Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts...Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.展开更多
The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderate-resolution imaging spectroradiometer(MODIS) data in China.Paddy rice fields were extracted by identify...The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderate-resolution imaging spectroradiometer(MODIS) data in China.Paddy rice fields were extracted by identifying the unique char-acteristic of high soil moisture in the flooding and transplanting period with improved algorithms based on rice growth calendar regionalization.The characteristic could be reflected by the enhanced vegetation index(EVI) and the land surface water index(LSWI) derived from MODIS sensor data.Algorithms for single,early,and late rice identification were obtained from selected typical test sites.The algorithms could not only separate early rice and late rice planted in the same fields,but also reduce the uncertainties.The areal accuracy of the MODIS-derived results was validated by comparison with agricultural statistics,and the spatial matching was examined by ETM+(enhanced thematic mapper plus) images in a test region.Major factors that might cause errors,such as the coarse spatial resolution and noises in the MODIS data,were discussed.Although not suitable for monitoring the inter-annual variations due to some inevitable factors,the MODIS-derived results were useful for obtaining spatial distribution maps of paddy rice on a large scale,and they might provide reference for further studies.展开更多
A newgeneration of solar spectroradiometer has been developed by CUST/JRSI to improve solarirradiance observation data under hyperspectral resolution. It is based on the grating spectroradiometer with a back-thinned C...A newgeneration of solar spectroradiometer has been developed by CUST/JRSI to improve solarirradiance observation data under hyperspectral resolution. It is based on the grating spectroradiometer with a back-thinned CCD linear image sensor and is operated in a hermetically sealed enclosure. The solar spectroradiometer is designed to measure the solar spectral irradiance from300 nm to 1100 nm wavelength range with the spectral resolution of 2 nm( the full width at half maximum). The optical bench is optimized to minimize stray light. The Peltier device is used to stabilize the temperature of CCD sensor to 25℃,while the change of temperature of CCD sensor is controlled to ±1℃ by the dedicated Peltier driver and control circuit.展开更多
Leaf area index(LAI)is a key measure of forest stand physiology and biomass production,and is essential within ecosystem modeling.There are two common approaches to obtaining LAI:(i)terrestrial forest inventory-based...Leaf area index(LAI)is a key measure of forest stand physiology and biomass production,and is essential within ecosystem modeling.There are two common approaches to obtaining LAI:(i)terrestrial forest inventory-based“bottom-up”,and(ii)satellite-based“top-down”techniques.The purpose of this study is to compare terrestrial LAI from allometric functions applied to more than 30,000 trees of the Austrian National Forest Inventory(NFI)vs.satellite-based LAI estimates obtained from moderate resolution imaging spectroradiometer(MODIS)and Sentinel(Sentinel-3 TOC reflectance and PROBA-V)data across Austrian forests.We analyzed a satellite pixelto-plot aggregation and obtained the full inventory data set for the LAI comparison.The results suggest that terrestrial vs.satellite(MODIS and Sentinel)driven LAI estimates are consistent,but(i)the variation of the terrestrial forest inventory LAI is larger vs.the pixel average LAI from satellite data,and(ii)any satellite LAI estimation needs a forest stand density correction if the crown competition factor(CCF),a measure for stand density,is<250 to avoid an overestimation in LAI.展开更多
Fires are one of the most destructive natural disasters and have serious long-term effects on the environment,economy,and human health.In Inner Mongolia Autonomous Region,China,frequent fire disturbance occurs due to ...Fires are one of the most destructive natural disasters and have serious long-term effects on the environment,economy,and human health.In Inner Mongolia Autonomous Region,China,frequent fire disturbance occurs due to the intensification of climate change and human activities.It is crucial to understand the fire regime and estimate the probability of regional fire occurrence and reducing fire losses.However,most studies have primarily focused on the dynamic changes,probability of occurrence,and driving mechanisms of wildfires in the grassland and forest land ecosystems in Inner Mongolia,while insufficient research has been conducted on the spatiotemporal variations in active fires and their impact on the wildfire risk in forest land and grassland.Therefore,in this study,we analyzed the active fire regime based on Moderate Resolution Imaging Spectroradiometer(MODIS)thermal anomalies and burned area products from 2000 to 2022.Combined with climate,topographic,landscape,anthropogenic,and