Satellite Application Center for Ecology and Environment

In response to ecosystem degradation from rapid economic development, China began investing heavily in protecting and restoring natural capital starting in 2000. We report on China's first national ecosystem assessment (2000-2010), designed to quantify and help manage change in ecosystem services, including food production, carbon sequestration, soil retention, sandstorm prevention, water retention, flood mitigation, and provision of habitat for biodiversity. Overall, ecosystem services improved from 2000 to 2010, apart from habitat provision. China's national conservation policies contributed significantly to the increases in those ecosystem services.

Abstract. Air quality observations by satellite instruments are global and have a regular temporal resolution, which makes them very useful in studying long-term trends in atmospheric species. To monitor air quality trends in China for the period 2005–2015, we derive SO2 columns and NOx emissions on a provincial level with improved accuracy. To put these trends into perspective they are compared with public data on energy consumption and the environmental policies of China. We distinguish the effect of air quality regulations from economic growth by comparing them relatively to fossil fuel consumption. Pollutant levels, per unit of fossil fuel, are used to assess the effectiveness of air quality regulations. We note that the desulfurization regulations enforced in 2005–2006 only had a significant effect in the years 2008–2009, when a much stricter control of the actual use of the installations began. For national NOx emissions a distinct decreasing trend is only visible from 2012 onwards, but the emission peak year differs from province to province. Unlike SO2, emissions of NOx are highly related to traffic. Furthermore, regulations for NOx emissions are partly decided on a provincial level. The last 3 years show a reduction both in SO2 and NOx emissions per fossil fuel unit, since the authorities have implemented several new environmental regulations. Despite an increasing fossil fuel consumption and a growing transport sector, the effects of air quality policy in China are clearly visible. Without the air quality regulations the concentration of SO2 would be about 2.5 times higher and the NO2 concentrations would be at least 25 % higher than they are today in China.

Abstract. The COVID-19 pandemic lockdowns led to a sharp drop in socio-economic activities in China in 2020, including reductions in fossil fuel use, industry productions, and traffic volumes. The short-term impacts of lockdowns on China's air quality have been measured and reported, however, the changes in anthropogenic emissions have not yet been assessed quantitatively, which hinders our understanding of the causes of the air quality changes during COVID-19. Here, for the first time, we report the anthropogenic air pollutant emissions from mainland China by using a bottom-up approach based on the near-real-time data in 2020 and use the estimated emissions to simulate air quality changes with a chemical transport model. The COVID-19 lockdown was estimated to have reduced China's anthropogenic emissions substantially between January and March in 2020, with the largest reductions in February. Emissions of SO2, NOx, CO, non-methane volatile organic compounds (NMVOCs), and primary PM2.5 were estimated to have decreased by 27 %, 36 %, 28 %, 31 %, and 24 %, respectively, in February 2020 compared to the same month in 2019. The reductions in anthropogenic emissions were dominated by the industry sector for SO2 and PM2.5 and were contributed to approximately equally by the industry and transportation sectors for NOx, CO, and NMVOCs. With the spread of coronavirus controlled, China's anthropogenic emissions rebounded in April and since then returned to the comparable levels of 2019 in the second half of 2020. The provinces in China have presented nearly synchronous decline and rebound in anthropogenic emissions, while Hubei and the provinces surrounding Beijing recovered more slowly due to the extension of lockdown measures. The ambient air pollution presented much lower concentrations during the first 3 months in 2020 than in 2019 while rapidly returning to comparable levels afterward, which have been reproduced by the air quality model simulation driven by our estimated emissions. China's monthly anthropogenic emissions in 2020 can be accessed from https://doi.org/10.6084/m9.figshare.c.5214920.v2 (Zheng et al., 2021) by species, month, sector, and province.

