Research progress on monitoring vegetation water content by using hyperspectral remote sensing

doi:10.17521/cjpe.2017.0313

Abstract

Abstract:

Aims Vegetation water content is an important biophysical property of terrestrial vegetation, and its remote estimation can be utilized for real-time monitoring of vegetation drought stress. This paper reviewed and summarized the conception and research progress of four commonly used vegetation water indicators: canopy water content, leaf equivalent water thickness, live fuel moisture content, and relative water content. The advantage and disadvantage of various research methods were evaluated by estimating vegetation water content and identifying the limitation in monitoring vegetation water content using optical hyperspectral remote sensing techniques. Finally, the future research tasks were discussed to address issues on accurate monitoring, early warning and evaluation of vegetation drought stress.

Key words: canopy water content, hyperspectral remote sensing, leaf equivalent water thickness, live fuel moisture content, vegetation water status

ZHANG Feng,ZHOU Guang-Sheng. Research progress on monitoring vegetation water content by using hyperspectral remote sensing[J]. Chin J Plan Ecolo, 2018, 42(5): 517-525.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2017.0313

https://www.plant-ecology.com/EN/Y2018/V42/I5/517

Figures/Tables 1

References 82

[1]	Atzberger C, Guérif M, Baret F, Werner W ( 2010). Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat. Computers and Electronics in Agriculture, 73, 165-173. DOI URL
[2]	Atzberger C, Darvishzadeh R, Immitzer M, Schlerf M, Skidmore A, Le Maire G ( 2015). Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 43, 19-31. DOI URL
[3]	Barrs HD, Weatherley PE ( 1962). A re-examination of the relative turgidity technique for estimating water deficits in leaves. Australian Journal of Biological Sciences, 15, 413-428. DOI URL
[4]	Bowyer P, Danson FM ( 2004). Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level. Remote Sensing of Environment, 92, 297-308. DOI URL
[5]	Burgan RE ( 1996). Use of remotely sensed data for fire danger estimation. EARSeL Advances in Remote Sensing, 4(4), 1-8.
[6]	Cao Z, Wang Q, Zheng C ( 2015). Best hyperspectral indices for tracing leaf water status as determined from leaf dehydration experiments. Ecological Indicators, 54, 96-107. DOI URL
[7]	Carlson TN, Capehart WJ, Gillies RR ( 1995). A new look at the simplified method for remote sensing of daily evapotranspiration. Remote Sensing of Environment, 54, 161-167. DOI URL
[8]	Ceccato P, Gobron N, Flasse S, Pinty B, Tarantola S ( 2002). Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach. Remote Sensing of Environment, 82, 188-197. DOI URL
[9]	Cho MA, Skidmore A, Corsi F, Wieren SE, Sobhan I ( 2007). Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. International Journal of Applied Earth Observation and Geoinformation, 9, 414-424. DOI URL
[10]	Chuvieco E, Rianõ D, Aguado I, Cocero D ( 2002). Estimation of fuel moisture content from multi temporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment. International Journal of Remote Sensing, 23, 2145-2162. DOI URL
[11]	Clevers JGPW, Kooistra L, Schaepman ME ( 2008). Using spectral information from the NIR water absorption features for the retrieval of canopy water content. International Journal of Applied Earth Observation and Geoinformation, 10, 388-397. DOI URL
[12]	Clevers JGPW, Kooistra L, Schaepman ME ( 2010). Estimating canopy water content using hyperspectral remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 12, 119-125. DOI URL
