July 16, 2025 • 11 mins
NDVI (Normalized Difference Vegetative Index) helps farmers detect how green and/or productive different areas of their fields are by measuring the ratio of near-infrared to red light reflected from plant canopies. This technology creates reliable productivity zone maps when analyzed consistently over multiple growing seasons, enabling targeted input management across variable field conditions.
In this Tiny Byte Sarah and Jodi cover:
• NDVI measures plant canopy density by comparing near-infrared light reflection to red light absorption
• Chlorophyll absorbs visible light while plant cell structures reflect near-infrared light
• NDVI values range from -1 to 1 (or ~0-100 in ADMS) with higher values indicating denser vegetation
• Creating reliable management zones requires multiple years of imagery, not just single snapshots
• Best imagery comes from periods when crop canopy is developing or starting to senesce
• NDVI data often correlates with yield potential and can identify consistently productive field areas
• Zone management allows targeted input application based on productivity potential
• The most valuable NDVI images show variation in greenness rather than complete saturation
Check out our backlog episode "Choosing Quality Imagery" in Season 1 for more information on selecting the best imagery for creating management zones. https://www.buzzsprout.com/2297340/episodes/16135793
https://gktechinc.com/
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Jodi (00:00):
And now it's time for a tiny bite of knowledge.
Sarah (00:13):
It ain't easy being green , being the colors of the leaves
.
Jodi (00:22):
It might not be easy being green, but what is relatively
easy is detecting how greensomething is, and that comes in
the form of NDVI, or normalizeddifference vegetative index.
So that's what NDVI stands for.
But let's break this down.
What is a vegetative index?
(00:42):
Even what is?
Sarah (00:43):
it.
A vegetative index is when weuse light, specifically using
different bands of light withinthe electromagnetic spectrum, to
understand vegetation.
So when we think about NDVInormalized difference vegetative
index it's an index where wehave a ratio and a specific
(01:05):
formula that compares nearinfrared light to red light and
that helps us understand howdensely green the plant canopy
is.
Basically, in dumbed down terms, when our human eyes see green,
it is because red light isgetting absorbed, and so plants
(01:25):
are absorbing red light.
Green is being reflected, andso when we take into comparison
near infrared light and redlight, we are able to understand
how green and how densely greenthe plant canopy is across the
landscape.
Jodi (01:45):
So what does that mean for us?
How do we use that number in apractical way?
Sarah (01:50):
So chlorophyll strongly absorbs visible light and plant
cell structures on the leavesstrongly reflect near-infrared
light.
So NDVI, specifically, is a wayof calculating the amount of
light that is being absorbed andused for photosynthesis and
also how much light is reflected.
This is important because it'sgoing to help us understand how
(02:14):
green and how densely green aplant canopy is across a
landscape.
For our purposes in agriculture, we're really thinking about
where is a field of crop more orless green than other areas,
because this is really anindication of photosynthesis.
This can be a great way ofthinking about the productivity
(02:38):
potential across the landscape.
And actually there's manyplaces and many times where NDVI
can actually correlate to yielddata Not always, but many times
it does correlate to yield data.
Jodi (02:54):
So does that mean a lot of what we use in terms of imagery
?
We'll extract the NDVI valuefrom that imagery and then use
that to build zones.
Sarah (03:05):
Absolutely Jodi.
Many times here at GKTechnology we actually use a lot
of satellite imagery to helpunderstand the productivity
potential of a field across alandscape.
It can help us understand wherethe crop has more potential to
photosynthesize and have greateryield potential, productivity
potential and areas of the fieldwhere there's less
(03:27):
photosynthesis happening andtherefore less productivity
potential.
So from that, when we can takea look at different satellite
images over the years, we canunderstand across a given field
where those areas areconsistently happening, where we
consistently seeing greateramounts of NDVI throughout the
(03:48):
growing season and lesser NDVIand photosynthesizing
photosynthesis potential acrossthat field.
Once we can figure out wherethat is happening consistently
over a number of years, we cancreate zone maps that can help
us understand where we should beinvesting in more inputs into
(04:09):
areas where the greaterproduction potential is
happening and where maybe weshould try to manage that input
cost so that we have less inputsgoing into lower productivity
areas.
Jodi (04:23):
So basically what I hear you saying is we want to find
out like where the NDVI value islowest, consistently the lowest
.
