259SOLA, 2020, Vol. 16, 259−264, doi:10.2151/sola.2020-043

©The Author(s) 2020. This is an open access article published by the Meteorological Society of Japan under a Creative Commons Attribution 4.0 International (CC BY 4.0) license (http://creativecommons.org/license/by/4.0).

AbstractBased on the drought severity index (DSI), which is derived

from the ratio of evapotranspiration (ET) to potential evapo-transpiration (PET) and normalized difference vegetation index (NDVI), as measured by remote sensing, we replaced the ET/PET ratio with soil moisture (0−10 cm) to establish an improved index (IDSI) and evaluated the drought situation in Inner Mongolia. The two components in IDSI are independently derived; however, the effects of evapotranspiration are contained in the soil moisture, and even precipitation changes could be reflected. We used month-ly IDSI data for Inner Mongolia to evaluate drought throughout the growing season (May–September) during 2001−2010. The IDSI showed that 2001, 2007, and 2009 were years with signifi-cant drought. Areas that experienced extreme drought were less extensive than those found by the DSI because we removed the ET/PET component, which already contains the effects of the NDVI. IDSI results not only contained the degree of precipitation change for a given month through soil moisture, but also reflected the influence of water and heat in the previous month through the NDVI. Moreover, IDSI results were generally consistent with agricultural drought disaster records.

(Citation: Wang, L., A. Kotani, T. Tanaka, and T. Ohta, 2020: Application of improved remotely sensed drought severity index based on soil moisture product in Inner Mongolia. SOLA, 16, 259−264, doi:10.2151/sola.2020-043.)

1. Introduction

The Inner Mongolia Autonomous Region, with a total area of 1.183 million km2, is in the northern frontier of China. It has a diverse ecosystem environment that includes mainly woodland, grassland, and unvegetated areas (Fig. 1). As an important agri-cultural and livestock province with abundant natural resources, it plays a significant role in China. With unevenly distributed or low levels of precipitation, much of Inner Mongolia consists of arid or semiarid areas. Hence, drought is the area’s most notable natural disaster, directly and severely affecting agriculture and animal husbandry (Wang et al. 2019). Wang and Zhang (2012) mentioned that drought disasters had occurred frequently in Inner Mongolia since 2000. Therefore, it was clear that monitoring drought in Inner Mongolia was necessary and could help improve the under-standing of causes and prevention of such disasters.

Drought is generally studied using a drought index. Moderate Resolution Imaging Spectroradiometer (MODIS) data play an increasingly important role in drought monitoring and assessment (Wan et al. 2004). Mu et al. (2007, 2011) developed a model for estimating evapotranspiration (ET) and potential ET (PET) and then set up the global MOD16ET product dataset. Mu et al. (2013a) integrated the operational MOD16 ET/PET ratio with Huete et al.’s (2002) MODIS normalized difference vegetation index (NDVI) products to obtain a remotely sensed global Drought Severity Index (DSI) for drought estimation (the item 1 in the

supplementary material).In DSI calculations, soil moisture and precipitation are not

used as input items (Mu et al. 2013a). When Wang et al. (2020) used Mu et al.’s (2013a) DSI to evaluate drought in Inner Mongo-lia during 2001−2010, they examined the relationship between the DSI and soil moisture and came to the following conclusions: (1) The relationship between the DSI and soil moisture was mainly controlled by the relationship between ET/PET and soil moisture, whereas the relationship between the NDVI and soil moisture was very limited. (2) ET/PET and the NDVI were not mutually inde-pendent, because the NDVI affected ET/PET. (3) The relationship between ET/PET and soil moisture was more significant in areas with abundant rainfall. Given the above conclusions, we chose to replace the ET/PET ratio with soil-moisture data to establish an improved index (IDSI). Accordingly, the two components of the IDSI are independent of each other, but the effects of evapotrans-piration are already contained in the soil moisture, and even the precipitation changes could be reflected. Moreover, soil moisture data makes up for the lack of DSI calculation due to the lack of ET/PET data in western Inner Mongolia.

