基于三维随机辐射传输模型的高分一号中国叶面积指数产品算法

张虎; 李静; 柳钦火; 张召星; 朱欣然; 刘畅; 赵静; 董亚冬; 徐保东; 蒙继华

doi:10.11834/jrs.20231708

植被参量反演与产品 | 浏览量 : 0 下载量: 307 CSCD: 2 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

基于三维随机辐射传输模型的高分一号中国叶面积指数产品算法
GF-1 leaf area index product across China based on three-dimensional stochastic radiation transfer model
2023年27卷第3期页码：677-688
纸质出版日期： 2023-03-07 ，
DOI： 10.11834/jrs.20231708

扫描看全文

张虎，李静，柳钦火，张召星，朱欣然，刘畅，赵静，董亚冬，徐保东，蒙继华.2023.基于三维随机辐射传输模型的高分一号中国叶面积指数产品算法.遥感学报，27（3）： 677-688

Zhang H，Li J，Liu Q H，Zhang Z X，Zhu X R，Liu C，Zhao J，Dong Y D，Xu B D and Meng J H. 2023. GF-1 leaf area index product across China based on three-dimensional stochastic radiation transfer model. National Remote Sensing Bulletin， 27（3）：677-688
张虎，李静，柳钦火，张召星，朱欣然，刘畅，赵静，董亚冬，徐保东，蒙继华.2023.基于三维随机辐射传输模型的高分一号中国叶面积指数产品算法.遥感学报，27（3）： 677-688 DOI： 10.11834/jrs.20231708.

Zhang H，Li J，Liu Q H，Zhang Z X，Zhu X R，Liu C，Zhao J，Dong Y D，Xu B D and Meng J H. 2023. GF-1 leaf area index product across China based on three-dimensional stochastic radiation transfer model. National Remote Sensing Bulletin， 27（3）：677-688 DOI： 10.11834/jrs.20231708.

摘要

叶面积指数LAI（Leaf Area Index）是研究植被生态系统结构和功能的核心参数之一，遥感是获取大范围动态LAI的一个主要技术手段。目前国际上没有高分辨率的LAI标准化产品。本文基于三维随机辐射传输（3D-SRT）模型查找表算法研究了适用于国产高分辨率卫星高分一号宽幅相机（GF-1 WFV）的叶面积指数反演算法。模型中单次散射反照率和不确定性等参数与波段设置和波段稳定性相关。算法在全国范围内选取不同植被类型的均质样点，统计地表反射率的差异特征，调整全国6种植被类型各波段的单次散射反照率、不确定性等算法参数，进而构造适用于GF-1 WFV传感器的查找表以进行LAI的反演。研究中使用新疆维吾尔自治区石河子地区、内蒙古自治区四道桥包含农作物、森林等共359组实测地面数据开展LAI验证。验证结果表明，和调整参数前的反演结果相比，优化后的算法均方根误差RMSE可由算法优化前的1.209下降至0.804，决定系数

由0.659提高至0.883，反演成功率RI可由25.3%提高至73.8%，算法精度和稳定性较高，更适用于GF-1叶面积指数的反演。将其应用于GF-1卫星影像上，生产了2018年—2020年全国16 m空间分辨率10天合成的叶面积指数产品，产品能够反映出不同植被类型的物候特征，有利于大面积农业林业等遥感监测应用。

Abstract

Leaf Area Index (LAI)

is a critical variable in models of climate

meteorology

hydrology

and biogeochemistry to characterize vegetation canopy structure. Remote sensing provides a practical approach to estimating dynamic LAI on a large scale and some global LAI products were generated in the past decades. However

these products are mainly focused on the low-medium resolution satellite data and there is no standardized high-resolution LAI product worldwide. The object of this work is to propose an LAI inversion algorithm for high-resolution satellite GF-1 Wide Field View (GF-1 WFV) images and generate the GF-1 LAI product across China.

Three-dimensional stochastic radiative transfer (3D-SRT) model

which can take the 3D-canopy architecture into consideration

is a widely-used model in LAI inversion. Parameters of Single Scattering Albedo (SSA) and uncertainty in the 3D-SRT model are highly correlated with the band setting and band stability. To acquire the optimal values of these parameters

94824 homogenous samples of six vegetation types across China are selected and the characteristics of the difference in their surface reflectance are analyzed. SSA and uncertainty are adjusted to the values when the GF-1 retrieved LAI and MODIS LAI share the most similarity across the homogeneous samples. Based on the 3D-SRT model and the adjusted key parameters

an look up table (LUT) was constructed for the LAI retrieval in this work.

