Tong Qingxi

Relating soil surface moisture to reflectance

Abstract The objective of this study was to explore the relationship between soil reflectance in the solar domain (400–2500 nm) and soil moisture. Ten soils covering a large range of composition have been sampled. To decrease the large dimensionality of the data set, we reduced the number of wavebands investigated thanks to a simple stepwise linear regression. Seven wavebands were selected, which represent the whole spectral domain for the 10 soils and all moisture conditions with a root mean square error (RMSE) better than 0.002, close to the experimental uncertainties. Each soil reflectance spectrum was normalized by the corresponding reflectance spectrum observed under the driest condition. This allows to minimize effects due to soil type, as well as those of other undesirable multiplicative factors such as roughness and measurement configuration. The relationship between the normalized soil reflectance and moisture was then investigated. For all the wavelengths and all the soils, results show that for low soil moisture levels, the reflectance decreased when the moisture increased. Conversely, after a critical point, soil reflectance increased with soil moisture. For some soils, the reflectance of the wettest conditions can overpass that of the driest conditions. The position of the critical point was related to soil hydrodynamic properties. For both low and high soil moisture levels, and the seven wavelengths selected, the relative reflectance was strongly correlated with moisture. Adjustment of the relationships over individual soil types provides better soil moisture retrieval performances. It also shows that the relationships are generally nonlinear. These results are discussed with regards to the underlying physical processes, as well as for application to soil moisture estimates from reflectance measurements.

Current issues in high-resolution earth observation technology

This paper reviewed the developments of the last ten years in the field of international high-resolution earth observation, and introduced the developmental status and plans for China’s high-resolution earth observation program. In addition, this paper expounded the transformation mechanism and procedure from earth observation data to geospatial information and geographical knowledge, and examined the key scientific and technological issues, including earth observation networks, high-precision image positioning, image understanding, automatic spatial information extraction, and focus services. These analyses provide a new impetus for pushing the application of China’s high-resolution earth observation system from a “quantity” to “quality” change, from China to the world, from providing products to providing online service.

A new architecture for Remote-Sensing Environmental Monitoring System REMS: design and implementation

A new architecture for REMS (Remote-sensing Environmental Monitoring System V1.0) is introduced in this paper. REMS is the first integrated system in China developed to meet the multi-resource, multi-temporal and multi-thematic data processing, analyzing and products distributing, especially to process and monitor inland water resource pollution. The most important components of REMS are presented in this paper including tools for multi-resource data input/output, preprocessing, data visualization and mapping, environment information extraction, conventional image analysis, advanced tools for eco-environment modeling, and integrated interface to connect with general spatial and spectral database and water quality monitoring database. REMS provide professional ability to extract the major characters of water resource such as Chlorophyll content, Total Suspended Matter (TSM), Yellow Substance or CDOM etc. In order to retrieve and predict the blue algae distribution and water quality evolvement, a group of statistical algorithms are also realized to analyses the result and at the same time data assurance measures are also given respectively. Finally, we select the Taihu Lake, JiangShu Province, South China, as the representative research area and some field applications constructed based on REMS are discussed

Spatial scale issue in temperature and emissivity separation from thermal hyperspectral imager

This paper analysis the scale issue using four scale transformation methods (center pixels method (CPM), simple average method (SAM), point spread function method (PSF), wavelet transform method (WTM)). The results show that Center pixels method is not fit for scale transformation; PSF method can keep best the spatial autocorrelation of images, and lowest standard deviation. The optimal spatial resolution for hyperspectral thermal infrared is 6.25m, using semi-variance.

An operational method for fast detecting abnormal channels in imaging spectrometers

Data from abnormal channels in an imaging spectrometer almost always exerts an undesired impact on spectrum matching, classification, pattern recognition and other applications in hyperspectral remote sensing. To solve this problem, researchers should get rid of the data acquired by these channels. Selecting abnormal channels just in the way of visually examining each band image in a imaging data set is a conceivably hard and boring job. To relieve the burden, this paper proposes a method which exploits the spatial and spectral autocorrelations inherent in imaging spectrometer data, and can be used to speed up and, to a great degree, automate the detection of abnormal channels in an imaging spectrometer. This method is applied easily and successfully to one PHI data set and one Hymap data set, and can be applied to remotely sensed data from other hyperspectral sensors.

Surface reflectance auto retrieval model based on hyperspectral remotely sensed images

This method presented in this paper was tested using CASI&SASI data collected from a site at the Huailai County, Hebei Province, China. Various methods for retrieving AOT and CWV specific to this region were assessed. Results show that retrieval of AOT from the remote sensing data required establishing empirical relationships between 465.6nm, 659nm and 2105nm augmented by ground-based reflectance validation data, and minimizing the merit function based on optimization of AOT@550nm. The paper also extends the SODA method using Powells method to optimize the retrieval of CWV. The resultant CWV image shows relatively less residual surface features compared with the standard methods. Comparison of derived remote sensing surface reflectance with ground spectra of comparable vegetation and soil targets show significant correlations (R2) is 0.942 and 0.786, with RMSE being 0.0387 and 0.0406 respectively. Therefore, the method proposed in this paper is reliable enough for integrated atmospheric correction and surface reflectance retrieval from hyperspectral remote sensing data. This case provides a good reference to the surface reflectance inversion under the condition of lack of synchronized atmospheric parameters.

Notice of Retraction Vegetation edge feather extracting based on wavelet transform

Because field imaging spectrometer has high spatial resolution, soil particles as noise can make many unreasonable edges for its vegetation images. In this paper, in order to gain satisfied edges, we present an edge detection method based on b-spline wavelet transform and 8-connected components. The results show that this method is better than Canny operator with 8-connected components.

Crop growth monitoring study by using multi-temporal index image cube analysis

Crop physiology analysis and growth monitoring are of great importance for precision agriculture. Remote sensing technology supplies us with more selections and available spaces in this dynamic change study by producing images of different spatial, different spectral and different temporal resolutions. Especially, the remote sensing data of high spectral and high temporal resolution will play a key role in land cover studies at national, regional and global scales. In this paper, a new effective data structure conception, temporal index image cube (MIIC) is proposed in aid of the parameterization of the multi-dimension spectral curve. MIIC is proved very useful for the dynamic analysis of vegetation. Based on multi-temporal meteorological satellite data and multi-temporal ground spectral measurements, the temporal characteristics of different vegetation physiological parameters are contrasted and analyzed from the temporal index image cube. It turned out, each crop has its differentiated temporal index curve. Based on this multi-temporal index image cube, the classification and identification on various crops is very practicable. Some spectral indices perform fairly well on monitoring the growth situation of crops. But more efforts still should be made to develop new effective and general spectral indices, which will be more sensible to land cover types and the growth situation, moreover, it will screen the effects of atmosphere and background to a large extent.

Mineral exploration by use of infrared multispectral remote sensing in China

A new airborne infrared multispectral technology has been used for study of the gold and other mineral exploration in the North-Western Part of China during past few years.In this study the following steps were involved:the measurements and analysis of spectral character-istics, airborne remote sensing data acquisition and processing , extracting information on mineralized features and the assessment of techniques.