Soil Moisture Condition Index Research Articles

Improved Agricultural Drought Monitoring with an Integrated Drought Condition Index in Xinjiang, China

Drought is a natural disaster with severe global agricultural and economic impacts. Accurate drought indices are needed for improved assessment and monitoring; however, most existing drought indices poorly represent agricultural drought due to complex interactions among meteorological factors, crop and soil conditions. Here, we compute an integrated drought condition index (IDCI) based on the 3-month standardized precipitation evapotranspiration index (SPEI3), vegetation cover index (VCI) and soil moisture condition index (SMCI). We apply the IDCI to monitoring agricultural drought in Xinjiang, China. After regional evaluations with soil moisture, precipitation and air temperature observations, as well as with the scaled crop yields index, the IDCI was used to describe spatiotemporal changes in regional drought in Xinjiang during 2000–2018, revealing adverse impacts on crop yield (beet, wheat and vegetables). The IDCI is strongly correlated with observed soil moisture and performs better than SMCI, VCI or SPEI3, demonstrating that the IDCI is suitable for agricultural drought monitoring. The most severe drought occurred in the spring to autumn of 2008. Droughts before 2008 were more serious than those after 2008, in terms of both severity and frequency. Droughts in northern, southern and eastern Xinjiang, as well as in the Tianshan Mountains, were generally increasing before 2008 and then weakened after 2008.

Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye

Abstract Remote sensing-based approaches have gained widespread usage in drought monitoring studies. However, relying on single-variable drought indices may be inadequate to provide a comprehensive understanding of drought dynamics. In this study, principal component analysis was employed to derive a combined index, namely, the combined drought index (CDI), from multiple indices such as vegetation condition index, temperature condition index, precipitation condition index, and soil moisture condition index. The CDI was subsequently employed to analyze drought occurrences in the Ergene Basin-Türkiye period from 2001 to 2020 (May to October) using MODIS data. Correlations were computed with standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) at 1-, 3-, and 6-month scales and crop yield. The results revealed that drought incidents transpired in the Ergene Basin for at least 1 month annually. May exhibited the wettest, while September stood as the driest month. The severity of drought and its spatial extent displayed an increasing trend followed by a subsequent decline during the aforementioned period. The CDI demonstrated stronger correlations with the 1-month standardized indices than the 3- and 6-month SPI-SPEI. A robust correlation of 0.79 was also observed between the CDI and the crop yield. In general, the CDI performed well in determining the spatial and temporal patterns of the historic droughts. As a result, the CDI could be leveraged to develop effective drought monitoring and management, which could help mitigate the negative impact of drought in the fragile environment of the Ergene Basin.

A novel composite drought index combining precipitation, temperature and evapotranspiration used for drought monitoring in the Huang-Huai-Hai Plain

Single drought index such as Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Evapotranspiration Condition Index (ETCI) and so on, has a limited capability to indicate drought state. It has been proved that the composite drought index proposed by integrating multiple single drought indices can indicate and predict drought state more efficiently and accurately. This paper presents the Precipitation Temperature Evapotranspiration Drought Index (PTEDI) combining the PCI, TCI and ETCI. The three-dimensional Euclidean PCI-TCI-ETCI space is built, and the PTEDI is defined as the magnitude of the vector in the PCI-TCI-ETCI space. To verify the rationality and effectiveness of the PTEDI as a desired drought index in the Huang-Huai-Hai Plain, it is compared with the PCI, TCI, ETCI and PCACDI (Principal Component Analysis Combined Drought Index), and is evaluated with the Drought Affected/Disaster Area (DAA/DDA) and Net Primary Productivity (NPP). The results indicate that the PTEDI outperforms the PCI, TCI, ETCI and PCACDI, and is highly correlated with the Standardized Precipitation Index (SPI-1) (R = 0.61), Soil Moisture Condition Index (SMCI) (R= 0.56), and NPP (R = 0.56). The time series of the PTEDI and four indices (SPI-1, SMCI, Crop Water Stress Index (CWSI) and Temperature Vegetation Drought Index (TVDI)) exhibits good spatial consistency. The PTEDI has a negative correlation with the DAA/DDA (R < −0.6). Furthermore, to illustrate the applicability of the PTEDI, the drought spatiotemporal variation is analyzed by taking it as a drought index. At the monthly scale, moderate drought occurs most frequently in March and July. At the annual scale, the PTEDI fluctuates between 0.34 and 0.60 without obvious periodicity, and the study area is mainly characterized by mild drought. Moreover, the drought evolution is analyzed by using the timing analysis methods. In conclusion, the PTEDI can be applied to monitor drought effectively.

