Xuejian Sun

Experimental system for the study of the directional thermal emission of natural surfaces

A new automatic experimental system was designed to improve the accuracy of multidirectional thermal infrared measurements. This experimental system mainly consists of two identical thermal cameras operating at 8–13 µm, one metal ring to keep the constant view area for different view angles and a goniometer, which is composed of: (1) a semicircular roadway of 2 m diameter to change the observation angle in the azimuth direction; (2) an elevator of 1 m height to adjust the measuring level to the target level; (3) a rotating arm equipped with one thermal camera for changing the observation angle in the zenith direction; and (4) a fixed arm equipped with another thermal camera to record at nadir the target temperature variation with time during the measurements. The system can be disassembled for easy transport and all of the data acquisition procedures are automatically monitored. For a given azimuth angle, the system needs about 2 minutes to make the directional measurements from about −70° to 70°, and for completing one hemispheric measurement it needs about 20 minutes if the multidirectional measurements are conducted by a step of 30° in the azimuth direction. The preliminary data acquired using our new system on bare soil and winter wheat are displayed and analysed. The results show that the angular variation of surface brightness temperature is measurable and presents some regular directional distribution and can be used quantitatively to study the directional thermal emission of the natural surfaces.

Enhancement of Spectral Resolution for Remotely Sensed Multispectral Image

Hyperspectral (HS) remote sensing has an important role in a wide variety of fields. However, its rapid progress has been constrained due to the narrow swath of HS images. This paper proposes a spectral resolution enhancement method (SREM) for remotely sensed multispectral (MS) image, to generate wide swath HS images using auxiliary multi/hyper-spectral data. Firstly, a set number of spectra of different materials are extracted from both the MS and HS data. Secondly, the approach makes use of the linear relationships between multi and hyper-spectra of specific materials to generate a set of transformation matrices. Then, a spectral angle weighted minimum distance (SAWMD) matching method is used to select a suitable matrix to create HS vectors from the original MS image, pixel by pixel. The final result image data has the same spectral resolution as the original HS data that used and the spatial resolution and swath were also the same as for the original MS data. The derived transformation matrices can also be used to generate multitemporal HS data from MS data for different periods. The approach was tested with three image datasets, and the spectra-enhanced and real HS data were compared by visual interpretation, statistical analysis, and classification to evaluate the performance. The experimental results demonstrated that SREM produces good image data, which will not only greatly improve the range of applications for HS data but also encourage more utilization of MS data.

An Analysis of Shadow Effects on Spectral Vegetation Indexes Using a Ground-Based Imaging Spectrometer

Sunlit vegetation and shaded vegetation are inseparable parts for most remotely sensed images, and the presence of shadows affects high spatial resolution remote sensing and multiangle remote sensing data. Shadows can lead to either a reduction in or a total loss of information in an image. This can potentially lead to the corruption of biophysical parameters derived from pixel values, such as vegetation indexes (VIs). VIs are widely used in remote sensing inversion applications. If the effects of shadows are not properly accounted for, retrieval may be uncertain when using a VI to calculate vegetation parameters. One of the major reasons that the effects of shadows are easy to be ignored in remote sensing is the spatial resolution of the measurement. High spatial and spectral resolutions are typically difficult to achieve simultaneously, and images that have one tend to not have the other. A ground-based imaging spectrometer brings a turning point to solve this problem as it can obtain both high spatial and high spectral resolutions to obtain feature and shadow images simultaneously. The resolution of the system used here was 1 mm at a height of 1 m, and the spectral resolution was better than 5 nm. For each pixel, the spectral curve of the image was almost a pure-pixel spectral curve, which allowed the differentiation of sunlit and shaded areas. To investigate the effects of shadows on different indexes, 14 hyperspectral VIs were calculated. Moreover, the vegetation fractional coverage calculated using the same 14 VIs was compared. The results show that shadows affect not only each narrowband of a VI but also vegetation parameters.

Determination of the effective emissivity for the regular and irregular cavities using Monte-Carlo method

When estimating the Land Surface Temperature (LST) using remotely sensed data, the emissivity of the land surface in thermal infrared wavelength is a key variable. A Monte-Carlo method was used to calculate the effective emissivity of cavities which the rough land surface is composed of. The cavitys effective emissivity was measured using the Sealing-Cavity method and then compared with the calculated results. The results show that a Monte-Carlo method can be used to compute the effective emissivity for cavities with either regular or irregular geometry.

Multi‐layer perceptron neural network based algorithm for estimating precipitable water vapour from MODIS NIR data

This Letter presents a multi‐layer perceptron neural network (MLP‐NN) based algorithm to quantitatively determine precipitable water vapour (PWV) directly from near infrared (NIR) radiance measured by the Moderate Resolution Imaging Spectroradiometer (MODIS). First, the background of the MLP‐NN based algorithm is discussed briefly. Then, the radiance of MODIS NIR channels simulated through a radiative transfer model with a set of input variables covering a broad range of surface reflectance and water vapour content are used to train MLP‐NN. Finally, PWV values derived by the MLP‐NN based algorithm are compared with radiosonde observations and a root mean squared error of 5.2 kg m−2 is found from this comparison.