vegetation datasets,logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models were chosen to estimate the probability of active fire occurrence at the seasonal timescale.The results revealed that:(1)a total of 100,343 active fires occurred in Inner Mongolia and the burned area reached 6.59×104 km².The number of ignition point exhibited a significant increasing trend,while the burned area exhibited a nonsignificant decreasing trend;(2)four active fire belts were detected,namely,the Hetao-Tumochuan Plain fire belt,Xiliao River Plain fire belt,Songnen Plain fire belt,and Hailar River Eroded Plain fire belt.The centroid of the active fires has shifted 456.4 km toward the southwest;(3)RF model achieved the highest accuracy in estimating the probability of active fire occurrence,followed by CNN,and LR and SVM models had lower accuracies;and(4)the distribution of the high and extremely high fire risk areas largely aligned with the four fire belts.The probability of active fire occurrence was the highest in spring,followed by that in autumn,and it gradually decreased in summer and winter.Our results revealed active fires migrated to the southwest and ignition sources increased,despite reduction of the burned area was not significant.The RF model outperformed the other models in predicting the probability of active fire occurrence.These findings contribute to future fire prevention and prediction in Inner Mongolia.展开更多
Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climat...Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.展开更多
The photolysis frequency of NO2, j（NO2）, is an important analytical parameter in the study of tropospheric chemistry. A chemical actinometer （CA） was built to measure the ambient j（NQ） based on a high precision ...The photolysis frequency of NO2, j（NO2）, is an important analytical parameter in the study of tropospheric chemistry. A chemical actinometer （CA） was built to measure the ambient j（NQ） based on a high precision NOx instrument with 1 min time resolution. Parallel measurements of the ambient j（NO2） by using the CA and a commercial spectroradiometer （SR） were conducted at a typical urban site （Peking University Urban Environmental Monitoring Station） in Beijing. In general, good agreement was achieved between the CA and SR data with a high linear correlation coefficient （R2 = 0.977） and a regression slope of 1.12. The regression offset was negligible compared to the measured signal level. Thej（NO2） data were calculated using the tropospheric ultraviolet visible radiation （TUV） model, which was constrained to observe aerosol optical properties. The calculated j（NO2） was intermediate between the results obtained with CA and SR, demonstrating the consistency of all the parameters observed at this site. The good agreement between the CA and SR data, and the consistency with the TUV model results, demonstrate the good performance of the installed SR instrument. Since a drift of the SR sensitivity is expected by the manufacturer, we propose a regular check of the data acquired via SR against those obtained by CA for long-term delivery of a high quality series ofj（NO2） data. Establishing such a time series will be invaluable for analyzing the long-term atmospheric oxidation capacity trends as well as O3 pollution for urban Beijing.展开更多
Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton plan...Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accurately on a fine scale.However,previous research studies have predominantly concentrated on specific years using remote sensing data.Challenges still exist in the extraction of cotton areas for long time series with high accuracy.To address this issue,a novel cotton sample selection method was proposed and the machine learning method is employed to effectively identify the long time series cotton planting areas at a 30-m resolution scale.Bortala and Shuanghe in Xinjiang,China,were selected as the study cases to demonstrate the approach.Specifically,the cropland in this study was extracted by using an object-oriented classification method with Landsat images and the results were optimized as the vectorized boundary of croplands.Then,the cotton samples were selected using the Normalized Difference Vegetation Index(NDVI)series of Moderate Resolution Imaging Spectroradiometer(MODIS)based on its phenological characteristics.Next,cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model.Finally,the performance was evaluated,and the spatiotemporal distribution characteristics of cotton planting areas were analyzed.The results showed that the proposed approach can achieve high accuracy at a fine spatial resolution.The performance evaluation indicated the applicability and suitability of the method,there is a good correlation between the extracted cotton areas and statistical data,and the cotton area of the study area showed an increasing trend.The cotton spatial distribution pattern developed from dispersion to agglomeration.The proposed approach and the derived 30-m cotton maps can provide a scientific reference for the optimization of agricultural management.展开更多