Dynamic Assessment of Drought Risk of Sugarcane in Guangxi, China Using Coupled Multi-Source Data

Globally, drought is an increasing threat to agricultural ecosystems, resulting in impaired crop yields, high food prices, and low incomes for farmers. Fluctuations in crop production and prices can exert a negative transboundary effect on food exporting and importing countries across the world through international trade. Therefore, it is important to regionally assess agricultural drought risk to reduce crop yield reduction by adapting existing systems. In this study, from the perspective of Chinese sugar security, a comprehensive assessment index of drought risk of sugarcane was constructed by considering the atmosphere–soil–crop continuum. Based on disaster-causing factors (hazards) and exposure, vulnerability, and mitigation capabilities of disaster subjects (disaster bearers), a risk assessment model of drought disaster of sugarcane in the growing season was established. Results of this study were three-fold. First, the maximum entropy model accurately reflected the reliability and relative importance of the disaster-causing factors of vegetation condition index (VCI), soil moisture condition index (SMCI), and standardized precipitation evapotranspiration index (SPEI), with the area under the curve value of the comprehensive drought risk of sugarcane being greater than 0.75. Second, the drought frequency and impact range in four growth stages of sugarcane significantly declined with the increasing drought severity. Light drought was prevalent in each growth stage, and the occurrence frequency of severe drought was relatively low. The drought frequency was significantly higher in the seedling and maturity stages than in the tillering and stem elongation stages, and the drought distribution was mainly concentrated in the southwest and central regions. Finally, the spatial distribution characteristics of drought risk significantly differed among the four growth stages of sugarcane. The risk level in the seedling stage declined from the southwest to the northeast. The high risk in the tillering stage was mainly concentrated in the southwest and northeast of the study region. In the stem elongation stage, the southwest became a low- risk area. In the maturity stage, the risk level was higher in the southeast than in the other areas. As sugarcane is majorly planted on dry slopes with uneven rainfall, a lack of good infrastructure, and the further intensification of global warming, sugarcane areas that were highly exposed to drought stress were highly vulnerable to drought risk, which in turn weakens farmers’ willingness to plant, thus threatening the security of sugar and biofuel production.

A comprehensive remote sensing-based Agriculture Drought Condition Indicator (CADCI) using machine learning

Agriculture drought is a decrease in soil moisture during a growing season. In this study, a comprehensive remote sensing-based Agriculture Drought Condition Indicator (CADCI) was developed to monitor the agriculture drought in semi-arid environments and assess its effectiveness in rainfed agriculture regions in (A) Jordan and (B) Syria. First, remote sensed-based drought-condition spectral indices [i.e., Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Evapotranspiration Condition Index (ETCI), Precipitation Condition Index (PCI), Soil Moisture Condition Index (SMCI), and Vegetation Health Index (VHI)] were calculated using data from Moderate Resolution Imaging Spectroradiometer satellite (MODIS) [Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and evapotranspiration (ET)]; the Global Precipitation Measurement (GPMs); the Soil Moisture Active Passive (SMAPs); and Sentinel-1A. Second, Random Forest (RF) was used to estimate and determine the relative importance of these indices based on Standardized Precipitation Index (SPI) values to select the three spectral indices that have the most monthly short-term relative importance in identifying the agriculture drought for semi-arid environments, which were PCI, TCI, and VCI. Third, these indices were integrated to identify the drought severity based on specific thresholds that compare the pixel-specific value with the study area average value. For instance, a severe drought condition is identified if all three indices indicate a drought condition, a moderate drought or mild drought conditions are identified if any two or any one of the indices indicate drought conditions, respectively. Lastly, a none drought condition is identified if none of the indices indicate a drought condition. Finally, the SPI sets for 1 and 3-months (SPI-1 and SPI-3) were used to evaluate the performance of the CADCI. The results showed the CADCI has a high agreement with SPI-1 classes in the study areas, with overall accuracy and Kappa-values of 85% and 0.80, for study area A and 83% and 0.76 for study area B, respectively. Consequently, the CADCI shows its ability to monitor agricultural drought in semi-arid environments. Perhaps, it could be applicable for larger areas due to the spatial resolution of the input dataset.

