Moderate Resolution Imaging Spectroradiometer Sea Surface Temperature Research Articles

Impact of Cloud Reduction on MODIS Thermal Infrared Sea Surface Temperature Retrieval

Considering the significant influence of clouds on thermal infrared (TIR) data sea surface temperature (SST) retrieval, this study focuses on the reduction of cloud influence on Moderate Resolution Imaging Spectroradiometer (MODIS) longwave SST retrieval. First, the quality level (QL) 3 MODIS SSTs are classified into cloudy or clear-sky pixels based on MODIS cloud mask products. The cloudy SST pixels, flagged as confident cloudy or probably cloudy in the cloud masks, are further classified into three groups according to their associated daytime cloud-top and optical properties. Taking the SSTs measured by 11 buoys over one year as the reference data, those three groups of cloudy SSTs are significantly underestimated, with biases of −33.45 °C, −6.35 °C, and −4.72 °C. The QL 3 clear-sky SSTs are identified as probably and confident clear in the cloud masks and are not influenced by clouds, with a bias of nearly 0 °C. Then, three support vector regression (SVR) models are individually proposed for the three groups of cloudy SSTs. Taking the cloud-top and optical parameters as inputs of the proposed SVR models, we can obtain SSTs with a bias of 0 °C and a root-mean-square error (RMSE) of less than 1.6 °C for the three groups of cloudy SSTs. The method proposed in this study shows the potential for TIR SST estimation under some cloudy conditions using satellite remote sensing cloud products. Considering the RMSE of 0.4 °C in operational sea surface temperature and sea ice analysis, further study is needed before actual applying the method proposed in this study.

An Evaluation of Autonomous In Situ Temperature Loggers in a Coastal Region of the Eastern Mediterranean Sea for Use in the Validation of Near-Shore Satellite Sea Surface Temperature Measurements

The coastal ocean is one of the most important environments on our planet, home to some of the most bio-diverse and productive ecosystems and providing key input to the livelihood of the majority of human society. It is also a highly dynamic and sensitive environment, particularly susceptible to damage from anthropogenic influences such as pollution and over-exploitation as well as the effects of climate change. These have the added potential to exacerbate other anthropogenic effects and the recent change in sea temperature can be considered as the most pervasive and severe cause of impact in coastal ecosystems worldwide. In addition to open ocean measurements, satellite observations of sea surface temperature (SST) have the potential to provide accurate synoptic coverage of this essential climate variable for the near-shore coastal ocean. However, this potential has not been fully realized, mainly because of a lack of reliable in situ validation data, and the contamination of near-shore measurements by the land. The underwater biotechnological park of Crete (UBPC) has been taking near surface temperature readings autonomously since 2014. Therefore, this study investigated the potential for this infrastructure to be used to validate SST measurements of the near-shore coastal ocean. A comparison between in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra SST data is presented for a four year (2014–2018) in situ time series recorded from the UBPC. For matchups between in situ and satellite SST data, only nighttime in situ extrapolated to the sea surface (SSTskin) data within ±1 h from the satellite’s overpass are selected and averaged. A close correlation between the in situ data and the MODIS SST was found (squared Pearson correlation coefficient-r2 > 0.9689, mean absolute error-Δ < 0.51 both for Aqua and Terra products). Moreover, close correlation was found between the satellite data and their adjacent satellite pixel’s data further from the shore (r2 > 0.9945, Δ < 0.23 for both Aqua and Terra products, daytime and nighttime satellite SST). However, there was also a consistent positive systematic difference in the satellite against satellite mean biases indicating a thermal adjacency effect from the land (e.g., mean bias between daytime Aqua satellite SST from the UBPC cell minus the respective adjacent cell’s data is δ = 0.02). Nevertheless, if improvements are made in the in situ sensors and their calibration and uncertainty evaluation, these initial results indicate that near-shore autonomous coastal underwater temperature arrays, such as the one at UBPC, could in the future provide valuable in situ data for the validation of satellite coastal SST measurements.

Discrepancies between satellite-derived and in situ SST data in the Cape Frio Upwelling System, Southeastern Brazil (23˚S)

ABSTRACTSatellite-derived sea surface temperature (SST) presents discrepancies in both magnitude and temporal variability in the wind-driven coastal upwelling region off the Cape Frio (23°S) in Southeastern Brazil. Level 2 (L2) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua, and Level 4 (L4) SST products are compared with in situ measurements. Strong positive biases up to 4°C are systematically observed in L4 products during upwelling events and decrease to less than 1°C in non-upwelling days. Lower biases were found in L2 MODIS SST, and the statistical performance of L4 SST products did not present significant improvements in days when high-resolution clear-sky observations from MODIS were available. This indicates that spatio-temporal interpolation and ingestion of microwave-based data sets in L4 SST analyses are likely the main cause of observed discrepancies during upwelling events. Improved L2 SST retrieval algorithms and L4 SST analyses that take into account upwelling-related regional characteristics are required for more accurate satellite SST products over coastal regions.

Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs

This study was undertaken to derive and analyze the advanced microwave scanning radiometer-Earth observing satellite (EOS) (AMSR-E) sea surface temperature (SST) footprint associated with the remote sensing systems (RSS) level-2 (L2) product. The footprint, in this case, is characterized by the weight attributed to each 4 × 4 km square contributing to the SST value of a given (AMSR-E) pixel. High-resolution L2 SST fields obtained from the moderate-resolution imaging spectroradiometer (MODIS), carried on the same spacecraft as AMSR-E, are used as the sub-resolution “ground truth” from which the AMSR-E footprint is determined. Mathematically, the approach is equivalent to a linear inversion problem, and its solution is pursued by means of a constrained least square approximation based on the bootstrap sampling procedure. The method yielded an elliptic-like Gaussian kernel with an aspect ratio ≈1.58, very close to the AMSR-E 6.93 GHz channel aspect ratio, ≈1.74. (The 6.93 GHz channel is the primary spectral frequency used to determine SST.) The semi-major axis of the estimated footprint is found to be aligned with the instantaneous field-of-view of the sensor as expected from the geometric characteristics of AMSR-E. Footprints were also analyzed year-by-year and as a function of latitude and found to be stable—no dependence on latitude or on time. Precise knowledge of the footprint is central for any satellite-derived product characterization and, in particular, for efforts to deconvolve the heavily oversampled AMSR-E SST fields and for studies devoted to product validation and comparison. A preliminary analysis suggests that use of the derived footprint will reduce the variance between AMSR-E and MODIS fields compared to the results obtained ignoring the shape and size of the footprint as has been the practice in such comparisons to date.

Pre- analysis assessment of Sea Surface Temperature (SST) products in the region of Malaysian coastal water

This paper presents the pre-analysis of validation between the acquisition satellite data and in situ data. To carry out this assessment, Sea Surface Temperature (SST) data are acquired to be regressed with SST In situ. With the launch of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite with a sensor on the Terra spacecraft, data sets of the global distribution of sea surface temperature are retrieved, and need to be validated and analyzed. Radar Altimeter Database System (RADS) also has an archived data of Optimal Interpolation SST (OISST) that can be retrieved based on satellite track of altimeter. The aim of this paper is to present intercomparison study between pixel based (MODIS SST) and point based (RADS SST). The value of root mean square error (rmse) is computed to see the performance of the data product. It is an assessment and evaluation to see the performance for both data. The objective of this paper is to evaluate Malaysian coastal area through validation with in situ data. To achieve the objective, we perform pre-analysis study of the MODIS products and RADS SST to see the performance of both data in terms of spatial value during seasonal changes. However, the scope of this analysis covers only on the spatial MODIS pixel value and the OISST point value during the southwest monsoon daytime. From the result, RADS SST/RADS show higher root mean square error (rmse) at 0.731/0.677 (before calibration) and 0.6951/0.476 (after calibration). From the rmse result, we could deduce that the RADS SST has random error arising from the fact that the interpolated points are based on the track.

Modelling the upwelling offthe east Hainan Island coast in summer 2010

A synoptic-scale upwelling event that developed offthe east coast of the Hainan Island (EHIU) in the summer of 2010 is defined well via processing the Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) data. The Regional Ocean Modeling System (ROMS) with high spatial resolution has been used to investigate this upwelling event. By comparing the ROMS results against tide station data, Argo float profiles and MODIS SST, it is confirmed that the ROMS reproduces the EHIU well. The cooler-water core (CWC) distinguished by waters < 27.5°C in the EHIU, which occurred in the east Qiongzhou Strait mouth area and was bounded by a high temperature gradient, was the focus of this paper. Vertical structure of the CWC suggests that interaction between the westward flow and the bathymetry slope played a significant role in the formation of CWC. Numerical experiments indicated that the westward flow in the Qiongzhou Strait was the result of tidal rectification over variable topography (Shi et al., 2002), thus tides played a critical role on the development of the CWC. The negative wind stress curl that dominated the east Qiongzhou Strait mouth area suppressed the intensity of the CWC by 0.2–0.4°C. Further, nonlinear interaction between tidal currents and wind stress enhanced vertical mixing greatly, which would benefit the development of the CWC.

A Robust Fixed Rank Kriging Method for Improving the Spatial Completeness and Accuracy of Satellite SST Products

Sea surface temperature (SST) plays a vital role in the Earth's atmosphere and climate systems. Complete and accurate SST observations are in great demand for forecasting tropical cyclones and projecting climate change. Satellite remote sensing has been used to retrieve SST globally, but missing values and biased observations impose difficulties on practical applications of these satellite-derived SST data. Conventional spatial statistics methods such as kriging have been widely used to fill the gaps. However, when such conventional methods are used to analyze a massive satellite data set of size $n$ , the inversion of the $n\times n$ covariance matrix may require $O(n^3) $ computations, which make the computation very intensive or even infeasible. The fixed rank kriging (FRK) performs dimension reduction through multiresolution wavelet analysis so that it can dramatically reduce the computation cost of various kriging methods. However, the FRK cannot directly be used for incomplete data over spatially irregular regions such as SSTs, and the potential bias in the satellite data is not addressed. In this paper, we construct a data-driven bias-correction model for the correction of the bias in satellite SSTs and develop a robust FRK (R-FRK) method so that the dimension reduction can be used to the satellite data in irregular regions with missing data. We implement the bias-correction model and the R-FRK to the level-3 mapped night Moderate Resolution Imaging Spectroradiometer SSTs. The accuracy of the resulting predictions is assessed using the colocated drifting buoy SST observations, in terms of mean bias (bias), root-mean-squared error, and R squared (R2) . The spatial completeness is assessed by the availability of ocean pixels. The assessment results show that the spatially complete SSTs with high accuracy can be obtained through the bias-correction model and the R-FRK method developed in this paper.

