The Best Vegetation Indices For Maize Growth Monitoring

Why it matters to match vegetation indices to maize growth stages

Vegetation indices respond differently depending on the growth phase of the crop because of variations in canopy structure, pigment concentration, and overall biomass. Understanding these correlations is key for accurate satellite-based maize growth monitoring. Indices suited to early corn growth stages minimize soil influence, while the ones for later stages track green biomass or photosynthetic activity.

While it’s common to divide growth into vegetative and reproductive phases, the BBCH scale gives a more detailed picture. That’s why we use BBCH phases as the basis for all index recommendations on EOSDA Crop Monitoring. To further guide users, in our digital agriculture platform, we’ve added a Typical index range feature, which reflects expected values under normal crop conditions. Together, these tools help detect problems early and ensure fertilizer, irrigation, or pest treatments are applied at the right moment.

What vegetation indices to use at each growth stage

Each growth stage of maize, from early leaf development to ripening, affects yield in different ways. That’s why you should monitor what is happening across the field during the whole season. Satellite-based indices reveal changes in chlorophyll, biomass, and water status that help guide crop management decisions. Let’s discover how to match the right indicator to the right phase for more precise field-level insights.

Early vegetative stage: Leaf development

During early growth of maize (VE–V6 / BBCH 09–16), the plants cover very little of the ground, which means the soil is still clearly visible in satellite images. This can distort vegetation signals and reduce the accuracy of standard indices. To improve reliability in such conditions, the best vegetation indices for maize early growth are the ones reducing the impact of soil background, such as:

• SAVI (soil adjusted vegetation index) helps reduce the influence of soil brightness in satellite data. This makes it more accurate for detecting crop development when canopy cover is limited and bare soil is still visible.
• MSAVI (modified soil adjusted vegetation index) further improves SAVI by automatically adjusting for varying soil levels. It is especially useful during early crop growth when maize plants are just emerging. MSAVI has been shown to improve the detection of uneven sprouting and overall crop vigor during this phase ^{Baret, F., Guyot, G. (1991). Potentials and limits of vegetation indices for LAI estimation. Remote sensing of environment, 35(2-3), 161-173. https://doi.org/10.1016/0034-4257(91)90009-U.}.

Farmers can view both SAVI and MSAVI maize maps in EOSDA Crop Monitoring. These digital farming tools help track maize establishment and detect any weak or delayed areas. You can also compare MSAVI values with typical ranges for the leaf development phase. If values are outside the expected range, field scouting is recommended to identify the cause.

Mid-vegetative stage: Stem elongation to heading

As maize plants move into active vegetative growth (V7–VT / BBCH 17–59), leaf area and chlorophyll levels become key signs of crop health. These traits affect how well the plant can photosynthesize and assimilate nitrogen fertilizer (essential for high yields). Index-based satellite monitoring of the maize vegetative stage can highlight these changes, helping farmers make timely fertilizer decisions.

The most useful metrics for the mid-vegetative period include:

• GNDVI (green normalized difference vegetation index) responds strongly to chlorophyll levels, especially in crops with thick, overlapping leaves like maize. It is more sensitive than NDVI when it comes to detecting changes in nitrogen status and overall photosynthetic activity ^{Zhao, X. et al. (2023, March). On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies. Open Life Sciences, 18(1). https://doi.org/10.1515/biol-2022-0566.}. This makes it a good indicator during fast growth.
• NDRE (normalized difference red edge index) also tracks chlorophyll but uses the red-edge band instead of near-infrared. This reduces the impact of saturation in dense canopies and provides clearer signals during later vegetative growth. NDRE is also useful for maize nitrogen management, especially when standard indices like NDVI level off.

With EOSDA Crop Monitoring, index values turn into clear, up-to-date field maps that help you see what’s really happening under the canopy. Whether chlorophyll levels are starting to dip or parts of the field are lagging behind, NDRE and GNDVI maize maps let you catch it early and act with purpose. For farmers managing large areas or working under tight timelines, analyzing these vegetation maps is essential.

Reproductive stage: Flowering to ripening

The reproductive stages of maize (R1–R6 / BBCH 63–87) are when grain development is most sensitive to stress. At this point, photosynthesis drives kernel fill, and water shortages can directly impact yield. So, it’s critical to monitor photosynthetic activity, plant water status, and biomass levels with:

• NDVI (normalized difference vegetation index) is widely used for estimating leaf area index (LAI), confirming that there’s steady photosynthetic activity to increase biomass ^{Xie, Q. et al. (2018, February). Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5). https://doi.org/10.1109/JSTARS.2018.2813281.}. NDVI imagery can also work well for crop stress tracking and yield prediction during the reproductive period.
• EVI (enhanced vegetation index) performs better in dense maize canopies where NDVI tends to saturate ^{Huete, A. et al. (2002, November). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2), 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2.}. If NDVI values stop increasing despite visible crop growth, EVI can provide a clearer signal of continued biomass accumulation.
• NDMI (normalized difference moisture index) picks up moisture shifts before plants show visible signs of drought stress. A dip in NDMI during grain fill often points to short-term water deficits that can reduce kernel weight even if leaves still appear healthy.

EOSDA Crop Monitoring integrates NDVI, EVI, and NDMI in one platform, so that you can track changes in crop health and biomass in different conditions. Time-series tools highlight when the crop’s progress slows or reverses for optimizing maize irrigation and management with satellite data during this yield-critical phase.

Practical ways to monitor maize with vegetation indices in EOSDA Crop Monitoring

Farmers using EOSDA Crop Monitoring for maize growth checks can rely on vegetation analytics to notice what’s changing and perhaps going wrong in their fields throughout the growing season. Here’s a wrap-up of how maize growers can use different indices in everyday activities:

• Early growth check. MSAVI helps detect uneven sprouting and poor plant stands early in the season.
• Nitrogen planning. GNDVI and NDRE maize maps highlight areas with low nitrogen availability so farmers can plan variable rate fertilizer use.
• Irrigation management. NDMI, NDVI, and EVI vegetation indices identify maize water stress during flowering and grain filling for more precise irrigation timing.
• Yield forecasts. Vegetation index trends, combined with weather and yield history, help estimate future harvests for maize yield optimization.
• Growth tracking. Changes in relevant index values over time show whether the crop is growing as expected or facing delays.

EOSDA Crop Monitoring also supports decision-making with features beyond vegetation index maps. Growers and other farming experts can use satellite images in agriculture from sources like Sentinel-2 and PlanetScope, log field activities, and combine weather data with satellite-derived insights.

Making sense of all the factors that affect maize yields is not easy, but EOSDA Crop Monitoring brings much more clarity. Over time, it builds a habit of farming based on signals from the maize field, not just the calendar. When used consistently, index-based agricultural analytics of maize help reduce risks and improve decision-making across the farm.