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Article

High-Resolution Drought Detection Across Contrasting Climate Zones in China

by
Ji Li
1,
Guoyong Leng
1,*,
Karim Pyarali
2,3,4 and
Jian Peng
2,5
1
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
Department of Remote Sensing, Helmholtz Centre for Environmental Research-UFZ, Permoserstrasse 15, 04318 Leipzig, Germany
3
Institute for Integrated Management of Material Fluxes and of Resources, United Nations University, Ammonstrasse 74, 01067 Dresden, Germany
4
Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, Helmholtzstraße 10, 01069 Dresden, Germany
5
Remote Sensing Centre for Earth System Research, Leipzig University, Talstr. 35, 04103 Leipzig, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(7), 1169; https://doi.org/10.3390/rs17071169
Submission received: 7 January 2025 / Revised: 3 March 2025 / Accepted: 21 March 2025 / Published: 26 March 2025
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Abstract

:
Droughts have been exacerbated by climate change, posing significant risks to ecosystems, hydrology, agriculture, and human society. In this paper, we present the development and evaluation of a high-resolution 1 km SPEI (Standardized Precipitation-Evapotranspiration Index) dataset to enhance drought monitoring at finer spatial scales. The high-resolution SPEI datasets, derived using high-resolution TPDC precipitation and satellite-based MODIS potential evapotranspiration data, were compared with a coarse-resolution 50 km SPEI dataset derived from CRU measurements, as well as vegetation health indices (VHIs) and root zone soil moisture (SM), over two climatically contrasting regions in China: Northeast China (NEC) and Southwest China (SWC). The evaluation highlights the MODIS-based high-resolution SPEI’s ability to capture regional drought dynamics and improved correlation with vegetation and soil moisture dynamics. NEC, with its relatively flat topography and recent experience of significant droughts, and SWC, characterized by complex terrain and high precipitation variability, provided ideal testbeds for examining the performance of the 1 km SPEI. The results demonstrate that the high-resolution dataset offered superior spatial detail in detecting drought conditions, making it valuable for agricultural planning and water resource management in diverse climates.

