Developing the vegetation drought response index for South Korea (VegDRI-SKorea) to assess the vegetation condition during drought events

South Korea has experienced severe droughts and water scarcity problems that have influenced agriculture, food prices, and crop production in recent years. Traditionally, climate-based drought indices using point-based meteorological observations have been used to help quantify drought impacts on the vegetation in South Korea. However, these approaches have a limited spatial precision when mapping detailed vegetation stress caused by drought. For these reasons, the development of a drought index that provides detailed spatial-resolution information on drought-affected vegetation conditions is essential to improve the country’s drought monitoring capabilities, which are needed to help develop more effective adaptation and mitigation strategies. The objective of this study was to develop a satellite-based hybrid drought index called the vegetation drought response index for South Korea (VegDRI-SKorea) that could improve the spatial resolution of agricultural drought monitoring on a national scale. The VegDRI-SKorea was developed for South Korea, modifying the original VegDRI methodology (developed for the USA) by tailoring it to the available local data resources. The VegDRI-SKorea utilizes a classification and regression tree (CART) modelling approach that collectively analyses remote-sensing data (e.g. normalized difference vegetation index (NDVI)), climate-based drought indices (e.g. self-calibrated Palmer drought severity index (PDSI) and standardized precipitation index (SPI)), and biophysical variables (e.g. elevation and land cover) that influence the drought-related vegetation stress. This study evaluates the performance of the recently developed VegDRI-SKorea for severe and extreme drought events that occurred in South Korea in 2001, 2008, and 2012. The results demonstrated that the hybrid drought index improved the more spatially detailed drought patterns compared to the station-based drought indices and resulted in a better understanding of drought impacts on the vegetation conditions. The VegDRI-SKorea model is expected to contribute to the monitoring of drought conditions nationally. In addition, it will provide the necessary information on the spatial variations of those conditions to evaluate local and regional drought risk assessment across South Korea and assist local decision-makers in drought risk management.     

Comparison of remotely sensed and meteorological data-derived drought indices in mid-eastern China

Numerous drought indices have been developed and applied to monitor the severity of drought. It has been demonstrated that the evaluation of the indices is very important for further utilization of remotely sensed and meteorological information. The objective of this article is to investigate and compare the different methods derived from satellite/meteorological data for drought monitoring during the typical dry year (2006) in mid-eastern China. The compared six drought indices include the vegetation condition index (VCI), percent of average seasonal greenness (PASG), temperature condition index (TCI), vegetation supply water index (VSWI), percentage of precipitation anomalies (PPA) and standardized precipitation index (SPI). These indices are calculated based on different data sources including reflective data, thermal data, the combination of reflective and thermal data and meteorological data. The correlation matrix and regression relationships among the integrals under all drought indices, the integral under the relative air humidity (RAH) curve and cumulative rainfall at the location of 11 agro-meteorological stations for 2006 were calculated. Spatial comparison analysis among the drought indices reveals that all the indices have certain coincidence in the detected regional-scale distribution of drought especially those derived from the same data set, while obviously local-scale distribution differences were found among the different groups of indices. Compared to curves of the reflective and thermal indices, the overall trend of VSWI series has better consistence with the PPA curve. Based on correlation and regression analysis, it is demonstrated that VSWI can better reflect both the amount of precipitation and the severity of drought due to lack of rainfall. Furthermore, land surface temperature (LST) contributes more to the result of hybrid index (VSWI) than reflective information. There is logarithmic relationship between integral of VSWI and cumulative precipitation, while obvious linear correlations were found between integral under VSWI curve and integral under the RAH/TCI/PASG curves. According to the filed observation of droughts from agro-meteorological stations in the study area, it can be concluded that any single index is not sufficient to precisely depicting drought characteristics. The combined use of different indices at the same time or indices which integrate various sources of information may obtain more consistent results with the actual situation.     

