Hyperspectral remote sensing progress for forage nutritional quality and quantity in natural grassland

doi:10.11686/cyxb2019207

Abstract

Abstract: Evaluation of the nutritional quality and quantity of natural grassland forage is central in the optimization of grassland animal husbandry systems, and provides important information to enhance the quality of animal products and growth and development of livestock. Therefore, rapid and accurate estimates of forage nutrient content will greatly assist the rational utilization and effective management of natural alpine grassland. The rapid development of hyperspectral remote sensing technology shows promise of providing a tool for the dynamic monitoring and evaluation of grassland forage nutrition quality and quantity. This paper reviews the available hyperspectral remote sensing systems, such as Hyperion, the compact high resolution imaging spectrometer (CHRIS), RapidEye, WorldView-2, Sentinel-2, HJ-1A hyper-spectrum imager (HSI), Tiangong-1, Gaofen-5 and Gaofen-6. We suggest that research progress in grassland nutrition will be greatly improved by making full use of these available data gathering resources. The commonly used remote sensing inversion methods for forage nutrient composition of natural grassland include traditional multivariate statistical analysis, regression analysis based on spectral features and spectral indexes, and physical modeling. Previous studies of the estimation and evaluation of key nutrients in forage focused on analysis of hyperspectral characteristics, spatial-temporal inversion models, spectral band selection and local area nutrient mapping. It is emphasized that the following issues remain: difficulties accessing data, lack of relevant research and performance deficiencies of existing software and hardware. Given the multiple observation platforms and related technology innovations, an important research direction is to improve the performance of hyperspectral instruments, and to improve the inversion accuracy of algorithms for determining key nutrients in future. Moreover, aerial and satellite data can be integrated to monitor the temporal and spatial nutritional changes at a landscape level in in natural grasslands and such integration thus represents a promising future development trend.

Key words: natural grassland, forage nutrition, quality, hyperspectral remote sensing, progress

GAO Jin-long, LIU Jie, YIN Jian-peng, GE Jing, HOU Meng-jing, FENG Qi-sheng, LIANG Tian-gang. Hyperspectral remote sensing progress for forage nutritional quality and quantity in natural grassland[J]. Acta Prataculturae Sinica, 2020, 29(2): 172-185.

References

[1] Xu P. Grassland resource survey and planning. Beijing: China Agriculture Press, 2000.
许鹏. 草地资源调查规划学. 北京: 中国农业出版社, 2000.
[2] Ren J Z. Research methods of grassland science. Beijing: China Agriculture Press, 1998: 319-322.
任继周. 草业科学研究方法. 北京: 中国农业出版社, 1998: 319-322.
[3] Pu R L, Gong P. Hyperspectral remote sensing and aplication. Beijing: Higher Education Press, 2000.
浦瑞良, 宮鹏. 高光谱遥感及其应用. 北京: 高等教育出版社, 2000.
[4] Tong Q X, Xue Y Q, Zhang L F. Progress in hyperspectral remote sensing science and technology in China over the past three decades. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 70-91.
[5] Guo Y J, Long R J, Zhang D G, et al. The seasonal dynamics of nutrient contents in some dominant shrubs and forage herbs on alpine meadow in eastern Qilian Mountain, China. Pratacultural Science, 2001, 18(6): 36-39.
郭彦军, 龙瑞军, 张德罡, 等. 东部祁连山高寒草甸灌木和牧草营养成分含量季节变化动态. 草业科学, 2001, 18(6): 36-39.
[6] Tessier J T, Raynal D J. Use of nitrogen to phosphorus ratios in plant tissue as an indicator of nutrient limitation and nitrogen saturation. Journal of Applied Ecology, 2003, 40(3): 523-534.
[7] Houborg R, Fisher J B, Skidmore A K. Advances in remote sensing of vegetation function and traits. International Journal of Applied Earth Observation and Geoinformation, 2015, 43: 1-6.
[8] Thenkabail P S, Enclona E A, Ashton M S, et al. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment, 2004, 91(3/4): 354-376.
[9] Jafari R, Lewis M M. Arid land characterisation with EO-1 Hyperion hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 2012, 19: 298-307.
[10] Blanco P D, Valle H F, Bouza P J, et al. Ecological site classification of semiarid rangelands: Synergistic use of Landsat and Hyperion imagery. International Journal of Applied Earth Observation and Geoinformation, 2014, 29: 11-21.
