NOAA/NESDIS Advanced Satellite Products Branch

Madison, Wisconsin, USA

Andrew Heidinger

MODULE 4 Spatial, Spectral and Temporal

Characteristics of Imagery

Where Am I?

Andrew Heidinger

NOAA/NESDIS

University of Wisconsin / CIMSS

Madison, Wisconsin, USA

• So far you have learned about the international

bodies that deal with satellite data (GEO and

CEOS).

• Week 3 you learned about radiative transfer and

the different types of measurements satellites

make.

• This week (4) you will focus on the technical

characteristics of satellite imagery.

• In upcoming weeks, you will learn about specific

imagery applications.

How Week 4 Fits In

• Introduction to Imagery

• Imager Characteristics

– Spatial Resolution

– Temporal Resolution

– Spectral Resolution

• Other Imagery Issues to Consider

– Bit Depth

– Bowtie

– Parallax

– Calibration

• Conclusions

WEEK 4 OUTLINE

• Imagers are cameras mounted on satellites. The

make images – unlike some other sensors like

sounders or limb profilers where images can not

be easily made.

• Imagers make multispectral observations –

observations at more than one frequency.

Typically 5 – 50 channels.

• Imagers record observations that can be

radiometrically calibrated and used to make

estimates of important parameters.

What is an Imager?

• Imagers measure in the VIS and IR.

• Solar reflectance channels have wavelengths from 0.4 to

2.5 mm.

• Thermal emission channels measure from 4 to 15 mm

What makes an Imager an Imager

• Sounders are not imagers. They measure at

higher spectral resolution (more channels) in IR

and are not used for making images.

• Microwave Imagers and Sounders measure in

wavelengths much longer than VIS/IR and

typically are not used applied in traditional

imagery applications.

• However, the line between imagery and

sounders is becoming harder to discern as

sounders offer finer and finer spatial resolution

and imagers offer more and more channels.

• Synthetic Aperture Radars (SAR) are also used

in many imagery applications.

What is not an Imager?

• The spatial scale of your phenomenon - for

example studying Lakes demands finer

resolution data than that used for Oceans

• Choose your temporal sampling needs.

Vegetation health changes slowly, land

temperatures change daily but clouds vary over

minutes.

• Spectral information dictates what imagery can

tell you.

• Choose your period of record. Some imagers –

like AVHRR, LandSat – offer multiple decades of

data. Others – VIIRS, offer shorter records and

this limits climate-scale studies.

How to Select which Imager to Use

• Level 0: unprocessed instrument and payload data at full resolution, with any and all

communications artifacts (e. g., synchronization frames, communications headers,

duplicate data) removed.

• Level 1a: unprocessed instrument data at full resolution, time-referenced, and

annotated with ancillary information, including radiometric and geometric calibration

coefficients and georeferencing parameters (e. g., platform ephemeris) computed and

appended but not applied to the Level 0 data (or if applied, in a manner that level 0 is

fully recoverable from level 1a data).

• Level 1b: Level 1a data that have been processed to sensor units (e. g., radar

backscatter cross section, brightness temperature, etc.); not all instruments have

Level 1b data; level 0 data is not recoverable from level 1b data.

• Level 2: Derived geophysical variables (e. g., ocean wave height, soil moisture, ice

concentration) at the same resolution and location as Level 1 source data.

• Level 3: Variables mapped on uniform spacetime grid scales, usually with some

completeness and consistency (e. g., missing points interpolated, complete regions

mosaicked together from multiple orbits, etc.).

• Level 4: Model output or results from analyses of lower level data (i. e., variables that

were not measured by the instruments but instead are derived from these

measurements).

Imager Observations are usually available in Level-1b format

Levels of Imagery Data

Orbital Impacts on Imagery • Spectral resolution is governed by how much signal you have.

• Polar orbiters are much closer to the earth and get much more signal –

signal strength decreases as orbit radius squared

• Geostationary are much further away but they can stare at same

location.

