Soil nutrition status information is an important premise for precise field management.Methods on remote sensing based soil nutrients estimation were categorized into direct and indirect methods based on an analysis and summarization of recent researches in the field.Direct method uses soil spectral characteristics to estimate soil nutrients,while indirect method uses the crop growing characteristics under different soil nutrition levels to evaluate soil nutrients.The comprehensive comparison of these two methods show that pointed that the methods have their own advantages and the combination of the two methods can effectively improve the precision and integrality of soil nutrients estimation.Prospects on soil nutrients monitoring with remote sensing were provided in conclusion.
Hydrological model is a powerful tool in water-related studies,which experienced three stages development:originated as black-box models,and then progressed as lumped conceptual models,currently became as physical-based distributed models.Remote sensing technology,due to its advantages of spatially-distributed observation,ready accessibility and relative long continuous data recording,has been widely applied in hydrological simulations.In recent years,remote sensing data has made great progress in some areas like detection capability,reliability,multi-source data fusion and satellite constellations.There are generally three ways of applying remote sensing in hydrologic simulations,namely forcing data acquisition,parameter extraction and state variables diagnosis.With the development of data assimilation in recent years,assimilating remote sensing observations into hydrological models to improve the simulation results has been a research hotpot.At present,there are still several challenges in promoting further application of remote sensing in hydrology studies,such as high uncertainties,scale effects,instantaneous measurement,and difficulty to get the information of deeper soil layer.To make the full use of remote sensing data that are obtained from multiple sources and of various resolutions will be an important future direction of hydrology research.
Extraction accuracy of snow-cover under forest is low,due to occlusion of vegetation canopy,the snow under the canopy is difficult to be extracted.In this study,the data is based on Landsat 8 OLI Sensor.Due to a large area of forest distribution in the north central of Manas river basin.This study extracted snow cover information through three methods respectively,including NDSI and S3,NDSI and S3 aid of NDVI data as well as the object-oriented image feature extraction.The results showed that,for the first method,the snow pixels in forest were difficult to identify.Extraction accuracies were 85.23% and 87.54%.For the second method,with the aid of NDVI data,the snow cover can be well extracted by two types of normalized snow index.Meanwhile,the results were quite similar to each other and of high accuracy.Extraction accuracies were 91.47% and 90.60%.So,when the spatial resolution of the image is high,and basin scale is small,and forest covers more cases,this method can be used to extract the snow cover.For the third method,with the increase of altitude,affected by terrain increased gradually,and the vegetation coverage decreased gradually,the number of snow pixels were extracted by NDVI assisted extraction method decreased gradually.So we used the object-oriented image feature extraction method with spectrum,texture and space information to extract snow cover.The method can identify the snow pixels affected by terrain.The extraction accuracy was slightly lower than the second methods mentioned above.Extraction accuracy was 89.75% which met the needs of practical application.
Snow albedo is an important parameter in the study of local or global energy balance and climate change.The snow physical properties such as particle size shape and the impurities are the key reason that affects the snow albedo.The snow bidirectional reflectance distribution function (BRDF) is the premise in the remote sensing retrieve snow parameters.In this paper,the radiative transfer theory (ART) considers the snow particle size,particle shape and the effect of the impurities which was used to retrieve the snow surface albedo.Base on the ART model,a new MODIS snow albedo retrieval algorithm is developed.Then using the new algorithm to get the MODIS 8-day snow albedo data.Taking the Greenland as a study area,the MODIS surface reflectance data (MOD09) and MODIS snow cover data (MOD10L2G) were acquired.Then the GC-Net in-situ measurements data were used to analyze the evaluation of the retrieval snow albedo.Compared with the snow albedo in-situ data,the root mean square error(RMSE) and correlation coefficient for the new MODIS snow albedo over the Greenland is 0.018 and 0.83.The result shows that the MODIS snow albedo based on the ART model works well and needs less parameter.
