Search

Flood disasters are one of the most serious meteorological disasters in China. With the rapid development of information technology, individual monitoring tools could not meet the need for flood disaster monitoring. Therefore, a new integrated air-space-ground method, based on combined satellite remote sensing, unmanned aerial vehicle remote sensing and field measurement technology, has been proposed to monitor and assess flood disasters caused by a dam failure in Poyang County, Jiangxi Province. In this paper, based on an air-space-ground investigation system, the general flooded areas, severely affected areas, and more severely affected areas were 53.18 km2, 12.61 km2 and 6.98 km2, respectively. The size of the dam break gap was about 65 m and 34.7 m on 22 and 23 June. The assessment precision was better than 98%, and the root mean square error (RMSE) was 0.86 m. The method could meet the needs for flood disaster information at different spatiotemporal scales, such as macro scale, medium scale and local small scale. The integrated monitoring of flood disasters was carried out to provide the whole process and all-round information on flood evolution dynamics, the disaster development process for flood disaster monitoring and emergency assessment, and holographic information for emergency rescue and disaster reduction, as well as to meet the need for different temporal and spatial scales of information in the process of disaster emergencies. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is primary factor for understanding, quantifying and predicting the marine resources and important parameter in earth science model. The variability of SST in the coastal water is increasing gradually over the recent years, which subsequently affects the marine habitats as well as the human beings located over the coastal zone. Gulf of Manner (GOM) is one of the coastal zone with marine national park receives several rivers covered with almost 21 small islands, pearl banks, coral reefs, and high variety of plants and animals. In this study, MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images were used to understand the long term changes and spatial variation of SST over the Gulf of Manner located in the south-eastern part of India. To achieve the goal, satellite data from 2000-2020 over the GOM were used to understand the SST variations. This analysis helps to predict the potential fishing zone and primary productivity. This SST analysis over the coastal environments regionally provides solution for several challenges in management and utilisation of coastal resources. [ABSTRACT FROM AUTHOR]

Rain Water Harvesting (RWH) as a solution for sustainable rainwater management is the focus of this research. To locate the potential sites for RWH, multi-criteria analysis following analytical hierarchy process using land-use/landcover, slope, drainage density, and runoff depth has been performed. By introducing continuous soil moisture accounting procedure in the globally used SCS-CN method, discrepancies in computed runoff values have been assessed. To appraise the usefulness of revised Soil Moisture Accounting (SMA) -enhanced SCS-CN models, a number of modifications have been compared. The models' performance has been evaluated using R2, Root Mean Square Error (RMSE), Nash Sutcliffe Efficiency (NSE), Percent BIAS (PBIAS) statistical indicators, and Rank Grading System (RGS) and the best has been selected to calculate the runoff depth for RWH potential zones. The resultant suitability map classifies Gurriala catchment into three suitability zones. 33.8% of the total area has been found as least suitable, comprised mainly of forest, residential land, and water bodies, while 46.8% and 19.4% of the area is recognized as moderately suitable and high suitable respectively. Selected suitable sites have been further classified into suitability zones for enhanced RWH structures and runoff volume contributed by each RWH structure has been computed. The total runoff potential of the area is 22.47 MCM that is enough to fulfill the water demands of suburban areas as a most inexpensive solution. [ABSTRACT FROM AUTHOR]

In the context of increased pressures on land for food and non‐food production, it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper, we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar‐based analysis is a relatively simple method to detect land abandonment in an early to longer term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions shows that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like Land Parcel Identification Systems data, in regions is one of the challenges in this respect. [ABSTRACT FROM AUTHOR]

Abstract The distribution of emperor penguins is circumpolar, with 54 colony locations currently reported of which 50 are currently extant as of 2019. Here we report on eight newly discovered colonies and confirm the rediscovery of three breeding sites, only previously reported in the era before Very High Resolution satellite imagery was available, making a total of 61 breeding locations. This represents an increase of ~20% in the number of breeding sites, but, as most of the colonies appear to be small, they may only increase the total population by around 5–10%. The discoveries have been facilitated by the use of Sentinel2 satellite imagery, which has a higher resolution and more efficient search mechanism than the Landsat data previously used to search for colonies. The small size of these new colonies indicates that considerations of reproductive output in relation to metabolic rate during huddling is likely to be of interest. Some of the colonies exist in offshore habitats, something not previously reported for emperor penguins. Comparison with recent modelling results show that the geographic locations of all the newly found colonies are in areas likely to be highly vulnerable under business‐as‐usual greenhouse gas emissions scenarios, suggesting that population decreases for the species will be greater than previously thought.

Mapping the mass concentration of near-surface atmospheric particulate matter (PM) using satellite observations has become a popular research niche, leading to the development of a variety of instruments, algorithms, and datasets over the past two decades. In this study, we conducted a holistic review of the major advances and challenges in quantifying PM, with a specific focus on instruments, algorithms, datasets, and modeling methods that have been developed over the past 20 years. The aim of this study is to provide a general guide for future satellite-based PM concentration mapping practices and to better support air quality monitoring and management of environmental health. Specifically, we review the evolution of satellite platforms, sensors, inversion algorithms, and datasets that can be used for monitoring aerosol properties. We then compare various practical methods and techniques that have been used to estimate PM mass concentrations and group them into four primary categories: (1) univariate regression, (2) chemical transport models (CTM), (3) multivariate regression, and (4) empirical physical approaches. Considering the main challenges encountered in PM mapping practices, for example, data gaps and discontinuity, a hybrid method is proposed with the aim of generating PM concentration maps that are both spatially continuous and have high precision.

