4.1. ETC-Based Soil Moisture Data Merging and Validation
The spatial distribution maps of correlation coefficients for the CCIA, CCIP, and VIC soil moisture products based on ETC are shown in
Figure 4. These coefficients reflect the correlation between the soil moisture of a given product and the unknown true soil moisture values at grid locations. The results reveal notable regional variations in the overall distribution of correlation coefficients among the three products. The CCIA product exhibits higher correlation coefficients in the Qinghai−Tibet Plateau and the northwest regions (including Xinjiang, Inner Mongolia, Gansu, Ningxia, etc.), while lower coefficients are observed in the areas south of the Heilongjiang and Yangtze River basin. The CCIP product has the highest overall correlation coefficients among the three, with relatively lower coefficients in provinces such as Ningxia, Heilongjiang, Jiangsu, Anhui, Fujian, Jiangxi, etc. In terms of the VIC product, its accuracy is notably higher in southern regions compared to northern areas. Specifically, in the Qinghai−Tibet Plateau and the northwest regions, the accuracy of the VIC product is lower than that of the CCIA and CCIP. However, in southern regions, the VIC product demonstrates higher accuracy than the CCIA and CCIP.
There are various methods to determine the weight of data merging, such as correlation coefficients, the coefficient of determination, and signal-to-noise ratio (SNR). In this paper, we select the correlation coefficient (
R) obtained from ETC as the weight of data merging. The formula for obtaining the weight is illustrated as follows:
where
and
represents the correlation coefficient and corresponding weight of each soil moisture product in ETC-based merging. The spatial distribution map of merging weight based on the ETC of the CCIA, CCIP, and VIC products is depicted in
Figure 5, reflecting the relative accuracy of the three products in different regions. The results indicate that, in southern regions, the VIC has the highest weight, ranging from 0.5 to 0.8, while the CCIA exhibits the lowest weight, with values less than 0.2. In northern regions, the merging weight for the CCIA, CCIP, and VIC products are relatively similar, with values of around 0.33. However, the merging weight for the VIC is notably lower than that for CCIA and CCIP products in the northwest regions and the Qinghai−Tibet Plateau, particularly in certain areas of Xinjiang, which indicates a low robustness for the VIC model in these regions.
To validate the accuracy of the merged soil moisture, we compared the VIC, the CCI, and the merged soil moisture product with in situ measured soil moisture. The correlation coefficients between each three sets of product and in situ measurements were calculated for all sites from 2008 to 2018. The spatial distribution of in situ measurement sites is shown in
Figure 1. The correlation coefficients used in this section for validation were directly calculated using different soil moisture products and an in situ measured soil moisture combination. The difference in correlation coefficients between the merged products and the in situ soil moisture compared to the correlation coefficients between (a) the VIC products, (b) CCI products, and in situ soil moisture is shown in
Figure 6, where the x-axis represents the improved correlation coefficients and the y-axis represents the number of sites. The results indicate that the merged product showed an average increase in correlation coefficient of 0.18 compared to the CCI product. Specifically, there was a slight decrease in the correlation coefficient with the merged product compared to the CCI product for 15% of the sites. The remaining 85% of sites of merged products had better correlation coefficient performances compared to the CCI product. The majority of the improvement in correlation coefficients with the merged product was concentrated between 0.15 and 0.2, with 15% of the sites showing an increase in the correlation coefficient of 0.30 or higher. Compared to the VIC product, the results indicate that the merged product exhibits an average increase in soil moisture accuracy of 0.09 in the correlation coefficient. For the majority of sites, the increase in correlation coefficient ranges between 0.05 and 0.15. Over 70% of the sites show an improvement in correlation coefficient with the merged product compared to the VIC product, with 10% of the sites showing an increase in correlation coefficient of 0.30 or higher.
The percentage improvement in accuracy by merged products compared to the (a) VIC and (b) CCI using the correlation coefficient as a metric is illustrated in
Figure 7. The results indicate that the merged product demonstrates a 28% improvement in accuracy compared to the CCI product on average. For the majority of sites, the merged product exhibits a precision enhancement ranging from 10% to 40% compared to the CCI product. Over 15% of the sites show a precision improvement exceeding 60%, with only a small fraction experiencing a precision decrease of over 10%. Compared with the VIC products, the average accuracy improvement of merged products is 15%. The results indicate that, in over 75% of the sites, the merged product exhibits a precision enhancement compared to the VIC product, with the majority of sites showing a precision enhancement ranging from 10% to 20%. Additionally, over 10% of the sites show a precision enhancement exceeding 60%.