The water index (WI) is designed to highlight inland water bodies in remotely sensed imagery. The application of WI for water body mapping is mainly based on the thresholding method. However, there are three primary difficulties with this method: (1) inefficient identification of mixed water pixels; (2) confusion of water bodies with background noise; and (3) variation in the threshold values according to the location and time of image acquisitions. Considering that mixed water pixels usually appear in narrow rivers or shallow water at the edge of lakes or wide rivers, an automated method is proposed for extracting rivers and lakes by combining the WI with digital image processing techniques to address the above issues. The data sources are the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) images for three representative areas in China. The results were compared with those from existing thresholding methods. The robustness of the new method in combination with different WIs is also assessed. Several metrics, which include the Kappa coefficient, omission and commission errors, edge position accuracy and completeness, were calculated to assess the method’s performance. The new method generally outperformed the thresholding methods, although the degree of improvement varied among WIs. The advantages and limitations of the proposed method are also discussed.

Abstract Accurate estimation of the satellite‐based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite‐based global terrestrial LE estimation by merging five process‐based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product algorithm, the revised remote‐sensing‐based Penman‐Monteith LE algorithm, the Priestley‐Taylor‐based LE algorithm, the modified satellite‐based Priestley‐Taylor LE algorithm, and the semi‐empirical Penman LE algorithm. We validated the BMA method using data for 2000–2009 and by comparison with a simple model averaging (SA) method and five process‐based algorithms. Validation data were collected for 240 globally distributed eddy covariance tower sites provided by FLUXNET projects. The validation results demonstrate that the five process‐based algorithms used have variable uncertainty and the BMA method enhances the daily LE estimates, with smaller root mean square errors (RMSEs) than the SA method and the individual algorithms driven by tower‐specific meteorology and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data provided by the NASA Global Modeling and Assimilation Office (GMAO), respectively. The average RMSE for the BMA method driven by daily tower‐specific meteorology decreased by more than 5 W/m 2 for crop and grass sites, and by more than 6 W/m 2 for forest, shrub, and savanna sites. The average coefficients of determination ( R 2 ) increased by approximately 0.05 for most sites. To test the BMA method for regional mapping, we applied it for MODIS data and GMAO‐MERRA meteorology to map annual global terrestrial LE averaged over 2001–2004 for spatial resolution of 0.05°. The BMA method provides a basis for generating a long‐term global terrestrial LE product for characterizing global energy, hydrological, and carbon cycles.

This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance–variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

Abstract. The contribution of meteorology and emissions to long-term PM2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used the combination of a machine learning model, statistical method, and chemical transport model to quantify the meteorological impacts on PM2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods to examine the meteorological contribution to PM2.5: a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM2.5 retrievals and the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. We found good agreements between GAM estimations and the CMAQ model estimations of the meteorological contribution to PM2.5 on a monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PM2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and when haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM2.5 pollution across the North China Plain and central China but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PM2.5 and gas precursors in these years. Although emissions dominated the long-term PM2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., −6.3 % to 4.9 % of the annual mean PM2.5 concentrations in the Beijing-Tianjin-Hebei region, −5.1 % to 4.3 % in the Fenwei Plain, −4.8 % to 4.3 % in the Yangtze River Delta, and −25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM2.5 and the possible worsening trend of meteorological conditions in the northern part of China where air pollution is severe and population is clustered, stricter clean air actions are needed to avoid haze events in the future.

The impact of atmospheric vapor pressure deficit (VPD) on plant photosynthesis has long been acknowledged, but large interactions with air temperature (T) and soil moisture (SM) still hinder a complete understanding of the influence of VPD on vegetation production across various climate zones. Here, we found a diverging response of productivity to VPD in the Northern Hemisphere by excluding interactive effects of VPD with T and SM. The interactions between VPD and T/SM not only offset the potential positive impact of warming on vegetation productivity but also amplifies the negative effect of soil drying. Notably, for high-latitude ecosystems, there occurs a pronounced shift in vegetation productivity's response to VPD during the growing season when VPD surpasses a threshold of 3.5 to 4.0 hectopascals. These results yield previously unknown insights into the role of VPD in terrestrial ecosystems and enhance our comprehension of the terrestrial carbon cycle's response to global warming.

