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Plasma bubbles represent notable ionospheric irregularities primarily observed in low latitudes, characterized by plasma depletions exhibiting large spatial scales, which can make a significant impact on the propagation of OTHR (over-the-horizon radar) waves. Firstly, we constructed a three-dimensional model of plasma bubbles, which is modulated by Gaussian function distribution in the horizontal direction, and then we analyzed the impact of EPBs (Equatorial Plasma Bubbles) on the ray path of OTHR shortwaves. When radio waves propagate through EPBs with different RMS ΔN/N, there is a significant difference in the propagation path of OTHR waves. For the EPB with an RMS ΔN/N of 75%, radio waves exhibit more pronounced refraction than those with lower RMS values, the focusing effect of radio waves is more obvious, and the focusing point is relatively lower. In terms of different seasons, OTHR shortwaves propagating through EPBs exhibit different degrees of refraction. In addition, radio waves show the effect of inward focusing in different seasons: the focusing effect is the most pronounced in spring, followed by autumn, then summer, and the weakest in winter. For different solar activities, the impact of EPBs on OTHR shortwaves is more significant in the high-solar-activity year.

Satellite-derived bathymetry (SDB) is an effective means of obtaining global shallow water depths. However, the effect of inherent optical properties (IOPs) on the accuracy of SDB under different water quality conditions has not been clearly clarified. To enhance the accuracy of machine learning SDB models, this study aims to assess the performance improvement of integrating the quasi-analytical algorithm (QAA)-derived IOPs using the Sentinel-2 and ICESat-2 datasets. In different water quality experiments, the results indicate that four SDB models (the Gaussian process regression, neural networks, random forests, and support vector regression) incorporating QAA-IOP parameters equal to or outperform those solely based on the remote sensing reflectance (Rrs) datasets, especially in turbid waters. By analyzing information gains in SDB, the most effective inputs are identified and prioritized under different water qualities. The SDB method incorporating QAA-IOP can achieve an accuracy of 0.85 m, 0.48 m, and 0.74 m in three areas (Wenchang, Laizhou Bay, and the Qilian Islands) with different water quality. Also, we find that incorporating an excessive number of redundant bands into machine learning models not only increases the demand of computing resources but also leads to worse accuracy in SDB. In conclusion, the integration of QAA-IOPs offers promising improvements in obtaining bathymetry and the optimal feature selection should be carefully considered in diverse aquatic environments.

High-resolution and high-accuracy mapping products of the Lunar South Pole (LSP) will play a vital role in future lunar exploration missions. Existing lunar global mapping products cannot meet the needs of engineering tasks, such as landing site selection and rover trajectory planning, at the LSP. The Lunar Reconnaissance Orbiter (LRO)’s narrow-angle camera (NAC) can acquire submeter images and has returned a large amount of data covering the LSP. In this study, we combine stereo-photogrammetry and photoclinometry to generate high-resolution digital orthophoto maps (DOMs) and digital elevation models (DEMs) using LRO NAC images for a candidate landing site at the LSP. The special illumination and landscape characteristics of the LSP make the derivation of high-accuracy mapping products from orbiter images extremely difficult. We proposed an easy-to-implement shadow recognition and contrast stretching method based on the histograms of the LRO NAC images, which is beneficial for photogrammetric and photoclinometry processing. In order to automatically generate tie points, we designed an image matching method considering LRO NAC images’ features of long strips and large data volumes. The terrain and smoothness constraints were introduced into the cost function of photoclinometry adjustment, excluding pixels in shadow areas. We used 61 LRO NAC images to generate mapping products covering an area of 400 km2. The spatial resolution of the generated DOMs was 1 m/pixel, and the grid spacing of the derived DEMs was 1 m (close to the spatial resolution of the original images). The generated DOMs achieved a relative accuracy of better than 1 pixel. The geometric accuracy of the DEM derived from photoclinometry was consistent with the lunar orbiter laser altimeter (LOLA) DEM with a root mean square error of 0.97 m and an average error of 0.17 m.