1. Introduction

Drought is a multifaceted natural hazard that significantly affects both ecosystems and human societies, leading to severe consequences, such as crop failure, food shortage, famine, and malnutrition, and even poverty, loss of life, and mass migration [1,2,3]. Severe drought events in the 21st century, such as those in central Europe (2003), Russia (2010), the Horn of Africa (2011), southeastern Australia (2000 and 2001–2009), California (2013–2014 and 2012–2019), northern China (2014), and southern Africa (2015–2018), and more recently in Australia (2019–2020), the western United States (2020–2021), and Brazil’s Pantanal region (2020), highlight these negative impacts [4,5,6,7,8,9,10,11,12]. The increasing frequency and severity of droughts, driven by rising global temperatures and atmospheric demand, exacerbate environmental and socioeconomic damage [13,14]. Anthropogenic factors, such as land-use changes and water management practices, further intensify these impacts [15,16,17]. As these trends are expected to continue under projected climate change, it is crucial to enhance our understanding of the spatial and temporal variability of drought. This will provide a foundation for quantifying drought impacts and evaluating societal, economic, and environmental exposure.
Droughts are typically categorized into four main types: meteorological, agricultural, hydrological, and socio-economic, with some classifications also including environmental droughts [17,18]. Each type can be characterized by its severity, duration, and intensity, and various indices were developed to monitor and quantify drought at different spatial and temporal scales. Such drought indices include the Palmer Drought Severity Index (PDSI) [19], Standardized Precipitation Index (SPI) [20], Standardized Precipitation–Evapotranspiration Index (SPEI) [21], Soil Moisture Deficit Index [22], Normalized Multi-band Drought Index (NMDI) [23], and Standardized Runoff Index (SRI) [24]. The SPI, recommended by the World Meteorological Organization (WMO), is particularly valued for its simplicity and effectiveness in monitoring and predicting droughts based on precipitation data alone [25]. However, the SPI does not consider other critical climatic variables, such as the atmospheric evaporative demand, which is essential under current climate change scenarios [26,27]. To address these limitations, the SPEI was developed to include both precipitation and temperature data to account for the atmospheric evaporative demand, providing a more accurate representation of drought severity [21]. This is particularly important in regions with significant temperature variability, where evapotranspiration plays a crucial role in drought development [28,29]. In addition, the SPEI is widely used in global and regional drought assessments due to its flexibility in timescales, making it applicable for both short-term and long-term drought analysis [30,31,32].
The SPEI, like the SPI, can be computed on a variety of time scales (1–48 months), but relies on both long-term, high-quality precipitation datasets and potential evapotranspiration datasets representative of the atmospheric evapotranspiration demand. These data can be sourced from ground stations, satellite observations, and reanalysis or multi-source datasets. Two widely used global SPEI datasets are SPEIbase [21] and the Global Precipitation Climatology Centre Drought Index (GPCC-DI) [33]. SPEIbase, available at a 0.5° resolution, is derived from the Climatic Research Unit (CRU) precipitation and potential evapotranspiration datasets [34]. GPCC-DI offers SPEI datasets at a 1.0° resolution using GPCC precipitation and NOAA’s Climate Prediction Center temperature data [35,36]. While these datasets are valuable for long-term assessments, their lower resolution limits their effectiveness in detailed spatial analysis, especially at regional or sub-basin scales [37].
Advancements in satellite technology have led to the development of high-resolution precipitation and potential evapotranspiration datasets, such as the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) [38] and the Global Land Evaporation Amsterdam Model (GLEAM) [39], which provide accurate precipitation and potential evapotranspiration data for large-scale drought monitoring. These datasets have enabled the generation of a high-resolution (5 km) SPEI dataset for different regions, such as Africa, Central Asia, and even the globe, covering the period from 1981 to recent years [26,27,40]. These SPEI datasets were shown to reliably characterize drought events and inform drought management strategies [29,41]. However, the performance of these SPEI datasets in different climatic and topographic conditions remains an important area for further research [42,43]. In addition, a spatial resolution of 5 km is still insufficient for regional and localized drought detection. High-resolution SPEI datasets with a kilometer or even sub-kilometer spatial resolution can enhance the assessment of spatial and temporal variations in drought duration, severity, and magnitude at finer scales, thereby supporting the development of site-specific adaptation measures. Recent advancements in SPEI calculation methods, including machine learning-based estimations and land-surface-characteristic-adjusted approaches, provide new opportunities to enhance the spatial resolution and accuracy of drought assessments [44,45,46]. In this study, we aimed to investigate the feasibility of generating a 1 km spatial resolution SPEI dataset using the recently released Chinese high-resolution precipitation dataset and MODIS potential evapotranspiration products. The new 1 km SPEI dataset represents an advancement over existing high-resolution 5 km SPEI products, offering a finer spatial resolution that enables a more detailed analysis of regional and local drought conditions. The improved resolution enhances the ability to capture localized climate and land surface variations, which is particularly valuable in complex landscapes where drought patterns can vary significantly over short distances [47]. This enhanced spatial detail not only improves the applicability of the data for agricultural practices and water resource management but also facilitates better integration with high-resolution remote sensing products, such as MODIS potential evapotranspiration and vegetation indices. These improvements contribute to a more comprehensive understanding of drought impacts at regional and local scales, filling gaps in the existing SPEI datasets. These 1 km SPEI datasets were evaluated against lower-resolution SPEI datasets and other variables, such as the root zone soil moisture and the vegetation health index, to demonstrate their superior performance at finer spatial scales. This study was conducted over Northeast China (NEC) and Southwest China (SWC) as comparison areas, both of which experienced severe drought in the past 40 years [48,49]. NEC experienced a significant drought in 2014, making it essential for analyzing drought impacts on agricultural productivity and water resources [50]. In contrast, SWC, characterized by its complex terrain and diverse climate, including monsoon influences and high variability in precipitation, serves as a comparison region [51]. This diversity offers a contrasting climatic and hydrological context to NEC, which has typically cooler and more uniform precipitation patterns [52]. In addition, NEC is characterized by a relatively flat terrain and extensive croplands, while SWC has a rugged topography, high elevations, and a mix of dense forests and agricultural lands, where groundwater interactions play a significant role in soil moisture dynamics (Figure 1). By studying these two different regions, this study aimed to capture the different drought dynamics in China and to comprehensively assess the performance of the 1 km SPEI dataset and its superior drought-monitoring capability compared with the existing SPEI datasets.

