Parametric and non-parametric indices for agricultural drought assessment using ESACCI soil moisture data over the Southern Plateau and Hills, India

IF 7.6 Q1 REMOTE SENSING
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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.

利用印度南部高原和丘陵地区 ESACCI 土壤水分数据评估农业干旱的参数和非参数指数
欧洲航天局(ESA)在气候变化倡议(CCI)下开发了一个多卫星全球每日土壤湿度(SM)数据集,为农业干旱研究铺平了道路。为了评估 ESACCI SM 的性能,计算了两个基于 SM 的指数,即基于参数分布的标准化土壤水分指数(SSMI)和基于非参数分布的经验标准化土壤水分指数(ESSMI),以描述 1991 年至 2020 年印度南部高原和丘陵地区(SPH)的农业干旱特征。然后将 SSMI 和 ESSMI 与标准化降水指数 (SPI) 和标准化降水蒸散指数 (SPEI) 进行比较。年度时间分析表明,所有四项指数之间存在一致的模式,2003 年和 2020 年分别是最干旱和最潮湿的年份。另一方面,月度时间分析表明,SSMI 和 ESSMI 滞后于 SPI 和 ESSMI,这表明水汽蒸发指数对降水的反应延迟。指数的空间分布表明,基于降水层的指数能有效捕捉不同季节干湿度的时间变化。与 ESSMI 相比,SPH 和 SSMI 更好地捕捉了极端干旱地区。此外,还引入了动态阈值运行理论(DTRT),根据干旱事件的持续时间、频率、强度和峰值来识别和描述干旱事件。研究结果表明,干旱持续时间和频率的空间分布具有相似性。干旱峰值和强度显示了干旱条件的温和性。总之,这项研究突出了 ESACCI SM 产品在描述农业干旱方面的有效性。
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来源期刊
International journal of applied earth observation and geoinformation : ITC journal
International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences
CiteScore
12.00
自引率
0.00%
发文量
0
审稿时长
77 days
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.
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