There are 359 ground-measured LAI data in Shihezi

Xinjiang

and Sidaoqiao

Inner Mongolia in the validation. The overall result shows compared with the inversion result before adjusting the parameters

the root mean square error (RMSE) of the optimized algorithm can be reduced from 1.209 to 0.804

the determination coefficient (

) can be increased from 0.659 to 0.883

and the retrieval index (RI) can be increased from 25.3% to 73.8 %

suggesting the higher accuracy and stability of the algorithm and more suitable for GF-1 LAI retrieval. The accuracy and stability of the algorithm also improved for each vegetation type individually. Based on the algorithm

the GF-1 leaf area index product of 16 m/10 days resolution across China from 2018 to 2020 was generated. The temporal profiles extracted from the product can indicate reasonable phenological characteristics of different vegetation types.

Based on the algorithm proposed in this study

the high-resolution (16 m /10 days) LAI products for 2018-2020 across China were generated based on domestic satellite GF-1 Wide Field View. It can provide accurate and effective data which supports vegetation change research

agricultural and forestry application

ecological environment monitoring

and government decision-making. However

due to the short revisit time of medium and high-resolution satellites and the cloud contamination

the miss rate of current products is still high. In the future

more works can focus on how to generate spatial and temporal continuous products.

关键词

遥感叶面积指数高分一号三维随机辐射传输模型

Keywords

remote sensingleaf area indexGF-1three dimensional radiative transfer model

references

Bai Z G. 2013. Technical characteristics of Gaofen-1 satellite. Aerospace China (08):5-9

白照广. 2013. “高分一号卫星的技术特点.” 中国航天 (08):5-9

Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B, Niño F, Weiss M, Samain O, Roujean J L and Leroy M. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: part 1: principles of the algorithm. Remote Sensing of Environment, 110(3): 275-286 [DOI: 10.1016/j.rse.2007.02.018http://dx.doi.org/10.1016/j.rse.2007.02.018]

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.2012.12.027http://dx.doi.org/10.1016/j.rse.2012.12.027]

Baret F, Weiss M and Smets B. 2016. ATBD for LAI, FAPAR and FCOVER from PROBA-V products at 300M resolution GEOV3) IMAGINES_RP2. 1_ATBD-LAI300M). https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/ImagineS_RP2.1_ATBD-FAPAR300 m_I1.73.pdf [2023-01-11]

Bonan G B. 1993. Importance of leaf area index and forest type when estimating photosynthesis in boreal forests. Remote Sensing of Environment, 43(3): 303-314 [DOI: 10.1016/0034-4257(93)90072-6http://dx.doi.org/10.1016/0034-4257(93)90072-6]

Breunig F M, Galvão L S, Formaggio A R and Epiphanio J C N. 2011. Directional effects on NDVI and LAI retrievals from MODIS: a case study in Brazil with soybean. International Journal of Applied Earth Observation and Geoinformation, 13(1): 34-42 [DOI: 10.1016/j.jag.2010.06.004http://dx.doi.org/10.1016/j.jag.2010.06.004]

Camacho F, Cernicharo J, Lacaze R, Baret F and Weiss M. 2013. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: validation and intercomparison with reference products. Remote Sensing of Environment, 137: 310-329 [DOI: 10.1016/j.rse.2013.02.030http://dx.doi.org/10.1016/j.rse.2013.02.030]

Chen J M and Black T A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4): 421-429 [DOI: 10.1111/j.1365-3040.1992.tb00992.xhttp://dx.doi.org/10.1111/j.1365-3040.1992.tb00992.x]

Colombo R, Bellingeri D, Fasolini D and Marino C M. 2003. Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sensing of Environment, 86(1): 120-131 [DOI: 10.1016/S0034-4257(03)00094-4http://dx.doi.org/10.1016/S0034-4257(03)00094-4]

Deng F, Chen J M, Plummer S, Chen M Z and Pisek J. 2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2219-2229 [DOI: 10.1109/TGRS.2006.872100http://dx.doi.org/10.1109/TGRS.2006.872100]

