Yincui Hu

Soil moisture retrieval from MODIS data in Northern China Plain using thermal inertia model

Soil moisture plays an important role in surface energy balances, regional runoff, potential drought and crop yield. Early detection of potential drought or flood is important for the local government and people to take actions to protect their crop. Traditionally measurement of soil moisture is a time‐consuming job and only limited samples could be collected. Many problems would be results from extending those point measurements to 2D space, especially for a regional area with heterogeneous soil characteristics. The emergency of remote‐sensing technology makes it possible to rapidly monitor soil moisture on a regional scale. Thermal inertia represents the ability of a material to conduct and store heat, and in the context of planetary science, it is a measure of the subsurfaces ability to store heat during the day and reradiate it during the night. One major application of thermal inertia is to monitor soil moisture. In this paper, a thermal inertia model was developed to be suitable in situations whether or not the satellite overpass time coincides with the local maximum and minimum temperature time. Besides, the sensibilities of thermal inertia with surface albedo and the surface temperature difference were discussed. It shows that the surface temperature difference has more effects on the thermal inertia than the surface albedo. When the temperature difference is less than 10 Kelvin degrees, 1 Kelvin degree error of temperature difference will lead to a big fluctuation of thermal inertia. When the temperature difference is more than 10 Kelvin degrees, 1 Kelvin degree error of temperature difference will cause a small change of thermal inertia. The temperature difference should be larger than 10 Kelvin degrees when the thermal inertia model is selected to derive soil moisture or other applications. Based on this thermal inertia model, the soil moisture map was obtained for North China Plain. It shows that the averaged difference between the soil moisture values derived from MODIS data and in situ measured soil moisture data is 4.32%. This model is promising for monitoring soil moisture on a large regional scale.

Preliminary Study on Unsupervised Classification of Remotely Sensed Images on the Grid

Grid is a new technology. With corresponding middleware it can give strong computing power. In this paper we mainly discuss the middleware technology and architecture used in remote sensing image classification algorithm. Because unsupervised classification middleware is the key of the classification middleware algorithms, we study the alternant-unsupervised middleware and put forward a non-alternant unsupervised middleware scheme. Based on this scheme, main factors which effect the performance of non-alternant unsupervised classification are analyzed.

Preliminary study of Grid computing for remotely sensed information

Observing the world‐wide concentration and distribution of ozone is important for monitoring the evolution of the ozone layer, to derive the amount of UV, to provide ozone and UV forecasts and to improve weather forecasting. Monitoring ozone is the primary function of the Global Ozone Monitoring Experiment. Each day, space missions download, from space to ground, many raw images that are stored in ground stations located all over the world. How to process this data resource in real time — or almost real time — and effectively share spatial information among the remote sensing community is a pressing task. Grid computing can provide access to a globally distributed computing environment via authentication, authorization, negotiation and security. It can create a computational environment handling many PetaBytes of geographically distributed data, tens of thousands of heterogeneous computing resources and thousands of simultaneous users from many research institutions. It can provide a powerful tool for sharing both remote sensing data and processing middleware. This paper introduces the concept of grid computing, followed by its applications for atmospheric ozone retrieval. The special remote sensing data analysis note for the Spatial Information Grid (SIG) is addressed in detail. A series of remotely sensed image processing middleware is shown. Experience shows that near‐real‐time products, such as maps of ozone, from the processing and analysis of remotely sensed data are possible.

Experience of Remote Sensing Information Modelling with Grid Computing

In this paper, we focused on the remote sensing information modeling and determination using Grid computing platform. We have underdone the experiments using remotely sensed images for thermal inertial modeling in Condor system that is one of the Grid Projects existed nowadays worldwide. We divided remote sensing data into several parts and run them on Condor pool and on one single machine. From these tests, the relationship among the work efficiency of image processing in Condor system and the number of the separated parts of image and the number of machines in this system is presented. Given a certain number of machines, a most efficient image size is existed among varies sized images. Besides, the possible causes of the longer put-off in this process are given, and some possible methods to resolve this problem are also presented. It is feasible to use Grid computing system such as Condor to process remote sensing data. And if the postpone problem can be resolved, the work efficiency of Grid systems will be high. Even with so many problems, it is a good thing that Grid systems do many things for you during all of us are in sleep. Our next major task will concentrate on realizing an arithmetic that can read and divide remote sensing images based on image size and number of machines in Grid system automatically, and transfer results back to the submitted machine as a whole data file.

