CalibrationValidation Efforts at UPRM

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Calibration/Validation Efforts at UPRM

• Hamed Parsiani,
• Electrical Computer Engineering Department
• University of Puerto Rico at Mayagüez
• parsiani_at_ece.uprm.edu

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Content

• Validation of cloud top height retrieval by MODIS
  and MISR instruments
• Calibration of Radar Remote Sensing as Applied to
  Soil Moisture and Vegetation Health Determination
• SEAWIFS validation in costal waters of western
  Puerto Rico
• Validation of Hydro-Estimator and the Tropical
  Rainfall Prediction

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Validation of cloud top height retrieval by
MODIS and MISR instruments
Ramon Vasquez, Hamed Parsiani (Ana Picon)
Cloud top heights can be good indicators of the
presence of different types of clouds over a
region. The information about clouds tops
provides an input to some climate models that can
predict total water content. The Caribbean data
of the MODIS were obtained from the EOS Data
Gateway (EDG). Available lidar instrumentation
does not provide sufficient information about
cloud profiles. However, Cross-comparisons of
MODIS and MISR instruments can retrieve cloud
top heights.
Cross-comparison between MODIS and MISR.
MODIS cloud top heights and MISR stereo heights.
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RESEARCH RESULTS Cloud Top Height Retrieval from
the Earth Observing System (EOS) Sensors
RESEARCH RESULTS Cloud Top Height Retrieval from
the Earth Observing System (EOS) Sensors

• variations between MODIS and MISR cloud top
  heights may indicate the retrieval of two
  different cloud heights over the same area.
• MISR retrieval performance for high clouds is
  twice the MODIS retrieval performance.
• MISR and MODIS cloud values coincide in less
  than 1 of the total observed area and the cloud
  height value is 14km.
• Results show the ability of MODIS to detect low
  clouds at tropical regions.
• MISR is a better instrument to measure high
  clouds. MODIS retrieval methods can identify
  thicker clouds which are low clouds and MISR
  retrieval methods can identify thinner clouds
  which are high clouds.

Temporal analysis that shows the variation of
MODIS cloud top heights over San Juan, Puerto Rico
latitude 13.1 N , 35.6 S, longitude -66.2 W,
-60.6 E
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• Calibration of Radar Remote Sensing as Applied
• to Soil Moisture and Vegetation Health
  Determination
• Hamed Parsiani (Mairim Torres, Enrico Mattei,
  Allen Lizarraga)
• The Material Characteristics in Frequency Domain
  (MCFD) algorithm calculates the MCFD for each GPR
  image which is used as a signature to determine
  soil moisture, soil type, and vegetation index.
  The usage of properly trained Neural Network acts
  as a calibrator for the GPR in soil moisture, or
  soil type determination.
• Vegetation Health is obtained by calibrating the
  power of MCFD, using the linear relationship
  between the NDVI obtained by spectroradiometer
  and the MCFD power.
• The range for calibration and its accuracy for
  the vegetation health have been determined.
• The basic accuracy in both soil characteristics
  and vegetation information depend on the
  reception of images with quality wavelets. An
  algorithm is developed which permit Automatic
  Quality Wavelet Extraction (AQWE). Currently a
  1.5 GHz antenna has been used for this research.

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GPR Produced Image
GPR operation at 1.5 GHz
Example Subsurface Image produced by GPR
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Vegetation Health Index
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Moisture Determination and validation database,
based on Ground Penetrating Radar Measurements
Advanced Land Observing Satellite computer
representation. Which includes PRISM(stereo
mapping), AVNIR (infrared radiometer), and PALSAR
(L-Band aperture radar)
Ground Penetrating Radar _at_ 2 GHz High speed soil
moisture determination
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• SEAWIFS VALIDATION IN COASTAL WATERS
• OF WESTERN PUERTO RICO
• Fernando Gilbes (Patrick Reyes)
• Mayagüez Bay is a semi-enclosed bay in the west
  coast of Puerto Rico that suffers spatial and
  temporal variations in phytoplankton pigments and
  suspended sediments due to seasonal discharge of
  local rivers.
• New methods and instruments have been used as
  part of NOAA CREST project, allowing a good
  understanding of the processes affecting the
  signal detected by remote sensors.
• A large bio-optical data set has been collected
  during several cruises in Mayagüez Bay. Remote
  Sensing Reflectance, Chlorophyll-a, Suspended
  Sediments, and absorption of Colored Dissolved
  Organic Matter (CDOM) were measured spatially and
  temporally. These values were used to evaluate
  SeaWiFS OC-2 and OC-4 bio-optical algorithms in
  the region.
• Remote sensed Chlorophyll-a concentrations were
  compared against in situ Chlorophyll-a
  concentrations. The results show that these
  algorithms overestimate the actual Chlorophyll-a.
  
• It is clearly demonstrated that the major
  sources of this error is the variability of CDOM
  and total suspended sediments. The main working
  hypothesis establishes a possible relationship
  between CDOM and the clays in those sediments.
• The analyses of SeaWiFS images also verify that
  its spatial resolution is not appropriate for
  these coastal waters. The available data
  demonstrate that improved algorithms and
  different remote sensing techniques are necessary
  for this coastal region.
• We plan to continue these efforts to validate and
  calibrate ocean color sensors in Mayagüez Bay,
  like MODIS and AVIRIS. We aim to improve the
  remote sensing techniques for a better estimation
  of water quality parameters in coastal waters,
  specifically Chlorophyll-a, CDOM absorption, and
  suspended sediments.

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Bio-optical Properties and Remote Sensing of
Mayagüez Bay
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Validation of Hydro-Estimator and the Tropical
Rainfall Prediction
Nazario Ramirez Ramon Vasquez (Beatriz Cruz)

• This is the first time that the Hydro-Estimator
  (HE) algorithm is validated over a tropical
  region.
• The USGS monitors, in Puerto Rico, 120
  rain-gauges records rainfall every 15 minutes.
  
• Estimation of precipitation was generated by
  the same spatial and temporal distribution using
  the HE algorithm.
• Preliminary results
• HE algorithm underestimates heavy
  precipitation
• A correlation coefficient of 0.6 is observed
  between estimated and observed rainfalls.

• From rain gauge (24 Hrs) observed data
• Hydro estimator (HE)

Comparison between H-E vs rain gauges ( Nov.
11-13, 2003.)