Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for agricultural production. Efficient monitoring of crop N content is the basis of precise fertilizer management, and therefore to increase crop yields and improve crop quality. Remote sensing has gradually replaced traditional destructive methods such as field surveys and laboratory testing for crop N diagnosis. With the rapid advancement of remote sensing, a review on crop N monitoring is badly in need of better summary and discussion. The purpose of this study was to identify current research trends and key issues related to N monitoring. It begins with a comprehensive statistical analysis of the literature on remote sensing monitoring of N in rice and wheat over the past 20 years. The study then elucidates the physiological mechanisms and spectral response characteristics of remote sensing monitoring of canopy N. The following section summarizes the techniques and methods applied in remote sensing monitoring of canopy N from three aspects: remote sensing platforms for N monitoring; correlation between remotely sensed data and N status; and the retrieval methods of N status. The influential factors of N retrieval were then discussed with detailed classification. However, there remain challenges and problems that need to be addressed in the future studies, including the fusion of multisource data from different platforms, and the uncertainty of canopy N inversion in the presence of background factors. The newly developed hybrid model integrates the flexibility of machine learning with the mechanism of physical models. It could be problem solving, which has the advantages of processing multi-source data and reducing the interference of confounding factors. It could be the future development direction of crop N inversion with both high precision and universality.

The air quality in China has experienced dramatic changes during the last few decades. To improve understanding of distribution, variations, and main influence factors of air pollution in central China, long-term multiple satellite observations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) are used to characterize particle pollution and their primary gaseous precursors, sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) in Hubei province during 2005–2017. Unlike other regions in eastern China, particle and gaseous pollutants exhibit distinct spatial and temporal patterns in central China due to differences in emission sources and control measures. OMI SO₂ of the whole Hubei region reached the highest value of ~0.2 Dobson unit (DU) in 2007 and then declined by more than 90% to near background levels. By contrast, OMI NO₂ grew from ~3.2 to 5.9 × 10¹⁵ molecules cm⁻² during 2005–2011 and deceased to ~3.9 × 10¹⁵ molecules cm⁻² in 2017. Unlike the steadily declining SO₂, variations of OMI NO₂ flattened out in 2016 and increased ~0.5 × 10¹⁵ molecules cm⁻² during 2017. As result, MODIS AOD at 550 nm increased from 0.55 to the peak value of 0.7 during 2005–2011 and then decreased continuously to 0.38 by 2017. MODIS AOD and OMI SO₂ has a high correlation (R > 0.8), indicating that annual variations of SO₂ can explain most changes of AOD. The air pollution in central China has notable seasonal variations, which is heaviest in winter and light in summer. While air quality in eastern Hubei is dominated by gaseous pollution such as O₃ and NO_x, particle pollutants are mainly concentrated in central Hubei. The high consistency with ground measurements demonstrates that satellite observation can well capture variations of air pollution in regional scales. The increasing ozone (O₃) and NO₂ since 2016 suggests that more control measures should be made to reduce O₃-related emissions. To improve the air quality in regional scale, it is necessary to monitor the dynamic emission sources with satellite observations at a finer resolution.

Fine particulate matter (PM_2.5) exposure has been linked to numerous adverse health effects, with some disadvantaged subgroups bearing a disproportionate exposure burden. Few studies have been conducted to estimate the exposure and inequality of different subgroups due to a lack of adequate characterization of disparities in exposure to air pollutants in urban areas, and a mechanistic understanding of the causes of these exposure inequalities. Based on a long-term series of PM_2.5 concentrations, this study analyzed the spatial and temporal characteristics of PM_2.5 in 31 provincial capital cities of China from 2000 to 2016 using the coefficient of variation and trend analyses. A health risk assessment of human exposure to PM_2.5 from 2000 to 2016 was then undertaken. A cumulative population-weighted average concentration method was applied to investigate exposures and inequality for education level, job category, age, gender and income population subgroups. The relationships between socioeconomic factors and PM_2.5 exposure concentrations were quantified using the geographically and temporally weighted regression model (GTWR). Results indicate that the PM_2.5 concentrations in most of the capital cities in the study experienced an increasing trend at a rate of 0.98 μg m⁻³ per year from 2000 to 2016. The proportion of the population exposed to high PM_2.5 (above 35 μg m⁻³) increased annually, mainly due to the increase of population migrating into north, east, south and central China. The higher educated, older, higher income and urban secondary industry share (SIS) subgroups suffered from the most significant environmental inequality, respectively. The per capita GDP, population size, and the share of the secondary industry played an essential role in unequal exposure to PM_2.5.