Agricultural drought monitoring using MODIS-based drought indices over the USA Corn Belt

In 2012, the USA Corn Belt, an intensive agricultural region of the USA, was hit by a widespread severe drought, affecting states such as Illinois, Iowa, Nebraska, and Indiana. In this study, time series (2000–2012) of Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were investigated to assess the 2012 drought conditions during the corn-growing season. Seven MODIS indices generated based on eight day MODIS reflectance and land surface temperature (LST) products were examined with standardized precipitation index (SPI) and Palmer-Z across the Corn Belt to evaluate the relative performance of each MODIS index to detect agricultural drought. The normalized difference infrared index (NDII6) anomaly shows the highest correlation coefficient (r) with SPI at three time scales and correlates best with Palmer-Z, which suggests good sensitivity of the NDII6 anomaly to precipitation and moisture deficiency in agricultural areas. The temporal and spatial features of drought provided by MODIS indices were compared with maps of the USA Drought Monitor (USDM), the current advanced tool for drought monitoring. The rapid intensification of drought across the Corn Belt in 2012 summer captured by MODIS index anomalies agreed with the changes of USDM maps quite well, especially in August and September when extreme drought occurred. Through comparison with the USDM drought map, the NDII6 anomaly demonstrated an advantage in monitoring drought condition over irrigated land and showed the potential to advance fine-scale agricultural drought monitoring by providing more detailed spatial characterization.     

An investigation of drought prediction using various remote-sensing vegetation indices for different time spans

Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural and repeatable phenomenon definable at specified time and area. In addition, social and economic issues can be affected by drought. Information such as intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, therefore lessen the risks associated with drought episodes. Lack of long-term meteorological data for many parts of the country is one of the most important problems for drought monitoring in Iran. One of the useful ways for gathering information about soil and vegetation conditions is using satellite-based imagery. In this study, remotely sensed image data were applied in order to forecast and model the drought. To this end, SPI (standardized precipitation index) drought indicator was used to represent the drought and its intensity in different time spans (1, 3, 6, 9, 12, and 24 months). Some vegetation indices (VIs) including normalized difference vegetation index, temperature condition index, vegetation condition index, and normalized difference vegetation index deviation were extracted using Advanced Very High Resolution Radiometer sensor imagery. These indices were plugged into the model to calculate the SPI. A unique Support Vector Machine classifier improved for all types of the SPI by applying various remotely sensed VIs. The best vegetation index for each kind of SPI was determined. In this framework, meteorological stations were clustered based on their land cover extracted from satellite-based indices before insertion to the model.     

重庆市墒情分析评价预测系统的设计与实现

结合重庆市墒情、水雨情等自动监测系统,考虑主要作物种类、分布区域、播种面积、耕作制度、生育期间各生长发育指标,以及不同区域、深度的田间持水量,对已建立土壤墒情监测点的地区,采用土壤相对湿度评估农业墒情;对于尚未建立墒情监测站但已建立雨量监测站点的雨养农业区,采用降水量距平法或连续无雨日数法,进行墒情分析评价,用衰减系数法预测墒情的变化趋势。采用B/S开发模式,利用Flex通过天地图在线服务进行地图显示,采取IIS发布模式,基于Web Services的数据服务模式,设计一套基于Web GIS的墒情监测分析评价预测系统,通过相关评价指标反映农林作物土壤的干旱情况,并能结合天气情况预测未来墒情数据,为安排农业用水提供技术支撑,减少干旱灾害损失。     

Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems

Drought monitoring is important to analyse the influence of rainfall deficiency patterns on bushfire behaviour. Remote sensing provides tools for spatially explicit monitoring of drought across large areas. The objective of this study was to assess the performance of MODIS-based reflectance spectral indices to monitor drought across forest and woodland vegetation types in the fire prone Sydney Basin Bioregion, NSW, Australia. A time series of eight spectral indices were created from 2000 to 2009 to monitor inter-annual changes in drought and were compared to the Standardized Precipitation Index (SPI), a precipitation deficit/surplus indicator. A pixel-to-weather station paired correlation approach was used to assess the relationship between SPI and the MODIS-based spectral indices at different time scales. Results show that the Normalised Difference Infrared Index—band 6 (NDIIb6) provided the most suitable indicator of drought for the high biomass vegetation types considered. The NDIIb6 had the highest sensitivity to drought intensity and was highly correlated with SPI at all time scales analysed (i.e., 1, 3 and 6-month SPI) suggesting that variations in precipitation patterns have a stronger influence on vegetation water content than vegetation greenness properties. Spatial similarities were also found between patterns of NDIIb6-based drought maps and SPI values distribution. NDIIb6 outperformed the spectral index currently in use for operational drought monitoring systems in the region (Normalised Difference Vegetation Index, NDVI) and its implementation in existing drought-monitoring systems is recommended.     