2. Study Area

This study focused on two critical regions in China: Northeast China (NEC) and Southwest China (SWC) (Figure 1), both of which are important not only to the nation’s agricultural output and food security but also represent contrasting climate zones, and thus, offer valuable insights into the broader environmental and societal impacts of drought [53]. Northeast China (NEC), known for its fertile black soil (chernozem), is one of China’s most important agricultural regions. Often referred to as the “breadbasket” of China, NEC produces significant quantities of corn, soybeans, and rice, contributing substantially to the nation’s food security by supplying enough grain to feed millions of people [54,55]. This region spans a large geographical area from approximately 115°E to 135°E in longitude and 38°N to 53°N in latitude, covering about 1.267 million m2, or 13% of China’s total land area. The climate of NEC, characterized by hot, rainy summers and cold, dry winters, has seen significant interannual variability in precipitation. For example, in 2001, the region experienced only 452 mm of rainfall, causing a severe drought that impacted 3.75 million hectares in Heilongjiang Province and 2.7 million hectares in Jilin Province, which led to massive economic losses [56,57]. In contrast, 2013 saw 749 mm of rainfall, illustrating the dramatic variability in precipitation [58]. This variability poses challenges not only for agriculture but also for water management, urban planning, and ecosystem services, making NEC a key region for studying how drought impacts multiple sectors. Southwest China (SWC), on the other hand, is a region characterized by diverse topography and climate. Spanning latitudes from approximately 21°N to 35°N and longitudes from 97°E to 110°E, SWC covers about 1.56 million m2, accounting for 16% of China’s total land area. This region is marked by a complex landscape of high mountains, deep valleys, plateaus, and basins [59]. SWC’s varied topography results in distinct climate conditions, with subtropical monsoon climates in the eastern areas and plateau monsoon climates in the west [60]. The annual precipitation in SWC ranges from 800 mm to 2000 mm, depending on the location and elevation. SWC has also been affected by severe droughts in recent years, particularly since 2006, drawing the attention of both the government and the research community [61,62]. These devastating droughts, especially in mountainous areas, have had widespread impacts on agriculture, hydropower, water resources, and biodiversity [63]. Despite extensive research efforts, the understanding of drought in SWC remains fragmented, necessitating the further integration of high-resolution data to improve monitoring and management strategies. By examining these two distinct regions, this study aimed to capture the diverse drought dynamics within China and provide a comprehensive assessment of the performance of the new high-resolution SPEI datasets, highlighting their superior capability in drought monitoring and prediction compared with coarser-resolution datasets.
Remotesensing 17 01169 g001
Figure 1. Study area. The precipitation was from the TPDC monthly mean value for December 2017. The grey-colored boxes are the selected areas: Northeast China (NEC) and Southwest China (SWC). In addition, the ESA CCI land-cover map from s2016 highlights the contrasting land-cover types in NEC and SWC, while the ETOPO topography dataset [64] illustrates the variations in elevation between these two regions.
Figure 1. Study area. The precipitation was from the TPDC monthly mean value for December 2017. The grey-colored boxes are the selected areas: Northeast China (NEC) and Southwest China (SWC). In addition, the ESA CCI land-cover map from s2016 highlights the contrasting land-cover types in NEC and SWC, while the ETOPO topography dataset [64] illustrates the variations in elevation between these two regions.
Remotesensing 17 01169 g001

3. Data and Methods

3.1. Data

High-resolution precipitation and potential evapotranspiration datasets were utilized to develop the multi-scale high-resolution SPEI (SPEI-HR). In order to assess the performance of this new SPEI-HR, several reference datasets were used, including the root zone soil moisture (SM) and the vegetation health index (VHI). Together with the SPEI, these datasets offer a robust framework for analyzing drought conditions across various climatic zones, facilitating a detailed comparison between the high-resolution datasets and other coarse-resolution drought indicators.

3.1.1. Precipitation

The “1-km monthly precipitation dataset for China (1901–2023)” was used to compute the SPEI. It is a high-resolution, long-term dataset specifically developed for climate and hydrological studies in China [65]. It provides monthly precipitation data at a spatial resolution of approximately 1 km (0.0083333°) from January 1901 to December 2023 and is available from the TPDC (National Tibetan Plateau Data Center). The dataset was generated using a Delta spatial downscaling method, which integrates the global 0.5° climate data from the Climate Research Unit (CRU) and the high-resolution climate data from WorldClim [65]. The downscaling process ensures that the dataset accurately represents the complex climatic conditions across China. Moreover, the dataset’s reliability was validated using data from 496 independent meteorological observation stations across the country, confirming its accuracy and suitability for a wide range of applications [66,67].

3.1.2. Potential Evapotranspiration

High-resolution potential evapotranspiration (PET) from the TPDC and MODIS were used to develop the SPEI dataset. The “1-km monthly potential evapotranspiration dataset for China (1901–2023)” provides detailed monthly potential evapotranspiration (PET) at a spatial resolution of approximately 1 km (0.0083333°) for the period from January 1901 to December 2023. This dataset was generated using the Hargreaves potential evapotranspiration equation with inputs including monthly maximum, minimum, and mean temperatures at a 1 km resolution. The temperature dataset, also a product of TPDC, was developed using sophisticated interpolation methods to ensure a high accuracy and spatial consistency [65].The TPDC PET can be obtained from the TPDC dataset. In addition to the TPDC dataset, this study also utilized the potential evapotranspiration (PET) from MODIS (Moderate Resolution Imaging Spectroradiometer). The MODIS PET provides global coverage with a spatial resolution of 500 m and an 8-day temporal resolution [68]. It is derived using the Penman–Monteith equation, which integrates multiple climatic variables, such as temperature, radiation, wind speed, and relative humidity [69]. This approach allows for a more comprehensive and dynamic estimation of evapotranspiration compared with simpler methods, such as the Hargreaves equation. The MODIS PET was comprehensively evaluated and used for various applications [70,71].

