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

目前，全球生物气候变量图仅在非常粗糙的空间分辨率下才存在（例如，World-Clim）。对于需要更高解析信息的生态研究而言，这种空间分辨率通常不足。这项研究的目的是从地球观测（EO）数据以1 km的更高空间分辨率估算蒙古的重要生物气候变量。该分析使用了两个不同的卫星时间序列数据集：中分辨率成像光谱仪（MODIS）的地表温度（LST），以及气候灾害组带站红外降水（CHIRPS）的降水（P）。使用随机森林（RF）回归模型从Terra MODIS卫星（集合6）LST时间序列乘积估算每月最高，平均和最低气温。每月总降水量数据是从CHIRPS 2.0版获得的。基于此原始数据，生成了19个生物气候变量的空间图，其空间分辨率为1 km，代表2002–2017年。我们使用确定系数（R ²），均方根误差（RMSE）和归一化均方根误差（nRMSE），发现总体上吻合良好。在19个WorldClim生物气候变量中，估计有17个的测定系数（R ²）大于0.7，归一化RMSE（nRMSE）小于8％，这证实了可以从卫星数据中检索空间模式和值范围。与WorldClim相比，空间分辨率更高。仅对与极端温度有关的两个生物气候变量（即年平均日范围和等温）建模，但仅具有中等精度（nRMSE的R ²约为0.4）约占11％）。通常，与温度相关的生物气候变量相比，与降水相关的生物气候变量与WorldClim的相关性更高。建模的总体成功归因于以下事实：卫星衍生的数据非常适合生成的降水和温度变量的空间场，尤其是在高海拔和高纬度地区。由于成功地以1 km的空间分辨率检索了生物气候变量，因此我们有信心估计的19种生物气候变量对于包括物种分布模型在内的一系列应用将非常有用。