China is expanding and sharing its capacity for Earth observation by developing sensors,platforms,and launch capabilities in tandem with growing lunar and deep space exploration.China is considering the Moon as a viab...China is expanding and sharing its capacity for Earth observation by developing sensors,platforms,and launch capabilities in tandem with growing lunar and deep space exploration.China is considering the Moon as a viable Earth observation platform to provide high-quality,planetary-scale data.The platform would produce consistent spatiotemporal data because of its long operational life and the geological stability of the Moon.China is also quickly improving its capabilities in processing and transforming Earth observation data into useful and practical information.Programs such as the Big Earth Data Science Engineering Program(CASEarth)provide opportunities to integrate data and develop“Big Earth Data”platforms to add value to data through analysis and integration.Such programs can offer products and services independently and in collaboration with international partners for data-driven decision support and policy development.With the rapid digital transformation of societies,and consequently increasing demand for big data and associated products,Digital Earth and the Digital Belt and Road Program(DBAR)allow Chinese experts to collaborate with international partners to integrate valuable Earth observation data in regional and global sustainable development.展开更多
In the context of urbanization,air pollution has emerged as a significant environmental challenge.A thorough understanding of their transport pathways,especially at a national scale,is essential for environmental prot...In the context of urbanization,air pollution has emerged as a significant environmental challenge.A thorough understanding of their transport pathways,especially at a national scale,is essential for environmental protection and policy-making.However,it remains partially elusive due to the constraints of available data and analytical methods.This study proposed a data-driven spatiotemporal correlation analysis method employing the Dynamic Time Warping(DTW).We represented the first comprehensive attempt to chart the long-term and nationwide transport pathways of PM_(2.5) utilizing an extensive dataset spanning from 2000 to 2021 across China,which is crucial for understanding long-term air pollution trends.Compared with traditional chemical transport models(CTMs),this data-driven method can generate transport pathways of PM_(2.5) without requiring extensive meteorological or emission data,and suggesting fundamentally consistent spatial distribution and trends.Our analysis reveals that China’s transport pathways are notably pronounced in the Northwest(34%of the total pathways in China),Southwest(22%),and North(21%)regions,with less significant pathways in the Northeast(10%)region and isolated occurrences elsewhere.Additionally,a notable decrease in the number of China’s PM_(2.5) transport pathways,similar to annual average concentrations,was observed after 2013,aligning with stricter environmental regulations.Furthermore,we have demonstrated the feasibility of applying our method to the transport pathways of other gaseous pollutants.The approach is effective in detecting and quantifying air pollutants’transport pathways,even in regions like the Northwest with limited monitoring infrastructure,which may aid in environmental decision-making.The study will notably improve the current understanding of air pollutants’transport process,providing a new perspective for studying the large-scale spatiotemporal correlations.展开更多
Accurate reconstruction of understory terrain is essential for environmental monitoring and resource management.This study integrates 1:10,000 Digital Elevation Model,Global Ecosystem Dynamics Investigation(GEDI),and ...Accurate reconstruction of understory terrain is essential for environmental monitoring and resource management.This study integrates 1:10,000 Digital Elevation Model,Global Ecosystem Dynamics Investigation(GEDI),and AW3D30 Digital Surface Model data,combined with three machine learning algorithms—Random Forest(RF),Back Propagation Neural Network(BPNN),and Extreme Gradient Boosting(XGBoost)—to evaluate the performance of canopy height inversion and understory terrain reconstruction.The analysis emphasizes the impact of topographic and vegetation-related factors on model accuracy.Results reveal that slope is the most influential variable,contributing three to five times more to model performance than other features.In low-slope areas,understory terrain tends to be underestimated,whereas high-slope areas often result in overestimation.Moreover,the Normalized Difference Vegetation Index(NDVI)and land cover types,particularly forests and grasslands,significantly affect prediction accuracy,with model performance showing heightened sensitivity to vegetation characteristics in these regions.Among the models tested,XGBoost demonstrated superior performance,achieving a canopy height bias of-0.06 m,a root mean square error(RMSE)of 4.69 m for canopy height,and an RMSE of 9.82 m for understory terrain.Its ability to capture complex nonlinear relationships and handle high-dimensional data underlines its robustness.While the RF model exhibited strong stability and resistance to noise,its accuracy lagged slightly behind XGBoost.The BPNN model,by contrast,struggled in areas with complex terrain.This study offers valuable insights into feature selection and optimization in remote sensing applications,providing a reference framework for enhancing the accuracy and efficiency of environmental monitoring practices.展开更多
Internal solitary waves(ISWs)have considerable energy to drive the mixing of water masses in the Sulu Sea.The propagation speed is one of the critical parameters in quantifying the energy budget of the ISWs.We collect...Internal solitary waves(ISWs)have considerable energy to drive the mixing of water masses in the Sulu Sea.The propagation speed is one of the critical parameters in quantifying the energy budget of the ISWs.We collected 1354 groups of ISWs’speeds from tandem satellite remote sensing images with temporal intervals shorter than 25 min and analyzed their spatial and multi-scale temporal variations in the Sulu Sea.We found that water depth plays an important role in modulating the spatial variation of wave speeds,which increase exponentially with water depth with a power of 0.26.Tidal currents,ocean stratification,background circulation,and climate affect the temporal variations of wave speeds from days to months or years.The fortnightly spring/neap tidal currents cause daily variations of wave speeds up to 40%by modulating the ISW amplitudes.In addition to the well-accepted results that monthly variations of wave speeds are related to density stratification,we found that enhanced stratification increases wave speeds,and the background circulation leads to a maximum decrease of 0.27 m/s in the linear counterparts of wave speed.Moreover,the averaged wave speed collected in October is lower than the corresponding linear one possibly due to some unknown dynamical processes or underestimation of background current.As for the interannual variations,we show that wave speed increases in La Niña years and decreases in El Niño years as a result of the climatic modulation on the depth of the maximum value of buoyancy frequency.展开更多