3.1.3. Vegetation Health Index

The vegetation health index (VHI) dataset used in this study is a global, high-resolution monthly dataset that covers the period from 1981 to 2021. This dataset integrates meteorological data, vegetation indices, and soil moisture information, significantly improving the efficiency of detecting vegetation stress and drought conditions [72]. With a spatial resolution of 4 km and a monthly temporal resolution, the dataset provides detailed insights into vegetation health dynamics on a global scale. The VHI dataset is particularly valuable for drought detection and has been used to evaluate the performance of drought indicators, such as the SPEI [73,74].

3.1.4. Root Zone Soil Moisture

ERA5-Land root zone soil moisture is a key parameter derived from the ERA5 reanalysis dataset that simulates the soil moisture content at various depths, particularly within the plant root zone [75]. Root zone soil moisture typically represents the water content from a few centimeters to approximately 1 m in depth. In this study, to calculate the soil moisture from 0 to 100 cm in depth, different soil layers were aggregated using weighted averages. The soil moistures from the 0–7 cm, 7–28 cm, and 28–100 cm layers were assigned weights of 0.07, 0.21, and 0.72, respectively, to derive the comprehensive moisture value for the full depth of 0–100 cm [76].

3.1.5. Coarse-Resolution SPEI

The global coarse-resolution (0.5°) SPEI (Standardized Precipitation Evapotranspiration Index) is a commonly used dataset for analyzing drought [21,77]. The SPEI-CR was constructed using the monthly precipitation and potential evapotranspiration (PET) data at a 0.5° spatial resolution from the Climate Research Unit (CRU-TS) [34]. The PET values for CRU-TS were computed using the FAO-56 Penman–Monteith equation, which served as the basis for developing the SPEI-CR. In this study, the SPEI-CR dataset spanning the period from 2000 to 2021 was employed to assess the performance of the SPEI-HR (high-resolution) datasets, which offered a benchmark for comparison and validation.

3.2. Methods

3.2.1. SPEI Calculation

The SPEI proposed by Vicente-Serrano, Beguería, and López-Moreno [21] is used to quantify drought conditions over a certain period by considering both the precipitation (P) and potential evapotranspiration (PET). The SPEI has been widely used in a range of applications [29] and is normally calculated with the following steps. First, a time series of high-resolution P and PET are estimated and the difference between the P and PET is calculated to obtain a climatic water balance. These values are then aggregated over different time scales (e.g., 1–24 months or more) to assess the varying temporal impacts of droughts. The next step is to fit the aggregated data to a statistical distribution. The log–logistic probability distribution is often selected to consider both positive and negative values, which was demonstrated to perform better than other probability distributions [77]. Once the distribution is fitted, the cumulative probabilities are calculated and then converted into standardized SPEI values using a z-score transformation. The calculated SPEI values allow for consistent comparisons across regions and time periods, with negative values indicating dry conditions and positive values showing wet conditions. Table 1 presents the classification of dry and wet conditions based on SPEI values [27]. In this study, the TPDC precipitation, PET, and MODIS PET were used to calculate the high-resolution SPEI at a 1 km spatial resolution from 2000 to 2021 over Northeast and Southwest China. This leads to two SPEI datasets: TPDC 1 km and MODIS 1 km.

3.2.2. Evaluation Strategy

The generated high-resolution SPEI dataset at 1 km resolution was evaluated against the coarse-resolution SPEI based on the CRU data at 50 km to assess its accuracy and performance. In addition, the performance of the high-resolution SPEI was analyzed by comparing it with the vegetation health index (VHI), as well as the root zone soil moisture (SM). Both the VHI and SM were standardized by removing the seasonal cycle, and their anomalies were expressed in terms of standard deviations from their long-term means [26]. To ensure a valid comparison, the high-resolution SPEI data were bilinearly aggregated to the respective spatial resolution as the coarse-resolution datasets, including the CRU SPEI, VHI, and SM. Pearson’s correlation coefficient was used to assess the agreement between the different datasets, providing a quantitative measure of how well the high-resolution SPEI captured the drought conditions in comparison with both coarse-resolution SPEI and the independent vegetation and soil moisture datasets. The evaluation over two contrasting areas further provided a detailed assessment of the high-resolution SPEI’s performance under different climatic and ecological conditions.