Diner D J, Martonchik J V, Borel C, Gerstl S A W, Gordon H R, Knyazikhin Y, Myneni R, Pinty B and Verstraete M M. 2008. Level 2 Surface Retrieval Algorithm Theoretical Basis. Jet Propulsion Laboratory, California Institute of Technology

Feng M, Huang C Q, Channan S, Vermote E F, Masek J G and Townshend J R. 2012. Quality assessment of Landsat surface reflectance products using MODIS data. Computers and Geosciences, 38(1): 9-22 [DOI: 10.1016/j.cageo.2011.04.011http://dx.doi.org/10.1016/j.cageo.2011.04.011]

Friedl M and Sulla-Menashe D. 2019. MCD12Q1-MODIS/Terra+aqua land cover type yearly L3 Global 500 m SIN grid V006. NASA EOSDIS Land Processes DAAC

Ganguly S, Nemani R R, Zhang G, Hashimoto H, Milesi C, Michaelis A, Wang W L, Votava P, Samanta A, Melton F, Dungan J L, Vermote E, Gao F, Knyazikhin Y and Myneni R B. 2012. Generating global leaf area index from landsat: algorithm formulation and demonstration. Remote Sensing of Environment, 122: 185-202 [DOI: 10.1016/j.rse.2011.10.032http://dx.doi.org/10.1016/j.rse.2011.10.032]

Ganguly S, Schull M A, Samanta A, Shabanov N V, Milesi C, Nemani R R, Knyazikhin Y and Myneni R B. 2008. Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: theory. Remote Sensing of Environment, 112(12): 4333-4343 [DOI: 10.1016/j.rse.2008.07.014http://dx.doi.org/10.1016/j.rse.2008.07.014]

García-Haro F J, Campos-Taberner M, Muñoz-Marí J, Laparra V, Camacho F, Sánchez-Zapero J and Camps-Valls G. 2018. Derivation of global vegetation biophysical parameters from EUMETSAT polar system. ISPRS Journal of Photogrammetry and Remote Sensing, 139: 57-74 [DOI: 10.1016/j.isprsjprs.2018.03.005http://dx.doi.org/10.1016/j.isprsjprs.2018.03.005]

Gong P, Liu H, Zhang M N, Li C C, Wang J, Huang H B, Clinton N, Ji L Y, Li W Y, Bai Y Q, Chen B, Xu B, Zhu Z L, Yuan C, Suen H P, Guo J, Xu N, Li W J, Zhao Y Y, Yang J, Yu C Q, Wang X, Fu H H, Yu L, Dronova I, Hui F M, Cheng X, Shi X L, Xiao F J, Liu Q F and Song L C. 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64(6): 370-373 [DOI: 10.1016/j.scib.2019.03.002http://dx.doi.org/10.1016/j.scib.2019.03.002]

Gonsamo A and Chen J M. 2014. Improved LAI algorithm implementation to MODIS data by incorporating background, topography, and foliage clumping information. IEEE Transactions on Geoscience and Remote Sensing, 52(2): 1076-1088 [DOI: 10.1109/TGRS.2013.2247405http://dx.doi.org/10.1109/TGRS.2013.2247405]

Huang D, Knyazikhin Y, Dickinson R E, Rautiainen M, Stenberg P, Disney M, Lewis P, Cescatti A, Tian Y H, Verhoef W, Martonchik J V and Myneni R B. 2007. Canopy spectral invariants for remote sensing and model applications. Remote Sensing of Environment, 106(1): 106-122 [DOI: 10.1016/j.rse.2006.08.001http://dx.doi.org/10.1016/j.rse.2006.08.001]

Huang D, Knyazikhin Y, Wang W L, Deering D W, Stenberg P, Shabanov N, Tan B and Myneni R B. 2008. Stochastic transport theory for investigating the three-dimensional canopy structure from space measurements. Remote Sensing of Environment, 112(1): 35-50 [DOI: 10.1016/j.rse.2006.05.026http://dx.doi.org/10.1016/j.rse.2006.05.026]

Knyazikhin Y, Glassy J, Privette J L, Tian Y, Lotsch A, Zhang Y, Wang Y, Morisette J T, Votava P, Myneni R B, Nemani R R and Running S W. 1999. MODIS leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by vegetation (FPAR) product (MOD15): algorithm theoretical basis document. [EB/OL] https://www.researchgate.net/publication/236770186_MODIS_Leaf_Area_Index_LAI_and_Fraction_of_Photosynthetically_Active_Radiation_Absorbed_by_Vegetation_FPAR_Product_MOD15_Algorithm_Theoretical_Basis_Document [2023-01-11]