Evaluating satellite-based precipitation products in monitoring drought events in southwest China

Southwest China (SWC) is one of the areas that has most frequently been affected by a variety of drought events in recent years. Satellite-based precipitation products with high spatial resolution, having greatly improved their accuracy and applicability, are expected to offer an alternative to improve drought monitoring. The purpose of this article is to evaluate the reliability of the Tropical Rainfall Measuring Mission (TRMM) V7 3B43 products using the observed monthly precipitation data obtained from 118 meteorological stations from 1998 to 2013 and to monitor the temporal and spatial variations of drought conditions using the Standardized Precipitation Index (SPI), which is derived by a non-parametric approach. The results showed that the TRMM 3B43 products performed well in terms of monthly precipitation, although they slightly overestimated the total precipitation amount, mainly in summer. They matched the observed data well, yielding high correlations and low biases in most parts of SWC. For drought assessment, the SPI based on monthly TRMM 3B43 data oscillated around zero and showed a consistent inter-annual variability compared with gauges. Moreover, the TRMM 3B43 showed similar temporal drought behaviour by capturing most of the drought events at various timescales, and both of them described similar spatial patterns of drought. The TRMM products precisely described the occurrence and development process of the 2009/2010 drought and revealed that compared to the 2009/2010 drought the 2011 drought was more severe and affected a larger area. In general, using the TRMM 3B43 product is suitable and credible for drought monitoring over SWC.     

Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data

While existing remote sensing-based drought indices have characterized drought conditions in arid regions successfully, their use in humid regions is limited. We propose a new remote sensing-based drought index, the Scaled Drought Condition Index (SDCI), for agricultural drought monitoring in both arid and humid regions using multi-sensor data. This index combines the land surface temperature (LST) data and the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite. Each variable was scaled from 0 to 1 to discriminate the effect of drought from normal conditions, and then combined with the selected weights. When tested against in-situ Palmer Drought Severity Index (PDSI), Palmer's Z-Index (Z-Index), 3-month Standardized Precipitation Index (SPI), and 6-month SPI data during a ten-year (2000-2009) period, SDCI performed better than existing indices such as NDVI and Vegetation Health Index (VHI) in the arid region of Arizona and New Mexico as well as in the humid region of North Carolina and South Carolina. The year-to-year changes and spatial distributions of SDCI over both arid and humid regions generally agreed to the changes documented by the United States Drought Monitor (USDM) maps.     

The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana,USA

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented. It was found that the VCI is sensitive to changes in land cover, especially deforestation, the land cover changes from evergreen and deciduous forests to other land-cover classes. However, because the scale of land-cover changes was very small across the study area, only trivial drought alerts were observed in the VCI-based drought maps during non-drought years. Because drought is a large-scale climate event, it is reasonable to neglect these alerts. Besides, when the VCI was averaged to climate division scale, the results obtained through the VCI method were in good agreement with those acquired by the meteorological data-based drought indices such as the Palmer drought severity index and standardized precipitation index.     

