Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region,which mounts the need for precise spatial water management.In this study,we investigated the irrigation water demand fo...Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region,which mounts the need for precise spatial water management.In this study,we investigated the irrigation water demand for seven major crops in cities at the prefecture level between 2000 and 2019.Using Logarithmic Mean Divisia Index(LMDI)decomposition and k-means clustering,we quantified how yield,area,water use efficiency,and cropping patterns affect water demand and identified five irrigation development clusters.Key water-saving areas were identified by tracking transitions among clusters,and NSGA-II was applied to optimize crop structure.The results revealed that the total irrigation demand in the Yellow River Basin averaged 50.09 billion m3/year,with wheat accounting for 54.7%.The increase in yield and area increased demand by 15.2 and 5.5 billion m3,respectively,which was partly offset by changes in water use efficiency and cropping pattern(−7.0 and−1.8 billion m^(3),respectively).Regions in the upper reaches,particularly within the Lanzhou-Toudaoguai section,were identified as critical for water conservation.Optimization of the cropping structure in key regions can reduce annual irrigation water demand by 280 million m3,which accounts for 4.9%of the total demand in these areas,with minimal impact on crop production.This study provides a spatially explicit basis for targeted water conservation strategies in water-scarce agricultural regions.展开更多
Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled t...Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled to the regional scale.To mitigate this issue,this study used a process-based and remote sensing driven crop yield model for winter wheat(PRYM-Wheat),which was derived from the boreal ecosystem productivity simulator(BEPS),to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield(Ya).The distribution of Ya across the North China Plain showed great heterogeneity,decreasing from southeast to northwest.The remote sensing-estimated results show that the average YG of the study area was 6400.6 kg ha^(–1).The YG of Jiangsu Province was the largest,at7307.4 kg ha^(–1),while the YG of Anhui Province was the smallest,at 5842.1 kg ha^(–1).An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation,but there was a weak negative correlation between YG and accumulated temperature.In addition,the YG was positively correlated with elevation.In general,studying the specific features of the YG can provide directions for increasing crop yields in the future.展开更多
The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission （TRMM） data with rain gauge data and further to use this TRMM data to drive a Dis- tributed Time-Variant Gain Model （DT...The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission （TRMM） data with rain gauge data and further to use this TRMM data to drive a Dis- tributed Time-Variant Gain Model （DTVGM） to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year （2001-2010） daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R2 varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years （2001-2007） of data on daily hydro- logical processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamfiow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.展开更多
Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Curre...Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Currently,in-situ drought monitoring based on weather stations and based on remote sensing data has limitations,including infrequent updates,limited coverage,and low accuracy.This study leverages multi-source remote sensing data to monitor agricultural drought in Heilongjiang Province,China.We developed multi-source composite drought indices(MCDIs)at various timescales(3,6,9,and 12 months)by integrating precipitation,land surface temperature,soil moisture,and vegetation indices.Utilizing remote sensing data from various sources,we calculated a series of single drought indices,which are the precipitation condition index,soil moisture condition index,vegetation condition index,and temperature condition index.These are then integrated into MCDIs using a multivariable linear regression approach.The analysis reveals that MCDIs correlate more with standardized precipitation evapotranspiration index(SPEI)than single drought indices.When examining the correlation between different MCDIs and the affected area of crops and major grain production,MCDI-9 showed the highest correlation with the affected area of crops,while MCDI-12 showed the highest correlation with grain production.This suggests that these two MCDIs at different timescales are better indicators of agricultural drought.The spatio-temporal analysis of MCDI indicates that drought in Heilongjiang Province primarily occurs in early spring,gradually spreading from the Greater Khingan Mountains region to the southeastern plains.The drought gradually alleviates during the summer,ending by the autumn harvest period.Therefore,the MCDIs constructed in this study can serve as effective methods and indicators for drought monitoring in Heilongjiang Province and similar regions.展开更多
In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data,it is necessary to understand what the information flow in quantit...In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data,it is necessary to understand what the information flow in quantitative remote sensing model inversion is,thus control the information flow.Aiming at this,the paper takes the linear kernel-driven model inversion as an example.At first,the information flow in different inversion methods is calculated and analyzed,then the effect of information flow controlled by multi-stage inversion strategy is studied,finally,an information matrix based on USM is defined to control information flow in inversion.It shows that using Shannon entropy decrease of the inversed parameters can express information flow more properly.Changing the weight of a priori knowledge in inversion or fixing parameters and partitioning datasets in multi-stage inversion strategy can control information flow.In regularization inversion of remote sensing,information matrix based on USM may be a better tool for quantitatively controlling information flow.展开更多