The precision of the merged products is compared to the CCI and VIC products across different regions, and the boxplot of the correlation coefficient between the (a) CCI, (b) VIC, (c) the merged soil moisture, and the in situ soil moisture in the north, northwest and south is shown in
Figure 8. It should be notied that the precision comparison in the Qinghai−Tibet Plateau region was not analyzed due to the limited number of observational sites in this area. Overall, the merged product has the highest precision in the northwest, north, and south regions. In the north region, while the precision of the CCI and VIC products is close, the merged product demonstrates a significant improvement. The median correlation coefficients for the CCI, VIC, and merged products are 0.75, 0.78, and 0.88, respectively, indicating a notable enhancement in precision with the merged product in the north region. In the northwest region, the VIC product exhibits the poorest precision, with a median correlation coefficient of only 0.73, followed by the CCI product with a median correlation coefficient of 0.77. Compared to the CCI and VIC products, the merged product shows improvements in correlation coefficients of 0.14 and 0.10, respectively, with a median correlation coefficient reaching 0.87. The precision of the VIC and merged products is similar in the south region, with median correlation coefficients of 0.85 and 0.86, respectively, while the CCI product shows a precision performance at 0.70. Due to the already high precision of the VIC product and its higher merge weight in the south region, along with the insufficient precision and lower merging weight of the CCI product, the precision enhancement of the merged product compared to the CCI product is substantial, but the improvement compared to the VIC product is limited. These results indicate a significant precision enhancement of the merged product compared to the CCI product across all regions. Moreover, compared to the VIC product, the merged product demonstrates substantial precision improvement in the northwest and north regions, where the precision of the VIC product is lower, while maintaining its high-precision characteristics in the south region, where the VIC product performs well.
4.2. Assessment of Various Soil Moisture Products in Drought Monitoring
The spatial distribution map of field capacity and wilting points across China is shown in
Figure 9a and
Figure 9b, respectively. The minimum field capacity in
Figure 9a is 0.08 m
3·m
−3, being predominantly found in the Taklimakan and Alashan deserts. The maximum field capacity is 0.60 m
3·m
−3, distributed in the southwestern Yangtze River basin and eastern Heilongjiang Province. The average field capacity is 0.31 m
3·m
−3, with field capacity values in the northern regions being generally lower than in the south, typically below 0.36 m
3·m
−3. The field capacity gradually increases from northwest to southeast, with most grids in south China exceeding 0.40 m
3·m
−3. The same colorbar is used for the distribution of wilting points in
Figure 9b. The minimum wilting point value is 0.04 m
3·m
−3, being primarily distributed in provinces such as Xinjiang, Qinghai, Tibet, and Inner Mongolia. The maximum wilting point is 0.36 m
3·m
−3 and is predominantly found in provinces like Guizhou and Guangxi. Spatially, wilting point values in the northwest are lower compared to the southern and northeastern regions. The wilting point gradually increases from the northwest to the southeast, with wilting soil moisture in south China ranging between 0.27 and 0.36 m
3·m
−3. In the northeastern region, the wilting point is slightly lower compared to the south, with values ranging between 0.15 and 0.24 m
3·m
−3.
The boxplot of drought accuracy evaluation using the merged, VIC, SMAP, SMOS, CCI, and soil moisture product-based SWDI considering (a) POD and (b) MI is presented in
Figure 10. The examples of actual drought events used for POD analysis included the southwest China Drought from spring 2009 to spring 2010, affecting the Yunnan, Guizhou, Sichuan, and Guangxi provinces; the northeast China Drought in spring 2011, impacting the Liaoning, Jilin, and Heilongjiang provinces; the north and northeast China Drought in summer 2013, which affected the Hebei, Shanxi, Inner Mongolia, and Liaoning provinces; the north China Drought in spring 2014, impacting Hebei, Beijing, and Tianjin; the South China Drought in spring 2015, affecting the Guangdong, Guangxi, and Fujian provinces; and the northeast Spring Drought in spring 2017, impacting the Liaoning, Jilin, and Heilongjiang provinces. For the POD metrics, the results indicate that the POD of the merged soil moisture for drought monitoring is the highest among the five products, with a median value of 0.98. This indicates that, when an actual drought occurs, the merged soil moisture product has a 98% probability of monitoring this drought. In comparison, the POD values for CCI, VIC, and SMAP are 0.95, 0.94, and 0.92, respectively, all lower than the POD value of the merged product. It is evident that the merged soil moisture exhibits higher drought monitoring accuracy than the pre-merged VIC and CCI data. Additionally, the results show that the merged product has a more consistent accuracy performance at different sites, with a POD value between 1 and 0.86. This suggests that its stability in usage across different stations is higher compared to other products.
