Advances in application of space hyperspectral remote sensing(invited)

Li Shengyang; Liu Zhiwen; Liu Kang; Zhao Zifei

doi:10.3788/IRLA201948.0303001

Volume 48 Issue 3

Mar. 2019

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Article Navigation > Infrared and Laser Engineering > 2019 > 48(3): 303001-0303001(15)

Li Shengyang, Liu Zhiwen, Liu Kang, Zhao Zifei. Advances in application of space hyperspectral remote sensing(invited)[J]. Infrared and Laser Engineering, 2019, 48(3): 303001-0303001(15). doi: 10.3788/IRLA201948.0303001

Citation:

Li Shengyang, Liu Zhiwen, Liu Kang, Zhao Zifei. Advances in application of space hyperspectral remote sensing(invited)[J]. Infrared and Laser Engineering, 2019, 48(3): 303001-0303001(15). doi: 10.3788/IRLA201948.0303001

Advances in application of space hyperspectral remote sensing(invited)

doi: 10.3788/IRLA201948.0303001

Li Shengyang^1,2,
Liu Zhiwen^1,2,
Liu Kang^1,2,
Zhao Zifei^1,2,3

1.
Key Laboratory of Space Utilization,Chinese Academy of Sciences,Beijing 100094,China;
2.
Technology and Engineering Center for Space Utilization,Chinese Academy of Sciences,Beijing 100094,China;
3.
University of Chinese Academy of Sciences,Beijing 100049,China

Received Date: 2018-11-05
Rev Recd Date: 2018-12-03
Publish Date: 2019-03-25

Abstract

With the rapid development of hyperspectral imaging technology, space hyperspectral remote sensing data have been successfully applied to various fields in recent years. The development of space hyperspectral imaging technology at home and abroad was reviewed, the technical standards of representative space hyperspectral imagers were introduced. The latest applications of hyperspectral data in land resources, agriculture and forestry, ocean and lake remote sensing, urban environment, disaster monitoring and other fields in the past five years were systematically summarized and analysed. The outlook of future hyperspectral remote sensing was provided including hyperspectral information extraction and application based on AI technology, the multi-source data fusion and applications, and the analysis and application of hyperspectral data for deep space exploration. Further developments of space hyperspectral imager technology driven by applications will promote the innovated use of hyperspectral data in a wider range of fields.
- aerospace hyperspectral,
- imaging spectrometer,
- application of hyperspectral data

