A split-window method to retrieving sea surface temperature from landsat 8 thermal infrared remote sensing data in offshore waters

Journal Pre-proof A split-window method to retrieving sea surface temperature from landsat 8 thermal infrared remote sensing data in offshore waters Jiaoqi Fu, Chao Chen, Biyun Guo, Yanli Chu, Hong Zheng PII:

S0272-7714(19)30934-5

DOI:

https://doi.org/10.1016/j.ecss.2020.106626

Reference:

YECSS 106626

To appear in:

Estuarine, Coastal and Shelf Science

Received Date: 2 October 2019 Revised Date:

17 January 2020

Accepted Date: 29 January 2020

Please cite this article as: Fu, J., Chen, C., Guo, B., Chu, Y., Zheng, H., A split-window method to retrieving sea surface temperature from landsat 8 thermal infrared remote sensing data in offshore waters, Estuarine, Coastal and Shelf Science (2020), doi: https://doi.org/10.1016/j.ecss.2020.106626. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Conceptualization and methodology, Jiaoqi Fu and Chao Chen; Data curation, Jiaoqi Fu; Investigation and validation, Jiaoqi Fu, Chao Chen and Yanli Chu; Supervision, Chao Chen; Writing - original draft, Jiaoqi Fu; Writing - review & editing, Chao Chen, Yanli Chu and Hong Zheng; Revising, Jiaoqi Fu, Biyun Guo and Chao Chen.

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A split-window method to retrieving sea surface temperature from

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Landsat 8 thermal infrared remote sensing data in offshore waters

4 Jiaoqi Fua, Chao Chena,*, Biyun Guoa, Yanli Chub, Hong Zhengc

5 6 7 8 9 10 11 12

a

Marine Science and Technology College, Zhejiang Ocean University, Zhoushan, Zhejiang 316022, China b

School of Economics and Management, Zhejiang Ocean University, Zhoushan, Zhejiang 316022, China c

National Engineering Research Center of Marine Facilities Aquaculture, Zhejiang Ocean University, Zhoushan, 316000, China *

Correspondence: ([email protected])

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Abstract: Sea surface temperature (SST) is an important parameter used to describe the air-sea

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interaction and the state of marine structures. Atmospheric water vapor has a significant attenuation

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effect on thermal infrared information, which will reduce the accuracy of SST inversion. However,

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the effect of atmospheric water vapor on the inversion accuracy is not considered carefully in the

17

existing SST inversion algorithm. In this study, a new method is proposed for retrieving SST from

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Landsat 8 Thermal Infrared Remote Sensing (TIRS) data based on the variation of atmospheric water

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vapor content (wvc). First, simulating atmospheric conditions by using moderate resolution

20

atmospheric transmission (MODTRAN) based on atmospheric profiles data (air temperature and

21

pressure). Second, calculating the bright temperature based on the radiation transfer equation and

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Planck’s law. Then, constructing the SST retrieval model of thermal infrared remote sensing based on

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wvc. Finally, evaluating the accuracy of the proposed model by using simulation data. The root mean

24

square errors (RMSEs) are within 0.5 K, indicating that the accuracy of the model is good in theory.

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In addition, taking the Zhoushan sea area as the research area, SST is retrieved by Landsat 8 TIRS

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data. The accuracy of inversion results is evaluated by advanced very high-resolution radiometer

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(AVHRR) SST products. The bias and RMSE based on the AVHRR SST products are within 1 K and

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2 K, respectively. The results show that the accurate SST with high spatial resolution was

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successfully obtained by using the method. The study is of great significance to the acquisition of

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marine structural parameters, the exploitation of marine resources, and the monitoring of marine

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disasters.

