Land Surface Temperature Reconstruction Model of FY-4A Cloudy Pixels Considering Spatial and Temporal Characteristics

WANG Aihui; YANG Yingbao; PAN Xin; HU Jiejunde

doi:10.13203/j.whugis20200039

Volume 46 Issue 6

Jun. 2021

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Geomatics and Information Science of Wuhan University > 2021 > 46(6): 852-862. > DOI: 10.13203/j.whugis20200039

WANG Aihui, YANG Yingbao, PAN Xin, HU Jiejunde. Land Surface Temperature Reconstruction Model of FY-4A Cloudy Pixels Considering Spatial and Temporal Characteristics[J]. Geomatics and Information Science of Wuhan University, 2021, 46(6): 852-862. DOI: 10.13203/j.whugis20200039

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Land Surface Temperature Reconstruction Model of FY-4A Cloudy Pixels Considering Spatial and Temporal Characteristics

1.
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

Funds:

The National Natural Science Foundation of China 42071346

The National Natural Science Foundation of China 41701487

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Author Bio:
WANG Aihui, postgraduate, specializes in the application of thermal infrared remote sensing.E-mail: 15195892293@163.com
Corresponding author:
YANG Yingbao, PhD, professor.E-mail: yyb@hhu.edu.cn
Received Date: May 30, 2020
Published Date: June 04, 2021

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Abstract

Abstract

Objectives Resorting to remote sensing technology, land surface temperature (LST) data of high spatial and temporal resolution can be conveniently acquired. However, due to the influence of bad weather conditions such as cloud and rain, there are many null values in those LST data, being an obstacle to the further application of LST data.
Methods A novel null reconstruction model of LST based on spatial and temporal characteristics is proposed.Firstly, the reconstruction of partial null values of LST data is realized by weighting the effective records of the same period within contiguous days in the time domain, followed the intrinsic assumption that the changes of LST data in adjacent time periods are similar. Secondly, the reconstruction of other null valued pixels are reconstructed in the spatial domain, based on the assumption that adjacent similar pixels have similar LST. The similar pixels are searched by the normalized vegetation index (NDVI). In addition, due to the influence of extreme weather conditions, the above two steps are repeated several times until all null pixels are entirely reconstructed. Finally, Savitzky-Golay (S-G) filter is employed to remove the noise of the reconstructed LST data.
Results Compared to the in-situ data, the root mean squared errors (RMSE) of the LST data of all the FY-4A before and after reconstruction are 4.805 K and 6.969 K, respectively. Their determination coefficients (R²) are 0.842 and 0.605, respectively. Under clear sky conditions, RMSEs of FY-4A LST data before and after reconstruction are 3.392 K and 5.016 K, respectively, and their R² are 0.948 and 0.874, respectively. Under cloudy weather conditions, RMSE and R² of reconstructed LST data are 5.053 K and 0.726, respectively. Under rainy weather conditions, RMSE and R² of reconstructed LST data are 7.872 K and 0.313, respectively. Compared with the original LST data of FY-4A, the RMSE of the reconstructed data ranges 0.483-0.507 K and the R² ranges 0.846-0.976 for the null value regions of different sizes in spatial domain. RMSE ranges 0.405-1.915 K and R² ranges 0.952-0.989 for invalid LST time series of different sizes in time domain.
Conclusions The proposed null reconstruction model of LST can accomplish effective reconstruction not only for clear weather, but also for cloudy weather of long time series. Excluding the error of FY-4A LST data, RMSE of reconstruction results reaches 2.171 K. When the number of LST valid pixels is very small, the invalid LST can also be effectively reconstructed by the proposed model, which is tough for the diurnal temperature cycle (DTC) model.
- land surface temperature,
- null value reconstruction,
- spatial and temporal characteristics,
- FY-4A

