联合PROSAIL模型和植被水分指数的低矮植被含水量估算
Estimation of water content for short vegetation based on PROSAIL model and vegetation water indices
- 2021年25卷第4期 页码:1025-1036
纸质出版日期: 2021-04-07
DOI: 10.11834/jrs.20219443
扫 描 看 全 文
浏览全部资源
扫码关注微信
纸质出版日期: 2021-04-07 ,
扫 描 看 全 文
江海英,柴琳娜,贾坤,刘进,杨世琪,郑杰.2021.联合PROSAIL模型和植被水分指数的低矮植被含水量估算.遥感学报,25(4): 1025-1036
Jiang H Y,Chai L N,Jia K,Liu J,Yang S Q and Zheng J. 2021. Estimation of water content for short vegetation based on PROSAIL model and vegetation water indices. National Remote Sensing Bulletin, 25(4):1025-1036
植被冠层含水量CWC(Canopy Water Content)和植被地上部分含水量VWC(Vegetation Water Content)对于植被健康状况和土壤干旱监测具有重要意义。本文联合PROSAIL辐射传输模型和植被水分指数NDWI(Normalized Difference Water Index),发展了一种简单、通用性较好的低矮植被CWC和VWC反演方法,可实现中、高空间分辨率下的CWC和VWC估算。首先对PROSAIL模型输入参数进行敏感性分析,明确各参数对模型输出反射率的影响机制,以优化PROSAIL模型输入参数设置并生成低矮植被的反射率模拟数据。基于模拟数据,计算了4个植被水分指数NDWI
(860,1240)
、NDWI
(860,1640)
、NDWI
(1240,1640)
和NDWI
(860,970)
用于反演低矮植被CWC和VWC。基于模拟数据的结果表明,4个植被水分指数与ln(CWC)都存在明显的线性关系,基于该关系建立了CWC估算模型。该模型可以直接用于低矮植被CWC估算,并通过VWC与CWC之间的经验关系间接计算得到VWC。模型模拟结果也表明,由于NDWI
(860,1640)
和NDWI
(1240,1640)
高度相关(
R
2
=0.99),两者可以提供相似且相对较好的低矮植被CWC估算精度。基于地面实测数据的验证结果与基于模拟数据的结果表现出很好的一致性,即基于NDWI
(860,1640)
和NDWI
(1240,1640)
估算的VWC都有相似且较高的精度,决定系数(
R
2
)都为0.88,均方根误差(RMSE)分别为0.4558 kg/m
2
和0.4380 kg/m
2
。利用Landsat 5 TM数据对NDWI
(860,1640)
估算效果的验证结果显示,模型估算CWC与地面实测CWC的
R
2
为0.84,RMSE为0.1342 kg/m
2
,估算VWC的RMSE为0.5651 kg/m
2
。本文提出的基于NDWI
(860,1640)
和NDWI
(1240,1640)
的CWC/VWC估算模型可被用于低矮植被的长势监测和干旱监测,为低矮植被覆盖地表的土壤水分反演提供高质量的植被水分信息。
As the dominant component by weight of live vegetation
vegetation moisture is one of the main factors determining plant photosynthesis
respiration
and biomass. Based on the type of remote sensed data used
retrieval algorithms for vegetation moisture retrieving algorithms fall into two categories
i.e.
microwave-data-based methods and optical-data-based methods. However
microwave-based methods are always characterized by low spatial resolutions and often have difficulty in separating out vegetation and soil signals. On the contrary
because of the high spatial resolution and good sensitivity to green vegetation
optical remote sensing techniques have been the baseline method for estimating Vegetation Water Content (VWC) of short vegetation (i.e.
Canopy Water Content
CWC). Here
we try to set up a universal
accurate and easy-to-apply way of retrieving CWC/VWC of short vegetation based on simulations from the PROSAIL model and generalized normalized Difference water index (NDWI)
i.e.
spectral indices taking the form of the NDWI formula.
The new proposed method is based on PROSAIL model and four NDWI variants
i.e.
NDWI
(860
970)
NDWI
(860
1240)
NDWI
(860
1640)
and NDWI
(1240
1640)
. First
the parameter sensitivity analysis is carried out to determine their different influence mechanisms on the output reflectance and to optimize the PROSAIL model's input parameters. After that
canopy reflectance simulations are generated for short vegetation. According to the simulated reflectance
simulations of the four NDWI variants are derived
which were used to construct relationships with the simulated CWC and VWC of short vegetation. It is found that
instead of the linear relationship derived in previous studies
the simulated CWC/VWC is best approximated as an exponential function of NDWI. Following the analysis of the PROSAIL-generated results
a newly NDWI-based scheme is proposed for estimating CWC for short vegetation. Furthermore
VWC can also be estimated by combining the empirical relationship between VWC and CWC.
