Land-cover mapping is one of the foundations of Earth science.As a result of the combined efforts of many scientists,numerous global land-cover(GLC)products with a resolution of 30 m have so far been generated.However...Land-cover mapping is one of the foundations of Earth science.As a result of the combined efforts of many scientists,numerous global land-cover(GLC)products with a resolution of 30 m have so far been generated.However,the increasing number of fineresolution GLC datasets is imposing additional workloads as it is necessary to confirm the quality of these datasets and check their suitability for user applications.To provide guidelines for users,in this study,the recent developments in currently available 30 m GLC products(including three GLC products and thematic products for four different land-cover types,i.e.,impervious surface,forest,cropland,and inland water)were first reviewed.Despite the great efforts toward improving mapping accuracy that there have been in recent decades,the current 30 m GLC products still suffer from having relatively low accuracies of between 46.0%and 88.9%for GlobeLand30-2010,57.71%and 80.36%for FROM_GLC-2015,and 65.59%and 84.33%for GLC_FCS30-2015.The reported accuracies for the global 30 m thematic maps vary from 67.86%to 95.1%for the eight impervious surface products that were reviewed,56.72%to 97.36%for the seven forest products,32.73%to 98.3%for the six cropland products,and 15.67%to 99.7%for the six inland water products.The consistency between the current GLC products was then examined.The GLC maps showed a good overall agreement in terms of spatial patterns but a limited agreement for some vegetation classes(such as shrub,tree,and grassland)in specific areas such as transition zones.Finally,the prospects for fine-resolution GLC mapping were also considered.With the rapid development of cloud computing platforms and big data,the Google Earth Engine(GEE)greatly facilitates the production of global fine-resolution land-cover maps by integrating multisource remote sensing datasets with advanced image processing and classification algorithms and powerful computing capability.The synergy between the spectral,spatial,and temporal features derived from multisource satellite datasets and stored in cloud computing platforms will definitely improve the classification accuracy and spatiotemporal resolution of fineresolution GLC products.In general,up to now,most land-cover maps have not been able to achieve the maximum(per class or overall)error of 5%–15%required by many applications.Therefore,more efforts are needed toward improving the accuracy of these GLC products,especially for classes for which the accuracy has so far been low(such as shrub,wetland,tundra,and grassland)and in terms of the overall quality of the maps.展开更多
Precipitation is one of the most important parameters in Earth system but is hard to measure.China began to develop satellites dedicated to precipitation measurements in the second generation of the FengYun polarorbit...Precipitation is one of the most important parameters in Earth system but is hard to measure.China began to develop satellites dedicated to precipitation measurements in the second generation of the FengYun polarorbiting meteorological satellite program(FY-3).The first of total 2 rainfall missions scheduled,FY-3G,was successfully launched on 16 April 2023 and became the world’s third satellite to measure precipitation with space-borne radar after the tropical rainfall measuring mission in 1997 and global precipitation measurement core observatory in 2014.In this manuscript,we illustrate the platform of FY-3G and instruments mounted in great detail,with additional information about ground segments,designed sensor-based products,and retrieval of geophysical parameters.During the 4 months after launch,the specifications of the platform and instruments are under inspection as calibration and validation are carefully conducted.The first images captured by FY-3G are encouraging,and initial results show a strong capability for providing insights into all kinds of precipitation phenomena.The important work of data processing,such as data assimilation,data fusion between space-based and ground-based radar,and that between polar and geostationary satellites,as well as future applications in weather modification,has been prepared in advance.As a pioneer of China’s rainfall missions,FY-3G greatly improves our ability to provide global precipitation measurements,understand Earth’s water and energy cycle,and forecast extreme events for the benefit of society.展开更多
Crop phenology is critical for agricultural management,crop yield estimation,and agroecosystem assessment.Traditionally,crop growth stages are observed from the ground,which is time-consuming and lacks spatial variabi...Crop phenology is critical for agricultural management,crop yield estimation,and agroecosystem assessment.Traditionally,crop growth stages are observed from the ground,which is time-consuming and lacks spatial variability.Remote sensing Vegetation Index(VI)time series has been used to map land surface phenology(LSP)and relate to crop growth stages mostly after the growing season.In recent years,high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season.This paper summarizes two classes of near-real-time mapping methods,i.e.,curve-based and trend-based approaches.The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages.The curve-based approaches are capable of a shortterm prediction.The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds.The trend-based approaches only use current observations.Both curve-based and trend-based approaches are promising in mapping crop growth stages timely.Nevertheless,mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages.The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season.Recent satellite datasets such as the harmonized Landsat and Sentinel-2(HLS)are promising for mapping crop phenology within the season over large areas.Operational applications in the near future are feasible.展开更多
Bottom depth(H)of optically shallow waters can be retrieved from multiband imagery,where remote sensing reflectance(R_(rs))are commonly used as the input.Because of the difficulties of removing the atmospheric effects...Bottom depth(H)of optically shallow waters can be retrieved from multiband imagery,where remote sensing reflectance(R_(rs))are commonly used as the input.Because of the difficulties of removing the atmospheric effects in coastal areas,quite often,there are no valid R_(rs) from satellites for the retrieval of H.More importantly,the empirical algorithms for H are hardly portable to new measurements.In this study,using data from Landsat-8 and ICESat-2 as examples,we present an approach to retrieve H directly from the top-of-atmosphere(TOA)data.It not only bypasses the requirement to correct the effects of aerosols but also shows promising portability to areas not included in algorithm development.Specifically,we use Rayleigh-corrected TOA reflectance(ρ_(rc))in the 443–2300nm range as input,along with a multilayer perceptron(MLP^(ρ_(rc))_(H)),for the retrieval of H.More than 78,000 matchup points ofρ_(rc)and H(0–25 m)were used to train MLP^(ρ_(rc))_(H),which resulted in a Mean Absolute Percentage Difference(MARD)of 8.8%and a coefficient of determination(R^(2))of 0.96.This MLP^(ρ_(rc))_(H)was further applied to Landsat-8 data of six regions not included in the training phase,generating MARD and R^(2)values of 8.3%and 0.98,respectively.In contrast,a conventional two-band ratio algorithm with R_(rs) as the input generated MARD and R^(2)values of 31.6%and 0.68 and significantly fewer H retrievals due to failures in atmospheric correction.These results indicate a breakthrough of algorithm portability of MLP^(ρ_(rc))_(H)in sensing H of optically shallow waters.展开更多
The Chinese High-resolution Earth Observation System(CHEOS)program has successfully launched 7 civilian satellites since 2010.These satellites are named by Gaofen(meaning high resolution in Chinese,hereafter noted as ...The Chinese High-resolution Earth Observation System(CHEOS)program has successfully launched 7 civilian satellites since 2010.These satellites are named by Gaofen(meaning high resolution in Chinese,hereafter noted as GF).To combine the advantages of high temporal and comparably high spatial resolution,diverse sensors are deployed to each satellite.GF-1 and GF-6 carry both high-resolution cameras(2m resolution panchromatic and 8m resolution multispectral camera),providing high spatial imaging for land use monitoring;GF-3 is equipped with a C-band multipolarization synthetic aperture radar with a spatial resolution of up to 1 meter,mostly monitoring marine targets;GF-5 carried 6 sensors including hyperspectral camera and directional polarization camera,dedicated to environmental remote sensing and climate research,such as aerosol,clouds,and greenhouse gas monitoring;and GF-7 laser altimeter system payload enables a three-dimensional surveying and mapping of natural resource and land surveying,facilitating the accumulation of basic geographic information.This study provides an overview of GF civilian series satellites,especially their missions,sensors,and applications.展开更多