MODerate resolution atmospheric TRANsmission(MODTRAN)is a commercial remote sensing(RS)software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth’s atmosp...MODerate resolution atmospheric TRANsmission(MODTRAN)is a commercial remote sensing(RS)software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth’s atmosphere and the radiation observed by a remote sensor.However,when very large RS datasets must be processed in simulation applications at a global scale,it is extremely time-consuming to operate MODTRAN on a modern workstation.Under this circumstance,the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task.This paper presents PMODTRAN,an implementation of a parallel task-scheduling algorithm based on MODTRAN.PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster.In addition,PMODTRAN can distribute tasks with different levels of granularity and has some extra features,such as dynamic load balancing and parameter checking.展开更多
The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China.Presently,most remote sensing process models use the“biomass...The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China.Presently,most remote sensing process models use the“biomass×harvest index(HI)”method to simulate regional-scale winter wheat yield.However,spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional scale.Time-series dry matter partition coefficients(Fr)can dynamically reflect the dry matter partition of winter wheat.In this study,Fr equations were fitted for each organ of winter wheat using site-scale data.These equations were then coupled into a process-based and remote sensingdriven crop yield model for wheat(PRYM-Wheat)to improve the regional simulation of winter wheat yield over the North China Plain(NCP).The improved PRYM-Wheat model integrated with the fitted Fr equations(PRYM-Wheat-Fr)was validated using data obtained from provincial yearbooks.A 3-year(2000-2002)averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination(R^(2)=0.55)and lower root mean square error(RMSE=0.94 t ha^(-1))than PRYM-Wheat with a stable HI(abbreviated as PRYM-Wheat-HI),which had R^(2) and RMSE values of 0.30 and 1.62 t ha^(-1),respectively.The PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years(2013-2015).In conclusion,the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model,making it a useful tool for the simulation of regional winter wheat yield.展开更多
The increase of water temperature, due to thermal discharges from two nuclear power stations, was one of the most significant environmental changes since 1982 in the Daya Bay, located in the north of the South China S...The increase of water temperature, due to thermal discharges from two nuclear power stations, was one of the most significant environmental changes since 1982 in the Daya Bay, located in the north of the South China Sea. This study investigates the long-term (1982-2012) environmental changes in Daya Bay in response to the increase of water temperature, via comprehensively interpreting and analyzing both satellite and in situ observations along with previous data. The results show that: 1) salinity, dissolved oxygen (DO), chemical oxygen demand (COD) and nutrients had been enhanced after the thermal discharges started in 1994;2) the concentration of Chl-a increased while the net-phytoplankton abundance decreased;3) diversity of the phytoplankton community had decreased;4) fishery production had declined;and 5) frequency of Harmful Algal Bloom occurrence had increased. Satellite images show clearly that a thermal plume from the power stations extended toward the interior of Daya Bay, and that surface temperature of the seawater increased as one approached the power stations. The analysis suggests that the thermal water discharged from the two power stations was a driver of the ecosystem’s change in Daya Bay. Several factors, including nutrients, salinity, DO, and COD, varied according to the increase of water temperature. These factors affected the water quality, Chl-a, and phytoplankton in the short term and impaired aquatic organisms and the whole ecosystem in the long term.展开更多
We proposed a method to estimate single scattering albedo of winter wheat over the North China Plain with AMSR-E passive microwave imagery. The relationships of single scattering albedo and optical depth between 6. 92...We proposed a method to estimate single scattering albedo of winter wheat over the North China Plain with AMSR-E passive microwave imagery. The relationships of single scattering albedo and optical depth between 6. 925 GHz and 10. 65 GHz were derived from simulations. To retrieve the single scattering albedo,the relationships were combined with the physical expressions of microwave vegetation indices derived from the first-order parameterized emission model. Comparisons with normalized difference vegetation index( NDVI) obtained from daily MODIS reflectance product showed that the variations in winter wheat single scattering albedo were similar to those of winter wheat NDVI. However,several differences were observed. NDVI showed saturation from the heading stage to the milky stage of winter wheat,whereas single scattering albedo remained sensitive to the growth of winter wheat. Single scattering albedo offers certain advantages in reflecting the growth status of winter wheat.展开更多
This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou Ci...This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.展开更多
The northern region of China is not only a sensitive area for global climate change and a key region with prominent monsoon climate,but also a“hotspot”for global land-atmosphere coupling.Terrain and geomorphology in...The northern region of China is not only a sensitive area for global climate change and a key region with prominent monsoon climate,but also a“hotspot”for global land-atmosphere coupling.Terrain and geomorphology in this area are complex with a large spatiotemporal variation in land surface characteristics,and the climate dynamics of land-atmosphere interaction is relatively significant.In addition,affected by interactions between circulation systems in the mid-to high latitudes and low latitudes,atmospheric circulations in this area are relatively active,which makes it easy to induce extreme meteorological events such as droughts,sand storms,rainstorms,and hail.In view of this,from the perspective of scientific innovation,the main research works in the field of land-air interaction in northern China since this century are systematically summarized.Seven new research advancements have been outlined,including the comprehensive observational and experimental system of landatmosphere interaction in northern China,the spatiotemporal changes in physical quantities involved in land surface processes and their responses to summer monsoon,the response characteristics of land surface evapotranspiration to climate warming,land surface process parameters and parameterization schemes,the