[13]	Cohen W, Maiersperger T, Gower S, Turner D ( 2003). An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sensing of Environment, 84, 561-571. DOI URL
[14]	Colombo R, Meroni M, Marchesi A, Busetto L, Rossini M, Giardino C, Panigada C ( 2008). Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sensing of Environment, 112, 1820-1834. DOI URL
[15]	Curran P ( 1989). Remote sensing of foliar chemistry. Remote Sensing of Environment, 30, 271-278. DOI URL
[16]	Curran P, Dungan J, Peterson D ( 2001). Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies. Remote Sensing of Environment, 76, 349-359. DOI URL
[17]	Danson FM, Steven MD, Malthus TJ, Clark JA ( 1992). High spectral resolution data for determining leaf water content. International Journal of Remote Sensing, 13, 461-470. DOI URL
[18]	de Jong SM, Addink EA, Hoogenboom P, Nijland W ( 2012). The spectral response of Buxus sempervirens to different types of environmental stres—A laboratory experiment. ISPRS Journal of Photogrammetry & Remote Sensing, 74, 56-65.
[19]	de Jong SM, Addink EA, Doelman JC ( 2014). Detecting leaf- water content in Mediterranean trees using high-resolution spectrometry. International Journal of Applied Earth Observation and Geoinformation, 27, 128-136. DOI URL
[20]	Darvishzadeh R, Skidmore A, Atzberger C, Wieren SV ( 2008 a). Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture. International Journal of Applied Earth Observation and Geoinformation, 10, 358-373. DOI URL
[21]	Darvishzadeh R, Skidmore A, Schlerf M, Atzberger C, Corsi F, Cho M ( 2008 b). LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS Journal of Photogrammetry & Remote Sensing, 63, 409-426. DOI URL
[22]	Darvishzadeh R, Atzberger C, Skidmore A, Schlerf M ( 2011). Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models. ISPRS Journal of Photogrammetry & Remote Sensing, 66, 894-906. DOI URL
[23]	Deng B, Yang WN, Mu N, Zhang C ( 2016). The research of vegetation water content based on spectrum analysis and angle slope index. Spectroscopy and Spectral Analysis, 36, 2546-2552. DOI URL
	[ 邓兵, 杨武年, 慕楠, 张超 ( 2016). 基于光谱分析与角度斜率指数的植被含水量研究. 光谱学与光谱分析, 36, 2546-2552.] DOI URL
[24]	Dzikiti S, Verreynne JS, Stuckens J, Strever A, Verstraeten WW, Swennen R, Coppin P ( 2010). Determining the water status of Satsuma mandarin trees (Citrus unshiu Marcovitch) using spectral indices and by combining hyperspectral and physiological data. Agricultural and Forest Meteorology, 150, 369-379. DOI URL
[25]	Gao BC ( 1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257-266. DOI URL
[26]	Gillies RR, Carlson TN ( 1995). Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. Journal of Applied Meteorology, 34, 745-756. DOI URL
[27]	Gillies RR, Carlson TN ( 1997). A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the normalized difference vegetation index (NDVI) and surface radiant temperature. International Journal of Remote Sensing, 18, 3145-3166. DOI URL
[28]	Goward SN, Hope AS ( 1989). Evaporation from combined reflected solar and emitted terrestrial radiation: Preliminary FIFE results from AVHRR data. Advances in Space Research, 9, 239-249. DOI URL
[29]	Ghulam A, Li ZL, Qin QM, Tong QX, Wang JH, Kasimu A, Zhu L ( 2007). A method for canopy water content estimation for highly vegetated surfaces-shortwave infrared perpendicular water stress index. Science in China Series D: Earth Sciences, 37, 957-965. DOI URL
	[ 阿布都瓦斯提•吾拉木, 李召良, 秦其明, 童庆禧, 王纪华, 阿里木江•卡斯木, 朱琳 ( 2007). 全覆盖植被冠层水分遥感监测的一种方法: 短波红外垂直失水指数. 中国科学D辑: 地球科学, 37, 957-965.] DOI URL
