How Remote Sensing Works

. . . the process of collecting information related to the electromagnetic energy reflected or emitted by an object on the ground using a device some distance away from the target.

Credit: NASA

The Remote Sensing Process

Energy radiating from the sun interacts with the atmosphere and objects on the ground before a portion is reflected back to the sensor

The Electromagnetic Spectrum

Units for measuring wavelengths will be listed either in micrometers (µm, µ ) or nanometers (nm)

The visible spectrum is: 0.4 – 0.7 µm Or 400 – 700 nm

0.5 µm 0.6 µm 0.7 µm 0.4 µm

The Electromagnetic Spectrum

Blue Band

0.4 – 0.5 µm

Green Band

0.5 – 0.6 µm

Red Band

0.6 – 0.7 µm

EM Band – Narrow range of wavelengths that can be measured by a sensor

The Electromagnetic Spectrum

Infrared Bands

Near infrared (NIR): 0.7 - 1.3µm Shortwave or middle infrared (SWIR/MIR): 1.3 – 3.0 µm Thermal infrared (TIR): 3.0 – 14.0 µm

0.4 µm 0.5 µm 0.6 µm 0.7 µm

NIR – CIR imagery; vegetation growth

SWIR/MIR – Moisture content; penetrate smoke/haze; minerals; man-made materials

TIR – Heat sources; radiant energy; crop monitoring (evapotranspiration)

NIR and SWIR Examples

NIR image displaying variations in mineral content, vegetation, and water cause patterns of light and dark in this near infrared

view of the Piqiang Fault in northwestern China.

NASA image, Robert Simmon

NIR and SWIR Examples

NASA image, Robert Simmon

SWIR color composite utilizes the differences between 3 shortwave infrared bands to highlight the mineral geology

surrounding China’s Piqiang Fault.

What Happens to Energy When Strikes a Ground Target?

Transmission – Energy simply passes through to act on something else. i.e. greenhouse.

Absorption – Energy is trapped and held rather than being reflected or transmitted. The reason we see colors.

Reflection – Energy bounces off target to be recorded by a sensor.

What Happens to Energy When Strikes a Ground Target?

Incident Energy (I) = Transmission (T) + Absorption (A) + Reflection (R)

Spectral Reflectance = % of total energy for each λ that is reflected by the target.

⍴ =𝑅

𝐼 * 100

What Happens to Energy When Strikes a Ground Target?

Spectral Reflectance = % of total energy for each λ that is reflected by the target.

⍴ =𝑅

𝐼 * 100

What do the sensors actually record?

Brightness Values Spatial Resolution – the smallest area that can be measured. One pixel.

What do the sensors actually record?

Spatial Resolution

(Pixel size)

Brightness Values

Satellite Sensor Characteristics

• What Level of Detail Spatial

Resolution

• What Colors or Bands Spectral

Resolution

• Revisit Time Temporal

Resolution

• Color Depth Radiometric Resolution

Spatial Resolution describes how much detail in an image is visible to the human eye. The ability to "resolve," or separate, small details is one way of describing what we call spatial resolution. Higher spatial resolution = larger file sizes; increased processing time;

and higher cost (if purchasing from a commercial vendor).

Spatial Resolution

Aerial photo with 1m spatial resolution (pixel size) Satellite image with 10m spatial resolution

Planimetric data – roads, buildings, driveways

Spatial Resolution

80 meter Landsat MSS w/ planimetric overlay

Spatial Resolution

30 meter Landsat TM w/ planimetric overlay

Spatial Resolution

10 meter SPOT w/ planimetric overlay

Spatial Resolution

1 meter DOQ w/ planimetric overlay

Spatial Resolution

Sub-meter data w/ planimetric overlay

Spatial Resolution

Multispectral Scanner

(MSS)

Landsat 1-5

Thematic Mapper

(TM) Landsat 4 & 5

Enhanced Thematic

Mapper Plus (ETM+)

Landsat 7

Operational Land

Imager (OLI) / Thermal

Infrared Sensor (TIRS)

Landsat 8

Spectral

Resolution

(mm)

• 0.5-0.6 (green)

• 0.6-0.7 (red)

• 0.7-0.8 (NIR)

• 0.8-1.1 (NIR)

1. 0.45-0.52 (B)

2. 0.52-0.60 (G)

3. 0.63-0.69 (R)

4. 0.76-0.90 (NIR)