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Keywords: Sea surface temperature; Landsat 8; thermal infrared remote sensing data; MODTRAN

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1. Introduction

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Sea surface temperature (SST) is the result of the interaction of ocean dynamic and thermodynamic

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processes with ocean and atmosphere, which is located at the junction of ocean and atmosphere

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(Noori et al., 2017; Wynsberge et al., 2017). It is a very important variable in the earth's climate

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system. It plays an important role in marine monitoring, numerical prediction, seasonal forecasting of

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marine and atmospheric system, and climate change monitoring (Leonardo et al., 2017; Kawai and

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Wada, 2007). Extreme weather events such as El Niño and La Niña have highly correlated

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relationship with the variation of SST (Liu et al., 2017). In addition, the ocean is a major component

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of the earth, covering 71% of the surface of the earth. Due to the large heat capacity of seawater, a

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small variation in ocean temperature will impact the local or even global weather and change the

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environment (Varelaa et al., 2018). Therefore, studying the SST is not only of great scientific value

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but also of great significance to human activities and the social economy.

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The traditional methods of SST measurement are mainly observation by in-suit ships and buoys (Li

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and He, 2014; Zhu et al., 2019). From those methods, it is difficult to obtain large-scale and

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synchronous SST data, especially in extreme weather conditions such as typhoons. In recent years,

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with the rapid growth of sensor technology and big data processing techniques, ocean satellite remote

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sensing has achieved remarkable development. Satellite remote sensing technology can effectively

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overcome various limitations by the unique characteristics of high time efficiency, wide range, and

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high dynamics. It is increasingly being used in ocean observation and has become an important

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technology in ocean monitoring (Luo et al., 2019). Satellite remote sensing includes thermal infrared

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remote sensing and microwave remote sensing (Schmitt and Zhu, 2016). A microwave radiometer is

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often used in meteorological satellites. It cannot accurately obtain observations in coastal waters

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because it can be greatly affected by land (Liu et al., 2017; Othman et al., 2002). Thermal infrared

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remote sensing has high spatial resolution and can effectively reduce the effect of land on offshore

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SST retrieval (Wang et al., 2013; Li et al., 2013; Mathieu et al., 2017). According to relevant

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research, thermal infrared remote sensing has the capability to invert SST in land-sea interaction areas

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and offshore areas (Jimenez-Munoz et al., 2009; Mo et al., 2018).

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Many domestic and foreign scholars have studied the algorithm of SST inversion using thermal

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infrared remote sensing. The most common algorithm is the split-through algorithm (Minnett et al.,

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2019). In 1975, MC Millin first proposed the split-window algorithm based on the radiation transfer

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equation (Mcmillin, 1975; Loncan et al. 2015), and achieved good results in the inversion of SST. In

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2006, Liu et al. proposed a multi-channel split window algorithm to retrieve the SST in the Yellow

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Sea and the East China Sea, which reflected the distribution of the SST in the Yellow Sea and the East

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China Sea (Liu and Zhou, 2006). In 2011, Kotaro Hosoda et al. tested the atmospheric correction

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model of MODIS data, and the results showed that the split window algorithm had the highest

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accuracy (Hosoda, 2011). In 2019, Bo Ai et al. proposed a new SST inversion model based on split

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window algorithm. The results of SST inversion in Bohai Sea were compared with MODIS SST

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products to verify the credibility of the model (Ai et al., 2019). At present, the technology of

2

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retrieving SST from satellite thermal infrared remote sensing data is becoming more and more

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mature. However, the research on SST in China and abroad is mostly focused on the ocean, but less

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on lakes, rivers, and offshore areas, mainly due to the constraints of remote sensing data sources and

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satellite sensors.

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In this study, a method is proposed to retrieve SST in coastal waters with thermal infrared remote

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sensing dataset collected from the Landsat 8 Thermal Infrared Sensor (TIRS). It is of great

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significance to the acquisition of marine structural parameters, the exploitation of marine resources,

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and the monitoring of marine disasters. In addition, atmospheric water vapor has a significant

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attenuation effect on thermal infrared information, which will reduce the accuracy of SST inversion

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(Jimenez-Munoz et al., 2010; CristAbal et al., 2009). Therefore, the effect of atmospheric water vapor

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content (wvc) on SST is also considered in this study.

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2. Establishment of the offshore SST retrieval model

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2.1. Data for model construction

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The atmospheric profiles data provided by the National Centers for Environmental Prediction

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(NCEP) were used in this study. The NCEP freely provides 17-layer global atmospheric parameter

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profiles at 00:00, 06:00, 12:00, and 18:00 every day since 1948. The spatial resolution is 2.5° × 2.5°.

