Agricultural drought assessment at disaggregated level using AWiFS/WiFS data of Indian Remote Sensing satellites

Spatial differences in drought proneness and intensity of drought caused by differences in cropping patterns and crop growing environments within a district indicate the need for agricultural drought assessment at disaggregated level. The objective of this study is to use moderate resolution satellite images for detailed assessment of the agricultural drought situation at different administrative units (blocks) within a district. Monthly time composite NDVI images derived from moderate resolution AWiFS (60 m) and WiFS (180 m) images from Indian Remote Sensing satellites were analysed along with ground data on rainfall and crop sown areas for the kharif seasons (June – November) of 2002 (drought year), 2004 (early season drought) and 2005 (good monsoon year). The impact of the 2002 meteorological drought on crop area in different blocks of the district was assessed. The amplitude of crop condition variability in a severe drought year (2002) and a good year (2005) was used to map the degree of vulnerability of different blocks in the district to agricultural drought. The impact of early season deficit rainfall in 2004 on the agricultural situation and subsequent recovery of the agricultural situation was clearly shown. Agricultural drought assessment at disaggregated level using moderate resolution images is useful for prioritizing the problem areas within a district to undertake, in season drought management plans, such as alternate cropping strategies, as well as for end of the season drought relief management actions. The availability of ground data on rainfall, cropping pattern, crop calendar, irrigation, soil type etc., is very crucial in order to interpret the seasonal NDVI patterns at disaggregated level for drought assessment. The SWIR band of AWiFS sensor is a potential data source for assessing surface drought at the beginning of the season.     

Analysis of China vegetation dynamics using NOAA-AVHRR data from 1982 to 2001

The authors derived the normalized difference vegetation index (NDVI) from the NOAA/AVHRR Land dataset, at a spatial resolution of 8km and 15-day intervals, to investigate the vegetation variations in China during the period from 1982 to 2001. Then, GIS is used to examine the relationship between precipitation and the Normalized Difference Vegetation Index (NDVI) in China, and the value of NDVI is taken as a tool for drought monitoring. The results showed that in the study period, China’s vegetation cover had tended to increase, compared to the early 1980s; mean annual NDVI increased 3.8%. The agricultural regions (Henan, Hebei, Anhui and Shandong) and the west of China are marked by an increase, while the eastern coastal regions are marked by a decrease. The correlation between monthly NDVI and monthly precipitation/temperature in the period 1982 to 2001 is significantly positive (R2=0.80, R2=0.84); indicating the close coupling between climate conditions (precipitation and temperature) and land surface response patterns over China. Examination of NDVI time series reveals two periods: (1) 1982–1989, marked by low values below average NDVI and persistence of drought with a signature large-scale drought during the 1982 and 1989; and (2) 1990–2001, marked by a wetter trend with region-wide high values above average NDVI and a maximum level occurring in 1994 and 1998.     

Investigating the vegetation and agricultural responses to El Nino/Southern Oscillation using AVHRR data

This study explores the possible linkages of El Nino/Southern Oscillation (ENSO) with vegetation and rainfall patterns, vegetation activity and food grain yields, in arid and semi-arid regions of western India. A sequence of 20-year (1981–2000) monthly maximum Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) and monthly rainfall from 160 stations were examined to study the seasonal patterns and their relation to ENSO activity. In addition, a direct (ENSO-crop yield) linkage and an intermediate (ENSO-NDVI) linkage of agricultural responses to ENSO were also investigated. The results indicate below-normal seasonal NDVI and rainfall associated with El Nino (warm) events, except during 1997, while positive anomalies occur during La Nina (cold) events. Sea surface temperature (SST) anomalies from NINO 3 region (5°N–5°S; 150°W–90°W), as an indicator of ENSO were significantly correlated with NDVI anomalies, rainfall anomalies and yield anomalies but the Southern Oscillation Index (SOI) was significantly related to NDVI anomalies only. NDVI anomaly patterns correspond to rainfall variability including that associated with ENSO activity. The observed strong intermediate linkage between yield anomalies and NDVI anomaly signal (r = 0.609) indicates that NDVI is an ideal index for understanding and analysing agricultural response to ENSO climate teleconnections.     

