Using MODIS 250 m Imagery to Estimate Total Suspended Sediment

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INTERNATIONAL JOURNAL OF SYSTEMS APPLICATIONS, ENGINEERING & DEVELOPMENTIssue 1, Volume 3, 2009and a relatively small range of TSS values. On the other hand,one of the most determinant aspects when using satelliteimages to retrieve water quality parameters is the atmosphericcorrection. In this study we have compared and evaluated theresults of two atmospheric correction methods available intwo different image analysis packages: ENVI (Dark Subtract)and SeaDAS (l2gen command). The present study is focusedin the development, evaluation and validation of site-specificalgorithms to estimate TSS based on MODIS reflectance band1.II. METHODOLOGYThis study aimed to establish empirical relationship betweenin situ measurements of TSS concentration, MODIS band 1reflectance and in situ remote sensing reflectance (R rs ) asmeasured with a field spectroradiometer. This approach willshow the potential of MODIS sensor for TSS estimations anddetermine the best method to develop an algorithm for thispurpose.A. In situ measurementsAll in situ data were collected during research cruisesbetween January 2004 and October 2006 in a set of stationsdistributed along the bay (Fig. 1). Table 1 shows the datesincluded in this analysis which correspond to research cruiseswhere MODIS data were available. R rs was computed withmeasurements collected with the GER-1500 fieldspectroradiometer and using the following equation:RrsL=0( λ) − ( f ∗ L ( λ))ds+E (0 , λ)Where ƒ is the Fresnell coefficient equal to 0.028 at 45 o [15].The curves were corrected for sky-light reflection subtractingthe minimum measured value between 900-920 nm, and in afew cases using lower spectral regions (730-900 nm). It wasnot possible to select a specific wavelength because sampledstations include both clear and turbid waters and theseconditions affect the determination of the most appropriatewavelength for this correction [16]. Water samples werecollected at approximately at the depth of 1 meter deep in sixstations to measure TSS, which corresponded to all thematerial larger than 0.7 μm. Concentration of TSS (in mg/l)was determined using the standard weight difference method[17].B. MODIS DataTwo different processing methods were used to generatefunctional MODIS products. One was based on preprocessingroutines available in ENVI software and thesecond used SeaDAS commands (Fig. 2). Images collectedby the Moderate Resolution Imaging Spectroradiometer(MODIS) during the same sampling dates were downloadedthrough two NASA Internet servers: LAADS Web andOceanColor Web. For the first method the product selected atLAADS Web was MOD02QKM, which includes reflectanceand radiance valuesFigure 1. Study area (Mayagüez Bay at western Puerto Rico)showing the location of sampling stations. Stations colored inwhite indicate specific sites that were sampled only duringspecial missions, while six stations in gray are permanentstations.of MODIS/Terra band 1 and 2. All these images wereprocessed using ENVI (v. 3.4) processing routines: MODISGeoreference and Dark Subtract. The spatial reference systemwas defined as UTM NAD83 for Puerto Rico region. The darksubtract atmospheric correction consists in the selection of thedarkest value in band 2 and subtract it to all band 1 data. Thisvalue was manually identified in each image and then definedin the “User Value” option of this routine. For the secondmethod, L0 data were downloaded at the OceanColor Webbrowser. This data were processed using SeaDAS MODIScommands from level 0 to level 2. The program L2gen usesas input L1b data and generates level 2 products by applyingatmospheric corrections and bio-optical algorithms [18].Conditions in these waters suggested that the best algorithmfor atmospheric correction available in SeaDAS is the one thatperforms multiscattering switching between Near Infrared(NIR) and Short Wave Infrared (SWI) bands (J. Trinanes,personal communication). However, additional algorithmcorrections, available within L2gen command, were evaluatedin order to improve the results. After generating L2 products,the data were projected as UTM NAD83 using SeaDAS MapProjection command.C. Algorithm Development and ValidationA The algorithm consisted in the combination of twoequations, one defining the relationship between field R rs andTSS, and other establishing the relationship between field R rsat 645 nm and MODIS band 1. Data collected during thirteensampled days were used to develop the first equation, while37