Knyazikhin Y, Martonchik J V, Diner D J, Myneni R B, Verstraete M, Pinty B and Gobron N. 1998a. Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere-corrected MISR data. Journal of Geophysical Research, 103(D24): 32239-32256 [DOI: 10.1029/98jd02461http://dx.doi.org/10.1029/98jd02461]

Knyazikhin Y, Martonchik J V, Myneni R B, Diner D J and Running S W. 1998b. 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, 103(D24): 32257-32275 [DOI: 10.1029/98jd02462http://dx.doi.org/10.1029/98jd02462]

Knyazikhin Y, Schull M A, Stenberg P, Mõttus M, Rautiainen M, Yang Y, Marshak A, Carmona P L, Kaufmann R K, Lewis P, Disney M I, Vanderbilt V, Davis A B, Baret F, Jacquemoud S, Lyapustin A and Myneni R B. 2012. Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences of the United States of America, 110(3): E185-E192 [DOI: 10.1073/pnas.1210196109http://dx.doi.org/10.1073/pnas.1210196109]

Liu Q H, Wen J G, Zhou X, Zhao J, Li Z Y, Li X, Ma M G, Wang W Z, Liao X H, Liu S M, Fan W J, Xiao Q, Zhong B, Li J, Xin X Z, Li L, Jia L, Gao Z H, Jin J D, Liang S, Xin J, Liao C J and Wu Y R. 2023. Technique system of remote sensing product generation and validation of GF common products. National Remote Sensing Bulletin， 27(3): 544-562

柳钦火, 闻建光, 周翔, 赵坚, 李增元, 李新, 马明国, 王维真, 廖小罕, 刘绍民, 范闻捷, 肖青, 仲波, 李静, 辛晓洲, 李丽, 贾立, 高志海, 金家栋, 梁师, 邢进, 廖楚江, 吴一戎. 2023. 高分遥感共性产品生成和真实性检验技术体系. 遥感学报, 27(3): 544-562 [DOI： 10.11834/jrs.20235022http://dx.doi.org/10.11834/jrs.20235022]

Liu S M, Li X, Xu Z W, Che T, Xiao Q, Ma M G, Liu Q H, Jin R, Guo J W, Wang L X, Wang W Z, Qi Y, Li H Y, Xu T R, Ran Y H, Hu X L, Shi S J, Zhu Z L, Tan J L, Zhang Y and Ren Z G. 2018. The heihe integrated observatory network: a basin-scale land surface processes observatory in China. Vadose Zone Journal, 17(1): 1-21 [DOI: 10.2136/vzj2018.04.0072http://dx.doi.org/10.2136/vzj2018.04.0072]

Liu S M, Qu Y H, Xu Z W. 2020. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Leaf area index of Sidaoqiao superstation, 2019). A Big Earth Data Platform for Three Poles

刘绍民, 屈永华, 徐自为. 2020. 祁连山综合观测网：黑河流域地表过程综合观测网（四道桥超级站叶面积指数-2019）. 时空三极环境大数据平台 [DOI: 10.11888/Meteoro.tpdc.270724. CSTR: 18406.11.Meteoro.tpdc.270724http://dx.doi.org/10.11888/Meteoro.tpdc.270724.CSTR:18406.11.Meteoro.tpdc.270724]

Liu Y, Liu R G and Chen J M. 2012. Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data. Journal of Geophysical Research, 117(G4): G04003 [DOI: 10.1029/2012JG002084http://dx.doi.org/10.1029/2012JG002084]

Meroni M, Colombo R and Panigada C. 2004. Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations. Remote Sensing of Environment, 92(2): 195-206 [DOI: 10.1016/j.rse.2004.06.005http://dx.doi.org/10.1016/j.rse.2004.06.005]

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)00074-3http://dx.doi.org/10.1016/S0034-4257(02)00074-3]

Pierce L L and Running S W. 1988. Rapid estimation of coniferous forest leaf area index using a portable integrating radiometer. Ecology, 69(6): 1762-1767 [DOI: 10.2307/1941154http://dx.doi.org/10.2307/1941154]

Qu Y H, Xu Z W. 2019. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Sidaoqiao, 2018). A Big Earth Data Platform for Three Poles

