China's first Mars exploration mission will carry out comprehensive global surveys of the planet from data collected by instruments carried in orbit and roving on the planet itself.Goals of the mission include det...China's first Mars exploration mission will carry out comprehensive global surveys of the planet from data collected by instruments carried in orbit and roving on the planet itself.Goals of the mission include detailed inspections and surveys of key areas on the surface of Mars.One of the main scientific payloads installed on the orbiter is the moderate resolution camera.Its mission is to image the surface of Mars sufficiently to produce a global remote sensing image map of the planet,and to explore and record changes to the topography of Mars,including major geological structures,and to advance research on topography and geomorphology in general.The moderate resolution camera uses a lightweight and compact integrated design;its primary components are an optical module,a focal plane module,a camera control module,a power and interface module,a camera support module,a thermal control module,and a reference module.Radiometric calibration,color calibration,and geometric calibration have been carried out to ensure that the camera can acquire sufficient accurate data to complete mission goals.This paper introduces the camera's detection mission,its system composition,and its working principle;it also describes the camera's ground calibration tests and their results,and provides a reference for processing the camera's scientific data and for future applications.展开更多
Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultiv...Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.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
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
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
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
Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated fro...Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.展开更多
Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this stud...Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness(PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer(MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index(DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.展开更多
e The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer （MODIS） data. We developed an algorithm for detect...e The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer （MODIS） data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index （EVI） and land surface water index with a central wavelength at 2130 nm （LSW12130）. In two intensive sites in Northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3x3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination （R^2） value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.展开更多
The spatial pattern of rice paddies is an essential parameter used for studies of greenhouse gas emissions,agricultural resource management,and environmental monitoring.On large spatial scales,previous studies have us...The spatial pattern of rice paddies is an essential parameter used for studies of greenhouse gas emissions,agricultural resource management,and environmental monitoring.On large spatial scales,previous studies have usually mapped rice paddies using a single vegetation index product based on a traditional classification method,or a combined analysis of various vegetation and water indices derived from the moderate resolution imaging spectroradiometer(MODIS)satellite data.However,different indices increase the computational cost and constrain the satellite data sources,and traditional classification methods(e.g.,maximum likelihood classification)may be time-consuming and difficult to carry out over a large area like China.In this study,we designed an auto-thresholding and single vegetation index(normalized difference vegetation index(NDVI))-based procedure to estimate the spatial distribution of rice paddies in China.The MOD09Q1 product,which was available at MODIS’s highest spatial resolution(250 m),was taken as the input source.An auto-threshold function was also introduced into the change detection process to distinguish rice paddies from other croplands.Our MODIS-derived maps were validated with ground surveys and then compared with China national statistical data of rice paddy areas.The results indicated that the best classification result was achieved for plain regions,and that the accuracy declined for hilly regions,where the complex landscape could lead to an underestimation of the rice paddy area.A comparison between the modeled results and other analyses using 500-m MODIS data suggests that rice paddies may be identified routinely using a single vegetation index with finer resolution on large spatial scales.展开更多
In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapo...In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.展开更多
