基于植被二向性反射统一模型的高分五号FAPAR遥感反演算法

田定方; 杨斯棋; 徐大伟; 任华忠; 范闻捷; 刘镕源

doi:10.11834/jrs.20210097

植被参量反演与产品 | 浏览量 : 0 下载量: 1494 CSCD: 0

R-PDF
PDF
导出
分享
收藏
专辑

基于植被二向性反射统一模型的高分五号FAPAR遥感反演算法
FAPAR retrieval from GF-5 hyperspectral images based on unified BRDF model
2023年27卷第3期页码：711-723
收稿：2020-04-10，

纸质出版：2023-03-07
DOI： 10.11834/jrs.20210097
稿件说明：

移动端阅览

田定方，杨斯棋，徐大伟，任华忠，范闻捷，刘镕源.2023.基于植被二向性反射统一模型的高分五号FAPAR遥感反演算法.遥感学报，27（3）： 711-723 DOI： 10.11834/jrs.20210097.

Tian D F，Yang S Q， Xu D W， Ren H Z， Fan W J and Liu R Y. 2023. FAPAR retrieval from GF-5 hyperspectral images based on unified BRDF model. National Remote Sensing Bulletin， 27（3）：711-723 DOI： 10.11834/jrs.20210097.

摘要

植被光合有效辐射吸收比率（FAPAR）是描述植被光合作用能量交换过程的重要参数，广泛应用于植被长势监测、植被生产力估算、全球变化等研究领域。遥感是大范围获取FAPAR的唯一途径，与多光谱传感器相比，高光谱传感器能更加精确、细致地观测植被的光谱特征，有利于分析植被冠层反射、吸收特性，进而反演植被冠层FAPAR。本文首先在植被BRDF统一模型和FAPAR-P模型的基础上，构建了BRDF-FAPAR统一模型UBFM（Unified BRDF-FAPAR Model）；进而基于高分五号高光谱传感器特征模拟了不同情况下植被冠层反射率和相应的FAPAR；然后运用改进的最佳指数法选择FAPAR反演的特征波段组合；在此基础上，将特征波段反射率与FAPAR模拟结果作为神经网络的输入参数，构建针对高光谱数据的FAPAR神经网络反演算法。研究结果表明，改进的最佳指数法能有效地筛选出FAPAR估算的敏感波段；综合考虑波段信息量和实际影像数据噪声影响，本研究针对高分五号高光谱传感器选择8个波段作为FAPAR反演特征波段。基于UBFM模型构建的神经网络反演精度较高，模拟实验算法误差约为0.014。选择内蒙古呼伦贝尔市谢尔塔拉草原为主要研究区，采用高分五号高光谱影像数据反演了研究区的FAPAR，并利用同步地面实测数据开展验证，反演误差为0.048。该算法简化了传统机理方法的中间环节和繁琐的参数设置，有较好的可行性、稳定性和精度，为国产卫星高光谱传感器地表植被参数定量反演提供了新途径。

Abstract

The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a key parameter in characterizing the photosynthesis process of vegetation and widely used in many study areas

such as vegetation monitoring

NPP estimation

and global change. Remote sensing is the only way to obtain FAPAR at large scales. Compared with the multispectral instrument

the hyperspectral instrument has an advantage in analyzing the canopy reflectance and absorption on the basis of the high accuracy of the spectrum measurement

which is important in FAPAR retrieval. This study developed a new FAPAR retrieval algorithm for the Chinese GF-5 Visible-shortwave Infrared Advanced Hyperspectral Imager (AHSI) data on the basis of BRDF unified model and the neural network (NNT). The validation was performed in Hulun Buir Xeltala

which is a grassland and farming-pastoral area in Inner Mongolia. First

the simulated GF-5 AHSI reflectance-FAPAR datasets were generated by the BRDF unified model

and the characteristics of the data set were analyzed. Five groups of NNT input bands were selected based on the Optimal Index Factor (OIF) and a new factor OIFR

which was modified by the relevance of the band reflectance and the FAPAR. Different groups of bands were used to build the NNT

and the results were assessed by a test set in the simulation dataset. Finally

the best feature bands and the NNT were selected to generate the FAPAR map of the study area from the GF-5 AHSI image. Validation with in-situ observations was made. Overall

the new factor OIFR is more efficient than the origin factor OIF in band selection. As the amount of input bands increases

the NNT accuracy gradually increases

but the trend stops when the amount reaches a certain level. Considering both band information and instrument noise

