Estimating Land Surface Temperature from Feng Yun-3C/MERSI Data Using a New Land Surface Emissivity Scheme

Meng, Xianghai; Cheng, Jie; Liang, Shunlin

doi:10.3390/rs9121247

Cited by 35 publications

(22 citation statements)

References 64 publications

(84 reference statements)

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“…As described in Section 2.2, GAPRI atmospheric profiles were used to derive the coefficients of the algorithms, whereas the Cloudless Land Atmosphere Radiosounding (CLAR) [57] with 382 profiles, Thermodynamic Initial Guess Retrieval (TIGR) [58] with 946 profiles, and 2762 SeeBor V5.0 global profiles (hereafter SeeBor) [59] were used for validation purposes. All the profiles are restricted to land under clear sky conditions [60] based on the method of Galve et al [57]. To improve the inversion accuracy of the land surface temperature, the coefficients of the enterprise algorithm were fitted based on the water vapor content, as shown in Table 2.…”

Section: Algorithm Coefficientsmentioning

confidence: 99%

Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm

et al. 2019

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Land surface temperature (LST) is one of the key parameters in hydrology, meteorology, and the surface energy balance. The National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm is adapted to Landsat-8 data to obtain the estimate of LST. The coefficients of the Enterprise algorithm were obtained by linear regression using the analog data produced by comprehensive radiative transfer modeling. The performance of the Enterprise algorithm was first tested by simulation data and then validated by ground measurements. In addition, the accuracy of the Enterprise algorithm was compared to the generalized split-window algorithm and the split-window algorithm of Sobrino et al. (1996). The validation results indicate the Enterprise algorithm has a comparable accuracy to the other two split-window algorithms. The biases (root mean square errors) of the Enterprise algorithm were 1.38 (3.22), 1.01 (2.32), 1.99 (3.49), 2.53 (3.46), and −0.15 K (1.11 K) at the SURFRAD, HiWATER_A, HiWATER_B, HiWATER_C sites and BanGe site, respectively, whereas those values were 1.39 (3.20), 1.0 (2.30), 1.93 (3.48), 2.53 (3.35), and −0.35 K (1.16 K) for the generalized split-window algorithm, 1.45 (3.39), 1.08 (2.41), 2.16 (3.67), 2.52 (3.58), and 0.02 K (1.12 K) for the split-window algorithm of Sobrino, respectively. This study provides an alternative method to estimate LST from Landsat-8 data.

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Section: Algorithm Coefficientsmentioning

confidence: 99%

Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm

et al. 2019

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“…In these years, space-based methods, like radio occultation and microwave satellite-borne passive microwave detectors, provide availability to measure meteorology parameters and PWV distribution from space [3,17,18]; they have lots of advantages compared to ground observation methods. Like RO, it has high vertical resolution and RO measurements are not significantly affected by clouds and precipitation [19][20][21][22]; besides, its observation coverage is global, not a matter of land or ocean area, so radio occultation is applied to obtain PWV in a global area under allweather conditions [23][24][25]. Precision statistical related studies reveal over ocean-dominated geographical areas; PWV retrieved from RO and ground-based GNSS exhibits a global mean difference of around 1 mm, a root-meansquare deviation of about 5 mm, and a correlation above 0.9 [26].…”

Section: Introductionmentioning

confidence: 99%

Precipitable Water Vapor Retrieval and Analysis by Multiple Data Sources: Ground-Based GNSS, Radio Occultation, Radiosonde, Microwave Satellite, and NWP Reanalysis Data

