scholarly journals Combined land surface emissivity and temperature estimation from Landsat 8 OLI and TIRS

2020 ◽  
Vol 166 ◽  
pp. 390-402 ◽  
Author(s):  
Quinten Vanhellemont
Keyword(s):  
Land Surface ◽  
Landsat 8 ◽  
Surface Emissivity ◽  
Landsat 8 Oli ◽  
Temperature Estimation ◽  
Land Surface Emissivity

scholarly journals Application of Geo-informatics Technology to Access the Surface Temperature Using LANDSAT 8 OLI/TIRS Satellite Data: A Case Study in Ampara District in Sri Lanka

2020 ◽  
Author(s):  
Ibra Lebbe Mohamed Zahir
Keyword(s):  
Sri Lanka ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Surface Emissivity ◽  
Land Surface Emissivity

Land Surface Temperature is a one of the key variable of Global climate changes and model which estimate radiating budget in heat balance as control of climate model. It is a major influenced factor by the ability of the surface emissivity. In this study, were used Landsat 8 satellite image that have Operational Land Imager and Thermal Infrared Sensor to calculate Land Surface Temperature through geospatial technology over Ampara district, Sri Lanka. The Land Surface Temperature was estimated with respect to Land Surface Emissivity and Normalized Difference Vegetation Index values determined from the Red and Near Infrared channels. Land Surface Emissivity was processed directly by the thermal Infrared bands. Pixels based calculation were used to effort at LANDSAT 8 images that thermal Band 10 various dates in this study. The results were achievable to compute Normalized Difference Vegetation Index, Land Surface Emissivity, and Land Surface Temperature with applicable manner to compare with land use/ land cover data. It determines and predicts the changes of surface temperature to favorable to decision making process for the society. Study area faces seasonal drought in Sri Lanka, the prediction method that how land can be efficiently used with the present condition. Therefore, the Land Surface Temperature estimation can prove whether new irrigation systems for agricultural activities or can transformed source of energy into useful form that introducing solar hubs for energy production in future.

scholarly journals Estimation of Vegetation Proportion Cover to Improve Land Surface Emissivity

2020 ◽  
Author(s):  
Elnaz Neinavaz ◽  
Andrew K. Skidmore ◽  
Roshanak Darvishzadeh
Keyword(s):  
Land Surface ◽  
Prediction Accuracy ◽  
Vegetation Index ◽  
Dynamic Range ◽  
Landsat 8 ◽  
Surface Emissivity ◽  
Threshold Method ◽  
Wide Dynamic Range ◽  
Land Surface Emissivity ◽  
The Impact

<p>Precise estimation of land surface emissivity (LSE) is essential to predict land surface energy budgets and land surface temperature, as LSE is an indicator of material composition. There exist several approaches to LSE estimation employing remote sensing data; however, the prediction of LSE remains a challenging task. Among the existing approaches for calculating LSE, the NDVI threshold method appears to hold well over vegetated areas. To apply the NDVI threshold method, it is necessary to know the proportion of vegetation cover (Pv). This research aims to investigate the impact of Pv's prediction accuracy on the estimation of LSE over the forest ecosystem. In this regard, a field campaign coinciding with a Landsat-8 overpass was undertaken for the mixed temperate forest of the Bavarian Forest National Park, in southeastern Germany. The Pv in situ measurements were made for 37 plots. Four vegetation indices, namely NDVI, variable atmospherically resistant index, wide dynamic range vegetation index, and three-band gradient difference vegetation index, were applied to predict Pv for further use in LSE computing. Unlike previous studies that suggested variable atmospherically resistant index can be estimated Pv with higher prediction accuracy compared to NDVI over the agricultural area, our results showed that the prediction accuracy of Pv is not different when using NDVI over the forest (R<sup>2</sup><sub>CV </sub>= 0.42, RMSE<sub>CV </sub>= 0.06). Pv was measured with the lowest accuracy using the wide dynamic range vegetation index (R<sup>2</sup><sub>CV </sub>= 0.014, RMSE<sub>CV </sub>= 0.197) and three-band gradient difference vegetation index (R<sup>2</sup><sub>CV </sub>= 0.032, RMSE<sub>CV </sub>= 0.018).  The results of this study also revealed that the variation in the prediction accuracy of the Pv has an impact on the results of LSE calculation.</p>

scholarly journals Integration of Landsat-8 Thermal and Visible-Short Wave Infrared Data for Improving Prediction Accuracy of Forest Leaf Area Index

