Xiaoguang Jiang

University of Chinese Academy of Sciences

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Hua Wu

University of Electronic Science and Technology of China

Caixia Gao

Aerospace Information Research Institute

Yazhen Jiang

University of Chinese Academy of Sciences

Zhao-Liang Li

Chinese Academy of Agricultural Sciences

Hongyuan Huo

Beijing University of Technology

Dong Fan

Kunming University of Science and Technology

Ronglin Tang

Institute of Geographic Sciences and Natural Resources Research

Yuze Zhang

University of Jinan

Enyu Zhao

Dalian Maritime University

Ru Chen

Chinese Academy of Sciences

Cooperative Institutions

Chinese Academy of Sciences

University of Chinese Academy of Sciences

Institute of Geographic Sciences and Natural Resources Research

Aerospace Information Research Institute

State Key Laboratory of Remote Sensing Science

Chinese Academy of Agricultural Sciences

Centre National de la Recherche Scientifique

Academy of Opto-Electronics

Institute of Agricultural Resources and Regional Planning

Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie

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Detection of Coal Fire Dynamics and Propagation Direction from Multi-Temporal Nighttime Landsat SWIR and TIR Data: A Case Study on the Rujigou Coalfield, Northwest (NW) China

Remote Sensing (2014)

Hongyuan Huo Xiaoguang Jiang Xianfeng Song Zhao-Liang Li Zhuoya Ni

Coal fires are common and serious phenomena in most coal-producing countries in the world. Coal fires not only burn valuable non-renewable coal reserves but also severely affect the local and global environment. The Rujigou coalfield in Shizuishan City, Ningxia, NW China, is well known for being a storehouse of anthracite coal. This coalfield is also known for having more coal fires than most other coalfields in China. In this study, an attempt was made to study the dynamics of coal fires in the Rujigou coalfield, from 2001 to 2007, using multi-temporal nighttime Landsat data. The multi-temporal nighttime short wave infrared (SWIR) data sets based on a fixed thresholding technique were used to detect and monitor the surface coal fires and the nighttime enhanced thematic mapper (ETM+) thermal infrared (TIR) data sets, based on a dynamic thresholding technique, were used to identify the thermal anomalies related to subsurface coal fires. By validating the coal fires identified in the nighttime satellite data and the coal fires extracted from daytime satellite data with the coal fire map (CFM) manufactured by field survey, we found that the results from the daytime satellite data had higher omission and commission errors than the results from the nighttime satellite data. Then, two aspects of coal fire dynamics were analyzed: first, a quantitative analysis of the spatial changes in the extent of coal fires was conducted and the results showed that, from 2001 to 2007, the spatial extent of coal fires increased greatly to an annual average area of 0.167 km2; second, the spreading direction and propagation of coal fires was analyzed and predicted from 2001 to 2007, and these results showed that the coal fires generally spread towards the north or northeast, but also spread in some places toward the east.

Anthracite

Thematic Mapper

10.3390/rs6021234

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Citations (49)

Study on dynamic snow monitoring by multisensor remote sensing data

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2003)

Xiaoguang Jiang Lingli Tang Hong Ma YU Su-fen

Observation by limited field observational stations is the major conventional method of research on snow and relevant problem, such as flood caused by melting snow. However, observation on only several ground observatories cannot provide enough information for large scale region accurately and timely. The advanced technology of remote sensing and geographic information system (GIS) is an effective tool to extract snow information and monitor snow change quickly and dynamically. This paper discusses the method of snow mapping and establishing dynamic snow monitoring information system by using multisensor, multispectral and multitemporal remote sensing data (NOAA/AVHRR, satellite borne SAR and TM).The study results show that the use of multisensor data and technique of GIS, combined with relative contemporaneous field observational data, enables the snow monitoring more rapid and accurate.

10.1117/12.539037

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Calculating the Marine Gravity Anomaly of the South China Sea based on the Inverse Stokes Formula

IOP Conference Series Earth and Environmental Science (2016)

Liang Liu Xiaoguang Jiang Shanwei Liu Lei Zheng Jinxia Zang

Marine gravity field information has a great significance for the resource, environment and military affairs. As a new way to get marine gravity data, the satellite altimetry technique makes up for what ship measuring means lack. The paper carries out the researches on how altimeter data applied for calculating marine gravity anomaly based on inverse Stokes formula. In the article, the editing of 14-track Jason-1 data over South China Sea for 7 years is for collinear processing and cross-point adjustment. The inverse Stokes formula and fast Flourier transform technique are applied to calculate marine gravity anomaly of the region (0°∼23°N, 103°∼120°E), and to draw gravity anomaly map. Compared with the gravity anomaly by ship observation, RMS is 12.6mGal, and single altimetry satellite has a good precision.