随着上海港海上运输业和石油产业链的日趋发达,海上溢油事故风险也随之加剧。本文就2012年发生在上海海域吴淞口和九段沙附近的2起重大溢油事故,基于美国NASA(National Aeronautics and Space Administration)中等分辨率MODIS(Moderate-...随着上海港海上运输业和石油产业链的日趋发达,海上溢油事故风险也随之加剧。本文就2012年发生在上海海域吴淞口和九段沙附近的2起重大溢油事故,基于美国NASA(National Aeronautics and Space Administration)中等分辨率MODIS(Moderate-resolution Imaging Spectroradiometer)与国产"环境一号"卫星HJ-1的多源卫星数据,对溢油信息进行对比,通过对油水敏感通道进行波段比值运算,突出油膜与背景海水的光谱反射率差异,再结合重柴油光谱特征,利用图像分割的阈值确定法,从疑似溢油区域中有效提取溢油信息,实现溢油区域定位、溢油面积和溢油量的诊断,为事发后海域应急响应工作提供基础性分析依据。展开更多
China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this pap...China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.展开更多
A Local Ensemble Transform Kalman Filter assimilation system has been implemented into an aerosol-coupled global nonhydrostatic model to simulate the aerosol mass concentration and aerosol optical properties of 3 dese...A Local Ensemble Transform Kalman Filter assimilation system has been implemented into an aerosol-coupled global nonhydrostatic model to simulate the aerosol mass concentration and aerosol optical properties of 3 desert sites(Ansai, Fukang, Shapotou) in northwestern China. One-month experiment results of April 2006 reveal that the data assimilation can correct the much overestimated aerosol surface mass concentration, and has a strong positive effect on the aerosol optical depth(AOD) simulation, improving agreement with observations. Improvement is limited with the?ngstr€om Exponent(AE) simulation, except for much improved correlation coefficient and model skill scores over the Ansai site. Better agreement of the AOD spatial distribution with the independent observations of Terra(Deep Blue) and Multi-angle Imaging Spectroradiometer(MISR) AODs is obtained by assimilating the Moderate Resolution Imaging Spectroradiometer(MODIS) AOD product, especially for regions with AODs lower than 0.30. This study confirms the usefulness of the remote sensing observations for the improvement of global aerosol modeling.展开更多
We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts ...We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature,precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area,a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01),and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China,NPP showed 16-d,48-d,and 96-d lagged correlation with air temperature,precipitation,and sunshine percentage,respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d,while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region,the spatial patterns of vegetation-climate relationship became complicated and diversiform,especially for precipitation influences on NPP. In the northern part of the study area,all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.展开更多
Double-and triple-cropping in a year have played a very important role in meeting the rising need for food in China.However,the intensified agricultural practices have significantly altered biogeochemical cycles and s...Double-and triple-cropping in a year have played a very important role in meeting the rising need for food in China.However,the intensified agricultural practices have significantly altered biogeochemical cycles and soil quality.Understanding and mapping cropping intensity in China′s agricultural systems are therefore necessary to better estimate carbon,nitrogen and water fluxes within agro-ecosystems on the national scale.In this study,we investigated the spatial pattern of crop calendar and multiple cropping rotations in China using phenological records from 394 agro-meteorological stations(AMSs)across China.The results from the analysis of in situ field observations were used to develop a new algorithm that identifies the spatial distribution of multiple cropping in China from moderate resolution imaging spectroradiometer(MODIS)time series data with a 500 m spatial resolution and an 8-day temporal resolution.According to the MODIS-derived multiple cropping distribution in 2002,the proportion of cropland cultivated with multiple crops reached 34%in China.Double-cropping accounted for approximately 94.6%and triple-cropping for 5.4%.The results demonstrat that MODIS EVI(Enhanced Vegetation Index)time series data have the capability and potential to delineate the dynamics of double-and triple-cropping practices.The resultant multiple cropping map could be used to evaluate the impacts of agricultural intensification on biogeochemical cycles.展开更多
The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry o...The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.展开更多
基金funded by the Third Xinjiang Scientific Expedition Program(2021xjkk1400)the National Natural Science Foundation of China(42071049)+2 种基金the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2019D01C022)the Xinjiang Uygur Autonomous Region Innovation Environment Construction Special Project&Science and Technology Innovation Base Construction Project(PT2107)the Tianshan Talent-Science and Technology Innovation Team(2022TSYCTD0006).
文摘Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.