Snow cover reflects most of the solar radiation due to its very high reflectivity;at the same time,snow cover depresses longwave radiation due to its low conductivity.As a consequence,the accumulation and depletion processes of seasonal snow cover change the radiation energy balance on top of the atmosphere,then feedback to the climate system.ERA-interim reanalysis data,MODIS cloud-free snow products were used to simulate the energy balance on top of the atmosphere in Tibetan Plateau between 2001~2010 using modified partial radiative perturbation method.Results showed that more than 99.5% of the study area has a negative radiation balance,which indicates a positive radiative forcing to the climate system.The annual snow radiative forcing of the study area is 3.97 W m-2 on average between 2001~2010.There is little difference in the annual snow radiative forcing while big difference lies inside of the study area;the spatial distribution of radiative forcing has a very high correlation with annual fraction of snow cover.Under most circumstances,shortwave radiative forcing is the determinant factor of snow radiative forcing.At last,snow feedback is estimated to be 9.35 W·m-2℃-1.
Monitoring the distribution of snow coverage via satellite remote sensing has an clear advantage over surface observation.And a few studies have been carried out about snow monitoring based on FY-3 data.A snow-discriminating method applying to FY-3/VIRR data with snow-satellite remote sensing monitoring algorithm is provided which is achieved by normalized difference snow index and multi-band comprehensive threshold,and regional snow cover classification maps are extracted.This algorithm is proved to be effective and feasible by case analysis,and results comparisons are carried out with MODIS snow products MOD10 and its first level data identification results,which demonstrated this algorithm has dominant discriminating results.The study shows that FY-3 data is a reliable source of snow telemetry and can be used in snow monitoring and disaster predictive warning service,which facilitates the application and popularization of domestic satellite.
The relationship between snow cover fraction and snow water equivalent at pixel scale is an effective approach of applying high resolution optical remote sensing data in hydrological models.Binggou watershed was selected as the study area in this paper this paper.Retrieved snow cover fraction (SCF) and snow water equivalent (SWE) information through the ENVISAT-ASAR data,thus resamples the SCF and SWE data in 500m resolution and analyzes the relation between SCF and SWE.The results show that:①Without the concern of topographic factors,snow cover fraction and snow water equivalent is a discrete distribution,however,snow cover fraction determines the maximum value of snow water equivalent,and the average snow water equivalent improves with the increased snow cover fraction;②When considering topographic factors,the multifactor regression analysis of snow water equivalent and snow cover fraction,slope,aspect,altitude show that the regression coefficient significance level is less than 0.05,and the correlation coefficient reaches at 0.841.Therefore,snow cover fraction can be converted into snow water equivalent according to the analysis of specific snow depletion curve,thich provides a solution to fuse the remote sensing data into cold region hydrological model.
It is the basis of classifying feature and matching that the characteristics of spectral analysis.Now the spectrum analysis is the main way in the fine species identification using the hyperspectral remote sensing technology.This paper focuses on exploring the nonlinear space in the inner similarity measure.Since the spectral curve of characterization can express the complex nonlinear spectral imaging process.The paper describes the non-empty set from the overall shape of space target.The distance between spectrum characteristic curve was defined Fr ′ echet distance,Hausdorff distance and Euclidean distance respectively and algorithm was designed to measure nonlinear similar degree between the spectral vector.The results show that the accuracy about Fr ′ echet distance,Hausdorff distance,the precision of the Euclidean distance to measure the spectral similarity is once weakened,but on the basis of Fr′echet distance algorithm time complexity is a little high.This method based on Fr′echet distance improves the accuracy and denoising performance,due to considering point position and trend of the curve,thereby which provides an effective tool to spectral analysis.
Phenology is the key indicator of climate change,and the rapid development of remote sensing provides a new method to monitor phenology.It is really significant of acknowledging the differences in extracting vegetation phenology to assess the availability of remote sensing product in monitoring phenology.Taking three northeast provinces of China as study area,our study exploits asymmetric Gaussian function fitting method to smooth the data,and uses dynamic threshold method to extract the start of the growing season (SGS),the end of the growing season (EGS),and the length of the growing season (LGS) of MODIS,CYCLOPES and GLASS leaf area index data product.The results show that MODIS and GLASS data product have similar results in extracting SGS,EGS and LGS,and the consistency between MODIS and GLASS data product is better on the whole; The SGS extracted from CYCLOPES is later than MODIS and GLASS data product in the overwhelming majority of cases,but the EGS is earlier than MODIS and GLASS data product,thus it causes the LGS shorter than the other two data products.The validation of phonological phase by taking advantages of the phonological observation data show that the SGS from MODIS and GLASS data product is similar to the phonological observations,and the EGS is a litter later than the phonological observations;The SGS and EGS from CYCLOPES data product are similar to the phonological observations and it indicates that the SGS and EGS from CYCLOPES data product in woodland are much more reliable.
