Improving Soil Moisture Estimation by Joint Assimilationof MODIS Land Surface Temperature and Airborne L band Microwave Brightness Temperature

In this work,a novel soil moisture data assimilation scheme was developed,which was based land surface model (CoLM,Common Land Model),microwave radioactive transfer model (L MEB,L band Microwave Emission of the Biosphere),and data assimilation algorithm (EnKS,Ensemble Kalman Smoother).This scheme is used to improve the estimation of soil moisture profile by jointly assimilatingMODIS land surface temperature and airborne L band passive microwave brightness temperature.The ground based data observed at DAMAN superstation,which is located at Yingke oasis desert area in the middle stream of the Heihe River Basin,are used to conduct this experiment and validate assimilation results.Three LAI products are used to analyze the influence of LAI on soil temperature.Three assimilation experiments are also designed in this work,including assimilation of MODIS LST,assimilation of microwave brightness temperature,and assimilation of MODIS LST and microwave brightness temperature.The results show that the uncertainties in LAI influence significantly soil temperature simulations in different soil layers.MODIS LAI product is seriously underestimated in this study area,which results soil temperature overestimation about 4~6 K.Three assimilation schemes can improve soil moisture estimations to different extend.Joint assimilation of MODIS LST and microwave brightness temperature achieved the best performance,which can reduce the RMSE of soil moisture to 31%~53%.     

Algorithms Comparison of Land Surface Temperature Retrieval from Landsat Series Data：A Case Study in Qiqihar，China

Land Surface Temperature（LST)is considered to be one of the significant indicators of urban environment analysis.Landsat thermal infrared series data is an important data source for retrieving surface temperature.In this paper,the thermal infrared band of the Landsat data in 2002,2008 and 2016 were used to retrieve LST by three different algorithms in municipal area of Qiqihar,China.These algorithms were the Mono-Window algorithm（MW algorithm),the Single Channel algorithm（SC algorithm) and the Radiation Transport Equation method（RTE algorithm).And the results of the retrieval were compared to each other and verified by MODIS surface temperature products.The LST distribution maps were accomplished according to the retrieval results.The results showed that：(1)The spatial distribution of the LST obtained by the retrieval of the Landsat series by the three algorithms is consistent,and the LSTof the urban center is higher and thetemperature of water is the lowest;(2)Based on ETM+ data,the consistency between SC and RTE algorithm results is good,among which the SC algorithm has the highest precision,and the MW algorithm has large errors in different land cover areas;(3)The retrieval results by MW algorithm based on the TM data has the highest accuracy,RTE algorithm results is second,and the LST form SC algorithm is less consistent with the corresponding MODIS temperature products;(4)Based on the Landsat 8 TIRS data,the SC algorithm has the highest accuracy and the RTE algorithm has a large error.     

Data Fusion and Visualization of Multi-time OceanColor Remote Sensing Data

In order to solve the problem that the ocean sensor data such as MODIS and SeaWiFs are easy to emptied,an algorithm combining multi\|time data with multiple fitting methods is proposed.The vacancy value is estimated by the least squares fitting method.To set a threshold named A according to the date of different time in corresponding position.When the effective value is larger than A that the vacancy value is estimated by the method of least squares fitting,when the effective value is smaller than A that the linear interpolation method is used to estimate the vacancy value.There is no valid value the bilinear interpolation method is used for the second repair with the valid data of the adjacent position.The experimental results show that the algorithm has a smooth visual effect when the threshold selection is appropriate,and the error of the data analysis results is relatively small,then the global data can be well restored and the effectiveness of data coverage will be improved.     

