China-Pakistan Economic Corridor Zone II Versions EN2 Vol 4 (3) 2019
A dataset of high-resolution land surface temperature inversion for the China-Pakistan Economic Corridor (2013 – 2018)
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Abstract & Keywords
Abstract: Land surface temperature provides important basic data for research on,hydrology, ecology, climate, natural disasters, environment and resources,etc. A series of scientific issues in earth sciences, including disaster prevention and mitigation, climate change, environmental evolution and human-land interaction, all require a complete understanding of the thermal state of ground surface. Based on the thermal infrared bands of LANDSAT 8, a high-resolution temperature inversion dataset for the China-Pakistan Economic Corridor from 2013 to 2018 was built by using multiple LST inversion models. The data has a spatial resolution of 30 m and a temporal resolution of 16 days. The study uses diﬀerent models to retrieve land surface temperature data, such as single-channel inversion model, double-channel split-window inversion model and data fusion model. For data quality verification, the temperature data are validated against MODIS MOD11A1 V6 surface temperature products. A sample test shows that the agreement index is greater than 0.84 and the Kling-Gupta coefficient is greater than 0.72. A comparison and verification shows that this data product has a higher spatial resolution. The dataset can be used to study the spatiotemporal change of natural environment, providing basic data for relevant scientific research, engineering construction and social service along the China–Pakistan Economic Corridor.
Keywords: China-Pakistan Economic Corridor; land surface temperature; data inversion; 2013 – 2018; Landsat 8
Dataset Profile
 Title A dataset of high-resolution land surface temperature inversion for the China-Pakistan Economic Corridor (2013–2018) Data corresponding author Zhang Yaonan (yaonan@lzb.ac.cn) Data authors Zhao Guohui, Zhang Yaonan, Kang Jianfang Time range 2013–2018 Geographical scope China-Pakistan Economic Corridor: 23°47′ N – 41°55′ N, 60°20′ E – 80°16′ E Spatial resolution 30 m Data volume 3.73 TB Data format GeoTIFF Data service system Sources of funding “Special environment & special function observation research station shared service platform” program of the National Science and Technology Infrastructure Platform (Y719H71001); Information technology program of the Chinese Academy of Sciences , Research on Environmental Evolution in Cold and Arid regions: Construction and Application of CSTCloud (XXH13506). Dataset composition This dataset contains six directories corresponding to the six years. Each directory contains 12 sub-directories corresponding to the 12 month. Data files are named in the format: CPEC_LST_Xm_PPPRRR_YYYYMMDD.tif, where X is spatial resolution, YYYYMMDD is the year, month and date of the observation, PPPRRR is the WRS path and row numbers (e.g., CPEC_LST_30m_152042_20170603.tif).
1.   Introduction
As the measurement of the thermal radiation from the land surface, land surface temperature is a good reflection of all the results of the interaction between the earth's surface and the atmosphere, as well as the exchange of energy between the atmosphere and the land. It serves as a significant component of the research of key parameter of thermal equilibrium of surface water and climate change,and also serves as an important part of surface cycle research.
The accurate inversion of the temporal and spatial variation of the regional land surface temperature not only plays a significant role in areas including climate change, ecological protection and resource assessment, but also of great value in environmental monitoring, disaster prevention and control, and engineering construction, among other sectors[1].
It is difficult to obtain the spatial and temporal distribution characteristic data with traditional land surface temperature monitoring technique. In this sense, it is quite limited in regional research and application. Thermal infrared remote sensing technology, on the contrary, can detect direct emission of energy from the earth's surface, and thus becoming an important way to obtain regional geothermal temperature. Land surface temperature parameter inversion by remote sensing provides a direct means to the preparation of the temporal and spatial data of surface temperature. At present, popular land surface temperature remote sensing data mainly come from the MODIS satellite, which covers a good region and is of good effect, and has a distinct advantage in the continuous observation of surface changes. However, it cannot meet the spatial resolution of local environmental changes monitoring. Thanks to the long-term continuous global coverage, Landsat series satellite data, with other advantages, including high resolution and scientific storage and distribution strategy of data, has become one of the most effective remote sensing data in terms of surface features and earth system science[3]. Since its successful launch back in 2013, Landsat 8 has provided free medium resolution data and a 16-day replay period. It has also become a significant data source in terms of regional thermal infrared remote sensing. Currently, it has stored data in thermal infrared band for over five years and has provided important support for land surface temperature inversion.
