Deep neural network (DNN)-based video denoising has demonstrated significant performance, but deploying state-of the-art models on edge devices remains challenging due to stringent real-time and energy efficiency requirements. Computing in-Memory (CIM) chips offer a promising solution by integrating computation within memory cells, enabling rapid matrix-vector multiplication (MVM). However, existing DNN models are often designed without considering CIM architectural constraints, thus limiting their acceleration potential during inference. To address this, we propose a hardware-algorithm co-design framework incorporating two innovations: (1) a CIM-Aware Architecture, CIM-NET, optimized for large receptive field operation and CIM's crossbar-based MVM acceleration; and (2) a pseudo convolutional operator, CIM-CONV, used within CIM-NET to integrate slide-based processing with fully connected transformations for high-quality feature extraction and reconstruction. This framework reduces the number of MVM operations, improving inference speed on CIM chips while maintaining competitive performance. Experimental results indicate that, compared to the conventional lightweight model FastDVDnet, our improved variant, CIM-NET, built upon it substantially reduces MVM operations with a slight decrease in denoising performance. With a stride value of 8, CIM-NET reduces MVM operations to 1/77th of the original, while maintaining competitive PSNR (35.03 dB vs. 35.57 dB of FastDVDnet).

Recent advancements in wireless technology have spurred the growth of indoor localization applications based on wireless signals. Most existing methods model the relationship between input data and the target's location using the$l_{2}$loss function, which assumes that the residual between the predicted location and the ground truth follows a Gaussian distribution with a fixed standard deviation. Nevertheless, this approach may not conform to the actual distribution and lacks confidence measures for individual predictions, potentially compromising accuracy. Furthermore, in the realm of regression, Semi-Supervised Learning (SSL) remains relatively unexplored due to the absence of a reliable method to quantify prediction uncertainty, especially when labeled data is scarce. To address these challenges, we developed a novel indoor localization algorithm that employs probabilistic distribution modeling. Our approach focuses on indoor localization with Channel Impulse Response (CIR) as the input. Crucially, it leverages Maximum Likelihood Estimation (MLE) to model the localization error as a Gaussian distribution, and utilizes Residual Log-likelihood Estimation (RLE) to capture arbitrary error distributions. This enables us to extract the confidence of each prediction and utilize the most reliable ones as pseudo-labels for the unlabeled data. By employing probabilistic distribution modeling, we observed a significant improvement in localization accuracy over the$l_{2}$loss function. Additionally, by integrating pseudo-labeled data for model retraining, our algorithm achieves superior performance compared to existing state-of-the-art machine learning-based and SSL-based methods. This is demonstrated by our evaluation on two public datasets, showcasing the efficiency of the proposed method.

Aiming at the phenomenon of joint motor position asynchrony that occurs when dual robotic arms cooperatively handle objects with uneven mass distribution, this paper proposes a position cross-coupling cooperative control strategy based on a sliding mode extended state observer (SMESO) to ensure smooth handling of objects by the dual arms. Using the cross-coupling control strategy, the real-time angle difference between the two motors is fed back to the controller to adjust the speed command. In order to speed up the speed adjustment, this paper designs SMESO for load torque difference estimation and feed-forward compensation of the estimated value to the system. The final realization of dual robotic arm joint motor speed and position are synchronized. Simulation results show that the proposed control strategy in the cooperative handling of uneven mass distribution of objects under the working conditions, position cross-coupling control to achieve rapid synchronization of the two motors position, SMESO significantly improves the position synchronization dynamic performance of the two motors and reduces the system dynamic adjustment time by 88%. In addition, the scheme does not need to rely on the robotic arm dynamics model, providing a model-free solution for dual robotic arm joint motor position synchronization control.

