Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E/AMSR-2 and MODIS/GOES Observations

Land surface temperature (LST) is an important input to the Atmosphere–Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) products, but clouds affect them. While passive microwave (e.g., AMSR-E and AMSR-2) sensors can penetrate non-rainy clouds and observe the Earth’s surface, but usually with a coarse spatial resolution, how to utilize multiple instruments’ advantages is an important methodology in remote sensing. In this study, we developed a new five-channel algorithm to derive LST from the microwave AMSR-E and AMSR-2 measurements and calibrate to the MODIS and GOES LST products. A machine learning method is implemented to further improve its performance. The MODIS and GOES LST products still show better performance than the AMSR-E and AMSR-2 LSTs when evaluated against the ground observations. Therefore, microwave LSTs are only used to fill the gaps due to clouds in the MODIS and GOES LST products. A gap filling method is further applied to fill the remaining gaps in the merged LSTs and downscale to the same spatial resolution as the MODIS and GOES products. With the daily integrated LST at the same spatial resolution as the MODIS and GOES products and available under nearly all sky conditions, the drought index, like the ESI, can be updated on daily basis. The initial implementation results demonstrate that the daily drought map can catch the fast changes of drought conditions and capture the signals of flash drought, and make flash drought monitoring become possible. It is expected that a drought map that is available on daily basis will benefit future drought monitoring.

[1]  J. Dozier,et al.  Land-surface temperature measurement from space: physical principles and inverse modeling , 1989 .

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

[3]  Jenq-Neng Hwang,et al.  Solving inverse problems by Bayesian iterative inversion of a forward model with applications to parameter mapping using SMMR remote sensing data , 1995, IEEE Trans. Geosci. Remote. Sens..

[4]  Jeff Dozier,et al.  A generalized split-window algorithm for retrieving land-surface temperature from space , 1996, IEEE Trans. Geosci. Remote. Sens..

[5]  Martha C. Anderson,et al.  A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing , 1997 .

[6]  Zhao-Liang Li,et al.  A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data , 1997, IEEE Trans. Geosci. Remote. Sens..

[7]  Claude N. Williams,et al.  Using the Special Sensor Microwave/Imager to Monitor Land Surface Temperatures, Wetness, and Snow Cover , 1998 .

[8]  Li Li,et al.  Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz , 1999, IEEE Trans. Geosci. Remote. Sens..

[9]  C. Long,et al.  SURFRAD—A National Surface Radiation Budget Network for Atmospheric Research , 2000 .

[10]  G. De’ath,et al.  CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .

[11]  Dean B. Gesch,et al.  Development of a seamless multisource topographic/bathymetric elevation model of Tampa Bay , 2001 .

[12]  Keiji Imaoka,et al.  The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies , 2003, IEEE Trans. Geosci. Remote. Sens..

[13]  Donglian Sun,et al.  Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES‐8) , 2003 .

[14]  Michel Fily,et al.  A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas , 2003 .

[15]  T. Oke,et al.  Thermal remote sensing of urban climates , 2003 .

[16]  S. Miller,et al.  Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach , 2003 .

[17]  Z. Wan,et al.  Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA , 2004 .

[18]  Rachel T. Pinker,et al.  Land Surface Temperature Estimation from the Next Generation of Geostationary Operational Environmental Satellites: GOES M¿Q , 2004 .

[19]  Matthew F. McCabe,et al.  Initial soil moisture retrievals from AMSR‐E: Multiscale comparison using in situ data and rainfall patterns over Iowa , 2005 .

[20]  S. Liang,et al.  An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations , 2006 .

[21]  Compton J. Tucker,et al.  Development of a daily long term record of NOAA-14 AVHRR land surface temperature over Africa , 2006 .

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

[23]  T. Carlson An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery , 2007, Sensors (Basel, Switzerland).

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

[25]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation , 2007 .

[26]  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 .

[27]  Rong Fu,et al.  A Practical Method for Retrieving Land Surface Temperature From AMSR-E Over the Amazon Forest , 2007, IEEE Transactions on Geoscience and Remote Sensing.

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

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

[30]  Simon J. Hook,et al.  Intercomparison of versions 4, 4.1 and 5 of the MODIS Land Surface Temperature and Emissivity products and validation with laboratory measurements of sand samples from the Namib desert, Namibia , 2009 .

[31]  Martha C. Anderson,et al.  Advances in thermal infrared remote sensing for land surface modeling , 2009 .

[32]  Christopher Justice,et al.  Towards a Generalized Approach for Correction of the BRDF Effect in MODIS Directional Reflectances , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Da Li,et al.  jag simple retrieval method of land surface temperature from AMSR-E passive icrowave data — A case study over Southern China during the strong snow isaster of 2008 , 2010 .

[34]  Martha C. Anderson,et al.  Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign , 2012 .

[35]  Ronglin Tang,et al.  Derivation of Daily Evaporative Fraction Based on Temporal Variations in Surface Temperature, Air Temperature, and Net Radiation , 2013, Remote. Sens..

[36]  Li Fang,et al.  Toward an Operational Land Surface Temperature Algorithm for GOES , 2013 .

[37]  N. Brunsell,et al.  The impact of temporal aggregation of land surface temperature data for surface urban heat island (SUHI) monitoring , 2013 .

[38]  Neil S. Grigg,et al.  The 2011–2012 drought in the United States: new lessons from a record event , 2014 .

[39]  M. Hoerling,et al.  Causes and Predictability of the 2012 Great Plains Drought , 2014 .

[40]  Yongming Du,et al.  Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China , 2014 .

[41]  Zhao-Liang Li,et al.  Bare surface soil moisture retrieval from the synergistic use of optical and thermal infrared data , 2014 .

[42]  Catherine Ottlé,et al.  Land surface temperature retrieval over circumpolar Arctic using SSM/I-SSMIS and MODIS data , 2015 .

[43]  Jiancheng Shi,et al.  A case study for intercomparison of land surface temperature retrieved from GOES and MODIS , 2015, Int. J. Digit. Earth.

[44]  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.

[45]  Tsegaye Tadesse,et al.  Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought , 2016 .

[46]  F. Aires,et al.  Toward “all weather,” long record, and real‐time land surface temperature retrievals from microwave satellite observations , 2016 .

[47]  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..

[48]  Martha C. Anderson,et al.  Predicting the U.S. Drought Monitor Using Precipitation, Soil Moisture, and Evapotranspiration Anomalies. Part II: Intraseasonal Drought Intensification Forecasts , 2017 .

[49]  Yuji Murayama,et al.  Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015) , 2017 .

[50]  Isabel F. Trigo,et al.  Modelling directional effects on remotely sensed land surface temperature , 2017 .