Land Degradation Due to Salinization in Arid and Semi-arid Regions with the Aid of Geo-information Techniques

This study applied a computerized parametric methodology to monitor, map, and quantify land degradation by saliniza-tion risk detection techniques at a 1:250 000 mapping scale using geo-information technology. The northern part of the Shaanxi province in China was taken as a case. Multi-temporal remotely sensed materials of both Landsat TM and thematic maps (ETM+) were used as the bases to provide comprehensive views of surface conditions such as vegetation cover and salinization detection. With ERDAS ver. 9.1 software, the Normalized Differential Salinity Index (NDSI) and Salinity Index (S.I.) were computed and then evaluated for land degradation by salinization. Arc/Info ver. 9.2 software was used along with field observation data (GPS) for analysis. Using spatial analysis methods, results showed that 19 973.1 km2 (72%) of land had no risk of land degradation by salini-zation, 3 684.7 km2 (13%) had slight land degradation by salinization risk, 2 797.9 km2 (10%) had moderate land degradation by salinization risk, and 1 218.9 km2 (4%) of the total land area was at a high risk of land degradation by salinization. The study area, in general, is exposed to a high risk of soil salinization.

Leaf pigment retrieval using the PROSAIL model: Influence of uncertainty in prior canopy-structure information

Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model inversion.Canopy structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment retrie-val.Using canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval accuracy.Validation using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy structure.This study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.

Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

To protect the environment,the discharged sewage’s quality must meet the state’s discharge standards.There are many water quality indicators,and the pH(Potential of Hydrogen)value is one of them.The natural water’s pH value is 6.0–8.5.The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard.This paper aims to study the deep learning prediction model of wastewater’s pH.Firstly,the research uses the random forest method to select the data features and then,based on the sliding window,convert the data set into a time series which is the input of the deep learning training model.Secondly,by analyzing and comparing relevant references,this paper believes that the CNN(Convolutional Neural Network)model is better at nonlinear data modeling and constructs a CNN model including the convolution and pooling layers.After alternating the combination of the convolutional layer and pooling layer,all features are integrated into a full-connected neural network.Thirdly,the number of input samples of the CNN model directly affects the prediction effect of the model.Therefore,this paper adopts the sliding window method to study the optimal size.Many experimental results show that the optimal prediction model can be obtained when alternating six convolutional layers and three pooling layers.The last full-connection layer contains two layers and 64 neurons per layer.The sliding window size selects as 12.Finally,the research has carried out data prediction based on the optimal CNN deep learning model.The predicted pH of the sewage is between 7.2 and 8.6 in this paper.The result is applied in the monitoring system platform of the“Intelligent operation and maintenance platform of the reclaimed water plant.”

A New Generation of Intelligent Mapping and Remote Sensing Scientific Test Satellite Luojia-301

With the continuous improvement of the performance and the increasing variety of optical mapping and remote sensing satellites,they have become an important support for obtaining global accurate surveying and mapping remote sensing information.At present,optical mapping and remote sensing satellites already have sub-meter spatial resolution capabilities,but there is a serious lag problem in mapping and remote sensing information services.It is urgent to develop intelligent mapping and remote sensing satellites to promote the transformation and upgrading to real-time intelligent services.Firstly,based on the three imaging systems of the optical mapping and remote sensing satellites and their realization methods and application characteristics,this paper analyzes the applicable system of the intelligent mapping and remote sensing satellites.Further,according to the application requirements of real-time,intelligence,and popularization,puts forward the design concept of integrated intelligent remote sensing satellite integrating communication,navigation,and remote sensing and focuses on the service mode and integrated function composition of intelligent remote sensing satellite.Then expounds on the performance and characteristics of the Luojia-301 satellite,a new generation of intelligent surveying and mapping remote sensing scientific test satellite.And finally summarizes and prospects the development and mission of intelligent mapping remote sensing satellites.Luojia-301 satellite integrates remote sensing and communication functions.It explores an efficient and intelligent service mode of mapping and remote sensing information from data acquisition to the application terminal and provides a real service verification platform for on-orbit processing and real-time transmission of remote sensing data based on space-ground internet,which is of great significance to the construction of China’s spatial information network.