Landsat 8 Data Mountain Shadow Removal based onBrightness Recovering Model

Mountain region in remotely sensed imagery are usually covered by shadows,which reduce the accuracy of information extraction.Therefore,in this paper a method based on intensity restoration is putting forward necessarily.First,Shadow Detection (SD) was constructed by the Max function and the band ratio to identify shadows.Thus,mountain shadows were extracted combined with the slope factor and SD,through the grid randomly arranged verification point verification accuracy.Second,the intensity curve model of the shadow area was fitted by ground data of the shadow and the transition rules of pixel intensity from the shadow to non|shaded area.Third,the intensity restoration model was established by the derivative function of intensity curve to remove shadows.The results of the model on Changting Landsat 8 imagery indicated the extraction accuracy of the mountain shadow was 99.06% and the Kappa coefficient was 98%;According to the cluster analysis,the restoration and non|shaded samples were the same type;Processed by the intensity restoration model,the average intensity of the shadow was increased by 13%,and the standard deviation was reduced by 80% and the clustering distances was reduced by 96%.respectively,average intensity of the shadow increased by 6.7%,the standard deviation was reduced by 73.7% and the clustering distances was reduced by 88.3% when compared with ATCOR_3,and average intensity of the shadow reduced by 1.8%,the standard deviation was increased by 6.7% and the clustering distances was reduced by 90% when compared with unitary linear restoration model.In the process of removing the mountain shadows,the intensity restoration method is neither replacing the shaded pixels nor interference with non|shaded pixels and could preserve the spectral and intensity characteristics of shaded pixels better.;     

Influence of Temporal and Spatial Uariability on Estimation of Air Temperatures from MODIS Land Surface Temperatures

A method to obtain air temperature by the remote sensing technology was discussed in this paper.Linear dependence relations between MODIS land surface temperature and minimum temperature,maximum temperature and mean temperature were explored in Ningxia Autonomous Region.In order to improve the accuracy of air temperature estimated from MODIS land surface temperature,the influence of time and space to correlation coefficients were discussed.Ten meteorological stations in Ningxia Autonomous Region were used.Remote sensing data including MOD11A1 and MYD11A1 and common meteorological temperature data during 2000~2010 were used for the linear correlation analysis.Correlation coefficients in 1×1,3×3,5×5,7×7,9×9 windows centered the station locations were compared.Studies have shown that linear dependence relations between MODIS land surface temperature and minimum temperature,maximum temperature and mean temperature were good enough to estimate air temperature. Correlation coefficient between maximum temperature and surface temperature was generally greater than minimum temperature. Correlations between minimum temperature and night surface temperature were better than maximum temperature.Moreover,correlation coefficient between Aqua night surface temperature and minimum temperature was greater than Terra due to the difference of the observing time.Linear dependence relations became worse no matter whether the entire windows were filled with the valid data,though effective data increased with the enlargement of extract windows.     

Land‐cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data

Land‐cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land‐cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM+) and Radarsat data. A wavelet‐merging technique was used to integrate Landsat ETM+ multispectral and panchromatic data or Radarsat data. Grey‐level co‐occurrence matrix (GLCM) textures based on Landsat ETM+ panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum‐likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land‐cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8–6.9% compared to Landsat ETM+ data, but data fusion of Landsat ETM+ multispectral and panchromatic data or Radarsat data cannot effectively improve land‐cover classification accuracies.     

Influence of Sea Surface Wind Vector on Sea SurfaceBrightness Temperature Gain in Different Seasons

Based on the correlation of the sea surface wind vectors to the sea surface roughness temperature in different seasons,wind field data of the Windsat L2 U10 wind fields in the northwest Pacific in January,April,July and October from 2012 to 2016 were selected,and used sea surface roughness Semi\|empirical and theoretical algorithm,the relationship between the brightness temperature gain caused by wind speed and wind vector under different seasons was analyzed.the results showed that the contribution of wind speed to horizontal brightness temperature gain was greater than that of vertical brightness temperature; the change of horizontal brightness temperature gain was the most significant in January and the least was in July; the maximum and minimum mean values of wind speed to vertical brightness gain were 0.19 K and 0.05 K respectively,indicating that the wind speed had little effect on the vertical brightness gain.It showed that the vertical brightness temperature gain was almost independent of the seasons by the standard deviation calculation; in April and October to form larger cyclone phenomenon in high latitude regions by the Pacific and Hawaii high pressure under the influence,and with brightness gain changes,showing obvious features of the North Pacific gyre.     