Temperature inversion plays an important role in the accumulation and diffusion of aerosols. In this study, the relationship between temperature inversion and the vertical distribution of aerosols is investigated based on Raman lidar observations taken from January 2010 to September 2015 at the Atmospheric Radiation Measurement site in the Southern Great Plains, USA. First, the diurnal and seasonal variations of the surface-based inversion (SBI) and elevated temperature inversion (EI) are investigated. The results indicate that the occurrence frequency of SBI and EI have different seasonal trends. SBI has the highest frequency in summer, while EI has the highest frequency in winter. The diurnal variation of SBI is obvious, with a higher frequency in nighttime and a lower frequency in daytime. The inversion intensity (ΔT) and inversion depth (ΔZ) of SBI and EI have consistent diurnal and seasonal trends. The effects of SBI and EI on the vertical distribution of aerosols are then analyzed. The mean aerosol optical depth (AOD) below the SBI height shows a clear seasonal variation, which is consistent with the seasonal trends of ΔT and ΔZ. This phenomenon also occurs on the AOD below EI top height. The sensitivity analysis shows that the mean AOD below SBI height or EI top height increases with an increase of the ΔT and ΔZ of SBI (EI). It indicates that ΔT and ΔZ are the key factors affecting the vertical distribution of aerosols. In addition, the variation of AOD below and above EI top height is opposite to that of AOD below and above EI bottom height under different ΔT and ΔZ conditions. The correlation coefficients between ΔT (ΔZ) of EI with AOD in EI were 0.62 (0.65). These results indicate that the space between EI bottom height and EI top height can store aerosols. The larger the ΔZ of EI, the more aerosols are stored. These findings contribute to our understanding of the effect of temperature inversion on the vertical distribution of aerosols.

In view of global warming, caused by the increase in the concentration of greenhouse gases, China has proposed a series of carbon emission reduction policies. It is necessary to obtain the spatiotemporal distribution of carbon emissions accurately. Nighttime light data is recognized as an important basis for carbon emission estimation. A large number of research results show that there is a positive correlation between nighttime light intensity and carbon emission. However, in the current context of China’s industrial reforms, this positive relationship may not be entirely correct. First, we correct the nighttime light data from different satellites and established a long-term series data set. Then, we verify the positive correlation between nighttime light and carbon emission. However, the time scale of emission data often lags, and the carbon concentration data are released earlier and are more accurate than emission data. Therefore, we propose to investigate the relationship between nighttime light and carbon concentration. It is found that there may be different correlations between nighttime light and the carbon concentration, due to different urban industrial structure and development planning. Therefore, by exploring the relationship between nighttime light and the carbon concentration, the existing carbon emission estimation model can be modified to improve the accuracy of the emission model.

With the increasing frequency of natural disasters becoming, it is very important to classify and identify disasters. We propose a lightweight disaster classification model, which has lower computation and parameter quantities and a higher accuracy than other classification models. For this purpose, this paper specially proposes the SDS-Network algorithm, which is optimized on ResNet, to deal with the above problems of remote sensing images. First, it implements the spatial attention mechanism to improve the accuracy of the algorithm; then, the depth separable convolution is introduced to reduce the number of model calculations and parameters while ensuring the accuracy of the algorithm; finally, the effect of the model is increased by adjusting some hyperparameters. The experimental results show that, compared with the classic AlexNet, ResNet18, VGG16, VGG19, and Densenet121 classification models, the SDS-Network algorithm in this paper has a higher accuracy, and when compared with the lightweight models mobilenet series, shufflenet series, squeezenet series, and mnasnet series, it has lower model complexity and a higher accuracy rate. According to a comprehensive performance comparison of the charts made in this article, it is found that the SDS-Network algorithm is still better than the regnet series algorithm. Furthermore, after verification with a public data set, the SDS-Network algorithm in this paper is found to have a good generalization ability. Thus, we can conclude that the SDS-Network classification model of the algorithm in this paper has a good classification effect, and it is suitable for disaster classification tasks. Finally, it is verified on public data sets that the proposed SDS-Network has good generalization ability and portability.

