Estimation of Diurnal Cycle of Land Surface Temperature at High Temporal and Spatial Resolution from Clear-Sky MODIS Data

Duan, Si‐Bo; Li, Zhao-Liang; Tang, Bo‐Hui; Wu, Hua; Tang, Ronglin; Bi, Yuyun; Zhou, Guoqing

doi:10.3390/rs6043247

Cited by 79 publications

(64 citation statements)

References 34 publications

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“…Using the aforementioned 946 cloud-free TIGR atmospheric profiles, we first used the new high accurate atmospheric radiative transfer model MODTRAN 5.2 to simulate the band effective atmospheric transmittance, and then we obtained the coefficients through regression, which resulted in c0 = −9.674, c1 = 0.653 and c2 = 9.087. The model analysis indicated that this method will obtain a CWV RMSE of about 0.5 g/cm 2 . The details about the CWV retrieval can be found in [40].…”

Section: Determination Of Atmospheric Cwvmentioning

confidence: 99%

“…Currently, LST can only be obtained over large spatial and temporal scales through remote sensing data, which have attracted much attention in the last three decades [2,3]. Consequently, many LST retrieval methods have been proposed from remotely sensed data, particularly multi-channel thermal infrared (TIR) data, and these methods can be roughly grouped into three categories: the single-channel algorithm, multi-channel methods (e.g., the split-window algorithm [4][5][6] and the temperature and emissivity separation method [7]) and multi-time methods (e.g., the temperature-independent spectral indices method [8], two-temperature method [9,10] and the physical day and night algorithm [11]).…”

Section: Introductionmentioning

confidence: 99%

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A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

et al. 2015

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This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance-variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.

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Section: Determination Of Atmospheric Cwvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

et al. 2015

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“…The LSTs under cloudy skies are not validated due to the lack of in situ measurements and remotely sensed data from other sources. Validation of LSTs at the satellite pixel scale is challenging because LSTs can vary significantly within a pixel and change within relatively short time periods [21]. However, the LSTs are generally similar to neighboring pixels.…”

Section: Application To Actual Msg-seviri Satellite Datamentioning

confidence: 99%

“…As discussed by Duan et al [20][21][22], the DTC model can only be used to describe the LST diurnal cycle under cloud-free skies, i.e., the LST estimated using Equation (8) denotes the LST under cloud-free conditions. If the sky is overcast, the LST daily evolution cannot be determined using the ΔS is calculated using Equation (14) The following prior information is assumed: (1) the fitted LST is equal to or slightly higher than the actual LST; (2) the observations collected before a cloud first appears or at some time (assumed to be 2 h in this study) after a cloud disappears are given higher weights (set to 2), while other observations under cloud-free conditions are given lower weights (set to 1).…”

Section: Determining Parameters In the Dtc Modelmentioning

confidence: 99%

Estimation of Land Surface Temperature under Cloudy Skies Using Combined Diurnal Solar Radiation and Surface Temperature Evolution

Zhang

Pang

2015

Remote Sensing

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Land surface temperature (LST) is a key parameter in the interaction of the land-atmosphere system. However, clouds affect the retrieval of LST data from thermal-infrared remote sensing data. Thus, it is important to determine a method for estimating LSTs at times when the sky is overcast. Based on a one-dimensional heat transfer equation and on the evolution of daily temperatures and net shortwave solar radiation (NSSR), a new method for estimating LSTs under cloudy skies (Tcloud) from diurnal NSSR and surface temperatures is proposed. Validation is performed against in situ measurements that were obtained at the ChangWu ecosystem experimental station in China. The results show that the root-mean-square error (RMSE) between the actual and estimated LSTs is as large as 1.23 K for cloudy data. A sensitivity analysis to the errors in the estimated LST under clear skies (Tclear) and in the estimated NSSR reveals that the RMSE of the obtained Tcloud is less than 1.5 K after adding a 0.5 K bias to the actual Tclear and 10 percent NSSR errors to the actual NSSR. Tcloud is estimated by the proposed method using Tclear and NSSR products of MSG-SEVIRI for southern Europe. The results indicate that the new algorithm is practical for retrieving the LST under cloudy sky conditions, although some uncertainty exists. Notably, the approach can only be used during the daytime due to the assumption of the variation in LST caused by variations in insolation. Further, if there are less than six Tclear observations on any given day, the method cannot be used.

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“…Retrievals using radiances from the Moderate Resolution Imaging Spectroradiometer (MODIS) have been both the target and standard for a number of LST verification studies (Wan et al, 2002(Wan et al, , 2004Coll et al, 2009;Jiménez et al, 2012). Duan et al (2014) used four daily observations from Terra and Aqua MODIS to capture the diurnal cycle of LST, which is critical for full characterization of the climate system. Wang et al (2014) conducted a three-way T s comparison using MODIS, in situ ground observations, and model simulations.…”

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confidence: 99%

Global clear-sky surface skin temperature from multiple satellites using a single-channel algorithm with angular anisotropy corrections

et al. 2017

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Abstract. Surface skin temperature (T s ) is an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared (TIR) method to retrieve T s over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of T s over the diurnal cycle in non-polar regions, while polar T s retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR), can complement the GEO measurements. The combined global coverage of remotely sensed T s , along with accompanying cloud and surface radiation parameters, produced in near-realtime and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-realtime hourly T s observations can be assimilated in high-temporal-resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived T s data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing and illumination angles. Therefore, T s validation with established references is essential, as is proper evaluation of T s sensitivity to atmospheric correction source.This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based T s product that is derived using a single-channel technique. The resulting clearsky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirically adjusted theoretical model of satellite land surface temperature (LST) angular anisotropy is tested to improve satellite LST retrievals. Application of the anisotropic correction yields reduced mean bias and improved precision of GOES-13 LST relative to independent Moderateresolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program ground station measurements. It also significantly reduces intersatellite differences between LSTs retrieved simultaneously from two different imagers. The implementation of these universal corrections into the SatCORPS product can yield significant improvement in near-global-scale, near-realtime, satellite-based LST measurements. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

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Estimation of Diurnal Cycle of Land Surface Temperature at High Temporal and Spatial Resolution from Clear-Sky MODIS Data

Cited by 79 publications

References 34 publications

A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

Estimation of Land Surface Temperature under Cloudy Skies Using Combined Diurnal Solar Radiation and Surface Temperature Evolution

Global clear-sky surface skin temperature from multiple satellites using a single-channel algorithm with angular anisotropy corrections

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