Monitoring Surface Deformation Using Distributed Scatterers InSAR

In the past two decades,extensive and in-depth research has been conducted on Time Series InSAR technology with the advancement of high-performance SAR satellites and the accumulation of big SAR data.The introduction of distributed scatterers in Distributed Scatterers InSAR(DS-InSAR)has significantly expanded the application scenarios of InSAR geodetic measurement by increasing the number of measurement points.This study traces the history of DS-InSAR,presents the definition and characteristics of distributed scatterers,and focuses on exploring the relationships and distinctions among proposed algorithms in two crucial steps:statistically homogeneous pixel selection and phase optimization.Additionally,the latest research progress in this field is tracked and the possible development direction in the future is discussed.Through simulation experiments and two real InSAR case studies,the proposed algorithms are compared and verified,and the advantages of DS-InSAR in deformation measurement practice are demonstrated.This work not only offers insights into current trends and focal points for theoretical research on DS-InSAR but also provides practical cases and guidance for applied research.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

Monitoring absolute vertical land motions and absolute sea-level changes from GPS and tide gauges data over French Polynesia

In this study,we estimate the absolute vertical land motions at three tidal stations with collocated Global Navigation Satellite System(GNSS)receivers over French Polynesia during the period 2007-2020,and obtain,as ancillary results,estimates of the absolute changes in sea level at the same locations.To verify our processing approach to determining vertical motion,we first modeled vertical motion at the International GNSS Service(IGS)THTI station located in the capital island of Tahiti and compared our estimate with previous independent determinations,with a good agreement.We obtained the following estimates for the vertical land motions at the tide gauges:Tubuai island,Austral Archipelago-0.92±0.17 mm/yr,Vairao village,Tahiti Iti:-0.49±0.39 mm/yr,Rikitea,Gambier Archipelago-0.43±0.17 mm/yr.The absolute variations of the sea level are:Tubuai island,Austral Archipelago 5.25±0.60 mm/yr,Vairao village,Tahiti Iti:3.62±0.52 mm/yr,Rikitea,Gambier Archipelago 1.52±0.23 mm/yr.We discuss these absolute values in light of the values obtained from altimetric measurements and other means in French Polynesia.

Assessment of total suspended sediment concentrations in Poyang Lake using HJ-1A/1B CCD imagery

We explored the potential of the environment and disaster monitoring and forecasting small satellite constellations (HJ-1A/1B satellites) charge-coupled device (CCD) imagery (spatial resolution of 30 m, revisit time of 2 days) in the monitoring of total suspended sediment (TSS) concentrations in dynamic water bodies using Poyang Lake, the largest freshwater lake in China, as an example. Field surveys conducted during October 17-26, 2009 showed a wide range of TSS concentration (3-524 mg/L). Atmospheric correction was implemented using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module in ENVI with the aid of aerosol information retrieved from concurrent Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) surveys, which worked well at the CCD bands with relatively high reflectance. A practical exponential retrieval algorithm was created between satellite remote sensing reflectance and in-situ measured TSS concentration. The retrieved results for the whole water area matched the in-situ data well at most stations. The retrieval errors may be related to the problem of scale matching and mixed pixel. In three selected subregions of Poyang Lake, the distribution trend of retrieved TSS was consistent with that of the field investigation. It was shown that HJ-1A/1B CCD imagery can be used to estimate TSS concentrations in Poyang Lake over synoptic scales after applying an appropriate atmospheric correction method and retrieval algorithm.

