matter in small and intermediate sized lakes and reservoirs

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1 WATER RESOURCES RESEARCH, VOL. 46,, doi: /2009wr008709, 2010 Assessing the potential of Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data for monitoring total suspended matter in small and intermediate sized lakes and reservoirs P. E. Tarrant, 1,2 J. A. Amacher, 1 and S. Neuer 1 Received 26 September 2009; revised 14 February 2010; accepted 19 May 2010; published 25 September [1] Satellite remote sensing has been used extensively for many years to monitor the open oceans and coastal waters. These methods have been extended more recently to the study of inland waters. In this study we consider the potential application of data from two ocean color sensors, Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS), for monitoring the levels of suspended solids in small and intermediate sized lakes and reservoirs. We measured total suspended matter (TSM) in four southwestern United States lakes, Roosevelt Lake, Saguaro Lake, Bartlett Lake, and Lake Pleasant, and compared these field data with images obtained from these medium resolution satellite sensors. Our regression analysis of the complete data set identified a linear relationship between the field TSM values and both MODIS 250 m data (r 2 = 0.461) and MERIS 290 m data (r 2 = 0.521). This relationship improved substantially when data from the smallest lake in the study (Saguaro Lake) were excluded from the analysis (r 2 = and r 2 = 0.888, respectively). The resultant linear models produced estimates with a root mean square error (RMSE) ranging from 3.14 mg/l (MODIS) and 2.04 mg/l (MERIS) for all four lakes combined, improving to 1.32 mg/l (MODIS) and 0.47 mg/l (MERIS) for a lake specific regression. These results suggest that these satellite sensors have the potential to effectively monitor TSM in lakes and reservoirs, although a minimum practical lake size does appear to exist. Citation: Tarrant, P. E., J. A. Amacher, and S. Neuer (2010), Assessing the potential of Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data for monitoring total suspended matter in small and intermediate sized lakes and reservoirs, Water Resour. Res., 46,, doi: /2009wr Introduction [2] Suspended matter in lakes and reservoirs comprises of both organic and inorganic particles. Components such as sediments, algae, and plant debris affect the degree of water turbidity and can have consequential effects on primary production and water quality [Cloern, 1987; May et al., 2003]. Monitoring particle loadings and their consequent effect is usually achieved through field sampling programs. However, conducting regular field sampling in lakes and reservoirs is labor intensive and expensive. In addition, it is often necessary to assume that field samples, which are limited both spatially and temporally, are representative of the total area of interest [Mukhopadhyay et al., 1992]. Alternative monitoring methods such as airborne imaging spectroscopy are also expensive, especially if carried out regularly. Therefore, the cost of field monitoring is a funding challenge for any agency responsible for water quality, a challenge that 1 School of Life Sciences, Arizona State University, Tempe, Arizona, USA. 2 Currently at the Global Institute of Sustainability, Arizona State University, Tempe, Arizona, USA. Copyright 2010 by the American Geophysical Union /10/2009WR increases proportionately with the number of water bodies to be monitored. With a threefold increase in global water withdrawals over the last 50 years [World Water Assessment Programme, 2009], identifying viable, cost effective methods for monitoring water characteristics has become a desirable goal. The wise use of water resources is a particularly pertinent challenge in central Arizona, where the urban heat island of the Phoenix metropolitan area, combined with predictions of reduced runoff due to climate change [Ellis et al., 2008], is likely to reduce the volume of water available for human consumption. [3] The use of remotely sensed satellite data to monitor open ocean biological and physical processes is well established within the field of oceanography. The sea viewing wide field of view sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) satellite sensors have been providing global coverage since 1997, 1999, and 2002, respectively, and the data they produce are readily available through Internet data portals. For most geographic areas, MODIS and SeaWIFS are able to provide daily images, whereas MERIS produces two to three images per week. Therefore, all three data sources provide excellent temporal resolution for observing changes over time [Martin, 2004]. However, the moderate spatial resolution ( m) of 1of7

