Sea-surface temperature measurements from the moderate-resolution imaging spectroradiometer (MODIS) on aqua and terra

Minnett, Peter J.; Brown, Otis B.; Evans, Robert H.; Key, E.; Kearns, Edward J.; Kilpatrick, K. A.; Kumar, Ajoy; Maillet, K. A.; Szczodrak, G.

doi:10.1109/igarss.2004.1370173

Cited by 51 publications

(35 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We are not attempting to directly estimate water temperature from LST (like, for example, direct estimates of sea surface temperature [30]). Instead, we use LST as a covariate in linear models, first in the John Day River (JDR; a tributary to the Columbia River, USA) and then in other interior Columbia River (ICR) basins (the Lemhi (LRB), Tucannon (TRB), Upper Grande Ronde (UGR) and Wenatchee (WRB) Rivers (Figure 1)), at spatial and temporal scales of reaches and days.…”

Section: Introductionmentioning

confidence: 99%

Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures

McNyset¹,

Volk²,

Jordan

2015

Water

View full text Add to dashboard Cite

Abstract:Although water temperature is important to stream biota, it is difficult to collect in a spatially and temporally continuous fashion. We used remotely-sensed Land Surface Temperature (LST) data to estimate mean daily stream temperature for every confluence-to-confluence reach in the John Day River, OR, USA for a ten year period. Models were built at three spatial scales: site-specific, subwatershed, and basin-wide. Model quality was assessed using jackknife and cross-validation. Model metrics for linear regressions of the predicted vs. observed data across all sites and years: site-specific r 2 = 0.95, Root Mean Squared Error (RMSE) = 1.25˝C; subwatershed r 2 = 0.88, RMSE = 2.02˝C; and basin-wide r 2 = 0.87, RMSE = 2.12˝C. Similar analyses were conducted using 2012 eight-day composite LST and eight-day mean stream temperature in five watersheds in the interior Columbia River basin. Mean model metrics across all basins: r 2 = 0.91, RMSE = 1.29˝C. Sensitivity analyses indicated accurate basin-wide models can be parameterized using data from as few as four temperature logger sites. This approach generates robust estimates of stream temperature through time for broad spatial regions for which there is only spatially and temporally patchy observational data, and may be useful for managers and researchers interested in stream biota.

show abstract

Section: Introductionmentioning

confidence: 99%

Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures

McNyset¹,

Volk²,

Jordan

2015

Water

View full text Add to dashboard Cite

show abstract

“…The MODIS C5 has more spatial coverage than C4 particularly at high altitude regions (Wan, 2012). The MODIS LST was retrieved from the day/night algorithm (Wan and Li, 1997), whereas MODIS SST was retrieved by the brightness temperature difference between 11 µm and 12 µm channels (Minnett et al, 2004). The accuracy of the MODIS LST retrieved from the day/night algorithm was reported to be better than 1 K for the clear sky in the temperature range of −10 • C to 50 • C (Wan et al, 2004).…”

Section: Methodsmentioning

confidence: 99%

Comparison between MODIS and AIRS/AMSU satellite-derived surface skin temperatures

et al. 2013

View full text Add to dashboard Cite

Abstract. Surface skin temperatures of the Level 3 products of MODIS Collection 5 (C5) and AIRS/AMSU version 5 (V5) have been compared in terms of monthly anomaly trends and climatologies over the globe during the period from September 2002 to August 2011. The MODIS temperatures in the 50° N–50° S region tend to systematically be ~1.7 K colder over land and ~0.5 K warmer over ocean than the AIRS/AMSU temperatures. Over high latitude ocean the MODIS sea surface temperature (SST) values are ~5.5 K warmer than the AIRS/AMSU. The discrepancies between the annual averages of the two sensors are as much as ~12 K in the sea ice regions. Meanwhile, the MODIS ice surface temperature product (MYD29E1D) over the ocean is in better agreement with AIRS/AMSU temperatures, showing a root mean square error of 3.7–3.9 K. The disagreement between the two sensors results mainly from the differences in ice/snow emissivity between MODIS infrared and AMSU microwave, and also in their observational local times. Both MODIS and AIRS/AMSU show cooling rates from −0.05 ± 0.06 to −0.14 ± 0.07 K 9 yr−1 over the globe, but warming rates (0.02 ± 0.12 –0.15 ± 0.19 K 9 yr−1) in the high latitude regions.

show abstract

“…So in this area, fusing of these sets of vectors makes SSCs more reliable. The main earlier studies about SSCs refer to SST tracking and intricacy of SSCs is shown by SST patterns and some events like El-Niño can be foretold by large scale SST anomalies in crucial oceanic regions (Park and Chung, 1999), (Minnett et al, 2004), (M. Hasanlou and M. R. Saradjian, 2006). To study SST imagery, we use MODIS SST products in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…To study SST imagery, we use MODIS SST products in this paper. MODIS data include ocean color and SST for oceanographic tasks (Minnett et al, 2004). We determine The SSCs from SST imageries by applying OF method.…”

Section: Introductionmentioning

confidence: 99%

Estimating and Fusing Optical Flow, Geostrophic Currents and Sea Surface Wind in the Waters Around Kish Island

Ghalenoei

Sharifi

Hasanlou

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:The aim of this study is calculation of sea surface currents (SSCs) which are estimated from satellite data sets and processed with the variance component estimation (VCE) algorithm to check role of each data set, in fused surface currents (FSCs). The satellite data used in this study are sea surface temperature (SST), satellite altimetry data and sea surface wind (SSW) that plays the important role to make the SSCs and is measured by Ascat satellite. We use optical flow (OF) method (Horn-Schunck algorithm) to extract sea surface movements from sequential SST imageries; in addition, geostrophic currents (GCs) are estimated by satellite altimetry data like sea surface height (SSH). Combining these data sets, has its pros and cons, the OF results are so dense and precise due to high spatial resolution of MODIS data (SST), but sometimes cloud covering over the sea, does not allow the MODIS sensor to measure the SST. In contrast the SST data, the altimetry data have poor spatial resolution and the GCs are not able to determine small scale SSCs. The VCE algorithm shows variances of our data sets and it can be shown their correlations with themselves and with the FSCs. We also calculate angular differences between FSCs and OF, GCs and SSW, and plot distributions of these angular differences. We discover that, the OF and SSW are homolographic, but OF and GCs are accordant to each other.

show abstract

Sea-surface temperature measurements from the moderate-resolution imaging spectroradiometer (MODIS) on aqua and terra

Cited by 51 publications

References 9 publications

Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures

Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures

Comparison between MODIS and AIRS/AMSU satellite-derived surface skin temperatures

Estimating and Fusing Optical Flow, Geostrophic Currents and Sea Surface Wind in the Waters Around Kish Island

Contact Info

Product

Resources

About