At present, scholars at home and abroad have developed different land surface temperature inversion models for different application fields and regions. In order to accurately invert the temporal and spatial variation of land surface temperature, a model set-including the single channel inversion model, double channel split window inversion model, and data fusion model -- was built as the applicability of model was taken into account. According to selection criteria, the dataset of land surface temperature remote sensing inversion for the China-Pakistan Economic Corridor (2013-2018) is automatically generated as shown in Figure 1. It is expected to provide basic data for the study on time-space monitoring of natural environment variation, engineering construction, and social services for the China-Pakistan Economic Corridor.

Figure 1   Coverage of dataset of LST remote sensing inversion for the China-Pakistan Economic Corridor (2013–2018)
2   Method for collecting and processing data
2.1   Basic data
Landsat 8, which carries with it two main sensor land imager (OLI) and thermal infrared sensor (TIRS), enjoys huge improvement in scanning mode, band set and radiation resolution compared with previous Landsat satellites. Particularly, the TIRS can detect radiation in two thermal infrared bands (10.6–11.2 μm and 11.5–12.5 μm) with a instantaneous field angle of 15 degree and the ground width reaching 185 km. It improves the atmospheric correction of thermal infrared data, and thus contribute to the accuracy of land surface temperature inversion. Therefore, the dataset, based on two thermal infrared channels (bands 10 and 11) of Landsat 8, inversely prepare land surface temperature products. The basic data are from the USGS EarthExplorer (https://earthexplorer.usgs.gov/), obtaining all the Landsat 8 OLI/TIRS C1 Level-1 data products (see Table 1) since 2013. See Figure 1 for the specific numbers.
Table 1   Landsat 8 OLI/TIRS C1 Level-1 data product description
 No. Band Description Wave length（μm） Spatial resolution（m） Sensor 1 Band 1 Coastal/Aerosol 0.435–0.451 30 OLI 2 Band 2 Blue 0.452–0.512 30 OLI 3 Band 3 Green 0.533–0.590 30 OLI 4 Band 4 Red 0.636 - 0.673 30 OLI 5 Band 5 Near Infrared 0.851–0.879 30 OLI 6 Band 6 Short-wave Infrared 1.566–1.651 30 OLI 7 Band 7 Short-wave Infrared 2.107–2.294 30 OLI 8 Band 8 Panchromatic 0.503–0.676 15 OLI 9 Band 9 Cirrus 1.363–1.384 30 OLI 10 Band 10 Thermal Infrared 10.60–11.19 30* TIRS 11 Band 11 Thermal Infrared 11.50–12.51 30* TIRS
* The first-class product released by USGS has undergone data preprocessing, including the resampling of TIRS band from 100 m to 30 m.
2.2   Data processing
The data processing of the dataset is shown in Figure 2, and specific steps are as follows:

Figure 2   The manufacturing and preparation process of the dataset of LST inversion for the China-Pakistan Economic Corridor
Step 1 First, screen the basic data of Landsat 8 according to the data quality. Remove any cloud cover above 20%, and then process the data from OLI and TIRS sensor respectively.
Step 2 Calculate the radiation luminance values $${L}_{\lambda }$$ in band 10 and 11 of TIRS respectively on the basis of the Landsat 8 data manual[4] and calculate the star brightness temperature BT based on radiant brightness values. Inverse the amount of the vapor of water ω based on the split window covariance ratio algorithm, so as to correct the effect of atmosphere and land surface emissivity. The algorithm is as follows:
$\omega ={c}_{0}+{c}_{1}×\frac{{\tau }_{j}}{{\tau }_{i}}+{c}_{2}{\left(\frac{{\tau }_{j}}{{\tau }_{i}}\right)}^{2} \left(1\right)$
$\frac{{\tau }_{j}}{{\tau }_{i}}=\frac{{\epsilon }_{i}}{{\epsilon }_{j}}{R}_{j,i}, { R}_{ji}=\frac{{\sum }_{k=1}^{N}\left({BT}_{i,k}-{\stackrel{-}{BT}}_{i}\right)\left(B{T}_{j,k}-{\stackrel{-}{BT}}_{j}\right)}{{\sum }_{k=1}^{N}{\left({BT}_{i,k}-{\stackrel{-}{BT}}_{i}\right)}^{2}} \left(2\right)$
In the formula, $${\mathrm{\tau }}_{\mathrm{i}}$$ and $${\mathrm{\tau }}_{\mathrm{j}}$$ are atmospheric transmittance at band 10 and band 11 respectively. $${\mathrm{\epsilon }}_{\mathrm{i}}$$and $${\mathrm{\epsilon }}_{\mathrm{j}}$$ are corresponding specific emissivity. $${\stackrel{-}{\mathrm{B}\mathrm{T}}}_{\mathrm{i}}$$and $${\stackrel{-}{\mathrm{B}\mathrm{T}}}_{\mathrm{j}}$$ are average brightness temperature of band 10 and band 11 in the moving window. k is the index of pixels in a window. N is the window size. $${c}_{0}$$, $${c}_{1}$$ and $${c}_{2}$$ are the regression coefficients generated by the simulation based on the atmospheric radiative transfer model MODTRAN and the atmospheric profile database TIGR (Thermodynamic Initial Guess Retrieval database).
