NASA ARSET: Fundamentals of Aquatic Remote Sensing

hello and welcome to rset's fundamentals of aquatic remote sensing webinar this on-demand webinar is meant to serve as a foundation in aquatic remote sensing and as a prerequisite for future aquatic webinars and in-person trainings provided by our set we appreciate your interest and participation in rset's programming so why take this course the objective of this course is to provide an overview of aquatic optics the remote sensing of water targets and nasa earth observation resources available for aquatic applications this image captured by the landsat 8 oli satellite sensor shows the view over western australia on may 12 2013 the image shows the rich sediment color dissolved organic matter and nutrient patterns in a tropical estuary and nearby vegetated areas this type of information can help us infer the biogeochemistry of the system in this webinar we will cover a broad range of information first will be an overview of light and how it interacts with the water column next we will go over the fundamentals of remote sensing third we will discuss the types of data products we can derive from imagery and how those data products may be used we will discuss nasa satellite imagery data sources as well as data processing tools this is a lot of material to cover in one session there will be helpful links throughout the webinar that you can use to refer to online resources why do we observe the ocean from space to understand the earth's processes on a global scale this animation shows a time series of chlorophyll from the sea with satellite sensor if you look in the lower right you can see the month and year in the time series from 1997 to 2005. watch the animation for a moment focus on the ocean and notice patterns in the false color display of chlorophyll purple is low chlorophyll and green to red are high chlorophyll note the patterns that you see across the globe for example in the south pacific the region is dark blue or even purple this indicates low chlorophyll now look up in the north atlantic watch how it changes over time notice how it greens up this occurs primarily during the spring gloom period and it slightly precedes summer green up on land at the same latitudes light nutrients and temperature drive phytoplankton productivity satellite observations of ocean chlorophyll give us the ability to make global estimates of carbon uptake by phytoplankton this information helps in our understanding of the global carbon budget what are some advantages of remote sensing of aquatic environments there's synoptic coverage meaning remote sensing imagery can collect data over large areas that would otherwise be impossible using ships or mooring observations alone next the ocean moves and so for many processes frequent satellite overpasses are needed to capture natural phenomena many satellite sensors collect data on near daily revisit rates and some of these processes can therefore be observed third satellites can go where ships cannot go understand remote sensing we must first have some knowledge of the water and its constituents and how light interacts with them in this section we will cover the nature of light and how it propagates through the atmosphere and water column and back out to the satellite sensor or airborne sensor we will also talk about the inherent optical properties of the constituents in the water and how they influence the color of light the first step in any discussion of remote sensing requires an overview of the electromagnetic spectrum energy is emitted from the sun and the photons travel at different wavelengths or different energy levels for our purposes we are mainly interested in the light in the visible range or 400 to 700 nanometers and in the thermal range if you are familiar with terrestrial or land imaging the mirror and short wave infrared regions are used but because of the absorption of light by water at those wavelengths we do not use those ranges for aquatic remote sensing so here's how it works light from the sun passes through the atmosphere and if it reaches the sea surface and reflect off of the surface or pass through it as i said before the fate of a photon is to be either scattered or absorbed if absorbed phytoplankton non-algal particles color dissolved organic matter or water itself will absorb the light if scattered it will do so in either the forward or backward direction if in the backward direction some of it will be re-emitted from the sea surface and will be detected by a sensor in aquatic remote sensing we are interested in the radiometric unit the remote sensing reflectance or rrs and this is sometimes what we think of as ocean color remote sensing reflectance is used in ocean color algorithms to compute the data products of interest for ocean and aquatic science like chlorophyll concentration remote sensing reflectance can be defined as the ratio of back scattering to the total absorption and back scattering as affected by the local sun and sky conditions alternatively it can be defined as the water leaving radiance or the light just above the surface to the incoming or downwelling irradiance incident on the sea surface with this equation we see the relationship of the inherent optical properties absorption and scattering of the material in the water to the quantity and quality of light in the underwater light field it is this remote sensing reflectance quantity that is derived from satellite remote sensing measurements because of these relationships of what is in the water to the color of the light emitted from the water we can infer concentrations of optically active constituents in the upper part of the water column that the satellite can detect or see here again is the equation for remote sensing reflectance light absorption and back scattering by the different constituents in the water column govern the light the color of light the sensor detects this landsat 8 oli image to the right is of rupert bay in northern canada doesn't