A new integrated index for drought stress monitoring based on decomposed vegetation response factors

Drought is a complex process that usually stems from aberrant climatic conditions such as precipitation deficits and temperature anomalies. Drought can pose a serious threat to vegetation growth, which in turn regulates the process of drought evolution. An in-depth understanding of the interaction between drought and vegetation is crucial not only for drought monitoring but also for drought mitigation. To this end, this study divided the drought process into two sub-processes: (1) from physical dry conditions to drought stress and (2) from drought stress to vegetation destruction, and introduced vegetation response factors to quantitatively depict the vegetation contributions in each sub-process. Response factors of different vegetation types were decomposed respectively from Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Precipitation Condition Index (PCI) in the first sub-process, and from Vegetation Condition Index (VCI) in the second sub-process. Further, this study proposed a novel integrated drought index named Type Response-Aided Drought Index (TRADI) based on the response factors of different vegetation types. Vegetation response factors were applied to fine-tune several commonly used single drought indices to verify their effectiveness and the proposed TRADI was evaluated by comparing it with several combined indices, including Vegetation Health Index (VHI), Scaled Drought Condition Index (SDCI), Microwave Integrated Drought Index (MIDI), and Integrated Drought Monitoring Index (IDMI). Moreover, the TRADI was qualitatively evaluated with the maps from U.S. Drought Monitor (USDM) in the 2011 and 2012 drought events across the CONtinental United States (CONUS). Our results showed that adjusted by vegetation response factors, single drought indices as well as combined drought indices showed a closer correlation with the Palmer Drought Severity Index (PDSI) than their original forms, indicating the strength of response factors in modifying existing indices. Furthermore, the proposed TRADI performed better than VHI, SDCI, MIDI, and IDMI, and was consistent with USDM in typical drought events. This study provides a new perspective on understanding the process of drought affecting vegetation and highlights the importance of considering vegetation response in drought index establishment.

Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

Drought has been identified as one of the significant complicated natural disasters exacerbated by land degradation and climate change. Hence, monitoring drought and evaluating its spatiotemporal dynamics are essential to manage regional drought conditions and protecting the natural environment. In this study, various single remote sensing-based drought indices including soil moisture condition index (SMCI), precipitation condition index (PCI), temperature condition index (TCI), and vegetation condition index (VCI) and combined RS-based drought Indices including optimized meteorological drought index (OMDI) and synthesized drought index (SDI) have been used to investigate the spatiotemporal variations of meteorological and agricultural droughts between 2000 and 2021 in Iran. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> drought indices, including the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) series of 1, 3, 6, 12, and 24 months were utilized to verify remote sensing-based drought indices and evaluate their applicability for analyzing drought conditions. The results indicated that the correlation coefficients of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> drought indices with the combined drought indices are higher than the RS-based single drought indexes. Generally, single-factor drought indexes, including VCI, TCI, PCI, and SMCI, have specific characteristics. The PCI and SMCI have an acceptable correlation with the short-term SPI and SPEI and are more applicable to monitoring short-term drought conditions. Further, the TCI has better performance in monitoring long-term drought conditions in Iran. This research concluded that the central, eastern, and southeastern parts of Iran mainly were experiencing exceptional and extreme drought conditions as the worst agricultural and meteorological drought conditions observed in the years 2008 and 2021 in the region during the last 20 years. The results also showed that, in 2019 and 2020, most areas of Iran had higher OMDI and SDI values and the severity of the drought has decreased in these years. Particularly, this research provides an essential reference for reasonably choosing RS-based drought indices for monitoring meteorological and agricultural droughts from a local to global scale.