Snowmelt is an important component of any snow-fed river system.The Jhelum River is one such transnational mountain river flowing through India and Pakistan.The basin is minimally glacierized and its discharge is larg...Snowmelt is an important component of any snow-fed river system.The Jhelum River is one such transnational mountain river flowing through India and Pakistan.The basin is minimally glacierized and its discharge is largely governed by seasonal snow cover and snowmelt.Therefore,accurate estimation of seasonal snow cover dynamics and snowmeltinduced runoff is important for sustainable water resource management in the region.The present study looks into spatio-temporal variations of snow cover for past decade and stream flow simulation in the Jhelum River basin.Snow cover extent(SCE) was estimated using MODIS(Moderate Resolution Imaging Spectrometer) sensor imageries.Normalized Difference Snow Index(NDSI) algorithm was used to generate multi-temporal time series snow cover maps.The results indicate large variation in snow cover distribution pattern and decreasing trend in different sub-basins of the Jhelum River.The relationship between SCE-temperature,SCE-discharge and discharge-precipitation was analyzed for different seasons and shows strong correlation.For streamflow simulation of the entire Jhelum basin Snow melt Runoff Model(SRM) used.A good correlation was observed between simulated stream flow and in-situ discharge.The monthly discharge contribution from different sub-basins to the total discharge of the Jhelum River was estimated using a modified version of runoff model based on temperature-index approach developed for small watersheds.Stream power - an indicator of the erosive capability of streams was also calculated for different sub-basins.展开更多
In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1....In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1.38 μm were chosen to extract the water body information under the thin cloud.Two study cases were selected to validate the thin cloud removal method.One case was applied with the Earth Observation System Moderate Resolution Imaging Spectroradiometer(EOS/MODIS) data,and the other with the Medium Resolution Spectral Imager(MERSI) and Visible and Infrared Radiometer(VIRR) data from Fengyun-3A(FY-3A).The test results showed that thin cloud removal method did not change the reflectivity of the ground surface under the clear sky.To the area contaminated by the thin cloud,the reflectance decreased to be closer to the reference reflectance under the clear sky after the thin cloud removal.The spatial distribution of the water body area could not be extracted before the thin cloud removal,while water information could be easily identified by using proper near infrared channel threshold after removing the thin cloud.The thin cloud removal method could improve the image quality and water body extraction precision effectively.展开更多
Data on aerosol optical thickness(AOT) and single scattering albedo(SSA) derived from Moderate Resolution Imaging Spectrometer(MODIS) and Ozone Monitoring Instrument(OMI) measurements,respectively,are used jointly to ...Data on aerosol optical thickness(AOT) and single scattering albedo(SSA) derived from Moderate Resolution Imaging Spectrometer(MODIS) and Ozone Monitoring Instrument(OMI) measurements,respectively,are used jointly to examine the seasonal variations of aerosols over East Asia.The seasonal signals of the total AOT are well defined and nearly similar over the land and over the ocean.These findings indicate a natural cycle of aerosols that originate primarily from natural emissions. In contrast,the small-sized aerosols represented by the fine-mode AOT,which are primarily generated over the land by human activities,do not have evident seasonalscale fluctuations.A persistent maximum of aerosol loadings centered over the Sichuan basin is associated with considerable amounts of fine-mode aerosols throughout the year.Most regions exhibit a general spring maximum. During the summer,however,the aerosol loadings are the most marked over north central China.This occurrence may result from anthropogenic fine particles,such as sulfate and nitrate.Four typical regions were selected to perform a covariation analysis of the monthly gridded AOT and SSA.Over southwestern and southeastern China,if the aerosol loadings are small to moderate they are composed primarily of the highly absorptive aerosols. However,more substantial aerosol loadings probably represent less-absorptive aerosols.The opposite covariation pattern occurring over the coastal-adjacent oceans suggests that the polluted oceanic atmosphere is closely correlated with the windward terrestrial aerosols.North central China is strongly affected by dust aerosols that show moderate absorption.This finding may explain the lower variability in the SSA that accompanies increasing aerosol loadings in this region.展开更多
The Moderate Resolution Imaging Spectroradiometer(MODIS)surface reflectance data were used to analyze the temporal and spatial distribution characteristics of water clarity(Z_(sd))in the Jiaozhou Bay,Qingdao,China,in ...The Moderate Resolution Imaging Spectroradiometer(MODIS)surface reflectance data were used to analyze the temporal and spatial distribution characteristics of water clarity(Z_(sd))in the Jiaozhou Bay,Qingdao,China,in the Yellow Sea from 2000 to 2018.Z_(sd)retrieval models were regionally optimized using in-situ data with coincident MODIS images,and then were used to retrieve the Z_(sd) products in Jiaozhou Bay from 2000-2018.The analysis of the Z_(sd) results suggests that the spatial distribution of relative Z_(sd) spatial characteristics in Jiaozhou Bay was stable,being higher Z_(sd) in the southeast and a lower Z_(sd) in the northwest.The annual mean Z_(sd) in Jiaozhou Bay showed a significant upward trend,with an annual increase of approximately 0.02 m.Water depth and wind speed were important factors affecting the spatial distribution and annual variation of Z_(sd) in Jiaozhou Bay,respectively.展开更多
