A Novel Dynamic Stretching Solution to Eliminate Saturation Effect in NDVI and Its Application in Drought Monitoring

The normalized difference vegetation index(NDVI) is one of the key input variables for developing drought indices.However,the NDVI quickly saturates in high vegetation surfaces,and thus,the generalization of a drought index over different ecosystems becomes a challenge.This paper presents a novel,dynamic stretching algorithm to overcome the saturation effect in NDVI.A scaling transformation function to eliminate saturation effects when the vegetation fraction(VF) is large is proposed.Dynamic range adjustment is conducted using three coefficients,namely,the normalization factor(a),the stretching range controlling factor(m),and the stretching size controlling factor(e).The results show that the stretched NDVI(S-NDVI) is more sensitive to vegetation fraction than NDVI when the VF is large,ranging from 0.75 to 1.00.Moreover,the saturation effect in NDVI is effectively removed by using the S-NDVI.Further analysis suggests that there is a good linear correlation between the S-NDVI and the leaf area index(LAI).At the same time,the proposed S-NDVI significantly reduces or even eliminates the saturation effect over high biomass.A comparative analysis is performed between drought indices derived from NDVI and S-NDVI,respectively.In the experiment,reflectance data from the moderate resolution imaging spectroradiometer(MODIS) products and in-situ observation data from the meteorological sites at a regional scale are used.In this study,the coefficient of determination(R2) of the stretched drought index(S-DI) is above 0.5,indicating the reliability of the proposed algorithm with surface soil moisture content.Thus,the S-DI is suggested to be used as a drought index in extended regions,thus regional heterogeneity should be taken into account when applying stretching method.

A Multi-scale Perspective on Drought Monitoring and Assessment for the Pearl River Basin

It is accepted that climate change has a great impact on water cycle and regional water balance,and thus it would change the formation and evolution conditions of drought in some degree. By using data at 42 meteorological stations of the Pearl River Basin,the Standardized Precipitation Evapotranspiration Index（ SPEI） at different time scales was calculated. Based on the SPEIs of 1- 12 months,a newly proposed index for drought--Joint Drought Index（ JDI） was established under the multi-scale perspective through the copula function. Since short-term SPEIs are essential for the identification of emerging droughts and long-term SPEIs are useful for prolonged droughts,the JDI,which integrates all the usefull informations of drought and can thus form an overall judgement,is superior than the single SPEI in drought monitoring. By the forcast evaluation system and comparison to the actual drought,the accuracy and effectiveness of JDI in drought monitoring were verified. In general,JDI can be used as a new ideal index for future drought monitoring and assessment. Additionly,we analyzed the spatio-temperal characteristics of drought across the Pearl River Basin using the JDI. The results indicate that mild drought was the most frequent drought occurred in the Pearl River Basin over the past half century,and moderate drought followed. Severe drought and extreme drought would appear occasionally while exceptional drought could hardly be found. A dry-wet-dry interdecadal variation pattern had been found from the 1960 s to the 2000 s. Since the 21 stcentury,an obvious trend toward drought can be observed in the whole basin,especially in the Xijiang subbasin,which,consequently,poses an increasing challenge for the water resource planning and management.

Drought monitoring and reliability evaluation of the latest TMPA precipitation data in the Weihe River Basin, Northwest China

The high resolution satellite precipitation products bear great potential for large-scale drought monitoring, especially for those regions with sparsely or even without gauge coverage. This study focuses on utilizing the latest Version-7 TRMM Multi-satellite Precipitation Analysis （TMPA 3B42V7） data for drought condition monitoring in the Weihe River Basin （0.135×10^6 km2）. The accuracy of the monthly TMPA 3B42V7 satellite precipitation data was firstly evaluated against the ground rain gauge observations. The statistical characteristics between a short period data series （1998-2013） and a long period data series （1961-2013） were then compared. The TMPA 3B42V7-based SPI （Standardized Precipitation Index） sequences were finally validated and analyzed at various temporal scales for assessing the drought conditions. The results indicate that the monthly TMPA 3B42V7 precipitation is in a high agreement with the rain gauge observations and can accurately capture the temporal and spatial characteristics of rainfall within the Weihe River Basin. The short period data can present the characteristics of long period record, and it is thus acceptable to use the short period data series to estimate the cumulative probability function in the SPI calculation. The TMPA 3B42V7-based SPI matches well with that based on the rain gauge observations at multiple time scales （i.e., 1-, 3-, 6-, 9-, and 12-month） and can give an acceptable temporal distribution of drought conditions. It suggests that the TMPA 3B42V7 precipitation data can be used for monitoring the occurrence of drought in the Weihe River Basin.

Agricultural drought monitoring: Progress, challenges, and prospects

In this paper, we compared the concept of agricultural drought and its relationship with other types of droughts and reviewed the progress of research on agricultural drought monitoring indices on the basis of station data and remote sensing. Applicability and limitations of different drought monitoring indices were also compared. Meanwhile, development history and the latest progress in agricultural drought monitoring were evaluated through statistics and document comparison, suggesting a transformation in agricultural drought monitoring from traditional single meteorological monitoring indices to meteorology and remote sensing-integrated monitoring indices. Finally, an analysis of current challenges in ag- ricultural drought monitoring revealed future research prospects for agricultural drought monitoring, such as investigating the mechanism underlying agricultural drought, identifying factors that influence agricultural drought, developing multi-spatiotemporal scales models for agricultural drought monitoring, coupling qualitative and quantitative agricultural drought evaluation models, and improving the application levels of remote sensing data in agricultural drought monitoring.

