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The development of lightweight sensors compatible with mini unmanned aerial vehicles (UAVs) has expanded the agronomical applications of remote sensing. Of particular interest in this paper are thermal sensors based on lightweight microbolometer technology. These are mainly used to assess crop water stress with thermal images where an accuracy greater than 1 • C is necessary. However, these sensors lack precise temperature control, resulting in thermal drift during image acquisition that requires correction. Currently, there are several strategies to manage thermal drift effect. However, these strategies reduce useful flight time over crops due to the additional in-flight calibration operations. This study presents a drift correction methodology for microbolometer sensors based on redundant information from multiple overlapping images. An empirical study was performed in an orchard of high-density hedgerow olive trees with flights at different times of the day. Six mathematical drift correction models were developed and assessed to explain and correct drift effect on thermal images. Using the proposed methodology, the resulting thermally corrected orthomosaics yielded a rate of error lower than 1 • C compared to those where no drift correction was applied.

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The disease caused by SARS-CoV-2 has affected many countries and regions. In order to contain the spread of infection, many countries have adopted lockdown measures. As a result, SARS-CoV-2 has negatively influenced economies on a global scale and has caused a significant impact on the environment. In this study, changes in the concentration of the pollutant Nitrogen Dioxide (NO2) within the lockdown period were examined as well as how these changes relate to the Spanish population. NO2 is one of the reactive nitrogen oxides gases resulting from both anthropogenic and natural processes. One major source in urban areas is the combustion of fossil fuels from vehicles and industrial plants, both of which significantly contribute to air pollution. The long-term exposure to NO2 can also cause severe health problems. Remote sensing is a useful tool to analyze spatial variability of air quality. For this purpose, Sentinel-5P images registered from January to April of 2019 and 2020 were used to analyze spatial distribution of NO2 and its evolution under the lockdown measures in Spain. The results indicate a significant correlation between the population’s activity level and the reduction of NO2 values.

Wildfires are becoming more frequent in different parts of the globe, and the ability to predict when and where they will occur is a complex process. Identifying wildfire events with high probability of becoming a large wildfire is an important task for supporting initial attack planning. Different methods, including those that are physics-based, statistical, and based on machine learning (ML) are used in wildfire analysis. Among the whole, those based on machine learning are relatively novel. In addition, because the number of wildfires is much greater than the number of large wildfires, the dataset to be used in a ML model is imbalanced, resulting in overfitting or underfitting the results. In this manuscript, we propose to generate synthetic data from variables of interest together with ML models for the prediction of large wildfires. Specifically, five synthetic data generation methods have been evaluated, and their results are analyzed with four ML methods. The results yield an improvement in the prediction power when synthetic data are used, offering a new method to be taken into account in Decision Support Systems (DSS) when managing wildfires.