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The atmospheric pressures of 17-layer are 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150,

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100, 70, 50, 30, 20, and 10 hPa.

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The emissivity data used in this study are from Moderate Resolution Imaging Spectrometer

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(MODIS) University of California, Santa Barbara (UCSB) Emissivity Library. The library collects

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emissivity measurements of natural and manmade materials that can be divided into four categories:

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water, ice, and snow; soils and minerals; vegetation; and manmade materials. It can be used as a

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source of spectral emissivity at the component level in the TIR BRDF models to calculate the scene

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emissivity in the split-window channels to be used in the land surface temperature (LST) algorithms.

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2.2. Model construction

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In our study, a new method is proposed for the retrieval of SST based on the characteristics of the

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Landsat 8 thermal infrared sensor. The detailed research plan is shown in Fig. 1 and described as

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follows:

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1.

Simulating atmospheric conditions and atmospheric wvc by using moderate resolution

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atmospheric transmission (MODTRAN) based on NCEP atmospheric profiles data (air

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temperature and relative humidity).

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2.

Calculating the bright temperature based on the radiation transfer equation and Planck’s law.

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3.

Constructing the SST retrieval model of thermal infrared remote sensing based on wvc by using

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the principle of the split-window algorithm. 4.

Evaluating the accuracy of the proposed model by using simulation data.

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Fig. 1. Technical flowchart. 2.2.1. Atmospheric conditions simulated by MODTRAN

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The radiation signal received by thermal infrared remote sensors consists of three parts: sea surface

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radiation after atmospheric attenuation, upward atmospheric radiation, and downward atmospheric

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radiation reflected by the sea surface. The expression is described as follows (Guillevic et al., 2003): ↑ ↓ Bi (Ti ) = ε i B( TS )τ i + Ratmi + (1 - ε i ) Ratmi τi

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(1)

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where Bi is the Planck function, Ti is the bright temperature observed at the top of atmosphere in

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channel i, TS is the sea surface temperature, ε i is the offshore seawater emissivity, τ i is the

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↑ ↓ atmospheric transmittance, Ratmi and Ratmi are the upward atmospheric radiation and downward

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atmospheric radiation, respectively.

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In order to retrieve the SST accurately, the effect of atmosphere on spectral radiation has to be

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eliminated (Xing et al., 2007; Passang and Peter, 2018; Yi et al., 2018). In this study, the NCEP

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atmospheric profiles at 12:00 in the period from January 1, 2013, to December 31, 2018, in the

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Zhoushan Sea area was extracted, totaling 2,191. Fig. 2 shows the atmospheric wvc and the bottom

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layer (1000 hpa) air temperature for these atmospheric profiles. The atmospheric wvc was mainly

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distributed between 1 g/cm2 and 5.5 g/cm2, among which the most was between 1.5 g/cm2 and 3.5

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g/cm2. The bottom layer air temperature was mainly between 285 K and 301 K and was concentrated

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between 290 K and 299 K.

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Fig. 2. The distribution of water vapor content (wvc) and bottom layer air temperature of the National

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Centers for Environmental Prediction atmospheric profiles.

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In our study, the bottom layer air temperature is regarded as the SST. The upward atmospheric

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↓ ↑ radiation Ratmi , downward atmospheric radiation Ratmi , and atmospheric transmittance τ i are

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simulated by the atmospheric radiation transfer software MODTRAN with the atmospheric profiles

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mentioned above (Berk et al., 2003). The results are shown in Fig. 3. In general, the main components

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of atmosphere are concentrated in the lower atmosphere (Li et al., 1994; Vitor et al., 2018; Wieke et

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al., 2017). This situation will result in upward radiation less than downward radiation. In addition, the

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atmospheric radiation of channel 11 is mostly larger than that of channel 10, and the transmittance of

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channel 11 is less than that of channel 10.

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Fig. 3. Two thermal infrared channels simulated by moderate resolution atmospheric transmission:

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(a) the upward atmospheric radiation, (b) downward atmospheric radiation, and (c) atmospheric

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transmittance. Channel 10 is shown in black; and channel 11 is shown in red.