Most volcanism on Earth is submarine, but volcanic gas emissions by submarine eruptions are rarely observed and hence largely unquantified. On 15 January 2022 a submarine eruption of Hunga Tonga-Hunga Ha’apai (HTHH) volcano (Tonga) generated an explosion of historic magnitude, and was preceded by ∼1 month of Surtseyan eruptive activity and two precursory explosive eruptions. We present an analysis of ultraviolet (UV) satellite measurements of volcanic sulfur dioxide (SO2) between December 2021 and the climactic 15 January 2022 eruption, comprising an unprecedented record of Surtseyan eruptive emissions. UV measurements from the Ozone Monitoring Instrument (OMI) on NASA’s Aura satellite, the Ozone Mapping and Profiler Suite (OMPS) on Suomi-NPP, the Tropospheric Monitoring Instrument (TROPOMI) on ESA’s Sentinel-5P, and the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) are combined to yield a consistent multi-sensor record of eruptive degassing. We estimate SO2 emissions during the eruption’s key phases: the initial 19 December 2021 eruption (∼0.01 Tg SO2); continuous SO2 emissions from 20 December 2021—early January 2022 (∼0.12 Tg SO2); the 13 January 2022 stratospheric eruption (0.06 Tg SO2); and the paroxysmal 15 January 2022 eruption (∼0.4–0.5 Tg SO2); yielding a total SO2 emission of ∼0.6–0.7 Tg SO2 for the eruptive episode. We interpret the vigorous SO2 emissions observed prior to the January 2022 eruptions, which were significantly higher than measured in the 2009 and 2014 HTHH eruptions, as strong evidence for a rejuvenated magmatic system. High cadence DSCOVR/EPIC SO2 imagery permits the first UV-based analysis of umbrella cloud spreading and volume flux in the 13 January 2022 eruption, and also tracks early dispersion of the stratospheric SO2 cloud injected on January 15. The ∼0.4–0.5 Tg SO2 discharged by the paroxysmal 15 January 2022 HTHH eruption is low relative to other eruptions of similar magnitude, and a review of other submarine eruptions in the satellite era indicates that modest SO2 yields may be characteristic of submarine volcanism, with the emissions and atmospheric impacts likely dominated by water vapor. The origin of the low SO2 loading awaits further investigation but scrubbing of SO2 in the water-rich eruption plumes and rapid conversion to sulfate aerosol are plausible, given the exceptional water emission by the 15 January 2022 HTHH eruption.

Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms—the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008–2011 via MERIS and 2017–2020 via OLCI when cloud-, ice-, and snow-free imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall’s tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.

Retrievals of cloud microphysical properties based on passive satellite imagery are especially difficult over snow-covered surfaces because of the bright and cold surface. To help quantify their uncertainties, single-layered overcast liquid-phase Arctic stratus cloud microphysical properties retrieved by using the Clouds and the Earth’s Radiant Energy System Edition 2 and Edition 4 (CERES Ed2 and Ed4) algorithms are compared with ground-based retrievals at the Atmospheric Radiation Measurement North Slope of Alaska (ARM NSA) site at Barrow, AK, during the period from March 2000 to December 2006. A total of 206 and 140 snow-free cases (Rsfc ≤ 0.3), and 108 and 106 snow cases (Rsfc>0.3), respectively, were selected from Terra and Aqua satellite passes over the ARM NSA site. The CERES Ed4 and Ed2 optical depth (t) and liquid water path (LWP) retrievals from both Terra and Aqua are almost identical and have excellent agreement with ARM retrievals under snow-free and snow conditions. In order to reach a radiation closure study for both the surface and top of atmosphere (TOA) radiation budgets, the ARM precision spectral pyranometer-measured surface albedos were adjusted (63.6% and 80% of the ARM surface albedos for snow-free and snow cases, respectively) to account for the water and land components of the domain of 30 km × 30 km. Most of the radiative transfer model calculated SW↓sfc and SW↑TOA fluxes by using ARM and CERES cloud retrievals and the domain mean albedos as input agree with the ARM and CERES flux observations within 10Wm-2 for both snow-free and snow conditions. Sensitivity studies show that the ARM LWP and re retrievals are less dependent on solar zenith angle (SZA), but all retrieved optical depths increase with SZA.

Area-volume-elevation (AVE) curves are critical for reservoir operation rules. However, such curves are not publicly available for most global reservoirs. Here, we present a framework to derive reservoir AVE curves from TanDEM-X data, using Lake Mead (~600 km2) as an example. First, the maximum water extent from 1984 to 2018-provided by the global surface water (GSW) dataset-was used as a mask to obtain the TanDEM-X data. Then, the TanDEM-X water indication mask (WAM) was applied to the extracted TanDEM-X data to obtain the visible bathymetry, which represents the topography between the maximum extent (according to GSW) and the water extent from WAM. Last, the AVE curve was generated by integrating the volume values from the top to bottom layers. TanDEM-X also captures the elevation values of the transitional waters, which are defined as the difference between the highest and lowest water levels. The transitional waters were obtained by thresholding amplitude and coherence images, and their elevations were then added to the visible bathymetry to extend the AVE curves with an elevation range extending from 344-369 m to 341-369 m. Validation results against in situ lidar survey values suggest a high-accuracy of elevation-area (E-A) relationships with R2 values of >0.99 and NRMSE values from 2.11% to 2.45%, and elevation-volume (E-V) relationships with R2 values of 1 and NRMSE values from 1.11% to 1.29%. Results also show that TanDEM-X data can capture the interannual variations due to multiple acquisitions, and that the elevation measurements for the lake shore areas are reliable.