5. 1.55-1.75 (MIR)

6. 10.4-12.5 (TIR)

7. 2.08-2.35 (MIR)

1. 0.45-0.52

2. 0.52-0.60

3. 0.63-0.69

4. 0.77-0.90

5. 1.55-1.75

6. 10.4-12.5

7. 2.09-2.35

8. 0.52-0.90 (Pan)

1. 0.43-0.45

2. 0.45-0.51

3. 0.53-0.59

4. 0.64-0.67

5. 0.85-0.88

6. 1.57-1.65

7. 2.11-2.29

8. 0.50-0.68 (Pan)

9. 1.36-1.38

10. 10.60-11.19 (TIRS)

11. 11.50-12.51 (TIRS)

Spatial

Resolution

(meter)

79 x 79

30 x 30

120 x 120 (TIR)

15 x 15 (Pan)

30 x 30

60 x 60 (TIR)

15 x 15 (Pan)

30 x 30

100 x 100 (TIRS)

Temporal

Resolution

(revisit days)

18 (Landsat 1,2,3)

16

16

16

Comparison of Landsat Sensors

Satellite Sensor Characteristics

• What Level of Detail Spatial

Resolution

• What Colors or Bands Spectral

Resolution

• Revisit Time Temporal

Resolution

• Color Depth Radiometric Resolution

Spectral Resolution

• Number of spectral bands (red, green, blue, NIR, Mid-IR, thermal, etc.)

• Width of each spectral band

• Certain spectral bands (or combinations) are good for identifying specific ground features

• Panchromatic – 1 spectral band (B&W)

• Color – 3 spectral bands (RGB)

• Multispectral – 4+ discrete spectral bands (e.g. RGBNIR)

Spectral Resolution: Sentinel-2

Sentinel 2-A, ESA Copernicus Programme

• Twin Satellite Constellation (2A – 2015 / 2B – 2017)

• 13 Band, Multi-resolution

• 290 Km FOV

Multispectral Scanner

(MSS)

Landsat 1-5

Thematic Mapper

(TM) Landsat 4 & 5

Enhanced Thematic

Mapper Plus (ETM+)

Landsat 7

Operational Land

Imager (OLI) / Thermal

Infrared Sensor (TIRS)

Landsat 8

Spectral

Resolution

(mm)

• 0.5-0.6 (green)

• 0.6-0.7 (red)

• 0.7-0.8 (NIR)

• 0.8-1.1 (NIR)

1. 0.45-0.52 (B)

2. 0.52-0.60 (G)

3. 0.63-0.69 (R)

4. 0.76-0.90 (NIR)

5. 1.55-1.75 (MIR)

6. 10.4-12.5 (TIR)

7. 2.08-2.35 (MIR)

1. 0.45-0.52

2. 0.52-0.60

3. 0.63-0.69

4. 0.77-0.90

5. 1.55-1.75

6. 10.4-12.5

7. 2.09-2.35

8. 0.52-0.90 (Pan)

1. 0.43-0.45

2. 0.45-0.51

3. 0.53-0.59

4. 0.64-0.67

5. 0.85-0.88

6. 1.57-1.65

7. 2.11-2.29

8. 0.50-0.68 (Pan)

9. 1.36-1.38

10. 10.60-11.19 (TIRS)

11. 11.50-12.51 (TIRS)

Spatial

Resolution

(meter)

79 x 79

30 x 30

120 x 120 (TIR)

15 x 15 (Pan)

30 x 30

60 x 60 (TIR)

15 x 15 (Pan)

30 x 30

100 x 100 (TIRS)

Temporal

Resolution

(revisit days)

18 (Landsat 1,2,3)

16

16

16

Comparison of Landsat Sensors

Discrete Spectral Coverage

Unique Spectral Properties / Spectral Response Curve

Spectral reflectance curves, or spectral signatures, of different types of ground targets provide the knowledge base for information extraction.

Spectral Response Curve

Spectral Response Curve

Spectral responses (brightness values) from ground targets are recorded in separate spectral bands by

sensors.