Updated and accurate land cover maps are essential and crucial for sustainable crop production and efficient land management. However, accurate and efficient land cover mapping is still a challenge for agricultural regions with complicated landscapes. This study proposed a novel spectral-phenological based land cover classification (SPLC) method to identify the land cover for fragmented agricultural landscapes, with less requirement of ground truth data. The SPLC method integrated a pixel-based support vector machine (SVM) algorithm for cropland and various non-cropland classification, and a phenology-based automatic decision tree algorithm for identification of various crop types. It was then tested and applied in two typical case areas (i.e., Jiyuan in the upstream and Yonglian in the downstream) of Hetao Irrigation District (Hetao) in the upper Yellow River basin (YRB), northwest China. The field survey sampling data and the regional visual interpretation maps were jointly used to evaluate the accuracy of land cover classification. Results indicated that stable phenological rules can be established for crop identification even with complex planting patterns, and the SPLC method performed well in land cover mapping in case areas. Four high-accuracy land cover maps were produced for Jiyuan in 2020 and 2021, Yonglian in 2021, and Hetao in 2021. The overall accuracies (OA) can reach 0.90–0.94 based on evaluation with abundant ground truth data, and land cover maps agreed well with the visual interpretation maps in space. Overall, the case application validated the applicability and efficiency of the SPLC method in land cover mapping for regions with fragmented agricultural landscapes, and also implied the potential use in other similar regions.

Large amounts of aerosols remain in the residual layer (RL) after sunset, which may be the source of the next day’s pollutants. However, the characteristics of the nocturnal residual layer height (RLH) and its effect on urban environment pollution are unknown. In this study, the characteristics of the RLH and its effect on fine particles with diameters 2.5) were investigated using lidar data from January 2017 to December 2019. The results show that the RLH is highest in summer (1.55 ± 0.55 km), followed by spring (1.40 ± 0.58 km) and autumn (1.26 ± 0.47 km), and is lowest in winter (1.11 ± 0.44 km). The effect of surface meteorological factors on the RLH were also studied. The correlation coefficients (R) between the RLH and the temperature, relative humidity, wind speed, and pressure were 0.38, −0.18, 0.15, and −0.36, respectively. The results indicate that the surface meteorological parameters exhibit a slight correlation with the RLH, but the high relative humidity was accompanied by a low RLH and high PM2.5 concentrations. Finally, the influence of the RLH on PM2.5 was discussed under different aerosol-loading periods. The aerosol optical depth (AOD) was employed to represent the total amount of pollutants. The results show that the RLH has an effect on PM2.5 when the AOD is small but has almost no effect on PM2.5 when the AOD is high. In addition, the R between the nighttime mean RLH and the following daytime PM2.5 at low AOD is −0.49, suggesting that the RLH may affect the following daytime surface PM2.5. The results of this study have a guiding significance for understanding the interaction between aerosols and the boundary layer.

As an important fraction of light-absorbing particles, black carbon (BC) has a significant warming effect, despite accounting for a small proportion of total aerosols. A comprehensive investigation was conducted on the characteristics of atmospheric aerosols and BC particles over Wuhan, China. Mass concentration, optical properties, and radiative forcing of total aerosols and BC were estimated using multi-source observation data. Results showed that the BC concentration monthly mean varied from 2.19 to 5.33 μg m−3. The BC aerosol optical depth (AOD) maximum monthly mean (0.026) occurred in winter, whereas the maximum total AOD (1.75) occurred in summer. Under polluted-air conditions, both aerosol radiative forcing (ARF) and BC radiative forcing (BCRF) at the bottom of the atmosphere (BOA) were strongest in summer, with values of −83.01 and −11.22 W m−2, respectively. In summer, ARF at BOA on polluted-air days was more than two-fold that on clean-air days. In addition, compared with clean-air days, BCRF at BOA on polluted-air days was increased by 76% and 73% in summer and winter, respectively. The results indicate an important influence of particulate air pollution on ARF and BCRF. Furthermore, the average contribution of BCRF to ARF was 13.8%, even though the proportion of BC in PM2.5 was only 5.1%.

