A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data

A stepwise downscaling method is proposed for generating high-resolution land surface temperature (LST) from advanced microwave scanning radiometer for the Earth observing system (AMSR-E) data to benefit the fusion of thermal infrared and microwave data for high-quality all-weather LST. This method sets a series of intermediate resolution levels between the initial (0.25°) and target (0.01°) resolutions, then downscales AMSR-E LST from one resolution to the next one step at a time, starting from 0.25° and ending with 0.01°. The geographically weighted regression model is adopted in each step to construct the relationship between LST and environmental variables, including normalized differential vegetation index, elevation, and slope. The stepwise method is verified over three regions in China that represent different characteristics of landscape heterogeneity varying from the highest to the lowest: the Yunnan-Guizhou Plateau (YGP), the border of Shanxi Province and Henan Province (BSH), and the central part of Inner Mongolia (CIM). Verified using the emulated AMSR-E LST resampled from reference MODIS LST available in 2010, the results show that the proportions of dates when the stepwise method is better are 100%, 78.1%, and 51.5% in the YGP, BSH, and CIM regions, respectively, which means the stepwise method has an advantage over the direct method in the regions with high heterogeneity. For real AMSR-E LST, the downscaled LST exhibits a similar spatial pattern to that of emulated data but suffers from reduced accuracy and contrast, which is caused by the smooth spatial pattern and low accuracy of the real AMSR-E LST.

[1]  K. Trenberth,et al.  Observations: Surface and Atmospheric Climate Change , 2007 .

[2]  H. Miller.  Tobler's First Law and Spatial Analysis , 2004 .

[3]  Xiaodong Zhang,et al.  Developing a temporally land cover-based look-up table (TL-LUT) method for estimating land surface temperature based on AMSR-E data over the Chinese landmass , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[4]  J. Nichol.  An Emissivity Modulation Method for Spatial Enhancement of Thermal Satellite Images in Urban Heat Island Analysis , 2009 .

[5]  Donglian Sun,et al.  Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E/AMSR-2 and MODIS/GOES Observations , 2019, Remote. Sens..

[6]  Zhao-Liang Li,et al.  A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data , 2017 .

[7]  Z. Wan.  New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products , 2008 .

[8]  Shunlin Liang,et al.  Simultaneous inversion of multiple land surface parameters from MODIS optical–thermal observations , 2017 .

[9]  Q. Cheng,et al.  A technique based on non-linear transform and multivariate analysis to merge thermal infrared data and higher-resolution multispectral data , 2010 .

[10]  Victor F. Rodriguez-Galiano,et al.  Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Peter M. Atkinson,et al.  Evaluating a thermal image sharpening model over a mixed agricultural landscape in India , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[12]  R. Jeu,et al.  Land surface temperature from Ka band (37 GHz) passive microwave observations , 2009 .

[13]  Shunlin Liang,et al.  Simultaneous Estimation of Multiple Land-Surface Parameters From VIIRS Optical-Thermal Data , 2018, IEEE Geoscience and Remote Sensing Letters.

[14]  R. Bindlish,et al.  Evaluation of SMAP downscaled brightness temperature using SMAPEx-4/5 airborne observations. , 2019, Remote sensing of environment.

[15]  Chris T. Kiranoudis,et al.  Improving the Downscaling of Diurnal Land Surface Temperatures Using the Annual Cycle Parameters as Disaggregation Kernels , 2016, Remote. Sens..

[16]  Jiancheng Shi,et al.  An L-Band Brightness Temperature Disaggregation Method Using S-Band Radiometer Data for the Water Cycle Observation Mission (WCOM) , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Yin Pan,et al.  Cloud Detection in Remote Sensing Images Based on Multiscale Features-Convolutional Neural Network , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[18]  José A. Sobrino,et al.  Satellite-derived land surface temperature: Current status and perspectives , 2013 .

[19]  Paul A. Zandbergen,et al.  Applications of Shuttle Radar Topography Mission Elevation Data , 2008 .

[20]  Jiancheng Shi,et al.  Recovering land surface temperature under cloudy skies for potentially deriving surface emitted longwave radiation by fusing MODIS and AMSR-E measurements , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[21]  Wenjiang Huang,et al.  A Novel Method to Estimate Subpixel Temperature by Fusing Solar-Reflective and Thermal-Infrared Remote-Sensing Data With an Artificial Neural Network , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[22]  C. Cartalis,et al.  Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation , 2009 .

[23]  F. Gao,et al.  Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data , 2014 .