The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development strategies.This study aims to establish a model that maps the correlation between change...The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development strategies.This study aims to establish a model that maps the correlation between changes in land use and land surface temperature(LST)in the Mashhad City,northeastern Iran.Employing the Google Earth Engine(GEE)platform,we calculated the LST and extracted land use maps from 1985 to 2020.The convolutional neural network(CNN)approach was utilized to deeply explore the relationship between the LST and land use.The obtained results were compared with the standard machine learning(ML)methods such as support vector machine(SVM),random forest(RF),and linear regression.The results revealed a 1.00°C–2.00°C increase in the LST across various land use categories.This variation in temperature increases across different land use types suggested that,in addition to global warming and climatic changes,temperature rise was strongly influenced by land use changes.The LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C,while forest lands experienced the smallest increase of 1.19°C.The developed CNN demonstrated an overall prediction accuracy of 91.60%,significantly outperforming linear regression and standard ML methods,due to the ability to extract higher level features.Furthermore,the deep neural network(DNN)modeling indicated that the urban lands,comprising 69.57%and 71.34%of the studied area,were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030,respectively.In conclusion,the LST predictioin framework,combining the GEE platform and CNN method,provided an effective approach to inform urban planning and to mitigate the impacts of UHI.展开更多
Earth observation technology has provided highly useful information in global climate change research over the past few decades and greatly promoted its development,especially through providing biological,physical,and...Earth observation technology has provided highly useful information in global climate change research over the past few decades and greatly promoted its development,especially through providing biological,physical,and chemical parameters on a global scale.Earth observation data has the 4V features(volume,variety,veracity,and velocity) of big data that are suitable for climate change research.Moreover,the large amount of data available from scientific satellites plays an important role.This study reviews the advances of climate change studies based on Earth observation big data and provides examples of case studies that utilize Earth observation big data in climate change research,such as synchronous satelliteeaerialeground observation experiments,which provide extremely large and abundant datasets; Earth observational sensitive factors(e.g.,glaciers,lakes,vegetation,radiation,and urbanization); and global environmental change information and simulation systems.With the era of global environment change dawning,Earth observation big data will underpin the Future Earth program with a huge volume of various types of data and will play an important role in academia and decisionmaking.Inevitably,Earth observation big data will encounter opportunities and challenges brought about by global climate change.展开更多
Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat h...Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.展开更多
The Revised Universal Soil Loss Equation （RUSLE） was applied to assess the spatial distribution and dynamic properties of soil loss with geographic information system （GIS） and remote sensing （RS） technologies. ...The Revised Universal Soil Loss Equation （RUSLE） was applied to assess the spatial distribution and dynamic properties of soil loss with geographic information system （GIS） and remote sensing （RS） technologies. To improve the accuracy of soil-erosion estimates, a new C-factor estimation model was developed based on land cover and time series normalized difference vegetation index （NDVI） datasets. The new C-factor was then applied in the RUSLE to integrate rainfall, soil, vegetation, and topography data of different periods, and thus monitor the distribution of soil erosion patterns and their dynamics during a 3o-year period of the upstream watershed of Miynn Reservoir （UWMR）, China. The results showed that the new C-factor estimation method, which considers land cover status and dynamics, and explicitly incorporates within-land cover variability, was more rational, quantitative, and reliable. An average annual soil loss in UWMR of 25.68, 21.04, and 16.8o t ha-1 a-1 was estimated for 1990, 2000 and 2010, respectively, corroborated by comparing spatial and temporal variation in sediment yield. Between 2000 and 2010, a 1.38% average annual increase was observed in the area of lands that lost less than 5 t ha-1 a^-1, while during 1990-2000 such lands only increased on average by o.46%. Areas that classified as severe, very severe and extremely severe accounted for 5.68% of the total UWMR in 2010, and primarily occurred in dry areas or grasslands of sloping fields. The reason for the change in rate of soil loss is explained by an increased appreciation of soil conservation by developers and planners. Moreover,we recommend that UWMR watershed adopt further conservation measures such as terraced plowing of dry land, afforestation, or grassland enclosures as part of a concerted effort to reduce on-going soil erosion.展开更多
Glaciers play an important role in the climate system. The elevation change of a glacier is an important parameter in studies of glacier dynamics. Only a few ground-based measurements of high mountain glaciers are ava...Glaciers play an important role in the climate system. The elevation change of a glacier is an important parameter in studies of glacier dynamics. Only a few ground-based measurements of high mountain glaciers are available due to their remoteness, high elevation, and complex topography. The acquisition from the German Tan DEM-X(Terra SAR-X add-on for Digital Elevation Measurement) SAR imaging configuration provides a reliable data sources for studying the elevation change of glaciers. In this study, the bistatic Tan DEM-X data that cover the Geladandong Mountain on the Tibetan Plateau were processed with SAR interferometry technique and the elevation changes of the mountain's glaciers during 2000–2014 were obtained. The results indicated that although distinct positive and negative elevation changes were found for different glacier tongues, the mean elevation change was about-0.14±0.26 m a-1. Geoscience Laser Altimeter System(GLAS) data were obtained for comparison and verification. The investigation using GLAS data demonstrated the efficacy of the proposed method in determining glacier elevation change. Thus, the presented approach is appropriate for monitoring glacier elevation change and it constitutes a valuable tool for studies of glacier dynamics.展开更多
The wheat canopy reflectance spectrum is affected by many internal and external factors such as diseases and growth stage. Separating the effects of disease stress on the crop from the observed mixed signals is crucia...The wheat canopy reflectance spectrum is affected by many internal and external factors such as diseases and growth stage. Separating the effects of disease stress on the crop from the observed mixed signals is crucial for increasing the precision of remote sensing monitoring of wheat stripe rust. The canopy spectrum of winter wheat infected by stripe rust was processed with the difference-in-differences(DID) algorithm used in econometrics. The monitoring accuracies of wheat stripe rust before and after processing with the DID algorithm were compared in the presence of various external factors, disease severity, and several simulated satellite sensors. The correlation between the normalized difference vegetation index processed by the DID algorithm(NDVI-DID) and the disease severity level(SL) increased in comparison with the NDVI before processing. The increase in precision in the natural disease area in the field in the presence of large differences in growth stage, growth, planting, and management of the crop was greater than that in the controlled experiment. For low disease levels(SL < 20%), the R2 of the regression of NDVI-DIDon SL was 38.8% higher than that of the NDVI and the root mean square error(RMSE) was reduced by 11.1%. The increase in precision was greater than that for the severe level(SL > 40%).According to the measured hyperspectral data, the spectral reflectance of three satellite sensor levels was simulated. The wide-band NDVI was calculated. Compared with the wide-band NDVI and vegetation indexes(VI) before DID processing, there were increases in the correlation between SL and the various types of VIS-DID, as well as in the correlation between SL and NDVI-DID. It is feasible to apply the DID algorithm to multispectral satellite data and diverse types of VISfor monitoring wheat stripe rust. Our results improve the quantification of independent effects of stripe rust infection on canopy reflectance spectrum,increase the precision of remote sensing monitoring of wheat stripe rust, and provide a reference for remote sensing monitoring of other crop diseases.展开更多