With the continuous enhancement of the computing power of Internet-of-Things (IoT) hardware and the development of lightweight AI algorithms, a new trend of IoT development has emerged, featuring the decentralization of intelligence and the differentiated user needs. Traditional centralized data processing and unified models are inapplicable to intelligent IoT devices considering privacy protection and personalized service requirements. In this paper, we propose a Decentralized Opportunistic Learning Architecture (DOLA) that enables device-to-device opportunistic collaboration based on semantic consensus. This framework effectively enhances the flexibility of device collaboration and meets the personalized needs of minority groups. Experiments conducted on three public datasets demonstrate that our framework can improve the average recognition accuracy by more than 1.46%.

Near real-time precipitation estimation from geostationary satellites plays an important role in flood forecasting, water resource management, and disaster prevention and reduction. Currently, near real-time precipitation products based on geostationary satellites still face great challenges in accurately detecting precipitation and monitoring small-scale precipitation. In this study, a novel Geostationary Satellite Precipitation Estimation (GSPE) algorithm for near real-time condition was developed to retrieve precipitation at a spatial resolution of 0.05°×0.05° every 10 minutes both day and night. The major highlight of the GSPE is that a new precipitation detection scheme was created by introducing a newly proposed precipitation detection index (PDI) and 24-hour continuous cloud microphysical parameters for the first time. Another highlight is that a dynamic updating scheme was proposed in building conversion models between brightness temperature of geostationary satellite and precipitation to keep the accuracy and stability of the estimated precipitation. Furthermore, the 10-minute temporal resolution of the estimated precipitation could accurately capture the evolution of a short precipitation process and improve the calculation of total precipitation amount. According to the validation using rain gauges observations from Chinese mainland, the Heidke Skill Score of the GSPE in hourly scale could reach up to 0.39 which was improved by 11.43% compared to the GSMaP_NOW. The root mean square error of precipitation from the GSPE in hourly, daily, and monthly scale are 1.66mm, 13.65mm, and 97.76mm respectively, and were improved by 15.74%, 13.17%, and 21.01% respectively compared to that of the GSMaP_NOW.

Ellipse detection is of great significance in the fields of image processing and computer vision. Accurate, stable and direct ellipse detection in real-world images has always been a key issue. Therefore, an ellipse detection method is proposed on the basis of the constructed three-intersection-chord-invariant. First, in the inflexion point detection, the PCA minimum bounding box considering the distribution characteristics of edge points is studied to achieve the more refined line segment screening. Second, a multi-scale inflexion point detection method is proposed to effectively avoid over-segmentation of small arc segments, providing assurance for more reasonable and reliable arc segment combinations. Then, the 20 precisely classified arc segment combinations are refined into 4 combinations. A number of non-homologous arc segment combinations can be quickly removed to reduce incorrect combinations by the constructed midpoint distance constraint and quadrant constraint. Moreover, in order to accurately reflect the strict arc segment combination constraints of geometric features of ellipses, a three-intersection-chord-invariant model of ellipses is established with strong constraint of relative distances among five constraint points, by which a more robust initial ellipse set of homologous arc segment combinations is further obtained. Finally, ellipse validation and clustering are performed on the initial set of ellipses to obtain the high-precision ellipses. The algorithm accuracy of the ellipse detection method is experimentally validated on 6 publicly available datasets and 2 established wheel rim datasets.

The uncertainty of water cloud feedback on radiative forcing is one of the largest obstacles to producing confident projections of the global climate. Sufficient measurements of water clouds are crucial to addressing this issue. However, existing techniques based on remote sensing or in situ instruments face limitations in data capacity attributed to the short lifetime, high temporal variability, and complex vertical structure of water clouds. In this study, taking advantage of a dual-field-of-view (dual-FOV) high-spectral-resolution lidar (HSRL), we developed a novel algorithm to obtain diurnal simultaneous profiles of water cloud optical and microphysical properties with high temporal-spatial resolution. This technique does not rely on the widely used subadiabatic assumption about the vertical structure of water clouds. The retrieval algorithm, validated by simulations and cloud radar measurements, was applied to field experiment data collected at the Beijing and Hangzhou sites in China. The relationship functions between water cloud properties are presented to enhance our understanding of the underlying processes. Furthermore, the vertical distributions of retrieved properties are compared to the subadiabatic assumption. The dual-FOV HSRL technique enables comprehensive observations, enhancing our understanding of water clouds and providing significant insights into the interactions among clouds, aerosols, precipitation, and radiation.