In recent years, as China’s peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO2, O3, and CO2 column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO2 decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO2, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO2 and O3 concentrations on changes in XCO2. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO2 concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O3 concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO2 concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O3 concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO2 concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O3 concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO2. Moreover, the results show that the effect of NO2 concentration on XCO2 is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO2 and the O3 concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan’s carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan’s XCO2 fell by about 1.12 ppm in February. At last, the government should consider reducing XCO2 and NO2 concentration at the same time to make a synergistic reduction.

For many years, Beijing has suffered from severe air pollution. At present, fine particulate matter (PM2.5) pollution in the winter and ozone (O3) pollution in the summer constitute serious environmental problems. In this study, the combination of a comprehensive air quality model with particulate matter source apportionment technology (CAMx-PAST) and monitoring data was used for the high-spatial resolution source apportionment of secondary inorganic components (SNA: SO42−, NO3−, and NH4+) in PM2.5; their corresponding precursor gases (SO2, NO2, and NH3); and O3 in the winter and summer over Beijing. Emissions from residents, industry, traffic, agriculture, and power accounted for 54%, 25%, 14%, 5%, and 2% of PM2.5 in the winter, respectively. In the summer, the emissions from industry, traffic, residents, agriculture, and power accounted for 42%, 24%, 20%, 10%, and 4% of PM2.5, respectively. The monthly transport ratio of PM2.5 was 27% and 46% in the winter and summer, respectively. The regional transport of residential and industrial emissions accounted for the highest proportion of PM2.5. The regional transport of emissions had a significant effect on the SO42− and NO3− concentrations, whereas SO2 and NO2 pollution were mainly affected by local emissions, and NH4+ and NH3 were mainly attributed to agricultural emissions. Industrial and traffic sources were two major emission sectors that contributed to O3 pollution in Beijing. The monthly transport ratios of O3 were 31% and 65% in the winter and summer, respectively. The high-spatial resolution regional source apportionment results showed that emissions from Langfang, Baoding, and Tangshan had the greatest impact on Beijing’s air pollution. This work’s methods and results will provide scientific guidance to support the government in its decision-making processes to manage the PM2.5 and O3 pollution issues.

Integrated-path differential absorption (IPDA) LiDAR is a promising means of measuring the global distributions of the column weighted xCO2 (dry-air mixing ratio of CO2) with adequate accuracy and precision. Most IPDA LiDARs are incapable of discerning the vertical information of CO2 diffusion, which is of great significance for studies on the carbon cycle and climate change. Hence, we developed an inversion method using the constrained linear least-squares technique for a pulsed direct-detection multi-wavelength IPDA LiDAR to obtain sliced xCO2. In the proposed inversion method, the atmosphere is sliced into three different layers, and the xCO2 of those layers is then retrieved using the constrained linear least-squares technique. Assuming complete knowledge of the water vapor content, the accuracy of the retrieved sliced xCO2 could be as high as 99.85% when the signal-to-noise ratio of central wavelength retrievals is higher than 25 (with a log scale). Further experiments demonstrated that different carbon characteristics can be identified by the sign of the carbon gradient of the retrieved xCO2 between the ABL (atmospheric boundary layer) and FT (free troposphere). These results highlight the potential applications of multiple wavelength IPDA LiDAR.