References

[1]	Barnsley M J, Settle J J, Cutter M A, et al. The PROBA/CHRIS mission:A low-cost smallsat for hyperspectral multiangle observations of the earth surface and atmosphere[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(7):1512-1520.
[2]	Folkman M, Pearlman J, Liao L, et al. EO-1/Hyperion hyperspectral imager design, development, characterization, and calibration[C]//SPIE, 2001, 4151:40-51.
[3]	Yuan Yan, Li Liying, Xiong Wang'e, et al. Mechanical structure design for hyperspectral imager of HJ-1A satellite[J]. Spacecraft Engineering, 2009, 18(6):97-105.
[4]	Lucke R L, Corson M, McGlothlin N R, et al. Hyperspectral imager for the coastal ocean:instrument description and first images[J]. Applied Optics, 2011, 50(11):1501-1516.
[5]	Gao Ming, Zhang Shancong, Li Shengyang. Tiangong-1 hyperspectral remote sensing and application[J]. Journal of Remote Sensing, 2014, 18(s1):2-10. (in Chinese)
[6]	Liu Yinnian. Visible-shortwave infrared hyperspectral imager of GF-5 satellite[J]. Spacecraft Recovery Remote Sengsing, 2018, 39(3):25-28. (in Chinese)
[7]	Murchie S, Arvidson R, Bedini P, et al. Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on Mars Reconnaissance Orbiter (MRO)[J]. Journal of Geophysical Research Atmospheres, 2007, 112(E5):431-433.
[8]	Li Xiaopeng, Chen Jianpeng, Wang Xiang. Inversion of lunar nearside FeO and Al2O3 based on Chang'e-1 reflectance data[J]. China Mining Magazine, 2018, 27(7):150-156. (in Chinese)
[9]	Staenz K, Held A. Summary of current and future terrestrial civilian hyperspectral spaceborne systems[C]//2012 IEEE International Geoscience and Remote Sensing Symposium, 2012:123-126.
[10]	Stefano P, Angelo P, Simone P, et al. The PRISMA hyperspectral mission:science activities and opportunities for agriculture and land monitoring[C]//2013 IEEE International Geoscience and Remote Sensing Symposium, 2013:4558-4561.
[11]	Guanter L, Kaufmann H, Segl K, et al. The EnMAP spaceborne imaging spectroscopy mission for earth observation[J]. Remote Sensing, 2015, 7(7):8830-8857.
[12]	Lee C M, Cable M L, Hook S J, et al. An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities[J]. Remote Sensing of Environment, 2015, 167:6-19.
[13]	Lefevre-Fonollosa M-J, Fratter I, Mandea M. HYPXIM:an innovative hyperspectral satellite for science, security and defence[J]. Egu General Assembly, 2013, 15:10129.
[14]	Chen S, Huang S, Liu Y, et al. Soil and vegetation spectral coupling difference (SVSCD) for minerals extraction from hyperion data in vegetation covered area[J]. Chinese Geographical Science, 2018, 28(6):957-972.
[15]	Lei L, Feng J, Rivard B, et al. Mapping alteration using imagery from the Tiangong-1 hyperspectral spaceborne system:Example for the Jintanzi gold province, China[J].International Journal of Applied Earth Observation Geoinformation, 2017, 64:31-41.
[16]	Lin Jian, Yan Baikun, Dong Xinfeng, et al. Evaluating of Tiangong-1 imaging spectrometer data oriented to geological applications[J]. Journal of Remote Sensing, 2014, 18(s1):74-83. (in Chinese)
[17]	Elatawneh A, Kalaitzidis C, Petropoulos G P, et al. Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data[J]. International Journal of Digital Earth, 2014, 7(3):194-216.
[18]	Xing C, Ma L, Yang X Q. Stacked denoise autoencoder based feature extraction and classification for hyperspectral images[J]. Journal of Sensors, 2016, 2016:3632943.
[19]	Li Zhuqiang, Zhu Ruifei, Gao Fang, et al. Hyperspectral remote sensing image classification based on three-dimensional convolution neural network combined with conditional random field optimization[J]. Acta Optica Sinica, 2018, 38(8):404-413. (in Chinese)
[20]	Chen B, Huang B, Xu B. Multi-source remotely sensed data fusion for improving land cover classification[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2017, 124:27-39.
[21]	Chen Bin, Huang Bo, Xu Bing. Multi-source remotely sensed data fusion for improving land cover classification[J]. Isprs Journal of Photogrammetry Remote Sensing, 2017, 124:27-39.
[22]	Lu P, Wang L, Niu Z, et al. Prediction of soil properties using laboratory VIS-NIR spectroscopy and Hyperion imagery[J]. Journal of Geochemical Exploration, 2013, 132:26-33.
[23]	Steinberg A, Chabrillat S, Stevens A, et al. Prediction of common surface soil properties based on Vis-NIR airborne and simulated EnMAP imaging spectroscopy data:prediction accuracy and influence of spatial resolution[J]. Remote Sensing, 2016, 8(8):613.
[24]	Castaldi F, Palombo A, Santini F, et al. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon[J]. Remote Sensing of Environment, 2016, 179:54-65.
[25]	Malec S, Rogge D, Heiden U, et al. Capability of spaceborne hyperspectral EnMAP mission for mapping fractional cover for soil erosion modeling[J].Remote Sensing, 2015, 7(9):11776-11800.