The POD metric evaluates the ability to distinguish between the presence and absence of droughts, while the MI metric further evaluates the drought index’s capability of identifying different drought severity levels. As depicted in
Figure 10b, the merged soil moisture product stands out as the most accurate among the five soil moisture products. The median value of the MI is 0.33, with maximum and 75th percentile values of 0.93 and 0.48, respectively, which accurately discern the observed drought severity levels. In contrast, the MI medians for the SMAP, VIC, CCI, and SMOS are 0.25, 0.21, 0.20, and 0.13, respectively. In summary, the result demonstrates that the merged soil moisture product shows the ability for both drought monitoring and drought severity identification.
To analyze the disparities in the accuracy of an SWDI across different regions, we made statistics regarding the averaged POD of different soil moisture product-based SWDIs over the northwest, north, and south China regions. It is worth noting that, due to the limited number of observational stations in the Qinghai−Tibet Plateau compared to other regions, only the statistical results of the other three regions are compared in this study. The mean values of POD and MI based on different soil moisture products in different regions are shown in
Table 2. Overall, merged soil moisture has the highest POD value among the five products for the three regions, reaching 0.95, 0.96, and 0.93 in the northwest, north, and south regions, respectively, demonstrating its reliability and stability in identifying drought occurrences across different regions. Overall, the north region exhibits the highest drought identification accuracy. For the SMAP, SMOS, and CCI remote sensing soil moisture, this result aligns with the distribution pattern of remote sensing soil moisture itself. However, for VIC soil moisture, this distribution pattern differs from the better simulation results of the VIC model in the south. This indicates that the VIC model performs better in simulating soil moisture under relatively wet conditions in the southern region but relatively lacks accuracy under dry conditions.
The averaged MI of different soil moisture product-based SWDIs over the northwest, north, and south China regions is shown in
Table 3. The result indicates that, in the northwest region, drought monitoring based on the SMAP has the highest accuracy, with a mean value of 0.36. The MI performance of merged soil moisture is slightly worse than the SMAP at 0.31, which is still much better than the VIC, SMOS and CCI products. In the north and south regions, drought monitoring based on merged soil moisture products has the highest accuracy, with mean values of 0.39 and 0.29, respectively. In the northwest, south, and north regions, the MI values of merged soil moisture are higher than those of the CCI and VIC, indicating that merged soil moisture products have advantages in more accurately quantifying drought severity levels compared to the pre-merged CCI and VIC soil moisture products.
4.3. Spatiotemporal Characteristics Analysis of Drought Monitoring Accuracy
The spatial distribution of the correlation coefficient between the AWD and (a) the CCI, (b) the VIC, (c) and the merged soil moisture-based SWDI is shown in
Figure 11. A higher correlation coefficient indicates a higher accuracy of drought monitoring for the respective soil moisture products. It can be observed that the spatial distribution trends of drought monitoring accuracy for the VIC and merged soil moisture products are relatively similar, although there are differences in the quantitative values. Generally, the correlation coefficients exhibit a pattern of being higher in the southern regions and lower in the northern regions. In the northern regions, the correlation coefficient decreases from east to west. The results demonstrate that the correlation coefficients for the merged and VIC products exceeds 0.5 in most grids of the southern region and the central part of the Qinghai−Tibet Plateau. In the majority of grids in the northern region, the correlation coefficients for merged soil moisture products is above 0.4, while the correlation coefficients for the VIC product is around 0.3. Furthermore, the CCI product has the lowest correlation coefficients in this region, with the value being around 0.2. In most grids of the northwestern region, both the correlation coefficients for merged and VIC products are below 0.2, with negative correlation coefficients being found in the northern part of Xinjiang. The correlation coefficients of the CCI are notably lower compared to the other two sets of products, especially in the southern region, where most grids exhibit correlation coefficients of around 0.25. In the southwest region and the central part of Tibet, CCI products have the highest correlation coefficients, reaching above 0.4.
