Cloud detection is the first step for the practical processing of meteorology satellite images, and also determines the accuracy of subsequent applications. For Chinese FY serial satellite, the National Meteorological Satellite Center (NSMC) officially provides the cloud detection products. In practical applications, there still are some misdetection regions. Therefore, this paper proposes a cloud detection method trying to improve NSMC’s products based on ensemble threshold and random forest. The binarization is firstly performed using ten threshold methods of the first infrared band and visible channel of the image, and the binarized images are obtained by the voting strategy. Secondly, the binarized images of the two channels are combined to form an ensemble threshold image. Then the middle part of the ensemble threshold image and the upper and lower margins of NSMC’s cloud detection result are used as the sample collection source data for the random forest. Training samples rely only on source image data at one moment, and then the trained random forest model is applied to images of other times to obtain the final cloud detection results. This method performs well on FY-2G images and can effectively detect incorrect areas of the cloud detection products of the NSMC. The accuracy of the algorithm is evaluated by manually labeled ground truth using different methods and objective evaluation indices including Probability of Detection (POD), False Alarm Rate (FAR), Critical Success Index (CSI) and the average and standard deviation of all indices. The accuracy results show that the proposed method performs better than the other methods with less incorrect detection regions. Though the proposed approach is simple enough, it is a useful attempt to improve the cloud detection result, and there is plenty of room for further improvement.

The Coronavirus Disease 2019 (COVID-19) firstly announced in Wuhan of Hubei province, China is rapidly spreading to all the other 31 provinces of China and to more than 140 countries. Quarantine strategies play the key role on the disease controlling and public health in the world with this pandemic of the COVID-19 defined by the World Health Organization. In this study, a SEIRQ epidemic model was developed to explore the dynamic changes of COVID-19 in Wuhan and mainland China, from January 27, 2020 to March 5, 2020. Moreover, to investigate the effects of the quarantine strategies, two perspectives are employed from the different quarantine magnitudes and quarantine time points. The major results suggest that the COVID-19 variations are well captured by the epidemic model with very high accuracy in the cumulative confirmed cases, confirmed cases, cumulative recovered cases and cumulative death cases. The quarantine magnitudes in the susceptible individuals play larger roles on the disease control than the impacts of the quarantines of the exposed individuals and infectious individuals. For the quarantine time points, it shows that the early quarantine strategy is significantly important for the disease controlling. The time delayed quarantining will seriously increase the COVID-19 disease patients and prolongs the days of the disease extinction. Our model can simulate and predict the COVID-19 variations and the quarantine strategies are important for the disease controlling, especially at the early period of the disease outbreak. These conclusions provide important scientific information for the government policymaker in the disease control strategies.

The rapid expansion of oil palm is a major driver of deforestation and other associated damage to the climate and ecosystem in tropical regions, especially Southeast Asia. It is therefore necessary to precisely detect and monitor oil palm plantations to safeguard the ecosystem services and biodiversity of tropical forests. Compared with optical data, which are vulnerable to cloud cover, the Sentinel-1 dual-polarization C-band synthetic aperture radar (SAR) acquires global observations under all weather conditions and times of day and shows good performance for oil palm detection in the humid tropics. However, because accurately distinguishing mature and young oil palm trees by using optical and SAR data is difficult and considering the strong dependence on the input parameter values when detecting oil palm plantations by employing existing classification algorithms, we propose an innovative method to improve the accuracy of classifying the oil palm type (mature or young) and detecting the oil palm planting area in Sumatra by fusing Landsat-8 and Sentinel-1 images. We extract multitemporal spectral characteristics, SAR backscattering values, vegetation indices, and texture features to establish different feature combinations. Then, we use the random forest algorithm based on improved grid search optimization (IGSO-RF) and select optimal feature subsets to establish a classification model and detect oil palm plantations. Based on the IGSO-RF classifier and optimal features, our method improved the oil palm detection accuracy and obtained the best model performance (OA = 96.08% and kappa = 0.9462). Moreover, the contributions of different features to oil palm detection are different; nevertheless, the optimal feature subset performed the best and demonstrated good potential for the detection of oil palm plantations.