4. Results and Discussion

4.1. Spatial Variability of High-Resolution and Coarse-Resolution SPEI Datasets

Figure 2 shows examples of drought in Northeast China (NEC) in April 2014 and Southwest China (SWC) in February 2010. These periods were chosen due to the significant droughts experienced in these regions, making them ideal for this analysis [50,61]. In NEC, all three SPEI datasets effectively captured the overall drought conditions during this period. The high-resolution MODIS-derived SPEI 1 km dataset (Figure 2a) revealed detailed spatial variability, with SPEI01 values that ranged from −3 to 3, indicating severe drought conditions in specific areas, particularly over the top-right corner of the study area. The TPDC-driven SPEI 1 km dataset (Figure 2b) also showed a high-detail spatial distribution, confirming the same severe drought areas but with smoother transitions due to the downscaling process. In contrast, the coarse-resolution SPEI 50 km dataset calculated based on CRU data (Figure 2c) showed a similar overall pattern but lacked the detail observed in the 1 km dataset and could obscure localized drought monitoring. For SWC, all three datasets consistently identified the overall drought conditions in February 2010. The high-resolution SPEI 1 km datasets (Figure 2d,e) showed significant spatial variability in the SPEI01 values, reflecting the region’s complex climate and notable spatial heterogeneity. The SPEI 50 km dataset (Figure 2f) provided similar dry and wet patterns but lacked the detailed spatial resolution shown in the high-resolution datasets. These results in both NEC and SWC demonstrate that while all three datasets captured similar drought patterns, the high-resolution SPEI datasets (MODIS-1 km and TPDC-1 km) provided much more spatial details. This enhanced resolution offered a clearer view of the localized drought conditions, which emphasized the advantages of high-resolution datasets in drought monitoring.

4.2. Comparison Between High-Resolution and Coarse-Resolution SPEI Datasets

To assess the differences between the high-resolution and coarse-resolution SPEI datasets, the correlations between these datasets over NEC and SWC from 2000 to 2021 were evaluated. As illustrated in Figure 3, in NEC, both high-resolution 1-month SPEI datasets exhibited strong correlations with the coarse-resolution CRU-driven SPEI dataset, which demonstrated their effectiveness at capturing drought conditions. The MODIS vs. CRU correlation (Figure 3a) showed that the correlation coefficients (R) for most of the region were above 0.7, indicating a strong agreement in spatial patterns between the datasets. Similarly, the TPDC vs. CRU correlation (Figure 3b) also showed high correlation coefficients, which confirmed the reliability of the TPDC data in representing drought conditions when compared with the CRU dataset. In SWC, the results were consistent, where both high-resolution datasets showed strong correlations with the CRU dataset. The MODIS vs. CRU correlation (Figure 3c) indicates high R values, reflecting the region’s complex climate and spatial variability in drought conditions. The TPDC vs. CRU correlation (Figure 3d) supported these findings, which also demonstrated the TPDC dataset’s effectiveness at capturing detailed drought patterns.
The temporal correlation between the 12-month high-resolution SPEI and coarse-resolution SPEI is shown in Figure 4, where high R values were found for both NEC and SWC. The results for the 12-month SPEI and the 1-month SPEI revealed that the high-resolution SPEI at both temporal scales exhibited similar spatial patterns of dry and wet conditions compared with the coarse-resolution SPEI across the studied regions. Moreover, the observed differences in correlation patterns between the 1-month and 12-month SPEIs also highlighted the distinct water deficits associated with different aggregation timescales. The 1-month SPEI was more responsive to short-term weather anomalies by capturing immediate moisture surpluses or deficits that could impact agriculture and short-term water management. In contrast, the 12-month SPEI integrated longer-term climatic trends and provided insights into prolonged hydrological and environmental droughts [29]. These differences in correlation patterns can further differentiate between agricultural, hydrological, environmental, and other types of droughts [40].

