Downscaling MODIS land surface temperature to Sentinel-2 spatial resolution in the Barrax test site

2019 ◽  
Author(s):  
Juan Manuel Sánchez ◽  
Joan Miquel Galve ◽  
José González-Piqueras ◽  
Ramón López-Urrea ◽  
Raquel Niclòs ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Test Site ◽  
Modis Land Surface Temperature ◽  
Sentinel 2

scholarly journals Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques

2019 ◽  
Vol 11 (21) ◽  
pp. 2588 ◽  
Author(s):  
Otgonbayar ◽  
Atzberger ◽  
Mattiuzzi ◽  
Erdenedalai
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Near Surface ◽  
Monthly Average ◽  
Modis Land Surface Temperature ◽  
Weather Stations

The objective of this research was to develop a robust statistical model to estimate climatologies (2002–2017) of monthly average near-surface air temperature (Ta) over Mongolia using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) time series products and terrain parameters. Two regression models were analyzed in this study linking automatic weather station data (Ta) with Earth observation (EO) images: partial least squares (PLS) and random forest (RF). Both models were trained to predict Ta climatologies for each of the twelve months, using up to 17 variables as predictors. The models were applied to the entire land surface of Mongolia, the eighteenth largest but most sparsely populated country in the world. Twelve of the predictor variables were derived from the LST time series products of the Terra MODIS satellite. The LST MOD11A2 (collection 6) products provided thermal information at a spatial resolution of 1 km and with 8-day temporal resolution from 2002 to 2017. Three terrain variables, namely, elevation, slope, and aspect, were extracted using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and two variables describing the geographical location of weather stations were extracted from vector data. For training, a total of 8544 meteorological data points from 63 automatic weather stations were used covering the same period as MODIS LST products. The PLS regression resulted in a coefficient of determination (R2) between 0.74 and 0.87 and a root-mean-square error (RMSE) from 1.20 °C to 2.19 °C between measured and estimated monthly Ta. The non-linear RF regression yielded even more accurate results with R2 in the range from 0.82 to 0.95 and RMSE from 0.84 °C to 1.93 °C. Using RF, the two best modeled months were July and August and the two worst months were January and February. The four most predictive variables were day/nighttime LST, elevation, and latitude. Using the developed RF models, spatial maps of the monthly average Ta at a spatial resolution of 1 km were generated for Mongolia (~1566 × 106 km2). This spatial dataset might be useful for various environmental applications. The method is transparent and relatively easy to implement.

scholarly journals A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

2021 ◽  
Vol 13 (11) ◽  
pp. 2211
Author(s):  
Shuo Xu ◽  
Jie Cheng ◽  
Quan Zhang
Keyword(s):  
Random Forest ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Learning Algorithm ◽  
Mainland China ◽  
Fusion Method ◽  
Moisture Condition

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

scholarly journals Mapping urban greenery to create the optimal cooling effect model against solar radiation under the conditions of a smart city

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Vladimír Sedlák ◽  
Katarína Onačillová ◽  
Michal Gallay ◽  
Jaroslav Hofierka ◽  
Ján Kaňuk ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Surface Temperature ◽  
Urban Environment ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Smart City ◽  
Research Study ◽  
Cooling Effect ◽  
Urban Greenery ◽  
Sentinel 2

<p><strong>Abstract.</strong> Current climate changes on a global scale require an optimal estimate of heat transfer in a complex urban environment as a part of the requirements for optimal urban planning in the conditions of a smart city. Urban greenery has a considerable impact on the cooling of the urban environment during thermal waves. Sentinel-2 as an Earth observation mission developed by the European Space Agency as part of the Copernicus Programme to perform terrestrial observations in support of various services could become a potential means also for quantified assessment of different urban scenarios where vegetation plays an essential role. The Sentinel-2 data provide higher spatial and temporal resolution than other similar missions allow.</p><p>The presented research study is aimed at exploiting the potential of Sentinel-2 in simulating the cooling effect of urban greenery as part of smart city mapping in assessing the quality of life of its inhabitants. The main objective of the research study is to define a methodical approach for spatial surface temperature modelling in selected urban areas based on the solar radiation modelling and parameterization of the land cover properties from the Sentinel-2 data. While solar irradiation can be accurately calculated at a fine scale using virtual 3D city models, it is difficult to find other important parameters for ground surface modelling such as surface thermal emissivity, broadband albedo and evapotranspiration. The research study was tested and verified in 4&amp;thinsp;sq.&amp;thinsp;km urban area in the selected central parts of the city of Košice in Slovakia (Figure 1). For a detailed survey, four sites (site 1 &amp;ndash; Moyzesova Street, site 2 &amp;ndash; Historical centre, site 3 &amp;ndash; City park, site 4 &amp;ndash; Hvozdíkov park) were chosen in the central city area. The virtual 3D urban model was created from the airborne LiDAR (Light Detection And Ranging, hereinafter referred to as the lidar) and photogrammetric data obtained in a single mission.</p><p>The aim of the research study was to assess the feasibility of using virtual 3D city models and multispectral satellite images to approximate surface temperature dynamics by modelling of the spatial distribution of solar radiation and land surface characteristics in a complex urban environment. A time-series of the Sentinel-2 data was collected for comparison with the reference time series of the terrestrial lidar (TLS &amp;ndash; Terrestrial Laser Scanning) data on urban greenery on four selected urban areas of the city of Košice. Between the vegetation metrics, the statistical linear relationship derived from the Sentinel-2 and TLS data was defined. Based on terrain mapping, a geobotanic database of urban trees was created. The algorithmic structure of a toolbox for the land surface temperature modelling in the open-source GRASS GIS was developed based on the Stefan-Boltzmann law and Kirchhoff rule.</p><p>This research study has highlighted how the Sentinel-2 data can be used to estimate of the broad-band albedo, surface emission, and solar transmittance to the vegetation of urban greenery. The main benefit of the research study is the developed algorithm for estimation of the land surface temperature in a GIS environment that provides a unique platform for integrating different types of data-sets to become usable in urban planning and for exploitation of the Sentinel-2 data in mitigation of a negative impact of the urban extreme heat islands on the quality of life of inhabitants. The resulting LST (Land Surface Temperature) was calculated for four scenarios using the detail of the study area of the site 1 (Figure 2) and whole study are (Figure 3) demonstrate. These figures also show the cooling effect of urban trees and shrubs.</p>

scholarly journals Validation of Collection 6 MODIS land surface temperature product using in situ measurements

2019 ◽  
Vol 225 ◽  
pp. 16-29 ◽  
Author(s):  
Si-Bo Duan ◽  
Zhao-Liang Li ◽  
Hua Li ◽  
Frank-M. Göttsche ◽  
Hua Wu ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
In Situ Measurements ◽  
Modis Land Surface Temperature
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