it look kind of like a latte this image illustrates the effect of the different absorbing constituents in the water in the image you see that the dark water up in the upper right of the image is characteristic of color dissolved organic matter or cedom this is coming from the rivers flowing into the bay from the south as well as from the east sedon rich water tends to look black as it absorbs light strongly but it doesn't reflect much light backward in the middle part of the bay tidal forces cause a resuspension of sediments making for a light brown color offshore of the mouth of the bay there are regions of bluer water and regions that are slightly green possibly due to phytoplankton if you are having a hard time seeing much of a difference among the chlorophyll water and sedom examples that i have here you're not alone part of the reason i use this example is to emphasize to you that water is a dark target remote sensing reflectance in the visible range is low for water as compared to land and so we must be very careful that instruments are sensitive enough for water targets are calibrated in the visible range and that we are careful not to over correct for the atmospheric effects on light absorption and scattering so we see these pictures with our eyes what would the remote sensing reflectance spectra look like for these different water types in the figure on the right you see wavelength across the x-axis and remote sensing reflectance along the y-axis note how the remote sensing reflectance spectrum is very low for cdom but has a higher magnitude for sediments this intuitively makes sense because a sediment image looks bright light brown and brighter to our eye the water appears blue chlorophyll reflects strongly in the green as you can see in the peak in the region around 550. so to our eye chlorophyll looks green look at the figure to the right the gray vertical bars represent the wavelength ranges where a typical human eye detects light humans have color detecting receptors that sense light and three ranges corresponding to blue green and red our eyes typically can only see three wavelengths whereas satellite sensors can detect even more depending on the spectral resolution of the sensor so let's learn a little bit more about satellite remote sensing in this section we will discuss image resolution some of nasa's satellites and sensors for aquatic applications image correction and satellite data processing levels when we talk about resolution three types come to mind spatial temporal and spectral first is spatial resolution or how the area on the ground translates to one pixel the smallest unit measured by the sensor for example one landsat 8 oli pixel can be about 15 or 30 meters in spatial resolution how often a sensor obtains an image of a particular part of the globe is referred to as temporal resolution for example landsat 8 oli returns only once every 16 days but modis has a more frequent return rate at about one to two times per day spectral resolution refers to how many colors or bands an imager can collect for example the moda sensor has 36 spectral bands whereas landsat 8 senses about just 9 spectral bands for the purposes of this webinar we are most interested in passive satellite sensors that measure reflected solar radiation in the visible range to use in our ocean color algorithms we are also interested in measuring the emitted infrared and microwave radiation in order to absorb observe sea surface temperature we call these sensors passive because they are detecting the reflected or emitted light energy from the sun for this webinar we will only refer to passive remote sensing although active remote sensing like lidar is gaining popularity for understanding particles in aquatic environments but we will not cover that in this webinar satellite measurements can be used to infer characteristics of several earth systems spheres depending on the type of sensor and which part of the electromagnetic spectrum is used information can be gained about the atmosphere including clouds aerosols and gases as well as the earth's surface including snow and ice land vegetation and water as mentioned before the remote sensing of water bodies is used to derive the properties of optically active water constituents these include suspended sediments algae like this coccolithophore bloom near norway in the upper right of this slide color dissolved organic matter and others we use passive sensors to measure the reflected solar radiation surface temperature can be observed using sensors that detect emitted thermal radiation knowing sea surface temperature can give insight into the ocean heat budget ocean current patterns and rates the rate of photosynthesis and primary productivity models among other information the image on the right shows an eight day average of sea surface temperature along the east coast of the united states the deep red color along the state of florida and northward to north carolina where it detaches from the coastline is the gulf stream a quick flowing western boundary current after it detaches from the continent turbulent flow encourages the formation of eddies which are evident on the eastern part of this image they look like little waves almost in this image there are currently several u.s satellites that are used for the remote sensing of open ocean coastal and inland waters these include landsat 7 and landsat 8 and the aqua and terra satellites the international space station also has served as a satellite for ocean imagery observations these next two slides provide a reference to the different nasa satellites and sensors that are commonly used for ocean color remote sensing the landsat series including the thematic mapper enhanced thematic mapper and operational land imager or oli are used to observe water quality these sensors provide high spatial resolution imagery which is particularly valuable in coastal systems where small scale processes can dominate and are otherwise neglected by coarser spatial