屈永华, 徐自为. 2019. 祁连山综合观测网：黑河流域地表过程综合观测网（四道桥叶面积指数-2018）. 时空三极环境大数据平台 [DOI: 10.11888/Meteoro.tpdc.270762. CSTR: 18406.11.Meteoro.tpdc.270762http://dx.doi.org/10.11888/Meteoro.tpdc.270762.CSTR:18406.11.Meteoro.tpdc.270762]

Qu Y H, Zhu Y Q, Han W C, Wang J D and Ma M G. 2014. Crop leaf area index observations with a wireless sensor network and its potential for validating remote sensing products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(2): 431-444 [DOI: 10.1109/JSTARS.2013.2289931http://dx.doi.org/10.1109/JSTARS.2013.2289931]

Sellers P J, Dickinson R E, Randall D A, Betts A K, Hall F G, Berry J A, Collatz G J, Denning A S, Mooney H A, Nobre C A, Sato N, Field C B and Henderson-Sellers A. 1997. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science, 275(5299): 502-509 [DOI: 10.1126/science.275.5299.502http://dx.doi.org/10.1126/science.275.5299.502]

Sulla-Menashe D and Friedl M. 2018. User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1Product). [EB/OL] [DOI: 10.5067/MODIS/MCD12Q1.006http://dx.doi.org/10.5067/MODIS/MCD12Q1.006]

Tan B, Hu J N, Huang D, Yang W Z, Zhang P, Shabanov N V, Knyazikhin Y, Nemani R R and Myneni R B. 2005. Assessment of the broadleaf crops leaf area index product from the Terra MODIS instrument. Agricultural and Forest Meteorology, 135(1-4): 124-134 [DOI: 10.1016/j.agrformet.2005.10.008http://dx.doi.org/10.1016/j.agrformet.2005.10.008]

Tum M, Günther K P, Böttcher M, Baret F, Bittner M, Brockmann C and Weiss M. 2016. Global gap-free MERIS LAI time series (2002-2012). Remote Sensing, 8(1): 69 [DOI: 10.3390/rs8010069http://dx.doi.org/10.3390/rs8010069]

Xiao Z Q, Liang S L, Wang J D, Chen P, Yin X J, Zhang L Q and Song J L. 2014. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 209-223 [DOI: 10.1109/TGRS.2013.2237780http://dx.doi.org/10.1109/TGRS.2013.2237780]

Yan K, Park T, Chen C, Xu B D, Song W J, Yang B, Zeng Y L, Liu Z, Yan G J, Knyazikhin Y and Myneni R B. 2018. Generating global products of LAI and FPAR from SNPP-VIIRS Data: theoretical background and implementation. IEEE Transactions on Geoscience and Remote Sensing, 56(4): 2119-2137 [DOI: 10.1109/tgrs.2017.2775247http://dx.doi.org/10.1109/tgrs.2017.2775247]

Yang F K, Fan M and Tao J H. 2021. An improved method for retrieving aerosol optical depth using gaofen-1 WFV camera data. Remote Sensing, 13(2): 280 [DOI: 10.3390/rs13020280http://dx.doi.org/10.3390/rs13020280]

Zhang H, Li J, Liu Q H, Dong Y D, Li S Z, Zhang Z X, Zhu X R, Liu L Y and Zhao J. 2021. Estimating leaf area index with dynamic leaf optical properties. Remote Sensing, 13(23): 4898 [DOI: 10.3390/rs13234898http://dx.doi.org/10.3390/rs13234898]

Zhao Y S. 2003. Principles and Methods of Analysis of Remote Sensing Applications. Beijing: Science Press

赵英时. 2003. 遥感应用分析原理与方法. 北京: 科学出版社

Zhong B, Yang A X, Liu Q H, Wu S L, Shan X J, Mu X H, Hu L F and Wu J J. 2021. Analysis ready data of the chinese gaofen satellite data. Remote Sensing, 13(9): 1709 [DOI: 10.3390/rs13091709http://dx.doi.org/10.3390/rs13091709]

文章被引用时，请邮件提醒。

提交

迁移学习方法提取高分一号影像汶川地震震后滑坡

高分一号卫星遥感数据驱动ETMonitor模型估算16 m分辨率蒸散发及验证

考虑植被端元变异性的光谱解混方法

智能手机农作物叶面积指数测量算法改进

山地森林叶面积指数（LAI）遥感估算研究进展