Jonathan Karr
The JERS-1 Amazon Multi-season Mapping Study (JAMMS): Science objectives
and implications for future missions
Notes
- Because much
of Amazon is covered by clouds regularly, optical and infared coverage from
Landsat and SPOT satellites is sporadic
- Uses Japanese Earth
Remote Sensing satellite (JERS-1)
- Carried an L-band
HH Synthetic Aperture Radar (SAR) system
- Travelled in
a 568km altitude orbit
- Payload included
an L-band, HH-polarized SAR with a nominal resolution of 21m×21m (processed
into 12.5m×12.5m pixels), which
imaged at incidence angles between 30° and 36°
- Could monitor
entire surface of earth
- On-board tape
recorder system allowed for collection of large amounts of data.
- Optomized for
land vegetation studies
- Goal was to use
JERS-1 SAR to map the world's tropical rain forest regions at high resolution
- Iniated by National
Space Development Agency of Japan (NASDA), part of the Global Rain Forest
Mapping (GRFM) project
- Establish land
cover and baseline water levels
- Establish maximum
extent of flooding by comparison to baseline level
References to Pursue
- Satellite specifications
- EORC (NASDA Earth
Observation Center), 1995, JERS-1 SAR Data Users Handbook.
NASDA EORC, Tokyo, Japan.
The JERS Amazon multi-season mapping study (JAMMS): Observation strategies
and data characteristics
Notes
- JERS-1 SAR specifications
(NASDA EOC 1995)
- Frequency band:
L (1275MHz)
- Polarization:
HH
- Bandwidth: 15MHz
- PRF (Pulse Repetition
Frequency): 1505–1606Hz
- Antenna size:
11.9 m×2.4m
- Transmitted power:
325W
- Repeat orbit:
44 days
- Incidence angle:
34–43°
- Orbit inclination:
98.6°
- Swath width:
75 km
- Look direction:
right looking
- JERS-1 SAR capabilities
- Almost half a
million square kilometres could be imaged in three days
- Take 62 passes
to map South America, and therefore 62 days
- Project difficultires
- The western end
of the basin generally floods earlier than the eastern end
- The two month
east-to-west mapping cycle necessarily encompasses changing floding conditions
from the beginning of the cycle to the end of
the cycle
- In some areas
that were mapped there is only marginal or out-of-synch seasonal flooding.
- Project compared
low (September to November) to high (May to July) seasonal water levels
Outgassing from Amazonian rivers and wetlands as a large tropical source
of atmospheric CO2
Notes
- River and floodplain
waters of the central Amazon basin maintain partial pressures of dissolved
CO2 (pCO2) that are supersaturated with respect to the atmosphere
- These concentrations
track the hydrograph, increasing with rising water (to over 12,000 microatmospheres,matm,
in some tributaries) and decreasing with
falling water.
- Average values
over the year were 4,350^ 1,900matm for all mainstem samples and 5,000^3,300matm
across the mouths of all major tributaries (compared to a mean of 3,200m
atm calculated for 47 large rivers from around the world12)
- pCO2 ranged from
2,950matm to over 44,000matm on the mainstem floodplain, averaging 14,100
^ 10,200matm upstream to 6,300 ^ 4,200matm downstream
- High partial pressures
of CO2 translate to large gas evasion fluxes from water to atmosphere
- Total basin evasion
470TgC/yr
Spatial and temporal variabilities of rainfall in tropical South America
as derived from climate prediction Center merged analysis of precipitation
Notes
- Climate Prediction
Center merged analysis of precipitation (CMAP) was studied 1979-1998
- Monthly precipitation
data with 2.5° × 2.5° spatial resolution
- composed of two
kinds of data: standard precipitation (STD) and enhanced precipitation (ENH)
- STD consists
of gauge observations
- ENH consists
of five kinds of satellite estimates
- Outgoing longwave
radiation (OLR)-based precipitation index
- Infrared-based
Geostationary Operational Environmental Satellite (GOES) precipitation index
- Microwave sounding
unit
- Microwave scattering
from Special Sensor Microwave/Imager (SSM/I)
- Microwave emission
from SSM/I
References to pursue
- Merging algorith
- Xie P, Arkin
PA. 1996. Analyses of global monthly precipitation using gauge observations,
satellite estimates, and numerical model
predictions. Journal of Climate 9: 840–858.
