A disaggregation approach for estimating high spatial resolution broadband emissivity for bare soils from Landsat surface reflectance

Cheng, Jie; Liang, Shunlin; Líu, Hao; Nie, Aixiu; Liu, Qiang

doi:10.1080/17538947.2017.1341559

Cited by 5 publications

(3 citation statements)

References 46 publications

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“…Generally, quartz-rich (SiO 2 ) sand has higher reflectivity and a lower emissivity but mafic minerals with lower SiO 2 content generally have lower reflectivity and a higher emissivity. Cheng et al proposed a disaggregation approach that utilizes the established BBE-reflectance relationship to estimate high spatial resolution BBE for bare soils from Landsat surface reflectance data [4]. The LAI is a dimensionless quality that characterizes plant canopies.…”

Section: Remote Sensing Datamentioning

confidence: 99%

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A New Linear Relation for Estimating Surface Broadband Emissivity in Arid Regions Based on FTIR and MODIS Products

Liu

Mamtimin

et al. 2021

Remote Sensing

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Broadband emissivity is a crucial parameter for calculating the radiation budget, still, it adopts a constant value in land surface models due to a lack of adequate observations. Arid regions have complex underlying surfaces and estimations of the broadband emissivity in such areas suffer from high spatial variation and uncertainty. Here, we propose a novel method for estimating broadband emissivity in the 8–14 µm range based on Fourier-transform infrared spectroscopy (FTIR) observations, moderate resolution imaging spectrometer (MODIS) emissivity, the leaf area index (LAI) and reflectance products. The proposed method exploits FTIR observations, MODIS single-channel emissivity, reflectance and the LAI to fit a linear regression of the broadband emissivity, so the optimal equation includes emissivity, reflectance and the LAI, with an R2 and root-mean-squared error of 0.942 and 0.08. Then we used the proposed method to generate a broadband emissivity map of Northwest of China, the broadband emissivity estimated by the method showed higher variations and finer distribution in arid areas and sparsely vegetated regions compared to data from the global land surface satellite and land model. An analysis of the relationship between the broadband emissivity, land-use type and soil moisture found an existing but not linear relationship, which indicated that the relationship was complicated under the inhomogeneous surface of wetness and vegetation. In conclusion, our results suggest that the proposed method can accurately estimate the broadband emissivity in arid regions. In future research, we will test the data in a land model.

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Section: Remote Sensing Datamentioning

confidence: 99%

“…The land surface thermal-infrared (TIR) broadband emissivity (BBE) is a key parameter used to calculate the Earth's surface energy budget [1][2][3][4][5]. The emissivity is defined as the ratio of energy emitted from natural material to that from an ideal blackbody at the same temperature [6].…”

Section: Introductionmentioning

confidence: 99%

A New Linear Relation for Estimating Surface Broadband Emissivity in Arid Regions Based on FTIR and MODIS Products

Liu

Mamtimin

et al. 2021

Remote Sensing

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“…L'évaluation de la qualité de la classification est réalisée par le calcul de la précision globale et du coefficient Kappa de Cohen. Lorsque le coefficient de Kappa est compris entre 0.61 et 1, la classification peut être jugée comme bonne (Cheng et al, 2018 ;Mubako et al, 2018 ;Vázquez-Jiménez et al, 2018).…”

Section: Méthodologieunclassified

Apport des images LANDSAT à l’étude de l’évolution de l’occupation du sol dans la plaine de SAÏSS au MAROC, pour la période 1987-2018

Garouani

AHARIK²

2021

RFPT

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Cet article concerne la plaine de Saïss au Maroc et porte sur l’évolution de l’occupation et de l'utilisation des sols pour la période allant de 1987 à 2018. Cette plaine s’avère très importante au niveau économique pour le pays. La méthodologie adoptée comporte successivement le calcul d’indices spectraux à partir d’images Landsat (NDVI : Normalized Difference Vegetation Index, NDWI : Normalized Difference Water Index et NDBI : Normalized Difference Built-up Index), puis l’utilisation de l’algorithme de vraisemblance afin de réaliser quatre classifications thématiques pour les années 1987, 2003, 2014 et 2018. La précision globale de ces classifications est déterminée à partir de la matrice de confusion, et varie entre 83 et 87% ; le coefficient kappa est, pour les quatre années, supérieur à 0,80. Entre 1987 et 2018, les surfaces correspondant aux terres irriguées, aux oliviers et au milieu urbain, ont progressé respectivement de 123%, 136% et 115%. À l’inverse, les forêts, les parcours et les terres arables ont vu leur surface diminuer respectivement de 10%, 6% et 29%.

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A practical machine learning approach to retrieve land surface emissivity from space using visible and near-infrared to short-wave infrared data

Li,

Wu,

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

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A disaggregation approach for estimating high spatial resolution broadband emissivity for bare soils from Landsat surface reflectance

Cited by 5 publications

References 46 publications

A New Linear Relation for Estimating Surface Broadband Emissivity in Arid Regions Based on FTIR and MODIS Products

A New Linear Relation for Estimating Surface Broadband Emissivity in Arid Regions Based on FTIR and MODIS Products

Apport des images LANDSAT à l’étude de l’évolution de l’occupation du sol dans la plaine de SAÏSS au MAROC, pour la période 1987-2018

A practical machine learning approach to retrieve land surface emissivity from space using visible and near-infrared to short-wave infrared data

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