Crop residue open burning has significant adverse effects on regional air quality, climate change, and human health. Emissions from crop residue open burning estimated by satellites are underestimated in central China due to long-term cloud cover and the limitation of spatial-temporal resolution of satellites. In this study, we used a statistical-based method to investigate the crop residue open burning emissions in central China from 2012 to 2020. The open burning proportion (OBP) of residue, updated annually by the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), and the latest observed emission factors (EF_S) were used to improve the accuracy of the estimated emissions. Annual emissions of pollutants were allocated into 0.1° × 0.1° spatial grid cells using fire counts and land cover data. The results showed that the total emissions of black carbon (BC), organic carbon (OC), sulfur dioxide (SO₂), nitric oxide (NO_X), carbon monoxide (CO), carbon dioxide (CO₂), fine particles (PM_2.5), coarse particles (PM₁₀), ammonia (NH₃), methane (CH₄) and non-methane volatile organic compound (NMVOC) were 34.84, 149.72, 41.06, 90.11, 2640.97, 78,094.91, 485.17, 481.05, 35.21, 246.38 and 499.59 Gg, respectively. The largest contributor of crop residue open burning was rice, followed by wheat, rapeseed and corn, with the contribution rates of 35.34–64.07%, 15.78–34.71%, 9.12–25.56%, and 5.69–14.06%, respectively. The pollutants emissions exhibit large annual variation, with the highest emissions in 2013 and a remarkable decrease from 2013 to 2015 under strict control measures. Since 2015, the emissions remained at a low level, which shows that air quality control policies play a role in recent years. The result indicates that using OBP updated by satellite active fire product in a statistical-based method can help to get more accurate and reliable multi-year emissions.

Wildfire is one of the main hazards affecting large areas and causes great damage all over the world, and the rapid development of the wildland-urban interface (WUI) increases the threat of wildfires that have ecological, social, and economic consequences. As one of the most widely used methods for tracking fire, remote sensing can provide valuable information about fires, but it is not always available, and needs to be supplemented by data from other sources. Social media is an emerging but underutilized data source for emergency management, contains a wealth of disaster information, and reflects the public’s real-time witness and feedback to fires. In this paper, we propose a fusion framework of multi-source data analysis, including social media data and remote sensing data, cellphone signaling data, terrain data, and meteorological data to track WUI fires. Using semantic web technology, the framework has been implemented as a Knowledge Base Service and runs on top of WUIFire ontology. WUIFire ontology represents WUI fire–related knowledge and consists of three modules: system, monitoring, and spread, and tracks wildfires happening in WUIs. It provides a basis for tracking and analyzing a WUI fire by fusing multi-source data. To showcase the utility of our approach in a real-world scenario, we take the fire in the Yaji Mountain Scenic Area, Beijing, China, in 2019 as a case study. With object information identified from remote sensing, fire situation information extracted from Weibo, and fire perimeters constructed through fire spread simulation, a knowledge graph is constructed and an analysis using a semantic query is carried out to realize situational awareness and determine countermeasures. The experimental results demonstrate the benefits of using a semantically improved multi-source data fusion framework for tracking WUI fire.

Various cities in China have been identified as “stove cities” either in contemporary or historical times, exposing residents to extremely high temperatures. Existing studies on the heat island effect in stove cities are not representative nationwide. The outdated nature of these studies also significantly diminishes the relevance of their findings. Thus, reassessing the urban heat island (UHI) effect of stove cities is necessary in the context of global climate change and urbanization. This study focuses on seven symbolic and geographically distributed stove cities in China, including Nanjing, Chongqing, Wuhan, Fuzhou, Beijing, Xi’an, and Turpan. Using land surface temperature (LST) data, this study investigates the summer heat island effect from 2013 to 2023 and analyzes changes in the spatial distribution of the heat island effect. This paper utilizes impervious surface data and urban clustering algorithms to define urban and suburban areas. It then examines the evolution and spatial distribution of surface urban heat island intensity (SUHII) over time. Incorporating urbanization variables like population density and urban area, the study analyzes the main factors affecting the heat island effect from 2013 to 2018. We find that all cities continuously expand, with the annual average heat island effect intensifying over the years. With the exception of Beijing, the summer heat island or cool island effects in the remaining six cities show an overall intensification trend. From 2013 to 2018, SUHII has been primarily related to urban expansion and planning layout, with minimal impact from factors such as population density.