A Hierarchical Classification and Iterative Model based Methodfor Remote Sensing Classification of Land Cover

Land cover classification based on remote sensing is an important means to analyze the change and spatial pattern of land use.In order to further improve the classification accuracy,this paper proposed a hierarchical classification and iterative CART model based method for remote sensing classification of landcover.Firstly,the extraction order of land cover classes was determined based on the class separability evaluation,which was water,vegetation,bare soil and built-up land.Secondly,we selected the optimal image segmentation parameters and a set of sensitive features for each class during the hierarchical classification process.Finally,object-based training samples were selected to be fed into the iterative CART algorithm for the successive extraction of the first three classes,with the remaining unclassified objects being directly assigned to the last class.Results demonstrated that the proposed method can significantly reduce the mixture between bare soil and built-up land,and is capable of achieving landcover classification with much higher accuracy.The proposed method achieved an overall accuracy of 85.76% and a Kappa efficient of 0.72,with the performance improvements ranging from 10.67% to 16.5% and 0.15 to 0.21 as compared SVM and CART single classification methods.The classification accuracy of a specific class can be flexibly adjusted using this method,giving different purposes of classification.This method can also be easily extended to other districts and disciplines involving remote sensing image classification.     

Retrieval of Forest above Ground Biomass Using 4-Scale GeometricalOptical Model and Remote Sensing Data

Forest Aboveground Biomass (AGB) is an important parameter for assessing the function of forest ecosystems.Remote sensing is an effective technique for retrieving AGB.A method for retrieving AGB from TM remote sensing data and field measurements taken at 33 plots in Genhe city,Inn Mongolia,China was developed.First,The empirical equations estimating AGB from canopy Surface Area (SA) was fitted using field measured data.Then,a look up table for the inversion of SA from canopy reflectance was set up through forward simulations of the 4\|scale geometrical optical model.SA determined from TM remote sensing data and the constructed look up table was used to estimate AGB.At all 33 plots,AGB estimated using the newly developed method was in good agreement with measured data,with RMSE=20.8 t·hm-2and R2=0.45,much better than the estimation using Difference Vegetation Index (DVI) (RMSE=27.7 t·hm-2,R²=0.09) and special mixture analysis (SMA) (RMSE=27.6 t·hm-2,R²=0.02) method.Validation at 19 conifer plots indicated that the RMSE and R² of AGB estimated using the method developed in this study were 20.8 t·hm-2 and 0.53,respectively.The corresponding values were 31.5 t·hm-2 and 0.18 for the DVI-based model and were 31.8 t·hm-2 and 0.14 for the SMA-based model.As to 14 broad-leaved plots,the RMSE and R² of AGB estimated using the method developed here were 20.9 t·hm--2 and 0.47.The corresponding values were 21.4 t·hm-2 and 0.01for the DVI-based model and were 20.6 t·hm-2 and 0.11 for the SMA-based model.The method which estimates AGB on the basis of SA inverted from optical remote sensing data was applicable for the retrieval of AGB.     

Depth‐variant spectral characteristics of submersed aquatic vegetation detected by Landsat 7 ETM+

Spectral variations along depth profiles were compared using two subsets of a Landsat 7 Enhanced Thematic Mapper (ETM+) scene to test the difference between submersed aquatic vegetation (SAV) and non‐vegetated bare substrate in their depth‐induced spectral variation. Field‐surveyed water depth and SAV cover along transects were overlaid with the satellite image of Lake Pontchartrain, LA, USA. Digital numbers on the survey transects for each band and for band ratios were correlated with depth and vegetation cover. Band 1/band 3 correlated well with depth in both SAV and bare substrates, indicating that this ratio least reflects the effect of SAV. The ratio of bands 2 and 1 correlated best with vegetation cover within the shallow estuarine waters. Correlations between depth and the ratio of band 2/band3 showed contrasting results between the two substrate types (SAV and bare), suggesting that the depth‐induced variations in the band ratio can be used as indicators of SAV.     

