Sea Surface Temperature Retrieval Research Articles

The annual surface temperature patterns across the Dead Sea as retrieved from thermal images

Satellite images capture a large scene instantaneously, enabling direct comparisons of surface–atmosphere interactions in a static time domain. The annual course of surface temperature of the Dead Sea is investigated using thermal images obtained from Band 10 onboard Landsat 8. Retrieved sea surface temperature varied from around 20 °C in February to ~ 35 °C in July. Retrieved sea surface temperature is commensurate with extensive field measurements, with a root mean square error of 1.25 °C. Satellite images reveal three distinct thermal patterns. The first one is the presence of temperature variations across the open sea which may reflect differences in local meteorology and/or differential mixing with the deeper layers. The second feature is the different temperature skewness patterns between winter and summer months. A slight negative temperature skewness is observed during the period November through February, and a steep positive one during the rest of the year, with large values occurring during summer. These skewness patterns reflect the variations in the annual course of heating and cooling mechanisms occurring in the open sea and along the shallow shorelines. The third distinct feature is the presence of cold finger plumes during the cold months, extending around 4 km inside the open sea and covering an area in excess of 3 km2. The influence of freshwater flux on evaporation from large hypersaline water bodies is investigated using a theoretical formulation derived in this paper. Results show that the effect of freshwater flux on evaporation from hypersaline lakes is a complex process determined by activity and temperature departures. Satellite images provide an excellent surrogate to field observations which are money and labor intensive, time consuming, and very limited in their spatial coverage.

The Effects of VIIRS Detector-Level and Band-Averaged Relative Spectral Response Differences Between S-NPP and NOAA-20 on the Thermal Emissive Bands

The Joint Polar Satellite System-1 (renamed NOAA-20 after reaching the polar orbit) was successfully launched on November 18, 2017, into an afternoon orbit with a local equator crossing time of ∼1:30 p.m. , in the same orbital plane as the Suomi National Polar-orbiting Partnership (S-NPP) but with a time separation of 50 min. The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) will become the primary operational imager succeeding the VIIRS onboard S-NPP, which has been in orbit for more than six years. Although the VIIRS onboard S-NPP and NOAA-20 have identical designs, there are small differences in the relative spectral response (RSR) in most bands. Previous studies have shown that minor differences in the S-NPP RSRs at thermal emissive bands (TEBs) can lead to several effects at the detector level, such as striping in sensor data record (SDR) products. Such differences may explain the striping pattern found in S-NPP VIIRS sea surface temperature (SST) products. This article analyzes the detector-level and operational band-averaged RSRs for NOAA-20 VIIRS TEBs and examines the radiometric response to them using the line-by-line radiative transfer model at a very high spectral resolution for convolving with the RSRs. We also evaluate the impact of RSR differences between S-NPP and NOAA-20 for radiometric biases and potential striping in VIIRS TEB SDR brightness temperature. This article will contribute toward measurement consistency for long-term observations in the thermal infrared bands and ensure the quality of retrieval data produced by VIIRS, such as SST, fire, and other retrievals.

On the use of NLSST and MCSST for the study of spatio-temporal trends in SST gradients

ABSTRACTOngoing efforts are dedicated by meteorological and oceanographic agencies to improve the accuracy of Sea Surface Temperature (SST) estimates from satellite observations via improved retrieval algorithms and validation data. An important application of satellite-based SST observations is the analysis of the spatio-temporal characteristics of ocean fronts, which depend on several parameters including the SST retrieval scheme from Top-of-Atmosphere Brightness Temperatures. In this study, we focus on two widely used SST retrieval algorithms, namely the Multichannel SST (MCSST) and the Nonlinear SST (NLSST). Using night-time Level 2 SST derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua from 2005 to 2015 over the Southwestern Atlantic Ocean (SAO), we show that 1) the spatial distribution and temporal variability of SST gradient magnitudes derived from these two SST retrieval schemes are different despite statistical consistency of SST fields 2) the widely used NLSST formulation introduces a correlation between SST gradient magnitudes and SST values. This correlation, likely due to the use of a first guess SST in the NLSST formulation, is not observed in the MCSST data and may affect the study of long-term changes in ocean dynamics.