The Ramsar Convention on Wetlands is an international framework through which countries identify and protect important wetlands.Yet Ramsar wetlands are under substantial anthropogenic pressure worldwide,and tracking e...The Ramsar Convention on Wetlands is an international framework through which countries identify and protect important wetlands.Yet Ramsar wetlands are under substantial anthropogenic pressure worldwide,and tracking ecological change relies on multitemporal data sets.Here,we evaluated the spatial extent,temporal change,and anthropogenic threat to Ramsar wetlands at a national scale across China to determine whether their management is currently sustainable.We analyzed Landsat data to examine wetland dynamics and anthropogenic threats at the 57 Ramsar wetlands in China between 1980 and 2018.Results reveal that Ramsar sites play important roles in preventing wetland loss compared to the dramatic decline of wetlands in the surrounding areas.However,there are declines in wetland area at 18 Ramsar sites.Among those,six lost a wetland area greater than 100 km^(2),primarily caused by agricultural activities.Consistent expansion of anthropogenic land covers occurred within 43(75%)Ramsar sites,and anthropogenic threats from land cover change were particularly notable in eastern China.Aquaculture pond expansion and Spartina alterniflora invasion were prominent threats to coastal Ramsar wetlands.The observations within China’s Ramsar sites,which in management regulations have higher levels of protection than other wetlands,can help track progress towards achieving United Nations Sustainable Development Goals(SDGs).The study findings suggest that further and timely actions are required to control the loss and degradation of wetland ecosystems.展开更多
This study mapped the areal extent,identified the species composition,and analyzed the changes of salt marshes in the intertidal zone of China during the period 1985–2019.With the aid of the cloud platform of the Goo...This study mapped the areal extent,identified the species composition,and analyzed the changes of salt marshes in the intertidal zone of China during the period 1985–2019.With the aid of the cloud platform of the Google Earth Engine,we selected Landsat 5/8 and Sentinel-2 images and used the support vector machine classification method to extract salt marsh information for the years of 1985,1990,1995,2000,2005,2010,2015,and 2019.Seven major species of salt marshes:Phragmites australis,Suaeda spp.,Spartina alterniflora,Scirpus mariqueter,Tamarix chinensis,Cyperus malaccensis,and Sesuvium portulacastrum were identified.Our results showed that salt marshes are mainly distributed in Liaoning,Shandong,Jiangsu,Shanghai,and Zhejiang,with varying patterns of shrinking,expansion,or wavering in different places.The distribution of salt marshes has declined considerably from 151,324 ha in 1985 to 115,397 ha in 2019,a drop of 23.7%.During the same period,the area of native species has dropped 95.4%from 77,741 ha to 3,563 ha for Suaeda spp.and 45.1%from 60,511 ha to 33,193 ha for P.australis;on the contrary,the area of exotic species,S.alterniflora,has exhibited a sharp rise from just 99 ha to 67,527 ha.For the past 35 years,the driving factors causing salt marsh changes are mainly land reclamation,variations in water and sand fluxes,and interspecific competition and succession of salt marsh vegetation.These results provide fundamental reference information and could form the scientific basis for formulating policies for the conservation and utilization of salt marsh resources in China.展开更多
Timely and accurate information on tree species(TS)is crucial for developing strategies for sustainable management and conservation of artificial and natural forests.Over the last four decades,advances in remote sensi...Timely and accurate information on tree species(TS)is crucial for developing strategies for sustainable management and conservation of artificial and natural forests.Over the last four decades,advances in remote sensing technologies have made TS classification possible.Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature,it is necessary to conduct an updated review on the status,trends,potentials,and challenges and to recommend future directions.The review will provide an overview on various optical and light detection and ranging(LiDAR)sensors;present and assess current various techniques/methods for,and a general trend of method development in,TS classification;and identify limitations and recommend future directions.In this review,several concluding remarks were made.They include the following:(1)A large group of studies on the topic were using high-resolution satellite,airborne multi-/hyperspectral imagery,and airborne LiDAR data.(2)A trend of“multiple”method development for the topic was observed.(3)Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy.(4)Recently,unmanned aerial vehicle-(UAV-)based sensors have caught the interest of researchers and practitioners for the topic-related research and applications.In addition,three future directions were recommended,including refining the three categories of“multiple”methods,developing novel data fusion algorithms or processing chains,and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.展开更多
Fractional vegetation cover(FVC)is a critical biophysical parameter that characterizes the status of terrestrial ecosystems.The spatial resolutions of most existing FVC products are still at the kilometer level.Howeve...Fractional vegetation cover(FVC)is a critical biophysical parameter that characterizes the status of terrestrial ecosystems.The spatial resolutions of most existing FVC products are still at the kilometer level.However,there is growing demand for FVC products with high spatial and temporal resolutions in remote sensing applications.This study developed an operational method to generate 30-m/15-day FVC products over China.Landsat datasets were employed to generate a continuous normalized difference vegetation index(NDVI)time series based on the Google Earth Engine platform from 2010 to 2020.The NDVI was transformed to FVC using an improved vegetation index(VI)-based mixture model,which quantitatively calculated the pixelwise coefficients to transform the NDVI to FVC.A comparison between the generated FVC,the Global LAnd Surface Satellite(GLASS)FVC,and a global FVC product(GEOV3 FVC)indicated consistent spatial patterns and temporal profiles,with a root mean square deviation(RMSD)value near 0.1 and an R^(2) value of approximately 0.8.Direct validation was conducted using ground measurements from croplands at the Huailai site and forests at the Saihanba site.Additionally,validation was performed with the FVC time series data observed at 151 plots in 22 small watersheds.The generated FVC showed a reasonable accuracy(RMSD values of less than 0.10 for the Huailai and Saihanba sites)and temporal trajectories that were similar to the field-measured FVC(RMSD values below 0.1 and R^(2) values of approximately 0.9 for most small watersheds).The proposed method outperformed the traditional VIbased mixture model and had the practicability and flexibility to generate the FVC at different resolutions and at a large scale.展开更多
Both leaf inclination angle distribution(LAD)and leaf area index(LAI)dominate optical remote sensing signals.The G-function,which is a function of LAD and remote sensing geometry,is often set to 0.5 in the LAI retriev...Both leaf inclination angle distribution(LAD)and leaf area index(LAI)dominate optical remote sensing signals.The G-function,which is a function of LAD and remote sensing geometry,is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD.Large uncertainties are thus introduced.However,because numerous tiny leaves grow on conifers,it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval.In this study,we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval.Specifically,a Multi-Directional Imager(MDI)was developed to capture stereo images of the branches,and the needles were reconstructed.The accuracy of the inclination angles calculated from the reconstructed needles was high.Moreover,we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and threedimensional(3D)tree models.Results show that the constant G assumption introduces large errors in LAI retrieval,which could be as large as 53%in the zenithal viewing direction used by spaceborne LiDAR.As a result,accurate LAD estimation is recommended.In the absence of such data,our results show that a viewing zenith angle between 45 and 65 degrees is a good choice,at which the errors of LAI retrieval caused by the spherical assumption will be less than 10%for coniferous canopies.展开更多