Burned area mapping is an essential step in the forest fire research to investigate the relationship between forest fire and cli- mate change and the effect of forest fire on carbon budgets. This study proposed an alg...Burned area mapping is an essential step in the forest fire research to investigate the relationship between forest fire and cli- mate change and the effect of forest fire on carbon budgets. This study proposed an algorithm to map forest fire burned area using the Moderate-Resolution Imaging Spectroradiameter （MODIS） time series data in Heilongjiang Province, China. The algorithm is divided into two steps： Firstly, the ＇core＇ pixels were extracted to represent the most possible burned pixels based on the comparison of the tem- poral change of Global Environmental Monitoring Index （GEMI）, Burned Area Index （BAI） and MODIS active fire products between pre- and post-fires. Secondly, a 15-km distance was set to extract the entire burned areas near the ＇core＇ pixels as more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre- and post-fires, which are represented by the MODIS fire products and the spectral index, respectively. Tahe, Mohe and Huma counties of Heilongjiang Province, China were chosen as the study area for burned area mapping and a time series of burned maps were produced from 2000 to 2011. The results show that the algorithm can extract burned areas more accurately with the hiehest accuracy of 96.61%.展开更多
Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Veg...Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Vegetation lndex （NDVI） derived from the Moderate Resolution Imaging Spectroradiometer （MODIS） data. Owing to the high contrast of greenness between wet season and dry season, the monsoon forest can be easily discriminated from other forests by combining the maximum and minimum annual NDVI. The MODIS-based monsoon forest maps （MODMF） from 2000 to 2009 are derived and evaluated using the ground-truth dataset. The MODMF achieves an average producer accuracy of 80.0% and the Kappa statistic of 0.719. The variability of MODMF among different years is compared with that calculated from MODIS land cover products （MCD 12Q 1）. The results show that the coefficient of variation of total monsoon forest area in MODMF is 7.3%, which is far lower than that in MCD12Q1 with 24.3%. Moreover, the pixels in MODMv which can be identified for 7 to 9 times between 200l and 2009 account for 53.1%, while only 7.9% ofMCD12QI pixels have this frequency. Additionally, the monsoon forest areas estimated in MODMF, Global Land Cover 2000 （GLC2000）, MCDI2Q1 and University of Maryland （UMD） products are compared with the statistical dataset at national level, which reveals that MODMv has the highest R^2 of 0.95 and the lowest RMSE of 14 014 km^2. This algorithm is simple but reliable for mapping the monsoon forests without complex classification techniques.展开更多
Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial ...Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.展开更多
Aerosol optical properties are simulated using the Spectral Radiation Transport Model I~）r Aerosol Species （SPRINTARS） coupled with the Non-hydrostatic ICosahedral Atmospheric Model （NICAM）. The 3-year global mea...Aerosol optical properties are simulated using the Spectral Radiation Transport Model I~）r Aerosol Species （SPRINTARS） coupled with the Non-hydrostatic ICosahedral Atmospheric Model （NICAM）. The 3-year global mean all-sky aerosol optical thickness （AOT） at 550 nm, theAngstr/Sm Exponent （AE） based on AOTs at 440 and 870 nm, and the single scattering albedo （SSA） at 550 nm are estimated at 0.123, 0.657 and 0.944, respectively. For each aerosol species, the mean AOT is within the range of the AeroCom models. Both the modeled all-sky and clear-sky results are compared with observations from the Moderate Resolution Imaging Spectroradiometer （MODIS） and the Aerosol Robotic Network （AERONET）. The simulated spatiotemporal distributions of all-sky AOTs can generally reproduce the MODIS retrievals, and the correlation and model skill can be slightly improved using the clear-sky results over most land regions. The differences between clear-sky and all-sky AOTs are larger over polluted regions. Compared with observations from AERONET, the modeled and observed all-sky AOTs and AEs are generally in reasonable agreement, whereas the SSA variation is not well captured. Although the spatiotemporal distributions of all-sky and clear-sky results are similar, the clear-sky results are generally better correlated with the observations. The clear-sky AOT and SSA are generally lower than the all-sky results, especially in those regions where the aerosol chemical composition is contributed to mostly by sulfate aerosol. The modeled clear-sky AE is larger than the all-sky AE over those regions dominated by hydrophilic aerosol, while the＇opposite is found over regions dominated by hydrophobic aerosol.展开更多
文摘China's first Mars exploration mission will carry out comprehensive global surveys of the planet from data collected by instruments carried in orbit and roving on the planet itself.Goals of the mission include detailed inspections and surveys of key areas on the surface of Mars.One of the main scientific payloads installed on the orbiter is the moderate resolution camera.Its mission is to image the surface of Mars sufficiently to produce a global remote sensing image map of the planet,and to explore and record changes to the topography of Mars,including major geological structures,and to advance research on topography and geomorphology in general.The moderate resolution camera uses a lightweight and compact integrated design;its primary components are an optical module,a focal plane module,a camera control module,a power and interface module,a camera support module,a thermal control module,and a reference module.Radiometric calibration,color calibration,and geometric calibration have been carried out to ensure that the camera can acquire sufficient accurate data to complete mission goals.This paper introduces the camera's detection mission,its system composition,and its working principle;it also describes the camera's ground calibration tests and their results,and provides a reference for processing the camera's scientific data and for future applications.