We want to those would be whatwe consider like a low
productivity zone and the areaswhere the NDVI value is the
highest.
Those are hypothetically andprobabilistically our areas of
highest productivity and we wantto manage those, like those are
(04:46):
our highest producing zones orareas with highest potential.
Sarah (04:51):
Absolutely, and so we are obviously transitioning this
conversation from NDVI to howwe're using these in a practical
sense for making zones so, jodi, would you ever use just one
NDVI?
To how we're using these in apractical sense for making zones
.
So, jodi, would you ever usejust one NDVI image to create
zones from one year?
Jodi (05:09):
Absolutely not.
It is a snapshot in time andthat doesn't necessarily reflect
any sort of variance in weatherpatterns.
Maybe, like a single snapshotis a culmination of conditions
that occurred that year, butthat doesn't necessarily mean
that that's consistent over time.
(05:30):
And so, no, I do not use asingle image to create zones,
and I do not promote that either, just because what we're trying
to do when we're building zonesis we're trying to predict
variability and predictconsistent variability, and so
with just one snapshot, youaren't able to really create
(05:52):
something that holds up overtime.
Sarah (05:56):
I couldn't agree with you more, jodi.
You just totally hit the nailon the head with that, and I do
the same thing when I'm lookingat imagery over time.
I want to find those areaswhere we are having consistently
lower productivity potentialand areas of the field where
we're having consistently higherproductivity potential, and
(06:17):
that just is not possible withonly one image in one year.
Jodi (06:22):
Yeah, absolutely.
And there's a couple things tojust mention about NDVI before
we wrap this up.
The calculation for NDVI justthe bare bones or textbook
version of this it's going toproduce a value that's either
from negative one to plus one,whereas if you get closer to one
that means that is the highestdensely green you could have,
(06:42):
whereas the whole negative onewould be the most opposite of
green you can have.
But what I want to make thispoint of is that in ADMS, if
you're an ADMS user and you'reextracting NDVI images, what we
do in the software is we domultiply that NDVI value by 100,
meaning that if you're used toseeing and looking at NDVI
(07:03):
values, this negative one to oneconcept is going to sound kind
of weird, but that's because wemultiply it by 100 to make it
easier to remember.
In our grand scheme of things,you probably are familiar with
looking at values from likenegative point something or like
very lowly negative to up tolike 83 to 90.
And so those are kind of likethe normal, what we would say
(07:27):
range of NDVI values you mightsee in ADMS.
Sarah (07:31):
And I think in ADMS, when we're looking at our histogram,
once you reach right aroundlike 35 or 40 in there, we're
starting to think about having,you know, some greenness on the
landscape.
You're kind of out of the areawhere green plant matter is
starting to dominate that landversus soil or something that's
(07:53):
non-vegetative, and it doesn'ttake very long to get up to that
complete saturation.
You know those numbers that are80, 80, that's.
It's really hard to findvegetative indices out there
that are different from eachother.
You know, when I'm looking atimagery and I'm trying to make
zones out of that imagery,something that has no vegetative
(08:16):
indice to it at all, it's likeless than that 30 value and you
can't tell differences on thelandscape from a vegetative
perspective.
That's not very helpful to mewhen I'm considering vegetative
uh indicee data only.
But also once I get up to thatplace where it's completely
saturated and I can't see anyany differences in the in the
(08:37):
green canopy, that's also notthe most helpful image to me
because I can't tell where thosedifferences are and a lot of
times we'll see the differencesin the plant canopy earlier in
the growing season and thenthere'll be that time when the
plant canopy is completelysaturated and you just won't be
able to tell a whole lot ofdifference.
And then, as you start to movemore towards senescence, you'll
(09:00):
start to see some variabilityagain and perhaps how that plant
canopy dries down again.
So the images that I find themost difficult to use when it
comes to NDVI are where there'sabsolutely no plant material out
there at all, or the imagesthat are absolutely 100%
(09:22):
saturated with greenness and Ican't tell any difference.
Jodi (09:27):
Right, we're trying to use these images to figure out
where those consistentdifferences are, and Sarah and I
could talk about this subjectfor a long time.
Sarah (09:36):
And in fact we have.
Jodi (09:39):
And if you want to hear more, too, about you know how we
can choose imagery that helpsus get better NDVI data to build
those zones, do check ourbacklog.