2 Figure 1. MERIS image of central Arizona showing the location of the four study lakes relative to the Phoenix metropolitan area (Source: European Space Agency, the default products produced by these sensors, combined with the complex optical properties of lake waters, means that these data are not generally suitable for smaller inland bodies of water. While a satellite sensor such as Landsat Enhanced Thematic Mapper provides an adequate spatial resolution (30 m) for monitoring lakes and reservoirs, the repeat frequency of 16 days is insufficient to capture the short term variability often observed in levels of chlorophyll biomass, total suspended matter (TSM), or turbidity [Cloern, 1987; May et al., 2003; Rantajarvi et al., 1998]. In spite of these technical limitations, the desire to monitor inland waters has been demonstrated by several investigators applying terrestrial satellite data such as Landsat Multispectral Scanner and Thematic Mapper to the problem of modeling water quality and TSM [Dekker et al., 2001; Dekker et al., 2002; Harrington et al., 1992; Ritchie et al., 1987; Ritchie et al., 1990]. [4] In recent years, several studies have explored the potential of MODIS 250 m data and MERIS full resolution (290 m) data for monitoring inland waters [Alikas and Reinart, 2008; Gons et al., 2002; Koponen et al., 2001; Koponen et al., 2002; Koponen et al., 2004; Vincent et al., 2004; Zhu et al., 2005]. Other studies, specifically focused on the measurement of TSM in lakes and coastal areas, have used satellite derived data with promising results. Miller and McKee [2004] observed good agreement between field measured suspended matter quantities and MODIS band 1 (645 nm) reflectance values in the northern Gulf of Mexico. Sipelgas et al. [2006] discovered a weaker relationship when applying the same technique to the Pakri Bay area of the Gulf of Finland. Comparisons of MERIS reduced resolution scattering coefficient of particles, b p (440) data and total suspendedmatterintheskagerrakalsoshowedastrong degree of correlation [Sorenson et al., 2007]. In contrast, Alikas and Reinart [2008] found that MERIS performed poorly when estimating TSM in three study lakes in Sweden and Estonia, with little observed correlation between measured field data and the MERIS TSM Level 2 product. [5] MERIS and MODIS data have the potential to support the monitoring of inland waters as these sensors collect data at 290 and 250 m native resolution, respectively. However, the adjacency effect, which results from the atmospheric scattering of light that is not visible in the field of view [Kaufman, 1989], may cause pixel contamination on smaller lakes and reservoirs. This contamination is likely to limit the minimum lake size suitable for monitoring using these satellite data and methods. [6] The purpose of this study was to test the practicality of monitoring the levels of TSM in multiple lakes using the medium resolution data available from the MERIS and MODIS sensors. We wished to assess the different sensor and product characteristics to ascertain which sensor might provide the best results for a long term TSM monitoring solution. We also compared lakes of varying sizes to determine the minimum lake size suitable for this type of observation. 2. Methods 2.1. Study Lakes [7] The study area is located in central Arizona and comprises of four reservoirs; all located within 56 km (straight line distance) of the Phoenix metropolitan area (Figure 1). Roosevelt Lake and Saguaro Lake are part of the Salt River watershed, whereas Bartlett Lake impounds the Verde River. Lake Pleasant is fed primarily by the Central Arizona Project canal system with a secondary feed provided by the Agua Fria River. [8] Roosevelt Lake (111.1 W, 33.7 N) is the largest reservoir in the study with a maximum capacity of 2039 million m 3 and a surface area of 9698 ha [Salt River Project, 2006]. When full, the reservoir is over 36 km long, 57 m deep, and has a shoreline of just under 206 km. Saguaro Lake (111.5 W, 33.6 N) is officially 16 km long and has a maximum capacity of 86.1 million m 3, but this includes the upriver stretch of the Salt River that feeds into the reservoir 2of7

3 basin. The basin itself is approximately km and a maximum depth of 35 m. Bartlett Lake (111.6 W, N) has a capacity of million m 3 and a maximum surface area of 1139 ha. Lake Pleasant ( W, N) is the closest lake to the Phoenix metropolitan area with a capacity in excess of 1367 million m 3 and a maximum surface area of 4046 ha [Bureau of Reclamation, 2009]. [9] These reservoirs vary substantially in size and by including Saguaro Lake, which is the smallest lake in the study, we hoped to explore the concept of a minimum practical size for monitoring TSM using these particular sensors. We considered the other three lakes to be large enough to produce a reasonable sample size of uncontaminated pixels from MERIS full resolution products and MODIS 250 m bands but too small to be reliably monitored using the coarser resolution ocean color products produced from these sensors. [10] All the reservoirs in the study are part of the water supply infrastructure of