基金supported by the National High-Tech Research and Development Program (863) of China(No.2006AA120101)the National Natural Science Foundation of China(No.40871158/D0106)the Key Technologies Research and Development Program of China(No.2006BAD10A01)
文摘The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderate-resolution imaging spectroradiometer(MODIS) data in China.Paddy rice fields were extracted by identifying the unique char-acteristic of high soil moisture in the flooding and transplanting period with improved algorithms based on rice growth calendar regionalization.The characteristic could be reflected by the enhanced vegetation index(EVI) and the land surface water index(LSWI) derived from MODIS sensor data.Algorithms for single,early,and late rice identification were obtained from selected typical test sites.The algorithms could not only separate early rice and late rice planted in the same fields,but also reduce the uncertainties.The areal accuracy of the MODIS-derived results was validated by comparison with agricultural statistics,and the spatial matching was examined by ETM+(enhanced thematic mapper plus) images in a test region.Major factors that might cause errors,such as the coarse spatial resolution and noises in the MODIS data,were discussed.Although not suitable for monitoring the inter-annual variations due to some inevitable factors,the MODIS-derived results were useful for obtaining spatial distribution maps of paddy rice on a large scale,and they might provide reference for further studies.
基金supported from Meteorology Industry Research Special Funds for Public Welfare Projects (GYHY201406037)
文摘A newgeneration of solar spectroradiometer has been developed by CUST/JRSI to improve solarirradiance observation data under hyperspectral resolution. It is based on the grating spectroradiometer with a back-thinned CCD linear image sensor and is operated in a hermetically sealed enclosure. The solar spectroradiometer is designed to measure the solar spectral irradiance from300 nm to 1100 nm wavelength range with the spectral resolution of 2 nm( the full width at half maximum). The optical bench is optimized to minimize stray light. The Peltier device is used to stabilize the temperature of CCD sensor to 25℃,while the change of temperature of CCD sensor is controlled to ±1℃ by the dedicated Peltier driver and control circuit.
基金part of the Areas of Forest Innovation Climate Smart Forestry(Project No.101726),Work Package Modeling,funded by the Austrian Ministry of Agriculture,Forestry,Regions,and Water Management.
文摘Leaf area index(LAI)is a key measure of forest stand physiology and biomass production,and is essential within ecosystem modeling.There are two common approaches to obtaining LAI:(i)terrestrial forest inventory-based“bottom-up”,and(ii)satellite-based“top-down”techniques.The purpose of this study is to compare terrestrial LAI from allometric functions applied to more than 30,000 trees of the Austrian National Forest Inventory(NFI)vs.satellite-based LAI estimates obtained from moderate resolution imaging spectroradiometer(MODIS)and Sentinel(Sentinel-3 TOC reflectance and PROBA-V)data across Austrian forests.We analyzed a satellite pixelto-plot aggregation and obtained the full inventory data set for the LAI comparison.The results suggest that terrestrial vs.satellite(MODIS and Sentinel)driven LAI estimates are consistent,but(i)the variation of the terrestrial forest inventory LAI is larger vs.the pixel average LAI from satellite data,and(ii)any satellite LAI estimation needs a forest stand density correction if the crown competition factor(CCF),a measure for stand density,is<250 to avoid an overestimation in LAI.
基金funded by the First-Class Discipline Research Special Project of Inner Mongolia(YLXKZX-NSD-040)the Natural Science Foundation of Inner Mongolia(2022LHQN04003,2023QN04009)+1 种基金the Fundamental Research Funds for the Inner Mongolia University of Finance and Economics(NCXKY25019,NCYWZ22003)the National Social Science Fund of China(22BZS134).
文摘Fires are one of the most destructive natural disasters and have serious long-term effects on the environment,economy,and human health.In Inner Mongolia Autonomous Region,China,frequent fire disturbance occurs due to the intensification of climate change and human activities.It is crucial to understand the fire regime and estimate the probability of regional fire occurrence and reducing fire losses.However,most studies have primarily focused on the dynamic changes,probability of occurrence,and driving mechanisms of wildfires in the grassland and forest land ecosystems in Inner Mongolia,while insufficient research has been conducted on the spatiotemporal variations in active fires and their impact on the wildfire risk in forest land and grassland.Therefore,in this study,we analyzed the active fire regime based on Moderate Resolution Imaging Spectroradiometer(MODIS)thermal anomalies and burned area products from 2000 to 2022.Combined with climate,topographic,landscape,anthropogenic,and vegetation datasets,logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models were chosen to estimate the probability of active fire occurrence at the seasonal timescale.The results revealed that:(1)a total of 100,343 active fires occurred in Inner Mongolia and the burned area reached 6.59×104 km².The number of ignition point exhibited a significant increasing trend,while the burned area exhibited a nonsignificant decreasing trend;(2)four active fire belts were detected,namely,the Hetao-Tumochuan Plain fire belt,Xiliao River Plain fire belt,Songnen Plain fire belt,and Hailar River Eroded Plain fire belt.The centroid of the active fires has shifted 456.4 km toward the southwest;(3)RF model achieved the highest accuracy in estimating the probability of active fire occurrence,followed by CNN,and LR and SVM models had lower accuracies;and(4)the distribution of the high and extremely high fire risk areas largely aligned with the four fire belts.The probability of active fire occurrence was the highest in spring,followed by that in autumn,and it gradually decreased in summer and winter.Our results revealed active fires migrated to the southwest and ignition sources increased,despite reduction of the burned area was not significant.The RF model outperformed the other models in predicting the probability of active fire occurrence.These findings contribute to future fire prevention and prediction in Inner Mongolia.
