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Remote Sensing, second half > Lectures 3 > Flashcards

Lectures 3 Flashcards

1
Q

XPGR

A

Cross-polarized gradient ratio
Developed by Waleed; other techniques also used

Combinging freq and polarization for amplified melt signal

Threshold allows classification of melt

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2
Q

ICESat

A

need stuff

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3
Q

GRACE

A

Gravity recovery and

can be used to measure ice balance

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4
Q

Mascon==

A

like a pixel, done by Luthcke

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5
Q

Assessing ice sheet contributions to sea level rise

A

Mass Balance = precipt - surface alblation - discharge

  • Flux Method= assess net different between mass input and mass output from observations and models of each component

Mass Balance = precept (ice cores and models) - surface ablation (models) - discharge (InSAR and others)

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6
Q

Snow measurement (RS)

A

MODIS monthly snow over with fractional snow…….

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7
Q

Spectral albedo

A

differs for different surfaces–can be used to ID

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8
Q

NDSI

A

Normalized Difference Snow Index, uses bands from Landsat ™ and MODIS. Measuring in dark forests is hard, particularly in the tropics. Sensitive to small changes in NSDI or NDVI,,,,

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9
Q

NDVI

A

define

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10
Q

SWE

A

Snow water equivalent–mount of water that comes from an icepack. Use 19 and 37 GHz. When no snow, similar. When snow, brightness temp is different.

Important for fire modeling, flood forecasting, hydroelectric power, watershed management.

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11
Q

Classification techniques

A

GENERAL CONCEPTS:

  • Each pixel has DN for each band
  • N-D scatterpolots of DN by band form clusters (plot band 4 and band 5 on x, y). Closest in cluster has similar spectral signature.
  • Each cluster has a mean distance from origin and angle. Together, these define the class vector.
  • Other stats describe shape of cloud.

PDF–define

SUPERVISED (need knowledge of what’s there):
Used to cluster pixels in an image into particular ground cover types. Many supervised algorithms are available (Paralellepiped, minimum distance, etc)
1. user picks types / groups
2. select pixels from each class (called training class)
3. use training classes to estimate parameters of algorithm
4. Classify every pixel into type
5. Produce image / map

UNSUPERVISED (don’t have knowledge of what’s there):
Used to cluster pixels in data set based on stats only, without training classes. Two algorithms: Isodata and k-means.
Fast. Does not assume normally distributed PDF.

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12
Q

HYDROSPHERE LECTURE

A

HYDROSPHERE LECTURE

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13
Q

Hydrosphere applications (IMPORTANT)

A

list from slide–add instruments

  • sea / lake surface temp–thermal, PM
  • wind over lakes / ocean–AM, scatterometry
  • chlorophyll–vis, NIR
  • sediment–vis, NIR
  • ocean circulation–TOPEX / POSEDISON, Jason, Metop, AM
  • volume & mass change: Altimimetry (ICESat and aircraft, Gravity (GRACE)
  • water extent–vis, NIR, AM
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14
Q

Cryosphere applications (IMPORTANT)

A

list from slide–add instruments

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15
Q

Water Cycle

A

see slide

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16
Q

Hydrological cycle

A

Global water cycle

three major pathways: precipitation, evaporation/transpiration and vapor transport

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17
Q

Total water radiance

A

surface, subsurfcae, volumetric, and bottom radiance
total (Lt) = Lp + Ls + Lv + Lb
Lp=unwanted path radiance (atmosphere)….etc

18
Q

Why does water appear blue to our eyes?

A

Scattering in water column is important in the bviolet, dark blue, and light blue portions of spectrum (400-500 nm)..Nearly all NIR and MID is absorbed (for clear water)

19
Q

Measuring surface extent of water

A

using active microwave (AM, e.g. Radarsat): surface roughness and moisture content affect reflection

  • water will have specular reflection (dark)
  • land will have diffuse reflection (some will return to sensor)
20
Q

What about sediment?

A
  • The spectral reflectance of sediment in surface water is function of quantity and characteristics (particle size, absorption, etc)
  • Remote sensing must be validated in situ
  • Can be done using a SECCHI DISK or spectroradiometer
21
Q

Spectroradiometer

A
  • measures radiation (in situ)
  • used to collect spectral reflectance at a of pure water and water with various suspended segment and chlorophyll concentrations
  • 252 bands between 368 and 1114 nm
22
Q

Measurements using a Spectroradiometer

A

study this slide

Take away: 580-690 nm provides info….

23
Q

Chlorophyll in water

A

Plankton: phyto, zoo, bacterio

  • Phytoplankton is important for carbon fixation / CO2 sink. (Use CO2 in photosynthesis)
  • Contain chlorophyll pigments that decrease blue and red reflectance, but increase green and NIR reflectance
  • Different spectral response if mineral also suspended
24
Q

Remote sensing of ocean color

A

color = life
Three main: CZCS, MODIS, SeaWIFS
Warmer the color (CZCS), the greater the chlorophyll concentration near the surface

Slide has details re platforms / spectral ranges

25
Q

Sea Surface Temp

A

The temperature of the sea is the most important parameter in understanding the role of the ocean as a heat reservoir. SST is an important geophysical parameter, providing the boundary condition used in the estimation of heat flux at the air-sea interface. On the global scale this is important for climate modeling, study of the earth’s heat balance, and insight into atmospheric and oceanic circulation patterns and anomalies (such as El Niño)

