The last decade has seen a considerable increase in the amount and availability of remotely
sensed data. This paper reviews the satellites, sensors and studies relevant to land surface …

Downscaling has an important role to play in remote sensing. It allows prediction at a finer
spatial resolution than that of the input imagery, based on either (i) assumptions or prior …

Soil organic carbon (SOC) plays an important role in soil fertility and carbon sequestration,
and a better understanding of the spatial patterns of SOC is essential for soil resource …

Accurate long-term temperature and precipitation estimates at high spatial and temporal
resolutions are vital for a wide variety of climatological studies. We have produced a new …

Estimation of carbon and nitrogen stocks is important for quantifying carbon and nitrogen
sequestration as well as greenhouse gas emissions and inventorying national carbon and …

Soil organic carbon (SOC) significantly influences soil fertility, soil water holding capacity,
and plant productivity. In this study, we applied two boosted regression tree (BRT) models to …

Air temperature (Tair) near the ground surface is a fundamental descriptor of terrestrial
environment conditions and one of the most widely used climatic variables in global change …

Landslides pose a serious risk to human life and the natural environment. Here, we compare
machine learning algorithms including the generalized linear model (GLM), mixture …

Vegetation cover is a key environmental variable often mapped from satellite and aerial
imagery. The derivation of ultra-high spatial resolution fractional vegetation cover (FVC) …

Abstract Land Surface Temperature (LST) is a key variable in the interactions and energy
fluxes between the Earth surface and the atmosphere. Satellite data provide consistent …