[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.