Dynamic Evaluation of Agricultural Drought Hazard in Northeast China Based on Coupled Multi-Source Data

As the climate warms, the impact of drought on plants has increased. We aimed to construct a comprehensive drought index (CDI), coupling soil-vegetation-atmosphere drought and heat conditions based on multi-source information, and to combine it with static and dynamic drought hazard evaluation models to analyze the spatial and temporal distribution characteristics of agricultural drought disasters and hazards during the growing season (May to September) in Northeast China (NEC). The results demonstrated that the CDI could combine the benefits of meteorology (standardized precipitation evapotranspiration index, SPEI), vegetation (vegetation health index, VHI), and soil (standardized soil moisture condition index, SMCI) indices. This was performed using a relative weighting method based on the remote sensing data of solar-induced chlorophyll fluorescence (SIF) to determine the weights of SPEI, VHI, and SMCI. The CDI for drought monitoring has the advantages of broad spatial range, long time range, and high accuracy, and can effectively reflect agricultural drought; the growing season in NEC showed a trend of becoming drier during 1982–2020. However, the trends of the drought index, the impact range of drought events, and the hazard of agricultural drought all turned around 2000. The drought hazard was highly significant (p &lt; 0.001) and decreased from 2000 to 2020. The frequency of drought disasters was the highest, and the hazard was the greatest in May. The best level of climatic yield anomalies in maize were explained by drought hazard in August (R2 = 0.28). In the center and western portions of the study area, farmland and grassland areas were where higher levels of hazard were most commonly seen. The dynamic hazard index is significantly correlated with climatic yield anomalies and can reflect the actual impact of drought on crop yield. The study results serve as a scientific foundation for drought risk assessment and management, agricultural planning, and the formulation of drought adaptation policies, as well as for ensuring food security in China.

Multisource data-based integrated drought monitoring index: Model development and application

In this study, we proposed a new integrated remote sensing drought monitoring indices, i.e. Multiple Remote Sensing Drought Index integrated by Principal Component Analysis (PSDI), Multiple Remote Sensing Drought Index integrated by multiple linear regression (MRSDI) and Multiple Remote Sensing drought index integrated by gradient boosting method (GBMDI), based on the Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Soil Moisture Condition Index (SMCI). The monitoring performance of PSDI, MRSDI and GBMDI was compared and verified based on the real-world observed droughts during 2002 to 2016. We also evidenced drought monitoring performance of the PSDI MRSDI and GBMDI by comparison between PSDI, MRSDI, GBMDI and SPEI, SPI and PDSI based on the in situ observed meteorological data. We found that the spatiotemporal characteristics of droughts monitored by the PSDI, MRSDI and GBMDI were generally in good agreement with those by the SPI and SPEI. The GBMDI performs better than PSDI and MRSDI in describing drought processes and spatial patterns of droughts of different drought intensities. Comparison between the real-world observed drought-affected croplands and those monitored by PSDI, MRSDI and GBMDI indicated better drought monitoring performance of GBMDI than PSDI and MRSDI in monitoring droughts across widespread drought-affected regions. Besides, the trend of GBMDI is in good agreement with standardized crop yield. Therefore GBMDI should be the first choice in drought monitoring practice. The GBMDI developed in this study can help to provide an alternative drought monitoring index for large-scale drought monitoring across China and also in other regions of the globe.

A new composite index for global soil plant atmosphere continuum drought monitoring combing remote-sensing based terrestrial water storage and vapor pressure deficit anomalies

Drought is known as a complex natural phenomenon because of its multifaceted effect on the environment. Composite drought indices have been considered a useful tool to capture the compound drought impacts, however, few of them are capable of operating at multi-timescales. Moreover, the vast of composite drought indices fail to depict the whole water deficit on terrestrial because of the ignoring of groundwater. In this study, we linearly combined two satellite observed variables, TWS (Terrestrial Water Storage) and VPD (Vapor Pressure Deficit), to develop a new index WDDI (Water Deficit Drought Index). It can provide the global composite drought conditions at multi-timescales by different weight combinations. The global multiscalar SPEI (Standardized Precipitation Evapotranspiration Index) from 2002 to 2011 and 2012 to 2017 were selected to train and assess the weights respectively. Weights assessment results showed that SPEI and WDDI reached moderate correlations (r > 0.4) over the globe, especially stronger in the summer season (0.5 < r < 0.88). Good agreements between WDDI and scPDSI (self-calibrated Palmer Drought Severity Index), SPEI were found in the global historical drought area statistics, especially in area statistics of severe or worse drought conditions. WDDI at short and long timescales were found to be sensitive to drought signals in the meteorological and hydrological system respectively as it produced moderate or stronger correlations with VsmCI (Volumetric soil moisture Condition Index) and RCI (Runoff Condition Index) in most parts of the world. The application of WDDI in the specific drought events (the 2010 Russian drought and the 2012–2016 California drought) further reinforced the above inferences and revealed the unique superiority of considering groundwater deficit in hydrological drought monitoring. The new proposed multiscalar WDDI could help practitioners better identify the drought conditions and understand the drought propagation mechanism from the meteorological to the hydrological system.