• Most imagers are in sun-synchronous orbits. (same time every day)

Comparison of Temporal and Spatial Characteristics of Common Imagers

Imager Pixel

Resolution

Swath

Width

Repeat

Frequency

LandSat 30 – 60 m 185 km 16 days

MODIS 1 km 2330 km 16 days

AVHRR 1.1 km 2900 km 9 days

VIIRS 375 – 750 m 3040 km 16 days

SEVIRI 3 km 6000 km 15 minutes

• Polar orbiting imagers overlap more and more as you move away

from equator.

• AVHRR is designed to have no gaps and no overlap at equator

• MODIS has small gaps only in Tropical Latitudes.

• VIIRS has no gaps and actually overlaps even at the equator

• Landsat has very large gaps between orbits.

Spatial Resolution

• Spatial: The size of each picture element or pixel.

• It determines the scale of features that can be

resolved.

Spatial Resolution Definition

• Spatial Resolution of Typical Imagers:

– Meteorological imagers have resolutions of 1-

5 km. (AVHRR, MODIS, SEVIRI)

– Land use imagers (LandSat, Spot) have

resolutions of << 1km but typically limited

coverage

– Newer reconnaissance imagers (GeoEye)

have resolutions of about 1 meter but are

typically provided at cost.

Spatial Resolution Options

Imagers and Other Satellite Sensors

LandSat – 30 m / every two weeks AVHRR – 1km – Every Day

• Choice of imagery depends on phenomena studied.

• If the feature is not resolved in your imagery, you risk contamination

from features in surrounding area.

LandSat Image of Lake Chad (NASA)

Spatial Resolution Examples

Imagery at 90 m (ASTER) and 1000 m (MODIS) of a Volcanic Lake.

ASTER is better of course but MODIS provides more channels – a common trade-off

Impact of Viewing Angle on Spatial Resolution

• For most imagers, the angular (q) pixel size stays the same as it scans.

• However, a pixel’s spatial resolution degrades as the viewing angle

increase.

• Pixels at the edge of swath can be much bigger than nadir pixels (as the

illustration below shows).

Pixel Growth with Angle for VIIRS

• VIIRS is a new meteorological imager flow on NPP by NASA and NOAA

• It limits its pixel size with angle by change how many sub-pixel it combines or

aggregates together.

• Near nadir, it aggregates 3 and at the end of scan, it aggregates just 1.

• This makes for a much improved imagery performance at edge of scan (as the

next slide will demonstrate).

NOAA-16 AVHRR 17:55Z

Edge of scan effects are rather severe

AVHRR & VIIRS True Color Comparisons

< Edge of scan Nadir >

VIIRS maintains its integrity

Example of Spatial Edge Effects in Imagery

3/8/2013

Terra-MODIS 19:45 Z

VIIRS Edge of Scan Improvement Example

MODIS versus VIIRS Edge of Scan Example

3/8/2013

Terra-MODIS 19:45 Z

Zoomed views

provide the

actual story

VIIRS Edge of Scan Improvement Example

3/8/2013

Temporal Resolution

• There are two distinct temporal characteristics that

impact the choice of imagery data to use

– Repeat cycle = how often will a polar orbiting satellite

see the same spot on the earth at the same angle.

– Time for global coverage = how long will it take for

an imager to see the whole globe.

– Geostationary images have high repeat cycles (on the

order minutes) but no one imager ever sees the

whole globe.

– Meteorological polar orbiters typically see the globe

twice a day (important for weather)

– Land surface polar orbit imagers sacrifice daily global

coverage for spatial resolution.

Temporal Resolution

• Temporal and spatial resolutions are

linked and image choice is trade-off!

• The higher spatial resolution, typically the

lower the temporal resolution (spatial

detail = less often data).

• Geostationary imagers provided data

continuously every 15 – 30 minutes.

• LandSat may not see the same spot on

the earth for many days.

Temporal Resolution

Trade Off Between Spatial and Temporal

A nice schematic of the typical spatial / temporal trade-off

Benefits of Temporal Sampling

• The more views one has of a target the more likely one can see a clear-view.

• Many features have a strong diurnal cycle (clouds), multiple views reveal that

cycle.

• The images below show a single MTSAT visible image and a 28-day composite

of the darkest images at the same time.

Spectral Resolution

• Spectral resolution refers to the number and

frequency of channels available on an imager.

