A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries

Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.

Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains,a national scenic spot in Nanjing,China

Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures.High spatial resolution remote sensing images can be used to detect subtle vegetation changes.The major objective of this study was to map and quantify forest vegetation changes in a national scenic location,the Purple Mountains of Nanjing,China,using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable management.We used Quickbird images acquired in 2004,IKONOS images acquired in 2009,and WorldView2 images acquired in 2015.Four pixel-based direct change detection methods including the normalized difference vegetation index difference method,multi-index integrated change analysis(MIICA),principal component analysis,and spectral gradient difference analysis were compared in terms of their change detection performances.Subsequently,the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes.An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results.The results showed that the MIICA method was the best pixel-based change detection method.And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA.From 2004 to 2009,areas of vegetation gain mainly occurred around the periphery of the study area,while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities,which contributed to 79%of the total area of vegetation loss.During 2009–2015,the greening initiatives around the construction areas increased the forest vegetation coverage,accounting for 84%of the total area of vegetation gain.In spite of this,vegetation loss occurred in the interior of the Purple Mountains due to infrastructure development that caused conversion from vegetation to impervious areas.We recommend that:(1)a local multi-agency team inspect and assess law enforcement regarding natural resource utilization;and(2)strengthen environmental awareness education.

High Spatial Resolution Mapping of Dykes Using Unmanned Aerial Vehicle(UAV) Photogrammetry: New Insights On Emplacement Processes

Remote sensing has played a pivotal role in our understanding of the geometry of dykes and dyke swarms on Earth,Venus and Mars(West and Ernst,1991;Mege and Masson,1995;Ernst et al.,2005).Since the 1970’s

Monitoring of Karst Rocky Desertification Control Projects Based on Remote Sensing Images with Medium and High Spatial Resolution——A Case Study of Disi River Basin in Puan County

[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.

DSFA: cross-scene domain style and feature adaptation for landslide detection from high spatial resolution images

Rapid and accurate landslide inventory mapping is significant for emergency rescue and post-disaster reconstruction.Nowadays,deep learning methods exhibit excellent performance in supervised landslide detection.However,due to differences between cross-scene images,the performance of existing methods is significantly degraded when directly applied to another scene,which limits the application of rapid landslide inventory mapping.In this study,we propose a novel Domain Style and Feature Adaptation(DSFA)method for cross-scene landslide detection from high spatial resolution images,which can leverage labeled source domain images and unlabeled target domain images to mine robust landslide representations for different scenes.Specifically,we mitigate the large discrepancy between domains at the dataset level and feature level.At the dataset level,we introduce a domain style adaptation strategy to shift landslide styles,which not only bridges the domain gap,but also increases the diversity of landslide samples.At the feature level,adversarial learning and domain distance minimization are integrated to narrow large feature distribution discrepancies for learning domain-invariant information.In addition,to avoid information omission,we improve the U-Net3+model.Extensive experimental results demonstrate that DSFA has superior detection capability and outperforms other methods,showing its great application potential in unsupervised landslide domain detection.

High Spatial Resolution and High Temporal Frequency(30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets:Method Development and Validation

High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

Observations of Dynamic Turbulence in the Lower Stratosphere over Inner Mongolia Using a High-resolution Balloon Sensor Constant Temperature Anemometer

We present characterizations of the dynamic turbulence in the lower stratosphere measured by a new balloon-based system designed for detecting finer scale dynamic turbulence. The balloon-based system included a constant temperature anemometer(CTA) operating at a sampling rate of 2 k Hz at an ascent speed of 5 m s^(-1)(corresponding to a vertical resolution of 2.5 mm), an industrial personal computer, batteries, sensors for ambient temperature and humidity, an A/D converter, and others. The system was successfully launched to 24 km altitude over Bayannur City, Inner Mongolia Province. Results show the spatial intermittence of the turbulence layers, with clear boundaries between turbulent and nonturbulent regions. This is the first time that the dynamic turbulence spectrum down to the viscous sub-range has been obtained throughout the lower stratosphere over China. With that, the energy dissipation rates of dynamic turbulence could be calculated with high precision. The profile of the dissipation rates varied from 7.37 × 10^(-7) to 4.23 W kg^(-1) and increased with altitude in the stratosphere.