A new drought index and its application based on geographically weighted regression (GWR) model and multi-source remote sensing data.

Drought is the most widespread natural disaster in the world. How to monitor regional drought scientifically and accurately has become a hot topic for many scholars. In this paper, Geographically Integrated Dryness Index (GIDI) was integrated from an assortment source including Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI) (as the dependent variable) based on geographically weighted regression method. Besides, the comprehensive drought situation and changing trends in China from 2001 to 2019 were also examined. The results showed that (1) GIDI has excellent performance in monitoring medium- and long-term droughts and the monitoring results shows 2003, 2016, and 2019 were relatively wet years, while 2007, 2009, and 2011 were major drought years, and spring and March were the most frequent droughts season and month, respectively, and (2) except for the middle and upper reaches of the Yellow River and Northeastern China, which have a tendency to become wet, other places have a tendency to fluctuating dry. This study took advantage of simple and efficient methods to integrate existing indices to obtain a new index for monitoring a wider range of droughts, taking into account the physical mechanism of drought formation and the time scale of drought development, so it can scientifically evaluate the spatial and temporal distribution characteristics of drought and changes.

Evaluating the impact of different normalization strategies on the construction of drought condition indices

Drought condition indices (CIs), including single condition (SCIs) and combined condition indices (CCIs), can reveal the characteristics of drought evolution and have been widely used in drought monitoring tasks. However, in practical applications, CIs are constructed in different normalization methods rather than based on a uniform standard. The diversely constructed CIs not only mislead the scientific community but also confuse farmers and stockholders to prevent them from taking timely measures when drought events occur. Thus, this study aims to comprehensively evaluate the differently constructed CIs and quantify the impact of different normalization strategies. To achieve this, four SCIs, including the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Soil Moisture Condition Index (SMCI), and the Precipitation Condition Index (PCI), together with three CCIs, including the Vegetation Health Index (VHI), the Scaled Drought Condition Index (SDCI), and the Microwave Integrated Drought Index (MIDI) were chosen as typical CI representatives. Each original CI was compared in the Continental United States (CONUS) with its variants by calculating the correlation coefficients with six in-situ indices (PDSI, Zindex, SPI-1, SPI-3, SPI-6, and SPI-9). Furthermore, the different CIs were qualitatively evaluated with the United States Drought Monitor in the extreme drought event in 2011. The results indicated that CI variants built through temporal normalization reduce the accuracy of drought severity estimation, and the CI variants built through spatial normalization lost the ability to track the spatiotemporal evolution of drought. The impacts of different normalization methods on single indices are transmissible and cumulative when integrating them into combined indices. These findings highlight the risks of diverse normalization methods in CIs establishment and underscore the necessity of considering seasonality and spatiality in drought applications.

Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine

As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R2 = 0.9606, p &lt; 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R2 = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (p &gt; 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and p values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and p values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future.

Assessing the performance of satellite soil moisture on agricultural drought monitoring in the North China Plain

Satellite soil moisture (SM) has been increasingly available and the use of SM-based indices has a great potential for agricultural drought monitoring. This study evaluates the performance of two satellite SM products, the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), on drought detection and assessment in the North China Plain (NCP) over the period of 2015–2018, with a comparison between two SM-based drought indices, Soil Water Deficit Index (SWDI) and Soil Moisture Condition Index (SMCI). Vegetation Condition Index (VCI) and Vegetation Drought Index (VDI) were adopted as the reference indices. Overall, SMAP outperformed SMOS in SM data validation as well as drought detection in the NCP region. A simple bias correction of the satellite SM data was able to improve their performance in characterizing agricultural drought via SM-based indices. Compared with SWDI, SMCI appeared to be the more appropriate index for drought monitoring, by showing a better agreement with both VCI and VDI in terms of drought characteristics (i.e. spatial distribution, drought category and frequency), though both SM-based indices overestimated drought severity to some extent. A simply SM-based drought indicator could be effective when satellite SM was applied for capturing agricultural drought, and a full consideration of longer term satellite data might be necessary.