Inner Mongolia is a large province with agriculture and animal husbandry as the main industry. In this study, we use the drought index based on land surface elements (vegetation activity and soil moisture), which is in line with the agricultural perspective (Passioura 2006). Mishra and Singh (2010) also attributed the lack of soil moisture to agricultural drought, and pointed out that precipitation is one of the important factors affecting soil mois-ture. We first used the IDSI to evaluate the drought situation in Inner Mongolia throughout the growing season (May–September) during 2001−2010, which is the same time period studied by Wang et al. (2020). Then we clarify the relationship between the IDSI and precipitation. By examining IDSI applicability to assess the water-resources situation, we expected to improve monitoring and provide empirical disaster warnings for agriculture in Inner Mongolia.

2. Data and methods

2.1 DataWe integrated soil-moisture data obtained from the Global

Land Data Assimilation System (GLDAS) (Rodell et al. 2004) with the Noah model in this study. Noah model is a land surface model that simulates processes of the heat transfer and water exchange between the ground surface and the atmosphere (Chen et al. 1996; Chen and Dudhia 2001). We used the monthly soil- moisture data from layer 1 (SML1, 0−10 cm depth, 0.25° spatial resolution) of the GLDAS–Noah v.2.1 dataset, which was devel-oped by the U.S. National Aeronautics and Space Administration’s Goddard Earth Sciences Data and Information Services Center (Beaudoing et al. 2016).

Monthly NDVI products obtained from MOD13C2 v.6, at a 0.05° Climate Modeling Grid resolution, were downloaded from the U.S. Geological Survey’s Land Processes Distributed Active Archive Center (Didan et al. 2015). The monthly NDVI data are resampled at 0.25° × 0.25° mean spatial resolution.

Monthly observation-based precipitation data from 23 Inner Mongolia Meteorological Administration stations (Fig. 1); there were no missing data. Precipitation grid data were obtained from

Application of Improved Remotely Sensed Drought Severity Index Based on Soil Moisture Product in Inner Mongolia

Lusha Wang, Ayumi Kotani, Takafumi Tanaka, and Takeshi OhtaLaboratory of Forest Hydrology and Disaster Mitigation Sciences, Graduate School of Bioagricultural Sciences,

Nagoya University, Nagoya, Japan

Corresponding author: Lusha Wang, Laboratory of Forest Hydrology and Disaster Mitigation Sciences, Graduate School of Bioagricultural Sci-ences, Nagoya University, Nagoya 464-8601, Japan. E-mail: wang.lusha@g.mbox.nagoya-u.ac.jp.

260 Wang et al., Drought Severity Index based on Soil Moisture

3. Results

3.1 Drought situation during the 10 yearsa. Dryness and wetness classification

Because the sample population was based on the original DSI, the new IDSI classification of dry and wet conditions also uses 11 categories. They were extreme drought (≤ −1.82), severe drought (−1.82 < IDSI ≤ −1.34), moderate drought (−1.34 < IDSI ≤ −1.04), mild drought (−1.04 < IDSI ≤ −0.74), incipient drought (−0.74 < IDSI ≤ −0.45), near normal (−0.45 < IDSI ≤ 0.44), in-cipient wet spell (0.44 ≤ IDSI < 0.74), slightly wet (0.74 ≤ IDSI < 1.04), moderately wet (1.04 ≤ IDSI < 1.34), very wet (1.34 ≤ IDSI < 1.89) and extremely wet (≥ 1.89).

b. Drought situation during 2001–2010 evaluated by improved drought severity index

On the basis of percentages of the moderate, severe, and extreme drought categories and the totals (TDs) for each month of the growing season during 2001−2010 in Inner Mongolia (Fig. 2a), we roughly divided the 10-year drought situation into three ranges according to average growing-season TDs for each year: < 10%, 10%−20%, and > 20% (area percentage).

The TDs for both 2001 and 2007 were > 20%. It is also clear from the graph that their drought occurrence is significant, particularly in 2001. For 2007, from June to the end of the grow-ing season, a large area of drought occurs almost every month, although it shows a decreasing trend. For 2004−2006 and 2009, the TDs were similar, 10%−20%, but each year during 2004−2006 had one or two months of significant drought, while the TDs of 2009 gradually increased during the second half of the growing season. In 2002, 2003, 2008, and 2010, the TDs were < 10% throughout the growing season.

