Estimating spatial distribution of aridity and moisture indices of Mongolia using remotely sensed time series data

Abstract

The aim of this study is to estimate the spatial distribution of aridity and moisture indices using remotely sensed time-series data. For the analysis, we have chosen two simple climatic indices. One of two indices was the De Martonne aridity index, and the other one was the Mezentsev moisture index. The study area covers the total territory of Mongolia (~1.566 × 10⁶ km²). Both indices could be estimated from meteorological station-based air temperature and precipitation. However, meteorological station-based recorded precipitation and temperature data with long coverage are only available from a limited number of stations with insufficient spatial coverage. In other words, these datasets suffer from uneven geographic coverage, with many areas of the Earth poorly represented. In this study, therefore, we have used satellite-derived temperature and precipitation data. Monthly mean air temperature has been estimated from MODIS LSTd, LSTn, and elevation using RF regression. Precipitation data has been extracted from Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS) datasets. CHIRPS is gauge-satellite combined precipitation data. Based on De Martonne and Mezensev formulas, and satellite-derived meteorological data, spatial distribution maps of aridity and moisture indices in Mongolia were generated. The study result showed that aridity was observed in all areas of southern Mongolia, and some areas in the west, and grassland areas in the east, which is largely included in the dry steppe, desert-steppe, and gobi desert zones. Moisture was observed in the forest and forest-steppe areas in north, central, northeastern, and eastern Mongolia. A comparison of the aridity index and moisture index shows the following: of the total territory, aridity is 31.9:31.4 percent, humidity 14.7:13.4 percent respectively. Dry steppe, desert steppe, and Gobi desert zones are extremely sensitive to water resource variability and availability.