Land degradation monitoring using multi‐temporal Landsat TM/ETM data in a transition zone between grassland and cropland of northeast China

Land degradation is one of the most pressing problems of environments. This research presents a methodology to monitor land degradation in a transition zone between grassland and cropland of northeast China, where soil salinization and grassland degradation, even desertification, have been observed in the past few decades. Landsat TM/ETM data in 1988, 1996 and 2001 were selected to determine the rate and status of grassland degradation and soil salinization together based on both decision tree (DT) classifier and the field investigation. The thermal radiance values of TM/ETM 6 data, the Normalized Difference Vegetation Index (NDVI), and new variables (brightness, greenness, and wetness) generated by the Kauth–homas Transforms (KT) algorithms from Landsat TM/ETM data served as the feature nodes of a DT classifer and contributed to improving the classification results. It showed an overall accuracy of more than 85% and a Kappa statistic of agreement of about 0.79 in 1996 and 2001 with the exception of about 0.69 in 1988. The statistical areas of land degradation in the observation periods revealed that land degradation, especially the salt‐affected soil, is accelerating. The distribution maps of land degradation in the years of 1988, 1996 and 2001 were generated respectively based on the classification results. Their change maps were created by the difference between the distribution maps from 1988 to 1996 and from 1996 to 2001 respectively. The changes of salt‐affected soil occurred near the water bodies due to variations of water sizes, and most of the degraded grassland appeared around the salt‐affected soil. Although climate variations play an important role in this region, human activities are also crucial to land degradation.     

Model Building and Application for Monitoring and Assessing the Aging Conditions of Asphalt Road Pavementsbased on Remote Sensing Data

To meet the demands in monitoring the health conditions of road pavements over a relatively large area,by means of derivative and continuum removal approaches this study analyzes the spectral features of asphalt road pavements aging degrees based on the field measurements of pavement spectra.Distinct spectral features of new and aged asphalt road pavements were observed in the wavelength regions of 400~680 nm and 860~970 nm.After that,a WorldView-2 image in Liangxiang area,Fangshan district,Beijing City were captured and the corresponding bands were used to create a Multiplication Aging Index (MAI) to reflect the aging conditions of asphalt road pavements.Comparison between the MAI and in-situ measurements of the spectra and aging conditions of the road pavements in the study area was performed,and statistical analysis was also conducted based on the Munsell brightness values collected in the field investigation.Through the contrast,the aging condition from MAI has good relevance to the in-situ measurements.Results indicate that the proposed MAI index can reflect the aging conditions well and is further used to monitor the pavement quality of the 14 road pavements in the study area.According to the evaluation,six roads in the study area need road maintenance.The research can offer a new technology for road management departments to make their road maintenance plans.     

The first detailed land‐cover map of Socotra Island by Landsat/ETM+ data

Present study has produced first detailed land‐cover map of Socotra Island. A Landsat 7 ETM+ dataset was used as a main source of remotely sensed data. From numerous reference points (more than 250) coming from the ground data verification the set of training fields and the set of evaluation fields were digitised. As a classification method the supervised maximum likelihood classification without prior probabilities was used in combination with rule‐based post‐classification sorting, providing results of sufficient accuracy and subject resolution. Estimates of the area and degree of coverage of particular land‐cover classes within Socotra Island have brought excellent overview on state of island biotopes. Overall accuracy of the map achieved is more than 80%, 19 terrestrial land‐cover classes (including three types of Shrublands, three types of Woodlands, two types of Forests and Mangroves) have been distinguished. It consequently allows estimates of the current and potential occurrence of endemic plant populations, proposals of management and conservation plans and agro‐forestry planning.     