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[1]	Li Z L, Tang B H, Wu H, et al. Satellite- Derived Land Surface Temperature: Current Status and Perspectives[J]. Remote Sensing of Environment, 2013, 131: 14-37 doi: 10.1016/j.rse.2012.12.008
[2]	向大享, 刘良明, 韩涛. FY-3A MERSI数据干旱监测能力评价[J]. 武汉大学学报·信息科学版, 2010, 35(3): 334-338 http://ch.whu.edu.cn/article/id/888 Xiang Daxiang, Liu Liangming, Han Tao. Estimation of Drought Monitoring Ability of FY-3A MERSI Data[J]. Geomatics and Information Science of Wuhan University, 2010, 35(3): 334-338 http://ch.whu.edu.cn/article/id/888
[3]	Ke L, Ding X, Song C. Reconstruction of Time-Series MODIS LST in Central Qinghai-Tibet Plateau Using Geostatistical Approach[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6): 1 602-1 606 doi: 10.1109/LGRS.2013.2263553
[4]	颜金彪, 段晓旗, 郑文武, 等. 顾及空间异质性的自适应IDW插值算法[J]. 武汉大学学报·信息科学版, 2020, 45(1): 97-104 doi: 10.13203/j.whugis20180213 Yan Jinbiao, Duan Xiaoqi, Zheng Wenwu, et al. Geomatics and Information Science of Wuhan University[J]. Geomatics and Information Science of Wuhan University, 2020, 45(1): 97-104 doi: 10.13203/j.whugis20180213
[5]	周义, 覃志豪, 包刚. GIDS空间插值法估算云下地表温度[J]. 遥感学报, 2012, 16(3): 492-504 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201203006.htm Zhou Yi, Qin Zhihao, Bao Gang. Land Surface Temperature Estimation Under Cloud Cover with GIDS[J]. Journal of Remote Sensing, 2012, 16(3): 492-504 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201203006.htm
[6]	Göttsche F M, Olesen F S. Modelling the Effect of Optical Thickness on Diurnal Cycles of Land Surface Temperature[J]. Remote Sensing of Environment, 2009, 113(11): 2 306-2 316 doi: 10.1016/j.rse.2009.06.006
[7]	Duan S B, Li Z L, Wang N, et al. Evaluation of Six Land-Surface Diurnal Temperature Cycle Models Using Clear-Sky in Situ and Satellite Data[J]. Remote Sensing of Environment, 2012, 124: 15-25 doi: 10.1016/j.rse.2012.04.016
[8]	刘紫涵, 吴鹏海, 吴艳兰, 等. 风云静止卫星地表温度产品空值数据稳健修复[J]. 遥感学报, 2017, 21(1): 40-51 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201701004.htm Liu Zihan, Wu Penghai, Wu Yanlan, et al. Robust Reconstruction of Missing Data in Feng Yun Geostationary Satellite Land Surface Temperature Products[J]. Journal of Remote Sensing, 2017, 21(1): 40-51 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201701004.htm
[9]	薛兴盛, 吴艳兰. 面向风云静止卫星地表温度产品的缺失数据修复方法对比[J]. 安徽农业大学学报, 2017, 44(2): 308-315 https://www.cnki.com.cn/Article/CJFDTOTAL-ANHU201702022.htm Xue Xingsheng, Wu Yanlan. A Comparison of Missing Data Reconstruction Methods for Feng Yun Geostationary Satellite Land Surface Temperature Products[J]. Journal of Anhui Agricultural University, 2017, 44(2): 308-315 https://www.cnki.com.cn/Article/CJFDTOTAL-ANHU201702022.htm
[10]	李国亮. 2000—2012年黑河流域上游植被覆盖变化遥感监测与分析[D]. 兰州: 西北师范大学, 2015 Li Guoliang. Remote Sensing Monitoring and Analysis of Vegetation Cover Change in the Heihe River Basin from 2000—2012[D]. Lanzhou: Northwest Normal University, 2015
[11]	张志清, 董瑶海, 丁雷, 等. 我国首颗第二代静止气象卫星风云-4升空[J]. 国际太空, 2016, 12: 6-12 https://www.cnki.com.cn/Article/CJFDTOTAL-GJTK201612003.htm Zhang Zhiqing, Dong Yaohai, Ding Lei, et al. China's First Second-Generation FY-4 Meteorological Satellite Launched[J]. Space International, 2016, 12: 6-12 https://www.cnki.com.cn/Article/CJFDTOTAL-GJTK201612003.htm