Satellite Altimetry

Free-air gravity anomaly

Anomaly (physics)

10.1088/1755-1315/46/1/012062

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An Improved Algorithm for Retrieving Land Surface Emissivity and Temperature From MSG-2/SEVIRI Data

IEEE Transactions on Geoscience and Remote Sensing (2013)

Caixia Gao Zhao-Liang Li Shi Qiu Bo‐Hui Tang Hua Wu

This paper presents an improved algorithm for simultaneously retrieving both land surface emissivity (LSE) and land surface temperature (LST) using data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the MSG-2 satellite. First, the temperature-independent spectral index-based method for LSE retrieval is reviewed and improved in terms of three aspects: atmospheric correction, fitting of the bidirectional reflectivity model, and retrieval of the LSE in SEVIRI channel 10. Then, the generalized split-window method with seven unknown coefficients is used to derive the LST. Finally, this improved algorithm is applied to several MSG-2/SEVIRI data sets over a study area with geospatial coverage of latitude 30 ° N-45 ° N and longitude 15 ° W-15 ° E, and using detailed cases, the modifications to the original LSE/LST retrieval methods are shown to be effective and reasonable. In addition, the SEVIRI-derived LSTs are cross-validated primarily using the Moderate Resolution Imaging Spectroradiometer-derived validated LST data extracted from the MOD11B1 product on two clear-sky days (August 22, 2009 and July 3, 2008). The validation results indicate that more than 70% of the differences are within 2.5 K and that the LST differences tend to be lower at night than in the day, which may result from the homogeneous thermal conditions at night.

Moderate-resolution imaging spectroradiometer

Longitude

10.1109/tgrs.2013.2271510

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Citations (14)

Land Surface Temperature Retrieval From Landsat 8 Thermal Infrared Data Over Urban Areas Considering Geometry Effect: Method and Application

IEEE Transactions on Geoscience and Remote Sensing (2021)

Ru Chen Si‐Bo Duan Xiaoguang Jiang Zhao-Liang Li Yazhen Jiang

Accurate retrieval of land surface temperature (LST) over urban areas is of great significance for urban thermal environment monitoring. In previous studies, most of the urban LST retrieval methods were developed based on the assumption of a flat surface without considering the influence of urban 3-D geometry structure, which has a significant impact on the retrieval accuracy of LST over urban areas. In this study, a radiative transfer equation (RTE)-based single-channel method was developed to retrieve LST with urban geometry effect correction from the Landsat 8 thermal infrared (TIR) data in band 10. The increase in adjacent radiance from the surrounding pixels and the decrease in atmospheric downwelling radiance caused by urban geometry structure were taken into account in this method. Because it is difficult to directly validate the retrieval accuracy of LST over urban areas using in situ LST measurements, the performance of the RTE-based LST retrieval method was evaluated via comparing brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the discrete anisotropic radiative transfer (DART) model and the urban RTE over three subregions. There is a good agreement between BT at the TOA simulated by the DART model and the urban RTE, with a root-mean-squared error (RMSE) of less than 0.25 K. The variations in LST retrieved with urban geometry effect correction over different local climate zones (LCZs) were analyzed. In general, built-up LCZs have relatively higher LST than land cover LCZs. The differences between LST retrieved without/with urban geometry effect correction over different LCZs are greater than 0.2 K. The largest average LST difference over built-up LCZs is approximately 0.9 K, whereas that over land cover LCZs is approximately 0.65 K. LST retrieved without/with urban geometry effect correction was used to calculate urban heat island intensity (UHII) in terms of the LCZ-based method. The results indicate that UHII calculated from LST with urban geometry effect correction is lower than that calculated from LST without urban geometry effect correction, with an average difference of approximately 0.5 K.