[26]	Liang Liang, Di Liping, Zhang Lianpeng, et al. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method[J]. Remote Sensing of Environment, 2015, 165:123-134.
[27]	Heiskanen J, Rautiainen M, Stenberg P, et al. Sensitivity of narrowband vegetation indices to boreal forest LAI, reflectance seasonality and species composition[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2013, 78:1-14.
[28]	Ali I, Greifeneder F, Stamenkovic J, et al. Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data[J]. Remote Sensing, 2015, 7(12):16398-16421.
[29]	Kattenborn T, Maack J, Fassnacht F, et al. Mapping forest biomass from space-Fusion of hyperspectral EO1-hyperion data and Tandem-X and WorldView-2 canopy height models[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 35:359-367.
[30]	Marshall M, Thenkabail P. Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM plus, and MODIS vegetation indices in crop biomass estimation[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2015, 108:205-218.
[31]	Xu Jin, Meng Jihua. Overview on estimating crop chlorophyll content with remote sensing[J]. Remote Sensing Technology and Application, 2016, 31(1):74-85. (in Chinese)
[32]	Liang L, Qin Z H, Zhao S H, et al. Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method[J]. International Journal of Remote Sensing, 2016, 37(13):2923-2949.
[33]	Yang X G, Yu Y, Fan W Y. Chlorophyll content retrieval from hyperspectral remote sensing imagery[J]. Environmental Monitoring and Assessment, 2015, 187(7):13.
[34]	Delloye C, Weiss M, Defourny P. Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems[J]. Remote Sensing of Environment, 2018, 216:245-261.
[35]	Stagakis S, Markos N, Sykioti O, et al. Tracking seasonal changes of leaf and canopy light use efficiency in a Phlomis fruticosa Mediterranean ecosystem using field measurements and multi-angular satellite hyperspectral imagery[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2014, 97:138-151.
[36]	Castaldi F, Castrignano A, Casa R. A data fusion and spatial data analysis approach for the estimation of wheat grain nitrogen uptake from satellite data[J]. International Journal of Remote Sensing, 2016, 37(18):4317-4336.
[37]	Jia Kun, Li Qiangzi. Review of features selection in crop classification using remote sensing data[J]. Resources Science, 2013, 35(12):2507-2516. (in Chinese)
[38]	Thenkabail P S, Mariotto I, Gumma M K, et al. Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):427-439.
[39]	Pan Z K, Huang J F, Wang F M. Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area[J]. International Journal of Applied Earth Observation and Geoinformation, 2013, 25:21-29.
[40]	Awad M M. Forest mapping:a comparison between hyperspectral and multispectral images and technologies[J].Journal of Forestry Research, 2018, 29(5):1395-1405.
[41]	Zhang Kang, Hei Baoqin, Zhou Zhuang, et al. CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification[J]. Journal of Remote Sensing, 2018, 22(1):87-96. (in Chinese)
[42]	Mariotto I, Thenkabail P S, Huete A, et al. Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission[J]. Remote Sensing of Environment, 2013, 139:291-305.
[43]	Zhang Q, Middleton E M, Cheng Y B, et al. Integrating chlorophyll fAPAR and nadir photochemical reflectance index from EO-1/Hyperion to predict cornfield daily gross primary production[J]. Remote Sensing of Environment, 2016, 186:311-321.
[44]	Deng Shiquan, Tian Liqiao, Li Jian, et al. A novel chlorophyll-a inversion model in turbid water for GF-5 satellite hyperspectral sensordir photochemical refle[J]. Journal of Huazhong Normal University(Natural Sciences), 2018, 52(3):409-415. (in Chinese)
[45]	Pan Banglong, Yi Weining, Wang Xianhua, et al. Geostatistics algorithm design on hyperspectral inversion of total phosphorus of lake[J]. Infrared and Laser Engineering, 2012, 41(5):1255-1260. (in Chinese)
[46]	Giardino C, Brando V E, Dekker A G, et al. Assessment of water quality in Lake Garda (Italy) using Hyperion[J]. Remote Sensing of Environment, 2007, 109(2):183-195.
[47]	Fang Xu, Duan Hongtao, Cao Zhigang, et al. Remote monitoring of cyanobacterial blooms using multi-source satellite data:A case of Yuqiao Reservoir, Tianjin[J]. Journal of Lake Sciences, 2018. 30(4):967-978. (in Chinese)
[48]	Casey B. Water and bottom properties of a coastal environment derived from Hyperion data measured from the EO-1 spacecraft platform[J]. Journal of Applied Remote Sensing, 2007, 1(1):011502.
[49]	Hu C, Feng L, Hardy R F, et al. Spectral and spatial requirements of remote measurements of pelagic Sargassum macroalgae[J]. Remote Sensing of Environment, 2015, 167:229-246.
[50]	Bell T W, Cavanaugh K C, Siegel D A. Remote monitoring of giant kelp biomass and physiological condition:An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) mission[J]. Remote Sensing of Environment, 2015, 167:218-228.