We do have an episode calledChoosing Quality Imagery in
Season 1, so please check thatout.
But what I think I've heardtoday, sarah, is that maybe it
isn't always easy being green,but hopefully with zone
(09:59):
management it's a lot easier tomanage those areas that are
green and hopefully improve theway that we're managing those
less green areas to get themcloser to that darker green, the
higher NDVI value.
Sarah (10:12):
It ain't easy being green , being the colors of the leaves
.
Tune in next time for a tinybite of knowledge from GK
Technology, where we have a mapand an app for that.
I love Kermit the Frog.
(10:36):
He makes me so happy.
And now it's time for a tiny bite of knowledge.
Sarah (00:13):
It ain't easy being green , being the colors of the leaves
.
Jodi (00:22):
It might not be easy being green, but what is relatively
easy is detecting how greensomething is, and that comes in
the form of NDVI, or normalizeddifference vegetative index.
So that's what NDVI stands for.
But let's break this down.
What is a vegetative index?
(00:42):
Even what is?
Sarah (00:43):
it.
A vegetative index is when weuse light, specifically using
different bands of light withinthe electromagnetic spectrum, to
understand vegetation.
So when we think about NDVInormalized difference vegetative
index it's an index where wehave a ratio and a specific
(01:05):
formula that compares nearinfrared light to red light and
that helps us understand howdensely green the plant canopy
is.
Basically, in dumbed down terms, when our human eyes see green,
it is because red light isgetting absorbed, and so plants
(01:25):
are absorbing red light.
Green is being reflected, andso when we take into comparison
near infrared light and redlight, we are able to understand
how green and how densely greenthe plant canopy is across the
landscape.
Jodi (01:45):
So what does that mean for us?
How do we use that number in apractical way?
Sarah (01:50):
So chlorophyll strongly absorbs visible light and plant
cell structures on the leavesstrongly reflect near-infrared
light.
So NDVI, specifically, is a wayof calculating the amount of
light that is being absorbed andused for photosynthesis and
also how much light is reflected.
This is important because it'sgoing to help us understand how
(02:14):
green and how densely green aplant canopy is across a
landscape.
For our purposes in agriculture, we're really thinking about
where is a field of crop more orless green than other areas,
because this is really anindication of photosynthesis.
This can be a great way ofthinking about the productivity
(02:38):
potential across the landscape.
And actually there's manyplaces and many times where NDVI
can actually correlate to yielddata Not always, but many times
it does correlate to yield data.
Jodi (02:54):
So does that mean a lot of what we use in terms of imagery
?
We'll extract the NDVI valuefrom that imagery and then use
that to build zones.
Sarah (03:05):
Absolutely Jodi.
Many times here at GKTechnology we actually use a lot
of satellite imagery to helpunderstand the productivity
potential of a field across alandscape.
It can help us understand wherethe crop has more potential to
photosynthesize and have greateryield potential, productivity
potential and areas of the fieldwhere there's less
(03:27):
photosynthesis happening andtherefore less productivity
potential.
So from that, when we can takea look at different satellite
images over the years, we canunderstand across a given field
where those areas areconsistently happening, where we
consistently seeing greateramounts of NDVI throughout the
(03:48):
growing season and lesser NDVIand photosynthesizing
photosynthesis potential acrossthat field.
Once we can figure out wherethat is happening consistently
over a number of years, we cancreate zone maps that can help
us understand where we should beinvesting in more inputs into
(04:09):
areas where the greaterproduction potential is
happening and where maybe weshould try to manage that input
cost so that we have less inputsgoing into lower productivity
areas.
Jodi (04:23):
So basically what I hear you saying is we want to find
out like where the NDVI value islowest, consistently the lowest
.
We want to those would be whatwe consider like a low
productivity zone and the areaswhere the NDVI value is the
highest.
Those are hypothetically andprobabilistically our areas of
highest productivity and we wantto manage those, like those are
(04:46):
our highest producing zones orareas with highest potential.
Sarah (04:51):
Absolutely, and so we are obviously transitioning this
conversation from NDVI to howwe're using these in a practical
sense for making zones so, jodi, would you ever use just one
NDVI?
To how we're using these in apractical sense for making zones
.
So, jodi, would you ever usejust one NDVI image to create
zones from one year?
Jodi (05:09):
Absolutely not.