Optical remote sensing(ORS)of reflected sun light has been used to assess oil spills in the ocean for several decades.While most applications are toward simple presence/absence detections based on the spatial contrast...Optical remote sensing(ORS)of reflected sun light has been used to assess oil spills in the ocean for several decades.While most applications are toward simple presence/absence detections based on the spatial contrast between oiled water and oil-free water,recent advances indicate the possibility of classifying oil types and quantifying oil volumes based on their spectral contrasts with oil-free water.However,a review of the current literature suggests that there is still confusion on whether this is possible and,if so,how.Here,based on the recent findings from numerical models,laboratory measurements,and applications to satellite or airborne imagery,we attempt to clarify this situation by summarizing(1)the optics behind oil spill remote sensing,and in turn,(2)how to interpret optical remote sensing imagery based on optical principles.In the end,we discuss the existing limitations and challenges as well as pathways forward to advance ORS of oil spills.展开更多
The importance of solar-induced chlorophyll fluorescence(SIF)to monitoring vegetation photosynthesis has attracted much attention from the ecological and remote sensing research communities.Space-borne SIF products ha...The importance of solar-induced chlorophyll fluorescence(SIF)to monitoring vegetation photosynthesis has attracted much attention from the ecological and remote sensing research communities.Space-borne SIF products have been obtained owing to the rapid development of atmospheric satellites in recent years.The SIF Imaging Spectrometer(SIFIS)is a payload onboard the upcoming Terrestrial Ecosystem Carbon Inventory Satellite(TECIS-1)that is specifically designed for SIF monitoring.We conducted an in situ experiment to evaluate the performance of SIFIS on spectral measurement and SIF retrieval through comparison to the commercial spectrometer QE Pro.Disregarding the spatiotemporal mismatch between the collected measurements of the two spectrometers,the radiance spectra obtained synchronously by SIFIS and QE Pro showed a high level of consistency.The SIF retrieval,normalized difference vegetation index(NDVI),and near-infrared radiance of vegetation(NIRvR)results for a push-broom image shows consistent spatial distributions over both vegetated and nonvegetated surfaces.A quantitative comparison was conducted by strictly filtering matching pixels.For the far-red band,a high correlation was obtained between the SIF retrieval performances of SIFIS and QE Pro with R^(2)=0:70 and RMSE=0:30mWm^(−2)sr^(−−1)nm^(−1).However,a relatively poor correlation was observed for the red band with an R^(2)value of 0.23 and an RMSE of 0.26 mWm^(−2)sr^(-−1)nm^(−1).Despite the large uncertainties associated with this experiment,the results indicate that TECIS-1 should offer a reliable SIF monitoring performance after its launch.展开更多
As the second largest producer of maize,China contributes 23%of global maize production and plays an important role in guaranteeing maize markets stability.In spite of its importance,there is no 30m spatial resolution...As the second largest producer of maize,China contributes 23%of global maize production and plays an important role in guaranteeing maize markets stability.In spite of its importance,there is no 30m spatial resolution distribution map of maize for all of China.This study used a time-weighted dynamic time warping method to identify planting areas of maize by comparing the similarity of time series of a satellite-based vegetation index at each pixel with a standard time series derived from known maize fields and mapped maize distribution from 2016 to 2020 over 22 provinces accounting for more than 99%of the maize planting area in China.Based on 18800 field-surveyed pixels at 30-meter spatial resolution,the distribution map yields 76.15%and 81.59%of producer’s and user’s accuracies averaged over the entire investigated provinces,respectively.Municipality-and county-level census data also show a good performance in reproducing the spatial distribution of maize.This study provides an approach to mapping maize over large areas based on a small volume of field survey data.展开更多
TanSat is China’s first greenhouse gases observing satellite.In recent years,substantial progresses have been achieved on retrieving column-averaged CO_(2)dry air mole fraction(XCO_(2)).However,relatively few attempt...TanSat is China’s first greenhouse gases observing satellite.In recent years,substantial progresses have been achieved on retrieving column-averaged CO_(2)dry air mole fraction(XCO_(2)).However,relatively few attempts have been made to estimate terrestrial net ecosystem exchange(NEE)using TanSat XCO_(2)retrievals.In this study,based on the GEOS-Chem 4D-Var data assimilation system,we infer the global NEE from April 2017 to March 2018 using TanSat XCO_(2).The inversion estimates global NEE at−3.46 PgC yr^(-1),evidently higher than prior estimate and giving rise to an improved estimate of global atmospheric CO_(2)growth rate.Regionally,our inversion greatly increases the carbon uptakes in northern mid-to-high latitudes and significantly enhances the carbon releases in tropical and southern lands,especially in Africa and India peninsula.The increase of posterior sinks in northern lands is mainly attributed to the decreased carbon release during the nongrowing season,and the decrease of carbon uptakes in tropical and southern lands basically occurs throughout the year.Evaluations against independent CO_(2)observations and comparison with previous estimates indicate that although the land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent,they are obviously overestimated in northern high latitudes and underestimated in tropical lands(mainly northern Africa),respectively.These results suggest that TanSat XCO_(2)retrievals may have systematic negative biases in northern high latitudes and large positive biases over northern Africa,and further efforts are required to remove bias in these regions for better estimates of global and regional NEE.展开更多
Global land cover map provides fundamental information for understanding the relationship between global environmental change and human settlement.With the development of data-driven deep learning theory,semantic segm...Global land cover map provides fundamental information for understanding the relationship between global environmental change and human settlement.With the development of data-driven deep learning theory,semantic segmentation network has largely facilitated the global land cover mapping activity.However,the performance of semantic segmentation network is closely related to the number and quality of training data,and the existing annotation data are usually insufficient in quantity,quality,and spatial resolution,and are usually sampled at local region and lack diversity and variability,making data-driven model difficult to extend to global scale.Therefore,we proposed a large-scale annotation dataset(Globe230k)for semantic segmentation of remote sensing image,which has 3 superiorities:(a)large scale:the Globe230k dataset includes 232,819 annotated images with a size of 512×512 and a spatial resolution of 1 m,including 10 firstlevel categories;(b)rich diversity:the annotated images are sampled from worldwide regions,with coverage area of over 60,000 km^(2),indicating a high variability and diversity;(c)multimodal:the Globe230k dataset not only contains RGB bands but also includes other important features for Earth system research,such as normalized differential vegetation index(NDVI),digital elevation model(DEM),vertical-vertical polarization(VV)bands,and vertical-horizontal polarization(VH)bands,which can facilitate the multimodal data fusion research.We used the Globe230k dataset to test several state-of-the-art semantic segmentation algorithms and found that it is able to evaluate algorithms in multiple aspects that are crucial for characterizing land covers,including multiscale modeling,detail reconstruction,and generalization ability.The dataset has been made public and can be used as a benchmark to promote further development of global land cover mapping and semantic segmentation algorithm development.展开更多
