Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method

Li, Aihua; Bo, Yanchen; Zhu, Yunlong; Guo, Pan; Bi, Jian; He, Yaqian

doi:10.1016/j.rse.2013.03.021

Cited by 128 publications

(60 citation statements)

References 45 publications

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“…In this paper, the BME method was used to blend the satellite LST products retrieved from TIR (MODIS) and PMW (AMSR-E) remote sensing data. In this method, the error model proposed by Li et al [46] was used to link the LST values at different spatial resolutions to generate soft data, and the residuals de-trended from MODIS LSTs were used to model the covariance functions. After the merging process, the estimated LSTs were spatiotemporally continuous with an availability of 100%.…”

Section: Discussionmentioning

confidence: 99%

“…The mean and variance of the Gaussian distribution were determined with the error model [46]. Readers are encouraged to refer to Li et al [46] for details about the error model.…”

Section: A Stepmentioning

confidence: 99%

“…It has not only been used in environmental risk assessment, soil property mapping, et al [41][42][43][44][45], but it has also been successfully applied in the combination of MODIS sea surface temperature (SST) with AMSR-E SST [46]. In this study, we investigated the feasibility of the BME method to estimate spatially and temporally complete LST time series at a relatively fine spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method

Kou

Jiang

et al. 2016

Remote Sensing

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Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared (TIR) remotely sensed data. Although passive microwaves (PMWs) are able to overcome these atmospheric influences while estimating LST, the data are constrained by low spatial resolution. In this study, to obtain complete and high-quality LST data, the Bayesian Maximum Entropy (BME) method was introduced to merge 0.01˝and 0.25˝LSTs inversed from MODIS and AMSR-E data, respectively. The result showed that the missing LSTs in cloudy pixels were filled completely, and the availability of merged LSTs reaches 100%. Because the depths of LST and soil temperature measurements are different, before validating the merged LST, the station measurements were calibrated with an empirical equation between MODIS LST and 0~5 cm soil temperatures. The results showed that the accuracy of merged LSTs increased with the increasing quantity of utilized data, and as the availability of utilized data increased from 25.2% to 91.4%, the RMSEs of the merged data decreased from 4.53˝C to 2.31˝C. In addition, compared with the filling gap method in which MODIS LST gaps were filled with AMSR-E LST directly, the merged LSTs from the BME method showed better spatial continuity. The different penetration depths of TIR and PMWs may influence fusion performance and still require further studies.

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Section: Discussionmentioning

confidence: 99%

“…The mean and variance of the Gaussian distribution were determined with the error model [46]. Readers are encouraged to refer to Li et al [46] for details about the error model.…”

Section: A Stepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method

Kou

Jiang

et al. 2016

Remote Sensing

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“…In this article the buoy density is defined as number of buoys/0.04° box and 0.25° box, respectively. So the buoy density in this study region is sufficient to validate the accuracy of satellitederived SST (Aihua Li 2013). Figure 6 is the scatter plots of satellite SSTs versus drifting …”

Section: Temporal Variation Of Error Featurementioning

confidence: 90%

Assessment of the MODIS and AMSR-E-Derived SST Products in joining area of Asia and Indian-Pacific Ocean

Zhu¹,

Bo²,

Cheng³

et al. 2013

AMOJ

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The Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer (AMSR-E) aboard the NASA Earth Observation System's Aqua satellite have been measuring the sea surface temperature (SST) since 2002. Although satellite-derived SST data complement the ground-based SST field data in better understanding the role of SST in coupling the ocean and atmosphere, they need to be validated in order to determine the spatiotemporal variation of their availability, and their error features, and then to decide how they can be used for further applications. In this paper we assess the spatiotemporal coverage and error variation of MODIS and AMSR-E level-3 mapped SSTs during 2003 to 2005 over the joining area of Asia and the Indian-Pacific Ocean. Drifting buoy SST data obtained from the Atlantic Oceanographic and Meteorological Laboratory (AOML) is used as a benchmark for the study. The results show that multi-year and annual average availabilities of MODIS SSTs are lower than those of AMSR-E SSTs due to their different cloud penetration. Weekly mean availability of AMSR-E SSTs is nearly constant during [2003][2004][2005] whereas that of MODIS SSTs shows obvious seasonal variation, i.e. it is low in summer and high in spring and autumn. The intra-annual variation of MODIS and AMSR-E availability is similar for the three year period. The geographical signal of annual mean availability of MODIS is more obvious than that of AMSR-E, i.e. it is high at about latitudes 20°N and 20°S and low around the equatorial regions. For error feature, the overall accuracy and precision of MODIS SSTs are lower than those of AMSR-E SSTs due to their slight differences in atmospheric effects, cloud penetration, spatial resolution, measured depths, and retrieval algorithms. Even so, annual mean biases of these two datasets are both within 0.5 °C. The intraannual variation of the error feature of MODIS is much more obvious than that of AMSR-E due to the wider range of mean bias and standard deviation. The intraannual variations of MODIS and AMSR-E error are similar for the three year period. We also find that the accuracy and precision of MODIS and AMSR-E have an obvious geographical signal for the three year period. That is, the accuracy and precision become higher moving from mid-latitudes of the northern hemisphere through equatorial regions to mid-latitudes of the southern hemisphere.

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“…In view of the highly complementary characteristics of IR and MW SST products, several SST fusion methods have been proposed (Kawamura, 2004;Alvera-Azcárate et al, 2005;Reynolds et al, 2005;Sakaida et al, 2005;Kawai et al, 2006;Wang and Xie, 2007;Chao et al, 2009;Donlon et al, 2012;Li et al, 2013b). Among them, the Objective Analysis (OA), Optimum Interpolation (OI) and VARiational (VAR) methods are the most commonly used algorithms for blending SSTs.…”

Section: Introductionmentioning

confidence: 99%

Merging daily sea surface temperature data from multiple satellites using a Bayesian maximum entropy method

2015

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Sea surface temperature (SST) is an important variable for understanding interactions between the ocean and the atmosphere. SST fusion is crucial for acquiring SST products of high spatial resolution and coverage. This study introduces a Bayesian maximum entropy (BME) method for blending daily SSTs from multiple satellite sensors. A new spatiotemporal covariance model of an SST field is built to integrate not only single-day SSTs but also time-adjacent SSTs. In addition, AVHRR 30-year SST climatology data are introduced as soft data at the estimation points to improve the accuracy of blended results within the BME framework. The merged SSTs, with a spatial resolution of 4 km and a temporal resolution of 24 hours, are produced in the Western Pacific Ocean region to demonstrate and evaluate the proposed methodology. Comparisons with in situ drifting buoy observations show that the merged SSTs are accurate and the bias and root-mean-square errors for the comparison are 0.15°C and 0.72°C, respectively.

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Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method

Cited by 128 publications

References 45 publications

Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method

Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method

Assessment of the MODIS and AMSR-E-Derived SST Products in joining area of Asia and Indian-Pacific Ocean

Merging daily sea surface temperature data from multiple satellites using a Bayesian maximum entropy method

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