In this study, we present a comprehensive comparison of the sea surface wind ?eld measured by scatterometer(Ku-band scatterometer) aboard the Chinese HY-2 A satellite and the full-polarimetric radiometer WindSat aboar...In this study, we present a comprehensive comparison of the sea surface wind ?eld measured by scatterometer(Ku-band scatterometer) aboard the Chinese HY-2 A satellite and the full-polarimetric radiometer WindSat aboard the Coriolis satellite. The two datasets cover a four-year period from October2011 to September 2015 in the global oceans. For the sea surface wind speed, the statistical comparison indicates good agreement between the HY-2 A scatterometer and WindSat with a bias of nearly 0 m/s and a root mean square error(RMSE) of 1.13 m/s. For the sea surface wind direction, a bias of 1.41° and an RMSE of 20.39° were achieved after excluding the data collocated with opposing directions. Furthermore,discrepancies in sea surface wind speed measured by the two sensors in the global oceans were investigated.It is found that the larger dif ferences mainly appear in the westerlies in the both hemispheres. Both the bias and RMSE show latitude dependence, i.e., they have signi?cant latitudinal ?uctuations.展开更多
River systems play an important role in the global carbon cycle. Rivers transport carbon to the ocean and also affect the carbon cycle in the coastal ocean. The flux from land to the ocean is thought to be a very impo...River systems play an important role in the global carbon cycle. Rivers transport carbon to the ocean and also affect the carbon cycle in the coastal ocean. The flux from land to the ocean is thought to be a very important part of the land carbon budget. To investigate the effect of dam-building on dissolved organic carbon(DOC)in rivers, three reservoirs of different trophic states in the Wujiang basin, Guizhou Province, were sampled twice per month between May 2011 and May 2012. Temporal and spatial distributions of DOC in the reservoirs and their released waters were studied. It was found that different factors controlled DOC in river water, reservoir water, and released water. DOC in the rivers tended to be affected by primary production. For reservoirs, the main controlling factors of DOC concentration varied by trophic state. For the mesotrophic Hongjiadu Reservoir, the effect of primary production on DOC concentration was obvious. For the eutrophic Dongfengdu Reservoir and the hypereutrophic Wujiangdu Reservoir, primary production was not significant and DOC came instead from soil and plant litter.展开更多
Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light...Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.展开更多
Evapotranspiration （ET） is the sum of soil or water body evaporation and plant transpiration from the earth surface and ocean to the atmosphere, and thus plays a significant role in regulating carbon and water resou...Evapotranspiration （ET） is the sum of soil or water body evaporation and plant transpiration from the earth surface and ocean to the atmosphere, and thus plays a significant role in regulating carbon and water resource cycles. The time-series data set from the remote sensing MOLDS product （MOD16） was used to study the spatial-temporal evolution of vegetation evapotranspiration in salinized areas during 2000-2014 by analyzing the variability, spatial patterns and Mann-Kendall （MK） nonparametric trends for the time series. The results indicate that inter-annual and intra-annual variations of ET across various vegetated areas show seasonal changes, with the abnormal months identified. The Cultivated land displays a greater degree of spatial heterogeneity and the spatial pattern of ET in the area covered by broadleaved deciduous forests corresponds to a higher ET rate and increased water consumption. Awidespread decline of ET is observed only in cultivated areas. However, agricultural cultivation doesn＇t worsen water shortage and soil salinization problems in the region, and water shortage problems are worsening for other vegetated areas. This research provides a basis of reference for the reasonable allocation of water resources and restructuring of vegetation patterns in salinized areas.展开更多
The SMOS(soil moisture and ocean salinity) mission undertaken by the European Space Agency(ESA) has provided sea surface salinity(SSS) measurements at global scale since 2009.Validation of SSS values retrieved from SM...The SMOS(soil moisture and ocean salinity) mission undertaken by the European Space Agency(ESA) has provided sea surface salinity(SSS) measurements at global scale since 2009.Validation of SSS values retrieved from SMOS data has been done globally and regionally.However,the accuracy of SSS measurements by SMOS in the China seas has not been examined in detail.In this study,we compared retrieved SSS values from SMOS data with in situ measurements from a South China Sea(SCS) expedition during autumn 2011.The comparison shows that the retrieved SSS values using ascending pass data have much better agreement with in situ measurements than the result derived from descending pass data.Accuracy in terms of bias and root mean square error(RMS) of the SSS retrieved using three different sea surface roughness models is very consistent,regardless of ascending or descending orbits.When ascending and descending measurements are combined for comparison,the retrieved SSS using a semi-empirical model shows the best agreement with in situ measurements,with bias-0.33 practical salinity units and RMS 0.74.We also investigated the impact of environmental conditions of sea surface wind and sea surface temperature on accuracy of the retrieved SSS.The SCS is a semi-closed basin where radio frequencies transmitted from the mainland strongly interfere with SMOS measurements.Therefore,accuracy of retrieved SSS shows a relationship with distance between the validation sites and land.展开更多
Background:Process-based models are widely used to simulate forest productivity,but complex parameterization and calibration challenge the application and development of these models.Sensitivity analysis of numerous p...Background:Process-based models are widely used to simulate forest productivity,but complex parameterization and calibration challenge the application and development of these models.Sensitivity analysis of numerous parameters is an essential step in model calibration and carbon flux simulation.However,parameters are not dependent on each other,and the results of sensitivity analysis usually vary due to different forest types and regions.Hence,global and representative sensitivity analysis would provide reliable information for simple calibration.Methods:To determine the contributions of input parameters to gross primary productivity(GPP)and net primary productivity(NPP),regression analysis and extended Fourier amplitude sensitivity testing(EFAST)were conducted for Biome-BGCMuSo to calculate the sensitivity index of the parameters at four observation sites under climate gradient from ChinaFLUX.Results:Generally,GPP and NPP were highly sensitive to C:Nleaf(C:N of leaves),Wint(canopy water interception coefficient),k(canopy light extinction coefficient),FLNR(fraction of leaf N in Rubisco),MRpern(coefficient of linear relationship between tissue N and maintenance respiration),VPDf(vapor pressure deficit complete conductance reduction),and SLA1(canopy average specific leaf area in phenological phase 1)at all observation sites.Various sensitive parameters occurred at four observation sites within different climate zones.GPP and NPP were particularly sensitive to FLNR,SLA1 and Wint,and C:Nleaf in temperate,alpine and subtropical zones,respectively.Conclusions:The results indicated that sensitivity parameters of China's forest ecosystems change with climate gradient.We found that parameter calibration should be performed according to plant functional type(PFT),and more attention needs to be paid to the differences in climate and environment.These findings contribute to determining the target parameters in field experiments and model calibration.展开更多
