Next Article in Journal
Analysis of Aerosol Radiative Forcing over Beijing under Different Air Quality Conditions Using Ground-Based Sun-Photometers between 2013 and 2015
Next Article in Special Issue
Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment
Previous Article in Journal
Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Remote Sens. 2016, 8(6), 509;

Time Series MODIS and in Situ Data Analysis for Mongolia Drought

Graduate Institute of Space Science, National Central University, Jhongli District, Taoyuan City 320, Taiwan
Center for Space and Remote Sensing Research, National Central University, Jhongli District, Taoyuan City 320, Taiwan
Information and Research Institute of Meteorology, Hydrology and Environment, Ulaanbaatar 15160, Mongolia
Taiwan Group on Earth Observation, Zhubei City 30274, Hsinchu County, Taiwan
Applied Hydrometeorological Research Institute, Nanjing University of Information Science & Technology, Nanjing 210044, China
Author to whom correspondence should be addressed.
Academic Editors: Yuriy Kuleshov, Alfredo R. Huete and Prasad S. Thenkabail
Received: 25 March 2016 / Revised: 9 May 2016 / Accepted: 2 June 2016 / Published: 16 June 2016
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
PDF [4407 KB, uploaded 16 June 2016]


Drought is a period of abnormally dry weather with a serious shortage of water supply. Drought indices can be an advantageous indicator to assess drought for taking further response actions. However, drought indices based on ground meteorological measurements could not completely reveal the land use effects over a regional scale. On the other hand, the satellite-derived products provide consistent, spatial and temporal comparisons of global signatures for the regional-scale drought events. This research is to investigate the drought signatures over Mongolia by using satellite remote sensing imagery. The evapotranspiration (ET), potential evapotranspiration (PET) and two-band Enhanced Vegetation Index (EVI2) were extracted from MODIS data. Based on the standardized ratio of ET to PET (ET/PET) and EVI2, the Modified Drought Severity Index (MDSI) anomaly during the growing season from May–August for the years 2000–2013 was acquired. Fourteen-year summer monthly data for air temperature, precipitation and soil moisture content of in situ measurements from sixteen meteorological stations for four various land use areas were analyzed. We also calculated the percentage deviation of climatological variables at the sixteen stations to compare to the MDSI anomaly. Both comparisons of satellite-derived and observed anomalies and variations were analyzed by using the existing common statistical methods. The results demonstrated that the air temperature anomaly (T anomaly) and the precipitation anomaly (P anomaly) were negatively (correlation coefficient r = −0.66) and positively (r = 0.81) correlated with the MDSI anomaly, respectively. The MDSI anomaly distributions revealed that the wettest area occupied 57% of the study area in 2003, while the driest (drought) area occurred over 54% of the total area in 2007. The results also showed very similar variations between the MDSI and T anomalies. The highest (wettest) MDSI anomaly indicated the lowest T anomaly, such as in the year 2003, while the lowest (driest) MDSI anomaly had the highest T anomaly in 2007. By comparing the MDSI anomaly and soil moisture content at a 10-cm depth during the study period, it is found that their correlation coefficient is 0.74. View Full-Text
Keywords: MODIS; evapotranspiration (ET); PET; two-band Enhanced Vegetation Index (EVI2); MDSI anomaly; Mongolia MODIS; evapotranspiration (ET); PET; two-band Enhanced Vegetation Index (EVI2); MDSI anomaly; Mongolia

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Dorjsuren, M.; Liou, Y.-A.; Cheng, C.-H. Time Series MODIS and in Situ Data Analysis for Mongolia Drought. Remote Sens. 2016, 8, 509.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top