On agricultural drought monitoring in Australia using Himawari-8 geostationary thermal infrared observations

Monitoring agricultural drought effectively and timely is important to support drought management and food security. Effective drought monitoring requires a suite of drought indices to capture the evolution process of drought. Thermal infrared signals respond rapidly to vegetation water stress, thus being regarded useful for drought monitoring at the early stage. Several temperature-based drought indices have been developed considering the role of land surface temperature (LST) in surface energy and water balance. Here, we compared the recently proposed Temperature Rise Index (TRI) with several agricultural drought indices that also use thermal infrared observations, including Temperature Condition Index (TCI), Vegetation Health Index (VHI) and satellite-derived evapotranspiration ratio anomaly (Δf_RET) for a better understanding of these thermal infrared drought indices. To do so, we developed a new method for calculating TRI directly from the top-of-atmosphere brightness temperatures in the two split-window channels (centered around ∼11 and 12 μm) rather than from LST. TRI calculated using the Himawari-8 brightness temperatures (TRI_BT) and LST retrievals (TRI_LST), along with the other LST-based indices, were calculated for the growing season (July–October) of 2015−2019 over the Australian wheatbelt. An evaluation was conducted by spatiotemporally comparing the indices with the drought indices used by the Australian Bureau of Meteorology in the official drought reports: the Precipitation Condition Index (PCI) and the Soil Moisture Condition Index (SMCI). All the LST-based drought indices captured the wet conditions in 2016 and dry conditions in 2019 clearly. Ranking of Pearson correlations of the LST-based indices with regards to PCI and SMCI produced very similar results. TRI_BT and TRI_LST showed the best agreement with PCI and SMCI (r > 0.4). TCI and VHI presented lower consistency with PCI and SMCI compared with TRI_BT and TRI_LST. Δf_RET had weaker correlations than the other LST-based indices in this case study, possibly because of outliers affecting the scaling procedure. The capability of drought early warning for TRI was demonstrated by comparing with the monthly time series of the greenness index Vegetation Condition Index (VCI) in a case study of 2018 considering the relatively slow response of the greenness index to drought. TRI_BT and TRI_LST had a lead of one month in showing the changing dryness conditions compared with VCI. In addition, the LST-based indices were correlated with annual wheat yield. Compared to wheat yields, all LST-based indices had a peak correlation in September. TRI_BT and TRI_LST had strong peak and average correlations with wheat yield (r ≥ 0.8). We conclude that TRI has promise for agricultural drought early warning, and TRI_BT appears to be a good candidate for efficient operational drought early warning given the readily accessible inputs and simple calculation approach.     

基于温度供水干旱指数的京津冀春旱监测及时空分布分析

为提高农业干旱监测效果和精度,在对传统干旱监测模型对比分析基础上,本文提出将温度植被干旱指数(TVDI)和植被供水指数(VSWI)加权联合构建温度供水干旱指数(TSWDI)的研究思路。以京津冀2006—2012年5月份数据作为实验统计数据,以京津冀2006—2016年3—5月份春旱监测为例进行了模型实验。实验结果证实,TSWDI指数相对其他两个指数与10、20和50 cm深处的土壤水分相关性更高,能够更精准地反映农业干旱状况。TSWDI计算结果显示,京津冀干旱分布具有如下特征:从时间角度看,2006—2016年整体干旱状况逐渐缓解,特别是自2010年至今,研究区域干旱程度逐步减轻;从空间角度看,京津冀区域整体干旱面积逐步减少。     

2000-2012年鄂尔多斯高原植被动态及干旱响应

针对鄂尔多斯高原植被覆盖变化受干旱胁迫的状况,该文结合降水和气温的协同变化,以2000-2012年生长季的MODIS-NDVI数据和同期降水、温度和帕尔默干旱指数为依据,采用线性趋势分析、标准偏差分析和相关性分析等方法,对鄂尔多斯高原植被与气候变化的相关关系和干旱异常变化对植被动态的影响进行了研究.结果表明:鄂尔多斯高原生长季及季节(春季、夏季和秋季)植被归一化植被指数主要受降水的控制和干旱的制约,秋季归一化植被指数更多地受到夏季干旱的影响.与气象因子的空间相关分析表明,春季温度上升有利于研究区北部归一化植被指数像元的增加.在荒漠草原和沙漠地区,夏季干旱与归一化植被指数的相关关系最强.秋季降水对典型草原归一化植被指数的提升显著.     