In Internet of Things (IoT), each object requires a unique identifier to identify itself and index its detailed profile to support mutual recognitions among multiple objects. However, existing IoT identifiers belonging to different identification schemes are heterogeneous from each other, which create a great challenge for the applications that need to resolve the heterogeneous identifiers. To address this challenge, we propose an algorithm to automatically recognize the heterogeneous identification schemes used by various IoT identifiers, based on a sequence-to-sequence (seq2seq) model consisting of an encoder and a decoder. The encoder uses one Long Short-Term Memory (LSTM) to map the identifier sequence to a vector of fixed dimensionality, and the decoder uses another LSTM to unfold the vector into a target sequence representing the identification scheme of this identifier. To evaluate our algorithm, we create a new dataset named ID-20 with 20 categories of IoT identifiers and conduct experiments on it. The results demonstrate the superiority of our algorithm against other state-of-the-art methods, with an identifier recognition accuracy of up to 94.57%.

Words in a natural language corpus obey a Zipfian distribution, which means only a minority percentage of the total are frequent but the vast majority are rare. Word embedding techniques heavily rely on the frequencies of words in the corpus and cannot provide reliable representations for words appearing infrequently during training. To address this problem, we propose a novel algorithm to induce embeddings for rare words by leveraging morphological decomposition, stemming and bidirectional translation. Compared to existing approaches, our algorithm maintains a relatively lightweight model but generates qualified representations for a wider range of vocabulary from the same corpus. We have performed evaluations on multiple general domain datasets and specific domain datasets, and the experimental results show that our algorithm achieves better performance than the other state-of-the-art techniques.

Surface soil moisture (SM) plays a decisive function during the grassland degradation and restoration process. In this paper, a surface soil moisture retrieval model in desert steppe area with improved spatial resolution is established using diurnal temperature range (DTR) and Normalized Difference Vegetation Index (NDVI). The model integrates land surface temperature (LST) at daytime and nighttime respectively derived from Landsat8 and MODIS images to improve the spatial resolution of soil moisture from 1km to 30m. The results are validated by in situ measurements from Frequency Domain Reflectometry (FDR) in study region, and the root mean square error (RMSE) is 1.3919. Compared with the MODIS-DTR, the Landsat-MODIS DTR improves the accuracy and the spatial resolution of soil moisture retrieval.

With the rapid development of information technology, it is possible to use new remote sensing technology to study flood monitoring and assessment. In this paper, flood disaster caused by dike break in Poyang County, Jiangxi Province was monitored and evaluated by a combined remote sensing approach with satellite images, unmanned aerial vehicle (UAV) aerial photography and ground observation. It is aimed to construct three-dimensional monitoring through the integration of ground-air-space observations to provide flood information at different scales. Satellite remote sensing has excellent advantages in providing large-scale flood monitoring, while UAV and ground remote sensing have advantages in providing small-scale and local point observations. An effective combination of these different remote sensing means can effectively meet the actual needs for overall large scale flood monitoring and small scale local dike break rescue, which truly realizes integrated air-space-ground monitoring and emergency assessment of flood disasters. We believe that a new era of flood monitoring and assessment is expectable to make full use of these abundant data resources, which will surely further improve the scientific and technological benefits of flood control and disaster reduction.