Step 3 Surface emissivity is a measure of surface heat dissipation efficiency and different underlying surfaces have different emittance values (0–1). The dataset adopts the vegetation coverage weighting method[6][7] and obtain surface emissivity ε based on band 4 and 5 of Landsat 8 OLI.
$\varepsilon ={\epsilon }_{v}FVC+{\epsilon }_{s}\left(1-FVC\right)+4 〈 d\epsilon 〉 FVC \left(1-FVC\right) \left(3\right)$
$FVC={\left(\frac{NDVI-{NDVI}_{s}}{{NDVI}_{v}-{NDVI}_{s}}\right)}^{2} \left(4\right)$
$NDVI=\left({\rho }_{5}-{\rho }_{4}\right)/\left({\rho }_{5}+{\rho }_{4}\right) \left(5\right)$
In the formula, the data of the emissivity of vegetation components $${\epsilon }_{v}$$ and surface background emissivity $${\epsilon }_{s}$$ are from the spectral database. <> refers to the cavity effect parameters formed by multiple scattering between components in a pixel. FVC is the vegetation coverage. NDVIs and NDVIv are normalized vegetation index NDVI for bare soil and dense vegetation respectively. $${\rho }_{4}$$ and $${\rho }_{5}$$ are surface reflectance of the Landsat 8 red band Band 4 and near-infrared band Band 5 corrected based on the atmospheric radiometric correction model.
Step 4 Surface thermal radiation transfer equation is the basis of remote sensing inversion of surface temperature. Based on the theory of surface thermal radiation, the inversion model set of surface temperature LST includes single channel inversion model of radiation transfer equation (SC1)[8], single channel emittance correction model (SC2)[9], two-channel split window algorithm model (SW)[10]and data fusion algorithm (DF). The detailed derivation process and parameters won’t be repeated here and users can refer to the corresponding literature of each algorithm. The main formula is as follows:
1)   SC1 model
${LST}_{SC1}=\gamma \left[\frac{1}{\epsilon }\left({\psi }_{1}{L}_{\lambda }+{\psi }_{2}\right)+{\psi }_{3}\right]+\delta \left(6\right)$
${\psi }_{i}={{b}_{i,2}\omega }^{2}+{b}_{i,1}\omega +{b}_{i,0} , \gamma \approx \frac{{BT}^{2}}{\mathrm{K}\bullet {L}_{\lambda }} , \delta \approx BT-\frac{{BT}^{2}}{\mathrm{K}} \left(7\right)$
In the formula, $${\psi }_{1}, {\psi }_{2}$$and $${\psi }_{3}$$ are atmospheric function approximately obtained by using atmospheric water vapour content ω.$${L}_{\lambda }$$ is the value of radiant brightness on the star. bi, 0 , bi, 1 and bi, 2 are regression parameter. K is the constant. All the other symbols are the same as previous ones.
2)   SC2 model
${LST}_{SC2}=\frac{{BT}_{10}}{1+\frac{\lambda \sigma {BT}_{10}}{hc}\mathrm{ln}\epsilon } \left(8\right)$
In the formula, $${BT}_{10}$$ is the brightness temperature in band 10 of Landsat 8. λ is the wave length in band 10 of Landsat 8. σ is the boltzmann constant. h is the Planck constant and c is the speed of light.