An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data

We propose a simple, spatially invariant and probabilistic year-round Empirical Standardized Soil Moisture Index (ESSMI) that is designed to classify soil moisture anomalies from harmonized multi-satellite surface data into categories of agricultural drought intensity. The ESSMI is computed by fitting a nonparametric empirical probability density function (ePDF) to historical time-series of soil moisture observations and then transforming it into a normal distribution with a mean of zero and standard deviation of one. Negative standard normal values indicate dry soil conditions, whereas positive values indicate wet soil conditions. Drought intensity is defined as the number of negative standard deviations between the observed soil moisture value and the respective normal climatological conditions. To evaluate the performance of the ESSMI, we fitted the ePDF to the Essential Climate Variable Soil Moisture (ECV SM) v02.0 data values collected in the period between January 1981 and December 2010 at South–Central America, and compared the root-mean-square-errors (RMSE) of residuals with those of beta and normal probability density functions (bPDF and nPDF, respectively). Goodness-of-fit results attained with time-series of ECV SM values averaged at monthly, seasonal, half-yearly and yearly timescales suggest that the ePDF provides triggers of agricultural drought onset and intensity that are more accurate and precise than the bPDF and nPDF. Furthermore, by accurately mapping the occurrence of major drought events over the last three decades, the ESSMI proved to be spatio-temporal consistent and the ECV SM data to provide a well calibrated and homogenized soil moisture climatology for the region. Maize, soybean and wheat crop yields in the region are highly correlated (r > 0.82) with cumulative ESSMI values computed during the months of critical crop growing, indicating that the nonparametric index of soil moisture anomalies can be used for agricultural drought assessment.     

Detecting Drought-Prone Areas of Rice Agriculture Using a MODIS-Derived Soil Moisture Index

This study examined the use of remote sensing in detecting and assessing drought in Iloilo Province, Philippines. A remote sensing-based soil moisture index (SMI), rainfall anomaly data from the Tropical Rainfall Measuring Mission (TRMM), and rice production departure (P_d ) data were used for drought detection and validation. The study was conducted using two drought years (2001, 2005) and one non-drought year (2002). According to SMI data, the drought distribution was classified into four major groups. SMI values > 0.3 were considered not to be drought and SMI values ≤ 0.3 were classified as slight, moderate, and severe drought. Results based on SMI revealed that the study area experienced drought in 2001 and 2005, while 2002 exhibited no drought. On the other hand, TRMM-based rainfall anomaly data revealed negative values in 2001 and 2005 and positive values in 2002. Below-normal P_d values were observed in 2005 and above-normal values in 2002, whereas nearly normal values prevailed in 2001. Yield indicator data were crucial for the assessment of drought impacts on rice production. In most cases, the pattern of rice production and productivity revealed that the decline in the production or productivity of rice for a particular year coincided with lower SMI values and greater rainfall departure or negative anomaly.     

Monitoring the impact of aerosol contamination on the drought-induced decline of gross primary productivity

Southwestern China experienced a period of severe drought from September 2009 to May 2010. It led to widespread decline in the enhanced vegetation index (EVI) and gross primary productivity (GPP) in the springtime of 2010 (March to May). However, this study observed a spatial inconsistency between drought-impacted vegetation decline and the precipitation deficit. Significant aerosol loads that correspond to inconsistent areas were also observed during the drought period. After analyzing both MODIS GPP/NPP Collection 5 (C5) and the newer Collection 5.5 (C55) data, a large area observed to be experiencing GPP decline in the eastern part of the study area proved to be unreliable. Based on EVI data, after atmospherically contaminated data were screened from analysis, approximately 20% of the study area exhibited browning whereas 33% displayed no change or greening and the remaining area (approximately 47%) lacked sufficient data to document changing conditions. Correlation analysis showed that fire occurrences, aerosol optical depth, and precipitation anomalies during the two drought periods (from September to February and from March to May) all contributed to a decrease in GPP. C55 data remains vulnerable to aerosol contamination due to a much higher correlation coefficient with aerosol optical depth compared to C5 data. In the future, users of remotely sensed data should be cautious of and take into account impacts related to atmospheric contamination, even during drought periods.     

Developing a satellite-based combined drought indicator to monitor agricultural drought: a case study for Ethiopia