resolution imagery the terra and aqua satellites each host a moderate resolution imaging spectrometer or modis which senses both reflected visible radiance and emitted thermal energy lotus is used for land ocean and atmospheric applications and used used to infer several parameters or data products used for understanding ocean biology carbon dynamics and circulation other ocean color satellites include the suomi national polar partnership or npp which hosts the visible infrared imaging radiometer suite or beers this sensor is used for spectral reflectance and to infer chlorophyll concentration the international space station can be considered a satellite used for ocean color observations the hyperspectral imager for the coastal ocean or hiko wrote aboard the iss for five years from 2009 to 2014. this was a high spectral resolution sensor sometimes referred to as an imaging spectrometer or hyperspectral sensor finally a satellite and sensor that is not yet launched but is under development is the plankton aerosols clouds ocean ecosystems or pace sensor this proposed hyperspectral sensor is scheduled to launch sometime in 2022 or 23. the landsat satellites have been collecting earth observations since july 1972. the satellite has a near polar orbit with a 10 am equator crossing time the revisit rate is every 16 days spatial resolution varies by satellite but it is typically possible to obtain 30 meter resolution imagery there is a commitment to continue with the landsat program as it has been so successful in tracking land use and land change and more recently with the launch of landsat 8 with its broader use in aquatic systems despite the relatively long revisit rate of 16 days landstat landsat is still a useful imaging system to understand some applied science questions for aquatic systems including marsh subsidence and the effects of eutrophication in inland waters the landsat 7 enhanced thematic mapper launched in 1999 and has remained operational since it has a 16 day revisit rate spectral bands include blue green green red panchromatic and reflected in thermal ir note in the figure that the reflectance of water is relatively lower than the reflectance of land recall me saying earlier that water is a dark target it also absorbs light strongly in the near infrared and beyond so the number of useful bands for aquatic applications is limited to the visible range the development of landsat 8's operational land imager included the addition of another spectral band to improve the remote sensing of aquatic systems this satellite sensor also has a 16-day revisit rate and with this additional coastal band algorithms have been developed to derive chlorophyll color dissolved organic matter and other water constituents the sentinel 2a satellite from the european space agency was recently launched and then serves as several similar spectral bands to landsat 8 oli and at a finer spatial and temporal resolution than landsat 8. it may be of interest to some people in this webinar but we will not be discussing it as a part of this part of this webinar the workhorse for much of ocean color remote sensing are the modis imagers on the terra and aqua satellites both of these satellites are polar orbiting and have global coverage they collect mere daily observations tara collects in the morning and aqua collects in the afternoon there are a number of other sensors on the terra and aqua satellites but mostly for ocean color remote sensing we are interested in the modis sensor so what is modis lotus is the moderate resolution imaging spectroradiometer it is designed as i've said before for land atmosphere ocean and cryosphere observations it has a spatial resolution of one kilometer but some of the bands sense at 250 meters and at 500 meters it is possible to interpolate the ocean color bands to 250 meters and 500 meters facial resolution using the image processing software cdas while this may introduce some error this enables the use of the imagery in coastal waters where the finer spatial resolution is needed for the scale of processes occurring in these systems another satellite is the national polar partnership also known as the suomi npp this satellite was launched in 2011 and provides global coverage it has a 1 30 pm equator crossing time on a near daily revisit rate the viirs sensor is on this satellite and is used for ocean color remote sensing the veer sensor is the visible infrared imaging radiometer suite it is designed to collect measurements of ocean colors clouds aerosols surface temperature fires and albedo between the modis and veer sensors we've got the ocean covered but satellites and their sensors do not operate indefinitely eventually they come to the end of their functional life this is why nasa and other space agencies are continuously investing effort in the development of new satellite sensors and in the process changing the specifications of these sensors so the data can be used to answer new questions in pure and applied science one example of an experimental sensor was haiko the hyperspectral imager for the coastal ocean this was a partnership among the u.s naval research lab or nrl the office of naval research oregon state university and eventually nasa it was a high spectral resolution imager this time type of sensor is sometimes referred to as imaging spectrometer or as a hyperspectral imagery depending on the research community hico was designed and calibrated to collect over dark aquatic targets this sensor was only supposed to have an operational lifetime of one year it was installed on the iss in 2009 and was able to collect data for five years and way beyond its expected lifetime the imager was tasked to collect observations of specific targets divided defined by nrl and the scientific user community these targets included open ocean coastal and inland waters more information about heiko can be found in the above links hico data are available through the nasa ocean color web level one and two browser another