Half a century of satellite remote sensing of sea-surface temperature

Sea-surface temperature (SST) was one of the first ocean variables to be studied from earth observation satellites. Pioneering images from infrared scanning radiometers revealed the complexity of the surface temperature fields, but these were derived from radiance measurements at orbital heights and included the effects of the intervening atmosphere. Corrections for the effects of the atmosphere to make quantitative estimates of the SST became possible when radiometers with multiple infrared channels were deployed in 1979. At the same time, imaging microwave radiometers with SST capabilities were also flown. Since then, SST has been derived from infrared and microwave radiometers on polar orbiting satellites and from infrared radiometers on geostationary spacecraft. As the performances of satellite radiometers and SST retrieval algorithms improved, accurate, global, high resolution, frequently sampled SST fields became fundamental to many research and operational activities. Here we provide an overview of the physics of the derivation of SST and the history of the development of satellite instruments over half a century. As demonstrated accuracies increased, they stimulated scientific research into the oceans, the coupled ocean-atmosphere system and the climate. We provide brief overviews of the development of some applications, including the feasibility of generating Climate Data Records. We summarize the important role of the Group for High Resolution SST (GHRSST) in providing a forum for scientists and operational practitioners to discuss problems and results, and to help coordinate activities world-wide, including alignment of data formatting and protocols and research. The challenges of burgeoning data volumes, data distribution and analysis have benefited from simultaneous progress in computing power, high capacity storage, and communications over the Internet, so we summarize the development and current capabilities of data archives. We conclude with an outlook of developments anticipated in the next decade or so.

Sea surface temperature retrieval from landsat8 thermal infrared remote sensing data in coastal waters

Since the launch of landsat8, high-quality surface observation data have been acquired. But the corresponding sea surface temperature (SST) products have not been seen. Moreover, the existing SST inversion algorithm do not carefully consider the effect of water vapor on the accuracy. In this study, a new method is proposed for the retrieve of SST from Landsat 8 Thermal Infrared Remote Sensing (TIRS) data based on the variation of atmospheric water vapor content. The steps are briefly described as follows: 1) constructing the SST retrieval model of thermal infrared remote sensing by using the radiation transfer equation with atmospheric profile data (air temperature and pressure); 2) retrieving SST of Zhoushan sea area, China from Landsat 8 TIRS Data; 3) evaluating the accuracy of proposed model by simulation data and AVHRR SST product. The bias and RMSE based on the simulation data are within 0.4 K. The bias and RMSE based on AVHRR SST product are 1.6063 K and 1.8507 K, respectively. The results show that the accurate SST with high spatial resolution could be obtained by using the method.

Removal of the MCSST MODIS SST Bias During Upwelling Events Along the Southeastern Coast of Brazil

Remotely sensed sea-surface temperature (SST) retrievals with a significant positive bias during the occurrence of upwelling phenomena along the southeastern coast of Brazil were reported in our companion paper. As a result, this paper proposes an automated bias correction algorithm to improve the MODIS long-wave multichannel SST (MCSST) retrievals during the abovementioned conditions in this region. In this paper, MODIS daytime SST data (SSTMODIS) and differences between brightness temperatures in MODIS channels 31 and 32 (BT31 − BT32) are analyzed simultaneously with hourly wind surface conditions, in situ SST at 0.3 and 10 m in depth (SSTbuoy03 and SSTbuoy10), and sensible and latent heat fluxes from the Cabo Frio buoy data (at 23° S, 42° W) during 2014. The obtained results show that some upwelling events present air temperature ( $T_{\mathrm {air}}$ ) greater than SSTbuoy03 and low-atmospheric water vapor content. A simultaneous occurrence of these factors during upwelling conditions may lead to a warm-skin layer effect and may cause BT31 to be greater than SSTbuoy03 and BT31 − BT32 to be small (−0.18 °C ± 0.22 °C), affecting the MCSST performance. The proposed bias correction algorithm uses a least-squares curve between SSTbuoy03 and SSTMODIS retrievals when BT31 − BT32 ≤ 0.5 °C (i.e., dry atmospheric conditions). The bias correction algorithm has significantly improved the SSTMODIS bias (RMSE) from 1.43 °C to −0.2 °C (1.60 °C to 0.58 °C) when applied to 22 cloud-free pixels of MODIS during January–March of 2015.