Effective mean atmospheric temperature,Ta is a key parameter for the mono-window algorithm in the land surface temperature,LST remote sensing.Using radiosonde data of 123 stations within Chinese continent from 2008 to 2011,the vertical distribution characteristics of water vapor in atmospheric profile is discovered and the feasibility of Ta is esimated by air temperature near land surface T0is displayed,then their relationship is discussed,the results show that the best estimated model of Ta is deduced for land surface temperature remote sensing in China,and that is Ta=44.97098+0.80512T0,its determination coefficient is 0.859 and the root-mean-square error,RMSE is 4.198 K.The sensitivity analysis of Ta model for the land surface temperature remote sensing is carried out by 44 HJ-1B/IRS thermal infrared images,and the results show that the RMSE of the retrieval LST is 1.734 K using the estimated Ta,and the estimated error of Ta is very sensitive for the retrieval LST when the atmospheric transmittance,τ is very low,and a small Ta estimated error will result in a big LST retrieval error,however,as the transmittance increases,the sensitivity of the retrieval error will decrease with the Ta estimated error.
Integrating the respective advantages of optical and microwave data for the vegetation,an optical and microwave synergistic method for the Above Ground Biomass (AGB) in the prairie of arid regions was developed in this paper.Vegetation coverage was combined in water cloud model as additional information.The total backscattering was divided into the amount attributed to areas covered with vegetation and attributed to areas of bare soil.Thus,the water cloud model can be applied in the sparse vegetation cover area.Using the modified water cloud model and dual-polarization ASAR data,the vegetation biomass was estimated by the established equations.The method was applied to estimate the AGB of Wutumeiren prairie.The results indicated that the method of active and passive remote sensing synergy was of the operational potential in AGB.And the better accuracy of the biomass retrieval was achieved(R2=0.8562,RMSE=0.1813 kg/m2).Finally,the error of biomass estimation using this method was analyzed.
Fraction of absorbed Photosynthetically Active Radiation(FPAR) is a basic physiological variable that describing vegetation structure and the process of related material and energy exchange .Focus on the meadow steppe in Xieertala,Hulunber,Inner Mongolia,by five field experiments in 2013,through the bridge of high spatial resolution HJ-1CCD images,statistical modes between measured FPAR and VIs that calculated from images was established,achieved validation and analysis of MODIS/FPAR product.The “true value”of 1 km HJ/FPAR is cosistent with the trend of MODIS/FPAR product value,and has a higher consistency,R2 reached to 0.6762.MODIS/FPAR product can reflect grassland accuracy and phenology of the whole growing season of meadow steppe in Hulunber,because there also has a consistent trend between HJ/FPAR and MODIS/FPAR on the scale of study area,and the correlation coefficient is up to 0.9148.MODIS/FPAR product has some overestimation phenomenon both in the same sacle(1 km) throughtout the study area.The results are important to understand and further use MODIS/FPAR product of Hulunber meadow steppe.
With regard to the lack of systemic evaluation of biodiversity in China,combining with the requirements of Biodiversity Assessment at county’s in the National Key Ecological Function Areas,based on the remote sensing technology advantages and characteristics,the paper proposed a monitoring and evaluation method for biodiversity at county’s scale using remote sensing.which can carry it out in a county and monitored the temporal and spatial biodiversity changes in the study area.The results showed that the decreased biodiversity zone mainly occupied the study area from 2000 to 2010,whose area accounted for 77.24% of the study area while the increased zone only accounted for 22.58%.The application showed that the method can monitor the current status and the temporal and spatial changes of biodiversity at county’s scale,which also can obtain spatial information of biodiversity at the pixel scale,as a result,managers can quantitatively analyze the biodiversity.Compared with the traditional method of field investigation,the proposed method has the advantages of fine timeliness,easy operation and high efficiency.Therefore,it is feasible to be used in monitoring and assessment of biodiversity at county’s scale.