Combining airborne lidar and Landsat ETM+ data with photoclinometry to produce a digital elevation model for Langjökull,Iceland

An incomplete airborne lidar survey of Langjökull, Iceland's second largest ice cap (?900 km²) and the surrounding area was undertaken in August 2007. Elevation data were interpolated between the lidar swaths using the technique of photoclinometry (PC), which relates Sun-parallel slope angles to image brightness. A Landsat Enhanced Thematic Mapper Plus (ETM+) image from March 2002 was used for this purpose. Different bands and band combinations were assessed and Band 4 (760–900 nm) was found to be the most appropriate. Parameters in the slope–brightness equation were derived empirically by comparing the image brightness with lidar elevation data in a 4 km × 4 km region in the centre of the ice cap. This relationship was then used to calculate the slopes, and, by integration between tie points of known lidar elevation, the elevations of the 30 m pixels that were not surveyed by lidar. The root-mean-square (RMS) precision (repeatability) of lidar elevations was 0.18 m and the accuracy was estimated to be 0.25 m. The 68.3% quantile of absolute difference relative to lidar (analogous to root-mean-square error (RMSE)) of all interpolated areas where PC assumptions are met was 5.44 m (4.66 m and 8.73 m for on- and off-ice areas, respectively). Where one or more PC assumptions were not met (e.g. self-shading, sensor saturation), the 68.3% quantile of absolute difference relative to lidar was 27.89 m (18.52 m on the ice cap and 32.91 m off-ice). These accuracies were applicable to 63%, 31%, and 6% of the ice cap and 59%, 28%, and 13% of the final digital elevation model (DEM), respectively. The area-weighted average 68.3% quantiles were 2.89 m for the ice cap and 6.75 m for the entire DEM. The PC technique applied to satellite imagery is a useful and appropriate method for interpolating a lidar survey of an ice cap.     

Estimation of Mediterranean forest attributes by the application of k‐NN procedures to multitemporal Landsat ETM+ images

Routine applications of nonparametric estimation methods to satellite data for assisting the creation of forest inventories in Northern European countries are stimulating interest in the possible extension of these methods to more complex Mediterranean areas. This is the subject of the current work, which presents an experiment based on the integration of remotely sensed images and sample field measurements aimed at producing forest attribute maps in central Italy. Testing was carried out in an area where 370 geocoded field plots, sampled on a single‐stage cluster design, were collected to characterize wood and non‐wood forest attributes. These ground data served to apply various k‐Nearest Neighbour (k‐NN) estimation procedures to multitemporal Landsat 7 ETM+ images in order to map major forest attributes (basal area and simulated leaf area index, LAI). More specifically, the investigation focused on evaluating the effects of using satellite images from different periods of the growing season and spectral metrics of increasing complexity. The results achieved by the examined methods are finally discussed in order to provide guidelines for possible operational utilization.     

Land‐cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling

A land‐cover classification is needed to deduce surface boundary conditions for a soil–vegetation–atmosphere transfer (SVAT) scheme that is operated by a geoecological research unit working in the Andes of southern Ecuador. Landsat Enhanced Thematic Mapper Plus (ETM+) data are used to classify distinct vegetation types in the tropical mountain forest. Besides a hard classification, a soft classification technique is applied. Dempster–Shafer evidence theory is used to analyse the quality of the spectral training sites and a modified linear spectral unmixing technique is selected to produce abundancies of the spectral endmembers. The hard classification provides very good results, with a Kappa value of 0.86. The Dempster–Shafer ambiguity underlines the good quality of the training sites and the probability guided spectral unmixing is chosen for the determination of plant functional types for the land model. A similar model run with a spatial distribution of land cover from both the hard and the soft classification processes clearly points to more realistic model results by using the land surface based on the probability guided spectral unmixing technique.     

Stress Detection in Human–Computer Interaction: Fusion of Pupil Dilation and Facial Temperature Features

In order to differentiate the affective state of a computer user as it changes from relaxation to stress, features derived from pupil dilation and periorbital temperature can be utilized. Absolute signal values and measurements computed from these can be fused to increase the accuracy of affective classification. In this study, entropy in a sliding window was used to accommodate the time differences in the physiological rise and fall profiles of pupil and thermal data. Two methods, decision tree and Adaboost with Random Forest (ABRF), were used for classification tests. Detection accuracy of stressful states varied between 65% and 83.8%. Best results can be reported as 83.9% for sensitivity and 83.8% for specificity. ABRF classifier outperformed the decision tree model. This study emphasizes the importance of data fusion, particularly when physiological signals differ with respect to their rise and fall windows across time. Use of entropy within a predefined time window provides a useful set of features to combine with actual measurements. Furthermore, the collection of pupil and thermal data is feasible because surface sensors are eliminated.     