Over the past 2 to 3 decades,Chinese forests are estimated to act as a large carbon sink,yet the magnitude and spatial patterns of this sink differ considerably among studies.Using 3 microwave(L-and X-band vegetation ...Over the past 2 to 3 decades,Chinese forests are estimated to act as a large carbon sink,yet the magnitude and spatial patterns of this sink differ considerably among studies.Using 3 microwave(L-and X-band vegetation optical depth[VOD])and 3 optical(normalized difference vegetation index,leaf area index,and tree cover)remote-sensing vegetation products,this study compared the estimated live woody aboveground biomass carbon(AGC)dynamics over China between 2013 and 2019.Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps(mean correlation value R=0.84),followed by L-VOD(R=0.83),which outperform the other VODs.An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019.The performance of the AGC estimation model was good(root mean square error=0.05 Pg C and R^(2)=0.90 with a mean relative uncertainty of 9.8% at pixel scale[0.25°]).Results of the AGC estimation model showed that carbon uptake by the forests in China was about+0.17 Pg C year^(-1) from 2013 to 2019.At the regional level,provinces in southwest China including Guizhou(+22.35 Tg C year^(-1)),Sichuan(+14.49 Tg C year^(-1)),and Hunan(+11.42 Tg C year^(-1))provinces had the highest carbon sink rates during 2013 to 2019.Most of the carbon-sink regions have been afforested recently,implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.展开更多
The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000.This review intends to summarize the history,development trends,scientific collaborations,disciplines involv...The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000.This review intends to summarize the history,development trends,scientific collaborations,disciplines involved,and research hotspots of these products.Its aim is to intrigue researchers and stimulate new research direction.Based on literature data from the Web of Science(WOS)and associated funding information,we conducted a bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020 using bibliometric and social network analysis(SNA)methods.We drew the following conclusions:(1)research based on the MODIS LAI/FPAR shows an upward trend with a multiyear average growth rate of 24.9%in the number of publications.(2)Researchers from China and the USA are the backbone of this research area,among which the Chinese Academy of Sciences(CAS)is the core research institution.(3)Research based on the MODIS LAI/FPAR covers a wide range of disciplines but mainly focus on environmental science and ecology.(4)Ecology,crop production estimation,algorithm improvement,and validation are the hotspots of these studies.(5)Broadening the research field,improving the algorithms,and overcoming existing difficulties in heterogeneous surface,scale effects,and complex terrains will be the trend of future research.Our work provides a clear view of the development of the MODIS LAI/FPAR products and valuable information for scholars to broaden their research fields.展开更多
Urbanization affects vegetation within city administrative boundary and nearby rural areas.Gross primary production(GPP)of vegetation in global urban areas is one of important metrics for assessing the impacts of urba...Urbanization affects vegetation within city administrative boundary and nearby rural areas.Gross primary production(GPP)of vegetation in global urban areas is one of important metrics for assessing the impacts of urbanization on terrestrial ecosystems.To date,very limited data and information on the spatial-temporal dynamics of GPP in the global urban areas are available.In this study,we reported the spatial distribution and temporal dynamics of annual GPP during 2000–2016 from 8,182 gridcells(0.5°by 0.5°latitude and longitude)that have various proportion of urban areas.Approximately 79.3%of these urban gridcells had increasing trends of annual GPP during 2000-2016.As urban area proportion(%)within individual urban gridcells increased,the means of annual GPP trends also increased.Our results suggested that for those urban gridcells,the negative effect of urban expansion(often measured by impervious surfaces)on GPP was to large degree compensated by increased vegetation within the gridcells,mostly driven by urban management and local climate and environment.Our findings on the continued increases of annual GPP in most of urban gridcells shed new insight on the importance of urban areas on terrestrial carbon cycle and the potential of urban management and local climate and environment on improving vegetation in urban areas.展开更多
Reforestation is an eco-friendly strategy for countering rising carbon dioxide concentrations in the atmosphere and the negative effects of forest loss and degradation.China,with one of the world’s most considerable ...Reforestation is an eco-friendly strategy for countering rising carbon dioxide concentrations in the atmosphere and the negative effects of forest loss and degradation.China,with one of the world’s most considerable afforestation rates,has increased its forest cover from 16.6%20 years ago to 23.0%by 2020.However,the maximum potential forest coverage achieved via tree planting and restoration is uncertain.To map potential tree coverage across China,we developed a random forest regression model relating environmental factors and appropriate forest types.We estimate 67.2 million hectares of land currently available for tree restoration after excluding existing forested areas,urban areas,and agriculture land covers/uses,which is 50%higher than the current understanding.Converting these lands to the forest would generate 3.99 gigatons of new above-and belowground carbon stocks,representing an important contribution to achieving carbon neutrality.This potential is spatially imbalanced,with the largest restorable carbon potential being located in the southwest(29.5%),followed by the northeast(17.2%)and northwest(16.8%).Our study highlights the need to align tree restoration areas with the uneven distribution of carbon sequestration potential.In addition to being a biological mitigation strategy to partially offset carbon dioxide emissions from fossil fuel burning,reforestation should provide other environmental services such as the restoration of degraded soils,conservation of biological diversity,revitalization of hydrological integrity,localized cooling,and improvement in air quality.Because of the collective benefits of forest restoration,we encourage that such activities be ecosystem focused as opposed to solely focusing on tree planting.展开更多
The MODerate Resolution Imaging Spectroradiometer(MODIS)MCD43A products have been extensively applied in the remote sensing field,but recent researchers have demonstrated that these products still had the potential to...The MODerate Resolution Imaging Spectroradiometer(MODIS)MCD43A products have been extensively applied in the remote sensing field,but recent researchers have demonstrated that these products still had the potential to be further improved by using the latest development of the kernel-driven model[RossThick-LiSparseReciprocal-Snow(RTLSRS)]in snow-covered areas,since the MCD43A product algorithm[RossThick-LiSparseReciprocal(RTLSR)]needed to be improved for the accurate simulation of snow bidirectional reflectance distribution function(BRDF)signatures.In this paper,we proposed a practical approach to improve the MCD43A products,which used the Polarization and Directionality of the Earth's Reflectance(POLDER)observations and random forest algorithm to establish the relationship between the BRDF parameters(MCD43A1)estimated by the RTLSR and RTLSRS models.We applied this relationship to correct the MCD43A1 product and retrieve the corresponding albedo(MCD43A3)and nadir reflectance(MCD43A4).The results obtained highlight several aspects:(a)The proposed approach can perform well in correcting BRDF parameters[root mean square error(RMSE)=~0.04].(b)The corrected BRDF parameters were then used to retrieve snow albedo,which matched up quite well with the results of the RTLSRS model.(c)Finally,the snow albedo retrieved by the proposed approach was assessed using ground-based albedo observations.Results indicated that the retrieved snow albedo showed a higher accuracy as compared to the station measurements(RMSE=0.055,bias=0.005),which was better than the results of the MODIS albedo product(RMSE=0.064,bias=-0.018),especially at large angles.These results demonstrated that this proposed approach presented the potential to further improve the MCD43A products in snow-covered areas.展开更多
基金funded by the National Natural Science Foundation of China(41825002)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19090200)the Key Research Program of the Chi-nese Academy of Sciences(grant number ZDRW-ZS-2019-1).