Spectral Response Curves

April and May Spectra for P. australis

and S. patens

0

0.1

0.2

0.3

0.4

0.5

0.6

350 450 550 650 750 850 950

Wavelength (nm)

Re

fle

cta

nc

e (

%)

April P. australis

May P. australis

April S. patens

May S. patens

Blue Green Red NIR

Discrete

Spectral

Coverage

P. australis (Phragmites) S. patens (Spartina)

Spectral Response Curves

April and May Spectra for P. australis

and S. patens

0

0.1

0.2

0.3

0.4

0.5

0.6

350 450 550 650 750 850 950

Wavelength (nm)

Re

fle

cta

nc

e (

%)

April P. australis

May P. australis

April S. patens

May S. patens

Blue Green Red NIR Mid-IR Violet

Discrete

Spectral

Coverage

P. australis (Phragmites) S. patens (Spartina)

Earth Observing-1: Hyperion – Hyperspectral sensor (2000-2017)

An imaging spectrometer having a 30 meter ground sample distance over a 7.5 kilometer swath and providing 10nm (sampling interval) contiguous bands of the solar reflected spectrum from 400-2500nm.

Hyperion Hyperspectral Data

• sensor was capable of resolving over 220 continuous spectral bands at 10 nm interval (from 0.4 to 2.5 µm)

• 30 meter spatial resolution.

The hundreds of bands in hyperspectral imagery enable researchers to differentiate

minerals and rocks that appear similar in visible light. Outcrops near Kirbhat en-Nahas

(Jordan) that are uniformly dark in natural color (top) are variegated in false-color (lower),

signifying different rock types. Credit: NASA, Robert Simmon, using Hyperion data.

JORDAN

SAUDI

ARABIA

SYRIA

Color Composites – Brightness Values to Colorized Renditions

Color composite images require 3 channels of information – Red, Blue, Green (RGB)

Color Composites – Brightness Values to Colorized Renditions

Red Channel Green Channel Blue Channel

Red Band BV Green Band BV Blue Band BV

+ +

=

True Color Composite

Color Composites – Brightness Values to Colorized Renditions

Red Channel Green Channel Blue Channel

NIR Band Red Band Green Band

+ +

=

False Color Composite

Combination NIR, R, G is referred to as a color infrared image. This combination is also known as the Standard False Color Composite.

Landsat ETM+ Spectral Resolution

Spectral Indices

Relationship between spectral bands can provide an increased level of understanding beyond what standard 3-band composites can show

12

a

3

4

0

5

10

15

20

25

30

35

Blue

(0.45 - 0.52 mm)

Per

cen

t R

efle

ctan

ce

Green leaf

Yellow

Red/orange

Brown 4

2

1

3

45

40

Green

(0.52 - 0.60 mm)Red

(0.63 - 0.69 mm)

Near-Infrared

(0.70 - 0.92 mm)

a.

b.

c.

d.

Spectral Indices – Normalized Difference Vegetation Index (NDVI)

NDVI =(𝑁𝐼𝑅 −𝑅𝑒𝑑)

(𝑁𝐼𝑅+𝑅𝑒𝑑)

• Common in agriculture for assessing vegetation health and crop monitoring

• Requires the presence of both NIR and Red bands

Spectral Indices – Normalized Difference Vegetation Index (NDVI)

NDVI =(𝑁𝐼𝑅 −𝑅𝑒𝑑)

(𝑁𝐼𝑅+𝑅𝑒𝑑)

NDVI values range from -1 to +1

• Higher values indicate better health

• Low or negative values highlight areas where nothing is growing

Spectral Indices – Many Available Based on Needs

Green Normalized Difference Vegetation Index (GNDVI) - more sensitive to the variation of chlorophyll content • GNDVI = (NIR-GREEN) /(NIR+GREEN) Enhanced Vegetation Index (EVI) - corrects for some atmospheric conditions and canopy background noise and is more sensitive in areas with dense vegetation • EVI = G * ((NIR – R) / (NIR + C1 * R – C2 * B + L)) Moisture Stress Index (MSI) - canopy stress analysis, productivity prediction and biophysical modeling • MSI = MidIR / NIR Normalized Burned Ratio Index (NBRI) - takes advantage of the near infrared and short wave infrared spectral bands, which are sensitive in vegetation changes, to detect burned areas and monitor the recovery of the ecosystem • NBR = (NIR – SWIR) / (NIR+ SWIR)

A few examples:

Spectral Index Database

https://www.indexdatabase.de/

Partial listing of indices for Landsat 8

Lecture Resources

Required Reading – NASA Earth Observatory, Interpreting False

Color Satellite Images

https://earthobservatory.nasa.gov/features/FalseColor/page1.php