Passive microwave radiance estimation by coupling a land surface emissivity model with CRTM

2012 ◽  
Author(s):  
Huoping Pan ◽  
Jiancheng Shi ◽  
Hu Yang ◽  
Tianxing Wang
Keyword(s):  
Land Surface ◽  
Passive Microwave ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Emissivity Model ◽  
Radiance Estimation

scholarly journals Estimation of Consistent Global Microwave Land Surface Emissivity from AMSR-E and AMSR2 Observations

2018 ◽  
Vol 57 (4) ◽  
pp. 907-919 ◽  
Author(s):  
Satya Prakash ◽  
Hamid Norouzi ◽  
Marzi Azarderakhsh ◽  
Reginald Blake ◽  
Catherine Prigent ◽  
...  
Keyword(s):  
Skin Temperature ◽  
Land Surface ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Passive Microwave ◽  
Accurate Estimation ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Earth Observing System ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractAccurate estimation of passive microwave land surface emissivity (LSE) is crucial for numerical weather prediction model data assimilation, for microwave retrievals of land precipitation and atmospheric profiles, and for a better understanding of land surface and subsurface characteristics. In this study, global instantaneous LSE is estimated for a 9-yr period from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and for a 5-yr period from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors. Estimates of LSE from both sensors were obtained by using an updated algorithm that minimizes the discrepancy between the differences in penetration depths from microwave and infrared remote sensing observations. Concurrent ancillary datasets such as skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) and profiles of air temperature and humidity from the Atmospheric Infrared Sounder are used. The latest collection 6 of MODIS skin temperature is used for the LSE estimation, and the differences between collections 6 and 5 are also comprehensively assessed. Analyses reveal that the differences between these two versions of infrared-based skin temperatures could lead to approximately a 0.015 difference in passive microwave LSE values, especially in arid regions. The comparison of global mean LSE features from the combined use of AMSR-E and AMSR2 with an independent product—Tool to Estimate Land Surface Emissivity from Microwave to Submillimeter Waves (TELSEM2)—shows spatial pattern correlations of order 0.92 at all frequencies. However, there are considerable differences in magnitude between these two LSE estimates, possibly because of differences in incidence angles, frequencies, observation times, and ancillary datasets.

scholarly journals Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale

2021 ◽  
Vol 13 (4) ◽  
pp. 817
Author(s):  
Zahra Sharifnezhad ◽  
Hamid Norouzi ◽  
Satya Prakash ◽  
Reginald Blake ◽  
Reza Khanbilvardi
Keyword(s):  
Land Cover ◽  
Diurnal Cycle ◽  
Land Surface ◽  
Global Scale ◽  
Passive Microwave ◽  
Surface Emissivity ◽  
Land Cover Types ◽  
Land Surface Emissivity ◽  
Microwave Imager ◽  
Microwave Sensors

Satellite-borne passive microwave radiometers provide brightness temperature (TB) measurements in a large spectral range which includes a number of frequency channels and generally two polarizations: horizontal and vertical. These TBs are widely used to retrieve several atmospheric and surface variables and parameters such as precipitation, soil moisture, water vapor, air temperature profile, and land surface emissivity. Since TBs are measured at different microwave frequencies with various instruments and at various incidence angles, spatial resolutions, and radiometric characteristics, a mere direct integration of them from different microwave sensors would not necessarily provide consistency. However, when appropriately harmonized, they can provide a complete dataset to estimate the diurnal cycle. This study first constructs the diurnal cycle of land TBs using the non-sun-synchronous Global Precipitation Measurement (GPM) Microwave Imager (GMI) observations by utilizing a cubic spline fit. The acquisition times of GMI vary from day to day and, therefore, the shape (amplitude and phase) of the diurnal cycle for each month is obtained by merging several days of measurements. This diurnal pattern is used as a point of reference when intercalibrated TBs from other passive microwave sensors with daily fixed acquisition times (e.g., Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2) are used to modify and tune the monthly diurnal cycle to daily diurnal cycle at a global scale. Since the GMI does not cover polar regions, the proposed method estimates a consistent diurnal cycle of land TBs at global scale. Results show that the shape and peak of the constructed TB diurnal cycle is approximately similar to the diurnal cycle of land surface temperature. The diurnal brightness temperature range for different land cover types has also been explored using the derived diurnal cycle of TBs. In general, a large diurnal TB range of more than 15 K has been observed for the grassland, shrubland, and tundra land cover types, whereas it is less than 5K over forests. Furthermore, seasonal variations in the diurnal TB range for different land cover types show a more consistent result over the Southern Hemisphere than over the Northern Hemisphere. The calibrated TB diurnal cycle may then be used to consistently estimate the diurnal cycle of land surface emissivity. Moreover, since changes in land surface emissivity are related to moisture change and freeze–thaw (FT) transitions in high-latitude regions, the results of this study enhance temporal detection of FT state, particularly during the transition times when multiple FT changes may occur within a day.