central and southern Arizona and in two cases (Roosevelt and Saguaro) are used to generate hydroelectric power. In addition to their primary roles, all four reservoirs provide recreational fishing opportunities and water sports facilities In Situ Data Collection [11] Field sampling of Saguaro Lake and Roosevelt Lake began in February 2007 as part of a larger study to examine the ecology of the algal community in these reservoirs (Neuer et al., in preparation). The lakes were initially sampled on a biweekly basis, which was reduced to a monthly sample in June At three evenly spaced stations, we measured visible depth and collected water samples for the measurement of TSM (by weight) as well as other parameters. Sampling of Bartlett Lake began in February 2008, and a monthly sample was collected from a single station in the main body of the lake. A single sampling station in Lake Pleasant was sampled from July 2008 onward with water samples being collected on a monthly basis. All water samples were processed in the laboratory using the same methods. [12] Total suspended matter: ml of sample was filtered onto a Glass Fiber Filter filter that had been predried in a drying oven (24 h at 55 C) and then preweighed. The filtered samples were dried at 55 C for 48 h. Each sample was then weighed on a Mettler micro balance [Environmental Protection Agency, 1979]. [13] Visible depth: To determine the visible depth, a weighted 20 cm diameter Secchi disk was deployed at each sample station. The disk was lowered into the water until it disappeared, then raised until it was just visible again. The visible depth was recorded as the midpoint between these two depths MERIS Data [14] Source files: We obtained, via download, level 2 (L2) MERIS full resolution data files (MER_FR_2P) for the date closest to the field sampling date (either coincident or ±1 day) from the European Space Agency North American rolling archive ( ks.eo.esa.int) and the Earth Observation Link (Stand Alone) (EOLISA) catalog system. The L2 MERIS files are georectified and adjusted for ozone absorption and aerosols using the current Antoine and Morel [2005] method and further adjusted using bright pixel correction if the case 2 turbid water flag is triggered [Aiken and Moore, 2000]. These files contain a number of derived products as well as the reflectance values for 13 bands in the visible/near infrared range. All the data products in these full resolution files have an approximate spatial resolution of m at nadir. [15] TSM estimate: The TSM estimate available in MERIS L2 images is derived from the inverse modeling of radiative transfer values using parameters produced by an iterative nonlinear regression procedure or neural network [Doerffer and Schiller, 1997]. The satellite images were processed using BEAM V4.5.1 (Brockmann Consult), and for each file that contained valid data, the MERIS TSM estimates were extracted for each of the pixels covering the lake sampling stations (n = 69). While it is common in this type of analysis to use a 3 3 pixel grid to reduce single pixel variation, the number of usable pixels in the smallest lake (Saguaro) did not permit this approach and for the sake of consistency single pixel values were evaluated for all four lakes. A regression data set was constructed for the four study lakes using these estimates and field derived TSM measurements from samples collected during sampling visits conducted between 28 February 2007 and 9 June The MERIS Level 2 flags were extracted for each sample to assess the robustness of the TSM estimates MODIS Data [16] Source files: Daily MODIS Aqua 250 m L2G surface reflectance (MYD09GQK V005) products for each of the field collection dates (±1 day) were downloaded from the Land Processes Distributed Active Archive Center ( edcdaac.usgs.gov/main.asp). This auxiliary material is part of NASA s Earth Observing System Data and Information System. These MODIS products provide an estimate of the surface spectral reflectance. The images are georectified and corrected for atmospheric effects, including aerosols and Rayleigh scattering [Vermote and Vermeulen, 1999]. These surface reflectance images are produced from band 1 (centered on 645 nm) and band 2 (centered on 856 nm), which both have a native 250 m (at nadir) spatial resolution. [17] Algorithm development: The MODIS satellite images were processed using a combination of ERDAS Imagine (ERDAS Inc.) and IDL (ITT VIS). We separated the two 250 m bands from each image into individual files and extracted the digital values for those pixels that matched the field collection locations. A regression data set (n = 154) was created containing values from band 1 and band 2, and coincident field TSM measurements calculated from field samples collected on sampling visits were carried out between 28 February 2007 and 9 June This data set was then analyzed to determine the degree of correlation that might exist between the 645 and 856 nm bands and the in situ TSM loading. 3. Results 3.1. Lake Characteristics [18] The field data suggested that each of the four lakes had different optical characteristics with respect to particle loading and overall transparency. These differences could 3of7

