Drought is a complex and recurring natural disaster that occurs throughout the world and often has negative impacts on many sectors of society [1
]. Droughts are increasing in frequency and severity, and their impact on human lives and the economy is accelerating due to growing levels of urbanization and an increasing number of extreme weather events [3
]. The effective assessment of drought is an essential means towards achieving sustainable development. Traditional drought monitoring is based on data from meteorological stations. The relatively mature meteorological drought index includes the Palmer Drought Severity Index (PDSI) [6
], the Standardized Precipitation Index (SPI) [7
], and the Standardized Precipitation Evapotranspiration Index (SPEI) [8
]. Despite the high accuracy of meteorological station data, the meteorological drought index is constrained by the insufficient spatial distribution of stations, and has difficulty in reflecting a wide range of drought information [9
]. In 2015, 17 sustainable development goals (SDGs) were formally adopted at the UN Sustainable Development Summit, which clearly indicates that remote sensing technology has become an important way to reduce the risk of loss from drought disaster and achieve the goal of sustainable development [10
]. Remote sensing technology makes up for the shortage of meteorological stations thanks to its advantages of objective, its timely, economic, and wide coverage, its continuous data, and its ability to extend traditional “point” measurements to information about the entire areas. Remote sensing has proved to be the most promising technology in drought monitoring, and is now widely used in drought prevention, response, recovery, and mitigation [12
Many drought-monitoring methods based on remote sensing have been developed, including the vegetation index method, thermal inertia method, canopy temperature method, and microwave remote sensing method. As vegetation growth is closely related to soil moisture levels, the vegetation index method has become the main approach for monitoring agricultural drought based on remote sensing [14
]. Kogan considered that the Vegetation Condition Index (VCI) is capable of monitoring drought and providing accurate drought information under different ecological environmental conditions in the United States. The VCI is suitable for monitoring the interannual dry–wet conditions and can eliminate the effects of climatic conditions, soil type, and topography, allowing comparison between different regions [15
]. The Temperature Condition Index (TCI) is defined by the principle of elevated temperature and deficient water, and vegetation canopy or soil surface temperature increases with the rise of water stress. The TCI can reflect the adverse effects of high temperature on the growth of crops [17
]. The Vegetation Health Index (VHI) is a health condition index that takes into account the effect of vegetation leaf surface and temperature on vegetation. The VHI is used to reflect the differences in the spatio–temporal patterns of drought and has a better effect of drought monitoring [18
]. Sandholt et al. indicated that there was a triangular or trapezoidal relationship between the vegetation index and land surface temperature, and developed the Temperature Vegetation Dryness Index (TVDI) based on the scattered point feature space of these two parameters [19
]. Wang et al. proposed the Modified Temperature Vegetation Dryness Index (MTVDI) based on the difference between one and the TVDI [20
]. The MTVDI combines the special physiological and ecological significance of the vegetation index and land surface temperature; it is easy to understand and calculate, and is widely used in drought monitoring. Abbas et al. revised the Vegetation Supply Water Index (VSWI), and discovered that the physical mechanism of the Normalized Vegetation Supply Water Index (NVSWI) is much clearer [21
]. The NVSWI is superior to the VSWI for the analysis of time series, it can express the actual drought situation, and has a prominent advantage in drought monitoring [22
There are many remotely sensed drought indices (RSDIs), which use data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for drought monitoring. While all of these RSDIs can be used to monitor drought, the capability of different indices varies with temporal and spatial patterns [23
]. Klisch et al. quantified drought strength by calculating the VCI at the pixel level from de-noised MODIS Normalized Difference Vegetation Index (NDVI) data, and successfully applied drought products to drought monitoring in Kenya [24
]. Zhang et al. used drought events during 2011 and 2012 to compare various RSDIs, and found that different RSDIs had differing characteristics and were suitable for specific environments [25