3)   SC2 model
${LST}_{SW}={b}_{0}+\left({b}_{1}+{b}_{2}\frac{1-\epsilon }{\epsilon }+{b}_{3}\frac{∆\epsilon }{{\epsilon }^{2}}\right)\frac{{BT}_{10}+{BT}_{11}}{2}+\left({b}_{4}+{b}_{5}\frac{1-\epsilon }{\epsilon }+{b}_{6}\frac{∆\epsilon }{{\epsilon }^{2}}\right)\frac{B{T}_{10}-{BT}_{11}}{2}+{b}_{7}{\left(B{T}_{10}-{BT}_{11}\right)}^{2} \left(9\right)$
In the formula, $${BT}_{11}$$ is the brightness temperature in band 11 of Landsat 8. bnn = 0,1,...7）is the regression coefficient of the model. $$Ε$$ and $$∆\epsilon$$ are the mean and difference of transmittance of the two channels of TIRS.
4)   DF model
According to spatial similarity, the surface temperature formula of each MOD11A1 data product pixel corresponding to Landsat 8 pixels of different surface types is:
$${P}_{M}=\frac{1}{N}\sum _{j=1}^{m}{f}_{j}\left({P}_{L,j}\right)$$ (10)
${f}_{j}={BT}_{10}+{a}_{0,j}\left({BT}_{10}-{BT}_{11}\right)+{{a}_{1,j}\left({BT}_{10}-{BT}_{11}\right)}^{2}+{{a}_{2,j}\left(NDVI\right)}^{2}+{a}_{3,j}\left(NDVI\right)+{a}_{4,j} \left(11\right)$
Step 5 In the formula, $${P}_{M}$$ is the pixel value of MOD11A1. N is the number of MOD11A1 pixels corresponding to Landsat pixels. m is the total number of corresponding high-resolution land cover types in N pixel. PL,j isthe pixel value of category j in m ground object categories. $${f}_{j}$$identifies the correspondence between high and low resolution of different surface types. $${a}_{i,j}$$(i= 0,1,...4) is regression parameters obtained by mixed pixel method of MODIS and Landsat.
Step 6 Select surface temperature products. The above four inversion model products and the bright temperature average products obtained by inversion of the 10th and 11th bands add up to five surface temperature products. In order to make correspond with MODIS MOD11A1 geothermal data product space grid cell, coordinate conversion and resampling are performed for each inversion product. And then a statistical comparative was conducted one by one about the pixel. The best inversion results were selected and included in the dataset according to the evaluation index.
3.   Data sample description
The surface temperature product dataset is named in the format of CPEC_LST_Xm_PPPRRR_YYYYMMDD.tif, in which X represents the resolution of surface temperature products. The Landsat inversion geothermal product is 30 m. YYYY is the year. MM refers to month. DD is the day. PPP is the stripe number of the Landsat WRS2 Global reference system, and RRR is the line number. Here’s CPEC_LST_30m_152042_20170603.tif as an example. Data in the China-Pakistan Economic Corridor are shown in figure 3. The current data, from 2013 to 2018, are updated in real time. Currently, there are 17 018 of data, and the data capacity is 3 822 GB. The data files are classified according to time, and the data format is Geotiff. The coordinate system is UTM system, the data type is Single, and the temperature unit is ℃. Display, fusion and analysis can be read directly by spatial analysis software (Figure 4).

Figure 3   Single data sample of the surface temperature products of China-Pakistan Economic Corridor （CPEC_LST_30m_149038_20170918.tif）

Figure 4   All-regional land surface temperature product data splicing and fusion example of the China-Pakistan Economic Corridor (CPEC_LST_1km_201807.tif)
4.   Data quality control and evaluation
3.1   Quality control
To produce the dataset, the model inversion results are compared with each other. The data products of each model inversion are spatially fused and then compared with the MODIS MOD11A1 V6 land surface temperature products by pixel. The optimal inversion products are selected according to the evaluation index. Five selection indicators including Nash-Sutcliffe Efficiency (NSE), Concordance Index (d), Kling-Gupta Efficiency (KGE)[11], Correlation Coefficient (R) and Percentage Bias (Pbias) are applied to this data set to control the quality of the products.
3.2   Data evaluation
Both overall and local assessments are adopted to the quality assessment of the data set. A random sampling method is used for the overall assessment where six groups of data are randomly selected from all data products for comparison experiments. The distribution of data samples is shown in Figure 5. The comparative evaluation results of each group of data products are shown in Table 2.

