A greenhouse is an important land-use type, which can effectively improve agricultural production conditions and increase crop yields. It is of great significance to obtain the spatial distribution data of greenhouses quickly and accurately for regional agricultural production and food security. Based on the Google Earth Engine cloud platform and Landsat 8 images, this study selected a total of 18 indicators from three aspects of spectral features, texture features and terrain features to construct greenhouse identification features. From a variety of classification algorithms for remote-sensing recognition of greenhouses, this study selected three classifiers with higher accuracy (classification and regression trees (CART), random forest model (randomForest) and maximum entropy model (gmoMaxEnt)) to construct an integrated classification algorithm, and then extracted the spatial distribution data of greenhouses in Jiangsu Province. The results show that: (1) Google Earth Engine with its own massive data and cloud computing capabilities, combined with integrated classification algorithms, can achieve rapid remote-sensing mapping of large-scale greenhouses under complex terrain, and the classification accuracy is higher than that of a single classification algorithm. (2) The combination of different spectral, texture and terrain features has a greater impact on the extraction of regional greenhouses, the combination of all three aspects of features has the highest accuracy. Spectral features are the key factors for greenhouse remote-sensing mapping, but terrain and texture features can also enhance classification accuracy. (3) The greenhouse in Jiangsu Province has significant spatial differentiation and spatial agglomeration characteristics. The most widely distributed greenhouses are mainly concentrated in the agriculturally developed areas such as Dongtai City, Hai’an County, Rudong County and Pizhou City.

Cropland abandonment is a widespread land-change process globally, which can stem from the accelerated outmigration of the population from rural to urban areas, socio-economic and political changes, catastrophes, and other trigger events. Clouds limit the utility of optical satellite data to monitor cropland abandonment in highly fragmented mountain agricultural landscapes of tropical and subtropical regions, including the south of China. Taking Nanjing County of China as an example, we developed a novel approach by utilizing multisource satellite (Landsat and Sentinel-2) imagery to map multiple trajectories of cropland abandonment (transitioning from cropland to grassland, shrubs and forest) in subtropical mountainous landscapes. Then, we employed a redundancy analysis (RDA) to identify the spatial association of cropland abandonment considering agricultural productivity, physiography, locational characteristics and economic factors. Results indicate the great suitability of harmonized Landsat 8 and Sentinel-2 images to distinguish multiple trajectories of cropland abandonment in subtropical mountainous areas. Our framework of mapping cropland abandonment resulted in good producer's (78.2%) and user's (81.3%) accuracies. The statistical analysis showed 31.85% of croplands cultivated in 2000 were abandoned by 2018, and more than a quarter of townships experienced cropland abandonment with high abandoned rates (>38%). Cropland abandonment mainly occurred in relatively unfavorable areas for agricultural production, for instance with a slope above 6°. Slope and the proximity to the nearest settlement explained 65.4% and 8.1% of the variation of cropland abandonment at the township level, respectively. The developed approaches on both mapping cropland abandonment and modeling determinants can be highly relevant to monitor multiple trajectories of cropland abandonment and ascribe their determinants not only in mountainous China but also elsewhere and thus promote the formulation of land-use policies that aim to steer cropland abandonment.

Excessive exposure to ambient (outdoor) air pollution may greatly increase the incidences of respiratory and cardiovascular diseases. Accurate reports of the spatial-temporal distribution characteristics of daily PM_2.5 exposure can effectively prevent and reduce the harm caused to humans. Based on the daily average concentration data of PM_2.5 in Beijing in May 2014 and the spatio-temporal kriging (STK) theory, we selected the optimal STK fitting model and compared the spatial-temporal prediction accuracy of PM_2.5 using the STK method and ordinary kriging (OK) method. We also reveal the spatial-temporal distribution characteristics of the daily PM_2.5 exposure in Beijing. The results show the following: (1) The fitting error of the Bilonick model (BM) model which is the smallest (0.00648), and the fitting effect of the prediction model of STK is the best for daily PM_2.5 exposure. (2) The cross-examination results show that the STK model (RMSE = 8.90) has significantly lower fitting errors than the OK model (RMSE = 10.70), so its simulation prediction accuracy is higher. (3) According to the interpolation of the STK model, the daily exposure of PM_2.5 in Beijing in May 2014 has good continuity in both time and space. The overall air quality is good, and overall the spatial distribution is low in the north and high in the south, with the highest concentration in the southwestern region. (4) There is a certain degree of spatial heterogeneity in the cumulative duration at the good, moderate, and polluted grades of China National Standard. The areas with the longest cumulative duration at the good, moderate and polluted grades are in the north, southeast, and southwest of the study area, respectively.

Long-term exposure to ozone pollution will cause severe threats to residents’ physical and mental health. Ground-level ozone is the most severe air pollutant in China’s Pearl River Delta Metropolitan Region (PRD). It is of great significance to accurately reveal the spatial–temporal distribution characteristics of ozone pollution exposure patterns. We used the daily maximum 8-h ozone concentration data from PRD’s 55 air quality monitoring stations in 2015 as input data. We used six models of STK and ordinary kriging (OK) for the simulation of ozone concentration. Then we chose a better ozone pollution prediction model to reveal the ozone exposure characteristics of the PRD in 2015. The results show that the Bilonick model (BM) model had the highest simulation precision for ozone in the six models for spatial–temporal kriging (STK) interpolation, and the STK model’s simulation prediction results are significantly better than the OK model. The annual average ozone concentrations in the PRD during 2015 showed a high spatial variation in the north and east and low in the south and west. Ozone concentrations were relatively high in summer and autumn and low in winter and spring. The center of gravity of ozone concentrations tended to migrate to the north and west before moving to the south and then finally migrating to the east. The ozone’s spatial autocorrelation was significant and showed a significant positive correlation, mainly showing high-high clustering and low-low clustering. The type of clustering undergoes temporal migration and conversion over the four seasons, with spatial autocorrelation during winter the most significant.