In Punjab(Pakistan),the increasing population and expansion of land use for agriculture have severely exploited the regional groundwater resources.Intensive pumping has resulted in a rapid decline in the level of the ...In Punjab(Pakistan),the increasing population and expansion of land use for agriculture have severely exploited the regional groundwater resources.Intensive pumping has resulted in a rapid decline in the level of the water table as well as its quality.Better management practices and artificial recharge are needed for the development of sustainable groundwater resources.This study proposes a methodology to delineate favorable groundwater potential recharge zones(FPRI)by integrating maps of groundwater potential recharge index(PRI)with the DRASTIC-based groundwater vulnerability index(VI).In order to evaluate both indexes,different thematic layers corresponding to each index were overlaid in ArcGIS.In the overlay analysis,the weights(for various thematic layers)and rating values(for sub-classes)were allocated based on a review of published literature.Both were then normalized and modified using the analytical hierarchical process(AHP)and a frequency ratio model respectively.After evaluating PRI and FPRI,these maps were validated using the area under the curve(AUC)method.The PRI map indicates that 53%of the area assessed exists in very low to low recharge zones,22%in moderate,and 25%in high to excellent potential recharge zones.The VI map indicates that 38%of the area assessed exists in very low to low vulnerability,33%in moderate,and 29%in high to very high vulnerability zones.The FPRI map shows that the central region of Punjab is moderately-to-highly favorable for recharge due to its low vulnerability and high recharge potential.During the validation process,it was found that the AUC estimated with modified weights and rating values was 79%and 67%,for PRI and VI indexes,respectively.The AUC was less when evaluated using original weights and rating values taken from published literature.Maps of favorable groundwater potential recharge zones are helpful for planning and implementation of wells and hydraulic structures in this region.展开更多
Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carb...Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.展开更多
Glacial lake mapping provides the most feasible way for investigating the water resources and monitoring the flood outburst hazards in High Mountain Region.However,various types of glacial lakes with different propert...Glacial lake mapping provides the most feasible way for investigating the water resources and monitoring the flood outburst hazards in High Mountain Region.However,various types of glacial lakes with different properties bring a constraint to the rapid and accurate glacial lake mapping over a large scale.Existing spectral features to map glacial lakes are diverse but some are generally limited to the specific glaciated regions or lake types,some have unclear applicability,which hamper their application for the large areas.To this end,this study provides a solution for evaluating the most effective spectral features in glacial lake mapping using Landsat-8 imagery.The 23 frequently-used lake mapping spectral features,including single band reflectance features,Water Index features and image transformation features were selected,then the insignificant features were filtered out based on scoring calculated from two classical feature selection methods-random forest and decision tree algorithm.The result shows that the three most prominent spectral features(SF)with high scores are NDWI1,EWI,and NDWI3(renamed as SF8,SF19 and SF12 respectively).Accuracy assessment of glacial lake mapping results in five different test sites demonstrate that the selected features performed well and robustly in classifying different types of glacial lakes without any influence from the mountain shadows.SF8 and SF19 are superior for the detection of large amount of small glacial lakes,while some lake areas extracted by SF12 are incomplete.Moreover,SF8 achieved better accuracy than the other two features in terms of both Kappa Coefficient(0.8812)and Prediction(0.9025),which further indicates that SF8 has great potential for large scale glacial lake mapping in high mountainous area.展开更多
Understanding the effects of land use changes on the spatiotemporal variation of soil organic carbon （SOC） can provide guidance for low carbon and sustainable agriculture. In this paper, based on the large-scale dat...Understanding the effects of land use changes on the spatiotemporal variation of soil organic carbon （SOC） can provide guidance for low carbon and sustainable agriculture. In this paper, based on the large-scale datasets of soil surveys in 1982 and 2009 for Pinggu District -- an urban-rural ecotone of Beijing, China, the effects of land use and land use changes on both temporal variation and spatial variation of SOC were analyzed. Results showed that from 1982 to 2009 in Pinggu District, the following land use change mainly occurred： Grain cropland converted to orchard or vegetable land, and grassland converted to forestland. The SOC content decreased in region where the land use type changed to grain cropland （e.g., vegetable land to grain cropland decreased by 0.7 g kg-1; orchard to grain cropland decreased by 0.2 g kg-l）. In contrast, the SOC content increased in region where the land use type changed to either orchard （excluding forestland） or forestland （e.g., grain cropland to orchard and forestland increased by 2.7 and 2.4 g kg-1, respectively; grassland to orchard and forestland increased by 4.8 and 4.9 g kg-1, respectively）. The organic carbon accumulation capacity per unit mass of the soil increased in the following order： grain cropland soil〈vegetable land/grassland soil〈orchard soil〈forestland soil. Therefore, to both secure supply of agricultural products and develop low carbon agriculture in a modern city, orchard has proven to be a good choice for land using.展开更多
基金Supported by the Chinese Academy of Sciences Strategic Priority Research Program of the Big Earth Data Science Engineering Program(XDA19090000,XDA19030000)。
文摘China is expanding and sharing its capacity for Earth observation by developing sensors,platforms,and launch capabilities in tandem with growing lunar and deep space exploration.China is considering the Moon as a viable Earth observation platform to provide high-quality,planetary-scale data.The platform would produce consistent spatiotemporal data because of its long operational life and the geological stability of the Moon.China is also quickly improving its capabilities in processing and transforming Earth observation data into useful and practical information.Programs such as the Big Earth Data Science Engineering Program(CASEarth)provide opportunities to integrate data and develop“Big Earth Data”platforms to add value to data through analysis and integration.Such programs can offer products and services independently and in collaboration with international partners for data-driven decision support and policy development.With the rapid digital transformation of societies,and consequently increasing demand for big data and associated products,Digital Earth and the Digital Belt and Road Program(DBAR)allow Chinese experts to collaborate with international partners to integrate valuable Earth observation data in regional and global sustainable development.
