Remote sensing-based spatiotemporal dynamics of agricultural drought on Prince Edward Island using Google Earth engine

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2025-02-10 DOI:10.1016/j.ecoinf.2025.103073

Fatima Imtiaz , Aitazaz A. Farooque , Gurjit S. Randhawa , Seyyed Ebrahim Hashemi Garmdareh , Xiuquan Wang , Travis J. Esau , Bishnu Acharya , Rehan Sadiq

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Abstract

Climate change is a primary factor contributing to widespread drought conditions worldwide. Therefore, assessing agricultural drought's spatial and temporal extent is crucial. This study explicitly applies remote sensing techniques to monitor drought in the cropland area of Prince Edward Island, Canada, with a particular emphasis on potato crops. The long-term drought was evaluated using MODIS for 2012–2022, while the seasonal drought at the field scale was calculated using Landsat-8 OLI/TIRS for the 2021 and 2022 crop growth seasons. The computed remote sensing drought indices include Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature Condition Index (TCI), which are derived using the Google Earth Engine platform. Examining long-term drought by MODIS revealed that 2020 was the most dominant drought year, according to all three drought indices. However, the seasonal variations of VCI, TCI, and VHI at the field scale observed in the three fields in 2021 and 2022 demonstrated that June went through considerable drought in both years. August was the second critical month following June for drought conditions. CHIRPS data indicated significant rainfall anomalies relative to the long-term seasonal average for the 2021 crop season, specifically in June (−38.5 %) and August (−38.2 %), while the rainfall in the crop season in 2022 exceeded the seasonal average. Based on Pearson correlation analysis, VHI correlated strongly with VCI (CC = 0.87 for 2021 and 0.93 for 2022) and moderately with rainfall (CC = 0.68 for 2021 and 0.63 for 2022). The spatial autocorrelation analysis revealed substantial positive autocorrelation of drought for 2019, 2020, 2021 and 2022. However, 2020 has the highest spatial autocorrelation, with Moran's I of 0.54 and a z-score of 24.8. Hence, this study will optimize irrigation, decrease crop loss, sustain crop yields, and enhance food security.

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基于谷歌Earth engine的爱德华王子岛农业干旱时空动态遥感研究

气候变化是造成全球广泛干旱的一个主要因素。因此，评估农业干旱的时空程度至关重要。本研究明确应用遥感技术监测加拿大爱德华王子岛农田旱情，重点监测马铃薯作物。使用MODIS对2012-2022年的长期干旱进行评估，而使用Landsat-8 OLI/TIRS对2021和2022年作物生长季节进行田间尺度的季节性干旱计算。计算得到的遥感干旱指数包括植被状况指数（VCI）、植被健康指数（VHI）和温度状况指数（TCI），它们是利用谷歌Earth Engine平台导出的。MODIS对长期干旱的研究显示，根据所有三个干旱指数，2020年是最主要的干旱年。然而，在野外观测的2021年和2022年三个地区VCI、TCI和VHI的季节变化表明，这两年的6月都经历了相当大的干旱。8月是继6月之后第二个干旱严重的月份。CHIRPS数据显示，与2021年作物季节的长期平均降雨量相比，6月（- 38.5%）和8月（- 38.2%）的降雨量明显异常，而2022年作物季节的降雨量超过了季节平均降雨量。基于Pearson相关分析，VHI与VCI（2021年CC = 0.87, 2022年CC = 0.93）相关性强，与降雨量（2021年CC = 0.68, 2022年CC = 0.63）相关性中等。空间自相关分析显示，2019年、2020年、2021年和2022年干旱呈显著正相关。然而，2020年的空间自相关性最高，Moran's I为0.54，z-score为24.8。因此，这项研究将优化灌溉，减少作物损失，保持作物产量，加强粮食安全。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.