Long-Term Variations in the Pixel-to-Pixel Variability of NOAA AVHRR SST Fields from 1982 to 2015

Sea surface temperature (SST) fields obtained from the series of space-borne five-channel Advanced Very High Resolution Radiometers (AVHRRs) provide the longest continuous time series of global SST available to date (1981–present). As a result, these data have been used for many studies and significant effort has been devoted to their careful calibration in an effort to provide a climate quality data record. However, little attention has been given to the local precision of the SST retrievals obtained from these instruments, which we refer to as the pixel-to-pixel (p2p) variability, a characteristic important in the ability to resolve structures such as ocean fronts characterized by small gradients in the SST field. In this study, the p2p variability is estimated for Level-2 SST fields obtained with the Pathfinder retrieval algorithm for AVHRRs on NOAA-07, 9, 11, 12 and 14-19. These estimates are stratified by year, season, day/night and along-scan/along-track. The overall variability ranges from 0.10 K to 0.21 K. For each satellite, the along-scan variability is between 10 and 20% smaller than the along-track variability (except for NOAA-16 nighttime for which it is approximately 30% smaller) and the summer and fall σ s are between 10 and 15% smaller than the winter and spring σ s. The differences between along-track and along-scan are attributed to the way in which the instrument has been calibrated. The seasonal differences result from the T 4 − T 5 term in the Pathfinder retrieval algorithm. This term is shown to be a major contributor to the p2p variability and it is shown that its impact could be substantially reduced without a deleterious effect on the overall p2p σ of the resulting products by spatially averaging it as part of the retrieval process. The AVHRR/3s (NOAA-15 through 19) were found to be relatively stable with trends in the p2p variability of at most 0.015 K/decade.

Improving the Calibration of Suomi NPP VIIRS Thermal Emissive Bands During Blackbody Warm-Up/Cool-Down

The Suomi National Polar-orbiting Partnership Program Visible Infrared Imaging Radiometer Suite (VIIRS) thermal emissive bands (TEB) have been performing well during nominal operations since launch. However, small but persistent calibration anomalies are observed in all TEBs during the quarterly blackbody (BB) warm-up/cool-down (WUCD) events. As a result, the time series of daytime sea surface temperature (SST) (derived from bands M15–M16) show warm spikes on the order of 0.25 K. This paper suggests that VIIRS TEB WUCD biases are band dependent, with daily-averaged biases about −0.04 and 0.05 K for I4 and I5, and −0.05, −0.05, 0.11, 0.09, and 0.05 K for M12–M16, respectively. Two correction methods—Ltrace and WUCD-C—have been implemented and evaluated using colocated observations from the Cross-track Infrared Sounder (CrIS), radiative transfer simulations, and SST retrievals. Also an error in the National Oceanic and Atmospheric Administration operational processing was identified and fixed. Both correction methods effectively minimize WUCD-induced SST anomalies. The Ltrace method works well for I5, M12, and M14–M16, with residual biases about 0.01 K. The WUCD-C method, on the other hand, performs well to correct WUCD biases in all TEBs, with residual biases also about 0.01 K. However, it introduces warm biases relative to CrIS at cold scene temperatures, which requires further study. Applying nonequal BB thermistor weights improves calibration at BB temperature set points, but its impact on daily-averaged WUCD biases is small. The proposed methodologies may also be applied to the VIIRS onboard the follow-on Joint Polar Satellite System satellites.

