关于遥感实验场数字孪生体构建的思考
Digital twin of remote sensing experiment field： Theory and key technology
- 2023年27卷第3期 页码：584-598
纸质出版日期： 2023-03-07
DOI： 10.11834/jrs.20232247
扫 描 看 全 文
浏览全部资源
扫码关注微信
摘要
遥感机理模型构建、地表参数反演、遥感产品生产以及真实性检验等均离不开完备的地面先验知识支持，然而目前实验观测、模型模拟等均难以满足观测的完备性需求。目前，基于遥感实验场的数字孪生体生成遥感先验知识以支持遥感基础研究的思路逐渐成熟：突破物理遥感实验场的协同观测技术瓶颈，实现场景三维结构的数字重建；耦合辐射传输、能量平衡和植物生长模型，实现遥感实验场模拟的动态演进；基于物理遥感实验场数字孪生体驱动和约束地表观测数据，通过同化观测与模拟数据反馈优化机理模型，生成精度高、时间连续的完备观测数据集作为先验知识，以支撑遥感机理模型、遥感反演方法和真实性检验等研究。遥感实验场数字孪生体构建方法有望成为小尺度地球系统数字孪生体构建理论的雏形，进而推动地球科学各个学科全面、协同发展。
Abstract
In the context of remote sensing research and application
complete and reliable “ground a priori knowledge” datasets play an essential role in physical-based model construction
land surface parameter inversion
and remote sensing product production and validation. Ill-posed inversion problems
such as the case in which the observation information is less than the inversion target parameters that results in underdetermined inversion parameters
lead to uncertainty in the solution. A priori knowledge is an important support to solving the ill-posed problem of parameter inversion based on physical and empirical models. However
its completeness
accuracy
and timeliness are limited. The traditional methods of obtaining ground a priori knowledge include experimental measurements using various surface/near-surface sensors and numerical simulations using many physical models
such as one- or three-dimensional radiative transfer models. These current methods have their own advantages and disadvantages but cannot meet the need of comprehensive dataset production in spectral
temporal
angular
and spatial aspects for supporting the research and development of remote sensing science and technology when used alone. On the basis of the studies on experimental measurement
modeling of radiative transfer and ecological processes
and land surface parameter inversion and validation
we propose an innovative strategy to support remote sensing research by building a digital twin of the remote sensing experimental field. Several steps are designed for generating remote sensing a priori knowledge on the basis of the digital twin of the remote sensing experimental field. The three-dimensional structure of a scene is digitally reproduced from the surface by multiple experimental measurements of structural descriptors or the near-surface by remotely obtained data
such as the high-resolution visible and near-infrared images and light detection and ranging (lidar) point-clouds from the observation on Unmanned Aerial Vehicle (UAV) or other platforms based on a cooperative observation technology
recorded in a format accessible by simulation models. The systemic evolution of the surface simulations of physical processes can be realized by coupling radiative transfer
energy balance
evapotranspiration
and plant growth modeling theories as a synthesized model and applying the model to an experimental site in virtual space to illuminate and realize the dynamic progression of the remote sensing experiment field. Driven and constrained by the surface/near-surface collaborative observation data processed by data science and statistical methods
such as data fusion and data augmentation
the synthesized model is optimized by the feedback from the data assimilation of observation measurements and corresponding simulation data
increasing the consistency of the simulation results with the actual dynamic evolution of the remote sensing experimental field in the real world. Through the optimized model and the field measurements
a complete and coherent a priori knowledge of the remote sensing experimental field is achieved with high numerical precision and temporal continuity
supporting the development of remote sensing mechanism model construction and remote sensing inversion method and validation and improving the level of basic remote sensing research. The conditions for the development and application of digital twins in remote sensing experimental sites are gradually maturing. The construction of the remote sensing experimental field digital twin is expected to become the rudiment of digital twin construction theory of a small-scale ecosystem
which may in turn promote the comprehensive and collaborative development of various disciplines in geoscience.
关键词
遥感实验场辐射传输计算机模拟数据同化数字孪生完备数据集
Keywords
remote sensing experiment fieldradiative transfercomputer simulationdata assimilationdigital twincomprehensive dataset
references
Åkerblom M, Raumonen P, Casella E, Disney M I, Danson F M, Gaulton R, Schofield L A, Kaasalainen M. 2018. Non-intersecting leaf insertion algorithm for tree structure models. Interface Focus 8, 20170045.
Allen M T, Prusinkiewicz P, DeJong T M. 2005. Using L-systems for modeling source-sink interactions, architecture and physiology of growing trees: the L-PEACH model. New Phytologist 166, 869-880.
Bai J H, Xiao Q, Liu Q H and Wen J G. 2015. The research of constructing the target ranges to validate remote sensing products. Remote Sensing Technology and Application, 30(3): 573-578
柏军华, 肖青, 柳钦火, 闻建光. 2015. 遥感产品真实性检验靶场构建方法初步研究. 遥感技术与应用, 30(3): 573-578 [DOI: 10.11873/j.issn.1004-0323.2015.3.0573http://dx.doi.org/10.11873/j.issn.1004-0323.2015.3.0573]
Bailey B N, Ochoa M H. 2018. Semi-direct tree reconstruction using terrestrial LiDAR point cloud data. Remote Sensing of Environment 208, 133-144.