- Blending algorithm
- Reynolds RW.
1988. A real-time global sea surface temperature analysis. Journal of Climate
1: 75–86.
- Xie P, Arkin
PA. 1997. Global precipitation: a 17-year monthly analysis based on gauge
observations, satellite estimates, and numerical
model outputs. Bulletin of the American Meteorological Society 78: 2539–2558.
- River discharge
data as an indicator of the basin-scale wet and dry conditions.
- Marengo JA. 1995.
Variations and changes in South American streamflow. Climatic Change 31:
99–117.
- Marengo JA, Tomasella
J, Uvo CR. 1998. Trends in streamflow and rainfall in tropical South America:
Amazonia, eastern Brazil, and
northwestern Peru. Journal of Geophysical Research 103: 1775–1783.
Amazon River mainstem Floodplain Landsat TM digital mosaic
Notes
- Radiometric rectification
method used to build the TM Landsat digital mosaic
- Assemblage of
overlapping images into a single picture
- Faces two problems
- The definition
of a reference system or projection on to which the scenes will be projected
- The blending
of the diVerent images into a single radiometrically balanced set
- Most commonly
used method for radiometric blending is histogram matching
- Involves the
equalization of the histogram of a given image to another or various images
to a reference image
- Statistical
method based on the cumulative distribution function of the data
- Des not assure
the in-between band relationship
- More comprehensive
approach consists of the use of regression equations among scenes
- This approach
is quite useful for normalizing images from the same region acquired at different
dates
- Another approach
to radiometric normalization is the use of pseudo-invariant features
- Uses a stochastic
solution to estimate the probability distributions of reflectivity of objects
with near invariant reflectivity
- It is also
a good approach for normalizing multiple date images acquired over the same
geographic area
- Not useful
for normalizing multiple images acquired in different areas under different
atmospheric and ground conditions
- Best solution
is the radiometric rectification
- Does not require
the existence of reference targets common to all images
- The mosaic
was built only for the Brazilian portion of the Amazon River main stem
- The first
step in building the mosaic was to choose a cartographic projection system.
An accurate assessment of the area occupied by the various floodplain ecosystems
has to rely on precise geometric rectification.
- The problems
faced during the geometric correction process were:
- Small number
of good ground control points for each scene
- Poor quality
of the cartographic data (1 250 000 topographic charts)
- Shape (2750
km wide and 875km long) and area represented by the mosaic
- The Geographical
Co-ordination System was adopted as a solution to the third problem since
it provides a general reference system which can be transformed into the
required projection
- The radiometric
rectifcation of the mosaic was based on the method (Hall et al., 1991) assumes
that the digital counts are first converted into reflectance according to
procedures proposed by Markham and Barker (1986)
- For the majority
of scenes the rectification was effective for the visible bands where atmospheric
eVects produced the largest environmental disturbances in the radiance measurements
- Mosaic proved
able to cope with various problems presented by the original dataset: spatial
and temporal changes in the atmospheric optical properties; and time changes
in the radiometric performance of the Landsat TM sensor during the 10 years
of data acquisition
References to pursue
- Amazon River floodplain
environmental changes over last three decades
- Recitification
method
Insolation, moisture balance and climate change on the South
American Altiplano since the Last Glacial Maximum
Notes
- Orbitally induced
changes in solar insolation, coupled with long-term changes in El Niño-Southern
Oscillation variability, are the most
likely driving forces behind millennial-scale shifts in lake level that reflect
regional-scale changes in the moisture balance of the Atlantic-Amazon-Altiplano
hydrologic system