[30]	Han LJ, Wang PX, Wang JD, Liu SM ( 2005). Study on vegetation indices-surface temperature characteristic space. Science in China Series D: Earth Sciences, 35, 371-377. DOI URL
	[ 韩丽娟, 王鹏新, 王锦地, 刘绍民 ( 2005). 植被指数-地表温度构成的特征空间研究. 中国科学D辑: 地球科学, 35, 371-377.] DOI URL
[31]	Hunt Jr ER, Rock BN, Nobel PS ( 1987). Measurement of leaf relative water content by infrared reflectance. Remote Sensing of Environment, 22, 429-435. DOI URL
[32]	Hunt Jr ER, Rock BN ( 1989). Detection of changes in leaf water content using near and middle-infrared reflectances. Remote Sensing of Environment, 30, 43-54. DOI URL
[33]	Inoue Y, Morinaga S, Shibayama M ( 1993). Non-destructive estimation of water status of intact crop leaves based on spectral reflectance measurements. Japanese Journal of Crop Science, 62, 462-469. DOI URL
[34]	Jackson RD ( 1985). Spectral response of cotton to suddenly induced water stress. International Journal of Remote Sensing, 6, 177-185. DOI URL
[35]	Jacquemoud S, Baret F ( 1990). Prospect—A model of leaf optical properties spectra. Remote Sensing of Environment, 34, 75-91. DOI URL
[36]	Jacquemoud S, Ustin S, Verdebout J, Schmuck G, Andreoli G, Hosgood B ( 1996). Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sensing of Environment, 56, 194-202. DOI URL
[37]	Kramer PJ ( 1983). Water Relations of Plants. New York Press, New York.
[38]	Kriedemamn PE, Barrs HD ( 1983). Photosynthetic Adaptation to Water Stress and Implication for Drought Resistance. Westbiew Press, Boulder.
[39]	Li BZ, Zhou GS ( 2014). Advance in the study on drought index. Acta Ecologica Sinica, 34, 1043-1052.
	[ 李柏贞, 周广胜 ( 2014). 干旱指标研究进展. 生态学报, 34, 1043-1052.]
[40]	Liu C, Sun PS, Liu SR ( 2016). A review of plant spectral reflectance response to water physiological changes. Chinese Journal of Plant Ecology, 40, 80-91.
	[ 刘畅, 孙鹏森, 刘世荣 ( 2016). 植物反射光谱对水分生理变化响应的研究进展. 植物生态学报, 40, 80-91.]
[41]	Maki M, Ishiahra M, Tamura M ( 2004). Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sensing of Environment, 90, 441-450. DOI URL
[42]	Michio S, Tsuyoshi A ( 1989). Seasonal visible, near-infrared and mid-infrared spectral of rice canopies in relation to LAI and aboveground dry phytomass. Remote Sensing of Environment, 27, 119-127. DOI URL
[43]	Mirzaie M, Darvishzadeh R, Shakiba A, Matkan AA, Atzberger C, Skidmore A ( 2014). Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements. International Journal of Applied Earth Observation and Geoinformation, 26, 1-11. DOI URL
[44]	Moran MS, Clarke TR, Inoue Y, Vidal A ( 1994). Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment, 49, 246-263. DOI URL
[45]	Peñuelas J, Filella I, Biel C, Serrano L, Save R ( 1993). The reflectance at the 950-970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 14, 1887-1905. DOI URL
[46]	Peñuelas J, Filella I, Sweeano L ( 1996). Cell wall elastivity and Water Index (R970 nm/R900 nm) in wheat under different nitrogen availabilities. International Journal of Remote Sensing, 7, 373-382. DOI URL
[47]	Price JC ( 1990). Using spatial context in satellite data to infer regional scale evaportranspiration. IEEE Transactions on Geoscience and Remote Sensing, 28, 940-948. DOI URL
[48]	Qi Y, Dennison PE, Jolly WM, Kropp RC, Brewer SC ( 2014). Spectroscopic analysis of seasonal changes in live fuel moisture content and leaf dry mass. Remote Sensing of Environment, 150, 198-206. DOI URL
[49]	Ren HR, Zhou GS, Zhang F, Zhang XS ( 2012). Evaluating cellulose absorption index (CAI) for non-photosynthetic biomass estimation in the desert steppe of Inner Mongolia. Chinese Science Bulletin, 57, 839-845. DOI URL
	[ 任鸿瑞, 周广胜, 张峰, 张新时 ( 2012). 基于纤维素吸收指数(CAI)的内蒙古荒漠草原非绿色生物量估算. 科学通报, 57, 839-845.] DOI URL