Spatial and temporal change patterns of net primary productivity and its response to climate change in the Qinghai–Tibet Plateau of China from 2000 to 2015

The vegetation ecosystem of the Qinghai–Tibet Plateau in China,considered to be the′′natural laboratory′′of climate change in the world,has undergone profound changes under the stress of global change.Herein,we analyzed and discussed the spatial-temporal change patterns and the driving mechanisms of net primary productivity(NPP)in the Qinghai–Tibet Plateau from 2000 to 2015 based on the gravity center and correlation coefficient models.Subsequently,we quantitatively distinguished the relative effects of climate change(such as precipitation,temperature and evapotranspiration)and human activities(such as grazing and ecological construction)on the NPP changes using scenario analysis and Miami model based on the MOD17A3 and meteorological data.The average annual NPP in the Qinghai–Tibet Plateau showed a decreasing trend from the southeast to the northwest during 2000–2015.With respect to the inter-annual changes,the average annual NPP exhibited a fluctuating upward trend from 2000 to 2015,with a steep increase observed in 2005 and a high fluctuation observed from 2005 to 2015.In the Qinghai–Tibet Plateau,the regions with the increase in NPP(change rate higher than 10%)were mainly concentrated in the Three-River Source Region,the northern Hengduan Mountains,the middle and lower reaches of the Yarlung Zangbo River,and the eastern parts of the North Tibet Plateau,whereas the regions with the decrease in NPP(change rate lower than–10%)were mainly concentrated in the upper reaches of the Yarlung Zangbo River and the Ali Plateau.The gravity center of NPP in the Qinghai–Tibet Plateau has moved southwestward during 2000–2015,indicating that the increment and growth rate of NPP in the southwestern part is greater than those of NPP in the northeastern part.Further,a significant correlation was observed between NPP and climate factors in the Qinghai–Tibet Plateau.The regions exhibiting a significant correlation between NPP and precipitation were mainly located in the central and eastern Qinghai–Tibet Plateau,and the regions exhibiting a significant correlation between NPP and temperature were mainly located in the southern and eastern Qinghai–Tibet Plateau.Furthermore,the relative effects of climate change and human activities on the NPP changes in the Qinghai–Tibet Plateau exhibited significant spatial differences in three types of zones,i.e.,the climate change-dominant zone,the human activity-dominant zone,and the climate change and human activity interaction zone.These research results can provide theoretical and methodological supports to reveal the driving mechanisms of the regional ecosystems to the global change in the Qinghai–Tibet Plateau.

Monitoring Zenithal Total Delays over the three different climatic zones from IGS GPS final products:A comparison between the use of the VMF1 and GMF mapping functions

The International GNSS Service(IGS) final products(ephemeris and clocks-correction) have made the GNSS an indispensable low-cost tool for scientific research, for example sub-daily atmospheric water vapor monitoring. In this study, we investigate if there is a systematic difference coming from the choice between the Vienna Mapping Function 1(VMF1) and the Global Mapping Function(GMF) for the modeling of Zenith Total Delay(ZTD) estimates, as well as the Integrated Precipitable Water Vapor(IPWV) estimates that are deduced from them. As ZTD estimates cannot be fully separated from coordinate estimates, we also investigated the coordinate repeatability between subsequent measurements.For this purpose, we monitored twelve GNSS stations on a global scale, for each of the three climatic zones(polar, mid-latitudes and tropical), with four stations on each zone. We used an automated processing based on the Bernese GNSS Software Version 5.2 by applying the Precise Point Positioning(PPP)approach, L3 Ionosphere-free linear combination, 7 cutoff elevation angle and 2 h sampling. We noticed an excellent agreement with the ZTD estimates and coordinate repeatability for all the stations w.r.t to CODE(the Center for Orbit Determination in Europe) and USNO(US Naval Observatory) products, except for the Antarctic station(Davis) which shows systematic biases for the GMF related results. As a final step, we investigated the effect of using two mapping functions(VMF1 and GMF) to estimate the IPWV,w.r.t the IPWV estimates provided by the Integrated Global Radiosonde Archive(IGRA). The GPS-derived IPWV estimates are very close to the radiosonde-derived IPWV estimates, except for one station in the tropics(Tahiti).