The purpose of this study was to assess soil heavy metal contamination and the potential risk for local residents in Suxian county of Hunan Province, southern China. Soil, rice and vegetable samples from the areas near the mining industrial districts were sampled and analyzed. The results indicate that the anthropogenic mining activities have caused local agricultural soil contamination with As, Pb, Cu and Cd in the ranges of 8.47-341.33 mg/kg, 19.91-837.52 mg/kg, 8.41-148.73 mg/kg and 0.35-6.47 mg/kg, respectively. GIS-based mapping shows that soil heavy metal concentrations abruptly diminish with increasing distance from the polluting source. The concentrations of As, Pb, Cu and Cd found in rice were in the ranges of 0.02-1.48 mg/kg, 0.66-5.78 mg/kg, 0.09-6.75 mg/kg, and up to 1.39 mg/kg, respectively. Most of these concentrations exceed their maximum permissible levels for contaminants in foods in China. Heavy metals accumulate to significantly different levels between leafy vegetables and non-leafy vegetables. Food consumption and soil ingestion exposure are the two routes that contribute to the average daily intake dose of heavy metals for local adults. Moreover, the total hazard indices of As, Pb and Cd are greater than or close to the safety threshold of 1. Long-term As, Pb and Cd exposure through the regular consumption of the soil, rice and vegetables in the investigated area poses potential health problems to residents in the vicinity of the mining industry.

The Chinese High-resolution Earth Observation System (CHEOS) program has successfully launched 7 civilian satellites since 2010. These satellites are named by Gaofen (meaning high resolution in Chinese, hereafter noted as GF). To combine the advantages of high temporal and comparably high spatial resolution, diverse sensors are deployed to each satellite. GF-1 and GF-6 carry both high-resolution cameras (2 m resolution panchromatic and 8 m resolution multispectral camera), providing high spatial imaging for land use monitoring; GF-3 is equipped with a C-band multipolarization synthetic aperture radar with a spatial resolution of up to 1 meter, mostly monitoring marine targets; GF-5 carried 6 sensors including hyperspectral camera and directional polarization camera, dedicated to environmental remote sensing and climate research, such as aerosol, clouds, and greenhouse gas monitoring; and GF-7 laser altimeter system payload enables a three-dimensional surveying and mapping of natural resource and land surveying, facilitating the accumulation of basic geographic information. This study provides an overview of GF civilian series satellites, especially their missions, sensors, and applications.

Abstract Global industrialization and urbanization processes enabled a diverse cement production boom over the past three decades, as cement is the most important building construction material. Consequently, the cement industry is the second-largest industrial CO 2 emitter (∼25% of global industrial CO 2 emissions) globally. In this study, the Global Cement Emission Database, which encompasses anthropogenic CO 2 emissions of individual production units worldwide for 1990–2019, was developed. A recently developed unit-level China Cement Emission Database was then applied to override China’s data and the combination of two databases is used to reveal the unit characteristics of CO 2 emissions and ages for global cement plants, assess large disparities in national and regional CO 2 emissions, growth rates and developmental stages from 1990–2019, and identify key emerging countries of carbon emissions and commitment. This study finds that globally, CO 2 emissions from the cement industry have increased from 0.86 Gt in 1990 to 2.46 Gt in 2019 (increasing by 186%). More importantly, the large CO 2 emissions and the striking growth rates from those emerging countries, including most of the developing countries in the Asia region and the Middle East and Africa region, are clearly identified. For example, the Middle East and Africa, including mostly developing or underdeveloped countries, only represented 0.07 Gt CO 2 in 1990 (8.4% of the total), in contrast to 0.26 Gt (10.4% of the total) CO 2 in 2019, which is a 4.5% average growth rate during 1990–2019. Further, the intensive expansion of large and new facilities since 2005 in Asia and the Middle East and Africa has resulted in heavy commitment (90.1% of global commitment in 2019), and mitigation threats in the future considering their increasing emissions (the national annual growth rate can be up to >80%) and growing infrastructure construction (∼50% of clinker capacity operating ⩽10 years). Our results highlight the cement industry’s development and young infrastructure in emerging economies; thus, future increasing cement demand and corresponding carbon commitment would pose great challenges to future decarbonization and climate change mitigation.