[51]	Han Y, Li J, Zhang Y, et al. Sea ice detection based on an improved similarity measurement method using hyperspectral data[J]. Sensors, 2017, 17(5):1124.
[52]	Han Y, Li P, Zhang Y, et al. Combining active learning and transductive support vector machines for sea ice detection[J]. Journal of Applied Remote Sensing, 2018, 12(2):026016.
[53]	Wang Dong, Gao Feng, Dong Junyu, et al. Sea ice classification from hyperspectral images based on self-paced boost learning[C]//Igarss 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 2018:7324-7327.
[54]	Petropoulos G P, Kalivas D P, Georgopoulou I A, et al. Urban vegetation cover extraction from hyperspectral imagery and geographic information system spatial analysis techniques:case of Athens, Greece[J]. Journal of Applied Remote Sensing, 2015, 9(1):096088.
[55]	Okujeni A, Linden S V D, Hostert P. Extending the vegetation-impervious-soil model using simulated EnMAP data and machine learning[J]. Remote Sensing of Environment, 2015, 158(10):69-80.
[56]	Li X K, Wu T X, Liu K, et al. Evaluation of the chinese fine spatial resolution hyperspectral satellite TianGong-1 in urban land-cover classification[J]. Remote Sensing, 2016, 8(5):17.
[57]	Zhang X R, Sun Y J, Zhang J Y, et al. Hyperspectral unmixing via deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(11):1755-1759.
[58]	Tang F H, Xu Q. Impervious surface information extraction based on hyperspectral remote sensing imagery[J]. Remote Sensing, 2017, 9(6):550.
[59]	Weng Q, Hu X, Lu D. Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery:a comparison[J]. International Journal of Remote Sensing, 2008, 29(11):3209-3232.
[60]	Jia G J, Burke I C, GoetzA F H, et al. Assessing spatial patterns of forest fuel using AVIRIS data[J]. Remote Sensing of Environment, 2006. 102(3-4):318-327.
[61]	Yebra M, Quan X, Riano D, et al. A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing[J].Remote Sensing of Environment, 2018, 212:260-272.
[62]	Mallinis G, Galidaki G, Gitas I. A comparative analysis of EO-1 hyperion, quickbird and landsat TM imagery for fuel type mapping of a typical mediterranean landscape[J]. Remote Sensing, 2014, 6(2):1684-1704.
[63]	Roberts D A, Dennison P E, Gardner M E, et al. Evaluation of the potential of hyperion for fire danger assessment by comparison to the airborne visible/infrared imaging spectrometer[J]. Geoscience Remote Sensing IEEE Transactions on, 2015, 41(6):1297-1310.
[64]	Numata I, Cochrane M A, Galvao L S. Analyzing the impacts of frequency and severity of forest fire on the recovery of disturbed forest using landsat time series and EO-1 hyperion in the southern brazilian amazon[J]. Earth Interactions, 2011, 15(13):1-17.
[65]	Zhang C, Ye F W, He H X, et al. Study on the forest vegetation restoration monitoring using HJ-1A hyperspectral data[C]//35th International Symposium on Remote Sensing of Environment, 2014.
[66]	Abrams M, Pieri D, Realmuto V, et al. Using EO-1 Hyperion data as HyspIRI preparatory data sets for volcanology applied to Mt Etna, Italy[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):375-385.
[67]	Amici S, Piscini A, Buongiorno M F, et al. Geological classification of Volcano Teide by hyperspectral and multispectral satellite data[J]. International Journal of Remote Sensing, 2013, 34(9-10):3356-3375.
[68]	Davies G A, Chien S, Baker V, et al. Monitoring active volcanism with the Autonomous Sciencecraft Experiment on EO-1[J]. Remote Sensing of Environment, 2006, 101(4):427-446.
[69]	Stefanov W L, Evans C A. Data collection for disaster response from the international space station[C]//36th International Symposium on Remote Sensing of Environment, 2015:851-855.
[70]	Crowley J K, Hubbard B E, Mars J C. Analysis of potential debris flow source areas on Mount Shasta, California, by using airborne and satellite remote sensing data[J]. Remote Sensing of Environment, 2003, 87(2-3):345-358.
[71]	Savage S H, Levy T E, Jones I W. Prospects and problems in the use of hyperspectral imagery for archaeological remote sensing:a case study from the Faynan copper mining district, Jordan[J]. Journal of Archaeological Science, 2012, 39(2):407-420.
[72]	Davies W H, North P R J. Synergistic angular and spectral estimation of aerosol properties using CHRIS/PROBA-1 and simulated Sentinel-3 data[J]. Atmospheric Measurement Techniques, 2015, 8(4):1719-1731.
[73]	Koppe W, Gnyp M L, Hennig S D, et al. Multi-temporal hyperspectral and radar remote sensing for estimating winter wheat biomass in the North China Plain[J]. Photogrammetrie Fernerkundung Geoinformation, 2012, 3(3):281-298.
[74]	Loizeau D, Mangold N, Poulet F, et al. History of the clay-rich unit at Mawrth Vallis, Mars:High-resolution mapping of a candidate landing site[J]. Journal of Geophysical Research-Planets, 2015, 120(11):1820-1846.