8 bands were selected as the feature bands of FAPAR retrieval with the FAPAR RMSE of NNT is 0.014. The FAPAR map of the study area was generated

and the comparison with in-situ FAPAR shows the applicability of the method with RMSE=0.048. The reflectance and absorption by the hyperspectral data when the NNT reduced the middle term and parameters of the traditional methods simultaneously can be analyzed

presenting a new approach to the surface parameters retrieval of domestic satellite hyperspectral instruments.

关键词

Keywords

references

Baret F , Weiss M , Lacaze R , Camacho F , Makhmara H , Pacholcyzk P and Smets B . 2013 . GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: principles of development and production . Remote Sensing of Environment , 137 : 299 - 309 [ DOI: 10.1016/j.rse.2012.12.027 http://dx.doi.org/10.1016/j.rse.2012.12.027 ]

Chavez P S . 1984 . Digital processing techniques for image mapping with Landsat TM and SPOT simulator data // Proceedings of the Eighteenth International Symposium on Remote Sensing of Environment . Paris : 101 - 116

Chen J M . 1996 . Canopy architecture and remote sensing of the fraction of photosynthetically active radiation absorbed by boreal conifer forests . IEEE Transactions on Geoscience and Remote Sensing , 34 ( 6 ): 1353 - 1368 [ DOI: 10.1109/36.544559 http://dx.doi.org/10.1109/36.544559 ]

Chen L F , Gao Y H , Li L , Liu Q H and Gu X F . 2008 . Forest NPPestimation based on MODISdata under cloudless condition . Science in China Series D: Earth Sciences , 51 ( 3 ): 331 - 338

Dong T F , Meng J H , Shang J L , Liu J G and Wu B F . 2015 . Evaluation of chlorophyll-related vegetation indices using simulated Sentinel-2 data for estimation of crop fraction of absorbed photosynthetically active radiation . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 8 ( 8 ): 4049 - 4059 [ DOI: 10.1109/JSTARS.2015.2400134 http://dx.doi.org/10.1109/JSTARS.2015.2400134 ]

Fan W J , Liu Y , Xu X R , Chen G X and Zhang B T . 2014 . A new FAPAR analytical model based on the law of energy conservation: a case study in China . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 7 ( 9 ): 3945 - 3955 [ DOI: 10.1109/JSTARS.2014.2325673 http://dx.doi.org/10.1109/JSTARS.2014.2325673 ]

Gallo K P , Daughtry C S T and Bauer M E . 1985 . Spectral estimation of absorbed photosynthetically active radiation in corn canopies . Remote Sensing of Environment , 17 ( 3 ): 221 - 232 [ DOI: 10.1016/0034-4257(85)90096-3 http://dx.doi.org/10.1016/0034-4257(85)90096-3 ]

King D A , Turner D P and Ritts W D . 2011 . Parameterization of a diagnostic carbon cycle model for continental scale application . Remote Sensing of Environment , 115 ( 7 ): 1653 - 1664 [ DOI: 10.1016/j.rse.2011.02.024 http://dx.doi.org/10.1016/j.rse.2011.02.024 ]

Knyazikhin Y , Martonchik J V , Myneni R B , Diner D J and Running S W . 1998 . Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data . Journal of Geophysical Research: Atmospheres , 103 ( D24 ): 32257 - 32275 [ DOI: 10.1029/98JD02462 http://dx.doi.org/10.1029/98JD02462 ]

Lahoz W . A . 2011 . Systematic Observation Requirements for Satellite-Based Products for Climate, 2011 Update, Supplemental Details to the Satellite-Based Component of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update).