4.3. Evaluation Against Root Zone Soil Moisture

The SPEI datasets were compared with the root zone soil moisture (RSM) to assess their applicability in drought detection. Figure 5 shows the temporal correlation between the 6-month high-resolution SPEI and the ERA5-Land root zone soil moisture over NEC from 2000 to 2021 (p < 0.05). The correlation maps (Figure 5a,b) illustrate that both high-resolution SPEI datasets had high correlation values with the root zone soil moisture over most parts of the study area. This high correlation indicates a strong agreement between the high-resolution SPEI and ERA5-Land root zone soil moisture, emphasizing the reliability of these datasets for monitoring drought conditions. We observed that the agreement over the right side of the study area, and in particular the bottom-right corner, was relatively low, likely due to localized geographic or climatic factors, such as the variations in land cover, soil type, or microclimate conditions, which could influence the soil moisture independently of the precipitation and evapotranspiration patterns [78]. In addition, the time series of the spatially averaged SPEI and root zone soil moisture are shown in Figure 5c. The high correlation coefficients of 0.7 and 0.72 demonstrate that the high-resolution SPEI could effectively capture the variations in the root zone soil moisture over time, further validating its application in understanding the effects of agricultural drought [26,27,40].
In contrast, Figure 6 presents the correlation between the 6-month high-resolution SPEI and ERA5-Land root zone soil moisture over SWC from 2000 to 2021. The correlation maps (Figure 6a,b) show that both high-resolution SPEI datasets had lower correlation values with the root zone soil moisture in this region compared with those over NEC. Figure 6c shows the time series of the averaged values with correlation coefficients of 0.49 and 0.46. This suggests a moderate agreement between the high-resolution SPEI and root zone soil moisture in SWC. The relatively lower correlation can be attributed to the SWC’s complex hydroclimatic and geomorphological characteristics. The presence of clay-rich and karst soils influences the soil moisture retention and infiltration rates, with karst formations promoting rapid subsurface drainage, thereby reducing the near-surface soil moisture sensitivity to precipitation variability [79]. Additionally, groundwater interactions, particularly in karst regions and lowland basins, contribute to sustained soil moisture levels through capillary rise, introducing a decoupling effect from SPEI-based drought estimates [80]. Furthermore, the influence of vegetation and land cover, particularly the role of forests and agricultural irrigation, modifies the soil moisture through evapotranspiration and an artificial water supply, weakening the direct linkage between precipitation anomalies and soil moisture dynamics [81]. The highly varied topography, which is characterized by steep slopes and deep valleys, further affects the water re-distribution, causing spatial heterogeneity in soil moisture that is not fully captured by the SPEI. Some studies attempted to improve the SPEI accuracy by integrating land surface characteristics and presented better performance [45]. However, research on methods to enhance the SPEI accuracy in complex terrains remains under-explored [40]. To address the challenge of complex terrain, we suggest exploring terrain-aware downscaling techniques, such as statistical correction methods or machine learning approaches incorporating topographic parameters, to improve the SPEI representation in mountainous regions in future research [46].

4.4. Evaluation Against Vegetation Health Index

Similar to the above analysis between the SPEI and the soil moisture, the comparison between the SPEI and the VHI is shown in Figure 7 and Figure 8. Figure 7 presents the correlation between the 6-month high-resolution SPEI and the VHI over NEC from 2000 to 2021. Figure 7a,b show that the correlations values (p < 0.05) between the high-resolution SPEI and VHI were generally lower compared with those observed with root zone soil moisture, with R values around 0.35 and 0.31 for the comparison of the averaged SPEI and the VHI. This lower correlation might have been due to the complex physiological processes associated with vegetation and the influence of factors other than water availability, such as temperature and soil fertility, which affect the VHI [82]. Figure 8 shows the correlation between the 6-month high-resolution SPEI and the VHI over SWC from 2000 to 2021. The spatial distribution of correlations (Figure 8a,b) indicates moderate correlation values, which are slightly higher than those observed in NEC. The moderate correlation can be attributed to the same factors affecting vegetation health, including complex interactions between the water availability, temperature, and other environmental factors [83,84].
The comparisons between the SPEI6 and the VHI in both NEC and SWC suggest that while there was a positive relationship, it was generally weaker than the correlation between the SPEI and the root zone soil moisture [85,86]. In addition, rapid land-use changes in China over the study period (2000–2021), particularly urbanization and agricultural expansion, may have also influenced the relationships between the SPEI and the SM/VHI [87]. Urbanization can alter the soil moisture availability and local microclimates, potentially weakening the correlation between the SPEI (climate-driven) and SM (land-surface-driven) due to increased impervious surfaces and altered hydrological processes [88]. Similarly, irrigation in agricultural regions may sustain vegetation productivity, even under SPEI-indicated drought conditions, leading to inconsistencies between the SPEI and the VHI. Despite this, the high-resolution SPEI datasets still showed a meaningful correlation with VHI, supporting their utility in drought monitoring and assessment. These findings indicate that the SPEI derived from high-resolution datasets could provide valuable insights into vegetation health and drought impacts, although they should be interpreted with an understanding of their limitations. This reinforces the potential and reliability of high-resolution datasets in drought monitoring and climate assessment [27]. It is also noted that this study primarily relied on satellite-derived and reanalysis products for the evaluation of the SPEI due to their extensive spatial coverage, long-term data availability, and robust validation across different regions. However, a key limitation is the absence of ground-based validation data, which could provide more localized accuracy assessments. In the study areas, publicly available ground-based observations, such as soil moisture, meteorological, and agricultural data, are sparse and lack sufficient spatial and temporal coverage for direct validation. Nevertheless, integrating in situ measurements would enhance the reliability of drought assessments. Future research should focus on incorporating diverse ground-based datasets, including station networks, field measurements, and survey data, to strengthen validation efforts. In addition, detailed uncertainty quantification should also be investigated through ensemble approaches and a sensitivity analysis of downscaling techniques.