High spatial resolution and broad spatial coverage data on fine particulate matter (PM2.5) are of great significance to estimating the exposure to PM2.5. However, this type of data is currently very limited worldwide. In addition, the COVID-19 pandemic in China, starting in January 2020, may have led to significant variations in the PM2.5 concentrations. To identify the variations and causes of PM2.5 concentrations before and after the COVID-19 pandemic from January 23 to March 24 during 2018–2020, in this article, a geographically weighted regression model with a 1 km spatial resolution covering all of mainland China was developed. The overall R and RMSE values of the model cross validation were 0.91 and 17.19 μg/m3, respectively, indicating that the model performed satisfactorily in estimating the PM2.5 values. Then, based on the satellite-based PM2.5 values, the results show that the PM2.5 values fluctuated significantly across mainland China before and after the COVID-19 outbreak. Additionally, the mean PM2.5 values decreased by 5.41 μg/m3 in 2020 compared to 2019. In Hubei Province, the mean PM2.5 values increased by 1.85 μg/m3 in 2019 compared to 2018, whereas they dramatically decreased by 23.18 μg/m3 in 2020 compared to 2019. Finally, the results show that anthropogenic factors were primarily responsible for the variations in the PM2.5 concentrations in Heilongjiang, Jilin, and Liaoning provinces; whereas, both meteorological and anthropogenic factors were responsible for the variations in Hubei, Henan, Anhui, Shandong, and Jiangsu provinces during the study period. These results provide an important reference for the future development of air pollution control policies in China.

The measurement of the concentration of suspended sediment in a water body is a very important content in the observation of hydrological elements, and it is also one of the important parameters for calculating the sediment resuspension flux. In order to accurately predict the distribution of lake sediment, this paper uses satellite remote sensing data to invert the suspended sediment concentration. The key to the quantitative inversion is the atmospheric correction and the suspended sediment concentration inversion algorithm. In this paper, satellite remote sensing technology and Internet of Things technology are combined to establish a new type of lake suspended sediment concentration distribution model. First of all, this paper combines the results of satellite remote sensing inversion and the results of on-site water sample inspections of the Internet of Things to obtain the original hydrological data of suspended sediment in the lake. Secondly, this paper combines ADAM with deep learning technology to simulate the lake flow field and predict the dynamic process of suspended sediment pollution under different conditions. Finally, through experimental simulation and field sampling experiments, the validity of the lake suspended sediment concentration model established in this paper is verified. This model can provide assistance for relevant agencies to grasp the temporal and spatial distribution of suspended sediment concentration in regional lakes in a comprehensive and timely manner, and can obtain the overall characteristics of the study area and the impact of humanistic engineering construction.

The COVID-19 global pandemic has posed a serious impact on human life and health. Facing this unknown, unexpected, and devastating disease, China launched a resolute battle to prevent and control its spread. This study focused on assessing the recovery of city activity of 17 administrative regions in China around the COVID-19 pandemic period in early 2020. The night-time light (NTL) satellite remote sensing data, NPP-VIIRS dataset, was applied to evaluate the NTL changes during the coronavirus epidemic to understand the impact of the epidemic on city activity and the economy of China. A series of median composites was first constructed to analyze short-term NTL changes. Based on these median composites, the ratio map, difference map, wave index, and recovery ratio (RR) were defined to indicate the NTL recovery pattern and its spatial distribution characteristics. The results are as follows:first, the adopted median composites are able to describe the NTL changes in the short term; second, the recovery of city activity could not be exactly described by RR of a single year but could be evaluated by the comparison of RR between two adjacent years; third, a partial and spatially heterogeneous recovery of city activity is observed after the 2020 holiday compared to 2019, fourth, a better degree of recovery is observed after China announced its fully work resumption.

The registration of multiresolution optical remote sensing images has been widely used in image fusion, change detection, and image stitching. However, traditional registration methods achieve poor accuracy in the registration of multiresolution remote sensing images. In this study, we propose a framework for generating deep features via a deep residual encoder (DRE) fused with shallow features for multiresolution remote sensing image registration. Through an L2 normalization Siamese network (L2-Siamese) based on the DRE, the multiscale loss function is used to learn the attribute characteristics and distance characteristics of two key points and obtain the trained feature extractor. Finally, the DRE is used to extract the deep features of the key points and their neighbors, which are concatenated with the shallow features into a fusion feature vector to complete the image registration. We performed comprehensive experiments on four sets of multiresolution optical remote sensing images and two sets of synthetic aperture radar images. The results demonstrate that the proposed registration model can achieve subpixel registration. The relative registration accuracy improved by 1.6%-7.5%, whereas the overall performance improved by 4.5%-14.1%.

Satellite vertical atmospheric sounding was initiated more than 50 years ago and has evolved to provide the most critical component of today's global observation system. However, the operational use of today's polar orbiting satellite hyperspectral infrared (IR) observations in numerical weather prediction (NWP) has been limited to a small fraction of the radiance information being provided. On the other hand, research systems are in operation that combines high vertical resolution polar hyperspectral radiance measurements with high spatial and time resolution geostationary multispectral radiance measurements that demonstrate the promise of future geo-hyperspectral sounding observations to significantly improve the forecast location and warning time for the development of localized tornadic storms. This article has a twofold objective: 1) to demonstrate that there is much more information available in current IR sounding data, than is being used to benefit the current NWP operation and 2) to illustrate the importance of the spectrometer technology (i.e., Fourier transform vs. dispersive grating) used for achieving the vertical profile resolution required to improve both extended range and localized severe weather forecasts. These objectives are achieved by performing both theoretical physics-based radiance information content (IC) studies and empirical analyses of current hyperspectral radiance measurements. The IC studies clearly demonstrate the unique importance of longwave IR (9−15 μm) radiance observations. The empirical studies demonstrate the importance of using Fourier transform spectrometers for providing the high spectral fidelity needed to resolve the small-scale vertical features in atmospheric temperature and moisture profiles, which impact weather forecast accuracy.

Water turbidity is an important proxy to measure water quality and environmental conditions. Based on extensive field data and Landsat data (2011–2018), this article developed a retrieval model suitable for turbidity. The determination coefficient (R2) of the model was 0.946 and the root-mean-square error was 23.82 NTU. The model was implemented to obtain the turbidity information of hundreds of lakes in Northeast China (1984–2018). The results revealed the distinctive spatial pattern of water turbidity values of the lakes (i.e., high turbidity in the south; low turbidity in the northwest; and moderate turbidity in the east). In terms of temporal pattern, the water turbidity values of most lakes trended downward at an average rate of 1.39 NTU/a (P < 0.05) with obvious seasonal differences (i.e., decreased from May to the lowest in July, and then increased from September onward). Finally, we quantitatively examined how several typical factors affect turbidity variation at different scales. We found that water turbidity was highly correlated with NDVI (R = 0.56, P < 0.001), followed by water temperature and wind speed (0.02 < R < 0.5, P < 0.05). Water turbidity varies because of the interaction of multiple factors (e.g., area, temperature, water depth, precipitation, and land use) instead of one factor. This article highlights the potential of remote sensing in large-scale and long-term monitoring of lake water quality, and provides important information and support for water quality management in China.

