scholarly journals Spatially Continuous and High-Resolution Land Surface Temperature Product Generation: A Review of Reconstruction and Spatiotemporal Fusion Techniques

2021 ◽  
pp. 0-0
Author(s):  
Penghai Wu ◽  
Zhixiang Yin ◽  
Chao Zeng ◽  
Si-Bo Duan ◽  
Frank-M. Gottsche ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Product Generation

scholarly journals Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging

2020 ◽  
Vol 12 (7) ◽  
pp. 1082 ◽  
Author(s):  
Jianhui Xu ◽  
Feifei Zhang ◽  
Hao Jiang ◽  
Hongda Hu ◽  
Kaiwen Zhong ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Emission ◽  
Independent Variables

Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

scholarly journals SPATIOTEMPORAL FUSION OF HIGH RESOLUTION LAND SURFACE TEMPERATURE USING THERMAL SHARPENED IMAGES FROM REGRESSION-BASED URBAN INDICES

2020 ◽  
Vol V-3-2020 ◽  
pp. 247-254
Author(s):  
M. Kim ◽  
K. Cho ◽  
H. Kim ◽  
Y. Kim
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Brightness Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Absolute Difference ◽  
Landsat 8 ◽  
Thermal Images ◽  
Urban Regions

Abstract. Obtaining spatially continuous, high resolution thermal images is crucial in order to effectively analyze heat-related phenomena in urban areas and the inherent high spatial and temporal variations. Spatiotemporal Fusion (STF) methods can be applied to enhance spatial and temporal resolutions simultaneously, but most STF approaches for the generation of Land Surface Temperature (LST) have not focused specifically on urban regions. This study therefore proposes a two-phase approach using Landsat 8 and MODIS images acquired on a study area in Beijing to first, investigate the sharpening of the fine resolution image input with urban-related spectral indices and second, to explore the potential of implementing the sharpened results into the Spatiotemporal Adaptive Data Fusion Algorithm for Temperature Mapping (SADFAT) to generate high spatiotemporal resolution LST images in urban areas. For this test, five urban indices were selected based on their correlation with brightness temperature. In the thermal sharpening phase, the Fractional Urban Cover (FUC) index was able to delineate spatial details in urban regions whilst maintaining its correlation with the original brightness temperature image. In the STF phase however, FUC sharpened results returned relatively high levels of correlation coefficient values up to 0.689, but suffered from the highest Root Mean Squared Error (RMSE) and Average Absolute Difference (AAD) values of 4.260 K and 2.928 K, respectively. In contrast, Normalized Difference Building Index (NDBI) sharpened results recorded the lowest RMSE and AAD values of 3.126 K and 2.325 K, but also the lowest CC values. However, STF results were effective in delineating fine spatial details, ultimately demonstrating the potential of using sharpened urban or built-up indices as a means to generate sharpened thermal images for urban areas, as well as for input images in the SADFAT algorithm. The results from this study can be used to further improve STF approaches for daily and spatially continuous mapping of LST in urban areas.

High spatial and temporal resolution land surface temperature for the Antarctic Dry Valleys

2020 ◽  
Author(s):  
Maite Lezama Valdes ◽  
Marwan Katurji ◽  
Hanna Meyer
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Temporal Resolution ◽  
Dry Valleys ◽  
Model Complex ◽  
The Antarctic

<p>Anthropogenic Climate Change is expected to take a toll on the Antarctic environment and its biodiversity, which is concentrated on the continent’s few ice-free areas, such as the McMurdo Dry Valleys (MDV). To model the current terrestrial habitat distribution and predict possible climate induced changes, high spatial and temporal resolution abiotic variables, especially land surface temperature (LST) and soil moisture are needed, but are currently unavailable.</p><p>The aim of this project is to fill this gap and create a high resolution LST dataset of the Antarctic Dry Valleys. This variable is acquired in a high temporal resolution (sub-daily) by the MODIS sensor aboard Terra and Aqua satellites. However, as LST varies greatly in space, the spatial resolution provided by this data source (1000 m) is too low to give a meaningful impression of LST and to study biodiversity patterns. Therefore, we use data from Landsat and ASTER sensors as a reference to downscale MODIS LST to a spatial resolution of 30 m. 7 year’s worth of satellite data as well as terrain-derived auxiliary variables went into the development of the model, which predicts 30 m LST for the Antarctic Dry Valleys. </p><p>To model complex relations between terrain, radiation, land cover and LST, machine learning models are used. Multiple algorithms (Random Forest, NN, SVM, Gradient Boosting) are compared to find the best approach for predicting high resolution LST based on MODIS data. Using the best performing model, a daily dataset is created that provides LST for the Antarctic Dry Valleys from 2002 on.</p>

scholarly journals Land Surface Temperature Retrieval Using High-Resolution Vertical Profiles Simulated by WRF Model

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1436
Author(s):  
Lucas Ribeiro Diaz ◽  
Daniel Caetano Santos ◽  
Pâmela Suélen Käfer ◽  
Nájila Souza da Rocha ◽  
Savannah Tâmara Lemos da Costa ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Atmospheric Correction ◽  
Wrf Model ◽  
Landsat 8 ◽  
Accuracy Evaluation ◽  
Vertical Profiles ◽  
Radiosonde Station

This work gives a first insight into the potential of the Weather Research and Forecasting (WRF) model to provide high-resolution vertical profiles for land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing. WRF numerical simulations were conducted to downscale NCEP Climate Forecast System Version 2 (CFSv2) reanalysis profiles, using two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03). We investigated the utility of these profiles for the atmospheric correction of TIR data and LST estimation, using the moderate resolution atmospheric transmission (MODTRAN) model and the Landsat 8 TIRS10 band. The accuracy evaluation was performed using 27 clear-sky cases over a radiosonde station in Southern Brazil. We included in the comparative analysis NASA’s Atmospheric Correction Parameter Calculator (ACPC) web-tool and profiles obtained directly from the NCEP CFSv2 reanalysis. The atmospheric parameters from ACPC, followed by those from CFSv2, were in better agreement with parameters calculated using in situ radiosondes. When applied into the radiative transfer equation (RTE) to retrieve LST, the best results (RMSE) were, in descending order: CFSv2 (0.55 K), ACPC (0.56 K), WRF G12 (0.79 K), and WRF G03 (0.82 K). Our findings suggest that there is no special need to increase the horizontal resolution of reanalysis profiles aiming at RTE-based LST retrieval. However, the WRF results were still satisfactory and promising, encouraging further assessments. We endorse the use of the well-known ACPC and recommend the NCEP CFSv2 profiles for TIR atmospheric correction and LST single-channel retrieval.

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