4 Table 1. Range, Mean, and Standard Deviation of Secchi Depth Estimates and Total Suspended Matter as Measured in Four Central Arizona Reservoirs (Roosevelt, Saguaro, Bartlett, and Pleasant) Between March 2007 and June 2009 Secchi Depth (m) TSM (mg/l) Lake (Sample Dates) Range Mean SD Range Mean SD No. of Visits Roosevelt (03/07 to 06/09) Saguaro (03/07 to 06/09) Bartlett (02/08 to 06/09) Pleasant (07/08 to 06/09) potentially affect the effectiveness of any satellite derived estimates of total suspended matter. [19] The range of TSM loading varied substantially both between the individual lakes and over time. Roosevelt Lake had the largest TSM range with a minimum of 0.30 mg/l (recorded in April 2007) and a maximum of 20.0 mg/l (February 2008) (Table 1 and Figure 2a). Saguaro Lake also experienced large differences in TSM over the sampling period with the lowest level of 0.92 mg/l being measured in April In contrast to Lake Roosevelt, Saguaro Lake s highest TSM levels occurred in June 2008 (19.2 mg/l) (Figure 2b and Table 1). In both cases, these maxima were considered to be extremely unusual because they were substantially higher than the equivalent periods in previous or subsequent years. While Bartlett Lake produced lower values, the mean was comparable to Saguaro Lake (Figure 2c and Table 1). Interestingly, throughout the study, Lake Pleasant produced much lower levels of TSM relative to the other three lakes with a maximum of only 1.68 mg/l measured in June 2009 (Figure 2d). [20] With respect to the visible depth as measured by Secchi disk, Saguaro Lake and Bartlett Lake had the shallowest measured water transparency with very similar characteristics. Saguaro Lake ranged from 0.5 m (June 2008) to 5.0 m (April 2009) with a mean of 1.96 m, whereas Bartlett Lake varied between 0.5 m (March 2008) and 4.6 m (October 2008) with a mean of 2.18 m. These depths were significantly shallower (t test, P = <0.0001) than Lake Pleasant where the Secchi depth minimum was 3.7 m (September 2008) and the maximum was 12.0 m (April 2009) with a mean of 6.99 m Figure 2. Variation in TSM over time showing annual variability for (a) Roosevelt Lake, AZ (March 2007 to July 2009), (b) Saguaro Lake, AZ (March 2007 to July 2009), (c) Bartlett Lake, AZ (February 2008 to July 2009), and (d) Lake Pleasant, AZ (July 2008 to July 2009). Note that Lake Pleasant shows a significantly lower range of TSM values than do any of the other three lakes in our study. 4of7

5 Table 2. Results of Regression Analysis Performed on Four Lakes in Central Arizona (Roosevelt, Saguaro, Bartlett, and Pleasant) a Lake Sensor (n) r 2 P value RMSE Equation All lakes MODIS < x MERIS < x w/o Saguaro MODIS < x MERIS < x Saguaro only MODIS x MERIS x Roosevelt only MODIS < x MERIS x a Using MODIS 250 m and MERIS full resolution data. These results show how the Saguaro Lake data negatively affected the quality of the linear relationship. (Table 1). Even during periods of maximum water transparency, we are confident that the lake bottom did not contribute to satellite reflectance values at any of the sampling sites as the lakes are significantly deeper than their respective Secchi depths. [21] The recorded differences in particle loading and water transparency indicate that the optical properties of these lakes showed considerable variability during the study period. Interestingly, these variations did not appear to negatively affect the results obtained from either sensor MODIS Data [22] We examined several possible relationships when comparing surface reflectance of the two 250 m bands with the field data, including the response of MODIS 645 alone as per Miller and McKee [2004] and Sipelgas et al. [2006]. Although, MODIS 645 produced a weak correlation with field data (r 2 = 0.284), the strongest relationship was obtained from a linear regression of field data and MODIS 645 MODIS 856. [23] The analyses we conducted showed differences in performance with respect to individual lakes when compared to a more generic comparison of multiple lakes. A linear regression of the digital satellite data and the field data from all four lakes (n = 154) produced an r 2 = with a RMSE of 3.14 mg/l (Table 2 and Figure 3). The regression performance improved dramatically when the Saguaro Lake data were removed from the data set (n = 105) resulting in an r 2 = and a RMSE = 1.67 mg/l (Table 2 and Figure 3). This suggested that the data from this lake did not compare well with the values obtained from the other three lakes. The regression fit improved again when the analysis was performed only on Roosevelt Lake data, rising to r 2 = with a RMSE of 1.32 mg/l. An analysis of the Saguaro Lake data alone (n = 49) produced a relatively