The atmospheric infrared sounder (AIRS) provides a robust and accurate data source to investigate the variability of mid-tropospheric CO₂ globally. In this paper, we use the AIRS CO₂ product and other auxiliary data to survey the spatiotemporal distribution characteristics of mid-tropospheric CO₂ and the controlling factors using linear regression, empirical orthogonal functions (EOFs), geostatistical analysis, and correlation analysis. The results show that areas with low mid-tropospheric CO₂ concentrations (20°S–5°N) (384.2 ppm) are formed as a result of subsidence in the atmosphere, the presence of the Amazon rainforest, and the lack of high CO₂ emission areas. The areas with high mid-tropospheric CO₂ concentrations (30°N–70°N) (382.1 ppm) are formed due to high CO₂ emissions. The global mid-tropospheric CO₂ concentrations increased gradually (the annual average rate of increase in CO₂ concentration is 2.11 ppm/a), with the highest concentration occurring in spring (384.0 ppm) and the lowest value in winter (382.5 ppm). The amplitude of the seasonal variation retrieved from AIRS (average: 1.38 ppm) is consistent with that of comprehensive observation network for trace gases (CONTRAIL), but smaller than the surface ground stations, which is related to altitude and coverage. These results contribute to a comprehensive understanding of the spatiotemporal distribution of mid-tropospheric CO₂ and related mechanisms.

Drought is a meteorological phenomenon that threatens ecosystems, agricultural production, and living conditions. Central Asia is highly vulnerable to drought due to its special geographic location, water resource shortages, and extreme weather conditions, and poor management of water resources and reliance on irrigated agriculture exacerbate the effects of drought. In this study, the latest version of the Global Land Data Assimilation System was employed to calculate the Standardized Precipitation Evapotranspiration Index at different time scales during the period from 1981 to 2020. The varimax Rotated Empirical Orthogonal Function was applied for subregional delineation of drought patterns in Central Asia, and various methods were employed for a comparative analysis of the spatiotemporal characteristics of drought in these Central Asian subregions. The results show that drought patterns vary considerably in the Central Asian subregions. Over the past 40 years, alternating wet and dry conditions occurred in Central Asia. North Kazakhstan experienced more drought events with lower severity. East and west differences appear after 2001, the west becoming drier and the east becoming wetter. Some regions near lakes, such as Balkhash, Issyk-Kul, and the Aral Sea, suffer from droughts of long duration and high severity. In the Tianshan region, droughts in the northern slopes occur more frequently, with shorter durations and higher intensity and peaks. Northwestern China and western Mongolia have extensive agricultural land and grasslands with highly fragile ecosystems that have become progressively drier since 2001.

In history, every occurrence of a desert locust plague has brought a devastating blow to local agriculture. Analyses of the potential geographic distribution and migration paths of desert locusts can be used to better monitor and provide early warnings about desert locust outbreaks. By using environmental data from multiple remote-sensing data sources, we simulate the potential habitats of desert locusts in Africa, Asia and Europe in this study using a logistic regression model that was developed based on desert locust monitoring records. The logistic regression model showed high accuracy, with an average training area under the curve (AUC) value of 0.84 and a kappa coefficient of 0.75. Our analysis indicated that the temperature and leaf area index (LAI) play important roles in shaping the spatial distribution of desert locusts. A model analysis based on data for six environmental variables over the past 15 years predicted that the potential habitats of desert locust present a periodic movement pattern between 40°N and 30°S latitude. The area of the potential desert locust habitat reached a maximum in July, with a suitable area exceeding 2.77 × 10⁷ km² and located entirely between 0°N and 40°N in Asia-Europe and Africa. In December, the potential distribution of desert locusts reached its minimum area at 0.68 × 10⁷ km² and was located between 30°N and 30°S in Asia and Africa. According to the model estimates, desert locust-prone areas are distributed in northern Ethiopia, South Sudan, northwestern Kenya, the southern Arabian Peninsula, the border area between India and Pakistan, and the southern Indian Peninsula. In addition, desert locusts were predicted to migrate from east to west between these areas and in Africa between 10°N and 17°N. Countries in these areas should closely monitor desert locust populations and respond rapidly.