The Irrawaddy River is the largest river in Myanmar, with a drainage area accounting for 60% of Myanmar's land area. Due to specific reasons, the basic geographic information of the basin has been very scarce. The land use is the direct reflect of human activities in the nature. The investigation of land use is of great significance to the studies of the basin. This research realizes the investigations and studies on the current status of land utilization around Irrawaddy River basin based on remote sensing, and analyzes the characteristics of land utilization around the drainage basin. It is found that the forest coverage rate of the Irrawaddy River Basin is relatively high, and the coverage of forest land has reached 53.15%. Cultivated land and grassland followed, accounting for 21.32% and 19.99% of the total area, respectively. The cultivated land is composed of paddy field and dry land, mainly distribute in low-lying flat plains. The research results are of great significance for clarifying the status of land use in the Irrawaddy River Basin.

The Irrawaddy River is the largest river in Myanmar with its river delta as the country's agricultural center. It is of great significance to investigate river channel erosion and ecosystem evolution in such a river delta based on remote sensing. Thus, its river channel erosion/deposition and ecosystem evolution from 1970s to 2011 were analyzed based on Landsat and HJ satellite data through object-oriented supervised classification with the NDVI and NDWI threshold segmentation methods were used. The results show that river channel of Irrawaddy River from Burma Aung to the estuary delta hardly changes in 34 years while area of the estuary shoal is increasing slowly. An overall trend of continuously enlargement of cultivated land and urban land is found in the delta area with a gradually reduced forest land and wet land.

In the summer of 2013, the Heilongjiang River basin witnessed its biggest floods since 1984. The once-in-a-century flood happened in its lower reaches and spread to a range of 950 km along the main channel, causing large area of farmland to be flooded and lots of houses to collapse. There were about 8.29 million people were affected. In this paper, wide and high frequency-featured HJ-1A/B satellite images were used to monitor this whole flooding process across time and space. Very good results were achieved and a practicable monitoring method based on HJ-1A/B data was established for the applications in the future.

This article gives a detailed description of the High-resolution Earth Observation System, which is currently being carried out in China. The GF data have been successfully launched and their characteristics in flood disaster monitoring are analyzed. Followed by the introduction of the remote monitoring system of flood disaster has been completed. At last some examples of flood disaster monitoring in recent years based on the GF data are introduced.

The Disaster Monitoring and Emergency Response Service(DIMERS) project was kicked off in 2017 in China, with the purpose to improve timely responsive service of the institutions involved in the management of natural disasters and man-made emergency situations with the timely and high-quality products derived from Space-based, Air-based and the in-situ Earth observation. The project team brought together a group of top universities and research institutions in the field of Earth observations as well as the operational institute in typical disaster services at national level. The project will bridge the scientific research and the response services of massive catastrophe in order to improve the emergency response capability of China and provide scientific and technological support for the implementation of the national emergency response strategy.

Intelligent interactions of IoT mean that devices can perform proper actions and communicate with each other to support specific application scenarios without human intervention. To do this, the biggest challenge is providing IoT objects with common knowledge and abilities to analyze data and to reason. In this paper, we present a semantic mechanism to realize intelligent interactions of IoT devices on account of the semantic knowledge and machine learning technology. Based on this mechanism, a three-layer IoT system named IoT-Book is built, and every action performed by a device is deduced through analyzing the environment data and personalized preferences of the user. Finally, a smart home use case is demonstrated, and our approach is proved to be effective.

Vegetation water content (VWC) is an important parameter of agriculture and forestry. In this letter, specific polarization ratios were evaluated for estimating VWC of corn and soybean. Backscattering coefficients (σhh, σvv, σvhand σhv), polarization ratios (σhh/σvv,σvv/σvh, and σhh/σhv), and the radar vegetation index derived from L-band (1.26 GHz) and S-band (3.15 GHz) radar data of the passive and active Land S-band sensor (PALS) in Soil Moisture Experiments 2002 were implemented to develop various linear relationship models with field VWC measurements for corn and soybean, respectively. L-band σhh/σvvwas found to be most correlated with corn VWC (R = 0.81), while for soybean, L-band σhh/σhvwas the best parameter to estimate VWC with an R of 0.90. Based upon these analyses, prediction equations for the estimation of corn and soybean VWC using the polarization ratios were developed. Results indicated that L-band σhh/σvvwas able to estimate corn VWC with a root mean square error (RMSE) of 0.53 kg/m2and a mean absolute relative error (MARE) of 11.48%. As for soybean, L-band σhh/σhvwas capable of estimating soybean VWC with an RMSE of 0.12 kg/m2and an MARE of 13.33%. The main reason for these differences is most likely due to the disparate structure features and VWC distribution of corn and soybean. This letter proposes an effective method for acquiring VWC in regional areas, and it is also considered to be a powerful supplement for the current methods based on optical remotely sensed data.