Developing a robust drought monitoring tool is vital to mitigate the adverse impacts of drought. A drought monitoring system that integrates multiple agrometeorological variables into a single drought indicator is lacking in areas such as Ethiopia, which is extremely susceptible to this natural hazard. The overarching goal of this study is to develop a combined drought indicator (CDI-E) to monitor the spatial and temporal extents of historic agricultural drought events in Ethiopia. The CDI-E was developed by combining four satellite-based agrometeorological input parameters – the Standardized Precipitation Index (SPI), Land Surface Temperature (LST) anomaly, Standardized Normalized Difference Vegetation Index (stdNDVI) and Soil Moisture (SM) anomaly – for the period from 2001 to 2015. The method used to combine these indices is based on a quantitative approach that assigns a weight to each input parameter using Principal Component Analysis (PCA). The CDI-E results were evaluated using satellite-based gridded rainfall (3-month SPI) and crop yield data for 36 intra-country crop growing zones for a 15-year period (2001 to 2015). The evaluation was carried out for the main rainfall season, Kiremt (June-September), and the short rainfall season, Belg (February-May). The results showed that moderate to severe droughts were detected by the CDI-E across the food insecure regions reported by FEWS NET during Kiremt and Belg rainfall seasons. Relatively higher correlation coefficient values (r > 0.65) were obtained when CDI-E was compared with the 3-month SPI across the majority of Ethiopia. The spatial correlation analyses of CDI-E and cereal crop yields showed relatively good correlations (r > 0.5) in some of the crop growing zones in the northern, eastern and southwestern parts of the country. The CDI-E generally mapped the spatial and temporal patterns of historic drought and non-drought years and hence the CDI-E could potentially be used to develop an agricultural drought monitoring and early warning system in Ethiopia. Moreover, decision makers and donors may potentially use CDI-E to more accurately monitor crop yields across the food-insecure regions in Ethiopia.     

Monitoring Horizontal and Vertical Cropping Pattern and Dynamics in Bihar over a Decade (2001–2012) Based on Time-Series Satellite Data

Increasing population and natural disasters like drought, flood, cyclone etc., has impacted global agriculture area and hence continuously modifying cropping pattern and associated statistics. The present study analysed agriculture dynamics over one of the densely populated and disaster prone state (Bihar) in India and derived vital statistics (single, double and triple cropping area, and monthly, seasonal, annual and long term status at the state and district level) for the years 2001–2012. The study used time-series MODIS vegetation index (EVI; MOD13A2, 1 km, 16 day, 2001–2012), MODIS annual Land Cover product (MCD12Q1, 500 m, 2001–2012) and Global Land Cover map (Scasia_V4, 1 km, 2000; Globcover_V2.2, 300 m, 2005/2006 and V2.3, 2009, 300 m), and extracted horizontal (i.e., area change) and vertical (i.e., cropping intensification) agriculture change pattern. The results were inter-compared, and validated using government reports as well as with high spatial resolution data (IRS-LISS III 23.5 m). From 2001–2006 to 2007–2012, the net horizontal and vertical change in agriculture area is +145.24 and +907.82 km², respectively, and net change in seasonal crop area (winter, summer and monsoon) is +959.21, +1009.84 and ?1061.64 km², respectively. The districts which are located along the eastern part of Ganges experienced maximum positive changes and the districts along Gandak river in the north-western part of the study area experienced maximum negative changes. Overall, the study has quantified and revealed interesting space–time agriculture change patterns over 12 years including impacts caused by droughts and floods in the study area.     

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia’s cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation (R² > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships (R² < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation (R²) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.     

基于MODIS图像的植被叶面水反演与农业干旱监测

研究了应用MODIS数据,采用与植被物种无关的GVMI指数模型反演出植被水含量指标EWT,并以气象局发布的实际温度降雨数据和旱灾报告验证了植被水含量与旱灾的相关性。     

Remote sensing based agricultural drought assessment in Palar basin of Tamil Nadu state,India

Agricultural drought has been a recurrent phenomenon in many parts of India. Remote sensing plays a vital role in real time monitoring of the agricultural drought conditions over large area, there by effectively supplementing the ground mechanism. Conventional drought monitoring is based on subjective data. The satellite based monitoring such as National Agricultural Drought Assessment and Monitoring System (NADAMS) is based on the crop condition, which is an integrated effect of soil, effective rainfall, weather, etc. Drought causes changes in the external appearance of vegetation, which can clearly be identified (by their changed spectral response) and judged using satellite sensors through the use of vegetation indices. These indices are functions of rate of growth of the plants and are sensitive to the changes of moisture stress in vegetation. The satellite based drought assessment methodology was developed based on relationship obtained between previous year’s Normalised Difference Vegetation Index (NDVI) profiles with corresponding agricultural performance available at district/block level. Palar basin, one of the major river basins in Tamil Nadu state was selected as the study area. The basin covers 3 districts, which contain 44 blocks. Wide Image Field Sensor (WiFS) of 188m spatial resolution from Indian Remote Sensing Satellite (IRS) data was used for the analysis. Satellite based vegetation index NDVI, was generated for Samba and Navarai seasons in the years 1998 and 1999. An attempt has been made to estimate the area under paddy. It was also observed that, there was reduction in the crop area as well as vigour in the vegetation in both Samba and Navarai seasons in 1999 when compared with 1998. Drought severity maps were prepared in GIS environment giving blockwise agricultural water deficiency status.     