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沈阳化工大学材料科学与工程学院沈阳 110142

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Advances in application of space hyperspectral remote sensing(invited)

1. Key Laboratory of Space Utilization,Chinese Academy of Sciences,Beijing 100094,China;

2. Technology and Engineering Center for Space Utilization,Chinese Academy of Sciences,Beijing 100094,China;

3. University of Chinese Academy of Sciences,Beijing 100049,China

Received Date: 2018-11-05

Rev Recd Date: 2018-12-03

Publish Date: 2019-03-25

Key words:

Abstract: With the rapid development of hyperspectral imaging technology, space hyperspectral remote sensing data have been successfully applied to various fields in recent years. The development of space hyperspectral imaging technology at home and abroad was reviewed, the technical standards of representative space hyperspectral imagers were introduced. The latest applications of hyperspectral data in land resources, agriculture and forestry, ocean and lake remote sensing, urban environment, disaster monitoring and other fields in the past five years were systematically summarized and analysed. The outlook of future hyperspectral remote sensing was provided including hyperspectral information extraction and application based on AI technology, the multi-source data fusion and applications, and the analysis and application of hyperspectral data for deep space exploration. Further developments of space hyperspectral imager technology driven by applications will promote the innovated use of hyperspectral data in a wider range of fields.

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Reference (74)