Figure 5   Spatial distribution map of random sampling data of land surface temperature dataset of the China-Pakistan Economic Corridor (red part)
Table 2   Overall statistical characteristics of sampling data of the land surface temperature dataset of the China-Pakistan Economic Corridor
 No NSE d KGE R RMSE Pbias Case1 0.686718 0.92459 0.80298 0.8275 2.5748 −0.059 Case2 0.58036 0.90692 0.80316 0.84465 2.5560 −2.2125 Case3 0.652364 0.87973 0.72429 0.88177 1.3074 −0.4122 Case4 0.520501 0.8498 0.59283 0.76165 4.7713 −3.9133 Case5 0.780329 0.95623 0.85319 0.97264 1.9507 4.32386 Case6 0.670532 0.91233 0.83199 0.85874 2.0782 3.16136
Then, in order to ensure the randomness of data statistics, a local sampling evaluation is carried out, in which sub-regions are randomly selected from each group of data products, and a comparative analysis is made to verify the spatial correlation (Figure 6). The results show that each R2 is above 0.66, which is consistent with the overall trend.

Figure 6   Statistical analysis of sub-region random sampling of the land surface temperature case data of the China-Pakistan Economic Corridor
5.   Data value
This dataset generally maintains a high spatial correlation with MODIS land surface temperature products. Due to its high spatial resolution, the Landsat 8 can invert clearer terrain features such as waters, residential areas and vegetation and shows more details and characteristics of land surface temperature (Figure 7). It can be directly used to characterize the temporal and spatial changes of land surface temperature along the China-Pakistan Economic Corridor, and explore the characteristics and laws of the spatial and temporal differentiation. It thus provides basic data for scientific issues such as freeze-thaw disaster, climate change, disaster prediction, urban heat island effect and engineering safety, and serves the social and economic development of China and Pakistan in road construction, ecological protection, resource survey, construction engineering, and disaster prevention and mitigation.

Figure 7   Local comparative diagram of the data product (a is the regional remote sensing image. b is the MODIS land surface temperature product. c is the dataset product.)
6.   Data usage and recommendations
This dataset can be downloaded from the National Special Environment and Function of Observation and Research Stations Shared Service Platform (http://www.crensed.ac.cn). Due to the large amount of data, you can also contact the data author to access the computing and data platform of the Lanzhou Supercomputing Center of Chinese Academy of Sciences for online data downloading, sharing, analysis, processing and application. The data files are in GeoTIFF format and can be directly viewed and used via GIS related software such as ENVI, GRASS and ArcGIS.
Since the remote sensing inversion of land surface temperature is affected by land surface emissivity, atmospheric transmittance and underlying surface condition, the current remote sensing inversion products for land surface temperature are suitable for the scientific applications based on relative temperature. The true value of land surface temperature shall be revised according to the emissivity of each pixel, the classification of terrain features, and the condition of the atmosphere.
Acknowledgments
Thanks to the sharing service platform of the national observation and research station with special environment and special function for the support of the project. Thanks to USGS EarthExplorer website for providing free Landsat 8 and MODIS data products.
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Data citation
Zhao GH, Zhang YN and Kang JF. A dataset of high-resolution land surface temperature inversion for the China-Pakistan Economic Corridor (2013 – 2018). Service Center, National Special Environment and Function of Observation and Research Stations Shared Service Platform, 2018. (August 27, 2018). DOI: 10.12072/casnw.054.2018.db.
Article and author information
Zhao GH, Zhang YN and Kang JF. A dataset of high-resolution land surface temperature inversion for the China-Pakistan Economic Corridor (2013 – 2018). China Scientific Data 4 (2019). (August 10, 2018). DOI: 10.11922/csdata.2018.0055.zh.
Zhao Guohui
Main responsibilities: remote sensing data inversion system development and data preparation.
male, born in Henan, Doctor, Engineer, His research orientation is geological calculation.
Zhang Yaonan
Main responsibilities: project organization and guidance.
yaonan@lzb.ac.cn
male, born in Gansu, Doctor, Researcher, His research orientation is big data of geoscience.
Kang Jianfang
Main responsibilities: data pre-processing.
female, born in Gansu, Master's degree, Engineer, Her research direction is big data application in cold and arid regions.
“Special environment & special function observation research station shared service platform” program of the National Science and Technology Infrastructure Platform (Y719H71001); Information technology program of the Chinese Academy of Sciences , Research on Environmental Evolution in Cold and Arid regions: Construction and Application of CSTCloud (XXH13506).
Publication records
Published: Aug. 13, 2019 （ VersionsEN1
Updated: Aug. 13, 2019 （ VersionsEN2
Released: Oct. 23, 2018 （ VersionsZH2
Published: Aug. 13, 2019 （ VersionsZH3
References

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