文摘Land-cover mapping is one of the foundations of Earth science.As a result of the combined efforts of many scientists,numerous global land-cover(GLC)products with a resolution of 30 m have so far been generated.However,the increasing number of fineresolution GLC datasets is imposing additional workloads as it is necessary to confirm the quality of these datasets and check their suitability for user applications.To provide guidelines for users,in this study,the recent developments in currently available 30 m GLC products(including three GLC products and thematic products for four different land-cover types,i.e.,impervious surface,forest,cropland,and inland water)were first reviewed.Despite the great efforts toward improving mapping accuracy that there have been in recent decades,the current 30 m GLC products still suffer from having relatively low accuracies of between 46.0%and 88.9%for GlobeLand30-2010,57.71%and 80.36%for FROM_GLC-2015,and 65.59%and 84.33%for GLC_FCS30-2015.The reported accuracies for the global 30 m thematic maps vary from 67.86%to 95.1%for the eight impervious surface products that were reviewed,56.72%to 97.36%for the seven forest products,32.73%to 98.3%for the six cropland products,and 15.67%to 99.7%for the six inland water products.The consistency between the current GLC products was then examined.The GLC maps showed a good overall agreement in terms of spatial patterns but a limited agreement for some vegetation classes(such as shrub,tree,and grassland)in specific areas such as transition zones.Finally,the prospects for fine-resolution GLC mapping were also considered.With the rapid development of cloud computing platforms and big data,the Google Earth Engine(GEE)greatly facilitates the production of global fine-resolution land-cover maps by integrating multisource remote sensing datasets with advanced image processing and classification algorithms and powerful computing capability.The synergy between the spectral,spatial,and temporal features derived from multisource satellite datasets and stored in cloud computing platforms will definitely improve the classification accuracy and spatiotemporal resolution of fineresolution GLC products.In general,up to now,most land-cover maps have not been able to achieve the maximum(per class or overall)error of 5%–15%required by many applications.Therefore,more efforts are needed toward improving the accuracy of these GLC products,especially for classes for which the accuracy has so far been low(such as shrub,wetland,tundra,and grassland)and in terms of the overall quality of the maps.
基金supported by the FY3-03 meteorological satellite project ground application system,and the International Space Water Cycle Observation Constellation Program(grant no.183311KYSB20200015).
文摘Precipitation is one of the most important parameters in Earth system but is hard to measure.China began to develop satellites dedicated to precipitation measurements in the second generation of the FengYun polarorbiting meteorological satellite program(FY-3).The first of total 2 rainfall missions scheduled,FY-3G,was successfully launched on 16 April 2023 and became the world’s third satellite to measure precipitation with space-borne radar after the tropical rainfall measuring mission in 1997 and global precipitation measurement core observatory in 2014.In this manuscript,we illustrate the platform of FY-3G and instruments mounted in great detail,with additional information about ground segments,designed sensor-based products,and retrieval of geophysical parameters.During the 4 months after launch,the specifications of the platform and instruments are under inspection as calibration and validation are carefully conducted.The first images captured by FY-3G are encouraging,and initial results show a strong capability for providing insights into all kinds of precipitation phenomena.The important work of data processing,such as data assimilation,data fusion between space-based and ground-based radar,and that between polar and geostationary satellites,as well as future applications in weather modification,has been prepared in advance.As a pioneer of China’s rainfall missions,FY-3G greatly improves our ability to provide global precipitation measurements,understand Earth’s water and energy cycle,and forecast extreme events for the benefit of society.
基金supported by the National Aeronautics and Space Administration(NASA)Land CoverLand Use MuSLI program(NNH17ZDA001NLCLUC)and the U.S.Geological Survey(USGS)Landsat Science Team program to FGsupported by the USDA grant GRANT12685068 and the NASA grant 80NSSC20K1337 to XZ.
文摘Crop phenology is critical for agricultural management,crop yield estimation,and agroecosystem assessment.Traditionally,crop growth stages are observed from the ground,which is time-consuming and lacks spatial variability.Remote sensing Vegetation Index(VI)time series has been used to map land surface phenology(LSP)and relate to crop growth stages mostly after the growing season.In recent years,high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season.This paper summarizes two classes of near-real-time mapping methods,i.e.,curve-based and trend-based approaches.The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages.The curve-based approaches are capable of a shortterm prediction.The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds.The trend-based approaches only use current observations.Both curve-based and trend-based approaches are promising in mapping crop growth stages timely.Nevertheless,mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages.The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season.Recent satellite datasets such as the harmonized Landsat and Sentinel-2(HLS)are promising for mapping crop phenology within the season over large areas.Operational applications in the near future are feasible.
基金Financial supports from the Chinese Ministry of Science and Technology through the National Key Research and Development Program of China(#2016YFC1400905)the National Natural Science Foundation of China(#41941008,#41890803,and#41830102)the Joint Polar Satellite System(JPSS)funding for the NOAA ocean color calibration and validation(Cal/Val)project,and the University of Massachusetts Boston are greatly appreciated。
文摘Bottom depth(H)of optically shallow waters can be retrieved from multiband imagery,where remote sensing reflectance(R_(rs))are commonly used as the input.Because of the difficulties of removing the atmospheric effects in coastal areas,quite often,there are no valid R_(rs) from satellites for the retrieval of H.More importantly,the empirical algorithms for H are hardly portable to new measurements.In this study,using data from Landsat-8 and ICESat-2 as examples,we present an approach to retrieve H directly from the top-of-atmosphere(TOA)data.It not only bypasses the requirement to correct the effects of aerosols but also shows promising portability to areas not included in algorithm development.Specifically,we use Rayleigh-corrected TOA reflectance(ρ_(rc))in the 443–2300nm range as input,along with a multilayer perceptron(MLP^(ρ_(rc))_(H)),for the retrieval of H.More than 78,000 matchup points ofρ_(rc)and H(0–25 m)were used to train MLP^(ρ_(rc))_(H),which resulted in a Mean Absolute Percentage Difference(MARD)of 8.8%and a coefficient of determination(R^(2))of 0.96.This MLP^(ρ_(rc))_(H)was further applied to Landsat-8 data of six regions not included in the training phase,generating MARD and R^(2)values of 8.3%and 0.98,respectively.In contrast,a conventional two-band ratio algorithm with R_(rs) as the input generated MARD and R^(2)values of 31.6%and 0.68 and significantly fewer H retrievals due to failures in atmospheric correction.These results indicate a breakthrough of algorithm portability of MLP^(ρ_(rc))_(H)in sensing H of optically shallow waters.
基金This study was supported by the National Natural Science Foundation of China(Grant Nos.41830109,42025504,42175152,41871254,and 41701406).
文摘The Chinese High-resolution Earth Observation System(CHEOS)program has successfully launched 7 civilian satellites since 2010.These satellites are named by Gaofen(meaning high resolution in Chinese,hereafter noted as GF).To combine the advantages of high temporal and comparably high spatial resolution,diverse sensors are deployed to each satellite.GF-1 and GF-6 carry both high-resolution cameras(2m resolution panchromatic and 8m resolution multispectral camera),providing high spatial imaging for land use monitoring;GF-3 is equipped with a C-band multipolarization synthetic aperture radar with a spatial resolution of up to 1 meter,mostly monitoring marine targets;GF-5 carried 6 sensors including hyperspectral camera and directional polarization camera,dedicated to environmental remote sensing and climate research,such as aerosol,clouds,and greenhouse gas monitoring;and GF-7 laser altimeter system payload enables a three-dimensional surveying and mapping of natural resource and land surveying,facilitating the accumulation of basic geographic information.This study provides an overview of GF civilian series satellites,especially their missions,sensors,and applications.