Interaction between Upwelling and Internal Tide in the East Qiongzhou Strait Areas

An upwelling event that occurred off the east Hainan Island coast in summer 2010 is captured by processing the Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) data. High resolution Regional Ocean Model System (ROMS) is employed to study this upwelling event, and is proved to provide good reproduction of this upwelling event by comparing ROMS SST with MODIS SST. It is detected that internal tide generated and interacted with the upwelling in the east Qiongzhou Strait areas. The upwelling and the internal tide modulated each other: the uplift of the isopycnal due to the fluctuation of the internal tide enhanced the upwelling; in another aspect, the upwelling changed the background stratification which is sufficient to control the amount of barotropic-to-baroclinic energy conversion and the preferred direction of baroclinic energy flux [21].

Remote Sensing Studies of an Upwelling Jet in Guangdong Coastal Water

Summer upwelling is one of the most significant dynamic phenomena in the Guangdong coastal water, which transports bottom nutrients into the surface layer, facilities a high level of primary production in the offshore water, and plays an important role in the coastal ecosystem. In this study, we present detailed observations of an upwelling event that occurred in the Guangdong coastal water during July 14–16, 2003 using satellite multisensor data including the moderate resolution imaging spectroradiometer (MODIS) sea surface temperature (SST), QuikSCAT winds. The successive MODIS SST images reveal that the upwelled water in the surface layer is in a jet-like wedge shape. A quantitative method is developed to estimate the upwelling vertical and horizontal velocities of the upwelled water from the MODIS successive SST images, which are in good agreement with the observations. Based on the coastline geometry in the area, we derive analytical solutions to the upwelling system, which explain well the dynamic of the upwelling jet by comparison of the analytical solutions with the cross-shore expansion velocities of the upwelled water.

Comparisons of regional satellite sea surface temperature gradients derived from MODIS and AVHRR sensors

Regional sea surface temperature (SST) gradients were examined for a 6-year (2003–2008) period using data from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on two NOAA satellite platforms. Two regions, one in the California Current System and the other in the Gulf Stream, representing an eastern boundary upwelling region and strong western boundary current, respectively, were chosen to investigate the seasonal variability, statistical differences and similarities, and correlations with respect to the two sets of SST gradients. Results indicated higher gradient magnitudes using MODIS SST in relative comparison to those derived from AVHRR that are attributed to instrument and algorithm differences. These observed differences are important for any studies that employ SST gradients, such as fisheries investigations that have traditionally relied on AVHRR SST gradients only.

Real-Time, High-Resolution, Space–Time Analysis of Sea Surface Temperatures from Multiple Platforms

Abstract A sea surface temperature (SST) analysis system designed to initialize short-term atmospheric model forecasts is evaluated for a month-long, relatively clear period in May 2004. System inputs include retrieved SSTs from the Geostationary Operational Environmental Satellite (GOES)-East and the Moderate Resolution Imaging Spectroradiometer (MODIS). The GOES SSTs are processed via a sequence of quality control and bias correction steps and are then composited. The MODIS SSTs are bias corrected and checked against the background field (GOES composites) prior to assimilation. Buoy data, withheld from the analyses, are used to bias correct the MODIS and GOES SSTs and to evaluate both the composites and analyses. The bias correction improves the identification of residual cloud-contaminated MODIS SSTs. The largest analysis system improvements are obtained from the adjustments associated with the creation of the GOES composites (i.e., a reduction in buoy/GOES composite rmse on the order of 0.3°–0.5°C). A total of 120 analyses (80 night and 40 day) are repeated for different experimental configurations designed to test the impact of the GOES composites, MODIS cloud mask, spatially varying background error covariance and decorrelation length scales, data reduction, and anisotropy. For the May 2004 period, the nighttime MODIS cloud mask is too conservative, at times removing good SST data and degrading the analyses. Nocturnal error variance estimates are approximately half that of the daytime and are relatively spatially homogeneous, indicating that the nighttime composites are, in general, superior. A 30-day climatological SST gradient is used to create anisotropic weights and a spatially varying length scale. The former improve the analyses in regions with significant SST gradients and sufficient data while the latter reduces the analysis rmse in regions where the innovations tend to be well correlated with distinct and persistent SST gradients (e.g., Loop Current). Data thinning reduces the rmse by expediting analysis convergence while simultaneously enhancing the computational efficiency of the analysis system. Based on these findings, an operational analysis configuration is proposed.