考虑下垫面类型的干旱指数比较研究

干旱作为常见的自然灾害，在世界各地发生的频率日渐增加，已对经济发展、农业生产和人类生活等方面产生了严重影响。但是干旱的类型较多，包括气象干旱、土壤干旱、水文干旱、农田干旱等，无法用单个干旱指数对不同类型的干旱进行监测。按照干旱发生类型，利用气象干旱指数（Standardized Precipitation Index SPI）、土壤水分干旱指数（Soil Moisture Index, SMI）和蒸发压力干旱指数（Evaporative Stress Index, ESI）对美国的旱情进行监测。研究结果表明：不同干旱指数之间呈显著相关，相关系数R在0.7以上。ESI整体监测精度较高，它能够真实反映地表水分盈亏状况，同时与遥感数据结合，可以实现从田块到全球不同尺度干旱实时监测。不同植被类型覆盖下垫面对不同类型干旱响应存在较大差异，草地下垫面对不同类型的干旱响应较为一致，但是随着地上生物量的增加，不同干旱指数监测结果之间差异逐渐增大。因此，在干旱监测时需要考虑植被的结构特征，植被与气候之间的相互作用，才能具体分析不同下垫面的受灾情况，进一步考虑更适合的方法以及干旱指数监测不同下垫面的干旱情况。     

基于多源干旱指数的黄淮海平原干旱监测

黄淮海平原是我国重要的粮食基地,由于季风、气候等的影响,干旱频发,严重影响了粮食生产,实时监测黄淮海平原的干旱情况,对于合理制定农业政策、指导农业生产具有重要意义。基于MODIS反射率产品、温度产品和气象站点降雨数据等,采用改进归一化水指数(MNDWI)、植被健康指数(VHI)和标准化降水指数(SPI),对黄淮海平原2001~2012年干旱情况进行监测,分析其空间、季节、年际变化规律及其潜在原因,并根据结果确定3个指数的使用条件。结果发现:黄淮海平原燕山山麓和太行山山麓受西伯利亚冬季风的影响,同时由于春天植被覆盖少,水份蒸发较快,易发生春旱;农作物区在海拔25~100m之间比其他地区要干旱;12年间2003年干旱最弱。所采用遥感指数由于对水分温度敏感适用于实时监测,而气象指数SPI适用于长时间序列的干旱变化监测,亦可用于干旱预测。     

考虑下垫面类型的干旱指数比较研究

干旱作为常见的自然灾害，在世界各地发生的频率日渐增加，已对经济发展、农业生产和人类生活等方面产生了严重影响。但是干旱的类型较多，包括气象干旱、土壤干旱、水文干旱、农田干旱等，无法用单个干旱指数对不同类型的干旱进行监测。按照干旱发生类型，利用气象干旱指数（Standardized Precipitation Index SPI）、土壤水分干旱指数（Soil Moisture Index, SMI）和蒸发压力干旱指数（Evaporative Stress Index, ESI）对美国的旱情进行监测。研究结果表明：不同干旱指数之间呈显著相关，相关系数R在0.7以上。ESI整体监测精度较高，它能够真实反映地表水分盈亏状况，同时与遥感数据结合，可以实现从田块到全球不同尺度干旱实时监测。不同植被类型覆盖下垫面对不同类型干旱响应存在较大差异，草地下垫面对不同类型的干旱响应较为一致，但是随着地上生物量的增加，不同干旱指数监测结果之间差异逐渐增大。因此，在干旱监测时需要考虑植被的结构特征，植被与气候之间的相互作用，才能具体分析不同下垫面的受灾情况，进一步考虑更适合的方法以及干旱指数监测不同下垫面的干旱情况。     

Characteristics of drought indices and rainfall in Lake Chad Basin

A basin-scale analysis of the spatial and temporal distribution of drought indices and rainfall characteristics was performed in Lake Chad Basin (LCB), located at the Sahelo–Sudanian transition zone of West Africa. The research aims to improve our understanding of distribution, scenarios, and location-specific probability distribution of rainfall in the basin. Dekadal variability and trends were constructed and analysed using a geographic information system geoprocessing tool. There is a good correlation between the Tropical Rainfall Measuring Mission (TRMM 3B43) monthly rainfall and Global Precipitation Climatology Centre (GPCC) gauge with a correlation coefficient of 0.98. Climate Prediction Centre (CPC) Rainfall Estimate (RFE), Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) (eMODIS) normalized difference vegetation index images, global net primary production (NPP) anomaly, and standard precipitation index (SPI) were characterized. Results indicate an increase in NPP and SPI values from 2002 to 2011, which supports the theory of recent greening of the Sahel. Autocorrelation analysis identified a very high drought index at the northernmost part of LCB (proximal to the Sahara Desert) with the northern part of LCB characterized as low–low, suggesting more likelihood of low rainfall, and southeast and southwest portions as high–low, suggesting a decrease in likelihood of high precipitation northward. This provides vital information to farmers and relevant authorities for making educated decisions in poor rainy seasons. The statistical coefficient of variance and rainfall average also provide crucial information on region-specific rainfall needs for crop production.     