[12]	谭仲辉, 马烁, 韩丁, 等. 基于随机森林算法的FY-4A云底高度估计方法[J]. 红外与毫米波学报, 2019, 38(3): 381-388 https://www.cnki.com.cn/Article/CJFDTOTAL-HWYH201903020.htm Tan Zhonghui, Ma Shuo, Han Ding, et al. Estimation of Cloud Base Height for FY-4A Satellite Based on Random Forest Algorithm[J]. Journal of Infrared and Milmillimeter Waves, 2019, 38(3): 381-388 https://www.cnki.com.cn/Article/CJFDTOTAL-HWYH201903020.htm
[13]	Holben B N. Characteristics of Maximum-Value Composite Images from Temporal AVHRR Data[J]. International Journal of Remote Sensing, 1986, 7(11): 1 417-1 434 doi: 10.1080/01431168608948945
[14]	Snyder W C, Wan Z, Zhang Y, et al. Thermal Infrared (3-14 μm) Bidirectional Reflectance Measurements of Sands and Soils[J]. Remote Sensing of Environment, 1997, 60(1): 101-109 doi: 10.1016/S0034-4257(96)00166-6
[15]	Snyder W C, Wan Z. BRDF Models to Predict Spectral Reflectance and Emissivity in the Thermal Infrared[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(1): 214-225 doi: 10.1109/36.655331
[16]	Yang Y, Cao C, Pan X, et al. Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression[J]. Remote Sensing, 2017, 9(8): 789-795 doi: 10.3390/rs9080789
[17]	Zhu X, Chen J, Gao F, et al. An Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model for Complex Heterogeneous Regions[J]. Remote Sensing of Environment, 2010, 114(11): 2 610-2 623 doi: 10.1016/j.rse.2010.05.032
[18]	Kustas W P, Norman J M, Anderson M C, et al. Estimating Subpixel Surface Temperatures and Energy Fluxes from the Vegetation Index-Radiometric Temperature Relationship[J]. Remote Sensing of Environment, 2003, 85(4): 429-440 doi: 10.1016/S0034-4257(03)00036-1
[19]	Gao F, Masek J, Schwaller M, et al. On the Blending of the Landsat and MODIS Surface Reflectance: Predicting Daily Landsat Surface Reflectance[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8): 2 207-2 218 doi: 10.1109/TGRS.2006.872081
[20]	Savitzky A, Golay M J E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures[J]. Analytical Chemistry, 1964, 36(8): 1 627-1 639 doi: 10.1021/ac60214a047
[21]	贾明明, 王宗明, 张柏, 等. 综合环境卫星与MODIS数据的面向对象土地覆盖分类方法[J]. 武汉大学学报·信息科学版, 2014, 39(3): 305-310 https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201403012.htm Jia Mingming, Wang Zongming, Zhang Bai, et al. Land Cover Classification of Compositing HJ-1 and MODIS Data Based on Object-based Method[J]. Geomatics and Information Science of Wuhan University, 2014, 39(3): 305-310 https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201403012.htm
[22]	马晋, 周纪, 刘绍民, 等. 卫星遥感地表温度的真实性检验研究进展[J]. 地球科学进展, 2017, 32(6): 615-629 https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201706006.htm Ma Jin, Zhou Ji, Liu Shaomin, et al. Review on Validation of Remotely Sensed Land Surface Temperature[J]. Advances in Earth Science, 2017, 32(6): 615-629 https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201706006.htm
[23]	Van den Bergh F, Van Wyk M A, Van Wyk B J, et al. Comparison of Data-Driven and Model-Driven Approaches to Brightness Temperature Diurnal Cycle Interpolation[C]//The 17th Annual Symposium of the Pattern Recognition Association of South Africa, Parys, South Africa, 2006

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Land Surface Temperature Reconstruction Model of FY-4A Cloudy Pixels Considering Spatial and Temporal Characteristics

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