During the growing seasons of the 2001−2010 decade, persis-tent severe droughts occurred in both 2001 and 2007, with 2001 being the most significant. During 2004−2006, occasional wide-spread moderate and severe droughts occurred. The year with the least drought was 2008, but 2009 saw persistent moderate or severe drought and then increasingly extreme drought late in the growing season, and the average of extreme drought area in the growing season is in the third place after 2001 and 2007.

3.2 Spatial distribution characteristics of improved drought severity index in years when severe to extreme drought occurred

The above results (Fig. 2a) show that in 2001 and 2007, both the area where moderate drought occurred and the degree of extreme drought were significant compared with other years. In 2009, although overall not the third driest among the 10 years, extreme droughts increased significantly after July.

Characteristics of the IDSI spatial distributions during 2001,

Yatagai et al.’s (2012) Asian Precipitation—Highly Resolved Ob-servational Data Integration Toward Evaluation dataset. We select-ed the Monsoon Asia product (version V1801R1), which covers our research area. The spatial resolution is on a 0.25° latitude– longitude grid.

2.2 Methods The first step was to calculate the IDSI for each pixel of each

month during May 2001 through September 2010. The equations are similar to those of the DSI; we replaced ET/PET with soil moisture, as follows:

ZSMSM

SM SM=

−σ

(1)

ZNDVINDVI

NDVI NDVI=

−σ (2)

Z Z ZI = +SM NDVI (3)

IDSI= −Z ZI I

ZIσ

(4)

where SM and NDVI represent the 10-year mean monthly soil moisture and NDVI, respectively; σSM , σNDVI and σZI

represent the standard deviations of the soil moisture, NDVI, and ZI , respective-ly; and ZSM and ZNDVI represent the normalization of soil moisture and NDVI, respectively. The monthly 0.05° NDVI data were resampled at 0.25° × 0.25° spatial resolution to calculate the IDSI.

We tentatively divided all the pixel values of IDSI for each month of the growing season during the 10 years 2001−2010 into 11-category classification of DSI (the item 1 in the supple-mentary material). Then, for each tentative category, to reach the confidence level of 0.95, we used statistical estimation analysis (Li and Hu 2015) to redetermine the thresholds (the item 2 in the supplementary material).

According to our reclassification, we generated the IDSI for each month of the growing season during the 10-year period. To evaluate the 2001−2010 drought situation, we focused on counting area percentages of moderate, severe, and extreme drought.

To address the relationship between the IDSI and precipita-tion, first we averaged monthly precipitation value of the 23 sta-tions as the data of the whole area, and investigated the monthly precipitation anomaly based on 10-year average value for each year. Then the grid precipitation data were normalized against the 10-year average values as anomaly percentages for each month of the growing season. We investigated the spatial distribution of the 10-year correlation coefficients between the IDSI and the normal-ized grid precipitation anomalies and also between ZNDVI and ZSM.

Fig. 1. Location and elevation of Inner Mongolia (left) and land cover map in 2005 (right). On the left, the area enclosed by the black line is Inner Mongo-lia, gray is the national and provincial boundaries of China, and white color is the water. Black dots represent the locations of 23 meteorological stations. On the right, the data comes from MCD12Q1 land cover data of the Terra and Aqua combined MODIS (Moderate Resolution Imaging Spectroradiometer), with a spatial resolution of 500 m, classified by the University of Maryland (UMD) classification criteria. The white parts include the urban, water body, small-scale cropland, etc.

261SOLA, 2020, Vol. 16, 259−264, doi:10.2151/sola.2020-043

2007, and 2009 were of particular interest (Fig. S1). The 2001 drought period extended throughout the entire growing season, spring through autumn, and severe and even extreme droughts occurred over a large area in the central and western regions. Summer drought occurred mainly in 2007, while severe and extreme droughts occurred in some areas in the central and north-eastern regions. In 2009, severe and extreme drought occurred during the summer and autumn in the central region.