26
Q

SST types of sensors

A

SST can be derived from two types of satellite data, thermal infrared and passive microwave:
 Thermal sensors
 Sensors used for deriving SST include AVHRR, Along Track scanning Radiometer (ATSR), GOES, MODIS
 good spatial resolution and accuracy
 long heritage (over 30 years)
 with thermal infrared (8-14 μm) the skin temperature (20 μm) at the sea-air interface can be measured
 TIR measurements are affected by water vapor and clouds in the atmosphere, thus
 atmospheric corrections are required
Sea Surface Temperature (SST)
Passive microwave sensors
 PM sensors include SMMR, SSM/I, TMI, AMSR
 clouds are transparent
 long heritage (over 30 years)
 relatively insensitive to atmospheric effects
 lower spatial resolution and accuracy than with thermal, Due to lower signal strength of the Earth’s Planck radiation curve in the microwave region
 sensitive to surface roughness (waves) and precipitation
 measures SST at depth of 1 mm

27
Q

Methods of TIR for SST

A

Three methods can be used:
* Split window: uses 2 channels (windows) that overlap in spectral sensitivity (formula coefficients differ day / night)

  • Dual window: uses 2 channels that do not overlap
  • Triple window: uses 3 channels
28
Q

Oceanic Circulation importance

A
  • Ocean is important as the atmosphere in transporting heat
  • Mechanism is ocean currents
  • Warm ocean currents transport warm water from the tropics northward where they release energy to the air
  • Cold ocean currents transport cold water from higher latitudes toward the equator
29
Q

Oceanic circulation, factors affecting

A 
 Winds
 Bottom topography
 Sea-surface height
 Sea-surface temperature
 Coriolis forces resulting from the rotation of the earth
30
Q

Platforms for Oceanic Circulation–Sea Surface Height

A

The factor influencing the oceanic circulation determines the platform
Sea Surface Height
• Early platforms are Geosat and ERS-1 were designed for the remote estimation of sea-surface height
• NASA/CNES satellite TOPEX/POSEIDON, a sea- surface height sensor of high accuracy with an RMS error of 4.7 cm for TOPEX and 5.1 cm for POSEIDON
• Jason-1 (similar to TOPEX/POSEIDON)

31
Q

Platforms for Oceanic Circulation–Ocean Winds

A 
NASA scatterometer (NSCAT)
 produced data from Sept 1996
- June 1997
 C-band
 50 km grid resolution
 QuikSCAT SeaWinds scatterometer
 June 1999-November 2009
 C-band
 25 km spatial resolution for wind vectors
 measures winds of 3- 20 ms-1, with 2 ms-1 accuracy
 ERS1/2 scatterometer,
 Advanced scatterometer (ASCAT)
METOP-A launched Oct. 2006 METOP-A launched Sept. 2012 METOP-C Planned 17
32
Q

What sensors use for height?

A

Altimeters

33
Q

How to measure wave direction and height

A
  • Backscattered energy indicates surface roughness
  • Orientation in imagery determines direction

ex: ERS-2 SAR

34
Q

El Niño

A

Holy Child–always happens during x-mas

Unusual warming

extensive ocean warming in the equatorial eastern Pacific along the coast of Peru and Ecuador.

brings nutrient-poor tropical water southward along the west coast of South America in major events that recur at intervals of 3-7 years.

associated with atmospheric circulations having important consequences for weather and climate around the globe.

35
Q

La Niña

A

unusually cold ocean temperatures in the Equatorial Pacific

36
Q

El Niño and La Niña Impact on the Climate

A

At higher latitudes, El Niño and La Niña are among a number of factors that influence climate

  • most clearly seen in wintertime
  • El Niño :
    warmer winter in the North Central States
    cooler in the Southeast and the Southwest
  • La Niña year, winter temperatures are warmer than normal in the Southeast and cooler than normal in
    the Northwest
37
Q

Passive Microwave Sensing of Rain

A

Over the ocean:

  • Microwave emissivity of rain (liquid water) is about 0.9
  • Emissivity of the ocean is much lower (0.5)
  • Changes in emissivity (as seen by the measured brightness temperature) provide and estimate of surface rain rate

Over the land surface:

  • Microwave scattering by frozen hydrometeors is used as a measure of rain rate
  • Physical or empirical models relate the scattering signature to surface rain rates
38
Q

Tropical Rainfall Measuring Mission

A
  • Precipitation Radar (PR)
  • TRMM Microwave Imager
    (TMI)
  • Visible and Infrared Scanner (VIRS)
  • Cloud and Earth Radiant Energy Sensor (CERES)
  • Lightning Imaging Sensor (LIS)
39
Q

Grace and the water cycle

A

Gravity Recovery and Climate Experiment

Lake water storage anomalies
Terrestrial water storage

40
Q

BIOSPHERE

A

BIOSPHERE

41
Q

Hyperion

A

Hyperspectral imager–more than 200 bands, with 10-nm bandwidth

42
Q

Applications: Biosphere

A
  • Physical basis
  • Spectral refelctance
  • Assessment of plant stress
  • Vegetation classification
  • Estimation of biomass
  • Angular reflectance characteristics
  • Vegetation indices
  • Phenological cycles
  • Multi-sensor, multi-resoltuion considerations

Remote Sensing, second half (3 decks)

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