Plant health indices
A broadband index like NDVI tells you how much green vegetation is in a pixel. A narrowband index can tell you what state it's in: how much chlorophyll the leaves hold, whether stress pigments are building up, and where the red edge sits. This page explains why that difference exists, which plant properties you can measure, and which indices Dragonette supports.
Background
Multispectral satellites such as Landsat and Sentinel-2 are the usual source for vegetation indices like NDVI, SAVI, and EVI. These measure the presence and general vigour of vegetation. What they can't do reliably is separate the individual things that make up plant health, such as chlorophyll content, carotenoid content, or leaf area, because the absorption features of those pigments are narrower than the bands and overlap with one another.
NDVI still does its own job well. In the images below it separates vegetation cleanly from everything else, picks out areas of denser growth, and even shows crop rows within fields.
Sentinel-2 true colour image and the NDVI calculated from it
Figure 1 shows why. A near-infrared band separates green vegetation from soil easily, because vegetation reflects far more near-infrared light. But the bands are wide: Sentinel-2's Band 8, the near-infrared band marked 4 in the figure, is about 100 nm across.
Figure 1: Typical multispectral bands (grey) against soil, vegetation, and water spectra. Each band averages over a wide stretch of the spectrum. [2]
Figure 2 shows how much detail a band that wide passes over. It marks the wavelengths researchers link to specific plant properties, and in the visible and red edge many of them are only a few tens of nanometres apart: pigments at 450, 515, 550, 570, and 650 nm, chlorophyll absorption at 687 nm, and stress at 705 and 720 nm. A 100 nm band averages several of them into one number. Narrow bands measure them separately.
Figure 2: Reflectance of four crops at different growth stages, annotated with the wavelengths linked to specific plant properties. Dragonette covers the region from 445 to 870 nm. [3]
What you can measure
Figure 3: Absorbance of chlorophyll b, carotene, and anthocyanin. Each peaks in a different place, but the curves overlap, so separating them takes bands narrow enough to sit on each peak. [1]
These are the properties of foliage that reflectance can measure:
- Chlorophyll: the green pigments a plant uses to absorb light for photosynthesis. Chlorophyll content is the most direct measure of how actively a plant is growing.
- Carotenoids: yellow, orange, and red pigments with several roles, including protecting leaves from excess light. A high ratio of carotenoids to chlorophyll is a sign of stress.
- Anthocyanins: red, purple, and blue pigments that are mostly absent through the growing season and build up towards its end. Along with carotenoids, they give leaves their autumn colours.
- Leaf area index (LAI): the area of green leaf per unit of ground area. A dimensionless measure of how dense the canopy is.
- Red edge position: the wavelength of the steep rise in reflectance between red and near-infrared, roughly 680 to 750 nm. It shifts with chlorophyll content, which makes it useful for crop identification, growth stage, disturbance detection, and canopy stress.
- Senescence: the aging of a plant or leaf, usually marked by falling chlorophyll and rising anthocyanins.
Each of these has indices designed to measure it. The table lists popular ones and which satellites can compute them.
This is a small selection. The Index Library has the full set, with formulas and per-instrument band mappings.
| Index | Applications | Sentinel‑2 | Landsat 7/8 | SuperDove | Wyvern Dragonette |
|---|---|---|---|---|---|
| NDVI | General plant health & presence of vegetation | * | |||
| EVI | General plant health & presence of vegetation, counteracts NDVI saturation problem | * | |||
| SAVI | General plant health & presence of vegetation, suppresses effect of soil pixels | * | |||
| RENDVI | Sensitive to small changes in senescence & foliage content | ||||
| VREI2 | Sensitive to chlorophyll concentration, leaf area index, and water content | ||||
| REPI | Crop identification, disturbance detection, canopy stress | ||||
| ARI1/2 | Sensitive to weakening vegetation due to high concentration of anthocyanins | ||||
| CRI1/2 | Sensitive to stressed vegetation due to high concentration of carotenoids | ||||
| SIPI/1 | Sensitive to ratio of carotenoids to chlorophyll |
Reading the table
The indices above fall into three groups, and knowing which group you are in tells you what the number can and cannot say.
Broadband greenness (NDVI, EVI, SAVI) measures how much green vegetation is present. Every sensor in the table can compute it, including Dragonette, which is what the asterisk marks: these indices were designed around wide bands, so on a hyperspectral sensor you either average several narrow bands together to imitate the original broadband response, or you substitute a narrowband index that measures the same thing better. Both approaches work, but neither one takes full advantage of hyperspectral data.
Red edge indices (RENDVI, VREI2, REPI) measure where the reflectance climb between red and near-infrared sits, and how steep it is. That position shifts with chlorophyll concentration and leaf area, and it shifts early, before a canopy looks any different to the eye or to a broadband sensor. Dragonette carries seven bands between 690 and 765 nm, so the red edge is sampled repeatedly across its width rather than straddled by one wide band.
Pigment indices (ARI1/2, CRI1/2, SIPI/1) target individual compounds: anthocyanins, carotenoids, and the ratio of carotenoids to chlorophyll. Their absorption features are narrow and close together in the 500 to 800 nm range, so they need bands placed precisely on the feature. A wide band that spans the feature and the flat spectrum either side averages the signal away.
The pattern down the Wyvern column follows from that. It is not that broadband satellites are poor instruments; it is that an index can only be computed if the sensor has bands at the wavelengths the formula uses, and narrow bands mean the formula can be applied where the feature actually is instead of near it.
Two caveats worth carrying into any of these. First, an index is a ratio, not a measurement: it responds to the feature you are after, and also to soil background, canopy geometry, illumination, and residual atmosphere. Compare values within a scene before comparing across dates. Second, all of these want L2A surface reflectance. On L1B radiance the solar spectrum and atmospheric absorption are larger than the differences the index is measuring, so the result is not meaningful. See processing levels for the difference.
Try it yourself
Formulas, band mappings for each Dragonette configuration, and the source papers for every index above are in the Index Library.
To compute one on a real scene, the index calculation tutorial walks through RENDVI on a Dragonette L2A scene in Python, selecting bands by wavelength rather than by position. The water quality indices tutorial applies the same approach to a different problem.
Citations
[1] Zhou, Ning & Dong, Weiming & Mei, Xing. (2006). Realistic Simulation of Seasonal Variant Maples. 295 - 301. 10.1109/PMA.2006.29.
[2] Pham, Binh. (2018). Satellite remote sensing of the variability of the continental hydrology cycle in the lower Mekong basin over the last two decades. 10.13140/RG.2.2.27136.07686.
[3] Hyperspectral Signatures by USGS Western Geographic Science Center