mechanism of land surface energy and water imbalance,the spatiotemporal changes and influence mechanisms of atmospheric boundary layer,and the relationship of land-atmosphere interaction with weather and climate.Based on the research progress summarized in this paper and the cutting-edge international study trend,we propose six key breakthroughs in the future for the study in this field:(1)the study should be based on the implementation and development of a new meteorologically integrated operational observation system that can observe and test conventional land-atmosphere interaction,(2)we need to improve our understanding of multi-interface exchange processes involved in land-atmosphere interaction,(3)mechanism study in the multi-scale land-atmosphere coupling process will be strengthened,(4)we need to deepen our understanding of the characteristics of land-atmosphere interaction in the specific environment of northern China,(5)the impact of land-atmosphere interaction on extreme weather and climate will be revealed,(6)multiple complicated feedback mechanisms between land-atmosphere interaction and climate warming will be explored.The information given in this paper will provide a scientific reference as well as a roadmap to promote land-atmosphere interaction study in northern China in the future.展开更多
We proposed a method to separate ground points and vegetation points from discrete return,small footprint airborne laser scanner data,called skewness change algorithm.The method,which makes use of intensity of laser s...We proposed a method to separate ground points and vegetation points from discrete return,small footprint airborne laser scanner data,called skewness change algorithm.The method,which makes use of intensity of laser scanner data,is especially applicable in steep,and forested areas.It does not take slope of forested area into account,while other algorithms consider the change of slope in steep forested area.The ground points and vegetation points can be used to estimate digital terrain model(DTM)and fractional vegetation cover,respectively.A few vegetation points which were classified into the ground points were removed as noise before the generation of DTM.This method was tested in a test area of 10000 square meters.A LiteMapper-5600 laser system was used and a flight was carried out over a ground of 700―800 m.In this tested area,a total number of 1546 field measurement ground points were measured with a total station TOPCON GTS-602 and TOPCON GTS-7002 for validation of DTM and the mean error value is-18.5 cm and the RMSE(root mean square error)is±20.9 cm.A data trap sizes of 4m in diameter from airborne laser scanner data was selected to compute vegetation fraction cover.Validation of fractional vegetation cover was carried out using 15 hemispherical photographs,which are georeferenced to centimeter accuracy by differential GPS.The gap fraction was computed over a range of zenith angles 10°using the gap light analyzer(GLA)from each hemispherical photograph.The R2 for the regression of fractional vegetation cover from these ALS data and the respective field measurements is 0.7554.So this study presents a method for synchronous estimation of DTM and fractional vegetation cover in forested area from airborne LIDAR height and intensity data.展开更多
Based on the row structure model of Kimes and the mean gap probability model in single direction, we develop a bidirectional gap probability model for row crop canopies. A concept of overlap index is introduced in thi...Based on the row structure model of Kimes and the mean gap probability model in single direction, we develop a bidirectional gap probability model for row crop canopies. A concept of overlap index is introduced in this model to consider the gaps and their correlation between the sun and view directions. Multiangular thermal emission data sets were measured in Shunyi, Beijing, and these data are used in model validation in this paper. By comparison with the Kimes model that does not consider the gap probability, and the model considering the gap in view direction only, it is found that our bidirectional gap probability model fits the field measurements over winter wheat much better.展开更多
The landscape index is a quantitative index which reflects characteristics of structure composition and spatial pattern in landscape studies,it is,therefore,expected to describe the spatial pattern of scientific resea...The landscape index is a quantitative index which reflects characteristics of structure composition and spatial pattern in landscape studies,it is,therefore,expected to describe the spatial pattern of scientific research in bibliometric analysis.In this study,a novel attempt to regard scientific research as a kind of‘landscape’was made,and landscape indices were improved for bibliometric analysis to measure the spatial pattern of scientific research.For illustrating the feasibility of our method,global geoscience research from 1994 to 2018 was presented as a case.Moreover,spatiotemporal migration of landscape centroids was visualized.The results indicated that global geoscience publications increased steadily and articles were highly concentrated at the country level.The top 10 countries published 69.93%of total articles and 84.68%of geoscience articles were from top 20 productive countries.The spatial migration of centroids was mainly reflected in the longitude because of significant increasing of articles in eastern countries,especially in China with the growth rate of 747.14%.At the patch scale,the change trend of improved landscape indices verified the spatiotemporal changes of global distribution of geoscience articles.At the landscape scale,the strengthening of global international collaboration is the main driving forces of spatial heterogeneity of global geoscience research.This study is expected to help readers to understand global trends of geoscience research in the past 25 years,and to promote the development of bibliometric analysis towards the directions of spatialization and visualization.展开更多