The uncertainty of carbon fluxes of the terrestrial ecosystem is the highest among all flux components, calling for more accurate and efficient means to monitor land sinks. Gross primary productivity (GPP) is a key index to estimate the terrestrial ecosystem carbon flux, which describes the total amount of organic carbon fixed by green plants through photosynthesis. In recent years, the solar-induced chlorophyll fluorescence (SIF), which is a probe for vegetation photosynthesis and can quickly reflect the state of vegetation growth, emerges as a novel and promising proxy to estimate GPP. The launch of Orbiting Carbon Observatory 2 (OCO-2) further makes it possible to estimate GPP at a finer spatial resolution compared with Greenhouse Gases Observing Satellite (GOSAT), Global Ozone Monitoring Experiment-2 (GOME-2) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). However, whether the relationship between GPP and SIF is linear or non-linear has always been controversial. In this research, we proposed a new model to estimate GPP using SIF and the atmospheric CO2 concentration from OCO-2 as critical driven factors simultaneously (SIF-CO2-GPP model). Evidences from all sites show that the introduction of the atmospheric CO2 concentration improves accuracies of estimated GPP. Compared with the SIF-CO2-GPP linear model, we found the SIF-GPP model overestimated GPP in summer and autumn but underestimated it in spring and winter. A series of simulation experiments based on SCOPE (Soil-Canopy Observation of Photosynthesis and Energy) was carried out to figure out the possible mechanism of improved estimates of GPP due to the introduction of atmospheric CO2 concentrations. These experiments also demonstrate that there could be a non-linear relationship between SIF and GPP at half an hour timescale. Moreover, such relationships vary with CO2 concentration. As OCO-2 is capable of providing SIF and XCO2 products with identical spatial and temporal scales, the SIF-CO2-GPP linear model would be implemented conveniently to monitor GPP using remotely sensed data. With the help of OCO-3 and its successors, the proposed SIF-CO2-GPP linear model would play a significant role in monitoring GPP accurately in large geographical extents.

Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13−0.88, p < 0.001; OCO-2 SIF: R2 = 0.11−0.99, p < 0.001; MODIS GPP: R2 = 0.15−0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.

Atmospheric aerosol is one of the major factors that cause environmental pollution. Light detection and ranging (LiDAR) is an effective remote sensing tool for aerosol observation. In order to provide a comprehensive understanding of the aerosol pollution from the physical perspective, this study investigated regional atmospheric aerosol pollution through the integration of measurements, including LiDAR, satellite, and ground station observations and combined the backward trajectory tracking model. First, the horizontal distribution of atmospheric aerosol wa obtained by a whole-day working scanning micro-pulse LiDAR placed on a residential building roof. Another micro-pulse LiDAR was arranged at a distance from the scanning LiDAR to provide the vertical distribution information of aerosol. A new method combining the slope and Fernald methods was then proposed for the retrieval of the horizontal aerosol extinction coefficient. Finally, whole-day data, including the LiDAR data, the satellite remote sensing data, meteorological data, and backward trajectory tracking model, were selected to reveal the vertical and horizontal distribution characteristics of aerosol pollution and to provide some evidence of the potential pollution sources in the regional area. Results showed that the aerosol pollutants in the district on this specific day were mainly produced locally and distributed below 2.0 km. Six areas with high aerosol concentration were detected in the scanning area, showing that the aerosol pollution was mainly obtained from local life, transportation, and industrial activities. Correlation analysis with the particulate matter data of the ground air quality national control station verified the accuracy of the LiDAR detection results and revealed the effectiveness of LiDAR detection of atmospheric aerosol pollution.

Since over 70% of carbon emissions are from urban areas, it is of great importance to develop an effective measurement technique that can accurately monitor atmospheric CO2 in global urban areas. Remote sensing could be an effective way to achieve this goal. However, due to high aerosol loading in urban areas, there are large, inadequately resolved areas in the CO2 products acquired by passive remote sensing. China is planning to launch the Atmospheric Environment Monitoring Satellite (AEMS) equipped with a CO2-light detecting and ranging (LIDAR) system. This work conducted a feasibility study on obtaining city-scale column CO2 volume mixing ratios (XCO2) using the LIDAR measurements. A performance framework consisting of a sensor model, sampling model, and environmental model was proposed to fulfill our demand. We found that both the coverage and the accuracy of the LIDAR-derived city-scale XCO2 values were highly dependent on the orbit height. With an orbit height of 450 km, random errors of less than 0.3% are expected for all four metropolitan areas tested in this work. However, random errors of less than 0.3% were obtained in only two metropolitan areas with an orbit height of 705 km. Our simulations also showed that off-nadir sampling would improve the performance of a CO2-Integrated Path Differential Absorption (IPDA) LIDAR system operating in a 705 km orbit. These results indicate that an active remote sensing mission could help to effectively measure XCO2 values in urban areas. More detailed studies are needed to reveal the potential of such equipment for improving the verification of carbon emissions and the estimation of urban carbon fluxes.