Results derived from simulations show that the four NDWI variants are all linear related to ln(CWC)
which were further used as CWC retrieving models. Moreover
the CWC retrieving models can also be used for VWC retrieving by combining the empirical relationship between VWC and CWC. Results derived from simulations also indicate that since NDWI
(860
1640)
and NDWI
(1240
1640)
are highly correlated (
R
2
=0.99)
both of the two variants can provide similar and relatively good CWC estimation accuracy. The validation results based on ground measurements show good consistency with simulated results
i.e.
the VWC estimates from NDWI
(860
1640)
and NDWI
(1240
1640)
variants have high accuracy with both R
2
=0.88 and RMSE respectively of 0.4558 kg/m
2
and 0.4380 kg/m
2
. The validation results based on Landsat 5 TM datasets also show that the R
2
between CWC estimates and CWC ground measurements is 0.84
with a corresponding RMSE of 0.1342 kg/m
2
,while the RMSE between VWC estimates and VWC ground measurements is 0.5651 kg/m
2
.
The proposed NDWI-based scheme for retrieving CWC/VWC of short vegetation is easy to implement and highly accurate. It can also be applied to agriculture for crop growth monitoring and drought indication. The estimation framework is also useful for CWC/VWC estimation of other short vegetation types. Moreover
since crop cover remains a challenging land cover for satellite-based soil moisture retrieval
this method can also be used to improve the quality of cropland vegetation information available as an ancillary input data for microwave-based soil moisture retrieval algorithms.
光学遥感PROSAIL冠层含水量植被含水量植被水分指数低矮植被
optical remote sensingPROSAILcanopy water contentvegetation water contentvegetation water indexshort vegetation
Becker F and Choudhury B J. 1988. Relative sensitivity of NDVI and microwave polarization difference index (MPDI) for vegetation and desertification monitoring. Remote Sensing of Environment, 24(2): 297-311 [DOI: 10.1016/0034-4257(88)90031-4http://dx.doi.org/10.1016/0034-4257(88)90031-4]
Chai L N, Jiang H Y, Crow W, Liu S M, Zhao S J, Liu J and Yang S Q. 2020. Estimating corn canopy water content from normalized difference water index (NDWI): an optimized NDWI-based scheme and its feasibility for retrieving corn VWC. IEEE Transactions on Geoscience and Remote Sensing. In Press [DOI: 10.1109/TGRS.2020.3041039http://dx.doi.org/10.1109/TGRS.2020.3041039]
Chapin F S, Matson P A and Mooney H A. 2002. Principles of terrestrial ecosystem ecology. New York: Springer-Verlag New York: 1-2 [DOI:10.1007/b97397http://dx.doi.org/10.1007/b97397]
Chen D, Huang J F, Jackson T J. 2005. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near and short wave infrared bands. Remote Sensing of Environment, 98(2-3): 222-236 [DOI: 10.1016/j.rse.2005.07.008http://dx.doi.org/10.1016/j.rse.2005.07.008]
Cosh M H, White W A, Colliander A, Jackson T J, Prueger J H, Hornbuckle B K, Hunt E R, McNairn H, Powers J, Walker V A and Bullock P R. 2019. Estimating vegetation water content during the soil moisture active passive validation experiment 2016. Journal of Applied Remote Sensing, 13(1): 1-12 [DOI: 10.1117/1.JRS.13.014516http://dx.doi.org/10.1117/1.JRS.13.014516]
Clevers J G P W, Kooistra L and Schaepman M E. 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(3): 388-397 [DOI: 10.1016/j.jag.2008.03.003http://dx.doi.org/10.1016/j.jag.2008.03.003]
