The first evaluation of the FY-3D/MERSI-2 sensor’s thermal infrared capabilities for deriving land surface temperature in volcanic regions: a case study of Mount Etna

Aveni, Simone

doi:10.1080/01431161.2022.2068360

Cited by 6 publications

(9 citation statements)

References 53 publications

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“…Wang et al [40] showed a good agreement between the LST from the MODIS and MERSI-2 in the Bohai Sea area of China. Aveni and Blackett [47] evaluated the MODIS and MERSI-2 LST of Mount Etna (Italy) during the active volcanic phase in 2019 and obtained an R 2 of 0.92.…”

Section: Errors From the Fy-3d Mwri Brightness Temperature Measurementsmentioning

confidence: 99%

A Physics-Based Method for Retrieving Land Surface Emissivities from FengYun-3D Microwave Radiation Imager Data

Zhou,

Han,

Tang

et al. 2024

Remote Sensing

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The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958).

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Section: Errors From the Fy-3d Mwri Brightness Temperature Measurementsmentioning

confidence: 99%

A Physics-Based Method for Retrieving Land Surface Emissivities from FengYun-3D Microwave Radiation Imager Data

Zhou,

Han,

Tang

et al. 2024

Remote Sensing

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“…1,22,23 The commonly used optical image data include Landsat TM (Thematic Mapper), 24,25 ETM+(Enhanced Thematic Mapper Plus), 26 OLI (Operational Land Imager), 27 SPOT imagery, 28 Sentinel-2 imagery, 20 hyperspectral data, 29 among others. [30][31][32] Light detection and ranging (LiDAR) data is also commonly used in mapping FCD. 4,[33][34][35] Landsat series images have been widely used due to their space-time continuity and significant advantages of easy access.…”

Section: Introductionmentioning

confidence: 99%

Comparison between biophysical analysis model and dimidiate pixel model for the estimation of forest canopy density

Tian

Dong

Xing

et al. 2023

J. Appl. Rem. Sens.

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.Forest canopy density (FCD) is an essential factor in forest resources investigation and plays a vital role in forest resources management. To replace the traditional time-consuming and labor-intensive survey methods, it is of great importance to study how to select a suitable model at specific temporal and spatial scales for rapid and accurate estimation of FCD in large areas. To reduce workload and errors, we present an approach combing Landsat 5 TM data and biophysical analysis algorithms without considering thermal index (TI) information to model FCD in a temperate forest in Northern China. The scaled vegetation index (SVD) and scaled shadow index were used as variables for modeling and mapping FCD. The dimidiate pixel model based on the normalized difference vegetation index was also used to estimate FCD. We compared the forest management inventory data of 2012 with the FCD results from the biophysical analysis model and the dimidiate pixel model. The results showed that the biophysical analysis model (prediction precision P = 93.62 % , coefficient of determination R2 = 0.886, root mean square error RMSE = 0.057) has higher accuracy than the dimidiate pixel model (P = 89.92 % , R2 = 0.725, RMSE = 0.078) in estimating FCD. This study shows that the biophysical analysis model without TI information has better performance than the dimidiate pixel model, and can be used for monitoring large scale FCD.

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“…Additionally, regardless of the amount or the resolution of the available data, all thermal satellite-based systems rely on the availability of scenes acquired in good weather conditions, thus not contaminated by clouds or volcanic plumes, where thermal sources can be clearly distinguished, and thermal energy quantified [8,19]. Furthermore, volcanic processes are typically transient in nature, and variations in the eruptive regime may occur over timescales of tens of minutes [4,[20][21][22][23][24][25]. To track the evolution and detect rapid variations of volcanic activity, researchers employed geostationary satellites, exploiting their high temporal resolution (i.e., from 5 to 15 min for MSG-SEVIRI) [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…Polar imageries acquired with short temporal gaps can also provide crucial information on the evolution of the volcanic activity and its dynamics, for instance, allowing the detection of rapid changes in the eruptive regime, thus in the Time Averaged Discharge Rate (TADR). The rapid response, in turn, allows scientists, stakeholders, and competent bodies to run and update lava flow models, obtaining detailed information on the forthcoming expected scenarios [25].…”

Section: Introductionmentioning

confidence: 99%

“…Ensuring the continuity of satellite-based thermal volcanic systems [14] revealed how the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard Suomi-NPP and NOAA-20 platforms is an excellent candidate to compensate for the forthcoming disposal of Terra and Aqua satellites. Other space agencies, however, have recently launched comparable platforms equipped with sensors able to detect thermal anomalies in volcanic settings, including the European Space Agency (ESA) (i.e., Sentinel-3) [41], the Japan Aerospace Exploration Agency (JAXA) (i.e., GCOM-C) [42] and the China Meteorological Administration (CMA) (i.e., FY-3D) [25]. Amongst these, the MEdium Resolution Spectral Imager-2 (MERSI-II) aboard Fengyun-3D (FY-3D) platform is of most interest.…”

Section: Introductionmentioning

confidence: 99%

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The Capabilities of FY-3D/MERSI-II Sensor to Detect and Quantify Thermal Volcanic Activity: The 2020–2023 Mount Etna Case Study

et al. 2023

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Satellite data provide crucial information to better understand volcanic processes and mitigate associated risks. In recent years, exploiting the growing number of spaceborne polar platforms, several automated volcanic monitoring systems have been developed. These, however, rely on good geometrical and meteorological conditions, as well as on the occurrence of thermally detectable activity at the time of acquisition. A multiplatform approach can thus increase the number of volcanological-suitable scenes, minimise the temporal gap between acquisitions, and provide crucial information on the onset, evolution, and conclusion of both transient and long-lasting volcanic episodes. In this work, we assessed the capabilities of the MEdium Resolution Spectral Imager-II (MERSI-II) sensor aboard the Fengyun-3D (FY-3D) platform to detect and quantify heat flux sourced from volcanic activity. Using the Middle Infrared Observation of Volcanic Activity (MIROVA) algorithm, we processed 3117 MERSI-II scenes of Mount Etna acquired between January 2020 and February 2023. We then compared the Volcanic Radiative Power (VRP, in Watt) timeseries against those obtained by MODIS and VIIRS sensors. The remarkable agreement between the timeseries, both in trends and magnitudes, was corroborated by correlation coefficients (ρ) between 0.93 and 0.95 and coefficients of determination (R2) ranging from 0.79 to 0.84. Integrating the datasets of the three sensors, we examined the effusive eruption of Mount Etna started on 27 November 2022, and estimated a total volume of erupted lava of 8.15 ± 2.44 × 106 m3 with a Mean Output Rate (MOR) of 1.35 ± 0.40 m3 s−1. The reduced temporal gaps between acquisitions revealed that rapid variations in cloud coverage as well as geometrically unfavourable conditions play a major role in thermal volcano monitoring. Evaluating the capabilities of MERSI-II, we also highlight how a multiplatform approach is essential to enhance the efficiency of satellite-based systems for volcanic surveillance.

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The first evaluation of the FY-3D/MERSI-2 sensor’s thermal infrared capabilities for deriving land surface temperature in volcanic regions: a case study of Mount Etna

Cited by 6 publications

References 53 publications

A Physics-Based Method for Retrieving Land Surface Emissivities from FengYun-3D Microwave Radiation Imager Data

A Physics-Based Method for Retrieving Land Surface Emissivities from FengYun-3D Microwave Radiation Imager Data

Comparison between biophysical analysis model and dimidiate pixel model for the estimation of forest canopy density

The Capabilities of FY-3D/MERSI-II Sensor to Detect and Quantify Thermal Volcanic Activity: The 2020–2023 Mount Etna Case Study

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