Monitoring drought dynamics in China using Optimized Meteorological Drought Index (OMDI) based on remote sensing data sets

Timely and accurate monitoring of the spatiotemporal changes in drought is very important for the reduction in the social losses caused by drought. The Optimized Meteorological Drought Index (OMDI), originally established in southwestern China, showed great potential for drought monitoring over large regions on a large scale. However, the applicability of the index requires further evaluation, especially when used throughout China, which has a different agricultural divisions, variable climatic conditions, complex terrain and diverse land cover. In addition, the OMDI model relies on training data to construct local parameters for the model. On a large scale, it is of great significance to use multisource remote sensing data sets to construct OMDI model parameters. In this paper, the constrained optimization method was used to establish weights for the MODIS-derived Vegetation Conditional Index (VCI), TRMM-derived Precipitation Condition Index (PCI), and GLDAS-derived Soil Moisture Condition Index (SMCI) and calculate the OMDI based on the Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and weather stations. The accuracy of the OMDI model was evaluated by using the correlation coefficient. Moreover, the spatiotemporal changes in drought were also analyzed through trend analysis, Mann-Kendall (MK) statistics and the Hurst index on the monthly and annual scales. The results showed that (1) the highest positive correlation between the OMDI and the SPI was SPI-1, which was higher than that for any other month interval, such as 3 months, 6 months, 9 months and 12 months of the SPI. The results indicated that the OMDI was suitable to monitor meteorological drought. (2) In the nine agricultural subareas in China, the degree of drought in the Yangtze River (DYR) area had the most severe evolution and change frequency. This region was very sensitive to drought in the past two decades. (3) The area with OMDI variation coefficient less than 0.1 accounted for 94%, indicating that the degree of drought fluctuates little; The linear tendency rate is 0.0004, and the area greater than 0 reaches 66.44%, indicating that the drought is developing in a lightning trend. (4) The Hurst index value is mostly higher than 0.5 (the area ratio is 56.31%), and the area of "Positive-Consistent" and "Negative- Opposite" accounted for 54.02%, indicating that more than half of China's area drought changes will show a trend of mitigation in the future.

Drought monitoring in arid and semi-arid region based on multi-satellite datasets in northwest, China.

Drought is a complex natural disaster affected by multiple climate factors and underlying surface. In recent years, drought monitoring indices of remote sensing have been widely applied to monitor drought in a certain region or global. However, some remote sensing drought monitoring indices do not consider the drought-causing factors enough to reflect the comprehensive drought situation of a region fully. In this paper, a new remote sensing drought monitoring index, called Remote Sensing Drought Evaluation Index (RSDEI), was constructed by combining Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI), and Soil Moisture Condition Index (SMCI) using the spatial principal component analysis (SPCA) method. The reasonableness of RSDEI was test and verified using Net Primary Productivity (NPP), Standardized Precipitation Evapotranspiration Index (SPEI), and unit area crop yield. The RSDEI was also applied to the drought condition monitoring of the northwest arid and semi-arid region from 2001 to 2019.The result demonstrated that the results showed that the RSDEI had a high correlation coefficient with SPEI-12 (R=0.85, p<0.01). It is concluded that the correlation coefficient between RSDEI and NPP is 0.74 at 95% confidence level, which indicates that RSDEI and NPP have a strong correlation. Then, the correlation between RSDEI and crop yield per unit area is 0.89. The results of RSDEI showed that the drought in northwest China started in May and lasted in September from 2001 to 2019. The lowest value of RSDEI appeared in May, which inflected the significant difference of drought level in different month in northwest China. The result of CV (coefficient of variation) showed that the drought variation in the study area had a stable low fluctuation condition as a whole, in the northwest and northeast of study area, which indicated that the changes of drought were different in the past 19 years. The Hurst exponent analysis showed that the area with the positive evolution of Hurst index (0.5<H<1) is 1,845,046.669 km2,which accounts for 75.9% of the total area, while the area with reverse evolution characteristics (H<0.5) accounts for 24.1% of the total area. The result obtained above reflected that the drought changes in most regions are better than that in the past 19 years. The trend gradually changes from drought to humid.