Clevers J G P W, Kooistra L and Schaepman M E. 2010. Estimating canopy water content using hyperspectral remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 12(2): 119-125 [DOI: 10.1016/j.jag.2010.01.007http://dx.doi.org/10.1016/j.jag.2010.01.007]
Deng R R, He Y Q, Qin Y, Chen Q D and Chen L. 2012. Measuring pure water absorption coefficient in the near-infrared spectrum (900—2500 nm). Remote Sensing, 16(1): 192-206
邓孺孺,何颖清,秦雁,陈启东,陈蕾.2012.近红外波段(900—2500 nm)水吸收系数测量.遥感学报,16(1):192-206 [DOI: 10.11834/jrs.20121188http://dx.doi.org/10.11834/jrs.20121188]
Fan L, Wigneron J P, Ciais P, Chave J, Brandt M, Fensholt R, Saatchi S S, Bastos A, Al-Yaari A, Hufkens K, Qin Y, Xiao X, Chen C, Myneni R B, Fernandez-Moran R, Mialon A, Rodriguez-Fernandez N J, Kerr Y, Tian F and Peñuelas J. 2019. Satellite-observed pantropical carbon dynamics. Nature Plants 5(9): 944-951 [DOI: 10.1038/s41477-019-0478-9http://dx.doi.org/10.1038/s41477-019-0478-9]
Féret J-B, François C, Gitelson A, Asner G P, Barry K M, Panigada C, Richardson A D and Jacquemoud S. 2011. Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sensing of Environment, 115(10): 2742-2750 [DOI: 10.1016/j.rse.2011.06.016http://dx.doi.org/10.1016/j.rse.2011.06.016]
Gao B C. 1996. NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257-266 [DOI: 10.1016/S0034-4257(96)00067-3http://dx.doi.org/10.1016/S0034-4257(96)00067-3]
Gao B C and Goetz A F. 1995. Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sensing of Environment, 52(3): 55-162 [DOI: 10.1016/0034-4257(95)00039-4http://dx.doi.org/10.1016/0034-4257(95)00039-4]
Houborg R, Soegaard H and Boegh E. 2007. Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using terra and aqua MODIS reflectance data. Remote Sensing of Environment, 106(1): 39-58 [DOI: 10.1016/j.rse.2006.07.016http://dx.doi.org/10.1016/j.rse.2006.07.016]
Houborg R, Anderson M and Daughtry C. 2009. Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale. Remote Sensing of Environment, 113(1):259-274 [DOI: 10.1016/j.rse.2008.09.014http://dx.doi.org/10.1016/j.rse.2008.09.014]
Huemmrich K F. 2001. The GeoSail model: a simple addition to the sail model to describe discontinuous canopy reflectance. Remote Sensing of Environment, 75(3): 423-431 [DOI: 10.1016/s0034-4257(00)00184-xhttp://dx.doi.org/10.1016/s0034-4257(00)00184-x]
Hunt E R and Rock B N. 1989. Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sensing of Environment, 30(1): 43-54 [DOI: 10.1016/0034-4257(89)90046-1http://dx.doi.org/10.1016/0034-4257(89)90046-1]
Hunt E R, Li L, Yilmaz M T and Jackson M J. 2011. Comparison of vegetation water contents derived from shortwave-infrared and passive-microwave sensors over central Iowa. Remote Sensing of Environment, 115(9):2376-2383 [DOI: 10.1016/j.rse.2011.04.037http://dx.doi.org/10.1016/j.rse.2011.04.037]
Jackson T J, Chen D Y, Cosh M, Li F Q, Anderson M, Walthall C, Doriaswamy P and Hunt E R. 2004.Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, 92(4): 475-782 [DOI: 10.1016/j.rse.2003.10.021http://dx.doi.org/10.1016/j.rse.2003.10.021]
Jackson T J, and Schmugge T J. 1991. Vegetation effects on the microwave emission of soils. Remote Sensing of Environment, 36(3):203-212 [DOI: 10.1016/0034-4257(91)90057-Dhttp://dx.doi.org/10.1016/0034-4257(91)90057-D]