[1]	Barnsley M J, Settle J J, Cutter M A, et al. The PROBA/CHRIS mission:A low-cost smallsat for hyperspectral multiangle observations of the earth surface and atmosphere[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(7):1512-1520.
[2]	Folkman M, Pearlman J, Liao L, et al. EO-1/Hyperion hyperspectral imager design, development, characterization, and calibration[C]//SPIE, 2001, 4151:40-51.
[3]	Yuan Yan, Li Liying, Xiong Wang'e, et al. Mechanical structure design for hyperspectral imager of HJ-1A satellite[J]. Spacecraft Engineering, 2009, 18(6):97-105.
[4]	Lucke R L, Corson M, McGlothlin N R, et al. Hyperspectral imager for the coastal ocean:instrument description and first images[J]. Applied Optics, 2011, 50(11):1501-1516.
[5]	Gao Ming, Zhang Shancong, Li Shengyang. Tiangong-1 hyperspectral remote sensing and application[J]. Journal of Remote Sensing, 2014, 18(s1):2-10. (in Chinese)
[6]	Liu Yinnian. Visible-shortwave infrared hyperspectral imager of GF-5 satellite[J]. Spacecraft Recovery Remote Sengsing, 2018, 39(3):25-28. (in Chinese)
[7]	Murchie S, Arvidson R, Bedini P, et al. Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on Mars Reconnaissance Orbiter (MRO)[J]. Journal of Geophysical Research Atmospheres, 2007, 112(E5):431-433.
[8]	Li Xiaopeng, Chen Jianpeng, Wang Xiang. Inversion of lunar nearside FeO and Al2O3 based on Chang'e-1 reflectance data[J]. China Mining Magazine, 2018, 27(7):150-156. (in Chinese)
[9]	Staenz K, Held A. Summary of current and future terrestrial civilian hyperspectral spaceborne systems[C]//2012 IEEE International Geoscience and Remote Sensing Symposium, 2012:123-126.
[10]	Stefano P, Angelo P, Simone P, et al. The PRISMA hyperspectral mission:science activities and opportunities for agriculture and land monitoring[C]//2013 IEEE International Geoscience and Remote Sensing Symposium, 2013:4558-4561.
[11]	Guanter L, Kaufmann H, Segl K, et al. The EnMAP spaceborne imaging spectroscopy mission for earth observation[J]. Remote Sensing, 2015, 7(7):8830-8857.
[12]	Lee C M, Cable M L, Hook S J, et al. An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities[J]. Remote Sensing of Environment, 2015, 167:6-19.
[13]	Lefevre-Fonollosa M-J, Fratter I, Mandea M. HYPXIM:an innovative hyperspectral satellite for science, security and defence[J]. Egu General Assembly, 2013, 15:10129.
[14]	Chen S, Huang S, Liu Y, et al. Soil and vegetation spectral coupling difference (SVSCD) for minerals extraction from hyperion data in vegetation covered area[J]. Chinese Geographical Science, 2018, 28(6):957-972.
[15]	Lei L, Feng J, Rivard B, et al. Mapping alteration using imagery from the Tiangong-1 hyperspectral spaceborne system:Example for the Jintanzi gold province, China[J].International Journal of Applied Earth Observation Geoinformation, 2017, 64:31-41.
[16]	Lin Jian, Yan Baikun, Dong Xinfeng, et al. Evaluating of Tiangong-1 imaging spectrometer data oriented to geological applications[J]. Journal of Remote Sensing, 2014, 18(s1):74-83. (in Chinese)
[17]	Elatawneh A, Kalaitzidis C, Petropoulos G P, et al. Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data[J]. International Journal of Digital Earth, 2014, 7(3):194-216.
[18]	Xing C, Ma L, Yang X Q. Stacked denoise autoencoder based feature extraction and classification for hyperspectral images[J]. Journal of Sensors, 2016, 2016:3632943.
[19]	Li Zhuqiang, Zhu Ruifei, Gao Fang, et al. Hyperspectral remote sensing image classification based on three-dimensional convolution neural network combined with conditional random field optimization[J]. Acta Optica Sinica, 2018, 38(8):404-413. (in Chinese)
[20]	Chen B, Huang B, Xu B. Multi-source remotely sensed data fusion for improving land cover classification[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2017, 124:27-39.
[21]	Chen Bin, Huang Bo, Xu Bing. Multi-source remotely sensed data fusion for improving land cover classification[J]. Isprs Journal of Photogrammetry Remote Sensing, 2017, 124:27-39.
[22]	Lu P, Wang L, Niu Z, et al. Prediction of soil properties using laboratory VIS-NIR spectroscopy and Hyperion imagery[J]. Journal of Geochemical Exploration, 2013, 132:26-33.
[23]	Steinberg A, Chabrillat S, Stevens A, et al. Prediction of common surface soil properties based on Vis-NIR airborne and simulated EnMAP imaging spectroscopy data:prediction accuracy and influence of spatial resolution[J]. Remote Sensing, 2016, 8(8):613.
[24]	Castaldi F, Palombo A, Santini F, et al. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon[J]. Remote Sensing of Environment, 2016, 179:54-65.
[25]	Malec S, Rogge D, Heiden U, et al. Capability of spaceborne hyperspectral EnMAP mission for mapping fractional cover for soil erosion modeling[J].Remote Sensing, 2015, 7(9):11776-11800.
[26]	Liang Liang, Di Liping, Zhang Lianpeng, et al. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method[J]. Remote Sensing of Environment, 2015, 165:123-134.