Active remote sensing of atmospheric XCO2 has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO2-IPDA (integrated path differential absorption Lidar) to measure columnar concentrations of atmospheric CO2 globally. As space and power are limited on the satellite, compromises have been made to accommodate other passive sensors. In this study, we evaluated the sensitivity of the system’s retrieval accuracy and precision to some critical parameters to determine whether the current configuration is adequate to obtain the desired results and whether any further compromises are possible. We then mapped the distribution of random errors across China and surrounding regions using pseudo-observations to explore the performance of the planned CO2-IPDA over these regions. We found that random errors of less than 0.3% can be expected for most regions of our study area, which will allow the provision of valuable data that will help researchers gain a deeper insight into carbon cycle processes and accurately estimate carbon uptake and emissions. However, in the areas where major anthropogenic carbon sources are located, and in coastal seas, random errors as high as 0.5% are predicted. This is predominantly due to the high concentrations of aerosols, which cause serious attenuation of returned signals. Novel retrieving methods must, therefore, be developed in the future to suppress interference from low surface reflectance and high aerosol loading.

Open, Grass- and Forb-Dominated (OGFD) ecosystems, including tundra, tropical grasslands and savanna, provide habitat for both wild and domesticated large ungulate herbivores. These ecosystems exist across a wide temperature gradient from the Arctic regions to the Equator, but are confined to a narrow set of moisture conditions that range from arid deserts to forest-dominated systems. Primary productivity in OGFD ecosystems appears extremely sensitive to environmental change. We compared global trends in the annual maximum and mean values of the Normalized Difference Vegetation Index (NDVI) and identified the key bioclimatic indices that controlled OGFD productivity changes in various regions for the period from 1982 to 2011. We found significantly increased or decreased annual maximum NDVI values of 36.3% and 4.6% for OGFD ecosystems, respectively. Trends in the annual mean NDVI are similar for most OGFD ecosystems and show greater area decreases and smaller area increases than trends in the annual maximum NDVI in global OGFD ecosystems during the study period. Ecosystems in which the productivity significantly increased were distributed mainly in the Arctic, mid-eastern South America, central Africa, central Eurasia and Oceania, while those with decreasing trends in productivity were mainly on the Mongolian Plateau. Temperature increases tended to improve productivity in colder OGFD ecosystems; and precipitation is positively correlated with productivity changes in grassland and savannas, but negatively correlated with changes in the Arctic tundra. Simple bioclimatic indices explain 42% to 55% of productivity changes in OGFD systems worldwide, and the main climatic predictors of productivity differed significantly between regions. In light of future climate change, the findings of this study will help support management of global OGFD ecosystems.

The Tibetan Plateau has experienced profound climate change with significant implication for spatial vegetation greenness. However, the spatiotemporal disparities of long-term vegetation trends in response to observed climate change remain unclear. Based on remote-sensing vegetation images indicated by the normalized difference vegetation index (NDVI) from two long-term combined datasets, GIMMS and MODIS, we identified two spatiotemporal evolution patterns (SEPs) in long-term vegetation anomalies across the Tibetan Plateau. This new perspective integrates spatial and temporal NDVI changes during the growing seasons over the past four decades. Notably, the dipole evolution pattern that rotates counterclockwise from May to September accounted for 62.8% of the spatial mean amplitude of vegetation trends, dominating the spatiotemporal disparities. This dominant pattern trend is attributed to simultaneous effects of spatial warming and rising CO2, which accounted for 75% and 15%, respectively, along with a lagged effect of dipole precipitation, accounting for 6%. Overall, wetting and warming promote greening evolution in the northern Tibetan Plateau, while slight drying and warming favor browning evolution in the southern Tibetan Plateau. These findings provide insights into the combined effects of climate change on spatiotemporal vegetation trends and inform future adaptive strategies in fragile regions.