low r 2 = with a larger RMSE of 4.01 mg/l (Table 2) MERIS Data [24] The regression analysis of the MERIS L2 TSM product and the field derived data showed a similar pattern to the MODIS analysis. Performance was comparable when examining all the study lakes (n = 69), producing an r 2 = and a RMSE of 2.04 mg/l (Table 2 and Figure 4). The data fit likewise improved substantially when the Saguaro Lake data were excluded (n = 48) from the analysis with an r 2 = and a RMSE of 1.10 mg/l (Table 2 and Figure 4b). However, in contrast to the MODIS analysis, Figure 3. Correlation between TSM and equivalent reflectance values of MODIS ( ) for all four lakes in central Arizona (Roosevelt, Saguaro, Bartlett, and Pleasant [white and black symbols, solid line regression]), and for Roosevelt Lake, Bartlett Lake, and Lake Pleasant but without Saguaro Lake (black symbols only, dotted line regression). the regression of Roosevelt Lake alone (n = 35) produced an r 2 = 0.835, not significantly different from the three lake models but with an improved RMSE of 0.47 mg/l. The MERIS sensor did not produce reliable results for Saguaro Lake (n = 21) producing an r 2 =0.064andanRMSEof 4.49 mg/l. [25] A comparison of L2 flags showed that these waters were rarely flagged as case 2, either with respect to sediments (6%) or yellow substance (7%). However, confidence flag PCD_15 was triggered in 78% of cases and flag PCD_16 was triggered in 32% of cases. 4. Discussion [26] The purpose of this study was to explore how we might use the finer spatial resolution of the 250 m MODIS Figure 4. Correlation between TSM and equivalent L2 MERIS full resolution total suspended matter estimates for all four lakes in central Arizona (Roosevelt, Saguaro, Bartlett, and Pleasant [black and white symbols, solid line regression]), and for Roosevelt Lake, Bartlett Lake, and Lake Pleasant but without Saguaro Lake (black symbols only, dotted line regression). 5of7

6 bands and the full resolution MERIS products to monitor TSM in lakes and reservoirs that would be considered too small to be monitored by the standard satellite ocean color products. The comparison of in situ data and the equivalent MODIS satellite reflectance values indicates that there is a relationship that responds to changes in TSM loading. Therefore, it is reasonable to expect that algorithms can be defined that will estimate TSM from MODIS 250 m data. The TSM estimates supplied in L2 MERIS files also show good agreement with the measured field data. This degree of correlation suggests that the MERIS product would be a good alternative source for producing TSM estimations. [27] The repeat frequency of the Aqua and Terra satellites that carry the MODIS sensors produces approximately one image per sensor per day resulting in up to two images of a particular geographic area in any 24 h period. Realistically, the differences in performance characteristics of the individual sensors make it more practical to use only one sensor for ongoing monitoring activities, and for this reason, we chose to only use MODIS Aqua data in this study. However, even on this basis and allowing for lost images resulting from cloud cover, data acquisition, and processing overhead, one could expect to produce several estimates per week of multiple lakes and reservoirs. [28] The results achieved in this study using MODIS band 1 (645 nm) and band 2 (856 nm) in combination, compare favorably with those achieved by Miller and McKee [2004] and Sipelgas et al. [2006] using only MODIS band 1 for estimating TSM. Using band 1 surface reflectance values and in situ measurements, Miller and McKee obtained an r 2 =0.89andSipelgas et al. [2006] obtained r 2 = While we were unable to recreate their results using only MODIS band 1, we could show that for our study lakes, MODIS data can be used to obtain reliable TSM estimates. The inclusion of band 2 data in our regression analysis appears to offset the residual atmospheric effects that may remain after the standard atmospheric correction is applied to these data. The MERIS L2 TSM product also performed well under the conditions of our study lakes. This sensor, while having a longer repeat period of 2 3 days, has the advantage that the estimates do not require postprocessing, as they are available as a derived product within level 2 MERIS files. Unfortunately, if individual images are lost because of cloud cover or atmospheric conditions, the lengthier repeat frequency will result in larger gaps in the data record. In areas that regularly experience significant cloud coverage, these gaps would make it more difficult to identify trends in the data. Although this is not a major issue in central Arizona where there are an average of 211 cloud free days and 85 partly cloudy days each year [Cerveny, 1996], it could be a much larger problem in other regions. [29] However, our results also suggest that each of our study lakes has its own distinctive set of optical properties