Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives
The surface urban heat island (SUHI), which represents the difference of land surface
temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually …
temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually …
Spatially continuous and high-resolution land surface temperature product generation: A review of reconstruction and spatiotemporal fusion techniques
Remotely sensed land surface temperature (LST) with spatial continuity and high
spatiotemporal resolution (hereafter referred to as high resolution) is a crucial parameter for …
spatiotemporal resolution (hereafter referred to as high resolution) is a crucial parameter for …
[PDF][PDF] WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas
SE Fick, RJ Hijmans - International journal of climatology, 2017 - researchgate.net
We created a new dataset of spatially interpolated monthly climate data for global land areas
at a very high spatial resolution (approximately 1 km2). We included monthly temperature …
at a very high spatial resolution (approximately 1 km2). We included monthly temperature …
SoilGrids250m: Global gridded soil information based on machine learning
This paper describes the technical development and accuracy assessment of the most
recent and improved version of the SoilGrids system at 250m resolution (June 2016 update) …
recent and improved version of the SoilGrids system at 250m resolution (June 2016 update) …
Large increase in global storm runoff extremes driven by climate and anthropogenic changes
Weather extremes have widespread harmful impacts on ecosystems and human
communities with more deaths and economic losses from flash floods than any other severe …
communities with more deaths and economic losses from flash floods than any other severe …
[PDF][PDF] Spatio-temporal interpolation using gstat.
We present new spatio-temporal geostatistical modelling and interpolation capabilities of the
R package gstat. Various spatio-temporal covariance models have been implemented, such …
R package gstat. Various spatio-temporal covariance models have been implemented, such …
Random forest spatial interpolation
For many decades, kriging and deterministic interpolation techniques, such as inverse
distance weighting and nearest neighbour interpolation, have been the most popular spatial …
distance weighting and nearest neighbour interpolation, have been the most popular spatial …
SoilGrids1km—global soil information based on automated mapping
Background Soils are widely recognized as a non-renewable natural resource and as
biophysical carbon sinks. As such, there is a growing requirement for global soil information …
biophysical carbon sinks. As such, there is a growing requirement for global soil information …
Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach
Land surface temperature (LST) is among the most important variables in monitoring land
surface processes. LST is often retrieved from thermal infrared remote sensing data, which …
surface processes. LST is often retrieved from thermal infrared remote sensing data, which …
Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment
In China, ozone pollution shows an increasing trend and becomes the primary air pollutant
in warm seasons. Leveraging the air quality monitoring network, a random forest model is …
in warm seasons. Leveraging the air quality monitoring network, a random forest model is …