The aim of this study was to estimate soil moisture from RADARSAT-2 Quad-polarization Synthetic Aperture Radar (SAR) data acquired in the agricultural areas. The adopted approach is based on the combination of semi-empirical Water-Cloud model and Dubois model. Firstly, VH backscattering coefficient was used to develop empirical relationship for crop water content estimation. Secondly, crop water content was then used to correct the semi-empirical Water-Cloud model for vegetation effects in order to get the VV and HH backscattering coefficient of soil surface in the absence of vegetation cover. Thirdly, the soil moisture was retrieved based on Dubois model using VV and HH backscattering coefficient of soil surface in the absence of vegetation cover. Finally, the soil moisture retrieved is evaluated over wheat crop fields using ground measurements. This paper proposes an effective method for acquiring soil moisture in the agricultural areas under any weather conditions.

Poyang Lake, the largest freshwater lake in China, is well known for its ecological importance as a wetland system.. Understanding dynamics change in wetlands may benefit from a joint variation analysis in changes of size, shape, position and extent of water bodies. In this study, we used Cosmo-Skymed ScanSAR mode (HH) data for one-year monitoring of seasonal changes in the water surface areas of Poyang Lake from January 2014 to December 2014. To delinate water surface area, a histogram analysis with a global threshold classification was performed, and the results were validated using Landsat8 OLI data. The results showed: Poyang Lake water body area showed a significant seasonal variation, minimum value was about 690 km2 and maximum value reached 3500 km2. A unique spatial pattern occurred during those months that a dramatic flooding or recession happened. The Cosmo-Skymed time series could capture temporal changes in the water body spatial distribution. Comparison of the resultant classification of Cosmo-Skymed and Landsat8 OLI yielded 95% of mapping accuracy and 87% of puduct accuracy. This demonstrates the X-band Synthetic Aperture Radar is applicable to detect the wetland inundation area and there is a great potential of Cosmo-Skymed constellation for water bodies monitoring, regarding relative great revisit frequency and high resolution.

In environments with limited network bandwidth, current web service execution mode will face challenges for its enormous demand for network bandwidth. The transmitted message which contains real payload and XML-based format data seems to be verbose. The response time and fail risk will be brought to increase as well. In this paper we address this issue and propose an agent-based compression method. A software agent is deployed between the requesters and invokers to execute services and return results to the requesters. The transmitted messages size is reduced by stripping off redundant data and adaptive data compression. We conduct a detailed evaluation of compression effectiveness and provide the results of execution time measurements. The effectiveness of the approach is proved by the experiment results and the conclusion is given.

Remote sensing is an important technology in the field of drought monitoring. Most traditional methods by remote sensing construct relationship between image features and land real data. Usually the models are complicated and the parameters are difficult to confirm. The surface water directly influence the irrigation and drinking, so monitoring water area changing can reflect drought situation. In this paper, Henan province in China is an example to construct monitoring model. By water body extraction and water area calculation from GF-1 and Landsat8 satellites in the drought and normal period, the relative deviation of water area is achieved to march drought grade according to soil moisture. The method is more simple and direct, and can supplement traditional methods to monitor drought.