Li S B , Lin H and Ge M . 2019 . Hyperspectral dimensionality reduction and classification of the East Dongting Lake wetland vegetation . Journal of Central South University of Forestry and Technology , 39 ( 11 ): 36 - 41

李世波 , 林辉 , 葛淼 . 2019 . 东洞庭湖湿地植被高光谱数据降维与分类 . 中南林业科技大学学报 , 39 ( 11 ): 36 - 41 [ DOI: 10.14067/j.cnki.1673-923x.2019.11.006 http://dx.doi.org/10.14067/j.cnki.1673-923x.2019.11.006 ]

Liu Y N . 2018 . Visible-shortwave infrared hyperspectral imager of GF-5 satellite . Spacecraft Recovery and Remote Sensing , 39 ( 3 ): 25 - 28

刘银年 . 2018 . “高分五号”卫星可见短波红外高光谱相机的研制 . 航天返回与遥感 , 39 ( 3 ): 25 - 28 [ DOI: 10.3969/j.issn.1009-8518.2018.03.003 http://dx.doi.org/10.3969/j.issn.1009-8518.2018.03.003 ]

Melis C , Szafrańska P A , Jędrzejewska B and Bartoń K . 2006 . Biogeographical variation in the population density of wild boar ( Sus scrofa ) in western Eurasia . Journal of Biogeography , 33 ( 5 ): 803 - 811 [ DOI: 10.1111/j.1365-2699.2006.01434.x http://dx.doi.org/10.1111/j.1365-2699.2006.01434.x ]

Monteith J L . 1972 . Solar radiation and productivity in tropical ecosystems . The Journal of Applied Ecology , 9 ( 3 ): 747 - 766 [ DOI: 10.2307/2401901 http://dx.doi.org/10.2307/2401901 ]

Monteith J L . 1977 . Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London . B : Biological Sciences , 281 ( 980 ): 277 - 294 [ DOI: 10.1098/rstb.1977.0140 http://dx.doi.org/10.1098/rstb.1977.0140 ]

Myneni R B and Williams D L . 1994 . On the relationship between FAPAR and NDVI . Remote Sensing of Environment , 49 ( 3 ): 200 - 211 [ DOI: 10.1016/0034-4257(94)90016-7 http://dx.doi.org/10.1016/0034-4257(94)90016-7 ]

Myneni R B , Hoffman S , Knyazikhin Y , Privette J L , Glassy J , Tian Y , Wang Y , Song X , Zhang Y , Smith G R , Lotsch A , Friedl M , Morisette J T , Votava P , Nemani R R and Running S W . 2002 . Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data . Remote Sensing of Environment , 83 ( 1/2 ): 214 - 231 [ DOI: 10.1016/S0034-4257(02)00074-3 http://dx.doi.org/10.1016/S0034-4257(02)00074-3 ]

Ridao E , Conde J R and Mı́Nguez M I . 1998 . Estimating FAPAR from nine vegetation indices for irrigated and nonirrigated faba bean and semileafless pea canopies . Remote Sensing of Environment , 66 ( 1 ): 87 - 100 [ DOI: 10.1016/S0034-4257(98)00050-9 http://dx.doi.org/10.1016/S0034-4257(98)00050-9 ]

Smolander S and Stenberg P . 2005 . Simple parameterizations of the radiation budget of uniform broadleaved and coniferous canopies . Remote Sensing of Environment , 94 ( 3 ): 355 - 363 [ DOI: 10.1016/j.rse.2004.10.010 http://dx.doi.org/10.1016/j.rse.2004.10.010 ]

Stenberg P , Mõttus M and Rautiainen M . 2016 . Photon recollision probability in modelling the radiation regime of canopies - A review . Remote Sensing of Environment , 183 : 98 - 108 [ DOI: 10.1016/j.rse.2016.05.013 http://dx.doi.org/10.1016/j.rse.2016.05.013 ]

Tan C W , Samanta A , Jin X L , Tong L , Ma C , Guo W S , Knyazikhin Y and Myneni R B . 2013 . Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies . International Journal of Remote Sensing , 34 ( 24 ): 8789 - 8802 [ DOI: 10.1080/01431161.2013.853143 http://dx.doi.org/10.1080/01431161.2013.853143 ]