HIGH RESOLUTION, HIGH SENSITIVITY MOIRE METHOD

Based on geometric moire method, moire interferometry and microscopic moire interferometry, a high spatial resolution and high sensitivity geometric microscopic moire method is presented. Geometric micron-moire patterns are produced by the superposition of two high frequency gratings through a microscope system. Compared with other grating-based photo-mechanics methods, microscopic moire method could provide whole-field moire patterns of both high spatial resolution and high sensitivity. The frequency of specimen and reference gratings used in this method can be from 1 line/mm to 10000 lines/mm. Additionally, a 4F optical filter system is used to enhance the contrast of microscopic moire patterns effectively.

A stopping layer concept to improve the spatial resolution of gas-electron-multiplier neutron detector

In recent years,gas electron multiplier(GEM)neutron detectors have been developing towards high spatial resolution and high dynamic counting range.We propose a novel concept of an Al stopping layer to enable the detector to achieve sub-millimeter(sub-mm)spatial resolution.The neutron conversion layer is coated with the Al stopping layer to limit the emission angle of ions into the drift region.The short track projection of ions is obtained on the signal readout board,and the detector would get good spatial resolution.The spatial resolutions of the GEM neutron detector with the Al stopping layer are simulated and optimized based on Geant4 Garfield Interface.The spatial resolution of the detector is 0.76 mm and the thermal neutron detection efficiency is about 0.01%when the Al stopping layer is 3.0μm thick,the drift region is 2 mm thick,the strip pitch is 600μm,and the digital readout is employed.Thus,the GEM neutron detector with a simple detector structure and a fast readout mode is developed to obtain a high spatial resolution and high dynamic counting range.It could be used for the direct measurement of a high-flux neutron beam,such as Bragg transmission imaging,very small-angle scattering neutron detection and neutron beam diagnostic.

Luojia-HSSR:A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet

High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained models.In this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China.To our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground coverage.It is an ideal experimental data for spatial-spectral feature extraction.Furthermore,a new deep learning model 3D-HRNet for interpreting HSSR images is proposed.The conv-neck in HRNet is modified to better mine the spatial information of the images.Then,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously.Subsequently,the 3D convolution is inserted into the HRNet to optimize the performance.The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.

Tb^(3+)-doped borosilicate glass scintillators for highresolution X-ray imaging

Scintillators are the vital component in X-ray perspective image technology that is applied in medical imaging,industrial nondestructive testing,and safety testing.But the high cost and small size of single-crystal commercialized scintillators limit their practical application.Here,a series of Tb^(3+)-doped borosilicate glass(BSG)scintillators with big production size,low cost,and high spatial resolution are designed and fabricated.The structural,photoluminescent,and scintillant properties are systematically investigated.Benefiting from excellent transmittance(87%at 600 nm),high interquantum efficiency(60.7%),and high X-ray excited luminescence(217%of Bi4Ge3O12),the optimal sample shows superhigh spatial resolution(exceeding 20 lp/mm).This research suggests that Tb^(3+)-doped BSG scintillators have potential applications in the static X-ray imaging field.

Nanoelectrochemical architectures for high-spatial-resolution single cell analysis

The analysis of single cells instead of cell populations is important for characterizing cellular heterogeneity and elucidating the cellular signalling pathways. Nanoelectrodes have emerged as an increasingly important tool for biomolecule analyses at the single-cell level with high spatial or temporal resolution. Various electrochemical methods, such as amperometry and scanning electrochemical microscopy(SECM), have been applied. Research to date has focused on the development of new nanoelectrochemical architectures, such as arrays, to achieve higher spatial resolution and faster analysis rates for single-cell analysis. In this review, the fabrication of these new nanoelectrochemical architectures and their applications in high spatial resolution single-cell analyses are discussed. The recent progress of Chinese researchers is highlighted.

Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties,the temporal resolution of the data is rather low,which can be easily made worse by cloud contamination.In contrast,although Moderate Resolution Imaging Spectroradiometer(MODIS)can only achieve a spatial resolution of 250 m in its normalised difference vegetation index(NDVI)product,it has a high temporal resolution,covering the Earth up to multiple times per day.To combine the high spatial resolution and high temporal resolution of different data sources,a new method(Spatial and Temporal Adaptive Vegetation index Fusion Model[STAVFM])for blending NDVI of different spatial and temporal resolutions to produce high spatial
temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM).STAVFM defines a time window according to the temporal variation of crops,takes crop phenophase into consideration and improves the temporal weighting algorithm.The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution.An application of the generated NDVI dataset in crop biomass estimation was provided.An average absolute error of 17.2%was achieved.The estimated winter wheat biomass correlated well with observed biomass(R^(2) of 0.876).We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail.There is potential to apply the approach in many other studies,including crop production estimation,crop growth monitoring and agricultural ecosystem carbon cycle research,which will contribute to the implementation of Digital Earth by describing land surface processes in detail.

Research on sea surface temperature retrieval by the one-dimensional synthetic aperture microwave radiometer, 1D-SAMR

Due to the low spatial resolution of sea surface temperature(T_S)retrieval by real aperture microwave radiometers,in this study,an iterative retrieval method that minimizes the differences between brightness temperature(T_B)measured and modeled was used to retrieve sea surface temperature with a one-dimensional synthetic aperture microwave radiometer,temporarily named 1 D-SAMR.Regarding the configuration of the radiometer,an angular resolution of 0.43°was reached by theoretical calculation.Experiments on sea surface temperature retrieval were carried out with ideal parameters;the results show that the main factors affecting the retrieval accuracy of sea surface temperature are the accuracy of radiometer calibration and the precision of auxiliary geophysical parameters.In the case of no auxiliary parameter errors,the greatest error in retrieved sea surface temperature is obtained at low T_S scene(i.e.,0.7106 K for the incidence angle of 35°under the radiometer calibration accuracy of0.5 K).While errors on auxiliary parameters are assumed to follow a Gaussian distribution,the greatest error on retrieved sea surface temperature was 1.3305 K at an incidence angle of 65°in poorly known sea surface wind speed(W)(the error on W of 1.0 m/s)over high W scene,for the radiometer calibration accuracy of 0.5 K.

Retrieval of Sea Surface Wind Speed by One-Dimensional Synthetic Aperture Microwave Radiometer

One-dimensional synthetic aperture microwave radiometers have higher spatial resolution and record measurements at multiple incidence angles.In this paper,we propose a multiple linear regression method to retrieve sea surface wind speed at an incidence angle between 0°65°.We assume that a one-dimensional synthetic aperture microwave radiometer operates at frequencies of 6.9,10.65,18.7,23.8 and 36.5 GHz.Then,the microwave radiative transfer forward model is used to simulate the measured brightness temperatures.The sensitivity of the brightness temperatures at 0°65°to the sea surface wind speed is calculated.Then,vertical polarization channels(VR),horizontal polarization channels(HR)and all channels(AR)are used to retrieve the sea surface wind speed via a multiple linear regression algorithm at 0°65°,and the relationship between the retrieval error and incidence angle is obtained.The results are as follows:(1)The sensitivity of the vertical polarization brightness temperature to the sea surface wind speed is smaller than that of the horizontal polarization.(2)The retrieval error increases with Gaussian noise.The retrieval error of VR first increases and then decreases with increasing incidence angle,the retrieval error of HR gradually decreases with increasing incidence angle,and the retrieval error of AR first decreases and then increases with increasing incidence angle.(3)The retrieval error of AR is the lowest and it is necessary to retrieve the sea surface wind speed at a larger incidence angle for AR.