In the era of big data, making full use of remote sensing images to automatically extract surface water bodies (WBs) in complex environments is extremely challenging. Due to the weak capability of existing algorithms in extracting small WBs and WB edge information from remote sensing images, we proposed a new method—Multiscale Fusion SegFormer (MF-SegFormer)—for WB extraction in the Weihe River Basin of China using Landsat 8 OLI images. The MF-SegFormer method adopts a cascading approach to fuse features output by the SegFormer encoder at multiple scales. A feature fusion (FF) module is proposed to enhance the extraction of WB edge information, while an Atrous Spatial Pyramid Pooling (ASPP) module is employed to enhance the extraction of small WBs. Furthermore, we analyzed the impact of four kinds of band combinations on WB extraction by the MF-SegFormer model, including true color composite images, false color images, true color images, and false color images enhanced by Gaussian stretch. We also compared our proposed method with several different approaches. The results suggested that false color composite images enhanced by Gaussian stretching are beneficial for extracting WBs, and the MF-SegFormer model achieves the highest accuracy across the study area with a precision of 77.6%, recall of 84.4%, F1-score of 80.9%, and mean intersection over union (mIoU) of 83.9%. In addition, we used the determination coefficient (R2) and root-mean-square error (RMSE) to evaluate the performance of river width extraction. Our extraction results in an overall R2 of 0.946 and an RMSE of 28.21 m for the mainstream width in the “Xi’an-Xianyang” section of the Weihe River. The proposed MF-SegFormer method used in this study outperformed other methods and was found to be more robust for WB extraction.

El Niño–Southern Oscillation (ENSO) has remarkable impacts on Tibetan Plateau (TP) summer precipitation. However, recently identified ENSO spatial diversity brings complexity to these impacts. This study investigates the distinct impacts of the Eastern Pacific (EP) and Central Pacific (CP) ENSOs on TP summer precipitation based on numerous precipitation data and satellite-observed and reanalyzed circulation data. The results show that the EP El Niño and the CP La Niña have opposite effects on summer precipitation in the southwestern TP, with significant decreases and increases, respectively, indicating a trans-type inversion. In contrast, the CP El Niño causes significant decreases in summer precipitation in the central-eastern TP. No significant anomaly occurs during the EP La Niña. Moisture budget and circulation analyses suggest that these distinct precipitation characteristics can be attributed to different atmospheric teleconnections, which provide varying vertical motion and moisture conditions. The EP El Niño triggers an atmospheric response similar to the Indian Summer Monsoon–East Asian Summer Monsoon teleconnection, and the CP La Niña generates a teleconnection in the opposite phase. In contrast, the CP El Niño mainly causes a teleconnection resembling the East Asian–Pacific pattern. This study may deepen our understanding of ENSO impacts on TP summer precipitation and have implications for regional climate predictions.

The vegetation on the Tibetan Plateau (TP), as a major component of the land–atmosphere interaction, affects the TP thermal conditions. And, as a direct climatic factor of vegetation, precipitation over the TP is significantly regulated by the Indian summer monsoon (ISM). Using remote-sensing-based vegetation images, meteorological observations, and reanalysis datasets, this study deeply explored the influence of the ISM on vegetation on the TP in its main growing season, where the vegetation on the TP is indicated by the normalized difference vegetation index (NDVI). The findings reveal that the ISM is a critical external factor impacting the TP vegetation and has a significantly positive correlation with the TP precipitation and NDVI. Corresponding to a strong ISM, the South Asia high moves northwestward toward the TP and Iranian Plateau with an increase in intensity, and the cyclonic circulation develops over the south of the TP in the middle-lower troposphere. This tropospheric circulation structure aids in the transportation of more water vapor to the TP and enhances convection there, which facilitates more precipitation and thus the TP vegetation growth, featuring a uniform NDVI pattern. Since the positive correlation between precipitation over the TP and NDVI is weaker than that between the ISM and NDVI, we suggest that the ISM can influence the TP vegetation growth not only through changing precipitation but also through other local climatic factors. The increased convection and precipitation over the TP induced by the ISM can also affect the surface thermal conditions, featuring an interaction between the TP vegetation and heat sources. The evapotranspiration of vegetation and its coverage affect local latent and sensible heat fluxes, while the TP thermal condition changes affect in return the vegetation growth. In addition, the changes in thermal conditions over the TP caused by the substantial increase in vegetation may have a de-correlation effect on the relationship between the ISM and uniform NDVI pattern after the TP vegetation reaches its maximum coverage.

