Temperature and Emissivity Retrieval From Hyperspectral Thermal Infrared Data Using Dictionary-Based Sparse Representation for Emissivity

Ma, Chenyang; Qian, Y. Q.; Li, Kun; Dou, Xianhui; Shen, Huanfeng; Tang, Hongzhao; Qiu, Shi; Zhang, Lihua; Jia, Yuanyuan; Ouyang, Guangzhou

doi:10.1109/tgrs.2023.3268860

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…Remote sensing technology offers several advantages over traditional field survey methods, including wide coverage, simplicity, and speed [19][20][21]. It enables the rapid mapping of the spatial pattern of paddy fields in both current and historical regions [19,22].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Rice Field Mapping in the Northern Region of China: An Asynchronous Flooding Signal and Object-Based Method

Li,

Zhou,

Liu

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate delineation of paddy fields holds importance in ensuring food security, efficient water resource management, and precise evaluation of greenhouse gas emissions. Here we propose an innovative approach, the Asynchronous Flooding and Object-Based (AF-OB) model, aimed at optimizing phenologybased paddy field mapping. The AF-OB model capitalizes on the asynchronous flooding phenomenon observed between paddy fields and non-paddy fields, along with the seasonal variations in the Normalized Difference Vegetation Index. The Simple Non-Iterative Clustering (SNIC) algorithm is integrated to mitigate the common issue of the "pretzel effect" encountered in paddy field mapping. Evaluation through independent samples yields compelling results, with the paddy field map generated by the AF-OB method achieving an Overall Accuracy (OA) of 94.28%. The paddy fields extracted using the AF-OB method exhibit alignment with statistical data, surpassing comparable algorithms relying on alternative land use products in terms of visual quality. Furthermore, the AF-OB model exhibits stability across time, space, and sensors, thus enhancing its applicability and robustness. The outputs of the AF-OB method offer reference data for informed agricultural production planning and the effective management of water resources.

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Section: Introductionmentioning

confidence: 99%

Optimizing Rice Field Mapping in the Northern Region of China: An Asynchronous Flooding Signal and Object-Based Method

Li,

Zhou,

Liu

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Satellite observations offer a precise method for obtaining LST on a global scale due to their large spatial resolution and regular temporal revisiting [11]. In recent decades, various algorithms have been developed to retrieve LST from thermal infrared instruments carried on satellites, such as the single-channel (SC) algorithm [14][15][16], the split-window (SW) algorithm [17,18], multiple angle methods [17,[19][20][21], and the temperature and emissivity separation (TES) algorithm [22][23][24][25][26]. A comprehensive overview of LST retrieval methods can be seen in previous research [11,13].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Land Surface Temperature from Chinese ZY1-02E IRS Data

Dou,

Li,

Zhang

et al. 2024

Remote Sensing

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The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data.

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“…Satellite observations offer a precise method for obtaining LST on a global scale, thanks to their large spatial resolution and regular temporal revisiting [14] . In the past decades, various algorithms have been developed to retrieve LST from thermal infrared instruments carried on satellites, such as the single-channel (SC) algorithm [15][16][17] , the split-window (SW) algorithm [18,19] , multiple angle methods [18,[20][21][22] , and the temperature and emissivity separation (TES) algorithm [23][24][25][26][27] , etc. A comprehensive overview of LST retrieval methods can be seen in previous research [13,14] .…”

Section: Introductionmentioning

confidence: 99%

Estimation of Land Surface Temperature from CHINESE ZY1-02E IRS Data

Dou,

Li,

Zhang

et al. 2023

Preprint

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The role of land surface temperature (LST) is of utmost importance in multiple academic disciplines such as climatology, hydrology, ecology, and meteorology. Until to now, many methods have been proposed to estimate LST from satellite thermal infrared data. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12μm, affected strongly by both atmospheric water vapor and ozone. Three study areas, namely Baotou, Heihe River Basin, and Yantai-sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted by cross-comparison between the retrieved LST and MODIS LST product. The results revealed that the WVS-based method exhibited an average bias of 0.63K and an RMSE of 1.62K compared to the in-situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77K and RMSE of 2.0K. These findings provide that the WVS-based method is effective to estimate LST from ZY1-02E IRS data.

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Temperature and Emissivity Retrieval From Hyperspectral Thermal Infrared Data Using Dictionary-Based Sparse Representation for Emissivity

Cited by 10 publications

References 39 publications

Optimizing Rice Field Mapping in the Northern Region of China: An Asynchronous Flooding Signal and Object-Based Method

Optimizing Rice Field Mapping in the Northern Region of China: An Asynchronous Flooding Signal and Object-Based Method

Estimation of Land Surface Temperature from Chinese ZY1-02E IRS Data

Estimation of Land Surface Temperature from CHINESE ZY1-02E IRS Data

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