Tian D F , Fan W J and Ren H Z . 2020 . Progress of fraction of absorbed photosynthetically active radiation retrieval from remote sensing data . Journal of Remote Sensing (Chinese) , 24 ( 11 ): 1307 - 1324

田定方 , 范闻捷 , 任华忠 . 2020 . 植被光合有效辐射吸收比率遥感研究进展 . 遥感学报 , 24 ( 11 ): 1307 - 1324 [ DOI: 10.11834/jrs.20208498 http://dx.doi.org/10.11834/jrs.20208498 ]

Traore A K , Ciais P , Vuichard N , Macbean N , Dardel C , Poulter B , Piao S , Fisher J B , Viovy N , Jung M and Myneni R . 2014 . 1982-2010 trends of light use efficiency and inherent water use efficiency in African vegetation: sensitivity to climate and atmospheric CO 2 concentrations . Remote Sensing , 6 ( 9 ): 8923 - 8944 [ DOI: 10.3390/rs6098923 http://dx.doi.org/10.3390/rs6098923 ]

Verger A , Baret F and Camacho F . 2011 . Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: evaluation over an agricultural area with CHRIS/PROBA observations . Remote Sensing of Environment , 115 ( 2 ): 415 - 426 [ DOI: 10.1016/j.rse.2010.09.012 http://dx.doi.org/10.1016/j.rse.2010.09.012 ]

Wang L , Fan W J , Xu X R and Liu Y . 2015 . Scaling transform method for remotely sensed FAPAR based on FAPAR-P model . IEEE Geoscience and Remote Sensing Letters , 12 ( 4 ): 706 - 710 [ DOI: 10.1109/LGRS.2014.2359051 http://dx.doi.org/10.1109/LGRS.2014.2359051 ]

Wang T X , Yan G J , Ren H Z and Mu X H . 2010 . Improved methods for spectral calibration of on-orbit imaging spectrometers . IEEE Transactions on Geoscience and Remote Sensing , 48 ( 11 ): 3924 - 3931 [ DOI: 10.1109/TGRS.2010.2067220 http://dx.doi.org/10.1109/TGRS.2010.2067220 ]

Xu X R , Fan W J , Li J C , Zhao P and Chen G X . 2017 . A unified model of bidirectional reflectance distribution function for the vegetation canopy . Science China Earth Sciences , 60 ( 3 ): 463 - 477 [ DOI: 10.1007/s11430-016-5082-6 http://dx.doi.org/10.1007/s11430-016-5082-6 ]

Yoshida Y , Joiner J , Tucker C , Berry J , Lee J E , Walker G , Reichle R , Koster R , Lyapustin A and Wang Y . 2015 . The 2010 Russian drought impact on satellite measurements of solar-induced chlorophyll fluorescence: insights from modeling and comparisons with parameters derived from satellite reflectances . Remote Sensing of Environment , 166 : 163 - 177 [ DOI: 10.1016/j.rse.2015.06.008 http://dx.doi.org/10.1016/j.rse.2015.06.008 ]

Zhao P , Fan W J , Liu Y , Mu X H , Xu X R and Peng J J . 2016 . Study of the remote sensing model of FAPAR over rugged terrains . Remote Sensing , 8 ( 4 ): 309 [ DOI: 10.3390/rs8040309 http://dx.doi.org/10.3390/rs8040309 ]

Zhu Z C , Bi J , Pan Y Z , Ganguly S , Anav A , Xu L , Samanta A , Piao S , Nemani R R and Myneni R B . 2013 . Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011 . Remote Sensing , 5 ( 2 ): 927 - 948 [ DOI: 10.3390/rs5020927 http://dx.doi.org/10.3390/rs5020927 ]

文章被引用时，请邮件提醒。

提交

基于混合式特征选择的高分五号影像农田识别

高分五号高光谱数据融合方法比较

融合激光测高数据的无控GF-7卫星影像矿区DSM改进方法

基于神经网络的星载GNSS-R海面高度反演误差补偿模型