Aerosols are a critical component of the climate system and a risk to human health. Here, the lockdown response to the coronavirus outbreak is used to analyse effects of dramatic reduction in anthropogenic aerosol sources on satellite-retrieved aerosol optical depth (AOD). A machine learning model is applied to estimate daily AOD during the initial lockdown in China in early 2020. The model uses information on aerosol climatology, geography and meteorological conditions, and explains 69% of the day-to-day AOD variability. A comparison of model-expected and observed AOD shows that no clear, systematic decrease in AOD is apparent during the lockdown in China. During March 2020, regional AOD is observed to be significantly lower than expected by the machine learning model in some coastal regions of the North China Plains and extending to the Korean peninsula. While this may possibly indicate a small lockdown effect on regional AOD, and potentially pointing trans-boundary effects of the lockdown measures, due to uncertainties associated with the method and the limited sample sizes, this AOD decrease cannot be unequivocally attributed to reduced anthropogenic emissions. Climatologically expected AOD is compared to a weather-adjusted expectation of AOD, indicating that meteorological influences have acted to significantly increase AOD during this time, in agreement with recent literature. The findings highlight the complexity of aerosol variability and the challenges of observation-based attribution of columnar aerosol changes.

Quantitative diagnosis and analysis of the cloud structure characteristics of northward typhoons is of great significance for forecasting. In the present study, sixteen northward typhoons occurred during 2011—2020 are selected to define the North-South symmetry factor S1 and the one-three quadrant symmetry factor S2 as the indexes to reflect the cloud structure of northward typhoons. When S1<0, there were usually more clouds on the south and southwest sides of the typhoons, which were often connected with the monsoon system, and the typhoons generally showed a“9”shaped structure. When S1>0, there were more clouds on the north and northeast sides of the typhoons, the cloud systems on the south side of the typhoons weakened, and the typhoons generally showed a“6”shaped structure. During the northward movement of the typhoons, S1 transited from <0 to >0, and the “9” shaped structure turned to a “6” shaped structure. At the same time, the convective cloud in the third quadrant decayed and the total cloud amount decreased. The one-three quadrant symmetry factor S2 became greater than 0 and increased rapidly, and the typhoons weakened rapidly. The North-South symmetry factor and the one-three quadrant symmetry factor are two important indexes for the diagnosis and analysis of the structure of the cloud systems of northward typhoons and they constitute a quantitative reference for the monitoring and forecasting of typhoon intensity and rainfall. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Offshore aquaculture is critical for a marine fishery economy. The spatial distribution of aquaculture that characterizes it plays a vital role in the sustainable development of marine resources and the protection of the marine environment. In recent years, China's aquaculture has developed rapidly; specifically, the scale of aquaculture has dramatically expanded, and large-scale aquaculture has gradually grown in popularity. Although high-resolution satellite data can accurately extract aquaculture areas, the extraction of a large area of the sea area requires a copious amount of data. In contrast, medium-resolution satellite images allow for the extraction of aquaculture areas from large sea areas with a smaller amount of data, offering significant advantages. Therefore, we used Landsat8 satellite data to extract and count the number of aquaculture rafts based on the Hough transform and Canny edge detection methods. We tested the accuracy of this method by selecting Haizhou Bay as the study area for the experiment and accuracy verification and found that the automatic extraction accuracy for the number of aquaculture rafts was more than 90%. Additionally, we calculated statistics on the number of aquaculture rafts in Haizhou Bay over the past seven years. The findings presented in this paper offer a significant reference value for local marine utilization, marine environment protection, and marine disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Rapid urbanization and industrialization lead to severe air pollution in China, threatening public health. However, it is challenging to understand the pollutants' spatial distributions by relying on a network of ground-based monitoring instruments, considering the incomplete dataset. To predict the spatial distribution of fine-mode particulate matter (PM2.5) pollution near the surface, we established models based on the back propagation (BP) neural network for PM2.5 mass concentration in North China using remote sensing products. According to our predictions, PM2.5 mass concentrations are affected by changes in surface reflectance and the dominant particle size for different seasons. The PM2.5 mass concentration predicted by the seasonal model shows a similar spatial pattern (high in the east but low in the west) influenced by the terrain, but shows high value in winter and low in summer. Compared to the ground-based data, our predictions agree with the spatial distribution of PM2.5 mass concentrations, with a mean bias of +17% in the North China Plain in 2017. Furthermore, the correlation coefficients (R) of the four seasons' instantaneous measurements are always above 0.7, indicating that the seasonal models primarily improve the PM2.5 mass concentration prediction. [ABSTRACT FROM AUTHOR]

In recent decades, the economy of China has developed rapidly, but this has brought widespread damage to the environment, which forces us to explore a sustainable, green, economic development model. Therefore, it is particularly necessary to clarify the relationship between economic development and environmental pollution. In this paper, we used satellite remote sensing tropospheric NO2 vertical column density (VCD) as an air quality indicator; the total exports, total imports, and industrial electricity consumption as the economic indicators; and the wind speed, temperature, and planetary boundary layer height as the meteorological factors to perform a Generalized Additive Modeling (GAM) analysis. By deducing the influence of meteorological factors, the relationship between economic indicators and the air quality indicator can be determined. When total exports increased by one billion USD (United States Dollar), the tropospheric NO2 VCDs of Nanjing and Suzhou increased by about 15% and 6%, respectively. The tropospheric NO2 VCDs of Suzhou increased by about 5% when the total imports increased by one billion USD. In addition, when the industrial electricity consumption increased by one billion kWh, the tropospheric NO2 VCDs of Nanjing, Suzhou and Xuzhou increased by about 25%, 12%, and 59%, respectively. This study provides a method to quantify the contribution of economic growth to air pollution, which is helpful for better understanding of the relationship between economic development and air quality. [ABSTRACT FROM AUTHOR]