A terrain openness index for the extraction of karst Fenglin and Fengcong landform units from DEMs

The Fenglin and Fengcong landform units are considered to be an important representation for defining the degree of development of Karst landforms. However, these terrain features have been proven difficult to delineate and extract automatically because of their complex morphology. In this paper, a new method for identifying the Fenglin and Fengcong landform units is proposed. This method consists of two steps:(1) terrain openness calculation and(2) toe line extraction. The proposed method is applied and validated in the Karst case area of Guilin by using ASTER GDEM with one arc-second resolution. The openness of both the positive and negative terrain and a threshold were used to extract toe lines for segmenting depressions and pinnacles in Fenglin and Fengcong landforms. A comparison between the extracted Fenglin and Fengcong landform units and their real units from high resolution images wascarried out to evaluate the capability of the proposed method. Results show the proposed method can effectively extract the Fenglin and Fengcong landform units, and has an overall accuracy of 93.28%. The proposed method is simple and easy to implement and is expected to play an important role in the automatic extraction of similar landform units in the Karst area.

Detection of different-time-scale signals in the length of day variation based on EEMD analysis technique

Scientists pay great attention to different-time-scale signals in the lengllh of day （LOD） variations △LOD, which provide signatures of the Earth＇s interior structure, couplings among different layers, and potential excitations of ocean and atmosphere. In this study, based on the ensemble empirical mode decomposition （EEMD）, we analyzed the latest time series of △LOD data spanning from January 1962 to March 2015. We observed the signals with periods and amplitudes of about 0.5 month and 0.19 ms, 1.0 month and 0.19 ms, 0.5 yr and 0.22 ms, 1.0 yr and 0.18 ms, 2.28 yr and 0.03 ms, 5.48 yr and 0.05 ms, respectively, in coincidence with the results of predecessors. In addition, some signals that were previously not definitely observed by predecessors were detected in this study, with periods and amplitudes of 9.13 d and 0.12 ms, 13.69 yr and 0.10 ms, respectively. The mechanisms of the LOD fluctuations of these two signals are still open.

A Fast Algorithm of Cartographic Sounding Selection

An effective strategy and framework that adequately integrate the automated and manual processes for fast cartographic sounding selection is presented. The important submarine topographic features are extracted for important soundings selection, and an improved "influence circle" algorithm is introduced for sounding selection. For automatic configuration of soundings distribution pattern, a special algorithm considering multi factors is employed. A semi automatic method for solving the ambiguous conflicts is described. On the basis of the algorithms and strategies a system named HGIS for fast cartographic sounding selection is developed and applied in Chinese Marine Safety Administration Bureau (CMSAB). The application experiments show that the system is effective and reliable. At last some conclusions and the future work are given.

Analysis of the impact on the gravity field determination from the data with the ununiform noise distribution using block-diagonal least squares method

The block-diagonal least squares method, which theoretically has specific requirements for the observation data and the spatial distribution of its precision, plays an important role in ultra-high degree gravity field determination. On the basis of block-diagonal least squares method, three data processing strategies are employed to determine the gravity field models using three kinds of simulated global grid data with different noise spatial distri- bution in this paper. The numerical results show that when we employed the weight matrix corresponding to the noise of the observation data, the model computed by the least squares using the full normal matrix has much higher precision than the one estimated only using the block part of the normal matrix. The model computed by the block-diagonal least squares method without the weight matrix has slightly lower precision than the model computed using the rigorous least squares with the weight matrix. The result offers valuable reference to the using of block-diagonal least squares method in ultra-high gravity model determination.

Estimation of Fractional Vegetation Cover Based on Digital Camera Survey Data and a Remote Sensing Model

The objective of this paper is to improve the monitoring speed and precision of fractional vegetation cover (fc). It mainly focuses on fc estimation when fcmax and fcmin are not approximately equal to 100% and 0%, respectively due to using remote sensing image with medium or low spatial resolution. Meanwhile, we present a new method of fc estimation based on a random set of fc maximum and minimum values from digital camera (DC) survey data and a di- midiate pixel model. The results show that this is a convenient, efficient and accurate method for fc monitoring, with the maximum error -0.172 and correlation coefficient of 0.974 between DC survey data and the estimated value of the remote sensing model. The remaining DC survey data can be used as verification data for the precision of the fc estimation. In general, the estimation of fc based on DC survey data and a remote sensing model is a brand-new development trend and deserves further extensive utilization.