exciting aspect of this sensor is that it now provides a five-year data set of hyperspectral remote sensing imagery that can be used for algorithm development for nasa's future hyperspectral satellite sensor the newest ocean color satellite sensor under development at nasa is the ocean color imager on the plankton aerosol clouds ocean ecosystems or pace satellite this will be a polar orbiting sensor with a two day revisit and one kilometer ground sample distance the imagery will collect observations at high spectral resolution on the right you see an image showing the spectral resolution of legacy and current ocean color sensors you can see czcs seawifs modis spheres and pace down the right hand side the pace sensor will have much higher spectral resolution in the visible range and will collect data in the shortwave infrared to improve on atmospheric correction an optional polarimeter is being considered for this satellite for cloud and aerosol studies and to aid in atmosphere correction of ocean radiometry the proposed launch date is in the 2022-23 time frame i encourage you to visit the link on this page to learn more about the preparatory activities for this new sensor and to think about how it might be used in answering applied science questions in the future radiometric observations of the earth are made at some distance and at some angle relative to the earth's surface it can be as close as from a ship or dock or as far away as from an airplane or a satellite along its path light interacts with material in the atmosphere such as dust small particles water vapor and gases on its way to the water's surface once at the surface it can enter or bounce off and then some will pass back out of the water column and upwards through the atmosphere the material in the atmosphere absorbs and scatters light changing the amount and spectral characteristics of the light in aquatic remote sensing we work hard to remove the effects of the material in the atmosphere so we can obtain an image of the water surface that is as close to accurate as possible this can be really difficult because water is such a dark target and has low signal and so mistakes in our correction can have a big impact as you have seen water bodies dominated by phytoplankton colored dissolved organic matter suspended sediments or detrital particles have distinctive water colors that are detectable in their remote sensing reflectances like material in the water material in the atmosphere also has spectrally distinct characteristics that influence what the satellite sensor sees almost 90 percent of the reflectance signal detected by the satellite sensor is due to the atmosphere and only 10 percent is from the water's surface we use complex atmospheric correction algorithms that take into account the optical properties of atmospheric particles and gases in order to to subtract that signal from the light the sensor detects so that we can arrive at a surface reflectance as i've said previously water is a dark target so very little light is being emitted compared to land or ice so the sensors used for aquatic remote sensing must have high signal to noise so that they can have the sensitivity to detect the water dark water surface even through the filter of the atmosphere we work hard to measure light at the sea surface in order to compare the so-called sea truth measurements to the results of our atmospheric correction algorithms how do we take these measurements from a boat or dock using a handheld field spectroradiometer we measure water leaving radiance and downwelling irradiance and use these measurements to derive remote sensing reflectance we consider these sea surface measurements to be the best estimate of the truth airborne or satellite sensors also measure the water leaving radiance but for much higher off of the water than a boat we compare the c truth measurements to our atmospherically corrected imagery which is a process also known as validation so how different is an image at the top of the atmosphere versus what we expect the surface to look like these images show the narragansett bay rhode island in the u.s on the left is an uncorrected image taken above the atmosphere on the right is the same image but this time with the effects of atmospheric gas aerosols and reflection off of the surface and water column vapor removed so that the only quantity that is represented is the water leaving radiance because water is a dark target a faulty atmospheric correction can introduce mistakes into the resulting image the algorithms that we use to estimate chlorophyll dissolved organic matter and suspended sediments are sensitive to variability in the remote sensing reflectance spectrum if an image is improperly atmospherically corrected then inaccurate estimates of these data products will result when we obtain satellite data we can get it at different stages of processing depending on what our needs are some users prefer to use data that has already been atmospherically corrected and the data products have been derived whereas other users want the raw state of data available so they can make choices about how to process the data such as which atmosphere correction algorithm to use in response data providers serve the data at different processing levels they are listed here on this slide and i'm going to walk through them level zero data are unprocessed instrument data at full resolution any artifacts of these data from the communication of the spacecraft to the ground station have been removed these data are the most raw format available and are only provided for a few of the missions level 1a data are reconstructed unprocessed instrument data at full resolution time referenced and annotated with ancillary information including radiometric and geometric calibration coefficients and geo-referencing parameters computed and appended but not yet applied to the level 0 data level 1b data are level 1a data that have had instrument and radiometric calibrations applied level 2 data