resulting from different ranges of TSM and water transparency. Our studies of these lakes indicate that the majority of the suspended material is organic in nature with a strong correlation between TSM and algal biomass, which varies over time due to the seasonal succession of algae in each lake (Tarrant, unpublished data). Reinart and Kutser [2006] noted that the turbid waters of the Baltic caused failures in the atmospheric correction processing for MERIS products. This failure in atmospheric correction will result in the incorrect calculation of the other derived products such as TSM. The MERIS L2 flags did highlight uncertainty in several product indexes. While PCD_15 normally highlights uncertainty in the algal pigment index, it can also be indicative of turbid waters. Flag PCD_16 indicates uncertain yellow substance, TSM, or rectified reflectance values, another indication of the optical complexity of these waters. Clearly, these different characteristics will influence the relative optical response of each lake, as well as affecting the optical depth observed by the satellite sensor. [30] While the analysis of TSM loadings and Secchi depths showed that there are differences between all four of the study lakes, these differences are most pronounced between Lake Pleasant and the other three lakes. Our results suggest that if applying the MODIS data to the monitoring of suspended matter loadings over time, lake specific algorithms will yield the most accurate estimates. The regression of Roosevelt Lake data produced the highest correlation coefficient of all the lakes in this study and the improved performance achieved by this lake specific model may be observed in other instances, dependent on prevailing conditions. [31] With respect to the size of lakes that could reliably be monitored using these techniques, the poor results produced by the Saguaro Lake analysis suggest that these data may have been contaminated by nearby land pixels. This contamination may be caused by either atmospheric scattering or the point spread function, which describes the response of a sensor to point signals [Huang et al., 2002]. These effects are especially noticeable with high contrast heterogeneous subjects with the result that light pixels appear darker and dark pixels appear lighter [Santer and Schmechtig, 2000]. Consequently, inland waters surrounded by brighter land surfaces will potentially show higher reflectance values than those expected from the TSM response alone. This is particularly evident in the desert landscape of central Arizona where land pixels covered with sparse vegetation show a higher than average difference of reflectance compared to water pixels relative to areas with more extensive ground cover. Santer and Schmechtig [2000] showed that water pixels could be significantly affected by adjacent land areas with different contrast levels and that this effect was most significant in the near infrared range. It has also been shown that this effect may extend kilometers rather than meters beyond the contrast boundary [Dana, 1982; Tanre et al., 1981]. These effects can be modeled and potentially reduced, but this would require additional image processing [Huang et al., 2002]. As we are exploring the possibility of nonspecialist personnel obtaining useful data from these sensors, we suggest that a simpler approach to avoid this contamination would be to leave a significant border of water pixels around those pixels used as a source of estimation. Consequently, Saguaro Lake with its 1 2 km basin size may be too small an area to be reliably monitored by these sensors. 5. Conclusion [32] The goal of this investigation was to test the application of satellite monitoring methods normally used in oceanographic and coastal studies for much smaller inland bodies of water. We were also interested in defining methods that could be used in an applied setting where ongoing 6of7

7 water quality monitoring may be the goal of a state or federal agency or a water utility company. We conclude that the MERIS derived TSM product is capable of predicting TSM in small to intermediate sized inland water bodies, although we would recommend the comparison with field data, if available, to confirm if the performance we observed can be repeated on a case by case basis. We also conclude that with the availability of relevant field data to facilitate algorithm development, accurate estimates of TSM loadings using MODIS 250 m data may also be produced on a continuing basis and with higher repeat frequency compared to MERIS. [33] Acknowledgments. This research was supported by the Water Quality Center at Arizona State University, Tempe AZ and Salt River Project, Tempe AZ. We acknowledge the help of Alexis Pasulka, Brian Eddie, and Ian Anderson in field data collection and laboratory analysis, and three anonymous reviewers for valuable comments that improved the manuscript. 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