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2.2.2. Bright temperature calculated by Planck’s law

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Planck imported quantum theory to blackbody radiation sources. Planck’s law shows that, in

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theory, the distribution function of blackbody radiation energy varies with wavelength. The

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expression is described as follows (Jimenez-Munoz et al., 2014): B ( λ,T ) =

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hc  2πhc 2  kλT  e − 1 5 λ  

−1

(2)

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where the units of B ( λ, T ) are W/(m2·µm), c represents light speed, h = 6.6262×10-34 J·S is the

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Planck constant, k = 1.3806×10-23 J/K and is the Boltzmann constant, and the units of wavelength λ

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and temperature T are µm and K, respectively.

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According to the bottom layer air temperature T0 of each profile, the SST was set to eight levels,

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Ts=T0 + [-10, -5, 0, 5, 10, 15, 20, 25] K. In addition, three seawater emissivity curves: seawater01,

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seawater02, and seawater03 were selected from the UCSB Emissivity Library for simulation, as

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shown in Figure 4. The data of seawater01 and seawatre02 are the emissivity averaged over 18 sets

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from Goleta, CA, USA. The data of seawater03 are the emissivity averaged over 10 sets. Based on the

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eight SST levels, three emissivity samples and the upward atmospheric radiation, downward

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atmospheric radiation, and atmospheric transmittance of the two channels, the radiance in 52,584

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cases was simulated by using the radiation transfer equation (Equation 1). Then, the bright

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temperature observed at the top of atmosphere can be calculated by Planck’s law (Equation 2).

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Fig. 4. The three seawater emissivity curves. 2.2.3. The split-window algorithm based on wvc

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Atmospheric water vapor has a clear attenuation effect on thermal infrared information, which will

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reduce the inversion accuracy of the SST. Therefore, atmospheric wvc is added as a variable into the

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algorithm. Based on the principle of the split-window algorithm, a new model is constructed to

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retrieve the SST from the infrared channels (Jimenez-Munoz et al., 2014; Hulley et al., 2011; Wang et

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al., 2015; Tang, 2018):

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SST = a0 + a1 ⋅ Ti1 + a2 ⋅ Ti 2 + a3 ⋅ ( Ti1 − Ti 2 ) + a4 ⋅ wvc + a5 ⋅ wvc 2 2

(3)

6

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where a k (k = 0,1…5) are coefficients, Ti1 and Ti2 are the brightness temperature in two thermal

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infrared channels, SST is the sea surface temperature, and wvc is the water vapor content obtained by

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the NCEP atmospheric profiles data. Based on the brightness temperature data of the two channels,

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the SST and the wvc, the coefficients a k were obtained by regression analysis according to equation

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(3). The results are shown in Table 1.

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Table 1. Coefficients of the split-window algorithm with wvc.

a0 -0.992

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a1 3.970

a2 -2.963

a3 0.044

a4 -0.328

a5 0.091

2.2.4. Model validation

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Accuracy evaluation is indispensable in temperature retrieval. It is not only a standard for

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evaluating the quality of the method and the influence on the subsequent application but also an

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important basis for evaluating the performance of the method, adjusting model parameters, and

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optimizing the extraction process (Li et al., 2019; Li et al., 2018). In order to study the influence of

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water vapor on the accuracy of SST inversion, the study proposed an SST inversion method without

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considering the variation of atmospheric wvc:

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SST = a0 + a1 ⋅ Ti1 + a2 ⋅ Ti 2 + a3 ⋅ (Ti1 − Ti 2 )

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Similarly, the coefficients of each water vapor interval are obtained by regression analysis. The

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results are shown in Table 2.