The Yellow River is well-known for its highest sediment contents of all rivers in the world. Due to the huge sediment to the coast, the Yellow river delta is the youngest land of China, and still in a state of continuous dynamic change. The evolution of the coastline of Yellow River delta is closely related to the environment change of the flow and sediment in the Yellow River estuary. In recent 10 years, environment of flow and sediment in the Yellow River estuary has been changed a lot. At first, in 1996, the riverway into the sea had been artificially changed; secondly, since 1999, Water resources unified collocation has been carried out in the whole basin; and thirdly, since 2002, water and sediment regulation has been carried out in the downstream of Yellow river. Change of flow and sediment condition resulted in change of coastline, land area, land cover and environment. The environment evolution of the Yellow River delta chiefly lies on the change of the coastline. As a kind of monitoring method, remote sensing technology are combined with geographic information system (GIS), which is a very useful tool for studying the dynamic change monitoring. Under this background, the study on the recent evolution of the coastline there through the integration of remote sensing and GIS can not only provide the foundation to evolution analysis but also serve for the sustainable development of the delta. In this paper, multi-phase and many kinds of remote sensing images are used to monitor the coastline change that happened in the Yellow River estuary since 1999. The directly extracted information from these images include coastline, land area and their increase or decrease between each two periods along time series. Furthermore, the relation between the changes and the main affecting factors are also analyzed. Based on the ten years of hydrological data, the flow and sediment feature of the Lijin station that is the last one along the Yellow River from 1996 to 2006 by month was analyzed. Then the relation between it and changes of coastline, land area, land use are analyzed. The result shows that: though the land area of the Yellow River estuary decreased sometimes, the general of it in recent years increased especially after 2003. It is nearly related to the changes of flow and sediment condition. It is clear that these changes are favorable but it is far from the perfect.

In recent years, the water and sediment were dropped significantly in Yellow River, the average annual water and sediment discharge was 143.5 × 108 m3 and 3.75 × 108 t respectively, accounting for 29.2% and 31.2% of the 1950-1969 values. In addition, the day of flow depletion was also increased continuously and there was 21 years of flow depletion from 1972 to 1998. Especially in 1997 the day of flow depletion was up to 226 days in the section below Lijin Hydraulic Station. In the new hydrological environment the Yellow River delta eco- environment must be changed. Based on Remote Sensing and Geographic Information System (RS & GIS)(RS & GIS) technology, we study what and how the Yellow River delta wetlands changed in the new hydrologic conditions in recent years using the Landsat TM/ETM+ image of 1996, 2000 and 2004. The results show that the area of the wetlands in Yellow River Delta was increased and from 1996 to 2004 the area of the wetlands has been increased 22838.55 hm2and the increase rate was 2854.82hm2/y. The area of the nature wetlands and the artificial wetlands were increased as a whole, but their change character was different. The area of the nature wetlands was increased quickly from 1996 to 2000, which is related to that the national park of Yellow River delta was changed in 1996 and from 2000 to 2004 it was increased tardily. On the contrary, the area of the artificial wetlands was decreased tardily from 1996 to 2000 and from 2000 to 2004 it was increased quickly. In the mean time the area of river, bottomland and paddy field was decreased and the area of swampland, reed marsh, reservoir, saline and acequia was increased. To scrutinize the dynamic changes of wetlands, the simulation for transformation of the wetland types has been carried out by means of the ecological Markov Process. The results showed that the whole transfer velocity of the wetlands is 2.99% from 1996 to 2000 and the transfer among the wetland types is simple, but from 2000 to 2004 the transfer velocity of the wetlands is 6.95% and the transfer among the different wetland types is complex correspondingly. Moreover the reason of the wetlands changing has been analyzed in this paper. Firstly, the reciprocity between the decrease of the water and sediment and the regulating water and sediment is the main reason of the wetlands change. Aiming at the decrease of the water and sediment entering the Yellow River, the water and sediment regulation is an important measure which has positive impact on the Yellow River Delta Wetlands. Secondly, the process of the agriculture exploitation, city building and wetlands protection is also an important factor of the wetlands change. In order to resolve the problems of the excessive exploitation, wetlands pollution and so on, many protection measures, such as strengthening the management of the Yellow River, doing better program and dynamic monitoring, keeping harmony between the exploitation and recovery of the wetlands and paying attention to the wetland pollution, were carried out. Thirdly, the climate change and storm surge disaster are explicit factors of the wetlands change. This study has significance for building and protecting of eco-environment in Yellow River delta.