Reconstruction of 3D Curvilinear Wireframe Model from 2D Orthographic Views

An approach for reconstructing wireframe models of curvilinear objects from three orthographic views is discussed. Our main stress is on the method of generating three-dimensional (3D) conic edges from two-dimensional (2D) projection conic curves, which is the pivotal work for reconstructing curvilinear objects from three orthographic views. In order to generate 3D conic edges, a five-point method is firstly utilized to obtain the algebraic representations of all 2D projection curves in each view, and then all algebraic forms are converted to the corresponding geometric forms analytically. Thus the locus of a 3D conic edge can be derived from the geometric forms of the relevant conic curves in three views. Finally, the wireframe model is created after eliminating all redundant elements generated in previous reconstruction process. The approach extends the range of objects to be reconstructed and imposes no restriction on the axis of the quadric surface.

Data-Parallel method for georeferencing of MODIS level 1b data using grid computing

Georeference is a basic function of remote sensing data processing. Geo-corrected remote sensing data is an important source data for Geographic Information Systems (GIS) and other location services. Large quantity remote sensing data were produced daily by satellites and other sensors. Georeferenceing of these data is time consumable and computationally intensive. To improve efficiency of processing, Grid technologies are applied. This paper focuses on the parallelization of the remote sensing data on a grid platform. According to the features of the algorithm, backwards-decomposition technique is applied to partition MODIS level 1B data. Firstly, partition the output array into evenly sized blocks using regular domain decomposition. Secondly, compute the geographical range of every block. Thirdly, find the GCPs triangulations contained in or intersect with the geographic range. Then extract block from original data in accordance with these triangulations. The extracted block is the data distributed to producer on Grid pool.

A remote sensing application workflow and its implementation in remote sensing service grid node

In this article we describe a remote sensing application workflow in building a Remote Sensing Information Analysis and Service Grid Node at Institute of Remote Sensing Applications based on the Condor platform. The goal of the Node is to make good use of physically distributed resources in the field of remote sensing science such as data, models and algorithms, and computing resource left unused on Internet. Implementing it we use workflow technology to manage the node, control resources, and make traditional algorithms as a Grid service. We use web service technology to communicate with Spatial Information Grid (SIG) and other Grid systems. We use JSP technology to provide an independent portal. Finally, the current status of this ongoing work is described.

Java-Based grid service spread and implementation in remote sensing applications

Remote sensing applications often concern very large volumes of spatio-temporal data, the emerging Grid computing technologies bring an effective solution to this problem. The Open Grid Services Architecture (OGSA) treats Grid as the aggregate of Grid service, which is extension of Web Service. It defines standard mechanisms for creating, naming, and discovering transient Grid service instances; provides location transparency and multiple protocol bindings for service instances; and supports integration with underlying native platform facilities. It is not effective used in data-intensive computing such as remote sensing applications because its foundation, Web Service, is not efficient in scientific computing. How to increase the efficiency of the grid services for a scientific computing? This paper proposes a mechanism Grid service spread (GSS), which dynamically replant a Grid service from a Grid node to the others. We have more computers to provide the same function, so less time can be spent completing a problem than original Grid system. This paper also provides the solution how to adept the service duplicate for the destination node’s Grid environment; how each service duplicate communicates with each other; how to manage the lifecycle of services spread etc. The efficiency of this solution through a remote sensing application of NDVI computing is demonstrated. It shows that this method is more efficient for processing huge amount of remotely sensed data.

High throughput computing for spatial information processing (HIT-SIP) system on grid platform

For many remote sensing application projects, the quality of the research or the product is heavily dependent upon the quantity of computing cycles available. Middleware is software that connects two or more otherwise separate applications across the Internet or local area networks. In this paper, we present the High Throughput Computing Spatial Information Processing (HIT-SIP) System (Prototype), which is developed in Institute of Remote Sensing Applications, Chinese Academy of Sciences, China. Several middleware packages developed in the HIT-SIP system are demonstrated. Our experience shows that it is feasible that our grid computing testbed can be used to do remote sensing information analysis.

Feasibility Study of Geo-spatial Analysis Using Grid Computing

Spatial applications will gain high complexity as the volume of spatial data increases rapidly. A suitable data processing and computing infrastructure for spatial applications needs to be established. Over the past decade, grid has become a powerful computing environment for data intensive and computing intensive applications. In this paper, we tested and analyzed the feasibility of using Grid platform for spatial analysis functionalities in Geographic Information System (GIS). We found that spatial interpolation, buffers, and spatial query can be easily migrated to Grid platform. Polygon overlay and transformation could achieve better results on Grid platform. To do network analysis and spatial statistical analysis on Grid platform could be no significant improvement of performance. The most un-suitable spatial analysis on Grid platform is the spatial measurement.