Exploring carbon dioxide(CO2)emissions from human activities is essential for urban energy conservation and resource management.Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in ...Exploring carbon dioxide(CO2)emissions from human activities is essential for urban energy conservation and resource management.Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in and a low-cost way of revealing CO2 emissions.Although many researches have documented the feasibility of the Suomi NPP-VIIRS data for assessing CO2 emissions,few have systematically revealed the ability of nighttime lights for evaluating CO2 emissions from different industries,such as service industry CO2 emissions(SC),traffic CO2 emissions(TC),and secondary industry CO2 emissions(IC).Here,China was selected as the experimental subject,and we comprehensively explored the relationships between the nighttime lights and SC,TC,and IC,and investigated the factors mediating these relationships.We found that without considering other factors,the nighttime lights only revealed up to 51.2%of TC,followed by 41.7%of IC and 22.7%of SC.When controlling for city characteristic variables,the models showed that there were positive correlations between the Suomi NPP-VIIRS data and SC,IC,and TC,and that nighttime lights have an Inverted-U relationship with SC.The Suomi NPP-VIIRS data are more suitable for revealing SC,TC,and IC in medium-sized and large-sized cities than in small-sized cities and megacities.展开更多
This paper generalizes the progress of algorithms in small target detection for hyperspectral imaging,and finds that whitening the image is the key point of many methods in small target detection.An al-gorithm is pres...This paper generalizes the progress of algorithms in small target detection for hyperspectral imaging,and finds that whitening the image is the key point of many methods in small target detection.An al-gorithm is presented to detect desired targets by converting large targets into small ones based on the weighted sample autocorrelation matrix.展开更多
Land use reflects human activities on land.Urban land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and urbanization.Urban areas have widespread effects on lo...Land use reflects human activities on land.Urban land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and urbanization.Urban areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2].However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city level.The mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping personnel.To address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping methods.This is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental administration.However,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same scale.Even at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://sdi4apps.eu/open_land_use/).展开更多
Significant areas of native forest in Kalimantan,on the island of Borneo,have been cleared for the expansion of plantations of oil palm and rubber.In this study multisource remote sensing was used to develop a time se...Significant areas of native forest in Kalimantan,on the island of Borneo,have been cleared for the expansion of plantations of oil palm and rubber.In this study multisource remote sensing was used to develop a time series of land cover maps that distinguish native forest from plantations.Using a study area in east Kalimantan,Landsat images were combined with either ALOS PALSAR or Sentinel-1 images to map four land cover classes(native forest,oil palm plantation,rubber plantation,non-forest).Bayesian multitemporal classification was applied to increase map accuracy and maps were validated using a confusion matrix;final map overall accuracy was>90%.Over 18 years from 2000 to 2018 nearly half the native forests in the study area were converted to either non-forest or plantations of either rubber or oil palm,with the highest losses between 2015 and 2016.Trending upwards from 2008 large areas of degraded or cleared forests,mapped as non-forest,were converted to oil palm plantation.Conversion of native forests to plantation mainly occurred in lowland and wetland forest,while significant forest regrowth was detected in degraded peatland.These maps will help Indonesia with strategies and policies for balancing economic growth and conservation.展开更多
Many physical laws, principles, models, measurement methods, etc., are applicable only to either a point on surface or a homogeneous surface. However, remote sensing deals with pixels which may range from meters to ki...Many physical laws, principles, models, measurement methods, etc., are applicable only to either a point on surface or a homogeneous surface. However, remote sensing deals with pixels which may range from meters to kilometers. Therefore scale effects of these laws and measurements are inevitable problems which must be faced. As an example, the spatial scale effect of Planck Law over nonisothermal blackbody surface is considered.展开更多
This paper focuses on interpreting the different spatial relationships between NDVI and Ts,a triangular or a trapezoid,and on analyzing transformation conditions,the physical and ecological meanings of the vegetation ...This paper focuses on interpreting the different spatial relationships between NDVI and Ts,a triangular or a trapezoid,and on analyzing transformation conditions,the physical and ecological meanings of the vegetation index-surface temperature space as well.Further,we use the Tempera-ture-Vegetation Dryness Index(TVDI)to explain the existent meaning of a triangular space after NDVI reaches its saturated state by employing the relationships between NDVI,LAI and evapotranspiration.The specific relations between NDVI and Ts are useful for describing,validating and updating land surface models.展开更多
基金National Natural Science Foundation of China,No.42041007。
文摘Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region,which mounts the need for precise spatial water management.In this study,we investigated the irrigation water demand for seven major crops in cities at the prefecture level between 2000 and 2019.Using Logarithmic Mean Divisia Index(LMDI)decomposition and k-means clustering,we quantified how yield,area,water use efficiency,and cropping patterns affect water demand and identified five irrigation development clusters.Key water-saving areas were identified by tracking transitions among clusters,and NSGA-II was applied to optimize crop structure.The results revealed that the total irrigation demand in the Yellow River Basin averaged 50.09 billion m3/year,with wheat accounting for 54.7%.The increase in yield and area increased demand by 15.2 and 5.5 billion m3,respectively,which was partly offset by changes in water use efficiency and cropping pattern(−7.0 and−1.8 billion m^(3),respectively).Regions in the upper reaches,particularly within the Lanzhou-Toudaoguai section,were identified as critical for water conservation.Optimization of the cropping structure in key regions can reduce annual irrigation water demand by 280 million m3,which accounts for 4.9%of the total demand in these areas,with minimal impact on crop production.This study provides a spatially explicit basis for targeted water conservation strategies in water-scarce agricultural regions.
