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.     

Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage

Red-attack damage caused by mountain pine beetle (Dentroctonus ponderosa Hopkins) infestation in stands of lodgepole pine (Pinus contorta) in the Prince George Forest Region of British Columbia was examined using multitemporal Landsat-7 ETM+ imagery acquired in 1999, 2000, and 2001. The image data were geometrically and atmospherically corrected, and processed using the Tasseled Cap Transformation (TCT) to obtain wetness indices. The final steps included pixel subtraction, enhancement, and thresholding of the wetness index differences. The resulting enhanced wetness difference index (EWDI) was used to interpret spectral patterns in stands with confirmed (through aerial survey) red-attack damage in 2001, and these EWDI patterns were compared to the patterns of reflectance in normal-colour composites. We stratified the aerial survey dataset into two levels and used the EWDI to discriminate classes of 10-29 red-attack trees and 30-50 red-attack trees, and a sample of healthy forest collected from inventory data. Classification accuracy of red-attack damage based on the EWDI ranged from 67% to 78% correct.     

Drought and elevation effects on MODIS vegetation indices in northern Arizona ecosystems

Northern Arizona ecosystems are particularly sensitive to plant-available moisture and have experienced a severe drought with considerable impacts on ecosystems from desert shrub and grasslands to pinyon-juniper and conifer forests. Long-term time-series from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) are used to monitor recent regional vegetation activity and temporal patterns across various ecosystems. Surface air temperature, solar radiation and precipitation are used to represent meteorological anomalies and to investigate associated impacts on vegetation greenness. Vegetation index anomalies in the northern Arizona ecosystem have a decreasing trend with increasing surface air temperature and decreasing precipitation. MODIS NDVI and EVI anomalies are likely sensitive to the amount of rainfall for northern Arizona ecosystem conditions, whereas inter-annual variability of surface air temperature accounts for MODIS NDVI anomaly variation. The higher elevation area shows the slow vegetation recovery through trend analysis from MODIS vegetation indices for 2000–2011 within the study domain and along elevation.     

A global regression method for thermal sharpening of urban land surface temperatures from MODIS and Landsat

ABSTRACT

Land surface temperatures (LST) in urban landscapes are typically more heterogeneous than can be monitored by the spatial resolution of satellite-based thermal infrared sensors. Thermal sharpening (TS) methods permit the disaggregation of LST based on finer-grained multispectral information, but there is continued debate over which spectral indices are most appropriate for urban TS, and how they should be configured in a predictive regression framework. In this study, we evaluate the stability of various TS kernels with respect to LST at different spatial (Landsat 8) and diurnal (MODIS) scales, and present a new TS method, global regression for urban thermal sharpening (SGRUTS), based on these findings. Of the spectral indices examined, the normalized difference built-up index (NDBI) and the normalized multi-band drought index (NMDI) were the most spatially stable for Landsat 8 and MODIS overall. Kernel performance varied diurnally, with the index-based impervious surface index (IBI) and broadband α selected for 1030 h, NDBI and NMDI selected for 1330 h, and IBI and NMDI selected for 2230 h and 130 h, respectively. Over a range of field-validated metrics, the SGRUTS scheme comprising a two-factor interaction between NDBI and NMDI was competitive with the best alternative TS models compared. This SGRUTS model is essentially a refinement of the Enhanced Physical Method for urban applications in terms of kernel selection and configuration, and has interpretative advantages over more complex statistical schemes.     

Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices

Early drought detection and impact assessment on the amount of pasture biomass are important in Mongolia, whose economy strongly depends on livestock production. The country's large area and a lack of information on grass availability due to the sparseness of biomass-observing and/or meteorological stations make it difficult to optimize nomadic livestock output in the Mongolian dry climate. The application of a new satellite-based method for drought detection and for assessment of wild biomass in Mongolia was investigated. Measurements of biomass at an experimental station in a semi-dry steppe ecosystem during 1985–1997 were compared with the Advanced Very High Resolution Radiometer (AVHRR)-based vegetation health (VH) indices. The results showed the indices can be used as proxies for biomass production estimation (biomass anomaly, BA) applying the following equation BA=43.201+0.881 VHI (R ²=0.658).     

