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.
Air pollution (AP) is a significant risk factor for public health, and its impact is becoming increasingly concerning in developing countries where it is causing a growing number of health issues. It is therefore essential to map and monitor AP sources in order to facilitate local action against them. This study aims at assessing the suitability of Sentinel-5 AP products based on Google Earth Engine (GEE) to monitor air pollutants, including CO, NO2, SO2, and O3 in Arak city, Iran from 2018 to 2019. Our process involved feeding satellite images to a cloud-free GEE platform that identified pollutant-affected areas monthly, seasonally, and annually. By coding in the JavaScript language in the GEE, four pollution parameters of Sentinel-5 satellite images were obtained. Following that, images with clouds were filtered by defining cloud filters, and average maps were extracted by defining average filters for both years. The employed model, which solely used Sentinel-5 AP products, was tested and assessed using ground data collected from the Environmental Organization of Central Province. Our findings revealed that annual CO, NO2, SO2, and O3 were estimated with RMSE of 0.13, 2.58, 4.62, and 2.36, respectively, for the year 2018. The annual CO, NO2, SO2, and O3 for the year 2019 were also calculated with RMSE of 0.17, 2.41, 4.31, and 4.6, respectively. The results demonstrated that seasonal AP was estimated with RMSE of 0.09, 5.39, 0.70, and 7.81 for CO, NO2, SO2, and O3, respectively, for the year 2018. Seasonal AP was also estimated with RMSE of 0.12, 4.99, 1.33, and 1.27 for CO, NO2, SO2, and O3, respectively, for the year 2019. The results of this study revealed that Sentinel-5 data combined with automated-based approaches, such as GEE, can perform better than traditional approaches (e.g., pollution measuring stations) for AP mapping and monitoring since they are capable of providing spatially distributed data that is sufficiently accurate.
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