2019 ◽  
Vol 11 (4) ◽  
pp. 390 ◽  
Author(s):  
Elnaz Neinavaz ◽  
Roshanak Darvishzadeh ◽  
Andrew Skidmore ◽  
Haidi Abdullah
Keyword(s):  
Land Surface ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Shortwave Infrared ◽  
Reflectance Data ◽  
Infrared Data

Leaf area index (LAI) has been investigated in multiple studies, either by means of visible/near-infrared and shortwave-infrared or thermal infrared remotely sensed data, with various degrees of accuracy. However, it is not yet known how the integration of visible/near and shortwave-infrared and thermal infrared data affect estimates of LAI. In this study, we examined the utility of Landsat-8 thermal infrared data together with its spectral data from the visible/near and shortwave-infrared region to quantify the LAI of a mixed temperate forest in Germany. A field campaign was carried out in August 2015, in the Bavarian Forest National Park, concurrent with the time of the Landsat-8 overpass, and a number of forest structural parameters, including LAI and proportion of vegetation cover, were measured for 37 plots. A normalised difference vegetation index threshold method was applied to calculate land surface emissivity and land surface temperature and their relations to LAI were investigated. Next, the relation between LAI and eight commonly used vegetation indices were examined using the visible/near-infrared and shortwave-infrared remote sensing data. Finally, the artificial neural network was used to predict the LAI using: (i) reflectance data from the Landsat-8 operational land imager (OLI) sensor; (ii) reflectance data from the OLI sensor and the land surface emissivity; and (iii) reflectance data from the OLI sensor and land surface temperature. A stronger relationship was observed between LAI and land surface emissivity compared to that between LAI and land surface temperature. In general, LAI was predicted with relatively low accuracy by means of the vegetation indices. Among the studied vegetation indices, the modified vegetation index had the highest accuracy for LAI prediction (R2CV = 0.33, RMSECV = 1.21 m2m−2). Nevertheless, using the visible/near-infrared and shortwave-infrared spectral data in the artificial neural network, the prediction accuracy of LAI increased (R2CV = 0.58, RMSECV = 0.83 m2m−2). The integration of reflectance and land surface emissivity significantly improved the prediction accuracy of the LAI (R2CV = 0.81, RMSECV = 0.63 m2m−2). For the first time, our results demonstrate that the combination of Landsat-8 reflectance spectral data from the visible/near-infrared and shortwave-infrared domain and thermal infrared data can boost the estimation accuracy of the LAI in a forest ecosystem. This finding has implication for the prediction of other vegetation biophysical, or possibly biochemical variables using thermal infrared satellite remote sensing data, as well as regional mapping of LAI when coupled with a canopy radiative transfer model.

Analysis of Landsat-8 OLI imagery for land surface water mapping

2014 ◽  
Vol 5 (7) ◽  
pp. 672-681 ◽  
Author(s):  
Zhiqiang Du ◽  
Wenbo Li ◽  
Dongbo Zhou ◽  
Liqiao Tian ◽  
Feng Ling ◽  
...  
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Land Surface Water ◽  
Water Mapping

scholarly journals Study of Aerosol Influence on Nighttime Land Surface Temperature Retrieval Based on Two Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Caixia Gao ◽  
Enyu Zhao ◽  
Chuanrong Li ◽  
Yonggang Qian ◽  
Lingling Ma ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Water Vapor ◽  
Standard Deviation ◽  
Land Surface ◽  
Simulated Data ◽  
Mean Square ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Instrument Noise ◽  
Temperature Retrieval

The aim of this study is to evaluate the aerosol influence on LST retrieval with two algorithms (split-window (SW) method and a four-channel based method) using simulated data under typical conditions. The results show that the root mean square error (RMSE) decreases to approximately 2.3 K for SW method and 1.5 K for four channel based method when VZA = 60° and visibility = 3 km; an RMSE would be increased by approximately 1.0 K when visibility varies from 3 km to 23 km. Moreover, a detailed sensitivity analysis under a visibility of 3 km and 23 km is performed in terms of uncertainties of land surface emissivity (LSE), water vapor content (WVC), and instrument noise, respectively. It is noted that the four-channel based method is more sensitive to LSE than SW method, especially for dry atmosphere; LST error caused by a WVC uncertainty of 20% is within 1.5 K for SW method and within 0.8 K for four-channel based method; the instrument noise would introduce LST error with a maximum standard deviation of 0.5 K and 0.04 K for the four-channel based method and SW method, respectively.

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