Validation of water vapor retrieval from Moderate Resolution Imaging Spectro-radiometer (MODIS) in near infrared channels using GPS data over IAO-Hanle, in the trans-Himalayan region

Ningombam, Shantikumar S.; Jade, Sridevi; Shrungeshwara, T.S.; Song, Hwan‐Jin

doi:10.1016/j.jastp.2015.11.019

Cited by 41 publications

(16 citation statements)

References 30 publications

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“…The descriptive revealed good GPSPWV-MODISPWV agreement at surface level (R = 0.96; RMSE = 3.81). This MODISPWV validation is within the range of acceptable results, when compared to similar research findings on PWV validation (Tsidu et al, 2015;Gui et al, 2017b) but slightly lower than Ningombam et al, (2016). From Table 2, we can also conclude that the computed alpha reliability, correlation coefficient, root mean square error and bias, were within the range of PWV estimation with GPS positioning.…”

Section: Effects Of Modisaod Concentrations On Gpspwvsupporting

confidence: 84%

Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates

Aliyu

Botai

2018

Atmospheric Environment

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In 2016, three Nigerian cities were listed amongst the World's most polluted in terms of particulate matter (PM). Acknowledging Nigeria's limited resources for outdoor air pollution monitoring, this study attempts to investigate the effects on atmospheric aerosol optical depth and ground PM on GPS derived-precipitable water vapour estimates. The study utilized available GPSderived precipitable water vapour (GPSPWV), the moderate resolution imaging spectroradiometer aerosol optical depth (MODISAOD) and the ground level particulate matter of less than 10 microns (GPM) datasets for December 2015 -November 2016. All the datasets were tested for normality. To evaluate the atmospheric aerosol properties, the MODISPWV estimates were pre-validated using the GPSPWV measurements. The results revealed GPSPWV-MODISPWV agreement (R = 0.964; RMSE = 3.810 mm). The GPSPWV-MODISAOD analysis showed relationship (R ≤ -0.636; RMSE ≤ 0.563) for the atmospheric aerosol experiment, while the collocating GPSPWV-GPM seasonal analysis also revealed significant correlation (R < -0.660). The correlation of combined seasonal datasets for the GPSPWV-MODISAOD and GPSPWV-GPM relationships showed high negative correlation values of 0.79 and 0.68 respectively. The findings of this study is in agreement with similar related studies, as well as serve as future position accuracy for similar related studies.

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Section: Effects Of Modisaod Concentrations On Gpspwvsupporting

confidence: 84%

Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates

Aliyu

Botai

2018

Atmospheric Environment

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“…For example, the snow community uses the shift from 1,030 nm of the imaginary index of refraction (i.e., absorption) of pure ice to 970 nm for liquid water in algorithms to estimate the liquid water content of melting snow (Green et al, 2002). This same absorption feature is shifted to 940 nm for water vapor and is often a channel used for estimating perceptible water vapor and other atmospheric remote sensing applications (Ningombam et al, 2016;Ibrahim et al, 2018). Hence, the liquid absorption features detectable in the whitecap reflectance measured at the sea surface may wholly or in part be obscured by water found in various forms in the atmosphere due to the close and overlapping absorption bands of water in its various forms (see analysis in Section Whitecap Modeling for the PACE sensor below).…”

Section: Model Of Average Whitecap Reflectance From Water Absorptionmentioning

confidence: 99%

Hyperspectral Measurements, Parameterizations, and Atmospheric Correction of Whitecaps and Foam From Visible to Shortwave Infrared for Ocean Color Remote Sensing

Dierssen

2019

Front. Earth Sci.