基金the Shandong Key Research and Development Project,China(2018GNC110025)the National Natural Science Foundation of China(41871253)+2 种基金the Central Guiding Local Science and Technology Development Fund of Shandong—Yellow River Basin Collaborative Science and Technology Innovation Special Project,China(YDZX2023019)the Natural Science Foundation of Shandong Province,China(ZR2020QD016)the“Taishan Scholar”Project of Shandong Province,China(TSXZ201712)。
文摘Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled to the regional scale.To mitigate this issue,this study used a process-based and remote sensing driven crop yield model for winter wheat(PRYM-Wheat),which was derived from the boreal ecosystem productivity simulator(BEPS),to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield(Ya).The distribution of Ya across the North China Plain showed great heterogeneity,decreasing from southeast to northwest.The remote sensing-estimated results show that the average YG of the study area was 6400.6 kg ha^(–1).The YG of Jiangsu Province was the largest,at7307.4 kg ha^(–1),while the YG of Anhui Province was the smallest,at 5842.1 kg ha^(–1).An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation,but there was a weak negative correlation between YG and accumulated temperature.In addition,the YG was positively correlated with elevation.In general,studying the specific features of the YG can provide directions for increasing crop yields in the future.
基金National Key Technology P&D Program,No.2012BAB02B00The Fundamental Research Funds for the Central Universities
文摘The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission （TRMM） data with rain gauge data and further to use this TRMM data to drive a Dis- tributed Time-Variant Gain Model （DTVGM） to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year （2001-2010） daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R2 varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years （2001-2007） of data on daily hydro- logical processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamfiow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.
基金supported by the National Key Research and Development Program of China(2022YFD2001105)。
文摘Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Currently,in-situ drought monitoring based on weather stations and based on remote sensing data has limitations,including infrequent updates,limited coverage,and low accuracy.This study leverages multi-source remote sensing data to monitor agricultural drought in Heilongjiang Province,China.We developed multi-source composite drought indices(MCDIs)at various timescales(3,6,9,and 12 months)by integrating precipitation,land surface temperature,soil moisture,and vegetation indices.Utilizing remote sensing data from various sources,we calculated a series of single drought indices,which are the precipitation condition index,soil moisture condition index,vegetation condition index,and temperature condition index.These are then integrated into MCDIs using a multivariable linear regression approach.The analysis reveals that MCDIs correlate more with standardized precipitation evapotranspiration index(SPEI)than single drought indices.When examining the correlation between different MCDIs and the affected area of crops and major grain production,MCDI-9 showed the highest correlation with the affected area of crops,while MCDI-12 showed the highest correlation with grain production.This suggests that these two MCDIs at different timescales are better indicators of agricultural drought.The spatio-temporal analysis of MCDI indicates that drought in Heilongjiang Province primarily occurs in early spring,gradually spreading from the Greater Khingan Mountains region to the southeastern plains.The drought gradually alleviates during the summer,ending by the autumn harvest period.Therefore,the MCDIs constructed in this study can serve as effective methods and indicators for drought monitoring in Heilongjiang Province and similar regions.
基金This work was supported by the Special Funds for the Major State Basic Research Project(Grant No.G2000077903)the National Natural Science Foundation of China(Grant No.40171068).
文摘In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data,it is necessary to understand what the information flow in quantitative remote sensing model inversion is,thus control the information flow.Aiming at this,the paper takes the linear kernel-driven model inversion as an example.At first,the information flow in different inversion methods is calculated and analyzed,then the effect of information flow controlled by multi-stage inversion strategy is studied,finally,an information matrix based on USM is defined to control information flow in inversion.It shows that using Shannon entropy decrease of the inversed parameters can express information flow more properly.Changing the weight of a priori knowledge in inversion or fixing parameters and partitioning datasets in multi-stage inversion strategy can control information flow.In regularization inversion of remote sensing,information matrix based on USM may be a better tool for quantitatively controlling information flow.
基金This work was mainly supported by the National High-Technology Research and Development Program(863)[grant number 2013AA122801]the National Science Foundation of the United States[Award No.1251095]+3 种基金Also it was partially supported by the Fundamental Research Funds for the Central Universities[grant number ZYGX2015J111]the project entitled‘Design and development of the parallelism for typical remote sensing image algorithm based on heterogeneous computing’from the Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciencesthe project entitled‘CAST Innovation Fund:the Study of Agent and Cloud Based Spatial Big Data Service Chain’also the National Natural Science Foundation of China[grant number 51277167].