An extreme biomass burning event occurred in the Amazonian rainforest from July through September 2019 due to the extensive wildfires used to clear the land, which allowed for more significant forest burning than previously occurred. In this study, we reclustered the clear-sky ambient aerosols to adapt the black carbon (BC) aerosol retrieval algorithm to Amazonia. This not only isolated the volumetric fraction of BC (fbc) from moderate-resolution imaging spectroradiometer (MODIS) aerosol data, but also facilitated the use of aerosol mixing and scattering models to estimate the absorption properties of smoke plumes. The retrieved MODIS aerosol dataset provided a space perspective on characterizing the aerosol changes and trends of the 2019 pollution event. A very high aerosol optical depth (AOD) was found to affect the source areas continuously, with higher and thus stronger aerosol absorption. These pollutants also affected the atmosphere downwind due to the transport of air masses. In addition, properties of aerosols emitted from the 2019 Amazonian wildfire events visualized a significant year-to-year enhancement, with the averaged AOD at 550 nm increased by 150%. A 200% increase in the aerosol-absorption optical depth (AAOD) at 550 nm was recognized due to the low single-scattering albedo (SSA) caused by the explosive BC emissions during the pollution peak. Further simulations of aerosol radiative forcing (ARF) showed that the biomass-burning aerosols emitted during the extreme Amazonian wildfires event in 2019 forced a significant change in the radiative balance, which not only produced greater heating of the atmospheric column through strong absorption of BC, but also reduced the radiation reaching the top-of-atmosphere (TOA) and surface level. The negative radiative forcing at the TOA and surface level, as well as the positive radiative forcing in the atmosphere, were elevated by ~30% across the whole of South America compared to 2018. These radiative effects of the absorbing aerosol could have the ability to accelerate the deterioration cycle of drought and fire over the Amazonian rainforest. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Optical satellite images of the nearshore water surface offer the possibility to invert water depths and thereby constitute the underlying bathymetry. Depth inversion techniques based on surface wave patterns can handle clear and turbid waters in a variety of global coastal environments. Common depth inversion algorithms require video from shore-based camera stations, UAVs or Xband-radars with a typical duration of minutes and at framerates of 1–2 fps to find relevant wave frequencies. These requirements are often not met by satellite imagery. In this paper, satellite imagery is augmented from a sequence of 12 images of Capbreton, France, collected over a period of ∼1.5 min at a framerate of 1/8 fps by the Pleiades satellite, to a pseudo-video with a framerate of 1 fps. For this purpose, a recently developed method is used, which considers spatial pathways of propagating waves for temporal video reconstruction. The augmented video is subsequently processed with a frequency-based depth inversion algorithm that works largely unsupervised and is openly available. The resulting depth estimates approximate ground truth with an overall depth bias of −0.9 m and an interquartile range of depth errors of 5.1 m. The acquired accuracy is sufficiently high to correctly predict wave heights over the shoreface with a numerical wave model and to find hotspots where wave refraction leads to focusing of wave energy that has potential implications for coastal hazard assessments. A more detailed depth inversion analysis of the nearshore region furthermore demonstrates the possibility to detect sandbars. The combination of image augmentation with a frequency-based depth inversion method shows potential for broad application to temporally sparse satellite imagery and thereby aids in the effort towards globally available coastal bathymetry data. [ABSTRACT FROM AUTHOR]

Arctic sea ice characteristics have been changing rapidly and significantly in the last few decades. Using a long-term time series of sea ice products from satellite observations—the extended AVHRR Polar Pathfinder (APP-x)—trends in sea ice concentration, ice extent, ice thickness, and ice volume in the Arctic from 1982 to 2020 are investigated. Results show that the Arctic has become less ice-covered in all seasons, especially in summer and autumn. Arctic sea ice thickness has been decreasing at a rate of −3.24 cm per year, resulting in an approximate 52% reduction in thickness from 2.35 m in 1982 to 1.13 m in 2020. Arctic sea ice volume has been decreasing at a rate of −467.7 km3 per year, resulting in an approximate 63% reduction in volume, from 27,590.4 km3 in 1982 to 10,305.5 km3 in 2020. These trends are further examined from a new perspective, where the Arctic Ocean is classified into open water, perennial, and seasonal sea ice-covered areas based on sea ice persistence. The loss of the perennial sea ice-covered area is a major factor in the total sea ice loss in all seasons. If the current rates of sea ice changes in extent, concentration, and thickness continue, the Arctic is expected to have ice-free summers by the early 2060s. [ABSTRACT FROM AUTHOR]

Rivers play an important role in water supply, waterway transport, and riverine species habitations. The underwater depth of a river channel is a fundamental geometric element and a key input to studies for the aforementioned and other applications. Traditional in-situ field surveys for flow depth measurements would incur high costs and encounter technical and/or logistical difficulties, particularly for river channels of large extents, very shallow channels, and remote, inaccessible channels. Recent advances in satellite remote sensing of inland water bodies like rivers, streams, and creeks have allowed mapping flow depth in an inexpensive, convenient, and efficient manner. The purpose of this paper is to demonstrate the retrieval of river flow depth from high-resolution (1.2 m) WorldView-3 satellite imagery. The depth retrieval methods are based on the ratio of top-of-atmosphere reflectance between two pairing wavelength bands of multispectral imagery. The originality of the methods lies in using analytical relationships without resorting to ground data of river flow depth for calibration, which improves from previous studies of remote sensing of river flow depth. The methods are successfully applied to mapping the underwater bathymetry of a 26 km reach of the Nicolet River in Quebec, Canada. This study shows the importance of geometric and radiometric corrections to the satellite images. The obtained flow depths using the ratio of reflectance of the red band (630–690 nm) to that of the green band (510–580 nm), among the eight bands in the visible spectrum, agree best with in-situ measurements. This study is perhaps the first use of the analytical approach for mapping river bathymetry. It is feasible to implement the approach to other river channels, with a good potential to reduce the costs and increase the efficiency of mapping river bathymetries. [ABSTRACT FROM AUTHOR]