4.5. Drought Detection with SPEI in NEC

Northeast China (NEC) experienced significant drought periods in recent years, and the high-resolution 6-month SPEI datasets proved effective at capturing these events. The 2016 drought in NEC (Figure 9) served as an important example, where the spatial and temporal changes in the SPEI and root zone soil moisture (SM) clearly reflected the progression of the drought. From August to November 2016, negative SPEI values below -1.5 indicate severe drought conditions, which coincided with a noticeable decline in the SM, highlighting the drought’s impact on the soil moisture availability. However, the correlation between the SPEI and the vegetation health index (VHI) during this period was weaker compared with the SM results. This weaker relationship may have been due to the more complex nature of vegetation health, which is influenced by factors beyond water availability, such as temperature, land-use changes, and the physiological responses of different vegetation types [84,89]. Additionally, agricultural management practices and the presence of drought-resistant crop varieties likely mitigated some of the drought’s impact on the vegetation health, reducing the expected drop in the VHI [90]. Localized water resources, such as groundwater or irrigation systems, may have also buffered vegetation against immediate drought impacts [91]. Despite the less-than-ideal correlation with the VHI, the high-resolution SPEI6 still provides a valuable tool for drought detection and monitoring, particularly when combined with the SM. The strong relationship between the SPEI and soil moisture underscores its reliability in assessing water availability and drought progression. The partial recovery in both soil moisture and vegetation by November 2016, as reflected in the corresponding SPEI values, demonstrates SPEI’s capability to monitor changes in drought conditions over time, providing crucial insights for agricultural and water resource management in NEC.

5. Conclusions

This study demonstrated the effectiveness of using the high-resolution 1 km SPEI to capture regional drought conditions compared with the existing coarser-resolution SPEI dataset. The high-resolution 1 km SPEI was developed with high-resolution TPDC precipitation and MODIS PET products. We evaluated the 1 km SPEI against the 50 km SPEI, root zone soil moisture, and vegetation health indices across two climatically contrasting regions of China: Northeast China (NEC) and Southwest China (SWC). Our analysis shows that the finer resolution SPEI significantly enhanced the detection of drought spatial variability in both regions. While both the high- and coarse-resolution SPEI datasets captured general drought trends, the 1 km SPEI provided more detailed information about the severity and spatial distribution of the drought events. This improvement is particularly critical for applications in agricultural planning, water resource management, and drought mitigation strategies, where regional information is very important. However, this study also highlights that despite the improved spatial detail, certain discrepancies remain, particularly in regions with extreme topographic variability, suggesting the need for further refinement of high-resolution datasets. Future research should explore the integration of machine learning methods to enhance terrain-adjusted SPEI estimations. By incorporating topographic features, land-cover characteristics, and soil properties, machine learning-based approaches may improve the drought-monitoring accuracy, particularly in complex and heterogeneous landscapes. Additionally, expanding validation efforts through ground-based measurements will strengthen the reliability of remote sensing-based drought assessments.

Author Contributions

Conceptualization, J.L., G.L., K.P. and J.P.; methodology, G.L.; validation, J.L.; formal analysis, J.L.; investigation, J.L.; resources, J.P.; data curation, K.P.; writing—original draft preparation, J.L.; writing—review and editing, G.L., K.P. and J.P.; visualization, J.L.; supervision, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA28060100) and the National Natural Science Foundation of China (No. 42471025).