文摘MODerate resolution atmospheric TRANsmission(MODTRAN)is a commercial remote sensing(RS)software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth’s atmosphere and the radiation observed by a remote sensor.However,when very large RS datasets must be processed in simulation applications at a global scale,it is extremely time-consuming to operate MODTRAN on a modern workstation.Under this circumstance,the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task.This paper presents PMODTRAN,an implementation of a parallel task-scheduling algorithm based on MODTRAN.PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster.In addition,PMODTRAN can distribute tasks with different levels of granularity and has some extra features,such as dynamic load balancing and parameter checking.
基金supported by the National Natural Science Foundation of China(42101382 and 42201407)the Shandong Provincial Natural Science Foundation China(ZR2020QD016 and ZR2022QD120)。
文摘The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China.Presently,most remote sensing process models use the“biomass×harvest index(HI)”method to simulate regional-scale winter wheat yield.However,spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional scale.Time-series dry matter partition coefficients(Fr)can dynamically reflect the dry matter partition of winter wheat.In this study,Fr equations were fitted for each organ of winter wheat using site-scale data.These equations were then coupled into a process-based and remote sensingdriven crop yield model for wheat(PRYM-Wheat)to improve the regional simulation of winter wheat yield over the North China Plain(NCP).The improved PRYM-Wheat model integrated with the fitted Fr equations(PRYM-Wheat-Fr)was validated using data obtained from provincial yearbooks.A 3-year(2000-2002)averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination(R^(2)=0.55)and lower root mean square error(RMSE=0.94 t ha^(-1))than PRYM-Wheat with a stable HI(abbreviated as PRYM-Wheat-HI),which had R^(2) and RMSE values of 0.30 and 1.62 t ha^(-1),respectively.The PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years(2013-2015).In conclusion,the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model,making it a useful tool for the simulation of regional winter wheat yield.
文摘The increase of water temperature, due to thermal discharges from two nuclear power stations, was one of the most significant environmental changes since 1982 in the Daya Bay, located in the north of the South China Sea. This study investigates the long-term (1982-2012) environmental changes in Daya Bay in response to the increase of water temperature, via comprehensively interpreting and analyzing both satellite and in situ observations along with previous data. The results show that: 1) salinity, dissolved oxygen (DO), chemical oxygen demand (COD) and nutrients had been enhanced after the thermal discharges started in 1994;2) the concentration of Chl-a increased while the net-phytoplankton abundance decreased;3) diversity of the phytoplankton community had decreased;4) fishery production had declined;and 5) frequency of Harmful Algal Bloom occurrence had increased. Satellite images show clearly that a thermal plume from the power stations extended toward the interior of Daya Bay, and that surface temperature of the seawater increased as one approached the power stations. The analysis suggests that the thermal water discharged from the two power stations was a driver of the ecosystem’s change in Daya Bay. Several factors, including nutrients, salinity, DO, and COD, varied according to the increase of water temperature. These factors affected the water quality, Chl-a, and phytoplankton in the short term and impaired aquatic organisms and the whole ecosystem in the long term.
基金National Natural Science Foundation of China(No.41171259)National Basic Research Program of China(973 Program)(No.2013CB733406)
文摘We proposed a method to estimate single scattering albedo of winter wheat over the North China Plain with AMSR-E passive microwave imagery. The relationships of single scattering albedo and optical depth between 6. 925 GHz and 10. 65 GHz were derived from simulations. To retrieve the single scattering albedo,the relationships were combined with the physical expressions of microwave vegetation indices derived from the first-order parameterized emission model. Comparisons with normalized difference vegetation index( NDVI) obtained from daily MODIS reflectance product showed that the variations in winter wheat single scattering albedo were similar to those of winter wheat NDVI. However,several differences were observed. NDVI showed saturation from the heading stage to the milky stage of winter wheat,whereas single scattering albedo remained sensitive to the growth of winter wheat. Single scattering albedo offers certain advantages in reflecting the growth status of winter wheat.
基金the Natural Science Foundation of China(41807285)Interdisciplinary Innovation Fund of Natural Science,NanChang University(9167-28220007-YB2107).