The distribution map for the difference in correlation coefficients between (a) the merged-based SWDI and AWD compared to (a) the CCI-based SWDI and AWD and (b) the VIC-based SWDI and AWD are shown in
Figure 12. A larger difference in correlation coefficients indicates that the merged product has a greater improvement in drought monitoring accuracy compared to other products. The results indicate significant improvements in drought monitoring accuracy nationwide with the merged product compared to the CCI, while the enhancement in correlation coefficients compared to the VIC is more concentrated in the northern and northwestern regions. Specifically, concerning CCI products, the merged product exhibits noticeable improvements in the southern and northeastern regions, with correlation coefficient increases exceeding 0.2. Conversely, in the southwestern part of Tibet, southern Xinjiang, and the northern Gansu regions, the merged product shows a reduction in correlation coefficients of around −0.15 compared to the CCI. In the Hebei, Shanxi, and Shaanxi provinces, the merged product maintains a similar level of accuracy to the CCI. Regarding VIC products, the merged product demonstrates correlation coefficient increases of 0.1 to 0.2 in the Huaihai Plain, the northwestern, and the southwestern regions while experiencing a decrease of approximately −0.05 compared to the VIC in certain areas of the Qinghai−Tibet Plateau. In the southern region, the accuracy of the merged results closely aligns with that of the VIC model.
The boxplot of the correlation coefficient between the CCI, VIC, and the merged soil moisture-based SWDI and AWD over the northwest, Tibet, and north and south China are presented in
Figure 13, according to geographical division depicted in
Figure 1. Generally, all three sets of products exhibit the highest correlation coefficients in the southern region and the lowest in the northwest region. Notably, the merged product consistently demonstrates the highest correlation coefficients in every region. In the northwest region, the average correlation coefficients of CCI and VIC products are close. The VIC product displays a larger range of correlation coefficients compared with other products. Overall, the merged product shows an increase of approximately 0.05 in correlation coefficient compared to CCI and VIC products. In the Qinghai−Tibet region, the accuracy of the merged product is similar to that of the VIC product, with an increase of around 0.15 in the correlation coefficient compared to the CCI product. In the northern region, the merged product exhibits an increase of approximately 0.1 and 0.05 in the correlation coefficient compared to the CCI and VIC products, respectively. In the southern region, the correlation coefficients of the merged product and VIC product are close and notably higher than in other regions, with a median correlation coefficient exceeding 0.5. Compared to the CCI product, the correlation coefficient of the merged product increases by more than 0.3.
To investigate the impact of different seasons on the drought monitoring based on merged soil moisture, we divide the study period into four seasons building upon the research conducted throughout the entire timeframe. The spatial distribution of correlation coefficient between the merged soil moisture-based SWDI and AWD in (a) spring, (b) summer, (c) autumn, and (d) winter are presented in
Figure 14. Overall, the correlation coefficient during summer is the highest nationwide. The correlation coefficient reaches above 0.5 in most regions, except for southern Xinjiang, where the coefficients are slightly lower. In the southern regions, the correlation coefficients generally exceed 0.7. Spring and autumn exhibit relatively consistent correlation coefficients, with slight spatial variations in the southern regions. In spring, the Yangtze River basin shows higher correlation coefficients, with the values surpassing 0.7. In autumn, elevated coefficients are observed in the southwest which exceed 0.7. Comparatively, winter has the lowest correlation coefficients among the four seasons, particularly in the northwest, northern, and Qinghai−Tibet Plateau regions, where coefficients hover around 0.2. Some grids in Tibet exhibit negative correlation coefficients. Nationally, drought monitoring based on merged soil moisture demonstrates relatively high accuracy in all four seasons in the southern regions. In the northern regions, winter drought monitoring accuracy is lower than in the other three seasons.
4.4. Drought Evolution Analysis and Drought Indexes Comparison
Based on the geographical and climatic characteristics of different regions, we have divided China into six major crop regions, as shown in
Figure 2. Compared to the previous zoning map, the new zoning map now categorizes the north region into the northeastern and northern regions, while the south region is divided into the southwestern, Yangtze River basin, and southern regions. Note that, in this study, the Yangtze River basin specifically refers to the middle and lower reaches of the Yangtze River basin. The Qinghai−Tibet Plateau and northwest regions have been merged into the northwest region. This zoning rationale is due to the fact that the food production in the northwest region accounts for only 3% of the national total, thus it will not be analyzed separately in subsequent studies. The more detailed division of the south and north regions is beneficial for analyzing and comparing the differences in drought affection on crops across different areas.
