Next Article in Journal
Developing an Ensemble Precipitation Algorithm from Satellite Products and Its Topographical and Seasonal Evaluations Over Pakistan
Previous Article in Journal
Towards a 20 m Global Building Map from Sentinel-1 SAR Data
Open AccessArticle

Capability of Remotely Sensed Drought Indices for Representing the Spatio–Temporal Variations of the Meteorological Droughts in the Yellow River Basin

1
School of Water Conservancy and Environment, Zhengzhou University, Zhengzhou 450001, China
2
China Institute of Water Resources and Hydropower Research, the State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing 100038, China
3
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(11), 1834; https://doi.org/10.3390/rs10111834
Received: 22 October 2018 / Revised: 17 November 2018 / Accepted: 18 November 2018 / Published: 20 November 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Due to the advantages of wide coverage and continuity, remotely sensed data are widely used for large-scale drought monitoring to compensate for the deficiency and discontinuity of meteorological data. However, few studies have focused on the capability of various remotely sensed drought indices (RSDIs) to represent the spatio–temporal variations of meteorological droughts. In this study, five RSDIs, namely the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Modified Temperature Vegetation Dryness Index (MTVDI), and Normalized Vegetation Supply Water Index (NVSWI), were calculated using monthly Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The monthly NDVI and LST data were filtered by the Savitzky–Golay (S-G) filtering method. A meteorological station-based drought index represented by the Standardized Precipitation Evapotranspiration Index (SPEI) was compared with the RSDIs. Additionally, the dimensionless Skill Score (SS) method was adopted to identify the spatiotemporally optimal RSDIs for presenting meteorological droughts in the Yellow River basin (YRB) from 2000 to 2015. The results indicated that: (1) RSDIs revealed a decreasing drought trend in the overall YRB consistent with the SPEI except for in winter, and different variations of seasonal trends spatially; (2) the optimal RSDIs in spring, summer, autumn, and winter were VHI, TCI, MTVDI, and VCI, respectively, and the average correlation coefficient between the RSDIs and the SPEI was 0.577 (α = 0.05); and (3) different RSDIs have time lags of zero–three months compared with the meteorological drought index. View Full-Text
Keywords: remotely sensed drought indices (RSDIs); Standardized Precipitation Evapotranspiration Index (SPEI); meteorological drought; Skill Score (SS); Yellow River basin (YRB) remotely sensed drought indices (RSDIs); Standardized Precipitation Evapotranspiration Index (SPEI); meteorological drought; Skill Score (SS); Yellow River basin (YRB)
Show Figures

Graphical abstract

MDPI and ACS Style

Wang, F.; Wang, Z.; Yang, H.; Zhao, Y.; Li, Z.; Wu, J. Capability of Remotely Sensed Drought Indices for Representing the Spatio–Temporal Variations of the Meteorological Droughts in the Yellow River Basin. Remote Sens. 2018, 10, 1834.

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.

Article Access Map by Country/Region

1
Back to TopTop