文摘This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.42230611,U2142208,41905011,42175088)。
文摘The northern region of China is not only a sensitive area for global climate change and a key region with prominent monsoon climate,but also a“hotspot”for global land-atmosphere coupling.Terrain and geomorphology in this area are complex with a large spatiotemporal variation in land surface characteristics,and the climate dynamics of land-atmosphere interaction is relatively significant.In addition,affected by interactions between circulation systems in the mid-to high latitudes and low latitudes,atmospheric circulations in this area are relatively active,which makes it easy to induce extreme meteorological events such as droughts,sand storms,rainstorms,and hail.In view of this,from the perspective of scientific innovation,the main research works in the field of land-air interaction in northern China since this century are systematically summarized.Seven new research advancements have been outlined,including the comprehensive observational and experimental system of landatmosphere interaction in northern China,the spatiotemporal changes in physical quantities involved in land surface processes and their responses to summer monsoon,the response characteristics of land surface evapotranspiration to climate warming,land surface process parameters and parameterization schemes,the mechanism of land surface energy and water imbalance,the spatiotemporal changes and influence mechanisms of atmospheric boundary layer,and the relationship of land-atmosphere interaction with weather and climate.Based on the research progress summarized in this paper and the cutting-edge international study trend,we propose six key breakthroughs in the future for the study in this field:(1)the study should be based on the implementation and development of a new meteorologically integrated operational observation system that can observe and test conventional land-atmosphere interaction,(2)we need to improve our understanding of multi-interface exchange processes involved in land-atmosphere interaction,(3)mechanism study in the multi-scale land-atmosphere coupling process will be strengthened,(4)we need to deepen our understanding of the characteristics of land-atmosphere interaction in the specific environment of northern China,(5)the impact of land-atmosphere interaction on extreme weather and climate will be revealed,(6)multiple complicated feedback mechanisms between land-atmosphere interaction and climate warming will be explored.The information given in this paper will provide a scientific reference as well as a roadmap to promote land-atmosphere interaction study in northern China in the future.
基金Supported by the National State Key Basic Research Project(Grant No.2007CB714404)the National Natural Science Foundation of China(Grant No.40871173)+1 种基金the State Key Laboratory of Remote Sensing Science,China(Grant No.03Q0030449)Key Science and Technology R&D Program of Qinghai Province(Grant No.2006-6-160-01)
文摘We proposed a method to separate ground points and vegetation points from discrete return,small footprint airborne laser scanner data,called skewness change algorithm.The method,which makes use of intensity of laser scanner data,is especially applicable in steep,and forested areas.It does not take slope of forested area into account,while other algorithms consider the change of slope in steep forested area.The ground points and vegetation points can be used to estimate digital terrain model(DTM)and fractional vegetation cover,respectively.A few vegetation points which were classified into the ground points were removed as noise before the generation of DTM.This method was tested in a test area of 10000 square meters.A LiteMapper-5600 laser system was used and a flight was carried out over a ground of 700―800 m.In this tested area,a total number of 1546 field measurement ground points were measured with a total station TOPCON GTS-602 and TOPCON GTS-7002 for validation of DTM and the mean error value is-18.5 cm and the RMSE(root mean square error)is±20.9 cm.A data trap sizes of 4m in diameter from airborne laser scanner data was selected to compute vegetation fraction cover.Validation of fractional vegetation cover was carried out using 15 hemispherical photographs,which are georeferenced to centimeter accuracy by differential GPS.The gap fraction was computed over a range of zenith angles 10°using the gap light analyzer(GLA)from each hemispherical photograph.The R2 for the regression of fractional vegetation cover from these ALS data and the respective field measurements is 0.7554.So this study presents a method for synchronous estimation of DTM and fractional vegetation cover in forested area from airborne LIDAR height and intensity data.
基金the National Natural Science Foundation of China(Grant No.40101020)Special Funds for Major State Basic Research Project(Grant No.G2000077900) the National High Technology Research and Development Program(Grant No.2001AA131030).
文摘Based on the row structure model of Kimes and the mean gap probability model in single direction, we develop a bidirectional gap probability model for row crop canopies. A concept of overlap index is introduced in this model to consider the gaps and their correlation between the sun and view directions. Multiangular thermal emission data sets were measured in Shunyi, Beijing, and these data are used in model validation in this paper. By comparison with the Kimes model that does not consider the gap probability, and the model considering the gap in view direction only, it is found that our bidirectional gap probability model fits the field measurements over winter wheat much better.
基金This work was jointly supported by the project of National Social Science Fund of China(17ZDA188)the National Natural Science Foundation of China(Grant Nos.41830648,41771453).
文摘The landscape index is a quantitative index which reflects characteristics of structure composition and spatial pattern in landscape studies,it is,therefore,expected to describe the spatial pattern of scientific research in bibliometric analysis.In this study,a novel attempt to regard scientific research as a kind of‘landscape’was made,and landscape indices were improved for bibliometric analysis to measure the spatial pattern of scientific research.For illustrating the feasibility of our method,global geoscience research from 1994 to 2018 was presented as a case.Moreover,spatiotemporal migration of landscape centroids was visualized.The results indicated that global geoscience publications increased steadily and articles were highly concentrated at the country level.The top 10 countries published 69.93%of total articles and 84.68%of geoscience articles were from top 20 productive countries.The spatial migration of centroids was mainly reflected in the longitude because of significant increasing of articles in eastern countries,especially in China with the growth rate of 747.14%.At the patch scale,the change trend of improved landscape indices verified the spatiotemporal changes of global distribution of geoscience articles.At the landscape scale,the strengthening of global international collaboration is the main driving forces of spatial heterogeneity of global geoscience research.This study is expected to help readers to understand global trends of geoscience research in the past 25 years,and to promote the development of bibliometric analysis towards the directions of spatialization and visualization.