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2

(4)

Table 2. Coefficients of the split-window algorithm without wvc.

a0 3.228

a1 4.072

a2 -3.081

a3 0.048

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In this study, the parameters that were obtained during modeling are substituted into the SST

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retrieval model to calculate the SST. The accuracy of the proposed algorithm is verified by comparing

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the initial SST with the inversion SST (Hu et al., 2015). The root means square error (RMSE) is used

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to measure the deviation between the initial SST and the inversion SST:

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RMSE =

1 N 2 ∑ ( SSTi − TSi ) N i =1

(5)

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where SSTi is the inverted SST, TSi is the initial SST, and N=52,584 is the number of SSTs. The

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results are shown in Fig. 5. The RMSE of the derived results is within 0.5 K. The accuracy of the

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algorithm is good in theory. However, the RMSE of the algorithm with wvc is 0.3961K, which is

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smaller than the RMSE of the algorithm without wvc. It shows that the method proposed in the paper

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is effective. This paper has improved the method of split-window algorithm to retrieve SST.

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Fig. 5. Scatterplots of the initial SST ( TSi ) and the inversion SST ( SSTi ): (a) the algorithm with wvc,

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and (b) the algorithm without wvc.

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3. Experiments and results

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3.1. Research area

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In this study, the Zhoushan sea area was chosen as a representative location at which to study the

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inversion of offshore SST. The Zhoushan sea area is located in northeastern Zhejiang province, near

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the East China Sea and Hangzhou Bay, as shown in Fig. 6. It is an open seaport and passageway for

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the Yangtze River Basin and the Yangtze River Delta (Fu et al., 2019).

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Fig. 6. A sketch map of the research area.

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3.2. Data

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In our study, Landsat 8 TIRS data are used to retrieve the offshore SST, with a spatial resolution of

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30 m, 15° viewing angle, and 16-day revisit period. Landsat 8 was launched by NASA on February

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11th, 2018 (Braga et al., 2017). It is mainly equipped with an Operational Land Imager and a TIRS.

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Compared with Landsat 5/TM and Landsat 7/ETM+, Landsat 8 TIRS have been greatly adjusted. It

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has two thermal infrared channels with a 10–12 micron wavelength. Therefore, the Landsat 8 satellite

8

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is more advantageous than the previous series of satellites in SST retrieval (Wang et al., 2019; Fu et

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al., 2018).

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3.3. Results

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In this study, the Landsat 8 remote sensing images of Zhoushan Sea on March 12, 2015, August 3,

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2015, and April 2, 2017, were selected for inversion of SST. According to the transit time of the

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satellite, the atmospheric wvc corresponding to the three remote sensing images was estimated to be

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4.171 g/cm2 (Figure 7 (a1)), 2.154 g/cm2 (Figure 7 (a2)), and 1.774 g/cm2 (Figure 7 (a3)).

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Generally, the Landsat 8 remote sensing images acquired by users are gray values without physical

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meaning. Thus, it is necessary to perform radiometric calibration, which converts gray value into

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radiance with physical significance, on the images according to the parameters of the file. The

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brightness temperature is calculated based on the radiance of the thermal infrared bands. The SST is

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obtained by the new SST retrieval model based on the wvc. The results of the inversion are shown in

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Fig. 7. On March 12, 2015, the inversion results showed that the lowest SST was 281.64 K, the

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highest SST was 296.81 K, the average SST was 285.43 K, and the SST was mainly between 280 K

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and 290 K. On August 3, 2015, the inversion results showed the maximum temperature difference,

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with the lowest SST being 296.75 K, the highest SST being 316.66 K, and the average SST being

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302.55 K. The SST was mainly between 298 K and 305 K. The lowest SST of the inversion results on

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April 2, 2017, was 285.11 K, the highest SST was 300.01 K, the average SST was 287.91 K, and the

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SST was concentrated between 285 K and 290 K.

230 231

(a1)

(b1)

(c1)

232 233

(a2)

(b2)

(c2)

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234 235

(a3)

(b3)

(c3)

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Fig. 7. The Landsat8 images of the Zhoushan sea area, China: (a) true color images, (b) thermal

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infrared images, and (c) SST inversion results.

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3.4. Accuracy evaluation

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The observation data are usually used for accuracy evaluation. However, in the absence of

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observation data, the accuracy of the derived results is evaluated from the Advanced Very

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High-Resolution Radiometer (AVHRR) SST products (Ai et al., 2019; Brewin et al., 2017; Carroll et

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al., 2012; Tyagi et al., 2017) in order to verify the validity and applicability of the SST retrieval model

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in coastal waters. AVHRR SST products were used to analyze the accuracy of the derived results.