A prototype web-based analysis platform for drought monitoring and early warning

It has long been recognized that an effective drought monitoring and early warning system,which provides functions for real-time condition monitoring and prediction,risk assessment,information dissemination and response recommendation,is very important for the preparedness for and mitigation of drought impacts.In this article,we review the currently existing drought monitor and early warning systems,discuss applicable remote sensing datasets and drought indicators and present the development of a web-based quasi-real-time Global Drought Monitoring&Analysis Platform(Web-GDMAP).The Web-GDMAP is built upon a series of indicators derived from multi-source satellite remote sensing data and various other sources of data.From a technical perspective,the Web-GDMAP system includes a series of components from data storage,model implementation and distribution,to client-side visualization and user intuitive interaction.From a theoretical perspective,the Web-GDMAP system integrates multi-indicators on different aspects of drought,including anomalies in precipitation,anomalies in land surface thermal and vegetation conditions,water deficit of soil and plants,etc.Several case studies on applying the developed Web-GDMAP in the Asian region are demonstrated.Further improvements and perspectives are discussed.

Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data

Meteorological drought is a natural hazard that can occur under all climatic regimes. Monitoring the drought is a vital and important part of predicting and analyzing drought impacts. Because no single index can represent all facets of meteorological drought, we took a multi-index approach for drought monitoring in this study. We assessed the ability of eight precipitation-based drought indices（SPI（Standardized Precipitation Index）, PNI（Percent of Normal Index）, DI（Deciles index）, EDI（Effective drought index）, CZI（China-Z index）, MCZI（Modified CZI）, RAI（Rainfall Anomaly Index）, and ZSI（Z-score Index）） calculated from the station-observed precipitation data and the Ag MERRA gridded precipitation data to assess historical drought events during the period 1987–2010 for the Kashafrood Basin of Iran. We also presented the Degree of Dryness Index（DDI） for comparing the intensities of different drought categories in each year of the study period（1987–2010）. In general, the correlations among drought indices calculated from the Ag MERRA precipitation data were higher than those derived from the station-observed precipitation data. All indices indicated the most severe droughts for the study period occurred in 2001 and 2008. Regardless of data input source, SPI, PNI, and DI were highly inter-correlated（R^2=0.99）. Furthermore, the higher correlations（R^2=0.99） were also found between CZI and MCZI, and between ZSI and RAI. All indices were able to track drought intensity, but EDI and RAI showed higher DDI values compared with the other indices. Based on the strong correlation among drought indices derived from the Ag MERRA precipitation data and from the station-observed precipitation data, we suggest that the Ag MERRA precipitation data can be accepted to fill the gaps existed in the station-observed precipitation data in future studies in Iran. In addition, if tested by station-observed precipitation data, the Ag MERRA precipitation data may be used for the data-lacking areas.

Detecting Agro-Droughts in Southwest of China Using MODIS Satellite Data

The normalized difference vegetation index （NDVI） has proven to be typically employed to assess terrestrial vegetation conditions. However, one limitation of NDVI for drought monitoring is the apparent time lag between rainfall deficit and NDVI response. To better understand this relationship, time series NDVI （2000-2010） during the growing season in Sichuan Province and Chongqing City were analyzed. The vegetation condition index （VCI） was used to construct a new drought index, time-integrated vegetation condition index （TIVCI）, and was then compared with meteorological drought indices-standardized precipitation index （SPI）, a multiple-time scale meteorological-drought index based on precipitation, to examine the sensitivity on droughts. Our research findings indicate the followings： （1） farmland NDVI sensitivity to precipitation in study area has a time lag of 16-24 d, and it maximally responds to the temperature with a lag of about 16 d. （2） We applied the approach to Sichuan Province and Chongqing City for extreme drought monitoring in 2006 and 2003, and the results show that the monitoring results from TIVCI are closer to the published China agricultural statistical data than VCI. Compared to VCI, the best results from TIVCI3 were found with the relative errors of -4.5 and 6.36% in 2006 for drought affected area and drought disaster area respectively, and 5.11 and -5.95% in 2003. （3） Compared to VCI, TIVCI has better correlation with the SPI, which indicates the lag and cumulative effects of precipitation on vegetation. Our finding proved that TIVCI is an effective indicator of drought detection when the time lag effects between NDVI and climate factors are taken into consideration.

Detecting Effects of the Recent Drought on Vegetation in Southwestern China

The severest drought on record occurred in southwestern China from September 2009 until March 2010. In order to measure the impact the drought imposed on vegetation, we developed an evaluation indicator called the Vegetation Index Anomaly （VIA） based on MODIS/EVI. The tempo- spatial pattern of this impact was analyzed. Given that this impact may be modulated by many factors, the responses of different vegetation types （woodland, grassland and cropland）, and the spatial pattern of meteorological drought were also analyzed. Results show that more than 50% of vegetation suffered because of this drought event, but there was significant tempo-spatial variability in the range and intensity of impact. This variability may be caused by many factors. Of the three major vegetation types, cropland was the most sensitive to drought, followed by grassland and then woodland. In addition, meteorological factors （precipitation and air temperature） also played a role; however, obvious differences exist between the spatial distribution pattern of drought-stricken vegetation and that of meteorological drought, which further demonstrates the intervention of other factors besides meteorological factors. So compared to meteorological drought, the vegetation index may be more useful for measuring the actual intensity, duration and impact of drought events. The limitations of vegetation indices are also considered.