[11] Christian B, Joshi N, Saini M, et al. Seasonal variations in phenology and productivity of a tropical dry deciduous forest from MODIS and Hyperion. Agricultural and Forest Meteorology, 2015, 214/215: 91-105.
[12] Marshall M, Thenkabail P. Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 108: 205-218.
[13] Zhang C Y. Multiscale quantification of urban composition from EO-1/Hyperion data using object-based spectral unmixing. International Journal of Applied Earth Observation and Geoinformation, 2016, 47: 153-162.
[14] Cheng Q, Huang J F, Wang R C, et al. Analyses of the correlation between rice LAI and simulated MODIS vegetation indices, red edge position. Transactions of the Chinese Society of Agricultural Engineering, 2003, 19(5): 104-108.
程乾, 黄敬峰, 王人潮, 等. 水稻叶面积指数与MODIS植被指数、红边位置之间的相关分析. 农业工程学报, 2003, 19(5): 104-108.
[15] Wang X Z, Huang J F, Li Y M, et al. The study on hyperspectral remote sensing estimation models about LAI of rice. Journal of Remote Sensing, 2004, 8(1): 81-88.
王秀珍, 黄敬峰, 李云梅, 等. 水稻叶面积指数的高光谱遥感估算模型. 遥感学报, 2004, 8(1): 81-88.
[16] Yi Q X, Huang J F, Wang X Z. Hyperspectral estimation models for crude fiber concentration of corn. Journal of Infrared and Millimeter Waves, 2007, 26(5): 393-395.
易秋香, 黄敬峰, 王秀珍. 玉米粗纤维含量高光谱估算模型研究. 红外与毫米波学报, 2007, 26(5): 393-395.
[17] Mirik M, Norland J E, Crabtree R L, et al. Hyperspectral one-meter-resolution remote sensing in Yellowstone National Park, Wyoming: I. Forage nutritional values. Rangeland Ecology & Management, 2005, 58(5): 452-458.
[18] Beeri O, Phillips R, Hendrickson J, et al. Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment, 2007, 110(2): 216-225.
[19] Skidmore A K, Ferwerda J G, Mutanga O. Forage quality of savannas-simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery. Remote Sensing of Environment, 2010, 114(1): 64-72.
[20] Ferner J, Linstadter A, Südekum K H, et al. Spectral indicators of forage quality in West Africa’s tropical savannas. International Journal of Applied Earth Observation and Geoinformation, 2015, 41: 99-106.
[21] Wu F L, Wang Z S, Yang Q, et al. Analysis of growth characteristics, nutritional components and feeding value of native forage grass from the high-cold steppes in the Luqu and Hezuo region of Gannan in summer and winter. Acta Prataculturae Sinica, 2014, 23(4): 31-38.
吴发莉, 王之盛, 杨勤, 等. 甘南碌曲和合作地区冬夏季高寒天然牧草生产特性、营养成分和饲用价值分析. 草业学报, 2014, 23(4): 31-38.
[22] Tong Q X, Zhang B, Zheng L F. Hyperspectral remote sensing theory, technology and application. Beijing: Higher Education Press, 2006.
童庆禧, 张兵, 郑兰芬. 高光谱遥感: 原理, 技术与应用. 北京: 高等教育出版社, 2006.
[23] Grossman Y L, Ustin S L, Jacquemoud S, et al. Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data. Remote Sensing of Environment, 1996, 56(3): 182-193.
[24] Dawson T P, Curran P J, North P R J, et al. The propagation of foliar biochemical absorption features in forest canopy reflectance: A theoretical analysis. Remote Sensing of Environment, 1999, 67(2): 147-159.
[25] Feng Y, Miller J R. Vegetation green reflectance at high spectral resolution as a measure of leaf chlorophyll content. Calgary Alberta: Proceedings of the 14th Canadian Symposium on Remote Sensing, 1991: 351-355.
[26] Mutanga O. Hyperspectral remote sensing of tropical grass quality and quantity. Wageningen, the Netherlands: International Institute for Geoinformation Science and Earth Observation and Wageningen University, 2004.
[27] Mutanga O, Skidmore A K. Red edge shift and biochemical content in grass canopies. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 62(1): 34-42.