Extraction and Analysis of Hyperspectral Data and Characteristics from Pedicularis on Bayanbulak Grassland in Xinjiang

Through the field acquisition of three vegetation spectral datas,flowering Pedicularis,non flowering Pedicularis and common vegetation on Bayanbulak grassland,the first derivative,the two derivative and the reciprocal logarithm transformation were used to the smoothed and denoised data to analyze the difference sensitive bands of vegetation.The results showed that in visible light,non flowering Pedicularis and common vegetation showed the overall consistency,but the spectral curve of flowering Pedicularis showed a significant difference.In the red band and near infrared band at 750nm,non flowering Pedicularis reflectance increased significantly,and the three kinds of spectral reflectance showed significant differences.The reciprocal logarithmic transformation in the visible 580~680 nm band could be used to distinguish the Pedicularis as sensitive area.The spectral reflectance difference between the three at 655 nm was the most obvious.That solved the non flowering Pedicularis and common vegetation confusable problems.The improved normalized difference vegetation index by calculation,to further validate and showed the reciprocal logarithmic transformed values NDVI RLR could be distinguished the difference of flowering Pedicularis,non flowering Pedicularis and common vegetation.The extraction and analysis of hyperspectral data and characteristics from Pedicularis provided a theoretical basis for remote sensing monitoring of Pedicularis,and Remote sensing technology has great significance in Pedicularis resource survey and monitoring application.     

Integration Framework and Key Technology of Big Earth Datafor SDGs and Beautiful China Evaluation

Construction of UN Sustainable Development Goals(SDGs) and Beautiful Chinashare the same meaning.Both of them endeavor to achieve national and regional social,environment and economy sustainable development.Accurate,reliable,timely and well classified data is the key for accurate evaluation of sustainable development.In order to address issues such as single data source,poor timeliness,lack of high accuracy and evaluation results unreliable,we puts forward the integration framework and standardization of the bigearth data which includes big network data,big remote sensing data,and big socioeconomic data facing to the evaluation of SDGs and Beautiful China.Then,the key technologies of network data acquisition and analysis,remote sensing data information intelligent extraction and socioeconomic data spatialization are analyzed from different perspectives.Taking the water contamination accident of SDG 6,forest information extraction of SDG 15,population spatialization of common requirements in SDGs as examples,the application of technological routes in supporting sustainable development evaluation based on big earth data are studied consequently.     

Error Sources and Post Processing Method for Land Use/cover Change Estimation of Dongguan Citybased on Landsat Remote Sensing Imagery with SVM

Urbanization is the world developing trend in the past century,which significantly changed the land use/cover of the urbanized area,and caused a series negative impacts,such as water shortage,flood increase,environment pollution,ecosystem degradation.How to estimate the land use/cover change more accurately has the prerequisite of studying the urbanization processes and its impacts,and is the research hot and challenge of the remote sensing and application communities.Dongguan city expressed the rapidest urbanization in China since China’s reform and opening door,and transferred from an agriculture county to a modern international metropolitan in less than 30 years,which has made a miracle in the world urbanization process.To prepare a high accuracy land use/cover change dataset for studying Dongguan’s urbanization process and its impacts,this paper first estimated the land use/cover change dataset by employing Support Vector Machine auto\|classification algorithm based on 12 Landsat remote sensing imageries from 1987 to 2015 at an average interval of 3 year.Then the error sources is analyzed by comparing the results estimated by using auto\|classification algorithm and visual interpretation,and a post data processing algorithm is proposed for refining the auto\|classification results.The final dataset of land use/cover change of Dongguan City is produced with the above method with an average accuracy of 86.87% and a Kappa coefficient of 0.83,which implies this product has a very good accuracy for analyzing the urbanization process of Dongguan city and its impacts.