The drought-affected crop area data were collected to analyze the reliability of the SWDI in drought monitoring across different regions. A time series of the drought-affected crop area, SWDI drought intensity, drought tendency in (a) mainland China and (b) the northeastern, (c) northern, (d) southwestern, (e) Yangtze River basin, and (f) southern region of China from 1992 to 2018 is presented in
Figure 15. In this figure, the red bars represent the yearly average drought-affected crop areas, the red dashed line signifies the average drought-affected crop areas during 1992–2018, and the blue line depicts the annual drought intensity. A 5-year moving window average was employed to show the drought tendency, indicated by the yellow line segment.
Figure 15a shows that the national average drought-affected crop area was 16.44 million hectares during 1992 to 2018. Among these, the northeastern region had an average drought-affected crop area of 4.18 million hectares, accounting for 25% of the total average drought-affected crop area of China; the northern region had an average drought-affected crop area of 5.47 million hectares, accounting for 33%; the southwestern region had an average drought-affected crop area of 2.38 million hectares, accounting for 14%; the Yangtze River basin had an average drought-affected crop area of 3.08 million hectares, accounting for 19%; and the southern China region had an average drought-affected crop area of 0.96 million hectares, accounting for 6%. At different times, the drought situation varied across the region. From 1992 to 1996, the northern region experienced the most severe drought, followed by the Yangtze River basin region. In 1996, the northeastern region was the most severely drought-affected area nationwide. From 1997 to 2002, the most severely drought-affected regions during the same period were the northern region, the northeastern region, and the Yangtze River basin region. From 2003 to 2009, the northeastern region surpassed the northern region, becoming the most severely drought-affected region nationwide. During 2010 to 2011, the southwestern region experienced the most severe drought nationwide. From 2014 to 2018, the northeastern and northern regions were the most severely drought-affected areas nationwide.
For mainland China (
Figure 15a), the drought-affected crop area has shown a gradual increase since 1992. The drought-affected crop area reached its peak in 2000–2001, with an affected area of 34.24 million hectares. During the same period, the drought intensity also reached its maximum value of −1.3. The drought intensity has been gradually decreasing annually from −1 to around −0.5 from 2002, leading to a gradual decline in the affected area to below 6 million hectares. The northeastern region (
Figure 15b) exhibited a large interannual variability in regard to drought-affected crop areas. The top three years in terms of drought-affected crop area were 2007, 2009, and 2000, while the lowest drought-affected crop areas were observed in 2013, 2011, and 2010, respectively. Since 2007, both the drought-affected crop area and drought intensity in the northeastern region have shown a declining trend annually. Overall, the trends in drought intensity and drought-affected crop area in the northeastern region were generally consistent. However, the drought intensity failed to reflect the two significant drought events in 2007 and 2009. From 1992 to 2001, the drought-affected crop area for the northern region (
Figure 15c) remained above the mean line, except for in 1998. There was a yearly increase in the drought-affected crop area from 1998, with the top value occurrence being at 2000 for over 12 million hectares, accounting for over 40% of the national drought-affected crop area during the same period. The drought intensity in the same year also reached its maximum value in nearly 30 years at −1.7. Beginning in 2002, the affected area notably decreased compared to previous years, except for 2009. After 2014, the drought-affected crop area decreased annually. The results indicate that the drought trend aligned with the affected area trend, remaining stable from 2004 to 2013 and decreasing annually after 2013.
The drought trend in the southwestern region (
Figure 15d) differed from that of the northeastern and northern regions. There was a gradual increase in drought intensity from 1992 to 2011. The peaks of drought-affected crop area occurred in 2006 and 2010, which exceeded the historical average affected area by two times. The same peaks of drought intensity were also found in the same years, which indicates the reliability of the SWDI-based drought index. Since 2012, there has been a noticeable decrease in drought intensity, consistent with the simultaneous decrease in drought-affected crop areas. The average drought-affected crop area from 2014 to 2018 was only 380,000 hectares, which is significantly lower than the historical average annual affected area of 2.38 million hectares. The Yangtze River basin region (
Figure 15e) encompasses major crop-producing provinces such as Jiangsu and Hunan. As a result, it has exhibited a higher drought-affected crop area compared to the southwestern region despite its lower drought intensity. From 1992 to 1999, the drought-affected crop area showed a decreasing trend annually. The drought-affected crop area surged to 8.94 million hectares in 2000, reaching its highest value in nearly 30 years, accompanied by the highest drought intensity in the same year. The drought intensity decreased annually from 2002, and the drought-affected crop area also showed a downward trend. The southern region (
Figure 15f) has a noticeably smaller drought-affected crop area compared to other regions. The drought-affected crop area remained relatively stable around the mean line of 960,000 hectares from 1992 to 2001. However, the drought intensity showed more significant fluctuations in the same period. The drought-affected crop area has increased annually since 2002, peaking at 2.3 million hectares in 2004. The highest drought intensity was found in the same years, with a value of −1.2. Both the drought-affected crop area and drought intensity have shown a consistent downward trend since 2005.
