INTERNATIONAL JOURNAL OF SYSTEMS APPLICATIONS, ENGINEERING & DEVELOPMENTIssue 1, Volume 3, 2009TSS estimated values corresponding <strong>to</strong> Mayagüez Bay areawere extracted and analyzed using basic statistics. Figure 6illustrates the distribution of TSS values using both fielddata (Study Period: January 2004-Oc<strong>to</strong>ber 2006) andgenerated TSS products for February 12, 2004. Directcomparison between these data sets has been madeconsidering the following statements: (i) sampling stationsare distributed along the bay covering inshore and offshoreareas (ii) TSS measurements used in this analysis includesonly surface samples (iii) selected date for this analysis(February 12, 2004) presents commonly occurrenceconditions in this bay. Mean values for all products (4.6,5.0, 5.6 mg/l) were highly comparable <strong>to</strong> in situ mean value(5.9 mg/l) and all showed positive skewed distributions(Fig. 6). Minimum values of algorithms 2 and 3 (> 3.0mg/l) indicates that these results can be overestimatedconsidering that this analysis includes areas where TSSconcentrations are normally lower than 2.0 mg/l (e.g.offshore in the southern part of the bay). Algorithm 1presented the higher similarity with the distribution of insitu TSS measurements (Fig. 6b). However, in order <strong>to</strong>determine which estimations better followed realconditions, spatial analysis of in situ measurement of thatparticular day should be incorporated.(a)(b)(c)(d)Figure 6. Descriptive statistics and his<strong>to</strong>grams illustrating TSS values distribution for (a) in situ measurements collected withinthe study period (January 2004-Oc<strong>to</strong>ber 2006) and TSS products generated using (b) algorithm 1, (c) algorithm 2 and (d)algorithm 3.43
INTERNATIONAL JOURNAL OF SYSTEMS APPLICATIONS, ENGINEERING & DEVELOPMENTIssue 1, Volume 3, 2009IV. CONCLUSION\Geometric and radiometric corrections utilized duringimage pre-processing routines are crucial for this type ofanalysis. Atmospheric correction included in L2gencommand (SeaDAS) is not suitable for application inMayagüez Bay waters. This same result is expected <strong>to</strong> findin other tropical bays. Fairly good empirical relationshipwere defined between in situ R rs , TSS and <strong>MODIS</strong> band 1data using linear and exponential equations. Application ofdeveloped equations resulted on TSS products able <strong>to</strong>detect spatial variations associated <strong>to</strong> typical patterns ofcoastal environments. Algorithm 3 showed the highercorrespondence between observed and estimated values(RMSE 4.76 mg/l). However, all three algorithms resultedin reasonable TSS pixel values when compared with datafrom in situ measurements therefore at this point none ofthe algorithms is excluded for future application. <strong>Using</strong> anexponential equation resulted in a more suitable approachfor this study purpose, since the algorithm including thisequation was more effective estimating low values whichare the dominant conditions in the studied bay. Validationresults can be improved by addressing limiting fac<strong>to</strong>rs suchas lacking of data corresponding <strong>to</strong> high concentrations,and contamination by the remaining atmospheric effect inthe derived reflectance of the sensor. The results obtainedin this study provide a baseline <strong>to</strong> develop TSS operationalproducts for tropical coastal waters by developingpreliminary products and identifying potential errors andlimiting fac<strong>to</strong>rs in the process.ACKNOWLEDGMENTWe are grateful <strong>to</strong> Dr. Nazario Ramirez who presented thiswork in The 8th WSEAS International Conference onInstrumentation, Measurement, Circuits and Systems(IMCAS ’09). We also would like <strong>to</strong> acknowledge Dr.Ramón López who provided great support during thedevelopment of the study, and Joaquín Trinanes for givinghelpful recommendations. Special thanks <strong>to</strong> Patrick Reyesand José Martinez for helping in getting and processing partthe data presented. 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