基金supported by the National Key R&D Program of China(grant numbers 2016YFC0500201 and 2016YFA0602301)the National Natural Science Foundation of China(grant numbers 41771383 and 41730643)+1 种基金the Science and Technology Development Program of Jilin Prov-ince(grant number 20200301014RQ)the funding from the Youth Innovation Promotion Association of Chinese Academy of Sciences(grant numbers 2017277 and 2012178)。
文摘The Ramsar Convention on Wetlands is an international framework through which countries identify and protect important wetlands.Yet Ramsar wetlands are under substantial anthropogenic pressure worldwide,and tracking ecological change relies on multitemporal data sets.Here,we evaluated the spatial extent,temporal change,and anthropogenic threat to Ramsar wetlands at a national scale across China to determine whether their management is currently sustainable.We analyzed Landsat data to examine wetland dynamics and anthropogenic threats at the 57 Ramsar wetlands in China between 1980 and 2018.Results reveal that Ramsar sites play important roles in preventing wetland loss compared to the dramatic decline of wetlands in the surrounding areas.However,there are declines in wetland area at 18 Ramsar sites.Among those,six lost a wetland area greater than 100 km^(2),primarily caused by agricultural activities.Consistent expansion of anthropogenic land covers occurred within 43(75%)Ramsar sites,and anthropogenic threats from land cover change were particularly notable in eastern China.Aquaculture pond expansion and Spartina alterniflora invasion were prominent threats to coastal Ramsar wetlands.The observations within China’s Ramsar sites,which in management regulations have higher levels of protection than other wetlands,can help track progress towards achieving United Nations Sustainable Development Goals(SDGs).The study findings suggest that further and timely actions are required to control the loss and degradation of wetland ecosystems.
基金This study was supported in part by the Ministry of Natural Resources(Blue Carbon Initiative and Policy)the Department of Science and Technology,Zhejiang Province(2016C04004)the Fundamental Research Funds for the Zhejiang Provincial Universities(2021XZZX012).
文摘This study mapped the areal extent,identified the species composition,and analyzed the changes of salt marshes in the intertidal zone of China during the period 1985–2019.With the aid of the cloud platform of the Google Earth Engine,we selected Landsat 5/8 and Sentinel-2 images and used the support vector machine classification method to extract salt marsh information for the years of 1985,1990,1995,2000,2005,2010,2015,and 2019.Seven major species of salt marshes:Phragmites australis,Suaeda spp.,Spartina alterniflora,Scirpus mariqueter,Tamarix chinensis,Cyperus malaccensis,and Sesuvium portulacastrum were identified.Our results showed that salt marshes are mainly distributed in Liaoning,Shandong,Jiangsu,Shanghai,and Zhejiang,with varying patterns of shrinking,expansion,or wavering in different places.The distribution of salt marshes has declined considerably from 151,324 ha in 1985 to 115,397 ha in 2019,a drop of 23.7%.During the same period,the area of native species has dropped 95.4%from 77,741 ha to 3,563 ha for Suaeda spp.and 45.1%from 60,511 ha to 33,193 ha for P.australis;on the contrary,the area of exotic species,S.alterniflora,has exhibited a sharp rise from just 99 ha to 67,527 ha.For the past 35 years,the driving factors causing salt marsh changes are mainly land reclamation,variations in water and sand fluxes,and interspecific competition and succession of salt marsh vegetation.These results provide fundamental reference information and could form the scientific basis for formulating policies for the conservation and utilization of salt marsh resources in China.
基金supported by the University of South Florida,USA.
文摘Timely and accurate information on tree species(TS)is crucial for developing strategies for sustainable management and conservation of artificial and natural forests.Over the last four decades,advances in remote sensing technologies have made TS classification possible.Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature,it is necessary to conduct an updated review on the status,trends,potentials,and challenges and to recommend future directions.The review will provide an overview on various optical and light detection and ranging(LiDAR)sensors;present and assess current various techniques/methods for,and a general trend of method development in,TS classification;and identify limitations and recommend future directions.In this review,several concluding remarks were made.They include the following:(1)A large group of studies on the topic were using high-resolution satellite,airborne multi-/hyperspectral imagery,and airborne LiDAR data.(2)A trend of“multiple”method development for the topic was observed.(3)Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy.(4)Recently,unmanned aerial vehicle-(UAV-)based sensors have caught the interest of researchers and practitioners for the topic-related research and applications.In addition,three future directions were recommended,including refining the three categories of“multiple”methods,developing novel data fusion algorithms or processing chains,and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.
基金financially supported by the National Natural Science Foundation of China(grant nos.42090013,42271338,and 41871230).
文摘Fractional vegetation cover(FVC)is a critical biophysical parameter that characterizes the status of terrestrial ecosystems.The spatial resolutions of most existing FVC products are still at the kilometer level.However,there is growing demand for FVC products with high spatial and temporal resolutions in remote sensing applications.This study developed an operational method to generate 30-m/15-day FVC products over China.Landsat datasets were employed to generate a continuous normalized difference vegetation index(NDVI)time series based on the Google Earth Engine platform from 2010 to 2020.The NDVI was transformed to FVC using an improved vegetation index(VI)-based mixture model,which quantitatively calculated the pixelwise coefficients to transform the NDVI to FVC.A comparison between the generated FVC,the Global LAnd Surface Satellite(GLASS)FVC,and a global FVC product(GEOV3 FVC)indicated consistent spatial patterns and temporal profiles,with a root mean square deviation(RMSD)value near 0.1 and an R^(2) value of approximately 0.8.Direct validation was conducted using ground measurements from croplands at the Huailai site and forests at the Saihanba site.Additionally,validation was performed with the FVC time series data observed at 151 plots in 22 small watersheds.The generated FVC showed a reasonable accuracy(RMSD values of less than 0.10 for the Huailai and Saihanba sites)and temporal trajectories that were similar to the field-measured FVC(RMSD values below 0.1 and R^(2) values of approximately 0.9 for most small watersheds).The proposed method outperformed the traditional VIbased mixture model and had the practicability and flexibility to generate the FVC at different resolutions and at a large scale.
基金supported by the key program of the National Natural Science Foundation of China(NSFC)(Grant No.42090013)Guangxi Innovative Development Grand Grant under the grant number:Guike AA18118038the China Scholarship Council,Grant No.201906040055.
文摘Both leaf inclination angle distribution(LAD)and leaf area index(LAI)dominate optical remote sensing signals.The G-function,which is a function of LAD and remote sensing geometry,is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD.Large uncertainties are thus introduced.However,because numerous tiny leaves grow on conifers,it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval.In this study,we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval.Specifically,a Multi-Directional Imager(MDI)was developed to capture stereo images of the branches,and the needles were reconstructed.The accuracy of the inclination angles calculated from the reconstructed needles was high.Moreover,we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and threedimensional(3D)tree models.Results show that the constant G assumption introduces large errors in LAI retrieval,which could be as large as 53%in the zenithal viewing direction used by spaceborne LiDAR.As a result,accurate LAD estimation is recommended.In the absence of such data,our results show that a viewing zenith angle between 45 and 65 degrees is a good choice,at which the errors of LAI retrieval caused by the spherical assumption will be less than 10%for coniferous canopies.
基金supported by the University of South Florida,the U.S.NOAA(NA15OAR4320064)the National Natural Science Foundation of China(No.42071387).