[28] Tian Q, Tong Q, Pu R, et al. Spectroscopic determination of wheat water status using 1650~1850 nm spectral absorption features. International Journal of Remote Sensing, 2001, 22(12): 2329-2338.
[29] Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environmental, 2002, 81(2/3): 337-354.
[30] Broge N H, Mortensen J V. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sensing of Environment, 2002, 81(1): 45-47.
[31] Haboudane D, Miller J R, Tremblay N, et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 2002, 81(2/3): 416-426.
[32] Gitelson A, Merzlyak M N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 1994, 143(3): 286-292.
[33] Datt B. A new reflectance index for remote sensing of chlorophyll content in higher plants: Tests using eucalyptus leaves. Journal of Plant Physiology, 1999, 154(1): 30-36.
[34] Clevers J G P W. Imaging Spectrometry in agriculture-plant vitality and yield indicators//Imaging spectrometry—a tool for environmental observations. Springer Netherlands, 1994: 193-219.
[35] Vogelmann J E, Rock B N, Moss D M. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 1993, 14(8): 1563-1575.
[36] Fourty T, Baret F, Jacquemoud S. Leaf optical properties with explicit description of its biochemical composition: Direct and inverse problems. Remote Sensing of Environment, 1996, 56(2): 104-117.
[37] Serrano L, Peñuelas J, Ustin S L. Remote sensing of nitrogen and lignin in mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals. Remote Sensing of Environment, 2002, 81(2/3): 355-364.
[38] Gamon J A, Penuelas J, Field C B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 1992, 41(1): 35-44.
[39] Gamon J A, Serrano L, Surfus J S. The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia, 1997, 112(4): 492-501.
[40] Penuelas J, Baret F, Filella I. Semiempirical indexes to assess carotenoids chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica, 1995, 31(2): 221-230.
[41] Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 1996, 55(2): 95-107.
[42] Richardson A J, Wiegand C L. Distinguishing vegetation from soil background information (by gray mapping of Landsat MSS data). Photogrammetric Engineering and Remote Sensing, 1997, 43(12): 1541-1552.
[43] Gitelson A A, Merzlyak M N. Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology, 1996, 148(supplement 3/4): 494-500.
[44] Marshak A, Knyazikhin Y, Davis A B, et al. Cloud-vegetation interaction: Use of normalized difference cloud index for estimation of cloud optical thickness. Geophysical Research Letters, 2000, 27(12): 1695-1698.
[45] Huete A R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environmental, 1988, 25(3): 295-309.
[46] Huete A, Didan K, Miura T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 2002, 83(1/2): 195-213.
[47] Hansen P M, Schjoerring J K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 2003, 86(4): 542-553.
[48] Schleicher T D, Bausch W C, Delgado J A, et al. Evaluation and refinement of the nitrogen reflectance index (NRI) for site-specific fertilizer management. St Joseph MI USA: ASAE Annual International Meeting Report, 2001.
[49] Inoue Y, Sakaiya E, Zhu Y, et al. Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sensing of Environment, 2012, 126: 210-221.
[50] Nagler P L, Inoue Y, Glenn E P, et al. Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes. Remote Sensing of Environment, 2003, 87(2/3): 310-325.
[51] Galvao L S, Formaggio A R, Tisot D A. Discrimination of surface varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sensing of Environment, 2005, 94(4): 523-534.
[52] Baret F, Fourty T. Estimation of leaf water content and specific leaf weight from reflectance and transmittance measurements. Agronomie, 1997, 17(9/10): 455-464.
[53] Colombo R, Merom M, Marchesi A, et al. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sensing of Environment, 2008, 112(4): 1820-1834.
[54] Darvishzadeh R, Skidmore A, Schlerf M, et al. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sensing of Environment, 2008, 112(5): 2592-2604.
[55] Feret J B, Francois C, Asner G P, et al. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, 2008, 112(6): 3030-3043.
[56] Omari K, White H P, Staenz K, et al. Retrieval of forest canopy parameters by inversion of the PROFLAIR leaf-canopy reflectance model using the LUT approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2): 715-723.
[57] Riano D, Vaughan P, Chuvieco E, et al. Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: Analysis at leaf and canopy level. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 819-826.