Data Availability Statement

Data are available on request due to privacy/ethical restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 2. Spatial variability of both high and coarse resolution 1-month SPEI over NEC in July 2014 (a–c) and SWC in March 2010 (d–f). The high-resolution 1 km SPEI maps were based on the TPDC precipitation with the MODIS PET and the TPDC precipitation with the TPDC PET. The coarse-resolution 50 km SPEI was calculated with the CRU precipitation and PET.
Figure 2. Spatial variability of both high and coarse resolution 1-month SPEI over NEC in July 2014 (a–c) and SWC in March 2010 (d–f). The high-resolution 1 km SPEI maps were based on the TPDC precipitation with the MODIS PET and the TPDC precipitation with the TPDC PET. The coarse-resolution 50 km SPEI was calculated with the CRU precipitation and PET.
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Figure 3. Temporal correlation between the 1-month high-resolution SPEI and the coarse resolution SPEI over NEC (a,b) and SWC (c,d) from 2000 to 2021. Note that the p-values of the statistics were less than 0.05.
Figure 3. Temporal correlation between the 1-month high-resolution SPEI and the coarse resolution SPEI over NEC (a,b) and SWC (c,d) from 2000 to 2021. Note that the p-values of the statistics were less than 0.05.
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Figure 4. Temporal correlation between the 12-month high-resolution SPEI and the coarse resolution SPEI over NEC (a,b) and SWC (c,d) from 2000 to 2021. Note that the p-values of the statistics were less than 0.05.
Figure 4. Temporal correlation between the 12-month high-resolution SPEI and the coarse resolution SPEI over NEC (a,b) and SWC (c,d) from 2000 to 2021. Note that the p-values of the statistics were less than 0.05.
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Figure 5. Temporal correlation between the 6-month high-resolution SPEI and the ERA5-Land root zone soil moisture over NEC (a,b) from 2000 to 2021 (p < 0.05). The time series of the averaged SPEI and root zone soil moisture are shown in (c).
Figure 5. Temporal correlation between the 6-month high-resolution SPEI and the ERA5-Land root zone soil moisture over NEC (a,b) from 2000 to 2021 (p < 0.05). The time series of the averaged SPEI and root zone soil moisture are shown in (c).
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Figure 6. Temporal correlation between the 6-month high-resolution SPEI and the ERA5-Land root zone soil moisture over SWC (a,b) from 2000 to 2021 (p < 0.05). The time series of the averaged SPEI and root zone soil moisture are shown in (c).
Figure 6. Temporal correlation between the 6-month high-resolution SPEI and the ERA5-Land root zone soil moisture over SWC (a,b) from 2000 to 2021 (p < 0.05). The time series of the averaged SPEI and root zone soil moisture are shown in (c).
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Figure 7. Temporal correlation between the 6-month high-resolution SPEI and the vegetation health index over NEC (a,b) from 2000 to 2021 (p < 0.05). The time series of the averaged SPEI and vegetation health index are shown in (c).
Figure 7. Temporal correlation between the 6-month high-resolution SPEI and the vegetation health index over NEC (a,b) from 2000 to 2021 (p < 0.05). The time series of the averaged SPEI and vegetation health index are shown in (c).
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Figure 8. Temporal correlation between the 6-month high-resolution SPEI and the vegetation health index over SWC (a,b) from 2000 to 2021 (p < 0.05). The time series of the averaged SPEI and vegetation health index are shown in (c).
Figure 8. Temporal correlation between the 6-month high-resolution SPEI and the vegetation health index over SWC (a,b) from 2000 to 2021 (p < 0.05). The time series of the averaged SPEI and vegetation health index are shown in (c).
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Figure 9. Temporal change of the 6-month high-resolution SPEI that was calculated by the TPDC precipitation and MODIS PET, ERA5-Land root zone soil moisture, and vegetation health index over NEC during the drought period August–October 2016.
Figure 9. Temporal change of the 6-month high-resolution SPEI that was calculated by the TPDC precipitation and MODIS PET, ERA5-Land root zone soil moisture, and vegetation health index over NEC during the drought period August–October 2016.
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Table 1. Classification of dry and wet conditions based on the Standardized Precipitation Evapotranspiration Index.
Table 1. Classification of dry and wet conditions based on the Standardized Precipitation Evapotranspiration Index.
ClassificationSPEI Values
Extremely wetSPEI > 2
Very wet1.5 < SPEI ≤ 2
Moderately wet1 < SPEI ≤ 1.5
Near normal−1 < SPEI ≤ 1
Moderately dry−1.5 < SPEI ≤ −1
Very dry−2 < SPEI ≤ −1.5
Extremely drySPEI ≤ −2
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Li, J.; Leng, G.; Pyarali, K.; Peng, J. High-Resolution Drought Detection Across Contrasting Climate Zones in China. Remote Sens. 2025, 17, 1169. https://doi.org/10.3390/rs17071169

AMA Style

Li J, Leng G, Pyarali K, Peng J. High-Resolution Drought Detection Across Contrasting Climate Zones in China. Remote Sensing. 2025; 17(7):1169. https://doi.org/10.3390/rs17071169

Chicago/Turabian Style

Li, Ji, Guoyong Leng, Karim Pyarali, and Jian Peng. 2025. "High-Resolution Drought Detection Across Contrasting Climate Zones in China" Remote Sensing 17, no. 7: 1169. https://doi.org/10.3390/rs17071169

APA Style

Li, J., Leng, G., Pyarali, K., & Peng, J. (2025). High-Resolution Drought Detection Across Contrasting Climate Zones in China. Remote Sensing, 17(7), 1169. https://doi.org/10.3390/rs17071169

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