Calibration/Validation Efforts at UPRM Hamed Parsiani, Electrical & Computer Engineering Department University of Puerto Rico at Mayagüez email@example.com 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 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 cloud top heights and MISR stereo heights. MODIS and MISR. 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. Temporal analysis that shows the variation • MISR is a better instrument to of MODIS cloud top heights over San Juan, measure high clouds. MODIS Puerto Rico retrieval methods can identify thicker clouds which are low clouds latitude 13.1 N , 35.6 S, longitude -66.2 W, -60.6 E and MISR retrieval methods can Retrieval Percentage Rate(%) identify thinner clouds which are Sensor High clouds Mid clouds Low clouds high clouds. MODIS 13.98 16.81 12.25 MISR 26.89 8.94 9.05 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. GPR Produced Image GPR operation at 1.5 GHz GPR image Example: Air/Sand surface Subsurface reflection Image produced by GPR Vegetation Health Index Moisture Determination and validation database, based on Ground Penetrating Radar Measurements MCFD-NN Moisture Vs. Theta Probe Moisture MCFD Power vs. NDVI 25.00 0.6 Trained Moisture y = -1.1214x + 1.0792 Tested Moisture y = 0.9931x + 0.0911 0.5 R2 = 0.9861 R2 = 0.9991 MCFD-NN Moisture (%) 20.00 Linear (Tested )Moisture y = 0.8442x + 2.3279 0.4 MCFD power R2 = 0.9246 15.00 0.3 10.00 0.2 0.1 5.00 0 0.5 0.6 0.7 0.8 0.9 1 0.00 0 5 10 15 20 25 NDVI Theta Probe Moisture (%) Advanced Land Observing Satellite computer representation. Which Ground Penetrating Radar @ 2 GHz includes PRISM(stereo mapping), AVNIR (infrared radiometer), and PALSAR High speed soil moisture determination (L-Band aperture radar) 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. Bio-optical Properties and Remote Sensing of Mayagüez Bay VALIDATION OF SEAWIFS ALGORITHMS IN MAYAGÜEZ BAY FOR CHLOROPHYLL-A 12.0 y = 3.0325x + 0.1654 R2 = 0.6871 OC2 10.0 OC4 y = 4.1792x + 0.1248 R2 = 0.6895 Estimated Chl-a (ug/l) 8.0 6.0 4.0 2.0 0.0 0.0 0.5 1.0 1.5 2.0 2.5 ABSORPTION COEFFICIENT OF CDOM Measured Chl-a (ug/l) 1 .0 St at ion 1 0 .9 St at ion 4 St at ion 5 St at ion 7 0 .8 St at ion 9 St at ion 11 0 .7 St at ion 13 St at ion 15 St at ion 17 0 .6 St at ion 19 St at ion 21 ag ( m-1 ) St at ion 23 0 .5 0 .4 0 .3 0 .2 0 .1 0 .0 350 400 450 500 550 600 650 700 Wavelengt h ( nm) Validation of Hydro-Estimator and the Tropical Rainfall Prediction Nazario Ramirez & Ramon Vasquez (Beatriz Cruz) (a) This is the first time that the Hydro- Estimator (HE) algorithm is validated over a tropical region. The USGS monitors, in Puerto Rico, 120 (b) rain-gauges & records rainfall every 15 minutes. Estimation of precipitation was generated by the same spatial and a) From rain gauge (24 Hrs) observed data temporal distribution using the HE b) Hydro estimator (HE) algorithm. Comparison between H-E vs rain gauges ( Nov. 11-13, 2003.) Preliminary results: HE algorithm underestimates heavy precipitation A correlation coefficient of 0.6 is observed between estimated and observed rainfalls.
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