Global biodiversity losses erode the functioning of our vital ecosystems. Functional diversity is increasingly recognized as a critical link between biodiversity and ecosystem functioning. Satellite earth observation was proposed to address the current absence of information on large-scale continuous patterns of plant functional diversity. This study demonstrates the inference and spatial mapping of functional diversity metrics through satellite remote sensing over a large key biodiversity region (Sabah, Malaysian Borneo, ~53,000 km2) and compares the derived estimates across a land-use gradient as an initial qualitative assessment to test the potential merits of the approach. Functional traits (leaf water content, chlorophyll-a and -b, and leaf area index) were estimated from Sentinel-2 spectral reflectance using a pre-trained neural network on radiative transfer modeling simulations. Multivariate functional diversity metrics were calculated, including functional richness, divergence, and evenness. Spatial patterns of functional diversity were related to land-use data distinguishing intact forest, logged forest, and oil palm plantations. Spatial patterns of satellite remotely sensed functional diversity are significantly related to differences in land use. Intact forests, as well as logged forests, featured consistently higher functional diversity compared to oil palm plantations. Differences were profound for functional divergence, whereas functional richness exhibited relatively large variances within land-use classes. By linking large-scale patterns of functional diversity as derived from satellite remote sensing to land-use information, this study indicated initial responsiveness to broad human disturbance gradients over large geographical and spatially contiguous extents. Despite uncertainties about the accuracy of the spatial patterns, this study provides a coherent early application of satellite-derived functional diversity toward further validation of its responsiveness across ecological gradients. [ABSTRACT FROM AUTHOR]

The atmospheric carbon dioxide (CO2) concentration has increased to more than 405 parts per million (ppm. 1ppm=10-6m/s2) in 2017 due to human activities such as deforestation, land-use change and burning of fossil fuels. Although there is broad scientific consensus on the damaging consequences of the change in climate associated with increasing concentrations of greenhouse gases, fossil CO2 emissions have continued to increase in recent years mainly from rapidly developing economies and China is now the largest emitter of CO2 generating about 30% of all emissions globally. To allow more reliable forecast of the future state of the carbon cycle and to support the efforts for mitigation greenhouse gas emissions, a better understanding of the global and regional carbon budget is needed. Space-based measurements of CO2 can provide the necessary observations with dense coverage and sampling to provide improved constrains on of carbon fluxes and emissions. The Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite (TanSat) was established by the National High Technology Research and Development Program of China with the main objective of monitoring atmospheric CO2 and CO2 fluxes at the regional and global scale. TanSat has been successfully launched in December 2016 and as part of the Dragon programme of ESA and the Ministry of Science and Technology (MOST), a team of researchers from Europe (UK and Finland) and China has evaluated early TanSat data and contrast it against data from the GOSAT mission and models. In this manuscript, we report on retrieval intercomparisons of TanSat data using two different retrieval algorithms, on validation efforts for the Eastern Asia region using GOSAT CO2 data and first assessments of TanSat and GOSAT CO2 data against model calculations using the GEOS-Chem model.

Epidemiological studies have reported the associations of adverse health outcomes with ambient particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5). While these studies have accumulated increasingly refined evidence on PM2.5-health associations, the needs for more advanced PM2.5 exposure models have also grown. For the last two decades, PM2.5 estimation approaches using satellite remote sensing have been developed and advanced, taking advantage of quantitative aerosol data (e.g., aerosol optical depth; AOD), the development of satellite instruments and data retrieval algorithms, and the application of statistical and machine learning techniques. Subsequently, the satellite-based PM2.5 concentrations have contributed to health effect studies by providing spatially resolved exposure estimates of ambient PM2.5 and thus reducing exposure misclassification. This article summarizes previous development and recent advancement of satellite-based PM2.5 exposure assessment in the context of satellite aerosol products and PM2.5 estimation methodologies. Furthermore, this article deals with enhanced satellite capabilities of generating the exposure estimates of PM2.5 composition and time-resolved PM2.5. Finally, the future directions of satellite-based exposure assessment are discussed based on research needs and the satellite remote sensing technology of addressing them.

Soil moisture is an important data which can be used for crop growth estimation, drought prediction, irrigation, and development of hydrological model. However, it is difficult to obtain soil moisture data from inaccessible area or very large area using only general field campaign. For this reason, many soil moisture retrieval algorithms have been developed based on satellite remote sensing technique. It should be noted that both satellite images and ground-based data for the region of interest are required to effectively develop the soil moisture retrieval algorithm using satellite images. Thus, Korea aerospace research institute, KARI, have collected ground-based data containing soil moisture, soil temperature, and crop height in collaboration with the university of Melbourne from wheat cropping fields in Australia which are suitable for the development of soil moisture retrieval algorithm. The ground-based data was collected from wheat cropping fields containing various types of soils for about 7 months from May 2019 to November 2019.