EFFECT OF ACUPUNCTURE STIMULATION AT SANYINJIAO (SP 6) ON CEREBRAL GLUCOSE METABOLISM IN DYSMENORRHEA PATIENTS

Objective: To study the central mechanism of acupuncture stimulation of Sanyinjiao (三阴交 SP 6) in relieving dysmenorrhea. Methods: A total of 6 dysmenorrhea volunteer patients were subjected into this study. On the first positron emission tomography (PET) scan examination, they were assigned to pseudo-acupuncture group by using the acupuncture needle just to prick the skin of Sanyinjiao (SP 6); while on the second PET scans, they were assigned to acupuncture group by inserting the needle into the same acupoint. 18F fluorodeoxyglucose ( 18F-FDG) PET of the whole brain was performed during pseudo-acupuncture and real acupuncture of Sanyinjiao (SP 6). The acquired PET data were analyzed by using statistical parametric mapping (SPM) software to determine changes of glucose metabolism in different cerebral regions. The patient’s pain intensity was rated by using 0-10 numerical pain intensity scale. Results: After pseudo-acupuncture stimulation of Sanyinjiao (SP 6), no significant changes were found in the pain intensity (P>0.05), while after real-acupuncture stimulation, the pain intensity declined significantly (P<0.01). Following acupuncture of the right Sanyinjiao (SP 6), multiple cerebral regions involving pain were activated (increase of glucose metabolism), including ipsilateral lenticular nucleus (globus pallidus, putamen), ipsilateral cerebellum and insular lobe, bilateral dorsal thalamus, ipsilateral paracentral lobule, bilateral amygdaloid bodies, contralateral substantia nigra of the midbrain, bilateral second somatosensory (SⅡ) areas, ispsilateral hippocampal gyrus, frontal part of the ipsilateral cingulated gyrus, and bilateral mammary bodies of the hypothalamus. In addition, fewer regions of the cerebral cortex responded with decrease of the glucose metabolism after real acupuncture.

Comparison of stratospheric evolution during the major sudden stratospheric warming events in 2018 and 2019

Using Modern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2)data in the northern hemisphere at the 10 hPa level,we compared the stratospheric evolution of temperature and geopotential height during two major sudden stratosphere warming events(SSWs)that occurred in the Arctic winter of 2018 and 2019.In the prewarming period,poleward temperature-enhanced regions were mainly located around 120°E with a displaced vortex and around 120°E and 60°W with splitting vortices.The evolution of geopotential height indicated that these temperature-enhanced regions were both on the western side of high-latitude anticyclones.In the postwarming period,the polar vortex turned from splitting to displacement in the 2018 SSW but from displacement to splitting in the 2019 SSW.Both transitions were observed over the Atlantic region,which may have been caused by anticyclones moving through the polar region.Our findings revealed that the evolution of the anticyclone is important during SSWs and is closely related to temperature-enhanced regions in the prewarming periods and to transitions of the polar vortices in postwarming periods.

Evaluation of vertical crustal movements and sea level changes around Greenland from GPS and tide gauge observations

To better monitor the vertical crustal movements and sea level changes around Greenland,multiple data sources were used in this paper,including global positioning system(GPS),tide gauge,satellite gravimetry,satellite altimetry,glacial isostatic adjustment(GIA).First,the observations of more than 50 GPS stations from the international GNSS service(IGS)and Greenland network(GNET)in 2007–2018 were processed and the common mode error(CME)was eliminated with using the principal component analysis(PCA).The results show that all GPS stations show an uplift trend and the stations in southern Greenland have a higher vertical speed.Second,by deducting the influence of GIA,the impact of current Gr IS mass changes on GPS stations was analysed,and the GIA-corrected vertical velocity of the GPS is in good agreement with the vertical velocity obtained by gravity recovery and climate experiment(GRACE).Third,the absolute sea level change around Greenland at 4 gauge stations was obtained by combining relative sea level derived from tide gauge observations and crustal uplift rates derived from GPS observations,and was validated by sea level products of satellite altimetry.The results show that although the mass loss of Gr IS can cause considerable global sea level rise,eustatic movements along the coasts of Greenland are quite complex under different mechanisms of sea level changes.