Selecting the Shule River Basin which locates in the west of Gansu Province as the study area,the automated extraction method for wetlands in arid regions was discussed．Using the Landsat 8 satellite images as the data sources,supported by the digital elevation model (DEM),the modified arid zone wetlands index (MAZWI),the normalized difference vegetation indices (NDVI) and the surface albedo,and the identification of dissimilarity index of the gray level co-occurrence matrix (GLCM),were used as the indicators to establish the decision tree model and the wetlands were extracted.Comparing with the results obtaining by the maximum likelihood supervised classification,it showed that the decision tree method based on the indices can improve the overall accuracy by 6.52% and the overall kappa coefficient by 0.1243.The results of this study suggested that the decision tree method based on indices is an effective tool for wetlands classification in arid zone.
Rapid urbanization has changed land surface properties and land-atmosphere interaction,which further may impact on the climate and environment.It is important for urban sustainability to timely and accurate monitor regional or global urbanization process.Currently,urban area in large scale could be derived from satellite images,and DMSP/OLS has already been widely used to detect global urbanization due to its time series.However,its saturation in urban area largely limited its further application.Therefore,an improved urban index (VANUI) was used to detect urbanization process in JING-JIN-JI area from 2001-2012 to examine its fitness in mega-city area,further provide reasonable information for urban planning and urban sustainability.Compared with the traditional night lights index,VANUI is indeed to improve the monitoring ability for urban center and reduce the influence of signal saturation on parameters derivation.The results showed that VANUI could catch the urbanization process that happened in JING-JIN-JI area during last decades.Spatial pattern of urban expansion from 2001-2012 indicated that the urban area of JING-JIN-JI was doubled from 2001-2012,and the fasted region about urbanization happened in Tangshan,Hebei Province.Linear trend was also detected in urban development by VANUI index from 2001-2012,and the most important increasing period showed in 2004-2007 and 2009-2012.
It is always difficult to find a proper threshold that used to the segmentation of remote sensing images to extract water bodies.Choosing the MODIS surface reflectance data as data source,the specific procedures for extracting water bodies are as follows:①Record many thresholds of 6th band of the MODIS surface reflectance data that used to segment the images to extract the water bodies,and then a prior threshold range can be obtained;②Expand the vector boundaries of the water bodies to make land and water have approximately the same areas;③Get the grey level histograms of the expanded regions and select the minimum values between the prior threshold range as the best thresholds to extract different water bodies.It is simply and efficiently to get the suitable thresholds to extract the water bodies with this method,and at the same time it can get rid of influences of complexity of surface features and image noise.Also,it is possible to automatically and very timesaving extract water bodies on remote sensing images.
Stripe noise,which not only hinders the visual interpretation of hyperspectral images,and restricts quantitative application of hyperspectral remote sensing.The commonly used wavelet transform filtering methods have difficulty in separating stripe noise and useful information of the original images.For this reason,with reference to the characteristic of directivity and “mathematic microscope” of wavelet transform,a new destriping method based on wavelet transform has been proposed In this study.Firstly,this method decomposes the original images containing stripe by a certain scale of wavelet transform,then the high frequency sub-images of every scale which have the same direction with stripe noise and the low frequency sub-images are decomposed separately by wavelet transform to a degree where stripe noise and useful information can be effectively identified.Consequently,the values of sub-images serious corrupted by stripe noise are set to zero.Finally,the destriped images are constructed by inverse wavelet transform.We take CHRIS hyperspectral data from European Space Agency satellite PROBA as an example.The proposed method is compared with moment matching,Fourier filtering method and wavelet threshold method by quantitatively evaluating those four destriping methods using three indices:correlation coefficient(R),Structure Similarity (SSIM) and Peak Signal to Noise Ratio(PSNR).The results show that the image processed by the new method has the highest R,SSIM and PSNR,Which indicate the method proposed in this study effectively eliminates stripe noise in hyperspectral image while preserving the useful information of the original image.