consist of derived geophysical variables at the same resolution as the source level 1 data these variables also called data products like chlorophyll include such things as chlorophyll sea surface temperature inherent optical properties among other things level three data are derived geophysical variables that have been aggregated or projected onto a well-defined spatial grid over a well-defined time period level four data are model output or results from analyses of the lower level data it is possible to obtain data at any one of these processing levels level 0 being the exception for some missions the effort needed to work with the data is more difficult and requires more skill from the lower levels or where the data are at the rawest as you move to the higher levels it can require less skill to process the data nasa's image processing software cdas enables the processing of data through these different levels so what kind of aquatic data products can a person obtain from satellite imagery aquatic remote sensing data products and their uses several ocean properties or data products can be derived from satellite remote sensing imagery chlorophyll a is contained in all eukaryotic phytoplankton and the cyanobacteria it is used as a proxy for biomass of photoautotrophs at the water's surface or near surface because it is optically active we can estimate its concentration using remote sensing methods other aquatic properties derived from remote sensing imagery include water turbidity color dissolved organic matter or sedom sea surface temperature we can also obtain surface winds and salinity from other data inputs one of the most common questions i am asked about aquatic road sensing is how is it possible to obtain chlorophyll a from remote sensing imagery in the next few slides i hope to convey to you a high level understanding of how we derive this biophysical data product from imagery on the right of this slide you'll see just a snapshot of the water surface and on the left here is the remote sensing reflectance across the visible range for this water on the right here is a schematic representation of the type of spectra one would obtain from waters with different chlorophyll concentrations the four example spectra on the left correspond to the images on the right spectrum one is from water with the highest chlorophyll concentration and each subsequent image has a decreasing chlorophyll concentration this difference is also noted in the magnitude of the spectra and the figures on the left as chlorophyll concentration decreases the peak height at around 550 nanometers also decreases and you'll note the peak at around 685 also decreases the chlorophyll-egg algorithm is a fourth-order polynomial relationship between a ratio of remote sensing reflectance at two wavelengths and chlorophyll a this type of algorithm was derived from the empirical data or data collected at c there is more than one chlorophyll algorithm that can be used depending on the environment being studied for example a chlorophyll algorithm for the open ocean may not be appropriate for use in the coastal ocean also which satellite sensor is being used can define which chlorophyll algorithm should be also used if you would like more details about the different chlorophyll algorithms i suggest you follow the link to the algorithm description listed below the spectrum here simply stated the ratio of two remote sensing reflectance measures are used as inputs into the chlorophyll algorithm and the result is an estimate for chlorophyll concentration validation of the chlorophyll algorithm is performed by collecting c truth measurements of chlorophyll within one hour on either side of the time that the satellite passed over these in-situ c-truth chlorophyll-a measurements are then compared to the chlorophyll derived from the satellite measurements and the uncertainty is estimated it is a surprisingly straightforward approach developed in the 1970s and 1980s that continues to be refined and used today to estimate chlorophyll a from space so now you have learned how chlorophyll is derived from the first principles of aquatic optics and how it is possible to make quantitative estimates of global chlorophyll from space as you see in this image here this is a composite of modis imagery from march april may 2014 or the springtime in the northern hemisphere note the chlorophyll concentration scale along the right and the patterns in the northern hemisphere during spring phytoplankton respond to increasing light temperature mixed layer depth and the abundance of nutrients available to them the north atlantic in particular noticeably greens up during this period next i will be going over how to obtain satellite data from a few of the data access tools commonly used by the research community and also i will introduce you to nasa's image processing software named cdas this webinar series is focused on nasa and u.s based satellite systems there are other international satellites and image processing tools available to the public but those are not a part of this webinar worldview is a web-based application for interactively browsing global full resolution satellite imagery and then downloading the underlying data the browse feature that lets you step through time is really useful if you want to search image scenes without first having to go through the effort of processing the data find features such as a phytoplankton bloom worldview makes available over 100 data products and most of them are updated in near real time for the entire earth this supports time critical applications such as flood monitoring air quality monitoring and wildfire management to start i wanted to just browse satellite data to see if there was a recent period when there was a phytoplankton bloom in the arabian sea so i zoomed into the arabian sea in the tool i can click the red add layers button towards the left here to call up a search window from here i navigate to other where a window will open and give me