A re‐examination of perpendicular drought indices

In this letter, the performance of newly developed drought indices, the perpendicular drought index (PDI) and modified perpendicular drought index (MPDI), are further explored for regional surface dryness monitoring to provide clear guidance on appropriate implementation of these indices over different eco‐systems through in‐depth analysis of their advantages and constraints. Spatio‐temporal patterns of surface drought derived by MODerate Resolution Imaging Spectroradiometer (MODIS)‐based PDI and MPDI are compared against field‐measured soil moisture (SM), rainfall, and regional hydrological conditions. Results indicate that there are significant negative correlations between the PDI, the MPDI, and mean 0–20 cm SM content and rainfall. The PDI and the MPDI provide similar results at the early stage of vegetation growth, but a greater agreement between the drought information extracted by the MPDI and field measurements is observed for vegetated surfaces where the PDI fails. Therefore, it is recommended that PDI be used for bare soil applications, since it does not require calculation of additional information such as the fraction of vegetation which may contain some uncertainties, but the MPDI should be used for vegetated regions.     

The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation

In this study we evaluated the potential of the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) for monitoring gross primary productivity (GPP) across fifteen eddy covariance towers encompassing a wide variation in North American vegetation composition. The across-site relationship between MTCI and tower GPP was stronger than that between either the MODIS GPP or EVI and tower GPP, suggesting that data from the MERIS sensor can be used as a valid alternative to MODIS for estimating carbon fluxes. Correlations between tower GPP and both vegetation indices (EVI and MTCI) were similar only for deciduous vegetation, indicating that physiologically driven spectral indices, such as the MTCI, may also complement existing structurally-based indices in satellite-based carbon flux modeling efforts.     

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.     

Earth observation of vegetation status in the Sahelian and Sudanian West Africa: comparison of Terra MODIS and NOAA AVHRR satellite data

The first year of Moderate Resolution Imaging Spectroradiometer (MODIS) data are compared with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data for derivation of biophysical variables in Senegal, West Africa. The dynamic range of the two MODIS vegetation indices (VIs)—the continuity vegetation index (CVI) and the enhanced vegetation index (EVI)—is generally much larger than for the NOAA AVHRR normalized difference vegetation index (NDVI) data, indicating the importance of the change in near-infrared wavelength configuration from the NOAA AVHRR sensor to the MODIS sensor. Senegal is characterized by a pronounced gradient in the vegetation density covering a range of agro-climatic zones from arid to humid and it is found that the MODIS CVI values saturate for high VI values while the EVI demonstrates improved sensitivity for high biomass. Compared to NOAA AVHRR the MODIS VIs generally correlate better to the MODIS fraction of absorbed photosynthetically active radiation (fAPAR) absorbed by vegetation canopies and the leaf area index (LAI; the one-sided green leaf area per unit ground area). CVI is found to correlate better to both fAPAR and LAI than is the case for EVI because of the larger dynamic range of the CVI data. This suggests that the problem of background contamination on VIs from soil is not as severe in Senegal as has been found in other semi-arid African areas.     

MODIS‐derived visible atmospherically resistant index for monitoring chaparral moisture content

Time series of normalized difference indices (NDIs) derived from MODIS surface reflectance data provide potentially useful information for monitoring fuel moisture content (FMC) for fire risk assessment. The visible atmospherically resistant index (VARI) and normalized difference water index (NDWI) were compared for monitoring live FMC of chaparral shrublands. Regression coefficients are encouraging given disparate spatial resolutions of ground‐based FMC measurements and MODIS‐derived NDIs. VARI exhibited greater temporal co‐variability (0.79>r ²<0.94) and spatial robustness with FMC than NDWI, even though the former is based solely on visible waveband reflectance data.     

Comparison of vegetation water contents derived from shortwave-infrared and passive-microwave sensors over central Iowa

Retrieval of soil moisture content using the vertical and horizontal polarizations of multiple frequency bands on microwave sensors can provide an estimate of vegetation water content (VWC). Another approach is to use foliar-water indices based on the absorption at shortwave-infrared wavelengths by liquid water in the leaves to determine canopy water content, which is then related to VWC. An example of these indices is the normalized difference infrared index (NDII), which was found to be linearly related to canopy water content using various datasets, including data from the Soil Moisture Experiments 2002 and 2005 in central Iowa. Here we compared independent estimates of VWC from WindSat to Moderate resolution Imaging Spectroradiometer (MODIS) NDII over central Iowa from 2003 to 2005. Results showed that there was a linear relationship between the MODIS and WindSat estimates of VWC, although WindSat-retrieved VWC was greater than MODIS-retrieved VWC. WindSat and MODIS have different satellite overpass times and in most climates we expect VWC to vary over a day due to transpiration and plant water stress. However, a sensitivity analysis indicated that the diurnal variation of VWC should not have a significant effect on retrievals of VWC by either method. The results of this study indicated that soil moisture retrievals from microwave sensors may be improved using VWC from optical sensors determined by foliar-water indices and classifications of land cover type.     

Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China

In this paper, drought status of northwestern China is evaluated using the Terra–Moderate Resolution Imaging Spectroradiometer (MODIS) data with a newly developed method called perpendicular drought index (PDI), which is defined as a line segment that is parallel with the soil line and perpendicular to the normal line of soil line intersecting the coordinate origin in the two‐dimensional scatter plot of red against near infrared (NIR) wavelength reflectance. To validate the PDI in macroscale applications, quantitative evaluation of drought conditions in Ningxia, Northwestern China is carried out by comparing the PDI with one of the well‐known drought indexes, namely, temperature‐vegetation index (TVX). Linear regression between ground‐measured soil moisture data and the PDI and the TVX was made. Results show that satellite based PDI and TVX has significant correlation with 0–20 cm averaged soil moisture obtained over the meteorological observing stations across the whole study area. The highest correlation of R ² = 0.48 for the PDI and R ² = 0.40 for the TVX is obtained when compared with average soil moisture from 0 to 20 cm soil depth. According to the drought critical values defined by soil hydrologic parameters including soil moisture, wilting coefficient and field moisture capacity, the PDI based drought guidelines are established, and then the drought status in the study area is evaluated using the PDI. It is evident from the results showing the spatial distribution of drought in northwestern China that the PDI is highly accordant with field drought status.     

基于MODIS数据的农业干旱监测方法对比分析

利用2004年4月～6月上旬的MODIS影像数据,结合实测的土壤墒情数据,对比不同深度及不同拟合方式下土壤湿度的估算精度。另外,分别选用植被供水指数(VSWI)、基于EVI的植被供水指数(E-VSWI)、归一化多波段干旱指数(NMDI)与对应的土壤湿度数据进行回归分析,并在此基础上利用最优指数实现研究区土壤湿度的估算及旱情监测。结果表明:在华北平原中部地区冬小麦生长季节,利用MODIS数据进行土壤湿度估算的最佳深度是10cm;最佳拟合方式是线性拟合方式;植被供水指数(VSWI)在研究区整个时间序列具有最好的稳定性,可为今后大区域作物生长期的干旱监测提供一个简单方法。     

Modelling and prediction of malaria vector distribution in Bangladesh from remote-sensing data

Epidemic malaria cases and satellite-based vegetation health (VH) indices were investigated to be used as predictors of malaria vector activities in Bangladesh. The VH indices were derived from radiances, measured by the Advanced Very High Resolution Radiometer (AVHRR) on National Oceanic and Atmospheric Administration (NOAA) afternoon polar orbiting satellites. Two indices characterizing moisture and thermal conditions were investigated using correlation and regression analysis applied to the number of malaria cases recorded in the entire Bangladesh region and three administrative divisions (Chittagong, Sylhet and Dhaka) during 1992–2001. It is shown that during the cooler months (November to March), when mosquitoes are less active, the correlation between number of malaria cases and two investigated indices was near zero. From April, when the mosquito activity season starts, the correlation increased, reaching a maximum value of 0.5–0.8 by the middle of the high season (June to July), reducing thereafter to zero by the beginning of the cool season in November. Following these results, regressional equations for the number of malaria cases as a function of VH indices were built and tested independently. They showed that, in the main malaria administrative division (Chittagong) and the entire Bangladesh region, the regressional equations can be used for early prediction of malaria development.     

遥感干旱指数在洛川苹果干旱监测中的适用性分析

目前对苹果干旱研究较少且主要运用站点数据,对空间信息表征有限,遥感干旱指数可用于大范围干旱时空动态监测,但在苹果干旱监测中的适用性还有待研究.基于2014～2018年MODIS反射率、地表温度以及地表覆被数据,结合土壤湿度数据和野外调查资料,分析洛川苹果区温度植被干旱指数(TVDI)、归一化植被水分指数(NDWI)、植...     