Due to limitations of 16-day revisit cycle and frequent cloud contamination,the images of Landsat8 satellite OLI sensor in fine spatial and temporal resolution is urgently needed.This paper took local spatial association of ground features into consideration,the application of fusing Moderate Resolution Imaging Spectroradiometer (MODIS) images with Landsat8 OLI images is studied,using a new algorithm model called Local Associated Spatial and Temporal Adaptive Reflectance Fusion Model (LASTARFM).The algorithm is tested in Yecheng County,Kashi Prefecture of Xinjiang Uygur Autonomous Region.The result showed that,the correlation r-square of LASTARFM fused NDVI against true Landsat NDVI reached 0.92;LASTARFM showed more ground feature details than STARFM,and a higher fusion accuracy in low local associated areas;while in the ground features significantly changed areas,the LASTARFM images and true Landsat images had distinctions in a certain extent.
With regard to model non-linear problems in data assimilation process,an Iterative Ensemble Kalman Filter (IEnKF) is derived by thoroughly analysis and comparison.Within the framework of Lorenz-63model,this paper compared the different performances among the following three methods,Ensemble Kalman Filter (EnKF) Iterative Ensemble Kalman Filter (IEnKF) and Iterative extended Kalman Filter (IEKF),by changing ensemble numbers,observation error variance,the inflation factors and the model steps.The final comparative studies show that the assimilation accuracy of all three algorithms can be improved when ensemble numbers increase.When we change the inflation factors,the assimilation results are becoming worse and the EnKF presents obvious multihill and multivalley phenomena.The RMSE of all three algorithms increase when observation error variance and the model steps increase,and the results of algorithms get worse as well.The results show that the IEnKF is the most optimal algorithms with a much better robust performance.
Detecting power transmission lines is useful in the management of power line network.With the increasing applications of LiDAR (Light Detection And Ranging) in the helicopter-based inspection,many studies have focused on the three dimension model of the transmission lines.However,reconstructing the bundled conductors from points cloud is more complex than the non-bundled ones.In this paper,we proposed an automatic algorithm to reconstruct the bundled conductors from the helicopter-borne LiDAR points cloud.The power line is divided into sections along its orientation at first.Secondly,the points in each section are projected onto a principle plane,the clustering is applied in that plane.Thridly,with a clusters merging process,the points from the same bundled conductors in the sections are combined.Finally,the power lines are reconstructed with curve fitting.The algorithm is validated by the points cloud of four-bundled conductors.The results indicated that the proposed method is self-adaptable and effective in bundled conductors reconstruction and the quality of data has low impact on the final results.
This paper summarizes six popular and cutting\|edge algorithms,including SPP\|N\|FINDR,VCA ,SPICE,PCOMMEND,MVSA and MVC\|NMF.A comprehensive comparison and analysis concludes the advantages and disadvantages of each of the six algorithms.From the experimental results show that this paper concludes that SPP\|N\|FINDR algorithm lacks the ability to resist noise when compared to the other five algorithms;VCA and MVSA are more stable than the other five algorithms; MVC\|NMF and SPICE can autonomously determine the number of endmembers and simultaneously can also obtain abundance matrix; The outcomes of PCOMMEND by true hyperspectral image which is best and gain abundance matrix,but the accuracy of this algorithm declines when the number of endmembers is prime.In the future,embracing these algorithms to process relational study,the research will offer theoretical support and consult.
Cellular Automata (CA) was frequently used to Sub-pixel mapping,which is a technique designed to obtain the spatial distribution of classes with information contained in mixed pixels.However,it has several drawbacks,such as large number of iterations,evolution “insufficiency” and equivocation of termination condition,which restricted the further application in remote sensing.In this study CA was improved in three aspects to overcome problems that mentioned above,which included the modification of initialization,the modification of CA progress and a clear termination condition .Two images were employed to test running efficiency of improved CA.Although the adjusted Kappa coefficients of final Sub-pixel mapping results increased only by 4.1% and 3.7%,number of Iterations reduced and the CA running time accounted for only 25.8% of the original.The result showed that the improved CA can significantly improve the running efficiency.In addition,the clear termination condition made it more practical application value.