基金funded by the National Natural Science Foundation of China(grant No.42376246)the Key Research and Development Project of Guangxi(grant No.GuikeAB24010046)the Joint Funds of the National Natural Science Foundation of China(grant No.U2268217).
文摘In the context of urbanization,air pollution has emerged as a significant environmental challenge.A thorough understanding of their transport pathways,especially at a national scale,is essential for environmental protection and policy-making.However,it remains partially elusive due to the constraints of available data and analytical methods.This study proposed a data-driven spatiotemporal correlation analysis method employing the Dynamic Time Warping(DTW).We represented the first comprehensive attempt to chart the long-term and nationwide transport pathways of PM_(2.5) utilizing an extensive dataset spanning from 2000 to 2021 across China,which is crucial for understanding long-term air pollution trends.Compared with traditional chemical transport models(CTMs),this data-driven method can generate transport pathways of PM_(2.5) without requiring extensive meteorological or emission data,and suggesting fundamentally consistent spatial distribution and trends.Our analysis reveals that China’s transport pathways are notably pronounced in the Northwest(34%of the total pathways in China),Southwest(22%),and North(21%)regions,with less significant pathways in the Northeast(10%)region and isolated occurrences elsewhere.Additionally,a notable decrease in the number of China’s PM_(2.5) transport pathways,similar to annual average concentrations,was observed after 2013,aligning with stricter environmental regulations.Furthermore,we have demonstrated the feasibility of applying our method to the transport pathways of other gaseous pollutants.The approach is effective in detecting and quantifying air pollutants’transport pathways,even in regions like the Northwest with limited monitoring infrastructure,which may aid in environmental decision-making.The study will notably improve the current understanding of air pollutants’transport process,providing a new perspective for studying the large-scale spatiotemporal correlations.
基金funded by the National Key Research and Development Program(Grants No.2023YFE0207900)。
文摘Accurate reconstruction of understory terrain is essential for environmental monitoring and resource management.This study integrates 1:10,000 Digital Elevation Model,Global Ecosystem Dynamics Investigation(GEDI),and AW3D30 Digital Surface Model data,combined with three machine learning algorithms—Random Forest(RF),Back Propagation Neural Network(BPNN),and Extreme Gradient Boosting(XGBoost)—to evaluate the performance of canopy height inversion and understory terrain reconstruction.The analysis emphasizes the impact of topographic and vegetation-related factors on model accuracy.Results reveal that slope is the most influential variable,contributing three to five times more to model performance than other features.In low-slope areas,understory terrain tends to be underestimated,whereas high-slope areas often result in overestimation.Moreover,the Normalized Difference Vegetation Index(NDVI)and land cover types,particularly forests and grasslands,significantly affect prediction accuracy,with model performance showing heightened sensitivity to vegetation characteristics in these regions.Among the models tested,XGBoost demonstrated superior performance,achieving a canopy height bias of-0.06 m,a root mean square error(RMSE)of 4.69 m for canopy height,and an RMSE of 9.82 m for understory terrain.Its ability to capture complex nonlinear relationships and handle high-dimensional data underlines its robustness.While the RF model exhibited strong stability and resistance to noise,its accuracy lagged slightly behind XGBoost.The BPNN model,by contrast,struggled in areas with complex terrain.This study offers valuable insights into feature selection and optimization in remote sensing applications,providing a reference framework for enhancing the accuracy and efficiency of environmental monitoring practices.
基金Supported by the National Natural Science Foundation of China(Nos.U23A2032,42006193)supported by the Hainan Provincial Excellent Talent Team Project(Space Observation of Deep-sea)。
文摘Internal solitary waves(ISWs)have considerable energy to drive the mixing of water masses in the Sulu Sea.The propagation speed is one of the critical parameters in quantifying the energy budget of the ISWs.We collected 1354 groups of ISWs’speeds from tandem satellite remote sensing images with temporal intervals shorter than 25 min and analyzed their spatial and multi-scale temporal variations in the Sulu Sea.We found that water depth plays an important role in modulating the spatial variation of wave speeds,which increase exponentially with water depth with a power of 0.26.Tidal currents,ocean stratification,background circulation,and climate affect the temporal variations of wave speeds from days to months or years.The fortnightly spring/neap tidal currents cause daily variations of wave speeds up to 40%by modulating the ISW amplitudes.In addition to the well-accepted results that monthly variations of wave speeds are related to density stratification,we found that enhanced stratification increases wave speeds,and the background circulation leads to a maximum decrease of 0.27 m/s in the linear counterparts of wave speed.Moreover,the averaged wave speed collected in October is lower than the corresponding linear one possibly due to some unknown dynamical processes or underestimation of background current.As for the interannual variations,we show that wave speed increases in La Niña years and decreases in El Niño years as a result of the climatic modulation on the depth of the maximum value of buoyancy frequency.
文摘The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development strategies.This study aims to establish a model that maps the correlation between changes in land use and land surface temperature(LST)in the Mashhad City,northeastern Iran.Employing the Google Earth Engine(GEE)platform,we calculated the LST and extracted land use maps from 1985 to 2020.The convolutional neural network(CNN)approach was utilized to deeply explore the relationship between the LST and land use.The obtained results were compared with the standard machine learning(ML)methods such as support vector machine(SVM),random forest(RF),and linear regression.The results revealed a 1.00°C–2.00°C increase in the LST across various land use categories.This variation in temperature increases across different land use types suggested that,in addition to global warming and climatic changes,temperature rise was strongly influenced by land use changes.The LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C,while forest lands experienced the smallest increase of 1.19°C.The developed CNN demonstrated an overall prediction accuracy of 91.60%,significantly outperforming linear regression and standard ML methods,due to the ability to extract higher level features.Furthermore,the deep neural network(DNN)modeling indicated that the urban lands,comprising 69.57%and 71.34%of the studied area,were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030,respectively.In conclusion,the LST predictioin framework,combining the GEE platform and CNN method,provided an effective approach to inform urban planning and to mitigate the impacts of UHI.