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Breaking waves are highly reflective features on the sea surface that change the spectral properties of the ocean surface in both magnitude and spectral shape. Here, hyperspectral reflectance measurements of whitecaps from 400 to 2,500 nm were taken in Long Island Sound, USA of natural and manufactured breaking waves to explore new methods to estimate whitecap contributions to ocean color imagery. Whitecap reflectance was on average ∼40% in visible wavelengths and decreased significantly into the near infrared and shortwave infrared following published trends. The spectral shape was well-characterized by a third order polynomial function of liquid water absorption that can be incorporated into coupled ocean-atmospheric models and spectral optimization routines. Localized troughs in whitecap reflectance correspond to peaks in liquid water absorption and depths of the troughs are correlated to the amount and intensity of the breaking waves. Specifically, baseline-corrected band depths at 980 and 1,200 nm explained 77 and 90% of the whitecap-enhanced reflectance on a logarithmic scale, respectively. Including these wavebands into future ocean color sensors could potentially provide new tools to estimate whitecap contributions to reflectance more accurately than with wind speed. An effective whitecap factor was defined as the optical enhancements within a pixel due to whitecaps and foam independent of spatial scale. A simple mixed-pixel model of whitecap and background reflectance explained as much of the variability in measured reflectance as more complex models incorporating semi-transparent layers of foam. Using an example atmosphere, enhanced radiance from whitecaps was detectable at the top of the atmosphere and a multiple regression of at-sensor radiance at 880, 1,038, 1,250, and 1,615 nm explained 99% of the variability in whitecap factor. A proposed model of whitecap-free reflectance includes contributions from water-leaving radiance, glint, and diffuse reflected skylight. The epsilon ratio at 753 and 869 nm commonly used for aerosol model selection is nearly invariant with whitecap factor compared to the ratio at shortwave infrared bands. While more validation data is needed, this research suggests several promising avenues to retrieve estimates of the whitecap reflectance and to use ocean color to further elucidate the physics of wave breaking and gas exchange.

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“…Numerous satellite instruments provide IWV data which have been widely inter-compared against reference ground-based measurements, namely, Global Ozone Monitoring Experiment-2 (GOME-2) (Grossi et al, 2015;Kalakoski et al, 2016;Noël et al, 2008;Román et al, 2015), MODerate-resolution Imaging Spectroradiometer (MODIS) (Bennouna et al, 2013;Chang et al, 2015;Gao and Li, 2008;Li et al, 2003;Ningombam et al, 2016;Prasad and Singh, 2009;Román et al, 2014), Meteosat (Hanssen et al, 2001;Schroedter-Homscheidt et al, 2008), MEdium Resolution Imaging Spectrometer (MERIS) (Diedrich et al, 2016;Li et al, 2006) or SCIA-MACHY (Bovensmann et al, 1999;Noël et al, 2005;Schrijver et al, 2009). Additionally, the Ozone Monitoring Instrument (OMI) also provides IWV data using the retrieval algorithm proposed by Wang et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Validation of integrated water vapor from OMI satellite instrument against reference GPS data at the Iberian Peninsula

Vaquero-Martínez

Antón

Galisteo

et al. 2017

Science of The Total Environment

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This paper shows the validation of integrated water vapor (IWV) measurements retrieved from the Ozone Monitoring Instrument (OMI), using as reference nine ground-based GPS stations in the Iberian Peninsula. The study period covers from 2007 to 2009. The influence of two factors, - solar zenith angle (SZA) and IWV -, on OMI-GPS differences was studied in detail, as well as the seasonal dependence. The pseudomedian of the relative differences is -1 ± 1% and the inter-quartile range (IQR) is 41%. Linear regressions calculated over each station show an acceptable agreement (R up to 0.77). The OMI-GPS differences display a clear dependence on IWV values. Hence, OMI substantially overestimates the lower IWV data recorded by GPS (∼ 40%), while underestimates the higher IWV reference values (∼ 20%). In connection to this IWV dependence, the relative differences also show an evident SZA dependence when the whole range of IWV values are analyzed (OMI overestimates for high SZA values while underestimates for low values). Finally, the seasonal variation of the OMI-GPS differences is also associated with the strong IWV dependence found in this validation exercise.

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Validation of water vapor retrieval from Moderate Resolution Imaging Spectro-radiometer (MODIS) in near infrared channels using GPS data over IAO-Hanle, in the trans-Himalayan region

Cited by 41 publications

References 30 publications

Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates

Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates

Hyperspectral Measurements, Parameterizations, and Atmospheric Correction of Whitecaps and Foam From Visible to Shortwave Infrared for Ocean Color Remote Sensing

Validation of integrated water vapor from OMI satellite instrument against reference GPS data at the Iberian Peninsula

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