Abstract Aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance are two useful sources of satellite data for estimating surface PM2.5 concentrations. Comparison of PM2.5 estimates between these two approaches remains to be explored. In this study, satellite observations of TOA reflectance and AOD from the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite in 2016 over Yangtze River Delta (YRD) and meteorological data are used to estimate hourly PM2.5 based on four different machine learning algorithms (i.e., random forest, extreme gradient boosting, gradient boosting regression, and support vector regression). For both reflectance-based and AOD-based approaches, our cross validated results show that random forest algorithm achieves the best performance, with a coefficient of determination (R2) of 0.75 and root-mean-square error (RMSE) of 18.71 µg m−3 for the former and R2 = 0.65 and RMSE = 15.69 µg m−3 for the later. Additionally, we find a large discrepancy in PM2.5 estimates between reflectance-based and AOD-based approaches in terms of annual mean and their spatial distribution, which is mainly due to the sampling difference, especially over northern YRD in winter. Overall, reflectance-based approach can provide robust PM2.5 estimates for both annual mean values and probability density function of hourly PM2.5. Our results further show that almost all population lives in non-attainment areas in YRD using annual mean PM2.5 from reflectance-based approach. This study suggests that reflectance-based approach is a valuable way for providing robust PM2.5 estimates and further for constraining health impact assessments.

In summer 2018, thick sea ice blocked the mouth of the Amundsen Gulf (AG), Canada, obstructing shipping through the North-west Passage. This study analysed multi-year ice motion to investigate the source of this thick ice and the reasons for its unusual movement. For this purpose, a daily multi-year ice distribution product was generated by ice tracking using gridded daily sea-ice velocities (2003–2018) derived from the AMSR-E and AMSR-2 data. From autumn 2017 to summer 2018, the area of multi-year ice extended westward to the Beaufort Sea and then migrated towards the AG mouth. The primary cause of the unusual ice cover was anomalous AG-ward wind in September 2018. It is known that multi-year ice has become increasingly moveable over the past decades, as indicated by the increasing wind factor (i.e., ratio of ice-drift speed and wind speed), but the unusual ice motion in the summer of 2018 cannot be explainable by the wind factor alone. Accurately, predicting monthly wind and monitoring old thick ice will reduce the risk posed by thick Arctic sea ice to shipping.

In recent years, revenue protection crop insurance is an innovative insurance that has been prioritized in China. But it still lacks the support of the third-party yield data around crop harvest time. Aiming to provide objective yield data for revenue protection crop insurance, satellite remote sensing production estimation technology was employed to discuss its application mode and applicability. Taking the soybean revenue protection insurance in Jiaxiang county, Shandong province as an example, we first extracted soybean planting plots, calculated vegetation index and crop physiological parameters based on Sentinel-2 satellite images in 2018 . Combining to TRMM precipitation data from TRMM precipitation-monitoring radar satellite and MODIS land surface temperature data from Terra/Aqua satellite and site yield data, we established a multi-parameter linear regression model, and estimated soybean yield per unit area. The crop extraction results showed that the soybean planting area in the study area was 1.24 km2, which was in good agreement with the 1.27 km2 reported by the local agricultural bureau; and with using the actual measurement plots, the remote sensing identification accuracy of the planting distribution plots reached 90%. The yield estimation results showed that the NDVI of the soybean pod stage on August 23 and the leaf area index of the soybean seedling stage on September 7 explained the soybean yield per hectare the best, and the average estimated yield of the whole area was 244,500 kg/m2, which reflects the severely affected agricultural conditions, comparing to 299,800 kg/km2 in previous years.The regression coefficient between the estimated yield data and the measured data reached 0.92, which meet the application needs.With this results, the estimated yield of different towns can be summarized, and the regional yield was present, and was used as the real yield in 2018, multiplying with the average soybean price around October 11 to December 10 from the local price bureau, the real revenue was obtained. Compared the real revenue to the expected revenue in the contract of insurance, the claims work was decided. The results indicated that the Sentinel-2 satellite data could be used to identify the soybean planting distribution in the study area accurately, and to complete the yield estimation as soon as one week after the soybean harvest, which could guide the insurance company's claims work. The whole methodology is capable of aiding the claims work in revenue protection crop insurance.

Mongolia’s forest-steppes are one of the most threatened terrestrial ecosystems, being exposed to multiple stressors, including forest loss, fire, inadequate livestock management, and climate change. This study used satellite remote sensing to assess land cover change and long-term trends in primary productivity and precipitations in the Khoid Mogoin Gol-Teel protected area, Arkhangai province, a region of high conservation relevance rich in forest-steppe ecosystems. To our surprise, forest cover and primary productivity were found to be stable or increasing in our study area. The distribution of water bodies and bare ground were found to be decreasing, while the distribution of croplands and grasslands were found to be increasing. The reported increases in primary productivity were interpreted as a result of increasing precipitation in the region. Our findings are expected to help assist the community-led management in securing economic wellbeing and food security for those depending on forest-steppe habitats. In particular, the reported increase in forest cover indicates that natural regeneration is possible in the area and should therefore be encouraged.

In this article, the potential of Satellite Remote Sensing (SRS) for large-scale monitoring of archaeological sites is analysed. This analysis focuses on the Portuguese Lower Alentejo region, where multiple sites have been destroyed over the last years, mainly due to intensive agriculture. The development of a surveillance system based on data from the Sentinel 1 and Sentinel 2 satellite constellations, named SENSEOS, had its pilot application in this region during the first half of 2019. In a vast region where authorities lacked the proper resources to timely detect events endangering the integrity of heritage sites, this system tried to overcome these issues. By using Synthetic Aperture Radar (SAR) and optical data, it was possible to detect such events through a technique labelled Normalized Difference Amplitude Index (NDAI). The identification of these changes on the surface, associated with potentially destructive events, contributed to minimize the damage and destruction of archaeological sites.