基金Under the auspices of the National Natural Science Foundation of China(No.42101342,U2243205)the Third Comprehensive Scientific Expedition to Xinjiang(No.2021XJKK1403)。
文摘Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.
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 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.
基金Under the auspices of Knowledge Innovation Programs of Chinese Academy of Sciences (No.KZCX2-YW-449,KSCX-YW-09)National Natural Science Foundation of China (No.40971025,40901030,50969003)
文摘Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.
基金Under the auspices of the National Natural Science Foundation of China(No.41306091)Public Science and Technology Research Funds Projects of Ocean(No.201505019-2)
文摘Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness(PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer(MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index(DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.
基金Project supported by the National High-Tech R&D Program (863) of China(No.2012AA12A30703)the Meteorology Industry Special Project of China Meteorological Administration(CMA)(No.GYHY 201306036)the Ph.D Programs Foundation of the Ministry of Education of China(No.20100101110035)
文摘e The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer （MODIS） data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index （EVI） and land surface water index with a central wavelength at 2130 nm （LSW12130）. In two intensive sites in Northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3x3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination （R^2） value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.
基金financially supported by the Strategic Priority Research Program of Chinese Academy of Sciences—Climate Change:Carbon Budget and Relevant Issues(No.XDA05020200)the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(NUIST),China(No.2016r036)the Innovation and Entrepreneurship Training Program for College Students of Jiangsu Provincial Department of Education,China(No.2017103000165)
文摘The spatial pattern of rice paddies is an essential parameter used for studies of greenhouse gas emissions,agricultural resource management,and environmental monitoring.On large spatial scales,previous studies have usually mapped rice paddies using a single vegetation index product based on a traditional classification method,or a combined analysis of various vegetation and water indices derived from the moderate resolution imaging spectroradiometer(MODIS)satellite data.However,different indices increase the computational cost and constrain the satellite data sources,and traditional classification methods(e.g.,maximum likelihood classification)may be time-consuming and difficult to carry out over a large area like China.In this study,we designed an auto-thresholding and single vegetation index(normalized difference vegetation index(NDVI))-based procedure to estimate the spatial distribution of rice paddies in China.The MOD09Q1 product,which was available at MODIS’s highest spatial resolution(250 m),was taken as the input source.An auto-threshold function was also introduced into the change detection process to distinguish rice paddies from other croplands.Our MODIS-derived maps were validated with ground surveys and then compared with China national statistical data of rice paddy areas.The results indicated that the best classification result was achieved for plain regions,and that the accuracy declined for hilly regions,where the complex landscape could lead to an underestimation of the rice paddy area.A comparison between the modeled results and other analyses using 500-m MODIS data suggests that rice paddies may be identified routinely using a single vegetation index with finer resolution on large spatial scales.
基金Under the auspices of National Basic Research Program of China (No. 2010CB951304-5)National Natural Science Foundation of China (No. 41101545,41030743)
文摘In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.
文摘Snowmelt is an important component of any snow-fed river system.The Jhelum River is one such transnational mountain river flowing through India and Pakistan.The basin is minimally glacierized and its discharge is largely governed by seasonal snow cover and snowmelt.Therefore,accurate estimation of seasonal snow cover dynamics and snowmeltinduced runoff is important for sustainable water resource management in the region.The present study looks into spatio-temporal variations of snow cover for past decade and stream flow simulation in the Jhelum River basin.Snow cover extent(SCE) was estimated using MODIS(Moderate Resolution Imaging Spectrometer) sensor imageries.Normalized Difference Snow Index(NDSI) algorithm was used to generate multi-temporal time series snow cover maps.The results indicate large variation in snow cover distribution pattern and decreasing trend in different sub-basins of the Jhelum River.The relationship between SCE-temperature,SCE-discharge and discharge-precipitation was analyzed for different seasons and shows strong correlation.For streamflow simulation of the entire Jhelum basin Snow melt Runoff Model(SRM) used.A good correlation was observed between simulated stream flow and in-situ discharge.The monthly discharge contribution from different sub-basins to the total discharge of the Jhelum River was estimated using a modified version of runoff model based on temperature-index approach developed for small watersheds.Stream power - an indicator of the erosive capability of streams was also calculated for different sub-basins.