• Channels with finer resolution are usually

superior to channels with wider resolutions.

Newer sensors tend to have narrower channels.

• Location of channels on an imager are dictated

by the intended use of the data.

• Radiometric resolution also refers to the number

of channels.

• Spectral resolution can also refer to width of the

channels.

• We will use spectral resolution to mean both.

Spectral Resolution of Imagery

• Spectral Resolution varies widely across the

imagers that available to you.

• Spectral channels of an imager are driven by its

intended application.

• Land Imagers (Landsat): visible, near-IR and IR

windows for surface temperature

• Weather Imagers: more IR bands in H2O and

CO2 bands for sensing atmospheric components

(water vapor and clouds).

• Ocean Imagers: IR bands for SST and visible

bands sensitive to chlorophyll for ocean color.

Spectral Resolution

Spectral Resolution • The table below shows the bands for MODIS and their application.

• Each band has a specific purpose.

• MODIS has a nearly full set of bands for every imager application.

• Most imagers will have subset of these bands.

• One of the important aspects of spectral

characteristics are the properties of the

absorbing gases within in channels.

• On imagers, some IR and a few solar

reflectance channels are located in H2O or

CO2 absorbing bands.

• Gas absorption limits how far you can see

into the atmosphere. This allows you tell

high from low clouds for example in 6.7

and 11 mm imagery.

Weighting Function Introduction

Gaseous Absorption in VIS/IR

• This cartoon shows the spectral location of important gases in the

VIS/IR

• Windows are where the surface can be seen under clear-conditions

SEVIRI Weighting Functions

• Weighting functions show what levels contribute the most to the signal.

• The more the absorption, the higher the peak of the weighting functions.

• The weighting functions below show the MSG/SEVIRI IR Channels.

Channel in “Solar Window”

Channel in “Shortwave IR Window – 4mm”

Channel in “Longwave IR Window – 11mm”

Channel in Water Vapor Absorption Band – 6.7mm”

• One of the best ways to comprehend the

spectral information in imagery is through

the use of false color imagery.

• True color imagery is made by the

combination of red-green-blue colors.

• Natural color imagery is the use of other

colors to approximate true color

• False color imagery uses any channel or

product to make an image to highlight

certain features.

False Color Imagery

The Concept of Color • The Three Primary Colors are red, blue and green.

• The can be combined to generate any color.

• Some satellite imagers (MODIS, VIIRS) measure blue (0.44), green (0.55) and red

(0.63) channels directly. Most do not have true color capability.

• These channels can be combined to make true color.

• False color imagery is a qualitative not a

quantitative application.

• The human eye is very good at detecting

features and false color imagery exploits

this.

• False color imagery is used regularly for

detecting burned areas, fog, dust, cloud

phases, air-masses, snow and many other

features.

False Color Imagery

• EUMETSAT has a very good site with

real-time meteorological examples (http://oiswww.eumetsat.org/IPPS/html/MSG/RGB/)

• Understanding false color imagery

requires a rudimentary understanding of

the physics of remote sensing. This is

what you learned last week.

• The following examples will highlight the

spectral features of imagery and how they

influence false color imagery.

False Color Imagery

Example Construction of RGB

Reflectance Spectra of Surfaces

• The spectral features of surface are often exploited in false color imagery.

• The rapid rise in vegetation reflectance (0.6 to 0.8 mm) is seen in many false

color images. For example the next slide shows a burn-scar example.

0.65mm 0.85mm

Using 0.63 and 0.86 in a false color highlights area where vegetation is burned

Gaseous Absorption in VIS/IR

• Again, we view the spectra of gaseous absorption.

• Images of channels in and out of gaseous absorption features allow

for visualization of important aspects of the atmosphere – like the

presence of dry layers or inversions.

• The following example comes from EUMETSAT

Reference VIS (0.63mm) Image

False color with water vapor – shows dry air masses and allows one to see how

moisture is flowing into areas of active storms.

Dust Example from IR Spectral Channels

Dust has a unique spectral behavior in the 8.5 to 12 mm spectral region that can be

exploited in rgb images.

