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Geog 272 > Final > Flashcards

Final Flashcards

1
Q

What does satellite data offer?

A

cost-effective, timely, transparent, information on crop type, plant health, stress, and productivity

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2
Q

What are early forecasts?

A

Critical

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3
Q

Crop Monitor for AMIS

A

develop transparent, timely, crop condition assessments in primary agricultural production areas highlighting potential hotspots of stress/ bumper crops

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4
Q

NASA’s Earth Observation for Food Security and Agriculture Consortium

A

increase food security awareness and understand of the applications of NASA’s and other satellite data products by users from a wide range of sectors

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5
Q

Major tasks of (4)

A

crop state assessment
crop yield assessment/ forecasting
crop type mapping
crop area estimation

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6
Q

Biophysical Parameters

A

total canopy chlorophyll, vegetation fraction, fraction of photosynthetically active radiation absorbed by photosyhntetically active vegetation, green leaf area index, gross primary production

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7
Q

information retrieval

A

spatial: courier transformation
spectral: spectral curvatures
Temporal: change detection, temporal profiles

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8
Q

Spectral curve of plants

A

typical vegetation reflectance curve with dominant factors controlling leaf reflectance

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9
Q

spectral curve of plants:PROSPECT

A

radiative transfer model that represents the optical properties of plant leaves from 400nm to 2500nm

simulates spectra based on input parameters of the plants

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10
Q

inverse problem

A

to estimate properties of the plants based on the given spectra

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11
Q

Chlorophyll content

A

relates closely to the crop primary production and is a good indicator of the physiological status and the carbon sequestration potential of crops, it is also a good predictor of crop yield

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12
Q

Vegetation fraction

A

defined as the percentage or fraction of occupation occupation of vegetation canopy in a given ground area in vertical projection

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13
Q

Leaf Area Index

A

the ratio of the one-sided green leaf area to the ground area underneath

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14
Q

Fraction of Absorbed Photosynthetically Active Radiation

A

influences vegetation production

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15
Q

Gross Primary Production

A

vegetation productivity is the basis of all the biosphereic functions on the land surface and is simply defined as the production of organic matter through photosynthesis

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16
Q

Crop type mapping

A

classification of multi-temporal multi-spectral satellite images into crop-related classes

cropland vs non-cropland

training/ calibration data: ground truth

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17
Q

soil moisture

A

soil moisture is the water that is held in the space between soil particles

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18
Q

what does soil moisture depend on

A

soil properties
topography
presence/ absence of vegetation
the frequency and amount of precipitation/ irrigation

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19
Q

soil moisture

A

SMAP: soil moisture Active Passive

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20
Q

What are the mapping key parameters from remote sensing?

A

Land surface albedo
downward shortwave radiation
land surface temperature

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21
Q

What are the determine factors for remote sensing?

A

surface- reflectance, emissivity, and temperature
Atmosphere

22
Q

what are sensor characteristics?

A

Wavelength- spectral signature
view geometry- angular signature
polarization etc

23
Q

inversion problem

A

Underdetermined
Ill-posed/ improperly posed problem

24
Q

Albedo

A

ratio between reflected solar radiation and incident solar radiation

25
Q

What direction in albedo in?

A

all directions and it is broadbanded

26
Q

What direction is RS signature in?

A

One direction
Narrow banded

27
Q

BRDF what does it stand for?

A

Bi-directional Reflectance Distribution Function

28
Q

What is BRDF

A

Ratio of incremental radiance, leaving surface through an infinitesimal solid angle in direction to incremental irradiance from illumination direction

29
Q

How are BDRF and albedo related?

A

Albedo depends on BRDF and incident angle

30
Q

BRDF modeling in…

A

radiative transfer models: physical explanation
canopy/ soil parameters

empirical models

empirical models: kernel-driven models

31
Q

Albedo Retrieval approaches

A

BDRF fittings, optimization, direct estimation, and BRDF database

32
Q

What are needed for BDRF fittings?

A

Multiangular RS data: MISR, POLDER
3 observations
temporal composite approach- assume no surface change

33
Q

to measure BRDF via RS you need?

A

multiple cameras on same platform
ex: multi-angle imaging spectroRadiometer

broad swath with large overlap so multiple orbits build up multiple view angles

Pointing capability: offers an oblique viewing capacity, the viewing angle is adjustable through 27 degrees off vertical

34
Q

Issues of BRDF fitting approach

A

error propagation
unable to capture rapidly changing albedo
large errors over snow albedo

35
Q

Unique features of NOAAs VIIRS land surface albedo product

A

high accuracy, gap-free, high temporal resolution, daily mean value

36
Q

Incident Solar Radiation

A

aka insolation
300nm - 3000nm
drives longwave emission, latent and sensible heat fluxes
one of the key parameters in earth surface radiation budget

37
Q

Photosynthetically active radiation

A

400nm-700nm
an essential input for most of the ecosystem models
important for quantifying earth’s carbon cycle

38
Q

Radiation transfer modeling

A

accuracy dependent on quality of input data
requiring inputs of atmospheric and surface parameters
computationally expensive

39
Q

Parameterization

A

approximation of RT models
Efficient

40
Q

Summary of forward methods

A

highly accurate when high quality atmospheric input variables are available

41
Q

Innovative LUT-based method

A

accurate and efficient
Directly use TOA radiance data
improved spatial resolution

two parameterization schemes
TOA radiance and Surface flux

Parameters are pre-calculated from accurate MODTRAN

42
Q

Steps of the algorithm using temporal signatures

A

a set of temporal observations for each pixel
covert blue-band TOA radiance to “surface reflectance” using a default clear atmosphere
identify a set of “clear” observations with the minimum blue-band reflectance
interpolate surface reflectance of other observations
interpolate surface reflectance of other observations

43
Q

Algorithm integration

A

individual retrieval methods have strengths and limitations

using machine learning methods to combine multiple retrieval algorithms

44
Q

Skin temperature retrieval approaches

A

sea surface temperature vs. land surface temperature
single channel algorithm
two channel algorithm

45
Q

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46
Q

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47
Q

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48
Q

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49
Q

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50
Q

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Geog 272 (3 decks)

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