基金Under the auspices of National Nature Science Foundation of China(No.40901231,41101517)
文摘In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1.38 μm were chosen to extract the water body information under the thin cloud.Two study cases were selected to validate the thin cloud removal method.One case was applied with the Earth Observation System Moderate Resolution Imaging Spectroradiometer(EOS/MODIS) data,and the other with the Medium Resolution Spectral Imager(MERSI) and Visible and Infrared Radiometer(VIRR) data from Fengyun-3A(FY-3A).The test results showed that thin cloud removal method did not change the reflectivity of the ground surface under the clear sky.To the area contaminated by the thin cloud,the reflectance decreased to be closer to the reference reflectance under the clear sky after the thin cloud removal.The spatial distribution of the water body area could not be extracted before the thin cloud removal,while water information could be easily identified by using proper near infrared channel threshold after removing the thin cloud.The thin cloud removal method could improve the image quality and water body extraction precision effectively.
基金supported by the Knowledge Innovation Program of the Chinese Academy of Sciences(Grant No.KZCX2-YW-Q11-03)
文摘Data on aerosol optical thickness(AOT) and single scattering albedo(SSA) derived from Moderate Resolution Imaging Spectrometer(MODIS) and Ozone Monitoring Instrument(OMI) measurements,respectively,are used jointly to examine the seasonal variations of aerosols over East Asia.The seasonal signals of the total AOT are well defined and nearly similar over the land and over the ocean.These findings indicate a natural cycle of aerosols that originate primarily from natural emissions. In contrast,the small-sized aerosols represented by the fine-mode AOT,which are primarily generated over the land by human activities,do not have evident seasonalscale fluctuations.A persistent maximum of aerosol loadings centered over the Sichuan basin is associated with considerable amounts of fine-mode aerosols throughout the year.Most regions exhibit a general spring maximum. During the summer,however,the aerosol loadings are the most marked over north central China.This occurrence may result from anthropogenic fine particles,such as sulfate and nitrate.Four typical regions were selected to perform a covariation analysis of the monthly gridded AOT and SSA.Over southwestern and southeastern China,if the aerosol loadings are small to moderate they are composed primarily of the highly absorptive aerosols. However,more substantial aerosol loadings probably represent less-absorptive aerosols.The opposite covariation pattern occurring over the coastal-adjacent oceans suggests that the polluted oceanic atmosphere is closely correlated with the windward terrestrial aerosols.North central China is strongly affected by dust aerosols that show moderate absorption.This finding may explain the lower variability in the SSA that accompanies increasing aerosol loadings in this region.
基金Supported by the National Key Research and Development Program of China(No.2017YFC0405804)the National Natural Science Foundation of China(Nos.41971318,41701402,41901272)the Science and Technology Service Network Initiative,Chinese Academy of Sciences(No.KFJ-STS-ZDTP-077)。
文摘The Moderate Resolution Imaging Spectroradiometer(MODIS)surface reflectance data were used to analyze the temporal and spatial distribution characteristics of water clarity(Z_(sd))in the Jiaozhou Bay,Qingdao,China,in the Yellow Sea from 2000 to 2018.Z_(sd)retrieval models were regionally optimized using in-situ data with coincident MODIS images,and then were used to retrieve the Z_(sd) products in Jiaozhou Bay from 2000-2018.The analysis of the Z_(sd) results suggests that the spatial distribution of relative Z_(sd) spatial characteristics in Jiaozhou Bay was stable,being higher Z_(sd) in the southeast and a lower Z_(sd) in the northwest.The annual mean Z_(sd) in Jiaozhou Bay showed a significant upward trend,with an annual increase of approximately 0.02 m.Water depth and wind speed were important factors affecting the spatial distribution and annual variation of Z_(sd) in Jiaozhou Bay,respectively.