A comparison of the correlation coefficient between drought-affected crop areas and drought intensity of the SWDI, SMI, SMAPI, and SPI in different crop regions is presented in
Table 4. The bold number represents the drought index with the highest correlation coefficient in this crop region. The results indicate a notably higher correlation with the drought index directly related to soil moisture, such as the SWDI and SMAPI, compared to the precipitation-related drought index SPI. The absolute values of the correlation coefficients between drought intensity and drought-affected crop area in China for the SWDI, SMI, SMAPI, and SPI are 0.88, 0.83, 0.81, and 0.51, respectively. For each region, the absolute values of the correlation coefficients based on the SWDI are all higher than those based on the SMAPI. This indicates that a soil moisture-related drought index, such as the SWDI, which not only considers soil moisture dynamics but also soil physical properties, can better monitor and reflect agricultural drought compared with other drought indexes. The southwestern region exhibits the closest correlation among the three indices, with absolute correlation coefficient values of 0.75, 0.74, 0.73, and 0.63 for the SWDI, SMI, SMAPI, and SPI, respectively. The Yangtze River basin is the area where the SWDI reflects the drought-affected crop area best, with a correlation coefficient of 0.89, followed by the northern region, with a correlation coefficient of 0.88. The northeastern region has the worst ability in regard to the SWDI reflecting the drought-affected crop area, with a correlation coefficient of 0.69. The correlation coefficient of the SPI is highest in the southwestern region, with an absolute value of 0.63, and worst in the northeastern region, with an absolute value of only 0.13. It is worth noting that, in the northeastern region, the accuracy of all drought indexes is poor, including soil moisture-related indexes and precipitation-related indexes. One possible reason for this is the long-term snow accumulation in the area.
Based on above analysis, the results indicate that the meteorological drought index SPI only represents cumulative precipitation deficits, making it challenging to objectively reflect drought conditions for land surface. As a result, it has the poorest drought monitoring accuracy. The SMAPI considers the soil moisture dynamic as a state variable of land surface. However, it lacks consideration of soil physical properties, thus exhibiting higher accuracy in drought monitoring compared to the SPI but lower than the SMI and SWDI. Both the SWDI and SMI consider soil moisture and field capacity. However, the SWDI considers not only field capacity but also the wilting point. Consequently, the SWDI exhibits the highest monitoring accuracy in agricultural drought monitoring. This underscores the advantages and potential of the enhanced SWDI, which integrates soil moisture data with multiple key soil hydraulic parameters for improving agricultural drought monitoring.
Nevertheless, the enhanced SWDI proposed in this study still exhibits certain limitations. First, the index does not account for variations in crop water requirements. Given that different crops demand differing amounts of water, the SWDI may not accurately reflect the actual drought conditions experienced by specific crops. This limitation could lead to misinterpretations of drought severity in regions with diverse agricultural practices. Secondly, the SWDI operates independently of long-term climatic background data. This feature, although simplifying its application, means that the index may not fully capture the impacts of climate change on hydrological cycles and soil moisture over extended periods. As climate patterns shift, there may be a need to recalibrate drought thresholds to maintain the index’s accuracy and relevance. Lastly, the exclusion of human interventions such as irrigation and water reservoir management in the SWDI’s formulation can lead to uncertainty in regions where such practices significantly influence soil moisture levels. This oversight might result in underestimating or overestimating drought conditions in areas heavily managed through artificial watering systems. Therefore, while the enhanced SWDI provides a straightforward and reliable approach to monitoring drought, the development and practical application of the enhanced SWDI still need to be explored in the future, particularly in regard to the consideration of crop types, human activities, and climate change.