Active remote sensing of atmospheric XCO2 has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO2-IPDA (integrated path differential absorption Lidar) to measure columnar concentrations of atmospheric CO2 globally. As space and power are limited on the satellite, compromises have been made to accommodate other passive sensors. In this study, we evaluated the sensitivity of the system's retrieval accuracy and precision to some critical parameters to determine whether the current configuration is adequate to obtain the desired results and whether any further compromises are possible. We then mapped the distribution of random errors across China and surrounding regions using pseudo-observations to explore the performance of the planned CO2-IPDA over these regions. We found that random errors of less than 0.3% can be expected for most regions of our study area, which will allow the provision of valuable data that will help researchers gain a deeper insight into carbon cycle processes and accurately estimate carbon uptake and emissions. However, in the areas where major anthropogenic carbon sources are located, and in coastal seas, random errors as high as 0.5% are predicted. This is predominantly due to the high concentrations of aerosols, which cause serious attenuation of returned signals. Novel retrieving methods must, therefore, be developed in the future to suppress interference from low surface reflectance and high aerosol loading. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) vegetation plays an important role in the local ecosystem, which responds significantly to climate change and can affect local and large-scale weather and climate anomalies. However, little attention has been paid to its year-to-year variation. In this paper, using two NDVI datasets (GIMMS and MODIS) originated from satellite remote sensing, the variability characteristics of NDVI over the TP on the interannual time scale and associated local climatic factors were investigated. The results show that two primary patterns of NDVI governed TP during the main growing season (June–September, JJAS) for the period 1982–2020. The first one is a uniform pattern, with a consistent spatial variation over the entire TP, and the second is a dipole pattern, with an out-of-phase spatial variation of NDVI between the northern and southern TP. Interannual variations of the different climatic factors regulate the NDVI variability over the different regions of the TP. The interannual variability of the uniform NDVI pattern is mainly affected by the two local climatic factors, the preceding May–August precipitation and simultaneous JJAS sunshine duration. Specifically, NDVIs over the southern and eastern TP have a more significant response to the preceding precipitation and simultaneous sunshine duration, respectively. The variability of the dipole NDVI pattern is primarily modulated by the preceding May–August precipitation and simultaneous surface air temperature, ground surface temperature, and sunshine duration. However, NDVIs over the northern and southern TP have different degrees of response to the four climatic factors, with the most significant response being to preceding precipitation. The combined effect of these factors contributes to the formation of the interannual variability in the uniform and dipole patterns. This paper may shed light on deeply understanding the reasons for the inconsistency in variations of vegetation over the different regions of the TP under climate change. In addition to the effect of local climatic factors that this study focuses on, the influence of external climatic factors on the variability of the TP NDVI deserves further research in the future.

Pavement markings could wear out before their expected service life expires, causing traffic safety hazards. However, assessing pavement-marking conditions at the city scale was a great challenge in previous studies. In this article, we advance the method of detecting and evaluating pavement-marking defects at the city scale with Baidu Street View (BSV) images, using a case study in Nanjing. Specifically, we employ inverse perspective mapping (IPM) and a deep learning-based approach to pavement-marking extraction to make efficient use of street-view imageries. In addition, we propose an evaluation system to assess three types of pavement-marking defects, with quantitative and qualitative results provided for each image. Factors causing pavement-marking defects are discussed by mapping the spatial distribution of pavement-marking defects at the city scale. Our proposed methods are conducive to pavement-marking repair operations. Beyond this, this article can contribute to smart urbanism development by creating a new road maintenance solution and ensuring the large-scale realization of intelligent decision-making in urban infrastructure management.