[58] Malenovsky Z, Albrechtova J, Lhotakova Z, et al. Applicability of the PROSPECT model for norway spruce needles. International Journal of Remote Sensing, 2006, 27(24): 5315-5340.
[59] Moorthy I, Miller J R, Noland T L. Estimating chlorophyll concentration in conifer needles with hyperspectral data: An assessment at the needle and canopy level. Remote Sensing of Environment, 2008, 112(6): 2824-2838.
[60] Zarco-Tejada P J, Miller J R, Harron J, et al. Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies. Remote Sensing of Environment, 2004, 89(2): 189-199.
[61] Fourty T, Baret F, Jacquemoud S, et al. Leaf optical properties with explicit description of its biochemical composition: Direct and inverse problems. Remote Sensing of Environment, 1996, 56(2): 104-117.
[62] Jacquemoud S, Ustin S L, Verdebout J, et al. Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sensing of Environment, 1996, 56(3): 194-202.
[63] Mutanga O, Skidmore A K, Kumar L, et al. Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain. International Journal of Remote Sensing, 2005, 26(6): 1093-1108.
[64] Mutanga O, Skidmore A K. Continuum-removed absorption features estimate tropical savanna grass quality in situ. Earsel Workshop on Imaging Spectroscopy, 2003, (3): 13-16.
[65] Thulin S, Hill M J, Held A, et al. Predicting levels of crude protein, digestibility, lignin and cellulose in temperate pastures using hyperspectral image data. American Journal of Plant Sciences, 2014, 5(7): 997-1019.
[66] Mutanga O, Skidmore A K. Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa. Remote Sensing of Environment, 2004b, 90(1): 104-115.
[67] Ramoelo A, Wolff E. Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data. International Journal of Applied Earth Observation and Geoinformation, 2015, 43: 43-54.
[68] Ramoelo A, Cho M A, Mathieu R, et al. Estimating grass nutrients and biomass as an indicator of rangeland (forage) quality and quantity using remote sensing in Savanna ecosystems. El Jadida, Morroco: 9th International Conference of the African Association of Remote Sensing and the Environment (AARSE), 2012.
[69] Capolupo A, Kooistra L, Berendonk C, et al. Estimating plant traits of grasslands from UAV-acquired hyperspectral images: A comparison of statistical approaches. ISPRS International Journal of Geo-Information, 2015, 4(4): 2792-2820.
[70] Villamuelas M, Fernández N, Albanell E, et al. The enhanced vegetation index (EVI) as a proxy for diet quality and composition in a mountain ungulate. Ecological Indicators, 2016, 61: 658-666.
[71] Gao J L, Meng B P, Liang T G, et al. Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing and a multi-factor machine learning algorithm in the east of Tibetan Plateau, China. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 147: 104-117.
[72] Douman S, Tohuta sheng, Zheng F L, et al. The perspective of using high resolution sensing on the grassland resources and ecology studies. Grass Food Livestock, 2009, (4): 12-14.
塞里克. 都曼, 托乎塔生, 郑逢令, 等. 高分遥感在新疆草地资源与生态研究中的应用前景. 草食家畜, 2009, (4): 12-14.
[73] Lin Y, Li Q, Wang H B, et al. Research summary of vegetation water content inversion with hyperspectral technology. Chinese Agricultural Science Bulletin, 2015, 31(3): 167-172.
林毅, 李倩, 王宏博, 等. 高光谱反演植被水分含量研究综述. 中国农学通报, 2015, 31(3): 167-172.
[74] Thomas J R, Namken L N, Oerther G F, et al. Estimating leaf water content by reflectance measurement. Agronomy Journal, 1971, 63(6): 845-847.
[75] Curran P J. Remote sensing of foliar chemistry. Remote Sensing of Environment, 1989, 30(3): 271-278.
[76] Carter G A. Primary and secondary effects of water content of the spectral reflectance of leaves. American Journal of Botany, 1996, 78(7): 916-924.
[77] Dobrowski S Z, Pushnik J C, Zarco-Tejada P J, et al. Simple reflectance indices track heat and water stress induced changes instead-state chlorophyll fluorescence at the canopy scale. Remote Sensing of Environment, 2005, 97(3): 403-414.
[78] Feng X W, Chen X, Bao A M, et al. Analysis on the cotton physiological change and its hyperspectral response under the water stress conditions. Arid Land Geography, 2004, 27(2): 250-255.