基金funded by the International Cooperation and Exchanges National Natural Science Foundation of China (41120114001)
文摘Earth observation technology has provided highly useful information in global climate change research over the past few decades and greatly promoted its development,especially through providing biological,physical,and chemical parameters on a global scale.Earth observation data has the 4V features(volume,variety,veracity,and velocity) of big data that are suitable for climate change research.Moreover,the large amount of data available from scientific satellites plays an important role.This study reviews the advances of climate change studies based on Earth observation big data and provides examples of case studies that utilize Earth observation big data in climate change research,such as synchronous satelliteeaerialeground observation experiments,which provide extremely large and abundant datasets; Earth observational sensitive factors(e.g.,glaciers,lakes,vegetation,radiation,and urbanization); and global environmental change information and simulation systems.With the era of global environment change dawning,Earth observation big data will underpin the Future Earth program with a huge volume of various types of data and will play an important role in academia and decisionmaking.Inevitably,Earth observation big data will encounter opportunities and challenges brought about by global climate change.
基金the National High-Tech R&D Program of China(2012AA12A30701)the National Natural Science Foundation of China(91125003,41222008)
文摘Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.
基金supported by the National Natural Science Foundation of China (Grant No.41101399)the open fund of State Key Laboratory of Remote Sensing ScienceJointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University,China
文摘The Revised Universal Soil Loss Equation （RUSLE） was applied to assess the spatial distribution and dynamic properties of soil loss with geographic information system （GIS） and remote sensing （RS） technologies. To improve the accuracy of soil-erosion estimates, a new C-factor estimation model was developed based on land cover and time series normalized difference vegetation index （NDVI） datasets. The new C-factor was then applied in the RUSLE to integrate rainfall, soil, vegetation, and topography data of different periods, and thus monitor the distribution of soil erosion patterns and their dynamics during a 3o-year period of the upstream watershed of Miynn Reservoir （UWMR）, China. The results showed that the new C-factor estimation method, which considers land cover status and dynamics, and explicitly incorporates within-land cover variability, was more rational, quantitative, and reliable. An average annual soil loss in UWMR of 25.68, 21.04, and 16.8o t ha-1 a-1 was estimated for 1990, 2000 and 2010, respectively, corroborated by comparing spatial and temporal variation in sediment yield. Between 2000 and 2010, a 1.38% average annual increase was observed in the area of lands that lost less than 5 t ha-1 a^-1, while during 1990-2000 such lands only increased on average by o.46%. Areas that classified as severe, very severe and extremely severe accounted for 5.68% of the total UWMR in 2010, and primarily occurred in dry areas or grasslands of sloping fields. The reason for the change in rate of soil loss is explained by an increased appreciation of soil conservation by developers and planners. Moreover,we recommend that UWMR watershed adopt further conservation measures such as terraced plowing of dry land, afforestation, or grassland enclosures as part of a concerted effort to reduce on-going soil erosion.
基金supported by the National Science Foundation of China (41590852, 41001264)the International Science & Technology Cooperation Program of China (2010DFB23380)+1 种基金International Partnership Program of Chinese Academy of Sciences (131C11KYSB20160061)supported by the DLR AO project (GEOL0447)
文摘Glaciers play an important role in the climate system. The elevation change of a glacier is an important parameter in studies of glacier dynamics. Only a few ground-based measurements of high mountain glaciers are available due to their remoteness, high elevation, and complex topography. The acquisition from the German Tan DEM-X(Terra SAR-X add-on for Digital Elevation Measurement) SAR imaging configuration provides a reliable data sources for studying the elevation change of glaciers. In this study, the bistatic Tan DEM-X data that cover the Geladandong Mountain on the Tibetan Plateau were processed with SAR interferometry technique and the elevation changes of the mountain's glaciers during 2000–2014 were obtained. The results indicated that although distinct positive and negative elevation changes were found for different glacier tongues, the mean elevation change was about-0.14±0.26 m a-1. Geoscience Laser Altimeter System(GLAS) data were obtained for comparison and verification. The investigation using GLAS data demonstrated the efficacy of the proposed method in determining glacier elevation change. Thus, the presented approach is appropriate for monitoring glacier elevation change and it constitutes a valuable tool for studies of glacier dynamics.
基金supported by the National Natural Science Foundation of China (42171394, 41601467)。
文摘The wheat canopy reflectance spectrum is affected by many internal and external factors such as diseases and growth stage. Separating the effects of disease stress on the crop from the observed mixed signals is crucial for increasing the precision of remote sensing monitoring of wheat stripe rust. The canopy spectrum of winter wheat infected by stripe rust was processed with the difference-in-differences(DID) algorithm used in econometrics. The monitoring accuracies of wheat stripe rust before and after processing with the DID algorithm were compared in the presence of various external factors, disease severity, and several simulated satellite sensors. The correlation between the normalized difference vegetation index processed by the DID algorithm(NDVI-DID) and the disease severity level(SL) increased in comparison with the NDVI before processing. The increase in precision in the natural disease area in the field in the presence of large differences in growth stage, growth, planting, and management of the crop was greater than that in the controlled experiment. For low disease levels(SL < 20%), the R2 of the regression of NDVI-DIDon SL was 38.8% higher than that of the NDVI and the root mean square error(RMSE) was reduced by 11.1%. The increase in precision was greater than that for the severe level(SL > 40%).According to the measured hyperspectral data, the spectral reflectance of three satellite sensor levels was simulated. The wide-band NDVI was calculated. Compared with the wide-band NDVI and vegetation indexes(VI) before DID processing, there were increases in the correlation between SL and the various types of VIS-DID, as well as in the correlation between SL and NDVI-DID. It is feasible to apply the DID algorithm to multispectral satellite data and diverse types of VISfor monitoring wheat stripe rust. Our results improve the quantification of independent effects of stripe rust infection on canopy reflectance spectrum,increase the precision of remote sensing monitoring of wheat stripe rust, and provide a reference for remote sensing monitoring of other crop diseases.