Zhang

et al. 2018

Journal of Sensors

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Precipitable water vapor (PWV) content detection is vital to heavy rain prediction; up to now, lots of different measuring methods and devices are developed to observe PWV. In general, these methods can be divided into two categories, ground-based or space-based. In this study, we analyze the advantages and disadvantages of these technologies, compare retrieved atmosphere parameters by different RO (radio occultation) observations, like FORMOSAT-3/COSMIC (Formosa Satellite-3 and Constellation Observing System for Meteorology, Ionosphere, and Climate) and FY3C (China Feng Yun 3C), and assess retrieved PWV precision with a radiosonde. Besides, we interpolate PWV from NWP (numerical weather prediction) reanalysis data for more comparison and analysis with RO. Specifically, ground-based GNSS is of high precision and continuous availability to monitor PWV distribution; in our paper, we show cases to validate and compare GNSS retrieving PWV with a radiosonde. Except GNSS PWV, we give two different radio occultation sounding results, COSMIC and FY3C, to validate the precision to monitor PWV from space in a global area. FY3C results containing Beidou (China Beidou Global Satellite Navigation System) radio occultation events need to be emphasized. So, in our study, we get the retrieved atmospheric profiles from GPS and Beidou radio occultation observations and derive atmosphere PWV by a variational retrieval method based on these data over a global area. Besides, other space-based methods, such as microwave satellite, are also useful in detecting PWV distribution situations in a global area from space; in this study, we present a case of retrieved PWV using microwave satellite observation. NWP reanalysis data ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim and the new-generation reanalysis data ERA5 provide global grid atmosphere parameters, like surface temperature, different-level pressures, and precipitable water. We show cases of retrieved PWV and validate the precision with radiosonde results and compare new reanalysis dataset ERA5 with ERA-Interim, finding that ERA5 can get higher precision-retrieved atmosphere parameters and PWV. In the end, from our comparison, we find that the retrieved PWV from RO (FY3C and COSMIC) and ECMWF reanalysis data (ERA-Interim and ERA5) have a high positive correlation and that almost all R2 values exceed 0.9, compare retrieved PWV with a radiosonde, and find that whether it is RO and ECMWF reanalysis data, ground-based GNSS, or microwave satellite, they all show small biases.

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“…Windahl et al [54] indicated that the overall LST RMSE retrieved from radiative transfer equation method for all Landsat datasets were 0.44 K larger than those values for the cloud-free Landsat dataset. Meng et al [44] also indicated the LST calculated from the radiative transfer equation method was underestimated when the study area was covered by clouds.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

“…The estimated LSTs determined using the RTE method were evaluated with the ground measurements from six in situ sites during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment [43]. There were two in situ sites over the vegetation surfaces and four in situ sites over bare soil, and detailed information about the in situ sites can be found in Meng et al [44]. The land surface emissivity were determined using the method proposed by Meng et al [8], which used the ASTER Global Emissivity Dataset (GED) and Vegetation Cover Method (VCM) to improve land surface emissivity accuracy.…”

Section: Real Datamentioning

confidence: 99%

Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data

Meng

Cheng

2018

Remote Sensing

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Global reanalysis products have been widely used for correcting the atmospheric effects of thermal infrared data, but their performances have not been comprehensively evaluated. In this paper, we evaluate eight global reanalysis products (NCEP/FNL; NCEP/DOE Reanalysis2; MERRA-3; MERRA-6; MERRA2-3; MERRA2-6; JRA-55; and ERA-Interim) commonly used in the atmospheric correction of Landsat 8 TIRS10 data by referencing global radiosonde observations collected from 163 stations. The atmospheric parameters (atmospheric transmittance, upward radiance, and downward radiance) simulated with MERRA-6 and ERA-Interim were accurate than those simulated with other reanalysis products for different water vapor contents and surface elevations. When global reanalysis products were applied to retrieve land surface temperature (LST) from simulated Landsat 8 TIRS10 data, ERA-Interim and MERRA-6 were accurate than other reanalysis products. The overall LST biases and RMSEs between the retrieved LSTs and LSTs that were used to generate the top-of-atmosphere radiances were less than 0.2 K and 1.09 K, respectively. When eight reanalysis products were used to estimate LSTs from thirty-two Landsat 8 TIRS10 images covering the Heihe River basin in China, the various reanalysis products showed similar validation accuracies for LSTs with low water vapor contents. The biases ranged from 0.07 K to 0.24 K, and the STDs (RMSEs) ranged from 1.93 K (1.93 K) to 2.02 K (2.04 K). Considering the above evaluation results, MERRA-6 and ERA-Interim are recommended for thermal infrared data atmospheric corrections.

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Estimating Land Surface Temperature from Feng Yun-3C/MERSI Data Using a New Land Surface Emissivity Scheme

Cited by 35 publications

References 64 publications

Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm

Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm

Precipitable Water Vapor Retrieval and Analysis by Multiple Data Sources: Ground-Based GNSS, Radio Occultation, Radiosonde, Microwave Satellite, and NWP Reanalysis Data

Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data

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