After the COVID-19 pandemic began in 2020, Urumqi, a remote area in northwest China, experienced two lockdowns, in January and July 2020. Based on ground and satellite observations, this study assessed the impacts of these lockdowns on the air quality in Urumqi and the seasonal differences between them. The results showed that, during the wintertime lockdown, PM10, PM2.5, NO2, CO, and SO2 levels decreased by 38, 40, 45, 27, 8%, respectively, whereas O3 concentrations increased by 113%. During the summer lockdown, PM10, PM2.5, NO2, CO, and SO2 levels decreased by 39, 24, 59, 2, and 13%, respectively, and the O3 concentrations increased by 21%. During the lockdowns, the NO2 concentrations decreased by 53% in winter and 13% in summer in the urban areas, whereas they increased by 23% in winter and 9% in summer in the suburbs. Moreover, large seasonal differences were observed between winter and summer SO2, CO, and O3. The lockdown played a vital role in the rapid decline of primary air pollutant concentrations, along with fewer meteorological impacts on air pollution changes in this area. The increase in O3 concentrations during the COVID-19 lockdowns reflects the complexity of air quality changes during reductions in air pollutant emissions.

The EPIC/DSCOVR observations of the Earth’s surface lit by the Sun made from the first Lagrange point several times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean photosynthetically available radiation (PAR) at the ice-free ocean surface. The PAR algorithm uses a budget approach, in which the solar irradiance reaching the surface is obtained by subtracting from the irradiance arriving at the top of the atmosphere (known), the irradiance reflected to space (estimated from the EPIC Level 1b radiance data), taking account of atmospheric transmission and surface albedo (modeled). Clear and cloudy regions within a pixel do not need to be distinguished, which dismisses the need for often-arbitrary assumptions about cloudiness distribution within a pixel and is therefore adapted to the relatively large EPIC pixels. A daily mean PAR is estimated on the source grid for each EPIC instantaneous daytime observation, assuming no cloudiness changes during the day, and the individual estimates are remapped and weight-averaged using the cosine of the Sun zenith angle. In the computations, wind speed, surface pressure, and water vapor amount are extracted from NCEP Reanalysis 2 data, aerosol optical thickness and Angström coefficient from MERRA-2 data, and ozone amount from EPIC Level 2 data. Areas contaminated by Sun glint are excluded using a threshold on Sun glint reflectance calculated using wind data. Ice masking is based on NSIDC near-real-time ice fraction data. The product is evaluated against in situ measurements at various locations and compared with estimates from sensors in polar and geostationary orbits (MODIS, AHI). Unlike with MODIS, the EPIC PAR product does not exhibit gaps at low and middle latitudes. Accuracy is satisfactory for long-term studies of aquatic photosynthesis, especially given the much larger uncertainties on the fraction of PAR absorbed by live algae and the quantum yield of carbon fixation. The EPIC daily mean PAR product is generated operationally on a Plate Carrée (equal-angle) grid with 18.4 km resolution at the equator and on an 18.4 km equal-area grid, i.e., it is fully compatible with the NASA Greenbelt OBPG ocean-color products. Data are available since the beginning of the DSCOVR mission (i.e., June 2015) from the NASA Langley ASDC website.

Background: Yellow rust (Puccinia striiformis f. sp. tritici) is a devastating hazard to wheat production, which poses a serious threat to yield and food security in the main wheat-producing areas in eastern China. It is necessary to monitor yellow rust progression during spring critical wheat growth periods to support its prediction by providing timely calibrations for disease prediction models and timely green prevention and control., Results: Three Sentinel-2 images for the disease during the three wheat growth periods (jointing, heading, and filling) were acquired. Spectral, texture, and color features were all extracted for each growth period disease. Then three period-specific feature sets were obtained. Given the differences in field disease epidemic status in the three periods, three period-targeted monitoring models were established to map yellow rust damage progression in spring and track its spatiotemporal change. The models' performance was then validated based on the disease field truth data during the three periods (87 for the jointing period, 183 for the heading period, and 155 for the filling period). The validation results revealed that the representation of the wheat yellow rust damage progression based on our monitoring model group was realistic and credible. The overall accuracy of the healthy and diseased pixel classification monitoring model at the jointing period reached 87.4%, and the coefficient of determination (R 2 ) of the disease index regression monitoring models at the heading and filling periods was 0.77 (heading period) and 0.76 (filling period). The model-group-result-based spatiotemporal change detection of the yellow rust progression across the entire study area revealed that the area proportions conforming to the expected disease spatiotemporal development pattern during the jointing-to-heading period and the heading-to-filling period reached 98.2% and 84.4% respectively., Conclusions: Our jointing, heading, and filling period-targeted monitoring model group overcomes the limitations of most existing monitoring models only based on single-phase remote sensing information. It performs well in revealing the wheat yellow rust spatiotemporal epidemic in spring, can timely update disease trends to optimize disease management, and provide a basis for disease prediction to timely correct model. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

Based on Ozone （O 3 ） Monitoring Instrument satellite remoting sensing data and reanalysis meteorological data, an analysis was conducted on the spatiotemporal distribution and trends of tropospheric nitrogen dioxide （NO 2 ） and formaldehyde （HCHO） in Guangdong Province from 2015 to 2020. The study also examined the changing characteristics of O 3 generation sensitivity in relation to meteorological parameters. The results indicated that during 2015-2020, the tropospheric NO 2 column concentration in Guangdong Province exhibited a distribution pattern with the Pearl River Delta （PRD） Region as the high-value center gradually decreasing towards the surrounding areas, with the most substantial decline observed in the Pearl River Delta. The HCHO column concentration showed a pattern of higher values in the PRD and lower values in the other regions. While most areas exhibited a decreasing trend in HCHO column concentration, the spatial distribution was more dispersed compared to that of NO 2 . In most regions, the ratio of HCHO to NO 2 （FNR） showed an increasing trend. The area under VOCs-NO x synergistic control decreased, whereas the area under NO x control increased. By 2020, the proportions of VOCs control area, VOCs-NO x synergistic control area, and NO x control area in Guangdong Province were 0.5%, 13.1%, and 86.4%, respectively. The FNR value showed an increasing trend； a positive correlation with 2 m temperature, relative humidity, downward surface shortwave radiation, and boundary layer height； and a negative correlation with 10 m wind speed and sea level pressure. The three most influential meteorological factors were downward surface shortwave radiation（DSSR）, relative humidity, and 2 m temperature, impacting the FNR value the most.