基金Under the auspices of Strategic Pilot Science and Technology Projects of Chinese Academic Sciences(No.XDA05090310)
文摘Burned area mapping is an essential step in the forest fire research to investigate the relationship between forest fire and cli- mate change and the effect of forest fire on carbon budgets. This study proposed an algorithm to map forest fire burned area using the Moderate-Resolution Imaging Spectroradiameter （MODIS） time series data in Heilongjiang Province, China. The algorithm is divided into two steps： Firstly, the ＇core＇ pixels were extracted to represent the most possible burned pixels based on the comparison of the tem- poral change of Global Environmental Monitoring Index （GEMI）, Burned Area Index （BAI） and MODIS active fire products between pre- and post-fires. Secondly, a 15-km distance was set to extract the entire burned areas near the ＇core＇ pixels as more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre- and post-fires, which are represented by the MODIS fire products and the spectral index, respectively. Tahe, Mohe and Huma counties of Heilongjiang Province, China were chosen as the study area for burned area mapping and a time series of burned maps were produced from 2000 to 2011. The results show that the algorithm can extract burned areas more accurately with the hiehest accuracy of 96.61%.
基金National Natural Science Foundation of China(No.41171285)Research and Development Special Fund for Public Welfare Industry(Meteorology)of China(No.GYHY201106014)
文摘Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Vegetation lndex （NDVI） derived from the Moderate Resolution Imaging Spectroradiometer （MODIS） data. Owing to the high contrast of greenness between wet season and dry season, the monsoon forest can be easily discriminated from other forests by combining the maximum and minimum annual NDVI. The MODIS-based monsoon forest maps （MODMF） from 2000 to 2009 are derived and evaluated using the ground-truth dataset. The MODMF achieves an average producer accuracy of 80.0% and the Kappa statistic of 0.719. The variability of MODMF among different years is compared with that calculated from MODIS land cover products （MCD 12Q 1）. The results show that the coefficient of variation of total monsoon forest area in MODMF is 7.3%, which is far lower than that in MCD12Q1 with 24.3%. Moreover, the pixels in MODMv which can be identified for 7 to 9 times between 200l and 2009 account for 53.1%, while only 7.9% ofMCD12QI pixels have this frequency. Additionally, the monsoon forest areas estimated in MODMF, Global Land Cover 2000 （GLC2000）, MCDI2Q1 and University of Maryland （UMD） products are compared with the statistical dataset at national level, which reveals that MODMv has the highest R^2 of 0.95 and the lowest RMSE of 14 014 km^2. This algorithm is simple but reliable for mapping the monsoon forests without complex classification techniques.
基金the National Natural Science Foundation of China (40461001)
文摘Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.
基金National Natural Science Funds of China (Grant Nos. 41130104, and 41475031)Open Research Program of Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration from Nanjing University of Information Science and Technology (Grant No. KDW1302)+4 种基金the Public Meteorology Special Foundation of MOST (Grant No. GYHY201406023)the National Key Basic Research and Development Program (973 Program, 2011CB403401)Teruyuki NAKAJIMA is supported by projects from JAXA/EarthC ARE, MEXT/VL for Climate System Diagnosticsthe MOE/Global Environment Research Fund A-1101, NIES/GOSAT, NIES/CGER, MEXT/RECCA/SALSAthe S-12 of the MOE
文摘Aerosol optical properties are simulated using the Spectral Radiation Transport Model I~）r Aerosol Species （SPRINTARS） coupled with the Non-hydrostatic ICosahedral Atmospheric Model （NICAM）. The 3-year global mean all-sky aerosol optical thickness （AOT） at 550 nm, theAngstr/Sm Exponent （AE） based on AOTs at 440 and 870 nm, and the single scattering albedo （SSA） at 550 nm are estimated at 0.123, 0.657 and 0.944, respectively. For each aerosol species, the mean AOT is within the range of the AeroCom models. Both the modeled all-sky and clear-sky results are compared with observations from the Moderate Resolution Imaging Spectroradiometer （MODIS） and the Aerosol Robotic Network （AERONET）. The simulated spatiotemporal distributions of all-sky AOTs can generally reproduce the MODIS retrievals, and the correlation and model skill can be slightly improved using the clear-sky results over most land regions. The differences between clear-sky and all-sky AOTs are larger over polluted regions. Compared with observations from AERONET, the modeled and observed all-sky AOTs and AEs are generally in reasonable agreement, whereas the SSA variation is not well captured. Although the spatiotemporal distributions of all-sky and clear-sky results are similar, the clear-sky results are generally better correlated with the observations. The clear-sky AOT and SSA are generally lower than the all-sky results, especially in those regions where the aerosol chemical composition is contributed to mostly by sulfate aerosol. The modeled clear-sky AE is larger than the all-sky AE over those regions dominated by hydrophilic aerosol, while the＇opposite is found over regions dominated by hydrophobic aerosol.