农业干旱遥感监测业务化运行方法研究

研究一种基于MODIS遥感数据的快速且易于实际应用的农业旱情监测方法及其系统实现。监测方法基于植被供水指数的思想,利用NDVI进行植被盖度分级来计算,标准化作物供水指数,再耦合近8旬的综合降水距平指数,由此实现对旱情的监测。在介绍监测方法的同时,又针对应用数据的特点,设计了有效的数据管理系统,为高效管理各种数据而服务。     

A new agricultural drought monitoring index combining MODIS NDWI and day–night land surface temperatures: a case study in China

The vegetation health index (VHI) is a widely utilized remote-sensing-based index for monitoring agricultural drought on the regional or global scale. However, the validity of VHI as a drought detection tool relies on the assumption that the normalized difference vegetation index (NDVI) and land-surface temperature (T_s) at a given pixel will vary inversely over time. This assumption may introduce large uncertainties in VHI for drought monitoring over areas with complex landforms, such as China. In order to monitor agricultural drought over the whole of China, a new drought detection index is suggested in this article, termed the vegetation drought index (VDI). VDI is developed from the classical VHI by substituting NDVI and T_s with the normalized difference water index (NDWI) and day–night T_s difference (?T_s), respectively. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11C3 and MOD13C2 products from 2001 to 2011, monthly precipitation data from 1970 to 2010, and yearly winter wheat yield data from 2000 to 2012 were utilized to evaluate VDI. Results indicated that (1) many areas in China show a positive correlation between NDVI and T_s, especially in the cold season, whereas most areas have a negative correlation between NDWI and ?T_s; (2) VDI has a significant linear correlation with VHI in areas and periods where the NDVI–T_s correlation and NDWI–?T_s correlation are both negative; (3) VDI presents a significant correlation with 3 and 6 month standardized precipitation indices, which is comparable to VHI; and (4) VDI has a significant correlation with normalized crop yield, and is better than VHI. As an example, the extreme drought event over southwestern China from winter 2009 to spring 2010 was successfully explored by VDI. It is concluded that the new index, VDI, has the potential to monitor agricultural drought over the whole of China, including areas and periods where the NDVI–T_s correlation is non-negative.     

An evaluation of satellite-based drought indices on a regional scale

We compared conventional and satellite-based drought indices from drought vulnerable sites in South Korea during 2004–2013. Satellite-based drought indices, the energy-based water deficit index (EWDI), and the standalone Moderate Resolution Imaging Spectroradiometer (MODIS)-based evaporative stress index (stMOD_ESI) were evaluated using MODIS imagery to assess its capability to analyse the complex topography of the Korean peninsula. Of the drought indices examined, the EWDI and stMOD_ESI were accurate when capturing moderate drought conditions, compared to the observed precipitation-based conventional drought indices (standardized precipitation index (SPI-3) and Palmer drought severity index (PDSI)). In addition, the satellite-basedsoil moisture index (SSMI) developed from the Advanced Microwave Scanning Radiometer (AMSR-E) and Advanced Scatterometer (ASCAT) soil moisture products were reasonably correlated with the EWDI and stMOD_ESI. These results suggest that the satellite-based drought indices (EWDI and stMOD_ESI) may be applicable on a regional scale.     