[27]	Heiskanen J, Rautiainen M, Stenberg P, et al. Sensitivity of narrowband vegetation indices to boreal forest LAI, reflectance seasonality and species composition[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2013, 78:1-14.
[28]	Ali I, Greifeneder F, Stamenkovic J, et al. Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data[J]. Remote Sensing, 2015, 7(12):16398-16421.
[29]	Kattenborn T, Maack J, Fassnacht F, et al. Mapping forest biomass from space-Fusion of hyperspectral EO1-hyperion data and Tandem-X and WorldView-2 canopy height models[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 35:359-367.
[30]	Marshall M, Thenkabail P. Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM plus, and MODIS vegetation indices in crop biomass estimation[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2015, 108:205-218.
[31]	Xu Jin, Meng Jihua. Overview on estimating crop chlorophyll content with remote sensing[J]. Remote Sensing Technology and Application, 2016, 31(1):74-85. (in Chinese)
[32]	Liang L, Qin Z H, Zhao S H, et al. Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method[J]. International Journal of Remote Sensing, 2016, 37(13):2923-2949.
[33]	Yang X G, Yu Y, Fan W Y. Chlorophyll content retrieval from hyperspectral remote sensing imagery[J]. Environmental Monitoring and Assessment, 2015, 187(7):13.
[34]	Delloye C, Weiss M, Defourny P. Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems[J]. Remote Sensing of Environment, 2018, 216:245-261.
[35]	Stagakis S, Markos N, Sykioti O, et al. Tracking seasonal changes of leaf and canopy light use efficiency in a Phlomis fruticosa Mediterranean ecosystem using field measurements and multi-angular satellite hyperspectral imagery[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2014, 97:138-151.
[36]	Castaldi F, Castrignano A, Casa R. A data fusion and spatial data analysis approach for the estimation of wheat grain nitrogen uptake from satellite data[J]. International Journal of Remote Sensing, 2016, 37(18):4317-4336.
[37]	Jia Kun, Li Qiangzi. Review of features selection in crop classification using remote sensing data[J]. Resources Science, 2013, 35(12):2507-2516. (in Chinese)
[38]	Thenkabail P S, Mariotto I, Gumma M K, et al. Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):427-439.
[39]	Pan Z K, Huang J F, Wang F M. Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area[J]. International Journal of Applied Earth Observation and Geoinformation, 2013, 25:21-29.
[40]	Awad M M. Forest mapping:a comparison between hyperspectral and multispectral images and technologies[J].Journal of Forestry Research, 2018, 29(5):1395-1405.
[41]	Zhang Kang, Hei Baoqin, Zhou Zhuang, et al. CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification[J]. Journal of Remote Sensing, 2018, 22(1):87-96. (in Chinese)
[42]	Mariotto I, Thenkabail P S, Huete A, et al. Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission[J]. Remote Sensing of Environment, 2013, 139:291-305.
[43]	Zhang Q, Middleton E M, Cheng Y B, et al. Integrating chlorophyll fAPAR and nadir photochemical reflectance index from EO-1/Hyperion to predict cornfield daily gross primary production[J]. Remote Sensing of Environment, 2016, 186:311-321.
[44]	Deng Shiquan, Tian Liqiao, Li Jian, et al. A novel chlorophyll-a inversion model in turbid water for GF-5 satellite hyperspectral sensordir photochemical refle[J]. Journal of Huazhong Normal University(Natural Sciences), 2018, 52(3):409-415. (in Chinese)
[45]	Pan Banglong, Yi Weining, Wang Xianhua, et al. Geostatistics algorithm design on hyperspectral inversion of total phosphorus of lake[J]. Infrared and Laser Engineering, 2012, 41(5):1255-1260. (in Chinese)
[46]	Giardino C, Brando V E, Dekker A G, et al. Assessment of water quality in Lake Garda (Italy) using Hyperion[J]. Remote Sensing of Environment, 2007, 109(2):183-195.
[47]	Fang Xu, Duan Hongtao, Cao Zhigang, et al. Remote monitoring of cyanobacterial blooms using multi-source satellite data:A case of Yuqiao Reservoir, Tianjin[J]. Journal of Lake Sciences, 2018. 30(4):967-978. (in Chinese)
[48]	Casey B. Water and bottom properties of a coastal environment derived from Hyperion data measured from the EO-1 spacecraft platform[J]. Journal of Applied Remote Sensing, 2007, 1(1):011502.
[49]	Hu C, Feng L, Hardy R F, et al. Spectral and spatial requirements of remote measurements of pelagic Sargassum macroalgae[J]. Remote Sensing of Environment, 2015, 167:229-246.
[50]	Bell T W, Cavanaugh K C, Siegel D A. Remote monitoring of giant kelp biomass and physiological condition:An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) mission[J]. Remote Sensing of Environment, 2015, 167:218-228.
[51]	Han Y, Li J, Zhang Y, et al. Sea ice detection based on an improved similarity measurement method using hyperspectral data[J]. Sensors, 2017, 17(5):1124.