冯先伟, 陈曦, 包安明, 等. 水分胁迫条件下棉花生理变化及其高光谱响应分析. 干旱区地理, 2004, 27(2): 250-255.
[79] Zhou S L, Xie R Z, Jiang H R, et al. Selection of the wavelengths of peak values in analyzing leaf water status by using leaf reflectance, transmittance and absorptance in corn plants. Transactions of the Chinese Society of Agricultural Engineering, 2006, 22(5): 28-31.
周顺利, 谢瑞芝, 蒋海荣, 等. 用反射率、透射率和吸收率分析玉米叶片水分含量时的峰值波长选择. 农业工程学报, 2006, 22(5): 28-31.
[80] Fu Y B, Fan Y M, Sheng J D, et al. Study on relationship between alfalfa canopy spectral reflectance and leaf water content. Spectroscopy and Spectral Analysis, 2013, 33(3): 766-769.
付彦博, 范燕敏, 盛建东, 等. 紫花苜蓿冠层反射光谱与叶片含水率关系研究. 光谱学与光谱分析, 2013, 33(3): 766-769.
[81] Liu L Y, Wang J H, Zhang Y J, et al. Detection of leaf EWT by calculating REWT from reflectance spectra. Journal of Remote Sensing, 2007, 11(3): 289-295.
刘良云, 王纪华, 张永江, 等. 叶片辐射等效水厚度计算与叶片水分定量反演研究. 遥感学报, 2007, 11(3): 289-295.
[82] Kokaly R F, Clark R N. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 1999, 67(3): 267-287.
[83] Wang X, Liu S J, Jia H F, et al. Study on the nutrition of alpine meadow based hyperspectral data. Spectroscopy and Spectral Analysis, 2012, 32(10): 2780-2784.
王迅, 刘书杰, 贾海峰, 等. 基于高光谱数据的高寒草地营养状况的研究. 光谱学与光谱分析, 2012, 32(10): 2780-2784.
[84] Ma W W, Gong C L, Hu Y, et al. Hyperspectral remote sensing estimation models for pasture quality. Spectroscopy and Spectral Analysis, 2015, 35(10): 2851-2855.
马维维, 巩彩兰, 胡勇, 等. 牧草品质的高光谱遥感监测模型研究. 光谱学与光谱分析, 2015, 35(10): 2851-2855.
[85] Zhang A W, Yan W Y, Guo C F. Inversion model of pasture crude protein content based on hyperspectral image. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(3): 188-194.
张爱武, 鄢文艳, 郭超凡. 基于高光谱图像的牧草粗蛋白含量反演模型. 农业工程学报, 2018, 34(3): 188-194.
[86] Mutanga O, Skidmore A K. Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrusciliaris) measured under controlled laboratory conditions. International Journal of Applied Earth Observation and Geoinformation, 2004, 5(2): 87-96.
[87] Ramoelo A, Skidmore A K, Cho M A, et al. Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 82: 27-40.
[88] Knox N M, Skidmore A K, Prins H H T, et al. Remote sensing of forage nutrients: Combining ecological and spectral absorption feature data. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 72: 27-35.
[89] Na Q. Hyperspectral and nutrition of Medicago sativa L. and Bromuscilitus L. correlation research. Hohhot: Inner Mongolia Agricultural University, 2010.
纳钦. 紫花苜蓿和缘毛雀麦高光谱与营养成分的相关性研究. 呼和浩特: 内蒙古农业大学, 2010.
[90] Gao J L, Hou Y C, Bai Y F, et al. Methods for estimating nitrogen, phosphorus and potassium content based on hyperspectral data from alpine meadow in Guinan and Maqin Counties, Qinghai Province. Acta Prataculturae Sinica, 2016, 25(3): 9-21.
高金龙, 侯尧宸, 白彦福, 等. 基于高光谱数据的高寒草甸氮磷钾含量估测方法研究—以青海省贵南县及玛沁县高寒草甸为例. 草业学报, 2016, 25(3): 9-21.
[91] Gao J L, Meng B P, Yang S X, et al. Estimation of nitrogen content of alpine grassland in Maqin and Guinan Counties, Qinghai Province, using remote sensing. Acta Prataculturae Sinica, 2016, 25(10): 11-20.