文摘Optical remote sensing(ORS)of reflected sun light has been used to assess oil spills in the ocean for several decades.While most applications are toward simple presence/absence detections based on the spatial contrast between oiled water and oil-free water,recent advances indicate the possibility of classifying oil types and quantifying oil volumes based on their spectral contrasts with oil-free water.However,a review of the current literature suggests that there is still confusion on whether this is possible and,if so,how.Here,based on the recent findings from numerical models,laboratory measurements,and applications to satellite or airborne imagery,we attempt to clarify this situation by summarizing(1)the optics behind oil spill remote sensing,and in turn,(2)how to interpret optical remote sensing imagery based on optical principles.In the end,we discuss the existing limitations and challenges as well as pathways forward to advance ORS of oil spills.
基金This research was funded by the National Natural Science Foundation of China(41825002).
文摘The importance of solar-induced chlorophyll fluorescence(SIF)to monitoring vegetation photosynthesis has attracted much attention from the ecological and remote sensing research communities.Space-borne SIF products have been obtained owing to the rapid development of atmospheric satellites in recent years.The SIF Imaging Spectrometer(SIFIS)is a payload onboard the upcoming Terrestrial Ecosystem Carbon Inventory Satellite(TECIS-1)that is specifically designed for SIF monitoring.We conducted an in situ experiment to evaluate the performance of SIFIS on spectral measurement and SIF retrieval through comparison to the commercial spectrometer QE Pro.Disregarding the spatiotemporal mismatch between the collected measurements of the two spectrometers,the radiance spectra obtained synchronously by SIFIS and QE Pro showed a high level of consistency.The SIF retrieval,normalized difference vegetation index(NDVI),and near-infrared radiance of vegetation(NIRvR)results for a push-broom image shows consistent spatial distributions over both vegetated and nonvegetated surfaces.A quantitative comparison was conducted by strictly filtering matching pixels.For the far-red band,a high correlation was obtained between the SIF retrieval performances of SIFIS and QE Pro with R^(2)=0:70 and RMSE=0:30mWm^(−2)sr^(−−1)nm^(−1).However,a relatively poor correlation was observed for the red band with an R^(2)value of 0.23 and an RMSE of 0.26 mWm^(−2)sr^(-−1)nm^(−1).Despite the large uncertainties associated with this experiment,the results indicate that TECIS-1 should offer a reliable SIF monitoring performance after its launch.
基金The research is funded by the China National Funds for Distinguished Young Scientists(41925001).
文摘As the second largest producer of maize,China contributes 23%of global maize production and plays an important role in guaranteeing maize markets stability.In spite of its importance,there is no 30m spatial resolution distribution map of maize for all of China.This study used a time-weighted dynamic time warping method to identify planting areas of maize by comparing the similarity of time series of a satellite-based vegetation index at each pixel with a standard time series derived from known maize fields and mapped maize distribution from 2016 to 2020 over 22 provinces accounting for more than 99%of the maize planting area in China.Based on 18800 field-surveyed pixels at 30-meter spatial resolution,the distribution map yields 76.15%and 81.59%of producer’s and user’s accuracies averaged over the entire investigated provinces,respectively.Municipality-and county-level census data also show a good performance in reproducing the spatial distribution of maize.This study provides an approach to mapping maize over large areas based on a small volume of field survey data.
基金This work is supported by the National Key R&D Program of China(Grant Nos.2016YFA0600204 and SQ2019YFE013078)the National Natural Science Foundation of China(Grant Nos.41807434 and 41907378)+3 种基金the Key Research Program of the Chinese Academy of Sciences(ZDRW-ZS-2019-1)We acknowledge all atmospheric data providers to obspack_co2_1_GLOBALVIEWplus_v6.0_2020-09-11.The TCCON data were obtained from the TCCON Data Archive hosted by CaltechDATA at https://tccondata.org.The CarbonTracker CT2019 results are provided by NOAA ESRL,Boulder,Colorado,USA,from the website at http://carbontracker.noaa.govWe are also grateful to the High-Performance Computing Center(HPCC)of Nanjing University for doing the numerical calculations in this paper on its blade cluster systemThe TanSat data product service is provided by IRCSD and CASA(131211KYSB20180002).
文摘TanSat is China’s first greenhouse gases observing satellite.In recent years,substantial progresses have been achieved on retrieving column-averaged CO_(2)dry air mole fraction(XCO_(2)).However,relatively few attempts have been made to estimate terrestrial net ecosystem exchange(NEE)using TanSat XCO_(2)retrievals.In this study,based on the GEOS-Chem 4D-Var data assimilation system,we infer the global NEE from April 2017 to March 2018 using TanSat XCO_(2).The inversion estimates global NEE at−3.46 PgC yr^(-1),evidently higher than prior estimate and giving rise to an improved estimate of global atmospheric CO_(2)growth rate.Regionally,our inversion greatly increases the carbon uptakes in northern mid-to-high latitudes and significantly enhances the carbon releases in tropical and southern lands,especially in Africa and India peninsula.The increase of posterior sinks in northern lands is mainly attributed to the decreased carbon release during the nongrowing season,and the decrease of carbon uptakes in tropical and southern lands basically occurs throughout the year.Evaluations against independent CO_(2)observations and comparison with previous estimates indicate that although the land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent,they are obviously overestimated in northern high latitudes and underestimated in tropical lands(mainly northern Africa),respectively.These results suggest that TanSat XCO_(2)retrievals may have systematic negative biases in northern high latitudes and large positive biases over northern Africa,and further efforts are required to remove bias in these regions for better estimates of global and regional NEE.
基金supported by the National Key Research and Development Program of China(2022YFB3903402)the National Natural Science Foundation of China(42222106,61976234,and 42201340).
文摘Global land cover map provides fundamental information for understanding the relationship between global environmental change and human settlement.With the development of data-driven deep learning theory,semantic segmentation network has largely facilitated the global land cover mapping activity.However,the performance of semantic segmentation network is closely related to the number and quality of training data,and the existing annotation data are usually insufficient in quantity,quality,and spatial resolution,and are usually sampled at local region and lack diversity and variability,making data-driven model difficult to extend to global scale.Therefore,we proposed a large-scale annotation dataset(Globe230k)for semantic segmentation of remote sensing image,which has 3 superiorities:(a)large scale:the Globe230k dataset includes 232,819 annotated images with a size of 512×512 and a spatial resolution of 1 m,including 10 firstlevel categories;(b)rich diversity:the annotated images are sampled from worldwide regions,with coverage area of over 60,000 km^(2),indicating a high variability and diversity;(c)multimodal:the Globe230k dataset not only contains RGB bands but also includes other important features for Earth system research,such as normalized differential vegetation index(NDVI),digital elevation model(DEM),vertical-vertical polarization(VV)bands,and vertical-horizontal polarization(VH)bands,which can facilitate the multimodal data fusion research.We used the Globe230k dataset to test several state-of-the-art semantic segmentation algorithms and found that it is able to evaluate algorithms in multiple aspects that are crucial for characterizing land covers,including multiscale modeling,detail reconstruction,and generalization ability.The dataset has been made public and can be used as a benchmark to promote further development of global land cover mapping and semantic segmentation algorithm development.
基金supported by the National Science Fund for Distinguished Young Scholars(41825020)the National Natural Science Foundation of China(42171339)+1 种基金the Postdoctoral Start-Up Project of Southwest University(SWU020016)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA05050200).