[24]  Jie Cheng,et al.  Using Very High Resolution Thermal Infrared Imagery for More Accurate Determination of the Impact of Land Cover Differences on Evapotranspiration in an Irrigated Agricultural Area , 2019, Remote. Sens..

[25]  Zhao-Liang Li,et al.  A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements , 2018, International Journal of Remote Sensing.

[26]  M. Owe,et al.  On the relationship between thermodynamic surface temperature and high-frequency (37 GHz) vertically polarized brightness temperature under semi-arid conditions , 2001 .

[27]  K. Oštir,et al.  Downscaling land surface temperature for urban heat island diurnal cycle analysis , 2012 .

[28]  Mario Chica-Olmo,et al.  Downscaling cokriging for image sharpening , 2006 .

[29]  Shi Jiancheng,et al.  A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data , 2007 .

[30]  Xin Pan,et al.  Downscaling Land Surface Temperature in Complex Regions by Using Multiple Scale Factors with Adaptive Thresholds , 2017, Sensors.

[31]  M. Vohland,et al.  Downscaling land surface temperatures at regional scales with random forest regression , 2016 .

[32]  Liping Yang,et al.  SRTM DEM and its application advances , 2011 .

[33]  Linna Chai,et al.  Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method , 2016, Remote. Sens..

[34]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[35]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[36]  Thomas V. Schuler,et al.  Severe cloud contamination of MODIS Land Surface Temperatures over an Arctic ice cap, Svalbard , 2014 .

[37]  Chenghu Zhou,et al.  A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data , 2009, Sensors.

[38]  Feng Gao,et al.  A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land , 2012, Remote. Sens..

[39]  J P Hollinger,et al.  DMSP Special Sensor Microwave/Imager Calibration/Validation , 1991 .

[40]  Christopher M. U. Neale,et al.  Land surface temperature derived from the SSM/I passive microwave brightness temperatures , 1990 .

[41]  Ji Zhou,et al.  A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Ji Zhou,et al.  Maximum Nighttime Urban Heat Island (UHI) Intensity Simulation by Integrating Remotely Sensed Data and Meteorological Observations , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[43]  Ji Zhou,et al.  Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats , 2013 .

[44]  Ruiliang Pu,et al.  Estimation of Subpixel Land Surface Temperature Using an Endmember Index Based Technique: A Case Examination on ASTER and MODIS Temperature Products Over a Heterogeneous Area , 2011 .

[45]  Zhao-Liang Li,et al.  Spatial Downscaling of MODIS Land Surface Temperatures Using Geographically Weighted Regression: Case Study in Northern China , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Martha C. Anderson,et al.  A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales , 2008 .

[47]  Jie Cheng,et al.  Spatio-Temporal Analysis of Urban Heat Island Using Multisource Remote Sensing Data: A Case Study in Hangzhou, China , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[48]  Shuo Xu,et al.  Reconstructing All-Weather Land Surface Temperature Using the Bayesian Maximum Entropy Method Over the Tibetan Plateau and Heihe River Basin , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[49]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[50]  Qinke Yang,et al.  SRTM Error Distribution and its Associations with Landscapes across China , 2016 .

[51]  William P. Kustas,et al.  A vegetation index based technique for spatial sharpening of thermal imagery , 2007 .

[52]  K. P. Sudheer,et al.  Development and verification of a non-linear disaggregation method (NL-DisTrad) to downscale MODIS land surface temperature to the spatial scale of Landsat thermal data to estimate evapotranspiration , 2013 .

[53]  M. Jin.  Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle , 2000 .

[54]  Okke Batelaan,et al.  Downscaling of thermal images over urban areas using the land surface temperature-impervious percentage relationship , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[55]  Xiaoguang Jiang,et al.  Intercomparison of AMSR2- and MODIS-Derived Land Surface Temperature Under Clear-Sky Conditions , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[56]  Okke Batelaan,et al.  Improved DisTrad for Downscaling Thermal MODIS Imagery over Urban Areas , 2017, Remote. Sens..

[57]  Martha C. Anderson,et al.  Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship , 2003 .

[58]  J. Kleissl,et al.  High-resolution urban thermal sharpener (HUTS) , 2011 .

[59]  Jie Cheng,et al.  An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR‐E Data Considering the Comprehensive Effects of Environmental Variables , 2020, Earth and Space Science.

[60]  Guangjian Yan,et al.  A Practical Two-Stage Algorithm for Retrieving Land Surface Temperature from AMSR-E Data—A Case Study Over China , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.