Abstract. Formaldehyde (HCHO) in the ambient air not only causes cancer but is also an ideal indicator of volatile organic compounds (VOCs), which are major precursors of ozone (O3) and secondary organic aerosol (SOA) near the surface. It is meaningful to differentiate between the direct emission and the secondary formation of HCHO for HCHO pollution control and sensitivity studies of O3 production. However, understanding of the sources of HCHO is still poor in China, due to the scarcity of field measurements (both spatially and temporally). In this study, tropospheric HCHO vertical column densities (VCDs) in the Yangtze River Delta (YRD), East China, where HCHO pollution is serious, were retrieved from the Ozone Mapping and Profiler Suite (OMPS) onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite from 2014 to 2017; these retrievals showed good agreement with the tropospheric HCHO columns measured using ground-based high-resolution Fourier transform infrared spectrometry (FTS) with a correlation coefficient (R) of 0.78. Based on these results, the cancer risk was estimated both nationwide and in the YRD region. It was calculated that at least 7840 people in the YRD region would develop cancer in their lives due to outdoor HCHO exposure, which comprised 23.4 % of total national cancer risk. Furthermore, the contributions of primary and secondary sources were apportioned, in addition to primary and secondary tracers from surface observations. Overall, the HCHO from secondary formation contributed most to ambient HCHO and can be regarded as the indicator of VOC reactivity in Hangzhou and in urban areas of Nanjing and Shanghai from 2015 to 2017, due to the strong correlation between total HCHO and secondary HCHO. At industrial sites in Nanjing, primary emissions more strongly influenced ambient HCHO concentrations in 2015 and showed an obvious decreasing trend. Seasonally, HCHO from secondary formation reached a maximum in summer and a minimum in winter. In the spring, summer, and autumn, secondary formation had a significant effect on the variation of ambient HCHO in urban regions of Nanjing, Hangzhou, and Shanghai, whereas in the winter the contribution from secondary formation became less significant. A more thorough understanding of the variation of the primary and secondary contributions of ambient HCHO is needed to develop a better knowledge regarding the role of HCHO in atmospheric chemistry and to formulate effective control measures to decrease HCHO pollution and the associated cancer risk.

Urban greening can enhance quality of life by generating ecosystem services and has been proposed as a way of mitigating adverse consequences of global warming for human health. However, there is limited knowledge on global trends in urban vegetation and their relation to economic development and climate change. Here we studied 1,688 major cities worldwide and show that 70% (1,181) show an increase in vegetation derived from satellite observations (2000–2018). For 68% (1,138) of the cities studied, the increase in the urban vegetation is less strong as compared to the vegetation increase found in the surroundings of these cities. Overall, positive vegetation trends are widely observed in cities in Europe and North America, whereas negative vegetation trends in cities occur primarily in Africa, South America and Asia. Gross Domestic Product growth, population growth as well as temperature are found to be the main underlying drivers of the observed contrasts in changes in urban vegetation as compared to surrounding areas across continents. From a global synthesis of urban vegetation change, we quantify the role of social-economic development and climate change in regulating urban vegetation growth, and the contrasting imprint on cities of developed and developing countries.

The distribution of urban impervious surface is a significant indicator of the degree of urbanization, as well as a major indicator of environmental quality. Hence, taking advantage of remotely sensed imagery to map impervious surface has become an important topic. Spectral indices have been developed due to its convenience to apply, among which feature extraction approach has shown superiority in reliability and applicability. However, impervious surface is often confused with bare soil when the current existing indices are used as well as their sensor-specific limitations. In this study, a new index, combinational build-up index (CBI), is proposed to extract impervious surface. The new index combines the first component of a principal component analysis (PC1), normalized difference water index (NDWI), and soil-adjusted vegetation index (SAVI), representing high albedo, low albedo, and vegetation, respectively, to reduce the original bands into three thematic-oriented features. The new index was tested using various remote sensing images at different spectral and spatial resolutions. Qualitative and quantitative assessments of the accuracy and separability of CBI, together with the comparison with other existing indices, were performed. The result of this study indicates that the proposed method is able to serve as an effective impervious index and can be applied widely.