文摘Over the past 2 to 3 decades,Chinese forests are estimated to act as a large carbon sink,yet the magnitude and spatial patterns of this sink differ considerably among studies.Using 3 microwave(L-and X-band vegetation optical depth[VOD])and 3 optical(normalized difference vegetation index,leaf area index,and tree cover)remote-sensing vegetation products,this study compared the estimated live woody aboveground biomass carbon(AGC)dynamics over China between 2013 and 2019.Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps(mean correlation value R=0.84),followed by L-VOD(R=0.83),which outperform the other VODs.An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019.The performance of the AGC estimation model was good(root mean square error=0.05 Pg C and R^(2)=0.90 with a mean relative uncertainty of 9.8% at pixel scale[0.25°]).Results of the AGC estimation model showed that carbon uptake by the forests in China was about+0.17 Pg C year^(-1) from 2013 to 2019.At the regional level,provinces in southwest China including Guizhou(+22.35 Tg C year^(-1)),Sichuan(+14.49 Tg C year^(-1)),and Hunan(+11.42 Tg C year^(-1))provinces had the highest carbon sink rates during 2013 to 2019.Most of the carbon-sink regions have been afforested recently,implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.
基金supported by the National Natural Science Foundation of China[grant number 41901298]the Open Fund of State Key Laboratory of Remote Sensing Science[grant number OFSLRSS201924]+1 种基金the Open Research Fund of Key Laboratory of Digital Earth Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences[grant number 2018LDE002]the Fundamental Research Funds for the Central Universities[grant number 2652018031].
文摘The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000.This review intends to summarize the history,development trends,scientific collaborations,disciplines involved,and research hotspots of these products.Its aim is to intrigue researchers and stimulate new research direction.Based on literature data from the Web of Science(WOS)and associated funding information,we conducted a bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020 using bibliometric and social network analysis(SNA)methods.We drew the following conclusions:(1)research based on the MODIS LAI/FPAR shows an upward trend with a multiyear average growth rate of 24.9%in the number of publications.(2)Researchers from China and the USA are the backbone of this research area,among which the Chinese Academy of Sciences(CAS)is the core research institution.(3)Research based on the MODIS LAI/FPAR covers a wide range of disciplines but mainly focus on environmental science and ecology.(4)Ecology,crop production estimation,algorithm improvement,and validation are the hotspots of these studies.(5)Broadening the research field,improving the algorithms,and overcoming existing difficulties in heterogeneous surface,scale effects,and complex terrains will be the trend of future research.Our work provides a clear view of the development of the MODIS LAI/FPAR products and valuable information for scholars to broaden their research fields.
基金This work was supported in part by research grants from the US National Science Foundation(grant numbers OIA-1301789,OIA-1946093,and 1911955)the NASA Geostationary Carbon Cycle Observatory Mission(grant number 80LARC17C0001)+1 种基金the National Natural Science Foundation of China(grant number 42071415)the Outstanding Youth Foundation of Henan Natural Science Foundation(grant number 202300410049).
文摘Urbanization affects vegetation within city administrative boundary and nearby rural areas.Gross primary production(GPP)of vegetation in global urban areas is one of important metrics for assessing the impacts of urbanization on terrestrial ecosystems.To date,very limited data and information on the spatial-temporal dynamics of GPP in the global urban areas are available.In this study,we reported the spatial distribution and temporal dynamics of annual GPP during 2000–2016 from 8,182 gridcells(0.5°by 0.5°latitude and longitude)that have various proportion of urban areas.Approximately 79.3%of these urban gridcells had increasing trends of annual GPP during 2000-2016.As urban area proportion(%)within individual urban gridcells increased,the means of annual GPP trends also increased.Our results suggested that for those urban gridcells,the negative effect of urban expansion(often measured by impervious surfaces)on GPP was to large degree compensated by increased vegetation within the gridcells,mostly driven by urban management and local climate and environment.Our findings on the continued increases of annual GPP in most of urban gridcells shed new insight on the importance of urban areas on terrestrial carbon cycle and the potential of urban management and local climate and environment on improving vegetation in urban areas.
基金This study was supported by the National Natural Science Foundation of China(grant no.42071022)the start-up fund provided by Southern University of Science and Technology(no.29/Y01296122).
文摘Reforestation is an eco-friendly strategy for countering rising carbon dioxide concentrations in the atmosphere and the negative effects of forest loss and degradation.China,with one of the world’s most considerable afforestation rates,has increased its forest cover from 16.6%20 years ago to 23.0%by 2020.However,the maximum potential forest coverage achieved via tree planting and restoration is uncertain.To map potential tree coverage across China,we developed a random forest regression model relating environmental factors and appropriate forest types.We estimate 67.2 million hectares of land currently available for tree restoration after excluding existing forested areas,urban areas,and agriculture land covers/uses,which is 50%higher than the current understanding.Converting these lands to the forest would generate 3.99 gigatons of new above-and belowground carbon stocks,representing an important contribution to achieving carbon neutrality.This potential is spatially imbalanced,with the largest restorable carbon potential being located in the southwest(29.5%),followed by the northeast(17.2%)and northwest(16.8%).Our study highlights the need to align tree restoration areas with the uneven distribution of carbon sequestration potential.In addition to being a biological mitigation strategy to partially offset carbon dioxide emissions from fossil fuel burning,reforestation should provide other environmental services such as the restoration of degraded soils,conservation of biological diversity,revitalization of hydrological integrity,localized cooling,and improvement in air quality.Because of the collective benefits of forest restoration,we encourage that such activities be ecosystem focused as opposed to solely focusing on tree planting.
基金supported by the Fundamental Research Funds for the Central Universities(No.JZ2023HGQA0148)the National Natural Science Foundation of China(No.41971288).
文摘The MODerate Resolution Imaging Spectroradiometer(MODIS)MCD43A products have been extensively applied in the remote sensing field,but recent researchers have demonstrated that these products still had the potential to be further improved by using the latest development of the kernel-driven model[RossThick-LiSparseReciprocal-Snow(RTLSRS)]in snow-covered areas,since the MCD43A product algorithm[RossThick-LiSparseReciprocal(RTLSR)]needed to be improved for the accurate simulation of snow bidirectional reflectance distribution function(BRDF)signatures.In this paper,we proposed a practical approach to improve the MCD43A products,which used the Polarization and Directionality of the Earth's Reflectance(POLDER)observations and random forest algorithm to establish the relationship between the BRDF parameters(MCD43A1)estimated by the RTLSR and RTLSRS models.We applied this relationship to correct the MCD43A1 product and retrieve the corresponding albedo(MCD43A3)and nadir reflectance(MCD43A4).The results obtained highlight several aspects:(a)The proposed approach can perform well in correcting BRDF parameters[root mean square error(RMSE)=~0.04].(b)The corrected BRDF parameters were then used to retrieve snow albedo,which matched up quite well with the results of the RTLSRS model.(c)Finally,the snow albedo retrieved by the proposed approach was assessed using ground-based albedo observations.Results indicated that the retrieved snow albedo showed a higher accuracy as compared to the station measurements(RMSE=0.055,bias=0.005),which was better than the results of the MODIS albedo product(RMSE=0.064,bias=-0.018),especially at large angles.These results demonstrated that this proposed approach presented the potential to further improve the MCD43A products in snow-covered areas.