INTERNATIONAL JOURNAL OF SYSTEMS APPLICATIONS, ENGINEERING & DEVELOPMENTIssue 1, Volume 3, 2009III. RESULTS AND DISCUSSIONFigure 2. Schematic illustration of two methods used toprocess MODIS data. Method 1 is based in ENVI imageprocessing software and Method 2 uses SeaDAS specializedcommands.this number was limited to five for the second equation. Thisdifference was mainly due to the lack of good quality imagessuitable for analyses, which affected the development of thesecond equation. MODIS reflectance values were extractedfrom pixels corresponding to stations monitored for R rs .Developed equations were applied to two images (February12, 2004 & March 8, 2008), which were not incorporated inthe previous analysis and in situ data were available in orderto validate developed algorithms. Application of theequations was possible using ENVI band math tool, whichcontains easy to use options to ingrate image bands inmathematical equations.Proficiency of the algorithms was evaluated by comparingestimated and observed values in a root mean square error(RMSE) analysis. Additionally, obtained TSS products werevisually analyzed in order to evaluate and identify spatialpatterns associated to this type of environment. Finally,resultant values corresponding to Mayagüez Bay area wereextracted for statistical analyses. The Mayagüez Bay area wasdigitalized by visual interpretation using orthorectified aerialphotograph (2006) and saved as a polygon shapefile (ArcGIS9.3, ESRI). This shapefile was then imported to ENVI usingits vector files menu options and converted to a Region ofInterest (ROI), from which was very simple to extract theinformation of all pixels in the area of interest.A. MODIS ProductsThe first method resulted on seven reflectance products at645 nm (band 1), one for each research cruise dates. Threedifferent products for each date were generated using thesecond method: Remote sensing reflectance (R rs ), LeavingWater Radiance (L w ) and Normalized Leaving Radiance(nL w ). For purposes of this study the target product was R rs at645 nm, however resultant values in this and the rest ofgenerated products were mostly negative and with flaggedvalues. This suggested that the standard method forprocessing MODIS data in SeaDAS is not suitable forapplication in this type of waters characterized by highconcentrations of TSS and other water constituents incomparison to oceanic waters. Low and negative valuesindicated that the applied atmospheric correction it removedalso part of the water leaving signal while flag values in thecoast suggest that the algorithm is identifying as errors highlycontrasting reflectance values. Variations between Case 1 andCase 2 water limits generation of standard satellite products[19]. These processing algorithms are mainly developed foroceanic waters applications, therefore they are not able torecognize as good values, patterns that are typical in coastalwaters. One potential option to minimize the number of flagvalues is to adjust the different parameters available in theflags menu option within the L2gen command, but thisapproach is not included in this analysis. Table 2 showsreflectance values and in situ R rs 645 nm values obtained inlocations of sampling stations during October 19, 2005. Thesatellite derived reflectance is considerably lower than in situR rs suggesting a significant effect of atmospheric scattering inthe signal.Stat.IDLatitude(N)Longitude(W)Method 1Reflectanceat 645 nmin situR rs at645 nmI1 18º 16.00’ 67º 12.00’ 0.047406 0.0130O1 18º 16.00’ 67º 15.20’ 0.018792 0.00118I2 18º 14.40’ 67º 11.40’ 0.027528 0.0041O2 18º 14.40’ 67º 11.40’ 0.019948 0.0007O4 18º 12.20’ 67º 12.95’ 0.016265 0.0005I6 18º 10.25’ 67º 11.10’ 0.027037 0.0081O6 18º 10.25’ 67º 14.80’ 0.014844 0.0005Table 2. Pixel values obtained in permanent sampling stationsusing two different image processing methods and in situ R rsat 645 nm.B. Total Suspended Sediment AlgorithmA significant relationship (R 2 =0.73; n=72) was definedbetween TSS (mg/l) and in situ R rs at 645 (Fig. 3a). Thisresult suggests that this region of the spectrum is suitable forTSS estimation using remotely sensed data in these waters.However, a large unknown variability was detected in thisequation that could be reduced by the incorporation of more38
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Using MODIS 250 m Imagery to Estimate Total Suspended Sediment

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