利用遥感技术评估泰米尔纳德邦的农业干旱情况

S. Janarth, R. Jagadeeswaran, S. Pazhanivelan, Balaji Kannan, K. Ragunath, N.K. Sathiyamoorthy
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引用次数: 0

摘要

背景:利用气候灾害组红外降水与站点数据(CHIRPS)分析了印度泰米尔纳德邦 2019 年至 2023 年收获季节的农业干旱。研究获得了 1991 年至 2023 年的月降水量数据,主要目的是评估研究地区气象和农业干旱的持续时间、空间范围、严重程度和滞后时间。研究方法中分辨率成像光谱仪(MODIS)数据生成了增强植被指数(EVI)。像素可靠性层是根据图像采集时的云层覆盖率计算得出的。一个月时间范围内的标准化降水指数 SPI 在任何脆弱性研究中都起着非常重要的作用,可准确预测任何事件。结果:在本研究中,考虑了 2019-2023 这五年的枯草季节 EVI,并将其与不同时间尺度的 SPI 进行了相关分析。SPI 的相关系数为 0.39,显著性水平为 0.1%,换言之,2022 年的 EVI 与 SPI-6 具有良好的相关性。此外,还测试了 EVI 与间隔为 9 个月的降水量之间的关系。间隔 12 个月的降水对植被的压力更大,会对农业活动产生负面影响,导致作物歉收。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of Agricultural Drought in Tamil Nadu using Remote Sensing Techniques
Background: The Agricultural drought during kharif seasons of the year 2019 to 2023 in Tamil Nadu state in India was analyzed in using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Monthly precipitation data from 1991 to 2023 were obtained for the study, with the main objective of evaluating the duration, spatial extent, severity and lag time of meteorological and agricultural drought in the study area. Methods: The Enhanced Vegetation Index (EVI) was generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) data. The pixel reliability layer was introduced based on the calculation of the cloud coverage at the moment of image acquisition. The Standardized Precipitation Index SPI in one month time scale plays very significant role in any vulnerability studies for accurate prediction of any events. Result: In the present study, the EVI for Kharif season was considered for five years i.e., 2019-2023 and it was correlated with the SPI at various time scales. The correlation coefficient of SPI was 0.39 with 0.1% level of significance, in other words, EVI 2022 was well correlated with SPI-6. Also, the relation between EVI and precipitation with 9 months interval was also tested. The 12 months interval of precipitation have more stress on vegetation and it can negatively impact the agriculture activities leading to crop failures.
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