基金Supported by the Hainan Provincial Department of Science and Technology(No.ZDKJ2016015)the National Natural Science Foundation of China(No.41406198)the Special Project of Chinese HighResolution Earth Observation System(No.41-Y20A14-9001-15/16)
文摘In this study, we present a comprehensive comparison of the sea surface wind ?eld measured by scatterometer(Ku-band scatterometer) aboard the Chinese HY-2 A satellite and the full-polarimetric radiometer WindSat aboard the Coriolis satellite. The two datasets cover a four-year period from October2011 to September 2015 in the global oceans. For the sea surface wind speed, the statistical comparison indicates good agreement between the HY-2 A scatterometer and WindSat with a bias of nearly 0 m/s and a root mean square error(RMSE) of 1.13 m/s. For the sea surface wind direction, a bias of 1.41° and an RMSE of 20.39° were achieved after excluding the data collocated with opposing directions. Furthermore,discrepancies in sea surface wind speed measured by the two sensors in the global oceans were investigated.It is found that the larger dif ferences mainly appear in the westerlies in the both hemispheres. Both the bias and RMSE show latitude dependence, i.e., they have signi?cant latitudinal ?uctuations.
基金financially supported by the National Key Research and Development Program of China(2016YFA0601001)the National Natural Science Foundation of China(Grant Nos.U1612441 and 41473082)
文摘River systems play an important role in the global carbon cycle. Rivers transport carbon to the ocean and also affect the carbon cycle in the coastal ocean. The flux from land to the ocean is thought to be a very important part of the land carbon budget. To investigate the effect of dam-building on dissolved organic carbon(DOC)in rivers, three reservoirs of different trophic states in the Wujiang basin, Guizhou Province, were sampled twice per month between May 2011 and May 2012. Temporal and spatial distributions of DOC in the reservoirs and their released waters were studied. It was found that different factors controlled DOC in river water, reservoir water, and released water. DOC in the rivers tended to be affected by primary production. For reservoirs, the main controlling factors of DOC concentration varied by trophic state. For the mesotrophic Hongjiadu Reservoir, the effect of primary production on DOC concentration was obvious. For the eutrophic Dongfengdu Reservoir and the hypereutrophic Wujiangdu Reservoir, primary production was not significant and DOC came instead from soil and plant litter.
基金funded by the Guangxi Natural Science Fund for Innovation Research Team (No. 2019JJF50001)the Natural Science Foundation of Fujian Province,China (No. 2019 J01396)+1 种基金the Special Fund for Guangxi Innovation and Driving Development (Major science and technology projects)(No. 2018AA13005)the Youth Innovation Promotion Association CAS (2019130)。
文摘Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.
基金financial support from the National Key Research and Development Program of China(2017YFC1502404)the National Natural Science Foundation of China(41601562 and 41761014)+1 种基金the China Institute of Water Resources and Hydropower Research Team Construction and Talent Development Project(JZ0145B752017)the Research Project for Young Teachers of Fujian Province,China(JAT160085)
文摘Evapotranspiration （ET） is the sum of soil or water body evaporation and plant transpiration from the earth surface and ocean to the atmosphere, and thus plays a significant role in regulating carbon and water resource cycles. The time-series data set from the remote sensing MOLDS product （MOD16） was used to study the spatial-temporal evolution of vegetation evapotranspiration in salinized areas during 2000-2014 by analyzing the variability, spatial patterns and Mann-Kendall （MK） nonparametric trends for the time series. The results indicate that inter-annual and intra-annual variations of ET across various vegetated areas show seasonal changes, with the abnormal months identified. The Cultivated land displays a greater degree of spatial heterogeneity and the spatial pattern of ET in the area covered by broadleaved deciduous forests corresponds to a higher ET rate and increased water consumption. Awidespread decline of ET is observed only in cultivated areas. However, agricultural cultivation doesn＇t worsen water shortage and soil salinization problems in the region, and water shortage problems are worsening for other vegetated areas. This research provides a basis of reference for the reasonable allocation of water resources and restructuring of vegetation patterns in salinized areas.
基金Supported by the National Natural Science Foundation of China(Nos.41006110,41106155)
文摘The SMOS(soil moisture and ocean salinity) mission undertaken by the European Space Agency(ESA) has provided sea surface salinity(SSS) measurements at global scale since 2009.Validation of SSS values retrieved from SMOS data has been done globally and regionally.However,the accuracy of SSS measurements by SMOS in the China seas has not been examined in detail.In this study,we compared retrieved SSS values from SMOS data with in situ measurements from a South China Sea(SCS) expedition during autumn 2011.The comparison shows that the retrieved SSS values using ascending pass data have much better agreement with in situ measurements than the result derived from descending pass data.Accuracy in terms of bias and root mean square error(RMS) of the SSS retrieved using three different sea surface roughness models is very consistent,regardless of ascending or descending orbits.When ascending and descending measurements are combined for comparison,the retrieved SSS using a semi-empirical model shows the best agreement with in situ measurements,with bias-0.33 practical salinity units and RMS 0.74.We also investigated the impact of environmental conditions of sea surface wind and sea surface temperature on accuracy of the retrieved SSS.The SCS is a semi-closed basin where radio frequencies transmitted from the mainland strongly interfere with SMOS measurements.Therefore,accuracy of retrieved SSS shows a relationship with distance between the validation sites and land.
基金This study was funded by the National Natural Science Foundation of China(grant number 41871279 and 41901364).