基金supported by the Key Research Program of Frontier Sciences,CAS(No.QYZDB-SSW-DQC011)the MOE(Ministry of Education in China)Project of Humanities and Social Sciences(No.18XJC790011)the Fundamental Research Founds for the Central Universities(No.XDJK2020B008).
文摘Exploring carbon dioxide(CO2)emissions from human activities is essential for urban energy conservation and resource management.Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in and a low-cost way of revealing CO2 emissions.Although many researches have documented the feasibility of the Suomi NPP-VIIRS data for assessing CO2 emissions,few have systematically revealed the ability of nighttime lights for evaluating CO2 emissions from different industries,such as service industry CO2 emissions(SC),traffic CO2 emissions(TC),and secondary industry CO2 emissions(IC).Here,China was selected as the experimental subject,and we comprehensively explored the relationships between the nighttime lights and SC,TC,and IC,and investigated the factors mediating these relationships.We found that without considering other factors,the nighttime lights only revealed up to 51.2%of TC,followed by 41.7%of IC and 22.7%of SC.When controlling for city characteristic variables,the models showed that there were positive correlations between the Suomi NPP-VIIRS data and SC,IC,and TC,and that nighttime lights have an Inverted-U relationship with SC.The Suomi NPP-VIIRS data are more suitable for revealing SC,TC,and IC in medium-sized and large-sized cities than in small-sized cities and megacities.
基金the National Natural Science Foundation of China(Grant Nos.40501041,40202031)the Key Innovation Project of Chinese Academy of Sci-ences(Grant No.KZCX3-SW-338-1)
文摘This paper generalizes the progress of algorithms in small target detection for hyperspectral imaging,and finds that whitening the image is the key point of many methods in small target detection.An al-gorithm is presented to detect desired targets by converting large targets into small ones based on the weighted sample autocorrelation matrix.
基金partially supported by the National Key Research and Development Program of China(2016YFA0600104)supported by donations made by Delos Living LLC,and the Cyrus Tang Foundation+2 种基金supported by the National Natural Science Foundation of China(41471419)Beijing Institute of Urban Planningsupported by the Fundamental Research Funds for the Central Universities(CCNU19TD002).
文摘Land use reflects human activities on land.Urban land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and urbanization.Urban areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2].However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city level.The mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping personnel.To address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping methods.This is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental administration.However,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same scale.Even at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://sdi4apps.eu/open_land_use/).
文摘Significant areas of native forest in Kalimantan,on the island of Borneo,have been cleared for the expansion of plantations of oil palm and rubber.In this study multisource remote sensing was used to develop a time series of land cover maps that distinguish native forest from plantations.Using a study area in east Kalimantan,Landsat images were combined with either ALOS PALSAR or Sentinel-1 images to map four land cover classes(native forest,oil palm plantation,rubber plantation,non-forest).Bayesian multitemporal classification was applied to increase map accuracy and maps were validated using a confusion matrix;final map overall accuracy was>90%.Over 18 years from 2000 to 2018 nearly half the native forests in the study area were converted to either non-forest or plantations of either rubber or oil palm,with the highest losses between 2015 and 2016.Trending upwards from 2008 large areas of degraded or cleared forests,mapped as non-forest,were converted to oil palm plantation.Conversion of native forests to plantation mainly occurred in lowland and wetland forest,while significant forest regrowth was detected in degraded peatland.These maps will help Indonesia with strategies and policies for balancing economic growth and conservation.
基金Project supported partly by China's Key-Importance Basic Research Program (95-Y-38), the National Natural Science Foundation of China (Grant No. 49671059), partly by NASA' s grants NAG 5-7217 and NAS 5-31369.
文摘Many physical laws, principles, models, measurement methods, etc., are applicable only to either a point on surface or a homogeneous surface. However, remote sensing deals with pixels which may range from meters to kilometers. Therefore scale effects of these laws and measurements are inevitable problems which must be faced. As an example, the spatial scale effect of Planck Law over nonisothermal blackbody surface is considered.
基金This research was supported by Funds for the Major State Basic Research Project(Grant No.G2000077908)the National Natural Science Foundation of China(Grant Nos.40371083 and 40201038).
文摘This paper focuses on interpreting the different spatial relationships between NDVI and Ts,a triangular or a trapezoid,and on analyzing transformation conditions,the physical and ecological meanings of the vegetation index-surface temperature space as well.Further,we use the Tempera-ture-Vegetation Dryness Index(TVDI)to explain the existent meaning of a triangular space after NDVI reaches its saturated state by employing the relationships between NDVI,LAI and evapotranspiration.The specific relations between NDVI and Ts are useful for describing,validating and updating land surface models.