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Although AVHRR SST products cannot fully represent SST, they can be used as an indirect indicator

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for the accuracy of the inversion results.

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AVHRR is a scanning radiometer with five spectral channels, which was launched and is

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maintained by the National Oceanic and Atmospheric Administration (NOAA). The AVHRR SST

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dataset is obtained by the NOAA National Centers for Environmental Information. The SST

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algorithm is based on the NOAA/National Environmental Satellite Data and Information Service

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nonlinear SST operational algorithm (NLSST). This dataset provides twice-daily (day and night)

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global SST Level 3 data with a 0.0417º × 0.0417º rectangular grid. It provides a high-quality Climate

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Data Record (CDR) of SST. It is available to the public for a wide variety of uses including scientific

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research and analysis. The spatial resolution of AVHRR SST products is different from that of

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Landsat 8 thermal infrared data. According to the longitude and latitude of AVHRR SST products,

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the corresponding pixel is found in Landsat 8 inversion results. And it is regarded as the center pixel.

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The SST of the center pixel is averaged by the 133 × 133 window for the Landsat 8 inversion results

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(Nguyen and Katsuaki, 2018). After expanding with a 133 * 133 window, the spatial range of Landsat

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8 inversion results is roughly the same as AVHRR SST products. Then, it is compared to the SST of

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the corresponding pixel to the AVHRR SST product.

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In this study, the average bias, RMSE, and Pearson correlation coefficient were used to evaluate the

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inversion accuracy of the SST (Meng et al., 2018). Bias is the inversion SST minus the AVHRR SST

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product. Pearson’s correlation coefficient, usually denoted as R, was used to measure the linear

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relationship between two variables,

10

∑ ( SST − SST )(T N

R=

264

i

Si

− TS

i=1

∑ ( SST − SST ) * ∑ (T N

i =1

2

i

N

i =1

Si

)

− TS

)

(6) 2

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where SSTi is the inversion SST, SST is the average of inversion SST, TSi is the initial SST, TS is

266

the average of the initial SST, and N is the number of SSTs. Figure 8 shows the comparison between

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the AVHRR SST and the inversion SST. The Pearson correlation coefficients of the three cases were

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0.8211, 0.8842, and 0.8408. The larger the R, the better the correlation between the inversion SST and

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AVHRR SST. Overall, the biases were within 1 K, and the RMSEs are within 2 K. The minimum bias

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of the inversion result on March 12, 2015, was 0.0019 K, the maximum bias was 4.9930 K, the

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average bias was 0.6666 K, and the RMSE was 1.8252 K. On August 3, 2015, the derived result had a

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minimum bias of 0.0002 K, maximum bias of -4.8765 K, average bias of 0.0037 K, and RMSE of

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1.5979 K. On April 2, 2017, the minimum bias of the inversion result was 0.0062 K, the maximum

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bias was 4.8806 K, the average bias was 0.7943 K, and the RMSE was 1.7643 K. The error is within

275

the acceptable range. The algorithm can be used to retrieve the SST.

276 277 278

Fig. 8. Scatterplots of the AVHRR SST and the inversion SST ( SSTi ). 4. Discussion

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Based on the variation of atmospheric wvc, the SST inversion model was constructed. The

280

influence of atmospheric wvc on SST inversion was revealed. Atmospheric wvc is an important input

281

parameter for SST inversion. The accuracy of the model depends on the accuracy of wvc. Inaccurate

282

wvc will reduce the accuracy of SST inversion model. However, the wvc used in this paper is derived

283

from NCEP atmospheric profiles data, which may have errors. In addition, the spatial resolution of

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NCEP atmospheric profiles data is low. There is only one point of atmospheric profiles data in the

285

entire Zhoushan sea area, which cannot fully represent the atmospheric conditions in the Zhoushan

286

sea area. The low spatial resolution of the data may affect the accuracy of SST inversion model.