Characteristics of the quasi-16-day wave in the mesosphere and lower thermosphere region as revealed by meteor radar,Aura satellite,and MERRA2 reanalysis data from 2008 to 2017

This study presents an analysis of the quasi-16-day wave(Q16DW)at three stations in the middle latitudes by using a meteor radar chain in conjunction with Aura Microwave Limb Sounder temperature data and MERRA2(Modern-Era Retrospective Analysis for Research and Applications,Version 2)reanalysis data from 2008 to 2017.The radar chain consists of three meteor radar stations located at Mohe(MH,53.5°N,122.3°E),Beijing(BJ,40.3°N,116.2°E),and Wuhan(WH,30.5°N,114.6°E).The Q16DW wave exhibits similar seasonal variation in the neutral wind and temperature,and the Q16DW amplitude is generally strong during winter and weak around summer.The Q16DW at BJ was found to have secondary enhancement around September in the zonal wind,which is rarely reported at similar latitudes.The latitudinal variations of the Q16DW in the neutral wind and temperature are quite different.The Q16DW at BJ is the most prominent in both neutral wind components among the three stations and the Q16DW amplitudes at MH and WH are comparable,whereas the wave amplitude in temperature decreases with decreasing latitude.The quasi-geostrophic refractive index squared at the three stations in the period from 2008 to 2017 was revealed.The results indicate that the Q16DW in the mesosphere and lower thermosphere(MLT)at MH has a limited contribution from the lower atmosphere.Around March and October,the Q16DW in the troposphere at BJ can propagate upward into the MLT region,whereas at WH,the contribution to the Q16DW in the MLT region is largely from the mesosphere.

Spatiotemporal estimation of hourly 2-km ground-level ozone over China based on Himawari-8 using a self-adaptive geospatially local model

Ground-level ozone(O_(3))is a primary air pollutant,which can greatly harm human health and ecosystems.At present,data fusion frameworks only provided ground-level O_(3) concentrations at coarse spatial(e.g.,10 km)or temporal(e.g.,daily)resolutions.As photochemical pollution continues increasing over China in the last few years,a high-spatial–temporal-resolution product is required to enhance the comprehension of ground-level O_(3) formation mechanisms.To address this issue,our study creatively explores a brand-new framework for estimating hourly 2-km ground-level O_(3) concentrations across China(except Xinjiang and Tibet)using the brightness temperature at multiple thermal infrared bands.Considering the spatial heterogeneity of ground-level O_(3),a novel Self-adaptive Geospatially Local scheme based on Categorical boosting(SGLboost)is developed to train the estimation models.Validation results show that SGLboost performs well in the study area,with the R2 s/RMSEs of 0.85/19.041 lg/m^(3) and 0.72/25.112 lg/m^(3) for the space-based cross-validation(CV)(2017–2019)and historical space-based CV(2019),respectively.Meanwhile,SGLboost achieves distinctly better metrics than those of some widely used machine learning methods,such as e Xtreme Gradient boosting and Random Forest.Compared to recent related works over China,the performance of SGLboost is also more desired.Regarding the spatial distribution,the estimated results present continuous spatial patterns without a significantly partitioned boundary effect.In addition,accurate hourly and seasonal variations of ground-level O_(3) concentrations can be observed in the estimated results over the study area.It is believed that the hourly 2-km results estimated by SGLboost will help further understand the formation mechanisms of ground-level O_(3) in China.