3.3 Relationship between improved drought severity index and precipitationa. Annual variation of monthly average precipitation anomaly

Figure 2b shows the annual variation of monthly average pre-cipitation anomaly from 2001 to 2010. Comparing Fig. 2a, it can be clearly seen that in 2001 and 2007, when droughts occurred,

the precipitation was relatively low throughout the growing season. In addition to a little positive anomaly in June of 2009, there was also almost a negative anomaly. It can be said that the occurrence of severe droughts characterized by IDSI is closely related to precipitation.

b. Spatial variation in relationship between improved drought severity index and precipitation

The spatial distributions of the 10-year correlation coeffi-cients between the IDSI and normalized precipitation anomalies for each month (Fig. 3) indicate a slightly positive correlation between IDSI and precipitation. In the northeast forested area, the relationship between the IDSI and precipitation changes was not significant throughout the growing season. Areas with sparse vegetation in the western part of the region show significant

Fig. 2. a) Percentage of areas where moderate, severe, extreme drought and their total (TD) occurred in each month from 2001−2010 in Inner Mongolia. The numbers above the histogram represent the annual average value of TD, and the numbers in brackets represent the average extreme drought areas throughout the growing season. b) The annual variation of monthly average precipitation anomaly from 2001 to 2010.

Fig. 3. Spatial distribution of correlation coefficients between IDSI and the normalized precipitation during the 10-year for each month of growth season in Inner Mongolia. Black dots represent areas where statistically significant at 95% significant level. a) May, b) June, c) July, d) August, e) September.

262 Wang et al., Drought Severity Index based on Soil Moisture

correlation between the IDSI and precipitation changes, and the extensive grasslands in the central and northeastern parts of the region almost always do as well.

The relationship between ZNDVI and precipitation changes is not very significant, as shown by the spatial distribution of correla-tion coefficients (Fig. 4). In the forested area in the northeastern part of the region, ZNDVI and normalized precipitation show a neg-ative correlation, but it is not significant. The positive correlation of ZNDVI with precipitation for the grassland area is significant in the first half of the growing season. The western region generally shows a seasonal change from positive to negative correlation, but it is almost insignificant.

In contrast, throughout the growing season, over much of

Inner Mongolia, ZSM and normalized precipitation present a signif-icant positive correlation (Fig. 5).

4. Discussion

4.1 Difference between original and improved drought severity indexes

For the three drought years of 2001, 2007 and 2009, we compared the spatial distribution of the IDSI and the original DSI, which was investigated by Wang et al. (2020). The DSI in the western part could not be calculated because of the lack of ET and PET. For 2007 and 2009, the IDSI indicates that there were

Fig. 5. Spatial distribution of correlation coefficients between ZSM and the normalized precipitation during the 10-year for each month of growth season in Inner Mongolia. Black dots represent areas where statistically significant at 95% significant level. a) May, b) June, c) July, d) August, e) September.

Fig. 4. Spatial distribution of correlation coefficients between ZNDVI and the normalized precipitation during the 10-year for each month of growth season in Inner Mongolia. Black dots represent areas where statistically significant at 95% significant level. a) May, b) June, c) July, d) August, e) September.

263SOLA, 2020, Vol. 16, 259−264, doi:10.2151/sola.2020-043

few widespread droughts in the west (Fig. S1). The distributions of our IDSI and Wang et al.’s DSI (2020) for each month during these three years are roughly the same except for the western area. However, we found that the percentage of areas where extreme drought occurred in 2007 and 2009 based on the IDSI were small-er than those based on the DSI for each month (Table 1).

According to Wang et al. (2020), the evapotranspiration component of DSI included NDVI effects, while soil moisture is almost unrelated to the NDVI. Replacing the ET/PET component with soil moisture is equivalent to removing the duplicate effect of the NDVI in the DSI. When evaluating drought with the DSI, if ZNDVI becomes larger, the effect of ZET/PET increases to the extent that it is affected by the NDVI. Accordingly, the degree of drought shown by the DSI can become serious owing to the duplicating influence of the NDVI. After removing ZET/PET from the DSI while retaining the ZNDVI, the IDSI value will become smaller than that of the DSI; the drought values are negative in IDSI, and part of the extreme drought of the IDSI will be reduced to become a severe drought.