Jacobs J M, Mohanty B P, Hsu E C and Miller Douglas. 2004. SMEX02: Field scale variability, time stability and similarity of soil moisture. Remote Sensing of Environment, 92(4): 436-446 [DOI: 10.1016/j.rse.2004.02.017http://dx.doi.org/10.1016/j.rse.2004.02.017]
Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada P J, Asner G P, Francois C and Ustin S L. 2009. PROSPECT+ SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment, 113(supp-S1), S56-S66 [DOI: 10.1016/j.rse.2008.01.026http://dx.doi.org/10.1016/j.rse.2008.01.026]
Kimes D S, Markham B L, Tucker C J, Iii M M. 1981. Temporal relationships between spectral response and agronomic variables of a corn canopy. Remote Sensing of Environment, 11(81): 401-411 [DOI: 10.1016/0034-4257(81)90037-7http://dx.doi.org/10.1016/0034-4257(81)90037-7]
Konings A G and Gentine P. 2017. Global variations in ecosystem-scale isohydricity. Global Change Biology, 23: 891-905 [DOI: 10.1111/gcb.13389http://dx.doi.org/10.1111/gcb.13389]
Kuusk A and Nilson T. 2001. Testing directional properties of a forest reflectance model. Journal of Geophysical Research, 106(D11): 12011-12021 [DOI: 10.1029/2000JD900439http://dx.doi.org/10.1029/2000JD900439]
Quan X W, He B B and Li X. 2015. A Bayesian network-based method to alleviate the ill-posed inverse problem: a case study on leaf area index and canopy water content retrieval. IEEE Transactions on Geoscience & Remote Sensing, 53(12), 6507-6517 [DOI: 10.1109/TGRS.2015.2442999http://dx.doi.org/10.1109/TGRS.2015.2442999]
Schaepman M E, Koetz B, Schaepman-Strub G and Itten K I. 2005. Spectrodirectional remote sensing for the improved estimation of biophysical and chemical variables: two case studies. Int. J. Appl. Earth Obser. Geoinfor, 6(3-4): 271-282 [DOI:10.1016/j.jag.2004.10.012http://dx.doi.org/10.1016/j.jag.2004.10.012]
Shi J C, Jackson T, Tao J, Du J, Bindlish R, Lu L, Chen K S. 2008. Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E. Remote Sensing of Environment, 112(12): 4285-4300 [DOI: 10.1016/j.rse.2008.07.015http://dx.doi.org/10.1016/j.rse.2008.07.015]
Li L, Ustin S L and Riano D. 2007. Retrieval of fresh leaf fuel moisture content using genetic algorithm partial least squares (GA-PLS) modeling. IEEE Geoscience and Remote Sensing Letters, 4(2): 216-220 [DOI: 10.1016/j.jag.2004.10.012http://dx.doi.org/10.1016/j.jag.2004.10.012]
Liu Y Y, Van Dijk A I J M, De Jeu R A M, Canadell J G, McCabe M F, Evans J P and Wang G. 2015. Recent reversal in loss of global terrestrial biomass. Nature Climate Change, 5: 470-474 [DOI: 10.1038/nclimate2581http://dx.doi.org/10.1038/nclimate2581]
Lobell D B and Asner G P. 2002. Moisture effects on soil reflectance. Soil Science Society of America Journal, 66(3): 722-727 [DOI: 10.2136/sssaj2002.0722http://dx.doi.org/10.2136/sssaj2002.0722]
Peñuelas J, Filella I, Biel C, Serrano L and Savé R. 1993. The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 14(10): 1887-1905 [DOI: 10.1080/01431169308954010http://dx.doi.org/10.1080/01431169308954010]
Peñuelas J, Pinol J, Ogaya R and Filella I. 1997. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18(3): 2869-2875 [DOI: 10.1080/014311697217396http://dx.doi.org/10.1080/014311697217396]
Trombetti M, Riaño D, Rubio M A, Cheng Y B and Ustin S L. 2008. Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA. Remote Sensing of Environment, 112(1): 203-215 [DOI: 10.1016/j.rse.2007.04.013http://dx.doi.org/10.1016/j.rse.2007.04.013]