[52]	Han Y, Li P, Zhang Y, et al. Combining active learning and transductive support vector machines for sea ice detection[J]. Journal of Applied Remote Sensing, 2018, 12(2):026016.
[53]	Wang Dong, Gao Feng, Dong Junyu, et al. Sea ice classification from hyperspectral images based on self-paced boost learning[C]//Igarss 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 2018:7324-7327.
[54]	Petropoulos G P, Kalivas D P, Georgopoulou I A, et al. Urban vegetation cover extraction from hyperspectral imagery and geographic information system spatial analysis techniques:case of Athens, Greece[J]. Journal of Applied Remote Sensing, 2015, 9(1):096088.
[55]	Okujeni A, Linden S V D, Hostert P. Extending the vegetation-impervious-soil model using simulated EnMAP data and machine learning[J]. Remote Sensing of Environment, 2015, 158(10):69-80.
[56]	Li X K, Wu T X, Liu K, et al. Evaluation of the chinese fine spatial resolution hyperspectral satellite TianGong-1 in urban land-cover classification[J]. Remote Sensing, 2016, 8(5):17.
[57]	Zhang X R, Sun Y J, Zhang J Y, et al. Hyperspectral unmixing via deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(11):1755-1759.
[58]	Tang F H, Xu Q. Impervious surface information extraction based on hyperspectral remote sensing imagery[J]. Remote Sensing, 2017, 9(6):550.
[59]	Weng Q, Hu X, Lu D. Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery:a comparison[J]. International Journal of Remote Sensing, 2008, 29(11):3209-3232.
[60]	Jia G J, Burke I C, GoetzA F H, et al. Assessing spatial patterns of forest fuel using AVIRIS data[J]. Remote Sensing of Environment, 2006. 102(3-4):318-327.
[61]	Yebra M, Quan X, Riano D, et al. A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing[J].Remote Sensing of Environment, 2018, 212:260-272.
[62]	Mallinis G, Galidaki G, Gitas I. A comparative analysis of EO-1 hyperion, quickbird and landsat TM imagery for fuel type mapping of a typical mediterranean landscape[J]. Remote Sensing, 2014, 6(2):1684-1704.
[63]	Roberts D A, Dennison P E, Gardner M E, et al. Evaluation of the potential of hyperion for fire danger assessment by comparison to the airborne visible/infrared imaging spectrometer[J]. Geoscience Remote Sensing IEEE Transactions on, 2015, 41(6):1297-1310.
[64]	Numata I, Cochrane M A, Galvao L S. Analyzing the impacts of frequency and severity of forest fire on the recovery of disturbed forest using landsat time series and EO-1 hyperion in the southern brazilian amazon[J]. Earth Interactions, 2011, 15(13):1-17.
[65]	Zhang C, Ye F W, He H X, et al. Study on the forest vegetation restoration monitoring using HJ-1A hyperspectral data[C]//35th International Symposium on Remote Sensing of Environment, 2014.
[66]	Abrams M, Pieri D, Realmuto V, et al. Using EO-1 Hyperion data as HyspIRI preparatory data sets for volcanology applied to Mt Etna, Italy[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):375-385.
[67]	Amici S, Piscini A, Buongiorno M F, et al. Geological classification of Volcano Teide by hyperspectral and multispectral satellite data[J]. International Journal of Remote Sensing, 2013, 34(9-10):3356-3375.
[68]	Davies G A, Chien S, Baker V, et al. Monitoring active volcanism with the Autonomous Sciencecraft Experiment on EO-1[J]. Remote Sensing of Environment, 2006, 101(4):427-446.
[69]	Stefanov W L, Evans C A. Data collection for disaster response from the international space station[C]//36th International Symposium on Remote Sensing of Environment, 2015:851-855.
[70]	Crowley J K, Hubbard B E, Mars J C. Analysis of potential debris flow source areas on Mount Shasta, California, by using airborne and satellite remote sensing data[J]. Remote Sensing of Environment, 2003, 87(2-3):345-358.
[71]	Savage S H, Levy T E, Jones I W. Prospects and problems in the use of hyperspectral imagery for archaeological remote sensing:a case study from the Faynan copper mining district, Jordan[J]. Journal of Archaeological Science, 2012, 39(2):407-420.
[72]	Davies W H, North P R J. Synergistic angular and spectral estimation of aerosol properties using CHRIS/PROBA-1 and simulated Sentinel-3 data[J]. Atmospheric Measurement Techniques, 2015, 8(4):1719-1731.
[73]	Koppe W, Gnyp M L, Hennig S D, et al. Multi-temporal hyperspectral and radar remote sensing for estimating winter wheat biomass in the North China Plain[J]. Photogrammetrie Fernerkundung Geoinformation, 2012, 3(3):281-298.
[74]	Loizeau D, Mangold N, Poulet F, et al. History of the clay-rich unit at Mawrth Vallis, Mars:High-resolution mapping of a candidate landing site[J]. Journal of Geophysical Research-Planets, 2015, 120(11):1820-1846.

Advances in application of space hyperspectral remote sensing(invited)

doi: 10.3788/IRLA201948.0303001

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