[50]	Ren H, Zhou G ( 2014). Estimating aboveground green biomass in desert steppe using band depth indices. Biosystems Engineering, 127, 67-78. DOI URL
[51]	Ridd MK ( 1995). Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for citied. International Journal of Remote Sensing, 16, 2165-2185. DOI URL
[52]	Rollin EM, Milton EJ ( 1998). Processing of high spectral resolution reflectance data for the retrieval of canopy water content information. Remote Sensing of Environment, 65, 86-92. DOI URL
[53]	Running SW, Gower ST ( 1991). Forest-BGC, a general model of forest ecosystem processes for regional applications II. Dynamic carbon allocation and nitrogen budgets. Tree Physiology, 9, 147-160. DOI URL PMID
[54]	Running SW, Nemani RR ( 1991). Regional hydrologic and carbon balance responses of forests resulting from potential climate change. Climatic Change, 19, 349-368. DOI URL
[55]	Sandholt I, Rasmussen K, Andersen J ( 2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79, 213-224. DOI URL
[56]	Shen Y, Niu Z, Yan CY ( 2005). Estimation models for vegetation water content at both leaf and canopy levels. Chinese Journal of Applied Ecology, 16, 1218-1223.
	[ 沈艳, 牛铮, 颜春燕 ( 2005). 植被叶片及冠层层次含水量估算模型的建立. 应用生态学报, 16, 1218-1223.]
[57]	Tang YL, Huang JF ( 2001). Study on hyperspectral remote sensing in agriculture. Remote Sensing Technology and Application, 16, 248-251. DOI URL
	[ 唐延林, 黄敬峰 ( 2001). 农业高光谱遥感研究的现状与发展趋势. 遥感技术与应用, 16, 248-251.] DOI URL
[58]	Tanner CB ( 1963). Plant temperature. Agronomy Journal, 50, 210-211.
[59]	Tian QJ, Gong P, Zhao CJ, Guo XW ( 2000). The feasibility of diagnosing wheat water status by using spectral reflectance. Chinese Science Bulletin, 45, 2645-2650. DOI URL
	[ 田庆久, 宫鹏, 赵春江, 郭晓维 ( 2000). 用光谱反射率诊断小麦水分状况的可行性. 科学通报, 45, 2645-2650.] DOI URL
[60]	Tian YC, Zhu Y, Cao WX, Dai TB ( 2004). Relationship between canopy reflectance and plant water status of wheat. Chinese Journal of Applied Ecology, 15, 2072-2076.
	[ 田永超, 朱艳, 曹卫星, 戴廷波 ( 2004). 小麦冠层反射光谱与植株水分状况的关系. 应用生态学报, 15, 2072-2076.]
[61]	Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO ( 2004). Using imaging spectroscopy to study ecosystem processes and properties. BioScience, 54, 523-534. DOI URL
[62]	Verhoef W ( 1984). Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sensing of Environment, 16, 125-141. DOI URL
[63]	Wang JH, Zhao CJ, Guo XW, Huan WJ, Tian QJ ( 2000). Study on the water content of wheat leaves by the remote sensing. Acta Agriculturae Boreali-Sinica, 15(4), 68-72. DOI URL
	[ 王纪华, 赵春江, 郭晓维, 黄文江, 田庆久 ( 2000). 利用遥感方法诊断小麦叶片含水量的研究. 华北农学报, 15(4), 68-72.] DOI URL
[64]	Wang JH, Zhao CJ, Guo XW, Tian QJ ( 2001). Study on the water status of the wheat leaves diagnosed by the spectral reflectance. Scientia Agricultura Sinica, 34, 104-107.
	[ 王纪华, 赵春江, 郭晓维, 田庆久 ( 2001). 用光谱反射率诊断小麦叶片水分状况的研究. 中国农业科学, 34, 104-107.]
[65]	Wang PX, Wan ZM, Gong JY, Li XW, Wang JD ( 2003). Advances in drought monitoring by using remotely sensed normalized difference vegetation index and land surface temperature products. Advance in Earth Sciences, 18, 527-533. DOI URL
	[ 王鹏新, Wan ZM, 龚健雅, 李小文, 王锦地 ( 2003). 基于植被指数和土地表面温度的干旱监测模型. 地球科学进展, 18, 527-533.] DOI URL
[66]	Wu DH, Fan WJ, Cui YK, Yan BY, Xu XR ( 2010). Review of monitoring soil water content using hyperspectral remote sensing. Spectroscopy and Spectral Analysis, 30, 3067-3071. DOI URL
	[ 吴代晖, 范闻捷, 崔要奎, 闫彬彦, 徐希孺 ( 2010). 高光谱遥感监测土壤含水量研究进展. 光谱学与光谱分析, 30, 3067-3071.] DOI URL
[67]	Yebra M, Dennison PE, Chuvieco E, Riaño D, Zylstra P, Hunt Jr ER, Danson FM, Qi Y, Jurdao S ( 2013). A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products. Remote Sensing of Environment, 136, 455-468. DOI URL
