Assessment of MODIS thermal emissive bands calibration performance using deep convective clouds

Chang, Tiejun; Xiong, Xiaoxiong; Shrestha, Ashish

doi:10.1117/1.jrs.13.044526

Cited by 4 publications

(8 citation statements)

References 31 publications

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“…Random noise effects, such as noise in the digital output, are not included. These uncertainties propagate to the linear coefficient b 1 and, with the first order effect, its uncertainty can be derived as (Chang, Xiong, & Shrestha, 2019):…”

Section: Modis Teb Calibrationmentioning

confidence: 99%

“…Starting Collection 6.1 (C6.1), the electronic crosstalk between the Terra MODIS photo‐voltaic (PV) long‐wave infrared (LWIR) TEBs is corrected using coefficients derived from monthly lunar observations (Wilson et al., 2017; Xiong, Aldoretta, et al., 2020). Earth scene observations have provided useful information for calibration assessments, including inter‐sensor comparisons and assessments using ocean, desert, Antarctic Dome Concordia (Dome‐C), and deep convective clouds (DCC) measurements (Chang, Xiong, & Shrestha, 2019; Chang, Xiong, Shrestha, & Diaz, 2020; Chang, Xiong, Shrestha, Wu, et al., 2018; Diaz et al., 2019; Shrestha et al., 2018; Wenny et al., 2007; Xiong, Wenny, et al., 2009; Xiong, Wu, & Wenny, 2009). In order to remove solar reflectance effects on the MWIR band measurements, the nighttime quasi‐deep convective clouds (qDCC) method is applied in this paper (Chang, Xiong, Shrestha, & Diaz, 2020).…”

Section: Introductionmentioning

confidence: 99%

\frac{Δ b_{1}}{b_{1}} = Δ ε_{B B}^{e f f} - \frac{Δ a_{0}}{L_{B B}} - \frac{L_{B B}}{b_{1}^{2}} Δ a_{2},

where

Δ ε_{B B}^{e f f} = Δ ε_{B B} + {[\frac{d L (T)}{d T}]}_{T = T_{B B}}

for simplification. The EV radiance retrieval uses:

L_{E V} = \frac{1}{R V S_{E V}} [a_{0} + b_{1} d n_{E V} + a_{2} d n_{E V}^{2} - (R V S_{S V} - R V S_{E V}) L_{S M}] .

…”

Section: Introductionmentioning

confidence: 99%

“…Typical scenes used for the TEBs calibration assessment include Dome‐C, the ocean, the desert, and DCC (Chang, Xiong, & Shrestha, 2019; Chang, Xiong, Shrestha, & Diaz, 2020; Diaz et al., 2019; Shrestha et al., 2018; Wenny et al., 2007; Xiong, Wenny, et al., 2009; Xiong, Wu, & Wenny, 2009). The previously mentioned normalization method is demonstrated using these four EV scene types.…”

Section: Introductionmentioning

confidence: 99%

“…Afterward, if the IR temperature standard deviation (STD) was higher than a 1 K threshold, or the reflectance STD larger than 3%, the potential DCC pixel was discarded (Doelling, Hong, et al., 2010; Doelling, Morstad, et al., 2013; Doelling, Nguyen, & Minnis, 2004). However, as presented in our previous work, using daytime DCC has constraints over the calibration assessment of the MWIR bands; thus the nighttime quasi‐DCC technique is used (Chang, Xiong, & Shrestha, 2019; Chang, Xiong, Shrestha, & Diaz, 2020). The visible reflectance homogeneity is ignored due to a lack of nighttime measurements.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Normalization Method and Application for MODIS TEB Assessments Using Earth Scene Measurements

Chang

Xiong

Shrestha

et al. 2021

Earth and Space Science

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The Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been on-orbit for more than 20 and 18 years, respectively, and have provided continuous global observations for science research and applications. On-orbit calibration updates are important to track instrument response changes and maintain data product quality. MODIS has 36 spectral bands, among which bands 20 to 25 and 27 to 36 are the thermal emissive bands (TEBs) with a wavelength range from 3.8 to 14.2 μm. The MODIS TEBs radiometric calibration uses a quadratic function for instrument response, and the calibration coefficients look-up tables (LUTs) are updated using its on-board blackbody's (BB) response (

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Section: Modis Teb Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

\frac{Δ b_{1}}{b_{1}} = Δ ε_{B B}^{e f f} - \frac{Δ a_{0}}{L_{B B}} - \frac{L_{B B}}{b_{1}^{2}} Δ a_{2},

where

Δ ε_{B B}^{e f f} = Δ ε_{B B} + {[\frac{d L (T)}{d T}]}_{T = T_{B B}}

for simplification. The EV radiance retrieval uses:

L_{E V} = \frac{1}{R V S_{E V}} [a_{0} + b_{1} d n_{E V} + a_{2} d n_{E V}^{2} - (R V S_{S V} - R V S_{E V}) L_{S M}] .

…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Normalization Method and Application for MODIS TEB Assessments Using Earth Scene Measurements

Chang

Xiong

Shrestha

et al. 2021

Earth and Space Science

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Terra and Aqua MODIS Thermal Emissive Bands Calibration and RVS Stability Assessments Using an In Situ Ocean Target

Díaz

Xiong

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data

Sun

et al. 2020

Remote Sensing

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An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.

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Assessment of MODIS thermal emissive bands calibration performance using deep convective clouds

Cited by 4 publications

References 31 publications

Normalization Method and Application for MODIS TEB Assessments Using Earth Scene Measurements

Normalization Method and Application for MODIS TEB Assessments Using Earth Scene Measurements

Terra and Aqua MODIS Thermal Emissive Bands Calibration and RVS Stability Assessments Using an In Situ Ocean Target

An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data

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