Based on the principles of sustainable development, land use planning often requires the compromise between economic development and environmental conservation while advocating social justice. Given that ideas, values, and attitudes vary among the stakeholders involved, land use planning inevitably incurs a variety of conflicts. The conflicts in land use planning can be described from the perspective of the conflicts among land use types and the conflicts among stakeholders. Accordingly, land use planning can be conceived as the process of dealing with conflicts among different land use types through resolving the conflicts among stakeholders. This study centers around two important issues in land use planning: land use allocation and specific land use proposal deliberation. A Conflict Resolution Framework was proposed based on GIS and Multi-criteria Decision Analysis techniques. A Consensus Building Model was established to address the conflicts among different stakeholders with competing interests in the process of land use allocation. A Spatial Conflict Resolution Strategy was developed to help stakeholders and planners formulate specific land use proposals through an iterative modification process. The both models were tested and evaluated in the context of Lantau, Island Hong Kong. Moreover, the challenges of this research and future work are also covered in this paper.

Rapid urbanization and economic development inevitably lead to light pollution, which has become a universal environmental issue. In order to reveal the spatiotemporal patterns and evolvement rules of light pollution in China, images from 1992 to 2012 were selected from the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and systematically corrected to ensure consistency. Furthermore, we employed a linear regression trend method and nighttime light index method to demonstrate China’s light pollution characteristics across national, regional, and provincial scales, respectively. We found that: (1) China’s light pollution expanded significantly in provincial capital cities over the past 21 years and hot-spots of light pollution were located in the eastern coastal region. The Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei regions have formed light pollution stretch areas; (2) China’s light pollution was mainly focused in areas of north China (NC) and east China (EC), which, together, accounted for over 50% of the light pollution for the whole country. The fastest growth of light pollution was observed in northwest China (NWC), followed by southwest China (SWC). The growth rates of east China (EC), central China (CC), and northeast China (NEC) were stable, while those of north China (NC) and south China (SC) declined; (3) Light pollution at the provincial scale was mainly located in the Shandong, Guangdong, and Hebei provinces, whereas the fastest growth of light pollution was in Tibet and Hainan. However, light pollution levels in the developed provinces (Hong Kong, Macao, Shanghai, and Tianjin) were higher than those of the undeveloped provinces. Similarly, the light pollution heterogeneities of Taiwan, Beijing, and Shanghai were higher than those of undeveloped western provinces.

Among the diverse graphitic carbon nitride (CN) nanostructures, CN nanotubes (CNNTs) are currently attracting increasing interest due to the appealing properties of CN and the geometric advantages of one-dimensional (1D) nanotubes.

This study presents a spectral–spatial self-attention network (SSSAN) for classification of hyperspectral images (HSIs), which can adaptively integrate local features with long-range dependencies related to the pixel to be classified. Specifically, it has two subnetworks. The spatial subnetwork introduces the proposed spatial self-attention module to exploit rich patch-based contextual information related to the center pixel. The spectral subnetwork introduces the proposed spectral self-attention module to exploit the long-range spectral correlation over local spectral features. The extracted spectral and spatial features are then adaptively fused for HSI classification. Experiments conducted on four HSI datasets demonstrate that the proposed network outperforms several state-of-the-art methods.

Land surface temperature (LST) is one of the key parameters in hydrology, meteorology, and the surface energy balance. The National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm is adapted to Landsat-8 data to obtain the estimate of LST. The coefficients of the Enterprise algorithm were obtained by linear regression using the analog data produced by comprehensive radiative transfer modeling. The performance of the Enterprise algorithm was first tested by simulation data and then validated by ground measurements. In addition, the accuracy of the Enterprise algorithm was compared to the generalized split-window algorithm and the split-window algorithm of Sobrino et al. (1996). The validation results indicate the Enterprise algorithm has a comparable accuracy to the other two split-window algorithms. The biases (root mean square errors) of the Enterprise algorithm were 1.38 (3.22), 1.01 (2.32), 1.99 (3.49), 2.53 (3.46), and −0.15 K (1.11 K) at the SURFRAD, HiWATER_A, HiWATER_B, HiWATER_C sites and BanGe site, respectively, whereas those values were 1.39 (3.20), 1.0 (2.30), 1.93 (3.48), 2.53 (3.35), and −0.35 K (1.16 K) for the generalized split-window algorithm, 1.45 (3.39), 1.08 (2.41), 2.16 (3.67), 2.52 (3.58), and 0.02 K (1.12 K) for the split-window algorithm of Sobrino, respectively. This study provides an alternative method to estimate LST from Landsat-8 data.