]. The studies of Hao et al. and Du et al. indicated that studying the capability of RSDIs can better reveal drought characteristics, by comparing the drought monitoring ability of several different RSDIs [26
]. RSDIs are used to quantitatively evaluate the effects of drought and directly determine the accuracy of drought monitoring; therefore, it is particularly important to study their capability under different spatio–temporal patterns. However, few studies have combined RSDIs with the meteorological drought index to assess whether the remotely sensed index is suitable for drought monitoring, and the research on the capability of RSDIs under different spatio–temporal patterns is insufficient [28
]. Moreover, most of the methods to evaluate capability are based on a single statistical indicator (e.g., average deviation and root-mean-square error). While average deviation can reflect the overall deviation degree of remotely sensed and meteorological drought results in time-field or space-field, it cannot measure the similarity degree. Likewise, the correlation coefficient can measure the similarity degree between remotely sensed and meteorological drought, but it cannot reflect the actual deviation information. Due to the obvious temporal and spatial differences in drought, it is urgent to improve the comprehensiveness and objectivity of drought assessment through a composite statistical indicator with explicit significance. The latest development of Skill Score (SS) is a composite indicator that considers deviation and correlation coefficient synthetically [30
]. By comparing the grade results of remotely sensed and meteorological drought, the spatio–temporal capability of RSDIs is quantitatively evaluated [32
The capability of RSDIs should be fully considered on temporal and spatial scales. However, the systematic investigation of the spatio–temporal capability of RSDIs has not been carried out in the Yellow River basin (YRB). In view of this, the composite indicator SS was used for the first time in the YRB to quantitatively evaluate the capability of RSDIs in order to obtain the optimal RSDIs under different spatio–temporal patterns. We can elaborately and systematically reveal the highly precise RSDIs used to assess the YRB under different temporal and spatial patterns based on SS, so as to improve the accuracy of drought monitoring. The research results can provide a reasonable scientific assessment of the drought situation in the YRB and provide reference and basis for drought relief measures. It is of great practical significance to study the evolution and scientific development of drought under the changing environment in the YRB.
The capability of RSDIs should be fully considered under different spatio–temporal patterns, which can improve the accuracy of drought monitoring in the YRB. Based on the SS method, five RSDIs (VCI, TCI, VHI, MTVDI, and NVSWI) were quantitatively evaluated with the meteorological drought index SPEI in order to determine the optimal RSDIs under different spatio–temporal patterns from 2000 to 2015.
Drought slowed down in the YRB during 2000–2015. The linear tendency rates of the VCI, TCI, VHI, MTVDI, NVSWI, and SPEI were 0.2/10a, 0.059/10a, 0.133/10a, 0.005/10a, 0.137/10a, and 0.148/10a, with the most obvious trend being seen for the VCI. The main drought type was moderate drought, as monitored by different RSDIs in the YRB. On the seasonal scale, agricultural drought showed a decreasing trend based on the RSDIs, and meteorological drought showed a decreasing trend based on spring, summer, and autumn SPEI and an increasing trend based on winter SPEI. The drought results based on remote sensing were slightly different from the results obtained for meteorological drought, and had time lags of zero–three months compared with meteorological drought. By investigating the capability of RSDIs under different spatio–temporal patterns, the optimal RSDIs in spring, summer, autumn, and winter were found to be the VHI, TCI, MTVDI, and VCI, respectively, and the average correlation coefficient between the RSDIs and the SPEI was 0.577 (α = 0.05). In the future, the optimal RSDIs should be adopted to monitor drought conditions in the YRB, which can provide a reasonable scientific basis for relevant departments to plan and make decisions relating to drought.
In this study, we combined remotely sensed and meteorological drought indices to study the characteristics of drought and quantitatively evaluated the capability of RSDIs in the YRB. However, the factors involved in this paper were limited, and the physical mechanism of drought was not taken into account. There are many other factors involved in the occurrence of drought, and we should take these into account. Furthermore, we should increase the category of RSDIs to expand the scope of selection in future research, which is highly necessary for accurate drought monitoring.