Alternating Decision Trees for Cloud Masking in MODIS and VIIRS NASA Sea Surface Temperature Products

AbstractIdentification and exclusion of clouds from satellite-based infrared fields is critical to achieve accurate retrievals of sea surface temperature (SST). Historically, identification of clouds has been driven primarily by a few uniformity tests involving a small number of pixels, brightness temperature range tests, and comparisons to low-resolution gap-free reference fields. Collectively these tests are adequate at identifying large, upper-level, very cold cumulus clouds, and uniformity tests identify moderately sized patchy cumulus clouds. But the efficacy of cloud identification often decreases at cloud edges, for small or thin cirrus clouds, and for the lower, more uniform stratus clouds, for which cloud-top temperature can be comparable to that of the sea surface, particularly at high latitudes. The heavy reliance on stringent uniformity thresholds often also has the unintended consequence of eliminating strong SST frontal regions from the pool of best-quality retrievals. This paper presents results for an ensemble cloud classifier based on a machine-learning approach, boosted alternating decision trees (ADtrees), applied to NASA MODIS and VIIRS SST imagery. The ADtree algorithm relies on the use of a majority vote from a collection of both “weak” and “strong” classifiers. This approach offers the potential to identify more cloud types and improve the retention of SST gradients in best-quality SST retrievals and also provides a per pixel confidence estimate in the classification.

The Copernicus Surface Velocity Platform drifter with Barometer and Reference Sensor for Temperature (SVP-BRST): genesis, design, and initial results

Abstract. To support calibration and validation of satellite sea surface temperature (SST) retrievals, over 60 high-resolution SST (HRSST) drifting buoys were deployed at sea between 2012 and 2017. Their data record is reviewed here. It is confirmed that sea state and immersion depth play an important role in understanding the data collected by such buoys and that the SST sensors need adequate insulation. In addition, calibration verification of three recovered drifters suggests that the sensor drift is low, albeit negative at around −0.01 K year−1. However, the statistical significance of these results is limited, and the calibration procedure could not be exactly reproduced, introducing additional uncertainties into this drift assessment. Based on lessons learnt from these initial buoys, a new sensor package for the Surface Velocity Platform with Barometer (SVP-B) was designed to serve calibration of SST retrievals by European Union's Copernicus satellites. The novel sensor package includes an HRSST sensor calibrated by a metrology laboratory. The sensor includes a pressure probe to monitor immersion depth in calm water and acquires SST data at 1 Hz over a 5 min interval every hour. This enables the derivation of mean SST as well as several percentiles of the SST distribution. The HRSST sensor is calibrated with an uncertainty better than 0.01 K. Analysis of the data collected by two prototypes deployed in the Mediterranean Sea shows that the buoys are able to capture small-scale SST variations. These variations are found to be smaller when the sea state is well mixed and when the buoys are located within eddy cores. This affects the drifter SST data representativeness, which is an aspect of importance for optimal use of these data.

Using Artificial Neural Network Ensembles With Crogging Resampling Technique to Retrieve Sea Surface Temperature From HY-2A Scanning Microwave Radiometer Data

The brightness temperature data acquired during 2012-2015 from the scanning microwave radiometer (SMR), onboard the first Chinese ocean dynamic environment satellite- Haiyang-2A, were matched up with the WindSat Polarimetric Radiometer (WindSat) 0.25° × 0.25° gridded daily sea surface temperature (SST) data. Then, the artificial neural network (ANN) ensemble (ANNE) method implementing the Crogging technique was used to build the SMR SST retrieval algorithm. Different from a regular ANN, an ANNE combines the outputs of its ANN members to generate an algorithm. The developed ANNE algorithm for SMR SST was validated based on the SMR/WindSat data pairs that were not used in the tuning of the algorithm. The SST comparison shows the root mean square (rms) of 1.16 °C for the ANNE algorithm. We further validate the SMR SST products using the in situ measurements from the National Oceanic and Atmospheric Administration iQuam System. The rms of the ANNE algorithm in comparison with the global iQuam SSTs is 1.46 °C. All validations showed that ANNEs were more accurate than the other statistically based SST retrieval algorithms for SMR, and generally had much smaller uncertainties than regular ANNs.