287

Atmospheric profile data can be obtained from more sources to solve the problem of accuracy of

288

wvc and improve the accuracy of inversion. We also can obtain wvc by acquiring mature water vapor

289

products or improving existing water vapor algorithms. Using the atmospheric profiles data from

290

multiple sources to build the model to improve the inversion accuracy. In addition, it is likely that the

11

291

low spatial resolution of the atmospheric profiles data can be solved by data interpolation. We can

292

interpolate the original atmospheric profiles data to obtain higher resolution data to improve the

293

inversion accuracy.

294

5. Conclusions

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SST is a basic and important parameter for understanding and studying the ocean. It can be used as

296

a reference for oceanographic research such as water mass, circulation, ocean front, seawater mixing

297

and upwelling. The change of SST not only determines the living environment of marine organisms,

298

but also affects human life, production, economic and social development. This study proposed a new

299

split-window algorithm that takes into account the variation of atmospheric wvc based on Landsat 8

300

TIRS. The SST inversion and verification were carried out with the Zhoushan sea area as the research

301

area. First, the SST retrieval model was established by wvc based on atmospheric correction with

302

MODTRAN. Then, the accuracy of the algorithm was evaluated using simulation data. The RMSEs

303

were within 0.5 K, indicating that the error of the algorithm was small. Landsat 8 remote sensing

304

images were used to retrieve SST. Finally, AVHRR SST products were used to evaluate the inverted

305

SST. The biases were within 1 K, and the RMSEs are within 2 K. The error of the algorithm was

306

within an acceptable range. It was shown that the algorithm can be used to retrieve SST. The study is

307

not only of great scientific value but also of great significance to human activities and the social

308

economy.

309

Although some achievements have been made, the following deficiencies still exist in this study.

310

First, in this study, we did not find the observation data of the Zhoushan sea area for model validation.

311

The results of the accuracy evaluation were not accurate enough to comprehensively analyze the

312

accuracy of the inversion results. Therefore, the observation data and more satellite SST products can

313

be selected to verify the accuracy of the algorithm in the follow-up research works of this study. In

314

addition, combined with the effect of suspended sediment on emissivity, the inversion of the offshore

315

SST requires further study.

316

Acknowledgement

317

We would like to thank the anonymous reviewers for their constructive comments and suggestions.

318

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this

319 320

manuscript. This research was funded by National Natural Science Foundation of China (41701447) and the

321

Training Program of Excellent Master Thesis of Zhejiang Ocean University.

322

Data Availability Statement

323

The input data for this research are all publicly available. The Landsat8 data can be obtained from

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the website of United States Geological Survey (https://earthexplorer.usgs.gov/), the emissivity data

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of seawater samples can be obtained from the website of MODIS (Moderate Resolution Imaging

12

326

Spectrometer) UCSB Emissivity Library (https://icess.eri.ucsb.edu/modis/EMIS/html/em.html), the

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AVHRR (Advanced Very High Resolution Radiometer) data can be obtained from the website of

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gridded

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(https://coastwatch.pfeg.noaa.gov/erddap/griddap/index.html?page=1&itemsPerPage=1000),

330

atmospheric profile data for atmospheric modeling can be obtained from the website of NOAA

331

ESRL

332

(https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.pressure.html). The data explicitly

333

generated in this research (atmospheric conditions data simulated by using MODTRAN based on

334

atmospheric profile data, sea surface temperature retrieved from Landsat8 TIRS data) will be

335

available on Mendeley Data (http://data.mendeley.com).

336

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(1) A split-window method is proposed for retrieving SST from Landsat 8 TIRS data based on the variation of atmospheric water vapor content (wvc). (2) The coefficients of model with atmospheric water vapor content (wvc) are fitted. (3) The root mean square errors (RMSEs) of the model are within 0.5 K, indicating that the accuracy of the model is good in theory. (4) Compared with the AVHRR SST products, the bias and RMSE of the retrieving result in the Zhoushan sea area, China are within 1 K and 2 K, respectively. (5) Accurate SST with the high spatial resolution is successfully obtained by using the proposed method in offshore waters.

The authors declare no conflict of interest.