4.2 Relationship between drought-severity index drought and agricultural drought disasters

We extracted agricultural drought disaster-affected area data that from the yearbook of the National Bureau of Statistics of China (http://www.stats.gov.cn/). As is apparent from by compar-ing annual changes of the estimated mean moderate, severe, and extreme drought areas during the growing season each year and areas affected by reported drought disaster each year, the annual trend of severe and extreme drought by IDSI were almost the same as the drought disaster areas (Fig. 6a). (Moderate drought areas did not match the disaster areas.) Although the extreme drought in 2001 was very serious, crop areas affected by drought did not show as among the most extensive in the 10 years, but this is because the drought in 2001 was significant in the western region, which is not an important agricultural area. Overall, however, the more-severe drought data as characterized by the IDSI are consistent with agricultural drought disaster records. Compared to the case of DSI (Fig. 6b), the correlation between IDSI and agricultural drought disasters was less than that of DSI,

because calculation area of IDSI includes western Inner Mongolia, where there is little agricultural activity as mentioned above.

4.3 Relationships among normalized difference vegetation index, normalized soil moisture, and precipitation

We found very few areas exhibiting significant correlation coefficients between ZNDVI and precipitation (Fig. 4). According to past research on the relationship between vegetation and the NDVI in Inner Mongolia (Chuai et al. 2013; Mu et al. 2013b; Zhang et al. 2013), the NDVI has a lagging response to precipitation and temperature. Chuai et al. (2013) also pointed out that grassland vegetation is more sensitive than woodland vegetation to water and heat, and the lag effect is more obvious for grassland, but lag time varies with season as well as vegetation type. Therefore, it can be considered that the IDSI reflects both precipitation change in a given month and water and heat effects from the previous month.

The response time of soil moisture and NDVI to meteorolog-ical elements is different, and the superposition result can reflect the actual drought situation. Therefore, equation (3) also has practical significance. However, this difference in response time may cause the superposition of positive and negative values, the calculated IDSI value may be close to the normal value and thus may not correctly reflect the actual drought situation. It could be considered that IDSI is clearer for more severe drought when both soil moisture and vegetation activity show negative values.

5. Conclusion

We replaced the ET/PET component in the DSI calculation formula with soil moisture, which could maintain the indepen-dence of the two items in the calculation formula. Furthermore, it makes up for the problem that the west has no evapotranspiration data and cannot calculate the drought index, thus constructing the IDSI used to study drought during the 2001−2010 growing sea-sons in entire area of Inner Mongolia. Consistent with the original DSI, the IDSI also pointed out that 2001, 2007, and 2009 were significant drought years in that region. Because ZET/PET has dupli-cate NDVI effect, the values of the IDSI become smaller, while the areas of extreme drought were less extensive than those shown by the DSI. The extreme drought characterized by the IDSI could reflect the record of agricultural drought disaster but the correla-tion with agricultural drought disasters was reduced because of IDSI includes areas with little agricultural activity. Particularly for grassland, the IDSI contains the variation of precipitation in the current month owing to using soil moisture while also reflecting the influence of water and heat in the previous period due to using the NDVI. As the response time of soil moisture and NDVI to precipitation is different, the IDSI value may be close to 0 and difficult to judge the drought condition.

Fig. 6. The annual change for the mean moderate, severe, extreme drought areas of growing season (solid line, the unit is %), and drought disaster-affected areas (dotted line, the unit is ha). The numbers represent the correlation coefficients of the three solid line and the dotted line, respectively. a) by using IDSI; b) by using DSI.

Table 1. Percentage of areas covered by extreme drought in 2007 and 2009 in DSI and IDSI. The unit is %.

Month2007 2009

DSI IDSI DSI IDSI

MayJuneJulyAugustSeptember

0.5912.979.89

11.107.04

0.194.709.605.921.12

1.160.506.99

13.927.79

0.190.151.946.697.76

264 Wang et al., Drought Severity Index based on Soil Moisture

Acknowledgments

This research was supported by National Scholarship from China Scholarship Council (CSC) (No. 201808050210).

Edited by: W. Sun

Supplement

Supplement 1 contains an instruction of DSI calculation and classification, a description about confidence interval estimation analysis, and one figure (Fig. S1) and its caption.

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Manuscript received 3 August 2020, accepted 6 November 2020SOLA: https://www. jstage. jst.go. jp/browse/sola/