降水时空关联特征可视分析

目的 降水是影响全球气候变化和系统环境的重要因素,面向降水数据开展时空关联分析,对于区域气候特征探索及异常情况监测具有重要的意义。然而,降水时空关联特征的分析是一个复杂且耗时的过程,与气象站点的空间分布以及降水的时间序列密切相关。本文综合考虑降水的时空变化特征,研究和设计面向降水数据时空关联特征分析的可视化系统工具。方法 利用地图和矩阵图呈现降水数据的空间分布和周期变化特征,设计径向盒须图对降水数据的时空变化异常特征进行捕获;通过局部Moran''s I指数的计算和热力图的呈现表达降水的空间相关性,支持用户交互式地探索空间相关性的时序变化特征;利用普通克里金插值模型获得降水空间插值图,并对插值结果的准确性进行可视化评估。结果 以中国安徽省1971-2014年气象观测站长时间序列月降水数据集为例进行分析,实验结果证明本研究可视化交互系统能够直观高效地探索区域降水长时间序列时空变化特征和极端降水情况;有效探究区域降水空间分布模式、不同站点降水信息间空间依赖性和异质性,并快速发现降水奇异点;分析区域不同时间尺度降水气候特征空间变化。结论 系统工具集成便捷的交互模式,支持用户探索式地分析降水数据的时空关联特征,进而有效地探究区域气候变化规律和特征分布关系。基于真实降水数据的实验结果以及降水领域专家的反馈,进一步验证了本文系统工具的有效性和实用性。     

Analysis of drought events for the semi-arid central plains of Iran with satellite and meteorological based indicators

The aim of this article is to study the spatial and temporal pattern of drought events in the Northeastern fringes of the Central Plateau of Iran using remote sensing and in situ meteorological data sets. Drought recognition is based on the analysis of the Standardized Precipitation Index (SPI) derived from meteorological variables such as rainfall, and indices derived from the Normalized Difference Vegetation Index (NDVI) obtained from the Advanced Very High Resolution Radiometer (AVHRR). The latter includes the Vegetation Condition Index (VCI), Land Surface Temperature (LST), thye Temperature Condition Index (TCI), Land Surface Moisture (LSM) and the Vegetation Health Index (VHI). Analysis is confined to the spring season from 1998 to 2004, inclusive. Results show that indices derived from the thermal bands have a higher sensitivity to drought conditions than indices derived from visible bands in this area. Indices derived from reflective bands such as NDVI and VCI seem to be better correlated to meteorological parameters than thermal band-derived indices like TCI. Indices that are calculated from both reflective and thermal bands such as LSM and VHI do not seem to be a reliable measure of drought conditions in this region.     

Automated estimation and analyses of meteorological drought characteristics from monthly rainfall data

The paper describes a new software package for automated estimation, display and analyses of various drought indices – continuous functions of precipitation that allow quantitative assessment of meteorological drought events to be made. The software at present allows up to five different drought indices to be estimated. They include the Decile Index (DI), the Effective Drought Index (EDI), the Standardized Precipitation Index (SPI) and deviations from the long-term mean and median value. Each index can be estimated from point and spatially averaged rainfall data and a number of options are provided for months' selection and the type of the analysis, including a running mean, single value or multiple annual values. The software also allows spell/run analysis to be performed and maps of a specific index to be constructed. The software forms part of the comprehensive computer package, developed earlier and designed to perform the multitude of water resources analyses and hydro-meteorological data processing. The 7-step procedure of setting up and running a typical drought assessment application is described in detail. The examples of applications are given primarily in the specific context of South Asia where the software has been used.     

Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery

Drought is one of the most frequent climate-related disasters occurring across large portions of the African continent, often with devastating consequences for the food security of agricultural households. This study proposes a novel method for calculating the empirical probability of having a significant proportion of the total agricultural area affected by drought at sub-national level. First, we used the per-pixel Vegetation Health Index (VHI) from the Advanced Very High Resolution Radiometer (AVHRR) averaged over the crop season as main drought indicator. A phenological model based on NDVI was employed for defining the start of season (SOS) and end of the grain filling stage (GFS) dates. Second, the per-pixel average VHI was aggregated for agricultural areas at sub-national level in order to obtain a drought intensity indicator. Seasonal VHI averaging according to the phenological model proved to be a valid drought indicator for the African continent, and is highly correlated with the drought events recorded during the period (1981-2009). The final results express the empirical probability of drought occurrence over both the temporal and the spatial domain, representing a promising tool for future drought monitoring.