scholarly journals DEVELOPMENT AND VALIDATION OF A NEW PASSIVE MICROWAVE BASED SOIL MOISTURE INDEX

2020 ◽  
Vol V-3-2020 ◽  
pp. 125-131
Author(s):  
J. Zeng ◽  
K.-S. Chen ◽  
C. Cui ◽  
H. Bi
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Temporal Variation ◽  
Land Surface ◽  
Passive Microwave ◽  
Cumulative Distribution ◽  
Surface Emissivity ◽  
Moisture Index ◽  
Land Surface Emissivity ◽  
Soil Moisture Index

Abstract. Knowledge on the spatial-temporal variation of soil moisture is essential to many hydrometeorology applications. In this study, we proposed a new soil moisture index (SMI) from passive microwave observations, aiming to capture the soil moisture variability. The new SMI is developed based on the underlying physical basis that vegetation and surface roughness exert similar effects on the variation of land surface emissivity and microwave polarization difference radio (MPDI), but they act in an opposite way compared with soil moisture. Hence, we can obtain the SMI value in a two-dimensional space by combining use of land surface emissivity and MPDI to isolate the contribution of soil moisture and that of vegetation and surface roughness. We calculated the SMI by using the L-band SMAP Level-3 datasets and validated it with five well calibrated and dense soil moisture networks and also compared it with SMAP and ESA CCI soil moisture products. The results show the SMI exhibits the highest R (0.87) and lowest RMSE (0.028 m3 m−3) value after removing the systematic bias by using the cumulative distribution function (CDF) matching technique among the satellite products during the whole study period, thus demonstrating its good capability of tracking the temporal variation of soil moisture and its potential usage in various hydrometeorology applications.

scholarly journals Study of Aerosol Influence on Nighttime Land Surface Temperature Retrieval Based on Two Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Caixia Gao ◽  
Enyu Zhao ◽  
Chuanrong Li ◽  
Yonggang Qian ◽  
Lingling Ma ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Water Vapor ◽  
Standard Deviation ◽  
Land Surface ◽  
Simulated Data ◽  
Mean Square ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Instrument Noise ◽  
Temperature Retrieval

The aim of this study is to evaluate the aerosol influence on LST retrieval with two algorithms (split-window (SW) method and a four-channel based method) using simulated data under typical conditions. The results show that the root mean square error (RMSE) decreases to approximately 2.3 K for SW method and 1.5 K for four channel based method when VZA = 60° and visibility = 3 km; an RMSE would be increased by approximately 1.0 K when visibility varies from 3 km to 23 km. Moreover, a detailed sensitivity analysis under a visibility of 3 km and 23 km is performed in terms of uncertainties of land surface emissivity (LSE), water vapor content (WVC), and instrument noise, respectively. It is noted that the four-channel based method is more sensitive to LSE than SW method, especially for dry atmosphere; LST error caused by a WVC uncertainty of 20% is within 1.5 K for SW method and within 0.8 K for four-channel based method; the instrument noise would introduce LST error with a maximum standard deviation of 0.5 K and 0.04 K for the four-channel based method and SW method, respectively.

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