{"title":"Parametric and non-parametric indices for agricultural drought assessment using ESACCI soil moisture data over the Southern Plateau and Hills, India","authors":"","doi":"10.1016/j.jag.2024.104175","DOIUrl":null,"url":null,"abstract":"<div><p>The European Space Agency (ESA) under the Climate Change Initiative (CCI) has developed a multi-satellite global, daily Soil Moisture (SM) dataset that has paved the ways for agricultural drought studies. To evaluate the performance of this ESACCI SM, two SM-based indices i.e. parametric distribution-based Standardized Soil Moisture Index (SSMI) and non-parametric distribution-based Empirical Standardized Soil Moisture Index (ESSMI) are computed to characterize agricultural drought in the Southern Plateau and Hills (SPH) in India from 1991 to 2020. SSMI and ESSMI are then compared with the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The yearly temporal analysis revealed a consistent pattern among all the four indices with 2003 and 2020 marked as the driest and wettest years, respectively. On the other hand, monthly temporal analysis indicated SSMI and ESSMI lagged behind SPI and ESSMI suggesting a delayed response of SM to precipitation. Spatial distributions of indices showed that the SM-based indices effectively capture temporal variations of dryness or wetness across seasons. The near normal and mild to moderate droughts predominated (both spatially and temporally) the SPH and SSMI better captured the extreme drought areas compared to ESSMI. Further, Dynamic Threshold Run Theory (DTRT) is introduced to identify and characterize drought events based on their duration, frequency, intensity and peak. The findings revealed a resemblance in spatial distribution between the duration and frequency. The drought peak and intensity revealed a moderate nature of drought conditions. Overall, this study highlights the effectiveness of ESACCI SM product to characterize the agricultural droughts.</p></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":null,"pages":null},"PeriodicalIF":7.6000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1569843224005314/pdfft?md5=ede9f920fa540967bbd120938d857116&pid=1-s2.0-S1569843224005314-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of applied earth observation and geoinformation : ITC journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569843224005314","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0
Abstract
The European Space Agency (ESA) under the Climate Change Initiative (CCI) has developed a multi-satellite global, daily Soil Moisture (SM) dataset that has paved the ways for agricultural drought studies. To evaluate the performance of this ESACCI SM, two SM-based indices i.e. parametric distribution-based Standardized Soil Moisture Index (SSMI) and non-parametric distribution-based Empirical Standardized Soil Moisture Index (ESSMI) are computed to characterize agricultural drought in the Southern Plateau and Hills (SPH) in India from 1991 to 2020. SSMI and ESSMI are then compared with the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The yearly temporal analysis revealed a consistent pattern among all the four indices with 2003 and 2020 marked as the driest and wettest years, respectively. On the other hand, monthly temporal analysis indicated SSMI and ESSMI lagged behind SPI and ESSMI suggesting a delayed response of SM to precipitation. Spatial distributions of indices showed that the SM-based indices effectively capture temporal variations of dryness or wetness across seasons. The near normal and mild to moderate droughts predominated (both spatially and temporally) the SPH and SSMI better captured the extreme drought areas compared to ESSMI. Further, Dynamic Threshold Run Theory (DTRT) is introduced to identify and characterize drought events based on their duration, frequency, intensity and peak. The findings revealed a resemblance in spatial distribution between the duration and frequency. The drought peak and intensity revealed a moderate nature of drought conditions. Overall, this study highlights the effectiveness of ESACCI SM product to characterize the agricultural droughts.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.