高金龙, 孟宝平, 杨淑霞, 等. 基于HJ-1A卫星数据的高寒草地氮素评估-以青海省贵南县及玛沁县高寒草地为例. 草业学报, 2016, 25(10): 11-20.
[92] Xiong Y, Liu B, Yue Y M. Inversion of nitrogen content of plant leaves based on ASD and FISS. Ecology and Environmental Sciences, 2013, 22(4): 582-587.
熊鹰, 刘波, 岳跃民. 基于ASD和FISS的植被叶片氮素含量反演研究. 生态环境学报, 2013, 22(4): 582-587.
[93] Fan Y M, Wu H Q, Jin G L. Hyperspectral properties analysis of grassland types in Xinjiang. Pratacultural Sinence, 2006, 23(6): 15-18.
范燕敏, 武红旗, 靳瑰丽. 新疆草地类型高光谱特征分析. 草业科学, 2006, 23(6): 15-18.
[94] Zhang K, Guo N, Wang R Y, et al. Research on spectral reflectance characteristics for desert meadow of northwest China. Advances in Earth Science, 2006, 21(10): 1063-1069.
张凯, 郭铌, 王润元, 等. 西北荒漠草甸植被光谱反射特征研究. 地球科学进展, 2006, 21(10): 1063-1069.
[95] Zhang K, Guo N, Wang R Y, et al. Comparison of spectral reflectance characteristics of two main grassland types in Gansu Province. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(supplement 2): 142-148.
张凯, 郭铌, 王润元, 等. 甘肃省两种主要草地类型的光谱反射特征比较. 农业工程学报, 2009, 25(S2): 142-148.
[96] Gai Y Y, Fan W J, Xu X R, et al. Flower species identification and coverage estimation based on hyperspectral remote sensing data in Hulunbeier grassland. Spectroscopy and Spectral Analysis, 2011, 31(10): 2778-2783.
盖颖颖, 范闻捷, 徐希孺, 等. 基于高光谱数据的呼伦贝尔草原花期物种识别和覆盖度估算. 光谱学与光谱分析, 2011, 31(10): 2778-2783.
[97] Liu B, Sheng W S, Li R, et al. Spectral characteristics of alpine grassland during degradation process in the source region of YarlungZangbo. Spectroscopy and Spectral Analysis, 2013, 33(6): 1598-1602.
刘波, 沈渭寿, 李儒, 等. 雅鲁藏布江源区高寒草地退化光谱响应变化研究. 光谱学与光谱分析, 2013, 33(6): 1598-1602.
[98] Xu L H, Xie D T, Wei C F, et al. Prediction of total nitrogen and total phosphorus concentrations in purple soil using hyperspectral data. Spectroscopy and Spectral Analysis, 2013, (3): 723-727.
徐丽华, 谢德体, 魏朝富, 等. 紫色土土壤全氮和全磷含量的高光谱遥感预测. 光谱学与光谱分析, 2013, (3): 723-727.
[99] Hu Y N, Cui X, Meng B P, et al. Spectral characteristics of typical poisonous weeds in Gannan alpine meadow. Pratacultural Science, 2015, 32(2): 160-167.
胡远宁, 崔霞, 孟宝平, 等. 甘南高寒草甸主要毒杂草光谱特征分析. 草业科学, 2015, 32(2): 160-167.
[100] Hu X B. Correlation analysis of grass spectrum and forage yield. Grass-feeding Livestock, 1996, 93(4): 43-47.
胡新博. 草地光谱与牧草产量的相关分析. 草食家畜, 1996, 93(4): 43-47.
[101] Liu Z Y, Huang J F, Wu X H, et al. Hyperspectral remote sensing models for the grassland biomass. Transactions of the Chinese Society of Agricultural Engineering, 2006, 22(2): 111-115.
刘占宇, 黄敬峰, 吴新宏, 等. 草地生物量的高光谱遥感估算模型. 农业工程学报, 2006, 22(2): 111-115.
[102] Xu H, Bao Y H, Bao G, et al. Typical grassland of Inner Mongolia hay biomass hyper-spectral remote sensing estimation research. Yinshan Academic Journal (Natural Science Edition), 2014, 28(4): 22-27.
胥慧, 包玉海, 包刚, 等. 内蒙古典型草原干草生物量高光谱遥感估算研究. 阴山学刊(自然科学版), 2014, 28(4): 22-27.