Verhoef W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the sail model. Remote Sensing of Environment, 16(2): 125-141 [DOI: 10.1016/0034-4257(84)90057-9http://dx.doi.org/10.1016/0034-4257(84)90057-9]
Wang L L, John J Q, Hao X J and Zhu Q P. 2008. Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices. International Journal of Remote Sensing, 29(23-24):7065-7075 [DOI: 10.1080/01431160802226034http://dx.doi.org/10.1080/01431160802226034]
Yebra M, Dennison P E, Chuvieco E, Riaño D, Zylstra P, Hunt E R, Danson F M, Qi Y and 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(5): 455-468 [DOI: 10.1016/j.rse.2013.05.029http://dx.doi.org/10.1016/j.rse.2013.05.029]
Yilmaz M T, Raymond E R, Goins L D, Ustin S L, Vanderbilt V C and Jackson T M. 2008a. Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery. Remote Sensing of Environment,112(2):350-362
doi.org/10.1016/j.rse.2007.03.029
Yilmaz M T, Hunt E R and Jackson T J. 2008b. Remote sensing of vegetation water content from equivalent water thickness using satellite imagery. Remote Sensing of Environment,112(5), 2514-2522 [DOI: 10.1016/j.rse.2007.11.014http://dx.doi.org/10.1016/j.rse.2007.11.014]
Zhang F and Zhou G. 2019. Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize. BMC. Ecology. 19(19-18):1-12 [DOI: 10.1186/s12898-019-0233-0http://dx.doi.org/10.1186/s12898-019-0233-0]
Zhao T J, Shi J C, Lv L Q, Xu H X, Chen D Q, Cui Q, Thomas J J, Yan G J, Jia L, Chen L F, Zhao K, Zheng X M, Zhao L M, Zheng C L, Ji D B, Xiong C, Wang T X, Li R, Pan J M, Wen J G, Yu C, Zheng Y M, Jiang L M, Chai L N, Lu H, Yao P P, Ma J W, Lv H S, Wu J J, Zhao W, Yang N, Guo P, Li Y X, Geng D Y and Zhang Z Q. 2020. Soil moisture experiment in the Luan River supporting new satellite mission opportunities. Remote Sensing of Environment, 240(111680):1-21 [DOI: 10.1016/j.rse.2020.111680http://dx.doi.org/10.1016/j.rse.2020.111680]
Yan G J, Zhao T J, Mu X H, Wen J G, Pang Y, Jia L, Zhang Y G, Chen D Q, Yao C B, Cao Z Y, Lei Y H, Ji D B, Chen L F, Liu Q H, Lyu L Q, Chen J M and Shi J C. 2021. Comprehensive Remote Sensing Experiment of Carbon Cycle, Water Cycle and Energy Balance in Luan River Basin. National Remote Sensing Bulletin, 25(4): 856-870
阎广建, 赵天杰, 穆西晗, 闻建光, 庞勇, 贾立, 张永光, 陈德清, 姚崇斌, 曹志宇, 雷永荟, 姬大彬, 陈良富, 柳钦火, 吕利清, 陈镜明, 施建成. 2021. 滦河流域碳、水循环和能量平衡遥感综合试验总体设计. 遥感学报, 25(4): 856-870
Zhao T J, Shi J C, Xu H X, Sun Y L, Chen D Q, Cui Q, Jia L ,Huang S, Niu S D, Li X W, Yan G J, Chen L F, Liu Q H, Zhao K, Zheng X M, Zhao L M, Zheng C L, Ji D B, Xiong C, Wang T X, Li R, Pan J M, Wen J G, Mu X H, Yu C, Zheng Y M, Jiang L M, Chai L N, Lu H, Yao P P, Ma J W, Lv H S, Wu J J, Zhao W, Yang N, Guo P, Li Y X, Hu L, Geng D Y, Zhang Z Q, Hu J F and Du A P. 2021. Comprehensive remote sensing experiment of water cycle and energy balance in the shandian river basin. National Remote Sensing Bulletin, 25(4): 871-887
赵天杰, 施建成, 徐红新, 孙彦龙, 陈德清, 崔倩, 贾立, 黄硕, 牛升达, 李秀伟, 阎广建, 陈良富, 柳钦火, 赵凯, 郑兴明, 赵利民, 郑超磊, 姬大彬, 熊川, 王天星, 李睿, 潘金梅, 闻建光, 穆西晗, 余超, 郑姚闽, 蒋玲梅, 柴琳娜, 卢麾, 姚盼盼, 马建威, 吕海深, 武建军, 赵伟, 杨娜, 郭鹏, 李玉霞, 胡路, 耿德源, 张子谦, 胡建峰, 杜爱萍. 2021. 闪电河流域水循环和能量平衡遥感综合试验. 遥感学报, 25(4): 871-887
Zheng X M, Ding Y L, Zhao K, Jiang T, Li X F, Zhang S T, Li Y Y, Wu L L, Sun J, Ren J H and Zhang X X. 2014. Estimation of vegetation water content from Landsat8 OLI Data. Spectroscopy and Spectral Analysis, 34(12): 3385-3390
郑兴明, 丁艳玲, 赵凯, 姜涛, 李晓峰, 张世轶, 李洋洋, 武黎黎, 孙建, 任建华, 张宣宣. 2014. 光谱学与光谱分析, 34(12): 3385-3390) [DOI: 10.3964/j.issn.1000-0593(201412-3385-06]
相关作者
相关机构