Estimation of bioclimatic variables of Mongolia derived from remote sensing data

摘要: Global maps of bioclimatic variables currently exist only at very coarse spatial resolution (e.g. World-Clim). For ecological studies requiring higher resolved information, this is often insufficient. The aim study to estimate important Mongolia from Earth Observation (EO) data a 1 km. analysis used two different satellite time series sets: land surface temperature (LST) Moderate Resolution Imaging Spectroradiometer (MODIS), and precipitation (P) Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). Monthly maximum, mean, minimum air were estimated Terra MODIS (collection 6) LST product using the random forest (RF) regression model. total obtained CHIRPS version 2.0. Based on primary data, 19 km generated, representing period 2002–2017. We tested relationship between (SatClim) WorldClim 2.0 (WorldClim) determination coefficient (R2), root mean square error (RMSE), normalized (nRMSE) found overall good agreement. Among set variables, 17 (R2) than 0.7 RMSE lower 8%, confirming that pattern value ranges can be retrieved much compared WorldClim. Only related extremes (i.e., annual diurnal range isothermality) modeled moderate accuracy (R2 about 0.4 nRMSE 11%). Generally, precipitation-related closer correlated temperature-related variables. success modeling was attributed fact satellite-derived are well suited generated fields especially high altitudes latitudes. As consequence successful retrieval resolution, we confident will useful for applications, including species distribution modeling.