温度植被旱情指数在吉林省干旱监测中的应用

遥感技术在干旱监测中具有其他技术不可替代的优势。利用2005年8~9月的MODIS产品,获取逐日地表温度数据和逐日植被指数数据,建立了LST\|NDVI特征空间,根据此特征空间建模,计算得出温度植被干旱指数作为表征干旱的监测指标,并结合2005年土壤湿度数据对该指标进行定量验证。在此基础上利用ArcGIS软件分析了2005年8~9月吉林省干旱时空分布特征。结果表明：吉林省干旱总体分布趋势从东南到西北呈现出湿润到正常-轻旱-中旱-重旱的变化规律,体现出吉林省旱情的多样性和复杂性,8月19日、8月25日、9月8日正常和轻旱分布区域面积所占总区域面积比例分别为26.84%和59.53%、41.31%和41.73%、40.40%和32.83%,9月中旬轻旱和中旱分布最广,其比例分别为38.27%和36.26%;重旱和中旱分布区主要位于白城和松原市,轻旱区主要分布在长春、四平和辽源市,正常分布区集中在吉林、通化和白山市境内,湿润分布区主要分布在延边市。     

An assessment of impacts of land-cover changes on root-zone soil moisture

Land cover exerts considerable control over the exchange of energy, water, and carbon dioxide and other greenhouse gases between land surface and the atmosphere. In China, dramatic land-cover changes have occurred along with rapid economic development in the past 30 years. However, research specifically on whether such land-cover changes have any influence on root-zone soil moisture in the region has started only in very recent few years. In this study, the performance of selected land-surface models (Noah 2.7.1, Noah 3.2, Common Land Model (CLM version 2.0), and Mosaic) implemented in National Aeronautics and Space Administration (NASA)’s Land Information System (LIS version 6.1.6) is first tested using quality-controlled soil moisture observations from 108 in situ sites of the China Meteorological Administration. The best-performing model (CLM2.0) is selected to estimate the influence of land-cover changes on root-zone soil moisture, as well as drought occurrence in Yunnan Province in China. Both the 1992–1993 Advanced Very High Resolution Radiometer (AVHRR) and 2007–2010 Moderate Resolution Imaging Spectroradiometer Collection 5 (MODIS) land-cover products at 1 km resolution are employed to represent 1990 and 2010 land-cover status, respectively. These are verified using the local ground records of Yunnan Province over the two time periods. Their differences are considered roughly as land-cover changes occurring during the period 1990–2010. It is found that land-cover changes from primeval forest to grassland may increase root-zone soil moisture, thus reducing drought, while changes from grassland and primeval forest to cropland or reforested areas have increased the likelihood of drought.     

TVDI在冬小麦春季干旱监测中的应用

应用冬小麦春季生长期的NOAA/AVHRR资料,反演归一化植被指数（NDVI）、土壤调整植被指数（SAVI）和下垫面温度（T_s）,分析了植被指数和下垫面温度空间特征,采用温度植被旱情指数（TVDI）,研究了河北省2005年3~5月的冬小麦旱情状况。结果表明：基于SAVI的温度植被旱情指数与土壤表层相对湿度的相关性好于基于NDVI的温度植被旱情指数。通过与气象站土壤水分观测资料进行相关性分析,表明温度植被旱情指数与10 cm土壤相对湿度关系最好,20 cm次之,50 cm较差。因此,基于SAVI的温度植被旱情指数更适于监测冬小麦春季的旱情。     

Operational multi-sensor monitoring of turbidity for the entire Mekong Delta

An operational satellite-based approach was implemented to monitor turbidity and organic absorption in the Mekong river system. Using physics-based algorithms linked together in a fully automated processing chain, more than 300 Landsat Enhanced Thematic Mapper (ETM) scenes and 1000 MODIS scenes, representing five years of data, were used to produce standardized, quantitative time series of turbidity and organic absorption across Vietnam, Thailand, Cambodia, Laos, and China. To set up this system, the specific inherent optical properties (SIOPs) of the Mekong river system were determined through three separate field campaigns, laboratory analysis, and subsequent optical closure calculations. Following this, a range of satellite data types was tested using the derived Mekong-specific inherent optical properties, including Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m data, Landsat ETM, Medium Resolution Imaging Spectrometer (MERIS), Satellite Pour l’Observation de la Terre (SPOT) 5, RapidEye, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and QuickBird. The satellite-based turbidity estimates were coincident with available field data, and comparisons showed them to be in good agreement. Overall, the derived SIOPs were suitable for water-quality monitoring of the Mekong, and the MODIS, MERIS, Landsat, and RapidEye sensors were found to be the most radiometrically stable and thereby suitable for ongoing operational processing. The implemented system delivers consistent results across the different satellite sensors and over time, but is limited to where the spatial resolution of the sensor is still able to resolve the river width. The system is currently applicable for the entire Mekong river system, both for near-real-time monitoring and for analysis of historical data archive.