基金Under the auspices of the National Key Research and Development Program of China(No.2019YFA0606603)。
文摘Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.
基金Acknowledgements We have profited from discussions with Dr. Franz Rohrer. We acknowledge financial support from the National Natural Science Foundation of China （Grant Nos. 91544225 and 41375124）, the Strategic Priority Research Program of the Chinese Academy of Sciences （No. XDB05010500）, and the special fund of the State Key Joint Laboratory of Environment Simulation and Pollution Control （13Z02ESPCP）.
文摘The photolysis frequency of NO2, j（NO2）, is an important analytical parameter in the study of tropospheric chemistry. A chemical actinometer （CA） was built to measure the ambient j（NQ） based on a high precision NOx instrument with 1 min time resolution. Parallel measurements of the ambient j（NO2） by using the CA and a commercial spectroradiometer （SR） were conducted at a typical urban site （Peking University Urban Environmental Monitoring Station） in Beijing. In general, good agreement was achieved between the CA and SR data with a high linear correlation coefficient （R2 = 0.977） and a regression slope of 1.12. The regression offset was negligible compared to the measured signal level. Thej（NO2） data were calculated using the tropospheric ultraviolet visible radiation （TUV） model, which was constrained to observe aerosol optical properties. The calculated j（NO2） was intermediate between the results obtained with CA and SR, demonstrating the consistency of all the parameters observed at this site. The good agreement between the CA and SR data, and the consistency with the TUV model results, demonstrate the good performance of the installed SR instrument. Since a drift of the SR sensitivity is expected by the manufacturer, we propose a regular check of the data acquired via SR against those obtained by CA for long-term delivery of a high quality series ofj（NO2） data. Establishing such a time series will be invaluable for analyzing the long-term atmospheric oxidation capacity trends as well as O3 pollution for urban Beijing.
基金supported by the National Natural Science Foundation of China[grant number 42101342]Third Comprehensive Scientific Expedition to Xinjiang[grant number 2021XJKK1403].
文摘Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accurately on a fine scale.However,previous research studies have predominantly concentrated on specific years using remote sensing data.Challenges still exist in the extraction of cotton areas for long time series with high accuracy.To address this issue,a novel cotton sample selection method was proposed and the machine learning method is employed to effectively identify the long time series cotton planting areas at a 30-m resolution scale.Bortala and Shuanghe in Xinjiang,China,were selected as the study cases to demonstrate the approach.Specifically,the cropland in this study was extracted by using an object-oriented classification method with Landsat images and the results were optimized as the vectorized boundary of croplands.Then,the cotton samples were selected using the Normalized Difference Vegetation Index(NDVI)series of Moderate Resolution Imaging Spectroradiometer(MODIS)based on its phenological characteristics.Next,cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model.Finally,the performance was evaluated,and the spatiotemporal distribution characteristics of cotton planting areas were analyzed.The results showed that the proposed approach can achieve high accuracy at a fine spatial resolution.The performance evaluation indicated the applicability and suitability of the method,there is a good correlation between the extracted cotton areas and statistical data,and the cotton area of the study area showed an increasing trend.The cotton spatial distribution pattern developed from dispersion to agglomeration.The proposed approach and the derived 30-m cotton maps can provide a scientific reference for the optimization of agricultural management.