[68]	Yi Q, Bao A, Wang Q, Zhao J ( 2013). Estimation of leaf water content in cotton by means of hyperspectral indices. Computers and Electronics in Agriculture, 90, 144-151. DOI URL
[69]	Yi Q, Wang F, Bao A, Jiapaer G ( 2014). Leaf and canopy water content estimation in cotton using hyperspectral indices and radiative transfer models. International Journal of Applied Earth Observation and Geoinformation, 33, 67-75. DOI URL
[70]	Yilmaz MT, Hunt Jr ER, Goins LD, Ustin SL, Vanderbilt VC, Jackson TJ ( 2008). Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery. Remote Sensing of Environment, 112, 350-362. DOI URL
[71]	Yuan WP, Zhou GS ( 2004). Theoretical study and research prospect on drought indices. Advances in Earth Science, 19, 982-991. DOI URL
	[ 袁文平, 周广胜 ( 2004). 干旱指标的理论分析与研究展望. 地球科学进展, 19, 982-991.] DOI URL
[72]	Zhan ZM, Qin QM, Ghulam A, Wang DD ( 2006). A new method monitoring soil moisture based on NIR-Red spectral space. Science in China Series D: Earth Sciences, 36, 1020-1026. DOI URL
	[ 詹志明, 秦其明, 阿布都瓦斯提•吾拉木, 汪冬冬 ( 2006). 基于NIR-Red光谱特征空间的土壤水分监测新方法. 中国科学D辑: 地球科学, 36, 1020-1026.] DOI URL
[73]	Zhang JH, Guo WJ, Yao FM ( 2007). The study on vegetation water content estimating model based on remote sensing technique. Journal of Basic Science and Engineering, 15, 45-53. DOI URL
	[ 张佳华, 郭文娟, 姚凤梅 ( 2007). 植被水分遥感监测模型的研究. 应用基础与工程科学学报, 15, 45-53.] DOI URL
[74]	Zhang JH, Xu Y, Yao FM, Wang PJ, Guo WJ, Li L, Yang LM ( 2010). Advances in estimation methods of vegetation water content based on optical remote sensing techniques. Science China Technological Sciences, 53, 1159-1167. DOI URL
[75]	Zhang JH, Li L, Yao FM ( 2010). Progress in retrieving vegetation water content under different vegetation coverage condition based on remote sensing spectral information. Spectroscopy and Spectral Analysis, 30, 1638-1642. DOI URL
	[ 张佳华, 李莉, 姚凤梅 ( 2010). 遥感光谱信息提取不同覆盖下植被水分信号的研究进展. 光谱学与光谱分析, 30, 1638-1642.] DOI URL
[76]	Zhang F, Zhou GS ( 2014). Estimating canopy photosynthetic parameters in maize field based on multi-spectral remote sensing. Chinese Journal of Plant Ecology, 38, 710-719. DOI URL
	[ 张峰, 周广胜 ( 2014). 玉米农田冠层光合参数的多光谱遥感反演. 植物生态学报, 38, 710-719.] DOI URL
[77]	Zhang F, John R, Zhou G, Shao C, Chen J ( 2014). Estimating canopy characteristics of Inner Mongolia’s grasslands from field spectrometry. Remote Sensing, 6, 2239-2254. DOI URL
[78]	Zhang F, Zhou G ( 2015). Estimation of canopy water content by means of hyperspectral indices based on drought stress gradient experiments of maize in the North Plain China. Remote Sensing, 7, 15203-15223. DOI URL
[79]	Zhang F, Zhou G, Christer N ( 2015). Remote estimation of the fraction of absorbed photosynthetically active radiation for a maize canopy in Northeast China. Journal of Plant Ecology, 8, 429-435. DOI URL
[80]	Zhang F, Zhou G ( 2017). Deriving a light use efficiency estimation algorithm using in situ hyperspectral and eddy covariance measurements for a maize canopy in Northeast China. Ecology and Evolution, 7, 4735-4744. DOI URL PMID
[81]	Zhao Z, Li X, Yin YB, Tang J, Zhou SB ( 2010). Analysis of spectral features based on water content of desert vegetation. Spectroscopy and Spectral Analysis, 30, 2500-2503.
	[ 赵钊, 李霞, 尹业彪, 唐金, 周生斌 ( 2010). 荒漠植物含水量的光谱特征分析. 光谱学与光谱分析, 30, 2500-2503.]
[82]	Zhou S, Duursma RA, Medlyn BE, Kelly JWG, Prentice IC ( 2013). How should we model plant responses to drought? An analysis of stomatal and non-stomatal responses to water stress. Agricultural and Forest Meteorology, 182- 183, 204-214. DOI URL