Remote sensing-derived combined index for agricultural drought assessment of rabi pulse crops in Bundelkhand region, India

In this study, a novel approach defined for drought severity of Bundelkhand region of India, especially for rabi (winter) season crops using analytical hierarchy process established a Multi-Criteria Decision-Making approach based combined drought index (CDI). The remote sensing multi-sensor derived datasets, as long-term MODIS satellite data, rainfall from CHIRPS datasets and soil moisture from Era Interim datasets, used for quantification for the 2001–2018 period. The CDI is generated by integrated multi-spectral indices as Vegetation Condition Index, Temperature Condition Index, Precipitation Condition Index, Soil Moisture Condition Index using analytical hierarchy process derived weightage with a consistency ratio of 4.2% and consistency index of 0.038 value. The results show that Jalaun, Hamirpur and Banda districts as Northern part affected more as compared to Lalitpur and parts of Jhansi districts as the Southern part of the study area. The statistical analysis illustrated a significant correlation between crop yield anomaly and CDI for all districts for rabi pulses crops. Thus, a geospatial platform-based approach using historical earth observations with analytical hierarchy process integrated expert advice to finalize variables and their weighing will make this methodology more realistic, easier and quicker to apply in future at any region. Eventually, remote sensing can address the drought risk or severity for all kind of crop ecosystem by using cohesive approach derived from multi-sensor satellite datasets.

Comparative evaluation of drought indices for monitoring drought based on remote sensing data.

Many indices are used to monitor drought events. However, different indices have different data requirements and applications. Hence, evaluating their applicability will help to characterize drought events and refine the development of effective drought indices. We constructed different drought indices based on multisource remote sensing data and comprehensively evaluated and compared their applicability for drought monitoring throughout China. The characteristics of drought events in 2009 and 2011 were compared using various drought indices. The different time scales of the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Index (SPI) were used to evaluate remote sensing drought indices in different regions. Single drought indices, including the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, the Precipitation Condition Index (PCI) derived from Tropical Rainfall Measurement Mission (TRMM) data, and the TCI and Soil Moisture Condition Index (SMCI) derived from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) data, as well as combined drought indices, including the Microwave Integrated Drought Index (MIDI), Optimized Vegetation Drought Index (OVDI), Optimized Meteorological Drought Index (OMDI), Scale Drought Conditions Index (SDCI), and Synthesized Drought Index (SDI), were analyzed and compared to evaluate their applicability. The results showed that different drought indices have specific characteristics under different land use types in China. The VCI and TCI can better monitor long-term drought conditions, but they have a weak correlation with the in situ drought index in forestland and grassland areas. The correlation of SPI-1 with the PCI is higher than that with other single indices, which indicates that the PCI is a good short-term drought index. The SMCI has a better correlation with the short-term in situ drought index, but it is not conducive to drought monitoring in areas such as densely forested land and grassland. The correlations of the in situ drought index with the combined drought indices (the MIDI, OVDI, OMDI, SDCI, and SDI) are better than those with the single drought indices.

Global drought monitoring with big geospatial datasets using Google Earth Engine

Drought or dryness occurs due to the accumulative effect of certain climatological and hydrological variables over a certain period. Droughts are studied through numerically computed simple or compound indices. Vegetation condition index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the temperature condition index (TCI) is used for studying the temperature change. Dryness or wetness of soil is a major indicator for agriculture and hydrological drought and for that purpose, the index, soil moisture condition index (SMCI), is computed. The deviation of precipitation from normal is a major cause for meteorological droughts and for that purpose, precipitation condition index (PCI) is computed. The years when the indices escalated the dryness situation to severe and extreme are pointed out in this research. Furthermore, an interactive dashboard is generated in the Google Earth Engine (GEE) for users to compute the said indices using country boundary, time period, and ecological mask of their choice: Agriculture Drought Monitoring. Apart from global results, three case studies of droughts (2002 in Australia, 2013 in Brazil, and 2019 in Thailand)computed via the dashboard are discussed in detail in this research.

Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco

In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000–2017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.