Optimization of Sensitivity of GOES-16 ABI Sea Surface Temperature by Matching Satellite Observations with L4 Analysis

Monitoring of the diurnal warming cycle in sea surface temperature (SST) is one of the key tasks of the new generation geostationary sensors, the Geostationary Operational Environmental Satellite (GOES)-16/17 Advanced Baseline Imager (ABI), and the Himawari-8/9 Advanced Himawari Imager (AHI). However, such monitoring requires modifications of the conventional SST retrieval algorithms. In order to closely reproduce temporal and spatial variations in SST, the sensitivity of retrieved SST to SSTskin should be as close to 1 as possible. Regression algorithms trained by matching satellite observations with in situ SST from drifting and moored buoys do not meet this requirement. Since the geostationary sensors observe tropical regions over larger domains and under more favorable conditions than mid-to-high latitudes, the matchups are predominantly concentrated within a narrow range of in situ SSTs >2 85 K. As a result, the algorithms trained against in situ SST provide the sensitivity to SSTskin as low as ~0.7 on average. An alternative training method, employed in the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear-Sky Processor for Oceans, matches nighttime satellite clear-sky observations with the analysis L4 SST, interpolated to the sensor’s pixels. The method takes advantage of the total number of clear-sky pixels being large even at high latitudes. The operational use of this training method for ABI and AHI has increased the mean sensitivity of the global regression SST to ~0.9 without increasing regional biases. As a further development towards improved SSTskin retrieval, the piecewise regression SST algorithm was developed, which provides optimal sensitivity in every SST pixel. The paper describes the global and the piecewise regression algorithms trained against analysis SST and illustrates their performance with SST retrievals from the GOES-16 ABI.

Improving satellite retrieved night-time infrared sea surface temperatures in aerosol contaminated regions

Satellite retrievals of sea surface temperature (SST) have become necessary for many applications. Satellite infrared imaging radiometers passively measure the radiance emitted and reflected by the surface of the Earth and atmosphere. Tropospheric aerosols increase the signal attenuation at the satellite height, degrading the accuracy of SST retrievals. In this study to assess the infrared radiative effects of aerosols on satellite-derived skin SSTs (SSTskin), MODIS (MODerate-resolution Imaging Spectroradiometer) SSTskin retrievals are compared with quality-controlled, collocated SSTskin measurements from the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) deployed on research cruises during the Aerosols and Ocean Science Expeditions (AEROSE), and sub-surface temperatures measured by thermistors on drifting buoys. In this region, SSTskin retrievals from the MODIS on the Aqua satellite are generally much cooler than the in-situ measurements. In the Saharan outflow area between 90° W to 90° E and 20° S to 35° N where aerosol optical depths may be >0.5, satellite SSTskin are often >1 K cooler than the in situ data. The goal of this research is to determine the characteristics of aerosol effects on satellite retrieved infrared SST, and to derive formulae for improving accuracies of infrared-derived SSTs in aerosol-contaminated regions. A new method to derive a night-time Dust-induced SST Difference Index (DSDI) algorithm based on simulated brightness temperatures and Principal component (PC) analysis (PCA) at infrared wavelengths of 3.8, 8.9, 10.8 and 12.0 μm, was developed using radiative transfer simulations. The satellite SSTskin biases and standard deviations, derive by comparisons with coincident and collocated surface and in situ measurements, are reduced by 0.263 K and 0.166 K after the DSDI correction. This method can also improve the fraction of useful data available compared to the usual approach of discarding measurements identified as being contaminated by the effects of aerosols.