利用GRACE数据研究美国干旱产生的原因

利用GRACE和GLDAS数据计算美国2003年1月—2013年12月陆地等效水高的时间序列,两者结果呈强相关,相关系数为0.91.GRACE数据计算陆地水储量最低在2012年7月,这与欧洲干旱研究中心发布的帕尔默干旱指数(scPDSI)结果一致,证明了GRACE时变地球重力场模型具备探测干旱的能力.利用NOAA(Na...     

Analyzing the impact of thermal stress on vegetation health and agricultural drought – a case study from Gujarat,India

Although poor precipitation due to delayed arrival and/or early retreat of the southwest monsoon is considered the chief architect of drought in India, heat waves may also play a crucial role in the intensification of droughts. In the Indian subcontinent, occurrence of heat waves during the pre-monsoon and high air-temperature in the subsequent monsoon season imparts thermal stress on vegetation causing degradation of vegetation health (VH). In the present study, various vegetation indices and land-use/land-cover data derived from multi-sensor satellite have been used to assess VH and agricultural drought in Gujarat during 1981–2010. This Geographical Information Systems-based study has also used heat wave and temperature data to analyze the adverse effects of high temperature on VH. The time series of Vegetation Condition Index and Temperature Condition Index (TCI) has shown that the combined influence of moisture-stress and thermal stress determines the occurrence and severity of drought, which is reflected in the Vegetation Health Index (VHI). A strong correlation among aboveground air-temperature, the TCI and the VHI indicates definite influence of thermal stress on VH. Further, a systematic variation and strong resemblance between temperature, crop yield, TCI and VHI has established the impact of thermal stress on agricultural productivity.     

利用NOAA-AVHRR数据分析1982-2001年间中国植被的动态变化(英文)

The authors derived the normalized difference vegetation index (NDVI) from the NOAA/AVHRR Land dataset, at a spatial resolution of 8km and 15-day intervals, to investigate the vegetation variations in China during the period from 1982 to 2001. Then, GIS is used to examine the relationship between precipitation and the Normalized Difference Vegetation Index (NDVI) in China, and the value of NDVI is taken as a tool for drought monitoring. The results showed that in the study period, China’s vegetation cover h...     

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

ABSTRACT

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.     

Establishing and assessing the Integrated Surface Drought Index (ISDI) for agricultural drought monitoring in mid-eastern China

Accurately monitoring the temporal, spatial distribution and severity of agricultural drought is an effective means to reduce the farmers’ losses. Based on the concept of the new drought index called VegDRI, this paper established a new method, named the Integrated Surface Drought Index (ISDI). In this method, the Palmer Drought Severity Index (PDSI) was selected as the dependent variable; for the independent variables, 12 different combinations of 14 factors were examined, including the traditional climate-based drought indicators, satellite-derived vegetation indices, and other biophysical variables. The final model was established by fully describing drought properties with the smaller average error (relative error) and larger correlation coefficients. The ISDI can be used not only to monitor the main drought features, including precipitation anomalies and vegetation growth conditions but also to indicate the earth surface thermal and water content properties by incorporating temperature information. Then, the ISDI was used for drought monitoring from 2000 to 2009 in mid-eastern China. The results for 2006 (a typical dry year) demonstrate the effectiveness and capability of the ISDI for monitoring drought on both the large and the local scales. Additionally, the multiyear ISDI monitoring results were compared with the actual drought intensity using the agro-meteorological disaster data recorded at the agro-meteorological sites. The investigation results indicated that the ISDI confers advantages in the accuracy and spatial resolution for monitoring drought and has significant potential for drought identification in China.     

MODIS光谱指数在中国西南干旱监测中的应用

利用标准化降雨指数SPI比较了基于MODIS反射率数据提取的8种光谱指数(NDVI、NDWI、VARI、EVI、NDIIB6、NDIIB7、D1640和SR)对中国西南四省市(四川、重庆、云南和贵州)2000年—2012年典型干旱事件的反映。研究结果表明:(1)SPI指数能直接反映监测站点附近的干旱情况,其中3个月和6个月尺度的SPI(SPI3和SPI6)对2006年和2009年—2010年该区的特大干旱事件的监测效果较好;(2)除了D1640外,其余7种光谱指数的距平值与3个月尺度的SPI3都具有显著的相关性,其中NDIIB7、NDIIB6和VARI与SPI3的相关性较高(R0.3),在一定程度上可以表征中国西南地区的干旱状况;(3)MODIS NDIIB7距平值与3个月尺度的SPI3相关性最高(R=0.35),本文以其为例,再现了云贵高原2009年—2010年特大持续干旱事件的时空演变过程。