The vegetation water condition is determined by soil water content. So soil water condition can be reflected by means of vegetation water condition indirectly. There have been many detailed studies on crop water stress index from the micrometeoological aspect before, for example, CWSI (crop water stress index) and crop water stress microclimatic model based on CWSI. The generation of these indices need many meteorological data and is not suitable to regional water stress study because of the difficulties in limitation of data acquisition of related data on the surface. Moreover, there are a few relevant studies using remote sensing techniques, such as WDI (water deficit index), VCI (vegetation condition index) and TCI (temperature condition index). Although these indices are suitable to regional study, they utilize the statistic value of remote sensing data for years and they are on the basis of pixel scale. Therefore, the precision of quantitatively assessing surface water is limited to some extent. In view of problems of these water stress indices, this article is going to discuss a not only simple but also reasonable method to derive vegetation water. Different vegetation water condition could cause the variation of vegetation spectrum, so vegetation water stress can be shown by remote sensing vegetation indices, such as vegetation condition index and anomaly vegetation index et al. In view of this, soil water content can be assessed indirectly. But vegetation indices neglect some environment factors, eg. temperature and precipitation. The less evaportranspiration, the higher vegetation and soil temperatures. Therefore the vegetation temperature is a direct indicator of vegetation water stressed and drought. In a word, the soil water is positive correlation with vegetation index and negative correlation with temperature. It takes the North-West semi-arid area - Xilingole district of Inner Mongolia as study area. Different degraded grasslands are selected as objects in this study. MODIS (moderate resolution imaging spectroradiometer) has thirty-six bands of visible/near-infrared and thermal infrared with abundant information. This study selects MODIS as data source. In consideration of the spectral character of MODIS data and different degraded grasslands, the reflective spectrum of vegetation is greatly affected by soil. MSAVI (modified soil- adjusted vegetation index) is selected to weaken the disturbanceof soil information. MSAVI and NDWI (Normalized Difference Water Index) are deduced using one visible band (0.66 mum) and two near-infrared bands (0.86 mum, 1.24 mum). In order to estimate the vegetation water more accurately. Vegetation component temperature is inversed using two thermal infrared bands (8.6 mum, 11 mum) according to the emissivity distribution of vegetation with the wavelength from eight to twelve micrometer and the correlation analysis of MODIS data. Sequentially, the vegetation water synthesis index is acquired by analyzing the coupling character of three indexes, which can reflect the vegetation water condition. Then the vegetation water content can be extracted using the synthesis index effectively. Lastly, the vegetation water content is validated using measured data. The matching results show that the synthesis index is directly proportional to the measured data. It proves that the synthesis index and the vegetation water content are credible and the method is reasonable. This study has discussed a new method to know the regional vegetation water condition from satellite remote sensing data directly and quickly.

Remotely sensed imagery can reflect characteristics of sceneries, such as the formation, structure and spatial correlation etc, in order to make people know the surface matters form a macroscopic and holistic view. However, two dimension plane showed in remote sensing image could not get the 3D space in the realistic world reappearance. As the 3D visualization technique is used wider and wider, the visualization processing on the basis of remote sensing technique not only ensures the realization of the real-time 3D visualization, but also is better for the interpretation of remote sensing image. This article discusses a simple and feasible way of recurring the 3D remote sensing image taking Mount Taishan as an example. At the same time, by means of intuitionistic character of the 3D image, the characteristic of geology structures, physiognomy and environment in this area could be evaluated combining with other correlated data. In the end, the types of physiognomy landscape are divided and the distribution character is depicted. This study enhances the practicability of remote sensing technique, as well as, it takes positive effect on the planning and development of tour resources of Mount Taishan