Polarized Spectral Measurement and Analysis of Sedum Spectabile Boreau Using a Field Imaging Spectrometer System

Polarized hyperspectral imaging is a new remote sensing method that combines the benefits of polarization and hyperspectral characteristics. Based on a new self-developed polarized field imaging spectrometer system (FISS-P), we collected polarized hyperspectral images of leaves of Sedum spectabile Boreau. Polarization analysis of the diffuse reflectance standard white plate indicated that the FISS-P produced accurate polarization measurements. Ten related polarization parameters (<i>I</i>, <i>Q</i>, <i>U</i>, DoLP, AoP, <i>R</i>₀, <i>R</i>₆₀, <i>R</i>₁₂₀, <i>R</i>, <i>R</i>_I) were analyzed in this study. The angle of polarization (AoP) spectral curves of the S. spectabile leaf had no unique spectral features. The degree of linear polarization (DoLP) spectral curves displayed distinct spectral characteristics. However, the DoLP and spectral reflectance curves of the leaf displayed contrasting trends. Different parts of the same leaf, or different S. spectabile leaves, produced different spectral curve shapes. Analysis of the five reflectance parameters demonstrated that <i>R</i>₀, <i>R</i>₆₀, <i>R</i>₁₂₀, <i>R</i>_I, and <i>R</i> were consistent for all spectral and spatial aspects.

Assessing the Effect of Temporal Interval Length on the Blending of Landsat-MODIS Surface Reflectance for Different Land Cover Types in Southwestern Continental United States

Capturing spatial and temporal dynamics is a key issue for many remote-sensing based applications. Consequently, several image-blending algorithms that can simulate the surface reflectance with high spatial-temporal resolution have been developed recently. However, the performance of the algorithm against the effect of temporal interval length between the base and simulation dates has not been reported. In this study, our aim was to evaluate the effect of different temporal interval lengths on the accuracy using the widely used blending algorithm, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), based on Landsat, Moderate-resolution Imaging Spectroradiometer (MODIS) images and National Land Cover Database (NLCD). Taking the southwestern continental United States as the study area, a series of experiments was conducted using two schemes, which were the assessment of STARFM with (i) a fixed base date and varied simulation date and (ii) varied base date and specific simulation date, respectively. The result showed that the coefficient of determination (R2), Root Mean Squared Error (RMSE) varied, and overall trend of R2 decreased along with the increasing temporal interval between the base and simulation dates for six land cover types. The mean R2 value of cropland was lowest, whereas shrub had the highest value for two schemes. The result may facilitate selection of an appropriate temporal interval when using STARFM.

An algorithm of remotely sensed hyperspectral image fusion based on spectral unmixing and feature reconstruction

In order to get high spatial resolution hyperspectral data, many studies have examined methods to combine spectral information contained in hyperspectral image with spatial information contained in multispectral/panchromatic image. This paper developed a new hyperspectral image fusion method base on the non-negative matrix factorization (NMF) theory. Data sets obtained by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) was used to evaluate the performance of the method. Experimental results show that the proposed algorithm can provide a good way to solve the problem of high spatial resolution hyperspectral data shortage.

The Spectral Crust project—Research on new mineral exploration technology

Considering Chinas increasing demands for energy and mineral resources, this article describes challenges in current mineral exploration, summarizes new mineral exploration techniques, and introduces the basic concept, research aims, and implementation of the Spectral Crust project. This project will use hyperspectral technology and remote sensing to map the Earths land surface and mineral alterations and to establish a crustal hyperspectral database. The Spectral Crust project will align with national strategy, strengthen technological innovation, and improve mineral resource detection by spaceborne and airborne hyperspectral remote sensing, establish platforms for ground-spectra measurement and core scanning, compilation, and analysis, and promote data integration. This project has great strategic significance as it will help advance the development of remote sensing technology and Chinas mineral exploration technology and will solve key problems in direct and deep mineral prospecting.

Cloud removal for hyperspectral remotely sensed images based on hyperspectral information fusion

ABSTRACT Hyperspectral remote sensing plays an important role in a wide variety of fields. However, its specific application for land surface analysis has been constrained due to the different shapes of thick, opaque cloud cover. The reconstruction of missing information obscured by clouds in remote-sensing images is an area of active research. However, most of the available cloud-removal methods are not suitable for hyperspectral images, because they lose the spectral information which is very important for hyperspectral analysis. In this article, we developed a new spectral resolution enhancement method for cloud removal (SREM-CR) from hyperspectral images, with the help of an auxiliary cloud-free multispectral image acquired at different times. In the fixed hyperspectral image, spectra of the cloud cover pixels are reconstructed depending on the relationship between the original hyperspectral and multispectral images. The final resulting image has the same spectral resolution as the original hyperspectral image but without clouds. This approach was tested on two experiments, in which the results were compared by visual interpretation and statistical indices. Our method demonstrated good performance.