10.1109/tgrs.2021.3088482

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Citations (32)

Land surface temperature retrieval from AMSR-E passive microwave data

Optics Express (2017)

Enyu Zhao Caixia Gao Xiaoguang Jiang Zhaoxia Liu

The land surface temperature (LST) is a key parameter for energy balance, evapotranspiration and climate change. In this study, two new methods of LST retrieval from passive microwave data are developed: one is deriving LST only using single-channel dual-polarized data based on the relationship between the emissivity and microwave polarization difference index (MPDI) (denoted as Method 1); the other one is deriving LST using the traditional multi-channel method with prior knowledge of the normalized difference vegetation index (NDVI) (denoted as Method 3). Taking Moderate Resolution Imaging Spectroradiometer (MODIS) LST products as the actual LSTs, the coefficients for these algorithms are determined. From the results for the year 2008, it is demonstrated that the root mean square errors (RMSEs) for the LST retrieval using Method 3 are the smallest and range from 2.92 K to 3.44 K, the RMSEs for the LST retrieval using traditional multi-channel method (denoted as Method 2) range from 3.07 K to 4.05 K, and the worst results come from Method 1, whose RMSEs range from 3.11 K to 4.13 K at a frequency of 89 GHz. This could be caused by the fact that the NDVI provides substantial emissivity knowledge in Method 3, and much richer vegetation could result in a more accurate emissivity estimation.

Moderate-resolution imaging spectroradiometer

Root mean square

Spectroradiometer

Dual-polarization interferometry

10.1364/oe.25.00a940

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Citations (16)

A Study of Coal Fire Propagation with Remotely Sensed Thermal Infrared Data

Remote Sensing (2015)

Hongyuan Huo Zhuoya Ni Caixia Gao Enyu Zhao Yuze Zhang

Coal fires are a common and serious problem in most coal-bearing countries. Thus, it is very important to monitor changes in coal fires. Remote sensing provides a useful technique for investigating coal fields at a large scale and for detecting coal fires. In this study, the spreading direction of a coal fire in the Wuda Coal Field (WCF), northwest China, was analyzed using multi-temporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) thermal infrared (TIR) data. Using an automated method and based on the land surface temperatures (LST) that were retrieved from these thermal data, coal fires related to thermal anomalies were identified; the locations of these fires were validated using a coal fire map (CFM) that was developed via field surveys; and the cross-validation of the results was also carried out using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images. Based on the results from longtime series of satellite TIR data set, the spreading directions of the coal fires were determined and the coal fire development on the scale of the entire coal field was predicted. The study delineated the spreading direction using the results of the coal fire dynamics analysis, and a coal fire spreading direction map was generated. The results showed that the coal fires primarily spread north or northeast in the central part of the WCF and south or southwest in the southern part of the WCF. In the northern part of the WCF, some coal fires were spreading north, perhaps coinciding with the orientation of the coal belt. Certain coal fires scattered in the northern and southern parts of the WCF were extending in bilateral directions. A quantitative analysis of the coal fires was also performed; the results indicate that the area of the coal fires increased an average of approximately 0.101 km2 per year.

Thermal infrared

10.3390/rs70303088

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Citations (39)

Comparison of the Thermal Sensors of SEVIRI and MODIS for LST Mapping

Remote sensing and digital image processing (2013)

Caixia Gao Xiaoguang Jiang Zhao-Liang Li Françoise Nerry

Zenith

Moderate-resolution imaging spectroradiometer

Thermal infrared

Land Cover

10.1007/978-94-007-6639-6_12

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Citations (8)

Effects of 2001 Spring Dust Storms on Regional Climate in China

AGU Fall Meeting Abstracts (2010)

Xiaoguang Jiang Zhenhao Yang Huaxia Liao

Source

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Land surface emissivity retrieval from airborne hyperspectral scanner thermal infrared data over urban surfaces

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2015)

Caixia Gao Y. G. Qian Na Wang L. L.Xiaoguang Jiang

Land surface emissivity (LSE) is a key parameter for characterizing the land surface, and is vital for a wide variety of surface-atmosphere studies. This paper retrieved LSEs of land surfaces over the city of Madrid, Spain from airborne hyperspectral scanner (AHS) thermal infrared data using temperature emissivity separation (TES) method. Six different kinds of urban surfaces: asphalt, bare soil, granite, pavement, shrub and grass pavement, were selected to evaluate the performance of the TES method in urban areas. The results demonstrate that the TES method can be successfully applied to retrieve LSEs in urban area. The six urban surfaces have similar curve shape of emissivity spectra, with the lowest emissivity in band 73, and highest in band 78; the LSE for bare soil varies significantly with spectra, approximately from 0.90 in band 72 to 0.98 in band 78, whereas the LSE for grass has the smallest spectral variation, approximately from 0.965 in band 72 to 0.974 in band 78, and the shrub presents higher LSE than other surfaces in bands 72, 73, 75-77, but a little lower in bands 78 and 79. Furthermore, it is worth noting that band 73 is suitable for discriminating different urban surfaces because large LSE differences exist in this channel for different urban surfaces.

Thermal infrared

10.1117/12.2220606

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Citations (1)