Earth Observation and Satellite Image Applications Laboratory (EOSIAL), School of Aerospace Engineering (SIA), Sapienza University of Rome, Via Salaria 851-881, 00138, Rome, Italy.
Department of Remote Sensing and GIS, University of Tabriz, Tabriz, Iran.
Sci Rep. 2023 Jan 19;13(1):1057. doi: 10.1038/s41598-023-28244-5.
The agriculture sector provides the majority of food supplies, ensures food security, and promotes sustainable development. Due to recent climate changes as well as trends in human population growth and environmental degradation, the need for timely agricultural information continues to rise. This study analyzes and predicts the impacts of climate change on food security (FS). For 2002-2021, Landsat, MODIS satellite images and predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, soil moisture, groundwater quality, soil types, digital elevation model, slope, and aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based on the Google Earth Engine (GEE) to detect agricultural land (AL). A remote sensing-based approach combined with the analytical network process (ANP) model was used to identify frost-affected areas. We then analyzed the relationship between climatic, geospatial, and topographical variables and AL and frost-affected areas. We found negative correlations of - 0.80, - 0.58, - 0.43, and - 0.45 between AL and LST, evapotranspiration, cloud ratio, and soil salinity, respectively. There is a positive correlation between AL and precipitation, sunny days, soil moisture, and groundwater quality of 0.39, 0.25, 0.21, and 0.77, respectively. The correlation between frost-affected areas and LST, evapotranspiration, cloud ratio, elevation, slope, and aspect are 0.55, 0.40, 0.52, 0.35, 0.45, and 0.39. Frost-affected areas have negative correlations with precipitation, sunny day, and soil moisture of - 0.68, - 0.23, and - 0.38, respectively. Our findings show that the increase in LST, evapotranspiration, cloud ratio, and soil salinity is associated with the decrease in AL. Additionally, AL decreases with a decreasing in precipitation, sunny days, soil moisture, and groundwater quality. It was also found that as LST, evapotranspiration, cloud ratio, elevation, slope, and aspect increase, frost-affected areas increase as well. Furthermore, frost-affected areas increase when precipitation, sunny days, and soil moisture decrease. Finally, we predicted the FS threat for 2030, 2040, 2050, and 2060 using the CA-Markov method. According to the results, the AL will decrease by 0.36% from 2030 to 2060. Between 2030 and 2060, however, the area with very high frost-affected will increase by about 10.64%. In sum, this study accentuates the critical impacts of climate change on the FS in the region. Our findings and proposed methods could be helpful for researchers to model and quantify the climate change impacts on the FS in different regions and periods.
农业部门提供了大部分的食物供应，确保了粮食安全，并促进了可持续发展。由于最近的气候变化以及人口增长和环境退化的趋势，对及时的农业信息的需求持续增长。本研究分析和预测了气候变化对粮食安全（FS）的影响。在 2002 年至 2021 年期间，使用了 Landsat、MODIS 卫星图像和前置变量（地表温度（LST）、蒸散、降水、晴天、云比、土壤盐分、土壤湿度、地下水质量、土壤类型、数字高程模型、坡度和方位）。首先，我们使用基于 Google Earth Engine（GEE）的深度学习卷积神经网络（DL-CNN）来检测农业用地（AL）。结合遥感和分析网络过程（ANP）模型，我们识别了受霜害影响的地区。然后，我们分析了气候、地理空间和地形变量与 AL 和受霜害地区之间的关系。我们发现，AL 与 LST、蒸散、云比和土壤盐分的负相关系数分别为-0.80、-0.58、-0.43 和-0.45。AL 与降水、晴天、土壤湿度和地下水质量的正相关系数分别为 0.39、0.25、0.21 和 0.77。受霜害地区与 LST、蒸散、云比、海拔、坡度和方位的相关系数分别为 0.55、0.40、0.52、0.35、0.45 和 0.39。受霜害地区与降水、晴天和土壤湿度的负相关系数分别为-0.68、-0.23 和-0.38。我们的研究结果表明，LST、蒸散、云比和土壤盐分的增加与 AL 的减少有关。此外，AL 随降水、晴天和土壤湿度的减少而减少。还发现，随着 LST、蒸散、云比、海拔、坡度和方位的增加，受霜害地区也会增加。此外，当降水、晴天和土壤湿度减少时，受霜害地区会增加。最后，我们使用 CA-Markov 方法预测了 2030 年、2040 年、2050 年和 2060 年的 FS 威胁。根据结果，2030 年至 2060 年期间，AL 将减少 0.36%。然而，在 2030 年至 2060 年期间，受霜害影响非常严重的地区将增加约 10.64%。总之，本研究强调了气候变化对该地区 FS 的重大影响。我们的研究结果和提出的方法可以帮助研究人员在不同地区和时期模拟和量化气候变化对 FS 的影响。