Snow Cloud Reflectance Spectra

• Reflectance spectra of snow and cloud (water phase)

EUMETSAT Natural Color Image

• Red = 1.6, Green=0.8 and Blue = 0.6 mm.

• Ice clouds absorb at 1.6 mm, vegetation is relatively bright at 0.8mm

Cloud Microphysics RGB Ice cloud absorb more at 3.9 mm than water and are much colder than water clouds

usually at 11 mm. In this 0.63 (R), 3.9 (G), 11 (B) false color image, ice cloud will

read and water cloud as whitish blue.

Seeing Snow in False Color

• As you learned, snow absorbs at 1.6 mm. If you stick the 1.6 mm channel

in the red gun of an RGB, snow (or ice cloud) will appear blue/green.

Another Example of Spectral Information

The GOES Sounder makes Imagery of Each of its SPECTRAL Channels

Another Example of Spectral Information

While this shows “Sounder Data”, many new imagers provide these channels

Other Imagery Issues

Imagery Issues at Edge of Scan

• As we said, pixels get bigger as you scan away from nadir.

• This causes the bow-tie effect which can make imagery hard to

interpret

• Pixels begin to overlap with their neighbors

• VIIRS data has gaps in it to remove these overlapping pixels.

Imagery Issues at Edge of Scan

• Techniques exist to fix this issue. They involve mapping to the earth and

resampling.

• Any mapped image (placed on the globe) will not have a bow-tie issue.

Example Image with Bowtie Effects Example Image after Bowtie Correction

• Bit Depth refers to number of bits used to represent each

number. Very old imagers used 8-bits so the numbers

varied from 0-255. Many still use 10 bits and most new

sensors use 14+ bits.

• For a 8 bits measurement, temperatures have a

resolution of about 1K or less.

• Saturation refers to the highest or lowest value a sensor

can record.

• Saturation effects are common with imagers at 4 mm

where land surfaces can can brightness temperatures >

340 K. Unless designed for fire applications, most 4 mm

channels saturate at 330 – 340 K.

• Saturation effects have been known to fool scientists!

Bit Depth and Saturation

Bit Depth Example

• This illustrates the impact of bit-depth on imagery quality.

• The plot shows the temperature range for each bit of a 12 bit image of the 4

mm channel.

• As the temperature becomes colder, the radiances in this channel get very

small.

• The non-linear radiance to temperature behavior at 4 mm results in very

large temperature increments for each bit.

• Even worse for 10-bit sensors (AVHRR)

Bit Depth and Saturation

Atmospheric Correction

• As we learned, pixels grow in size as one views them at higher angles. At high

angles, the impact of atmosphere also increases.

• Many applications of imagery data you’ll learn about require atmospheric

correction.

• This means the impact of the atmosphere is removed. The atmospheric signal

can obscure the desired signal. Effects of smoke, aerosol and Rayleigh

scattering can be corrected for (see below).

NDVI from Landsat before (left) and after (right) atmospheric correction.

• Parallax is when the height of feature causes it to be

displaced in an image.

• As the cartoon illustrates, features viewed at an angle

and are vertically high will be displaced in image.

• Parallax displacement = Height / cosine(viewing angle)

• Feature will appear to move when viewed by multiple

sensors.

Parallax

A B

Cloud located at “A” will be

located at “B” on the image.

Parallax Example

Here is an example of how parallax affects the apparent displacement of convective cloud

top features when viewed from GOES vs. the polar-orbiting MODIS instrument — note how

the coldest cloud top pixel on the “MODIS IR Window” image is about a half a county farther

south that on the corresponding “GOES IR Satellite” image (in this case, half a county ends

up being about 20 miles).

• An important part of selecting which imagery

data to use is calibration.

• Old imagers like AVHRR and LANDSAT have

larger calibration errors than newer sensors

(MODIS, VIIRS).

• Solar reflectance channels are often neglected.

• Know your calibration source and be prepared to

deal with poorly calibrated data!

Calibration

• Today we have many imager data sets to apply

to any given remote sensing problem.

• Trade-offs between temporal, spatial and

spectral characteristics dictate which imager to

choose.

• It later lectures, you be exposed to the tools and

applications that exist to help you conduct this

analysis.

Summary

Extra Material