Highly spatial imaging of electrochemical activity on the wrinkles of graphene using all-solid scanning electrochemical cell microscopy

Here,all-solid scanning electrochemical cell microscopy(SECCM)is first established by filling polyacrylamide(PAM)into nanocapillaries as a solid electrolyte.A solid PAM nanoball at the tip of a nanocapillary contacts graphene and behaves as an electrochemical cell for simultaneously measuring the morphology and electrochemical activity.Compared with liquid droplet-based SECCM,this solid nanoball is stable and does not leave any electrolyte at the contact regions,which permits accurate and continuous scanning of the surface without any intervals.Accordingly,the resolutions in the lateral(x-y)and vertical(z)directions are improved to〜10 nm.The complete scanning of the wrinkles on graphene records low currents at the two sidewalls of the wrinkles and a relatively high current at the center of the wrinkles.The heterogeneity in the electrochemical activity of the wrinkle illustrates different electron transfer features on surfaces with varied curvatures,which is hardly observed by the current electrochemical or optical methods.The successful establishment of this high spatial electrochemical microscopy overcomes the current challenges in investigating the electrochemical activity of materials at the nanoscale,which is significant for a better understanding of electron transfer in materials.

Real Time Monitoring of Extreme Rainfall Events with Simple X-Band Mini Weather Radar

Real time rainfall events monitoring is very important for a large number of reasons: Civil Protection, hydrogeological risk management, hydroelectric power purposes, road and traffic regulation, and tourism. Efficient monitoring operations need continuous, high-resolution and large-coverage data. To monitor and observe extreme rainfall events, often much localized over small basins of interest, and that could frequently causing flash floods, an unrealistic extremely dense rain gauge network should be needed. On the other hand, common large C-band or S-band long range radars do not provide the necessary spatial and temporal resolution. Simple short-range X-band mini weather radar can be a valid compromise solution. The present work shows how a single polarization, non-Doppler and non-coherent, simple and low cost X-band radar allowed monitoring three very intense rainfall events occurred near Turin during July 2014. The events, which caused damages and floods, are detected and monitored in real time with a sample rate of 1 minute and a radial spatial resolution of 60 m, thus allowing to describe the intensity of the precipitation on each small portion of territory. This information could be very useful if used by authorities in charge of Civil Protection in order to avoid inconvenience to people and to monitor dangerous situations.

Study of muon tomographic imaging for high-Z material detection with a Micromegas-based tracking system

Purpose To study the cosmic ray muon tomographic imaging of high-Z material with Micromegas-based tracking system.Method A high-spatial-resolution tracking system was set up with the micro-mesh gaseous structure(Micromegas)detec-tors in order to study the muon tomographic imaging technique.Six layers of 90 mm×90 mm one-dimensional readout Micromegas were used to construct a tracking system.Result and conclusion The imaging test using some metallic bars was performed with cosmic ray muons.A two-dimensional imaging of the test object was presented with a newly proposed ratio algorithm.The result of this work shows that the ratio algorithm is well performed.

Mapping informal settlement indicators using object-oriented analysis in the Middle East

Mapping informal settlements is crucial for resource and utility management and planning.In 2003,the UN-Habitat developed a process for mapping and monitoring urban inequality to support reporting against the sustainable development goals(SDGs).Informal settlement indicators are used as a framework to carry out image analysis,and include vegetation extent,lacunarity of housing structures/vacant land,road segment type and materials,texture measures of built-up areas,roofing extent of built-up areas and dwelling size.Objectbased image analysis(OBIA)methods are recommended to identify informal settlements.This paper documents the application of OBIA to map informal settlements,drawing on the ontology of Kohli et al.(2012)and the indicators of Owen and Wong(2013)for a Middle Eastern city.Three informal settlements with different land use histories were selected to represent old and new informal settlements in the city of Jeddah,Saudi Arabia.Vegetation extent was the most successful indicator detected,with 100% producer accuracy and over 84% user accuracy,followed by the road network,with 84% producer and user accuracies in older informal settlements and 73% producer accuracy and 96% user accuracy across all case studies.Lacunarity of housing structures/vacant land was detected well in informal settlements.The texture measure indicator was detected using GLCM_(Ent)(R)with low producer accuracy across all case studies.The roofing extent of the built-up area is detected with better producer and user accuracies than texture measures.The dwellings size indicator generally failed to distinguish formal from informal settlements.Informal and formal were distinguished with an overall accuracy of 83%.This research concludes that OBIA is a useful method to map informal settlement indicators in Middle Eastern cities.However,a generic ruleset for mapping informal settlements remains elusive,and each indicator requires significant localised‘tuning’.