Agricultural greenhouses (AGs) are an important facility for the development of modern agriculture. Accurately and effectively detecting AGs is a necessity for the strategic planning of modern agriculture. With the advent of deep learning algorithms, various convolutional neural network (CNN)-based models have been proposed for object detection with high spatial resolution images. In this paper, we conducted a comparative assessment of the three well-established CNN-based models, which are Faster R-CNN, You Look Only Once-v3 (YOLO v3), and Single Shot Multi-Box Detector (SSD) for detecting AGs. The transfer learning and fine-tuning approaches were implemented to train models. Accuracy and efficiency evaluation results show that YOLO v3 achieved the best performance according to the average precision (mAP), frames per second (FPS) metrics and visual inspection. The SSD demonstrated an advantage in detection speed with an FPS twice higher than Faster R-CNN, although their mAP is close on the test set. The trained models were also applied to two independent test sets, which proved that these models have a certain transability and the higher resolution images are significant for accuracy improvement. Our study suggests YOLO v3 with superiorities in both accuracy and computational efficiency can be applied to detect AGs using high-resolution satellite images operationally.

The natural ecological lands, such as forest land, grassland, wetland, etc., constitute the most important factor for maintaining and preserving the earth’s ecosystem, which must be well concerned in the regional function-oriented planning for the sustainability of human economic development. We analyzed and evaluated the change of natural ecological land in the function-oriented planning regions where we applied the major function-oriented zones introduced as a new concept in China. Using the land-use data from 2009 to 2018 that were produced by the National Land Use Survey, we re-classified natural ecological land types into the forest, grassland, wetland, and bare land, and then addressed the changes of natural ecological land types from 2009 to 2018 in the major function-oriented zones. As a result, the area of natural ecological lands generally tended to decrease from 2009 to 2018, while the decreasing trend of natural ecological land areas was controlled after 2015 with the implementation of governmental policies for environment protection and eco-logical projects. Especially, the decrease of forest land area significantly tended to be zero in 2018 in optimal development zones. The decreased areas of natural ecological lands were mostly converted from artificial land from 2008 to 2019. On the other side, the forest lands mostly changed from cropland and grassland in key development zones, agricultural production zones, and key ecological function zones, due to the fact that grassland conversed in afforestation during this period. The evaluation of natural ecological land changes, which could be implemented by using the annual updates of national land-use data in China, is significant to support the government’s spatial regulation design, to reshape the planned regions, and make policies for environmental restoration and protection management.

Wetlands have been degrading and reducing under the influences of human activity and climate change. Landsat long-term observations can help us better track the changing evidences of wetland habitats that would be valuable for guiding the restoration and conservation of wetland. In this study, we demonstrated the results of tracking the changing evidence of wetland habitats using Landsat observations from 1984 to 2017 through the case study of Baiyangdian wetland in China. We extracted the open water and classified the wetland habitats using collected 190 scenes from Landsat observations. As a result, we found that the yearly variations of wetland present phasic changes in three phases: 1988–1998, 1999–2011 and 2013–2017. The landscape of wetland habitats presented during 1989–1999 mostly show us the natural spatial pattern with less human disturbance traces compared to that during 2013–2017. The water environment, moreover, changed for the better after the 2010s, which indicated the encouraging effects of the environmental restoration project implemented from the year 2010. The current landscapes of wetland habitats, however, present lots of linear belts that are blocking the water cycles and ecological channels of aquatic plants and animals in the wetland. The areas in the northwestern wing and around the northeastern edge of the wetland are changing to be drier due to cropping activities and are at risk of wetland loss. These historical changing evidences could be a guideline for planning and designing restoration for the wetland.

The spatial and temporal variations in the atmospheric CO₂ concentrations evidently respond to anthropogenic CO₂ emission activities. NO₂, a pollutant gas emitted from fossil fuel combustion, comes from the same emission sources as CO₂. Exploiting the simultaneous emissions characteristics of NO₂ and CO₂, we proposed an XCO₂ prediction approach to reconstruct XCO₂ data based on the data-driven machine learning algorithm using multiple predictors, including satellite observation of atmospheric NO₂, to resolve the issue of data gaps in satellite observation of XCO₂. The prediction model showed good predictive performance in revealing CO₂ concentrations in space and time, with a total deviation of 0.17 ± 1.17 ppm in the cross-validation and 1.03 ± 1.15 ppm compared to ground-based XCO₂ measurements. As a result, the introduction of NO₂ obtained better improvements in the CO₂ concentration responding to the anthropogenic emissions in space. The reconstructed XCO₂ data not only filled the gaps but also enhanced the signals of anthropogenic CO₂ emissions by using NO₂ data, as NO₂ strongly responds to anthropogenic CO₂ emissions (R² = 0.92). Moreover, the predicted XCO₂ data preferred to correct the abnormally low XCO₂ retrievals at satellite observing footprints, where the XCO₂_uncertainity field in the OCO-2 and OCO-3 products indicated a larger uncertainty in the inversion algorithm.

Anthropogenic CO₂ emissions are one of the primary drivers of the increase in atmospheric CO₂ concentrations. It has been indicated that reducing emitted pollution gases can simultaneously bring out anthropogenic CO₂ reduction, known as the synergistic effects of pollution and carbon reduction for controlling increases in CO₂ and pollution gas concentrations. This study aims to assess these synergistic effects, which are still not clearly understood, by analyzing the mechanisms of atmospheric CO₂ and NO₂ concentration variability in response to human emission reduction activities. We utilize satellite-observed NO₂, which is a short-lived anthropogenic pollution gas with the same emission sources as CO₂, along with CO₂ concentration data to detect their simultaneous response to anthropogenic CO₂ emissions, thereby assessing and comparing the synergistic effects of pollution and carbon reduction in the two study areas of China and the United States, as well as in a special scenario of abrupt reductions in anthropogenic CO₂ emissions. The results show that the synergistic effects of pollution and carbon reduction in the United States are likely better than those in China, as the United States demonstrates a stronger response (R² = 0.53) between atmospheric NO₂ and anthropogenic CO₂ emission compared with China (R² = 0.36). This difference is attributable to the CO₂ emissions from coal-fired power generation in China are much more than those in the United States, where oil and natural gas dominate. Furthermore, the analysis of special scenarios during the COVID-19 pandemic (2020–2022) in China demonstrates that the types of anthropogenic emission sources are the main factors influencing the synergistic effects of pollution and carbon reduction. Specifically, the megacity regions, where fossil fuel power plants and transportation are the main emission sources, presented stronger synergistic effects of pollution and carbon reduction than those regions dominated by coal-based metallurgical and chemical plants.