Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm

Tang, Kai; Zhu, Hao; Ni, Ping; Li, Ruibo; Fan, Cheng

doi:10.1109/jstars.2021.3075698

Cited by 16 publications

(2 citation statements)

References 86 publications

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“…Despite FY-3D being launched with the main purpose of collecting meteorological data, several studies demonstrated how MERSI-II MIR and TIR bands are suitable for detecting and quantifying fire hotspots and anomalous heat sources [50][51][52][53]. Indeed, the spectral distribution of MERSI-II channels, as well as its characteristics, make the sensor compared with the bands of MODIS and VIIRS, both in the MIR and TIR regions of the spectrum, i.e., [45,48,49,52,80] (Table 1). In the MIR range-between 3.5 to 4.10 µm-MERSI-II features two InfraRed (IR) channels, namely band 20 and band 21, centred at 3.80 and 4.05 µm, respectively.…”

Section: Fenguyn-3d and Mersi-ii Sensormentioning

confidence: 99%

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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Section: Fenguyn-3d and Mersi-ii Sensormentioning

confidence: 99%

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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“…The algorithms developed for LST estimation are generally named based on the number of bands used. For example, single-channel or mono-window algorithms use only one thermal band whereas split window requires two TIR bands (Qin et al, 2004;Sobrino and Romaguera, 2004;Meng et al, 2023). Landsat 5 (TM), Landsat 7 ETM+ and Landsat 8 OLI/TIRS data have been used for LST retrieval (Sekertekin, 2019) using Radiative Transfer Equation (RTE) (Becker and Li, 1990;Gillespie et al, 1998;Jiménez-Muñoz and Sobrino, 2003), Single Channel Algorithm (SCA) (Jiménez-Muñoz and Sobrino, 2009) and Mono Window Algorithm (MWA) (Zhihao Qin, .…”

Section: Introductionmentioning

confidence: 99%

Downscaling Land Surface Temperature at Farm-Scale Using Multi-Model Approaches over Diverse Agro Climatic Zones

Roy,

Das,

Rathore

et al. 2024

Preprint

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Land surface temperature (LST) is a critical parameter for land surface and atmospheric interactions. However, the applicability of current land surface temperature estimates for field-level hydrological, agricultural, and ecological operations is still challenging due to their coarse spatiotemporal resolution. In the current article, we have downscaled 100 m LST to 10 m by using high-resolution Sentinel-1,2 optical-microwave data and three different models- 1) Thermal Sharpening (TsHARP); 2) Thin Plate Spline (TPS); and 3) Random Forest (RF). The extensive analysis was performed at agricultural farms in the semi-arid (IARI) region of India during the winter and summer seasons of 2020-21 and 2021-22, arid (CAZRI) and per-humid (UBKV) region (2021-2022). The calibration accuracy of the RF model was shown to better agreement with the coefficient of determination (R2) of 0.982, root means square error (RMSE) 0.181 and normalized root means square error (RMSE) 0.061 with their lower values of standard errors over three diverse agro-climatic zones. The findings demonstrate that the validation accuracy of models' varied according to the agroclimatic zones, although RF and TPS consistently outperformed as compared to the TsHARP models.

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Retrieving leaf area index from FY-3D MERSI-II data using a sensor-adaptive algorithm

Chen

et al. 2023

International Journal of Remote Sensing

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Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm

Cited by 16 publications

References 86 publications

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

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

Downscaling Land Surface Temperature at Farm-Scale Using Multi-Model Approaches over Diverse Agro Climatic Zones

Retrieving leaf area index from FY-3D MERSI-II data using a sensor-adaptive algorithm

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