the plots a good correlation appeared, and in four plots it was excellent, with cross-validated R-squared values above 0.8 and RPIQ above 2.1, the study’s benchmark for a reliable relationship. A 2011 Gala plot posted an R-squared of 0.84 with an RPIQ of 2.3 using the seasonal maximum, and an Arian plot planted in 2015 reached an extraordinary 0.93 with the seasonal average and an RPIQ of 2.3. Intriguingly, no clean pattern by cultivar emerged, and even some mixed-variety plots performed well, suggesting that factors beyond varietal composition, presumably the interplay of local soil, irrigation and canopy architecture, exert the stronger influence. The logic is elegant: within a single plot, soil conditions, cultivation practice, tree age and variety are held essentially constant, so the only thing changing from season to season is the actual physiological state of the trees, which is precisely what NDVI measures.

The broader implication inverts a cherished assumption of satellite crop monitoring. For annual crops, yield models grow stronger when scaled up over large regions, because averaging smooths out plot-level noise. For perennial orchards, the team found the exact opposite: models built for large tracts of mixed-age, mixed-variety plantations are unreliable, while models built separately for each individual plot are robust. The practical recipe that follows is clear: forecast apple yields from orbit by treating every orchard block as its own modeling unit, using seasonal mean NDVI rather than seasonal peaks, and restricting the approach to stands in their middle fruiting years. The authors argue that existing Sentinel-2 archives already contain enough multi-year data to build such per-plot series from planting through peak fruiting, and that future gains will come from better satellite-based recognition of orchard variety and age, and from the hunt for entirely new spectral indicators of fruit yield.

For an industry where national yield statistics are often slow, error-prone or simply missing, and where a single forecasting surprise can move prices across a continent, the message is quietly revolutionary. The satellites were never blind; the analysts were simply asking them to see an entire heterogeneous orchard as one thing. Look at the right patch of trees, of the right age, over enough seasons, and a 10-meter pixel painted in invisible infrared light starts telling you how many apples the world will eat next harvest. It is a finding that will shape how precision horticulture, from the Caspian lowlands to the world’s great fruit belts, is practiced in the decade ahead.

Subject of Research: Satellite NDVI-based yield prediction in apple orchards

Article Title: Sentinel-2 NDVI as an indicator of apple orchard yield

Article References: Sentinel-2 NDVI as an indicator of apple orchard yield. (n.d.). https://doi.org/10.3389/fhort.2026.1904481

Image Credits: AI Generated

DOI: 10.3389/fhort.2026.1904481

Keywords: remote sensing, NDVI, Sentinel-2, apple orchards, yield prediction, precision agriculture, perennial crops, Azerbaijan, vegetation index, crop monitoring, horticulture, regression modeling