文摘Background:Process-based models are widely used to simulate forest productivity,but complex parameterization and calibration challenge the application and development of these models.Sensitivity analysis of numerous parameters is an essential step in model calibration and carbon flux simulation.However,parameters are not dependent on each other,and the results of sensitivity analysis usually vary due to different forest types and regions.Hence,global and representative sensitivity analysis would provide reliable information for simple calibration.Methods:To determine the contributions of input parameters to gross primary productivity(GPP)and net primary productivity(NPP),regression analysis and extended Fourier amplitude sensitivity testing(EFAST)were conducted for Biome-BGCMuSo to calculate the sensitivity index of the parameters at four observation sites under climate gradient from ChinaFLUX.Results:Generally,GPP and NPP were highly sensitive to C:Nleaf(C:N of leaves),Wint(canopy water interception coefficient),k(canopy light extinction coefficient),FLNR(fraction of leaf N in Rubisco),MRpern(coefficient of linear relationship between tissue N and maintenance respiration),VPDf(vapor pressure deficit complete conductance reduction),and SLA1(canopy average specific leaf area in phenological phase 1)at all observation sites.Various sensitive parameters occurred at four observation sites within different climate zones.GPP and NPP were particularly sensitive to FLNR,SLA1 and Wint,and C:Nleaf in temperate,alpine and subtropical zones,respectively.Conclusions:The results indicated that sensitivity parameters of China's forest ecosystems change with climate gradient.We found that parameter calibration should be performed according to plant functional type(PFT),and more attention needs to be paid to the differences in climate and environment.These findings contribute to determining the target parameters in field experiments and model calibration.
基金financially supported by the National Key Research and Development Program of China(Grant Nos.2016YFA0602302 and 2016YFB0502502)。
文摘In Punjab(Pakistan),the increasing population and expansion of land use for agriculture have severely exploited the regional groundwater resources.Intensive pumping has resulted in a rapid decline in the level of the water table as well as its quality.Better management practices and artificial recharge are needed for the development of sustainable groundwater resources.This study proposes a methodology to delineate favorable groundwater potential recharge zones(FPRI)by integrating maps of groundwater potential recharge index(PRI)with the DRASTIC-based groundwater vulnerability index(VI).In order to evaluate both indexes,different thematic layers corresponding to each index were overlaid in ArcGIS.In the overlay analysis,the weights(for various thematic layers)and rating values(for sub-classes)were allocated based on a review of published literature.Both were then normalized and modified using the analytical hierarchical process(AHP)and a frequency ratio model respectively.After evaluating PRI and FPRI,these maps were validated using the area under the curve(AUC)method.The PRI map indicates that 53%of the area assessed exists in very low to low recharge zones,22%in moderate,and 25%in high to excellent potential recharge zones.The VI map indicates that 38%of the area assessed exists in very low to low vulnerability,33%in moderate,and 29%in high to very high vulnerability zones.The FPRI map shows that the central region of Punjab is moderately-to-highly favorable for recharge due to its low vulnerability and high recharge potential.During the validation process,it was found that the AUC estimated with modified weights and rating values was 79%and 67%,for PRI and VI indexes,respectively.The AUC was less when evaluated using original weights and rating values taken from published literature.Maps of favorable groundwater potential recharge zones are helpful for planning and implementation of wells and hydraulic structures in this region.
基金supported by the CAS Strategic Priority Research Program(No.XDA19030402)the National Key Research and Development Program of China(No.2016YFD0300101)+2 种基金the Natural Science Foundation of China(Nos.31571565,31671585)the Key Basic Research Project of the Shandong Natural Science Foundation of China(No.ZR2017ZB0422)Research Funding of Qingdao University(No.41117010153)
文摘Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.
基金funded by the National Key R&D Program of China(Grant No.2017YFE0100800)the International Partnership Program of the Chinese Academy of Sciences(Grant No.131551KYSB20160002/131211KYSB20170046)the National Natural Science Foundation of China(41701481)。
文摘Glacial lake mapping provides the most feasible way for investigating the water resources and monitoring the flood outburst hazards in High Mountain Region.However,various types of glacial lakes with different properties bring a constraint to the rapid and accurate glacial lake mapping over a large scale.Existing spectral features to map glacial lakes are diverse but some are generally limited to the specific glaciated regions or lake types,some have unclear applicability,which hamper their application for the large areas.To this end,this study provides a solution for evaluating the most effective spectral features in glacial lake mapping using Landsat-8 imagery.The 23 frequently-used lake mapping spectral features,including single band reflectance features,Water Index features and image transformation features were selected,then the insignificant features were filtered out based on scoring calculated from two classical feature selection methods-random forest and decision tree algorithm.The result shows that the three most prominent spectral features(SF)with high scores are NDWI1,EWI,and NDWI3(renamed as SF8,SF19 and SF12 respectively).Accuracy assessment of glacial lake mapping results in five different test sites demonstrate that the selected features performed well and robustly in classifying different types of glacial lakes without any influence from the mountain shadows.SF8 and SF19 are superior for the detection of large amount of small glacial lakes,while some lake areas extracted by SF12 are incomplete.Moreover,SF8 achieved better accuracy than the other two features in terms of both Kappa Coefficient(0.8812)and Prediction(0.9025),which further indicates that SF8 has great potential for large scale glacial lake mapping in high mountainous area.
基金supported by the Hundred Talent Program of the Chinese Academy of Sciences(Huang Wenjiang)the Innovation“135”Program from Chinese Academy of Sciences(Y3SG0100CX)the Science&Technology Basic Research Program of China(2014FY210100)
文摘Understanding the effects of land use changes on the spatiotemporal variation of soil organic carbon （SOC） can provide guidance for low carbon and sustainable agriculture. In this paper, based on the large-scale datasets of soil surveys in 1982 and 2009 for Pinggu District -- an urban-rural ecotone of Beijing, China, the effects of land use and land use changes on both temporal variation and spatial variation of SOC were analyzed. Results showed that from 1982 to 2009 in Pinggu District, the following land use change mainly occurred： Grain cropland converted to orchard or vegetable land, and grassland converted to forestland. The SOC content decreased in region where the land use type changed to grain cropland （e.g., vegetable land to grain cropland decreased by 0.7 g kg-1; orchard to grain cropland decreased by 0.2 g kg-l）. In contrast, the SOC content increased in region where the land use type changed to either orchard （excluding forestland） or forestland （e.g., grain cropland to orchard and forestland increased by 2.7 and 2.4 g kg-1, respectively; grassland to orchard and forestland increased by 4.8 and 4.9 g kg-1, respectively）. The organic carbon accumulation capacity per unit mass of the soil increased in the following order： grain cropland soil〈vegetable land/grassland soil〈orchard soil〈forestland soil. Therefore, to both secure supply of agricultural products and develop low carbon agriculture in a modern city, orchard has proven to be a good choice for land using.