the choice to add chlorophyll a for the terra modis and aquamodus sensors i can see the chlorophyll data layer overlaying on top of the true color image if i click on the camera icon in the upper right corner of the webpage i can define the region to save and save it in one of a number of formats including jpeg and geotiff so just looking at this image here if you look at the horn of africa you can see this swirl or an eddy and what that is is it's an aggregation of phytoplankton into the physical current and that tells me in the date being december 1st that december 1st 2015 will be a good day to study because i can already see this swirl of chlorophyll just offshore of muscat oman the nasa ocean color web is another data access resource here you can obtain level one and two data from a number of satellite sensors including modis viirs and hico here is an example of the level one and two browser you can select the sensor month and year date range and provide a location it is possible to download image data from this search tool and then load it into nasa's image processing software cdas for further processing and analysis there are other data portals out on the web three of them are listed here and i encourage you to look into them more the noaa tool has helpful regional information but also can provide global coverage data these tools are the noaa coast watch tool nasa giovanni and the usgs earth explorer which i use for accessing landsat data so how do you process the data after you download it in this next section i will introduce you to nasa's cds image processing tool which can be used to visualize and process the remote sensing data you just learned how to obtain you've already seen the level 1 and 2 browser from nasa ocean color web nasa's ocean color web is supported by the ocean biology processing group or obpg at nasa's goddard space flight center think of it as a go-to resource for data data processing tools and an enthusiastic community of researchers the obpg responsibilities include the collection processing calibration validation archive and distribution of ocean related products from a large number of operational satellite-based remote sensing missions providing ocean color sea surface temperature and sea surface salinity data to the international research community since 1996. when you explore the website you will find information about the missions they support the data access tools they have available documentation related to the data products and the algorithms that are used calibration and validation of data and under the services tab you'll have find helpful links to the user form and their image processing software cdas this website provides a wealth of helpful information becoming proficient at accessing accessing processing and understanding ocean color satellite imagery it is at this website that you can find the image processing software cds cds was originally developed for the sea with sensor and derives its name from the seawifs data analysis system but now it supports most u.s and international ocean color missions it is a comprehensive image analysis package for the processing display analysis and quality control of ocean color data and it's free it's freely available through the nasa ocean color web website and the link at the top of this page cds is well supported with online tutorials help pages and active user community at the ocean color forum and an attentive and very friendly support team based at nasa goddard the cdas team at goddard has put together a suite of freely available on-demand tutorials that you can see a listing of here and a webinar on how to install and use cdas it is through these tutorials webinar and through your own use of the tool that you can learn this program cdas is intuitive to use and it does not take very long to learn and since cdas supports such a wide variety of satellite sensors including international sensors your time investment in learning it is time well spent if you are interested in building your knowledge of in-water optics and the fundamentals of remote sensing beyond this fundamentals of aquatic remote sensing webinar i highly recommend the ocean optics web book this is a free online resource that is perfect for beginners and advanced users alike in my own research search i still refer back to this book when i need to clarify a concept there's also the ioccg summer lecture series from 2016 that has a wealth of information about in-water optics and also remote sensing imagery and as noted previously for more information on remote sensing data access and processing tools this third link of the nash nasa ocean color web has a wealth of information so to recap this webinar in this webinar we have discussed the nature of light and how it propagates through the atmosphere and water column and back out to the sensor we have talked about the inherent optical properties of the constituents in the water and how they influence the color of light you've had an overview of the fundamentals of remote sensing including spatial temporal and spectral resolution we've talked about the different nasa satellites and sensors that can be used for aquatic applications we've touched on the idea of atmosphere correction and we've reviewed satellite processing levels and what they mean i've given an overview of data products used or commonly used in aquatic remote sensing we've talked about a few of the data portals you can use to obtain remote sensing imagery for free and finally i've directed you towards nasa's ocean biology processing group where you can obtain and learn more about the freely available image processing software named cdas this concludes the fundamentals of aquatic remote sensing webinar we thank you for your interest in participation in this rset webinar i hope you will consider future our set webinars for your future training needs thank you

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Length: 43min 18sec (2598 seconds)

Published: Thu May 14 2020