Exploring MERRA-2 global meteorological and aerosol reanalyses for improved SST retrieval

This study explores the use of Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) global meteorological and aerosol reanalysis for improving the simulation of satellite sensor infrared brightness temperatures (BTs), and the retrieval of sea surface temperature (SST) in the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) system, with a particular focus on ACSPO long-term reprocessing efforts. Using MERRA-2 upper-air pressure, temperature, and humidity profiles, rather than the currently used NCEP Global Forecast System (GFS) real-time data, as input into the Community Radiative Transfer Model (CRTM), reduces the global clear-sky observation-minus-model (O–M) BT biases in the infrared bands centered at 3.7, 8.6, 11, and 12 μm of the Visible Infrared Imaging Radiometer Suite onboard Suomi-NPP. The improvements are largely due to more accurate water vapor (total amount and/or vertical distribution) at low latitudes in MERRA-2, which brings the modeled BTs closer to observations. Additional stand-alone simulations, performed using RTTOV model and MERRA-2 aerosol profiles, further reduce the ACSPO global O–M BT biases and the dependence of O–M BT biases on the dust aerosol optical depth. The potential skill of MERRA-2 aerosol reanalysis for reducing dust-caused regional biases in the ACSPO global regression SST product is also demonstrated. Preliminary results suggest that MERRA-2 is a viable alternative to NCEP GFS for ACSPO reprocessing efforts.

Physical retrieval of sea-surface temperature from INSAT-3D imager observations

This paper presents results of physical retrieval of sea-surface temperature (SST) from the INSAT-3D imager observations. Radiance measurements from two thermal infrared imager channels are used for retrieving SST. Prior to their use in the retrieval process, the INSAT-3D radiances are corrected for the biases using a newly developed algorithm. The physical retrieval of SST is performed using an optimal estimation method. The optimisation is performed under two scenarios. In the first scenario, the SST retrieval problem is assumed to be linear while in the second one, the assumption of linearity is relaxed. Further, retrievals are performed by considering the reduced, as well as, the full state vector. Overall, physical retrieval performs much better than the regression-based approach. Moreover, in the physical retrieval, the best performance is obtained in the second scenario with the use of the full state vector. Validation of these retrievals with the iQuam SST resulted in a standard deviation (bias) of ∼0.65 (0.19) K while those from the regression-based method are found to have standard deviation (bias) of ∼0.68 (–0.17) K.

Sea Surface Temperature Retrieval from the First Korean Geostationary Satellite COMS Data: Validation and Error Assessment

Korea’s first geostationary satellite, the “Communication, Ocean, and Meteorological Satellite” (COMS), has been operating since 2010. The Meteorological Imager (MI), an sensor on-board the COMS, has observed sea-surface radiances for the estimation of sea surface temperature (SST) in the western Pacific Ocean and eastern Indian Ocean. To derive the SST coefficients of COMS, quality-controlled surface drifting buoy data were collected for the period of April 2011 to March 2015. A collocation procedure between COMS/MI data and the surface drifter data produced a matchup database for 4 years from 2011 to 2015. The coefficients for the COMS/MI SST were derived by applying appropriate algorithms, i.e., the Multi-channel SST (MCSST) and Non-linear SST (NLSST) algorithms, for daytime and nighttime data using a regression method. Validation results suggest the possibility of the continuous use of the coefficients as representative SST coefficients of COMS. The estimated SSTs near the edge of a full disk with high satellite zenith angles over 60° revealed relatively large errors compared to drifter temperatures. Most of NLSST formulations exhibited overestimation of SSTs at low SSTs (<10 °C). This study suggests an approach by which SST can be measured accurately in order to contribute to tracking climate change.

