Problem, research strategy, and findings: Historical patterns of discrimination and disinvestment have shaped the current landscape of vulnerability to heat in U.S. cities but are not explicitly co...

Urban Heat Management and the Legacy of Redlining

Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data

A practical method for reducing terrain effect on land surface temperature using random forest regression

In terrestrial landscape architecture, land surface temperature (LST) is a key estimator of local climate, vegetation growth, and urban transition. It also represents the environmental factors that influence the land cover patterns using temperature variation over land use land cover (LULC) classes. In the present study, various geospatial techniques have been implemented to analyze the spatio-temporal trends in temperature among different LULC of an arid Potohar region of Pakistan using Landsat 7 (ETM+) and 8 (OLI & TIRS) and the relationship between different normalized satellite indices and LST. Results of the seasonal fluctuation in winter showed temperature range of 0–57, 0–50, 04–31 and 7–39 °C for the year 2000, 2005, 2010, and 2015, respectively, while the summer exhibited the temperature range of 24–48, 27–57, 22–48, and 12–41 °C for the year 2000, 2005, 2010, and 2015, respectively. The analysis established a direct correlation between LST and normalized difference vegetation index and normalized difference water index, and an indirect correlation among LST and normalized difference soil index, normalized difference built-up index and built-up index. The findings are critically important for planning and development division for sustainable use of land resources for urbanization extension projects. Future research will highlight the change in the area occupied by different land featured classes and their impacts on LST over a specified period.

Land surface temperature relation with normalized satellite indices for the estimation of spatio-temporal trends in temperature among various land use land cover classes of an arid Potohar region using Landsat data

Multi-city comparison of the relationships between spatial pattern and cooling effect of urban green spaces in four major Asian cities

In mountainous areas, land surface temperature (LST) exhibits a high spatial heterogeneity due to the influences from the changes in surface topographic factors (elevation, aspect, and slope), vege...

The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas

Downward shortwave radiation (DSR) plays a very important role in land surface radiation budget and land-surface processes modeling. Although there are several radiation products developed based on satellite observations, the coarse spatial resolution greatly limits their applications in regional or local scales. To get high-resolution and accuracy-reliable DSR data, a practical downscaling method for clear-sky condition was proposed by using the scale-invariant relationship of the radiative transfer process to decompose the global radiation into direct and diffuse components at horizontal level and conducting topographic correction finally. Based on this method, the time series of Meteosat Second Generation (MSG) DSR product covering part of Navarre province in the northern Spain was disaggregated into 30-m level with the use of the ALOS World 3D-30m digital elevation model (DEM) data. The downscaled results not only presented high spatial heterogeneity with respect to the changes in surface topography but also showed reasonable values at different times over different days in one year. The in situ validation indicated that the hourly downscaled DSR had quite high correlation with the surface measurements at each day with the coefficient of determination above 0.97 and the root-mean-squared error lower than 35 W/m2. Overall, the evaluation allows concluding on the proposed method that can be a good way to get reliable and high-resolution DSR data from coarse-resolution DSR product under clear-sky condition.

Spatial Downscaling of MSG Downward Shortwave Radiation Product Under Clear-Sky Condition

Downward shortwave radiation (DSR) is an essential parameter in land surface energy budget. However, current DSR products are mainly generated at coarse-resolution scales (more than 5 km) and fail to accurately depict DSR distribution over different topographic and land cover conditions. Meanwhile, the existence of frequent cloud cover constrains the high-resolution DSR estimation. To overcome the above issues, a novel spatial downscaling method for high-resolution DSR estimation was proposed in this study by incorporating coarse-resolution Meteosat Second Generation (MSG) DSR product and Landsat-8 observations. Through decomposing the downscaling scheme into three separate models: fully cloudy, partial cloudy, and cloud-free, the 3-km MSG DSR data were spatially downscaled to 30-m scale under all-sky conditions, based on the assumption of scale-invariant of the models established at 3-km scale. An empirical model for DSR estimation under cloud cover condition was constructed between the top of atmosphere radiance from Landsat-8 and MSG DSR. The downscaled results showed reasonable DSR values under different cloud cover conditions, and the spatial heterogeneity of the downscaled DSR was also well depicted with the variation of surface topography. Meanwhile, the validation with in situ measurements also revealed the significant improvement in terms of the coefficient of determination ( $R^{2}$ ) (from 0.53 to 0.79) and the root mean squared error (RMSE) (from 198.5 to 140.41 W/ $\text{m}^{2}$ ). In general, the proposed downscaling method in this study shows good potential for high-resolution DSR estimation without regard to the atmospheric information required in traditional DSR estimation under all-sky condition.

A Spatial Downscaling Method for Deriving High-Resolution Downward Shortwave Radiation Data Under All-Sky Conditions

On 22 December 2018, volcano Anak Krakatau, located in Indonesia, erupted and experienced a major lateral collapse. The triggered tsunami killed at least 437 people by the 13-m-high tide. Traditional optical imagery plays a great role in monitoring volcanic activities, but it is susceptible to cloud and fog interference and has low temporal resolution. Synthetic aperture radar (SAR) imagery can monitor volcanic activities at a high temporal resolution, and it is immune to the influence of clouds. In this paper, we propose an automatic method to accurately extract the volcano boundary from SAR images by combining multi-polarized water enhancement and the Nobuyuki Otsu (OTSU) method. We extract the area change of the volcano in 2018–2019 from Sentinel-1 images and ALOS-2 imagesThe area change and evolution are verified and analyzed by combing the results from SAR and optical data. The results show that the southeastern part of the volcano expanded significantly after the eruption, and the western part experienced collapse and recovery. The volcano morphology change experienced a slow-fast-slow process in the two years.

/pdf/revealing-the-morphological-evolution-of-krakatau-volcano-by-li4rv2mq.pdf

Revealing the Morphological Evolution of Krakatau Volcano by Integrating SAR and Optical Remote Sensing Images

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Papers

The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas

Spatial Downscaling of MSG Downward Shortwave Radiation Product Under Clear-Sky Condition

A Spatial Downscaling Method for Deriving High-Resolution Downward Shortwave Radiation Data Under All-Sky Conditions

Revealing the Morphological Evolution of Krakatau Volcano by Integrating SAR and Optical Remote Sensing Images