It is a snapshot in time andthat doesn't necessarily reflect
any sort of variance in weatherpatterns.
Maybe, like a single snapshotis a culmination of conditions
that occurred that year, butthat doesn't necessarily mean
that that's consistent over time.
(05:30):
And so, no, I do not use asingle image to create zones,
and I do not promote that either, just because what we're trying
to do when we're building zonesis we're trying to predict
variability and predictconsistent variability, and so
with just one snapshot, youaren't able to really create
(05:52):
something that holds up overtime.
Sarah (05:56):
I couldn't agree with you more, jodi.
You just totally hit the nailon the head with that, and I do
the same thing when I'm lookingat imagery over time.
I want to find those areaswhere we are having consistently
lower productivity potentialand areas of the field where
we're having consistently higherproductivity potential, and
(06:17):
that just is not possible withonly one image in one year.
Jodi (06:22):
Yeah, absolutely.
And there's a couple things tojust mention about NDVI before
we wrap this up.
The calculation for NDVI justthe bare bones or textbook
version of this it's going toproduce a value that's either
from negative one to plus one,whereas if you get closer to one
that means that is the highestdensely green you could have,
(06:42):
whereas the whole negative onewould be the most opposite of
green you can have.
But what I want to make thispoint of is that in ADMS, if
you're an ADMS user and you'reextracting NDVI images, what we
do in the software is we domultiply that NDVI value by 100,
meaning that if you're used toseeing and looking at NDVI
(07:03):
values, this negative one to oneconcept is going to sound kind
of weird, but that's because wemultiply it by 100 to make it
easier to remember.
In our grand scheme of things,you probably are familiar with
looking at values from likenegative point something or like
very lowly negative to up tolike 83 to 90.
And so those are kind of likethe normal, what we would say
(07:27):
range of NDVI values you mightsee in ADMS.
Sarah (07:31):
And I think in ADMS, when we're looking at our histogram,
once you reach right aroundlike 35 or 40 in there, we're
starting to think about having,you know, some greenness on the
landscape.
You're kind of out of the areawhere green plant matter is
starting to dominate that landversus soil or something that's
(07:53):
non-vegetative, and it doesn'ttake very long to get up to that
complete saturation.
You know those numbers that are80, 80, that's.
It's really hard to findvegetative indices out there
that are different from eachother.
You know, when I'm looking atimagery and I'm trying to make
zones out of that imagery,something that has no vegetative
(08:16):
indice to it at all, it's likeless than that 30 value and you
can't tell differences on thelandscape from a vegetative
perspective.
That's not very helpful to mewhen I'm considering vegetative
uh indicee data only.
But also once I get up to thatplace where it's completely
saturated and I can't see anyany differences in the in the
(08:37):
green canopy, that's also notthe most helpful image to me
because I can't tell where thosedifferences are and a lot of
times we'll see the differencesin the plant canopy earlier in
the growing season and thenthere'll be that time when the
plant canopy is completelysaturated and you just won't be
able to tell a whole lot ofdifference.
And then, as you start to movemore towards senescence, you'll
(09:00):
start to see some variabilityagain and perhaps how that plant
canopy dries down again.
So the images that I find themost difficult to use when it
comes to NDVI are where there'sabsolutely no plant material out
there at all, or the imagesthat are absolutely 100%
(09:22):
saturated with greenness and Ican't tell any difference.
Jodi (09:27):
Right, we're trying to use these images to figure out
where those consistentdifferences are, and Sarah and I
could talk about this subjectfor a long time.
Sarah (09:36):
And in fact we have.
Jodi (09:39):
And if you want to hear more, too, about you know how we
can choose imagery that helpsus get better NDVI data to build
those zones, do check ourbacklog.
We do have an episode calledChoosing Quality Imagery in
Season 1, so please check thatout.
But what I think I've heardtoday, sarah, is that maybe it
isn't always easy being green,but hopefully with zone
(09:59):
management it's a lot easier tomanage those areas that are
green and hopefully improve theway that we're managing those
less green areas to get themcloser to that darker green, the
higher NDVI value.
Sarah (10:12):
It ain't easy being green , being the colors of the leaves
.
Tune in next time for a tinybite of knowledge from GK
Technology, where we have a mapand an app for that.
I love Kermit the Frog.
(10:36):
He makes me so happy.
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