Initial Evaluation of the Sensor-Specific Error Statistics in the NOAA Advanced Clear-Sky Processor for Oceans SST System: Diurnal Variation Signals Captured

The newly designed sensor-specific error statistics (SSES) for sea surface temperature (SST) in the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear-Sky Processor for Oceans (ACSPO) system is evaluated using six months (September 15, 2017–March 14, 2018) of ACSPO SST derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) flown onboard the Suomi National Polar-orbiting Partnership satellite, SSTVIIRS. SSES bias field is shown to generally capture the temperature differences between the surface SSTVIIRS and the in situ SSTs (0.2–1 m) in both daytime and nighttime, especially at low-mid latitudes. Daytime SSES can capture, in addition to several other physical factors, the diurnal variation differences between the SSTVIIRS and SST in situ over most of the global ocean. The SSES biases also accurately pick up the cooling effect on satellite SST retrievals caused by the Saharan dust over the eastern tropical–subtropical Atlantic Ocean. However, at high latitudes, especially over the Southern Ocean where in situ SST measurements are sparse, and strong winds are persistent, the current ACSPO SSES biases may be suboptimal.

Subpixel variability and quality assessment of satellite sea surface temperature data using a novel High Resolution Multistage Spectral Interpolation (HRMSI) technique

A novel interpolation technique is applied to assessment of the quality of sea surface temperature (SST) observations and quantitative analysis of the subpixel variability within satellite footprints of different size. Using retrieved satellite data as input, the new, global, multistage interpolation technique generates a trigonometric polynomial, providing a representation of the underlying physical SST field in functional form. The resulting interpolating function can be efficiently and accurately evaluated anywhere within the domain over which it was derived and its moments calculated to estimate the mean and variance of the field over desired sub-regions. Application of the technique is demonstrated for SST retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), Spinning Enhanced Visible and Infrared Imager (SEVIRI), and Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensors. Comparison of the functional form with the data from which it was derived demonstrates how the technique can potentially help to identify small observational artifacts such as MODIS scan striping and residual cloud contamination. Integrals of the interpolating functions over successively larger spatial scales successfully emulate the retrieved SST at the different effective spatial resolutions and the second moments are consistent with the direct sample variances, and hence representative of the spatial SST variability of the available finer-resolution observations over the coarser scales. Using the approach, the variability of 1-km-resolution SST observations on open ocean grids of both 5- and 25-km resolution is found to be ~0.07 K. In regions of sharper gradients such as associated with strong localized diurnal warming, the variability within 25-km-resolution grids increases to as much as 0.4 K for sampling at 1-km resolution. The variability of 1-km observations on a 25-km-resolution grid is about 2.4 times greater than that on a 5-km-resolution grid. Broader application of the technique globally could help better quantify regional variations in the spatial variability, which would subsequently improve uncertainty estimates for existing satellite-based SST products.

Comparison of the Retrieval of Sea Surface Salinity Using Different Instrument Configurations of MICAP

The Microwave Imager Combined Active/Passive (MICAP) has been designed to simultaneously retrieve sea surface salinity (SSS), sea surface temperature (SST) and wind speed (WS), and its performance has also been preliminarily analyzed. To determine the influence of the first guess values uncertainties on the retrieved parameters of MICAP, the retrieval accuracies of SSS, SST, and WS are estimated at various noise levels. The results suggest that the errors on the retrieved SSS have not increased dues poorly known initial values of SST and WS, since the MICAP can simultaneously acquire SST information and correct ocean surface roughness. The main objective of this paper is to obtain the simplified instrument configuration of MICAP without loss of the SSS, SST, and WS retrieval accuracies. Comparisons are conducted between three different instrument configurations in retrieval mode, based on the simulation measurements of MICAP. The retrieval results tend to prove that, without the 23.8 GHz channel, the errors on the retrieved SSS, SST, and WS for MICAP could also satisfy the accuracy requirements well globally during only one satellite pass. By contrast, without the 1.26 GHz scatterometer, there are relatively large increases in the SSS, SST, and WS errors at middle/low latitudes.

A comparison of two mono-window algorithms for retrieving sea surface temperature from Landsat8 data in coastal water of Hongyan River nuclear power station

A comparison of two mono-window algorithms for retrieving sea surface temperature from Landsat8 data in coastal water of Hongyan River nuclear power station