文摘随着上海港海上运输业和石油产业链的日趋发达,海上溢油事故风险也随之加剧。本文就2012年发生在上海海域吴淞口和九段沙附近的2起重大溢油事故,基于美国NASA(National Aeronautics and Space Administration)中等分辨率MODIS(Moderate-resolution Imaging Spectroradiometer)与国产"环境一号"卫星HJ-1的多源卫星数据,对溢油信息进行对比,通过对油水敏感通道进行波段比值运算,突出油膜与背景海水的光谱反射率差异,再结合重柴油光谱特征,利用图像分割的阈值确定法,从疑似溢油区域中有效提取溢油信息,实现溢油区域定位、溢油面积和溢油量的诊断,为事发后海域应急响应工作提供基础性分析依据。
文摘China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.
基金supported by the funds from the National Natural Science Funds of China (41475031, 41130104)the Public Meteorology Special Foundation of MOST (GYHY201406023)+1 种基金the special fund of State Key Joint Laboratory of Environment Simulation and Pollution Control(15K02ESPCP)the JAXA/Earth CARE, the MEXT/VL for Climate System Diagnostics, the MOE/Global Environment Research Fund S-12 (14426634)and A-1101, the NIES/GOSAT, theS/ NIECGER, and the MEXT/RECCA/SALSA
文摘A Local Ensemble Transform Kalman Filter assimilation system has been implemented into an aerosol-coupled global nonhydrostatic model to simulate the aerosol mass concentration and aerosol optical properties of 3 desert sites(Ansai, Fukang, Shapotou) in northwestern China. One-month experiment results of April 2006 reveal that the data assimilation can correct the much overestimated aerosol surface mass concentration, and has a strong positive effect on the aerosol optical depth(AOD) simulation, improving agreement with observations. Improvement is limited with the?ngstr€om Exponent(AE) simulation, except for much improved correlation coefficient and model skill scores over the Ansai site. Better agreement of the AOD spatial distribution with the independent observations of Terra(Deep Blue) and Multi-angle Imaging Spectroradiometer(MISR) AODs is obtained by assimilating the Moderate Resolution Imaging Spectroradiometer(MODIS) AOD product, especially for regions with AODs lower than 0.30. This study confirms the usefulness of the remote sensing observations for the improvement of global aerosol modeling.
基金Project supported by the National High-Tech Research and Development Program (863) of China (No. 2006AA120101)the National Natural Science Foundation of China (Nos. 40871158 and 40875070)the Key Technologies Research and Development Program of China (No. 2006BAD10A01)
文摘We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature,precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area,a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01),and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China,NPP showed 16-d,48-d,and 96-d lagged correlation with air temperature,precipitation,and sunshine percentage,respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d,while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region,the spatial patterns of vegetation-climate relationship became complicated and diversiform,especially for precipitation influences on NPP. In the northern part of the study area,all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.
基金Under the auspices of Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues of Chinese Academy of Sciences(No.XDA05050602)Major State Basic Research Development Program of China(No.2010CB950904)+1 种基金National Natural Science Foundation of China(No.40921140410,41071344)Land Cover and Land Use Change Program of National Aeronautics and Space Administration,USA(No.NAG5-11160,NNG05GH80G)
文摘Double-and triple-cropping in a year have played a very important role in meeting the rising need for food in China.However,the intensified agricultural practices have significantly altered biogeochemical cycles and soil quality.Understanding and mapping cropping intensity in China′s agricultural systems are therefore necessary to better estimate carbon,nitrogen and water fluxes within agro-ecosystems on the national scale.In this study,we investigated the spatial pattern of crop calendar and multiple cropping rotations in China using phenological records from 394 agro-meteorological stations(AMSs)across China.The results from the analysis of in situ field observations were used to develop a new algorithm that identifies the spatial distribution of multiple cropping in China from moderate resolution imaging spectroradiometer(MODIS)time series data with a 500 m spatial resolution and an 8-day temporal resolution.According to the MODIS-derived multiple cropping distribution in 2002,the proportion of cropland cultivated with multiple crops reached 34%in China.Double-cropping accounted for approximately 94.6%and triple-cropping for 5.4%.The results demonstrat that MODIS EVI(Enhanced Vegetation Index)time series data have the capability and potential to delineate the dynamics of double-and triple-cropping practices.The resultant multiple cropping map could be used to evaluate the impacts of agricultural intensification on biogeochemical cycles.
基金This work was funded by the National Natural Science Foundation of China(U1603242)the Major Science and Technology Projects in Inner Mongolia,China(ZDZX2018054).
文摘The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.