Bauer P, Dueben P D, Hoefler T, Quintino T, Schulthess T C and Wedi N P. 2021. The digital revolution of Earth-system science. Nature Computational Science, 1(2): 104-113 [DOI: 10.1038/s43588-021-00023-0http://dx.doi.org/10.1038/s43588-021-00023-0]
Bian Z J, Du Y M, Li H, Cao B, Huang H G, Xiao Q and Liu Q H. 2017. Modeling the temporal variability of thermal emissions from row-planted scenes using a radiosity and energy budget method. IEEE Transactions on Geoscience and Remote Sensing, 55(10): 6010-6026 [DOI: 10.1109/TGRS.2017.2719098http://dx.doi.org/10.1109/TGRS.2017.2719098]
Bian Z J, Qi J B, Gastellu-Etchegorry J P, Roujean J L, Cao B, Li H, Du Y M, Xiao Q and Liu Q H. 2022. A GPU-Based Solution for Ray Tracing 3-D Radiative Transfer Model for Optical and Thermal Images. IEEE Geoscience and Remote Sensing Letters 19:1-5.
Cao B, Liu Q H, Du Y M, Roujean J L, Gastellu-Etchegorry J P, Trigo I F, Zhan W F, Yu Y Y, Cheng J, Jacob F, Lagouarde J P, Bian Z J, Li H, Hu T and Xiao Q. 2019. A review of earth surface thermal radiation directionality observing and modeling: historical development, current status and perspectives. Remote Sensing of Environment, 232: 111304 [DOI: 10.1016/j.rse.2019.111304http://dx.doi.org/10.1016/j.rse.2019.111304]
Combal B, Baret F, Weiss M, Trubuil A, Macé D, Pragnère A, Myneni R, Knyazikhin Y, Wang L. 2003. Retrieval of canopy biophysical variables from bidirectional reflectance: Using prior information to solve the ill-posed inverse problem. Remote Sensing of Environment 84, 1-15.
Côté J F, Widlowski J L, Fournier R A and Verstraete M M. 2009. The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial lidar. Remote Sensing of Environment, 113(5): 1067-1081 [DOI: 10.1016/j.rse.2009.01.017http://dx.doi.org/10.1016/j.rse.2009.01.017]
Dente L, Satalino G, Mattia F and Rinaldi M. 2008. Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote Sensing of Environment, 112(4): 1395-1407 [DOI: 10.1016/j.rse.2007.05.023http://dx.doi.org/10.1016/j.rse.2007.05.023]
de Wit A J W and van Diepen C A. 2007. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agricultural and Forest Meteorology, 146(1/2): 38-56 [DOI: 10.1016/j.agrformet.2007.05.004http://dx.doi.org/10.1016/j.agrformet.2007.05.004]
Disney M I, Lewis P and North P R J. 2000. Monte Carlo ray tracing in optical canopy reflectance modelling. Remote Sensing Reviews, 18(2/4): 163-196 [DOI: 10.1080/02757250009532389http://dx.doi.org/10.1080/02757250009532389]
España M L, Baret F, Aries F, Chelle M, Andrieu B and Prévot L. 1999. Modeling maize canopy 3D architecture: application to reflectance simulation. Ecological Modelling, 122(1/2): 25-43 [DOI: 10.1016/S0304-3800(99)00070-8http://dx.doi.org/10.1016/S0304-3800(99)00070-8]
Favorskaya M N and Jain L C. 2017. Tree modelling in virtual reality environment//Favorskaya M N and Jain L C, eds. Handbook on Advances in Remote Sensing and Geographic Information Systems. [s.l.]: Springer: 141-179 [DOI: 10.1007/978-3-319-52308-8_5http://dx.doi.org/10.1007/978-3-319-52308-8_5]
Gastellu-Etchegorry J P. 2008. 3D modeling of satellite spectral images, radiation budget and energy budget of urban landscapes. Meteorology and Atmospheric Physics, 102(3): 187-207 [DOI: 10.1007/s00703-008-0344-1http://dx.doi.org/10.1007/s00703-008-0344-1]
Gastellu-Etchegorry J P, Demarez V, Pinel V and Zagolski F. 1996. Modeling radiative transfer in heterogeneous 3-D vegetation canopies. Remote Sensing of Environment, 58(2): 131-156 [DOI: 10.1016/0034-4257(95)00253-7http://dx.doi.org/10.1016/0034-4257(95)00253-7]
Gastellu-Etchegorry J P, Lauret N, Yin, T, Landier L, Kallel A, Malenovský Z, Bitar A A, Aval J, Benhmida S, Qi J, Medjdoub G, Guilleux J, Chavanon E, Cook B, Morton D, Chrysoulakis N, Mitraka Z, 2017. DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, 2640-2649.
Goel N S, Rozehnal I and Thompson R L. 1991. A computer graphics based model for scattering from objects of arbitrary shapes in the optical region. Remote Sensing of Environment, 36(2): 73-104 [DOI: 10.1016/0034-4257(91)90032-2http://dx.doi.org/10.1016/0034-4257(91)90032-2]
Govaerts Y M and Verstraete M M. 1998. Raytran: a Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media. IEEE Transactions on Geoscience and Remote Sensing, 36(2): 493-505 [DOI: 10.1109/36.662732http://dx.doi.org/10.1109/36.662732]
Guérif M and Duke C. 1998. Calibration of the SUCROS emergence and early growth module for sugar beet using optical remote sensing data assimilation. European Journal of Agronomy, 9(2/3): 127-136 [DOI: 10.1016/S1161-0301(98)00031-8http://dx.doi.org/10.1016/S1161-0301(98)00031-8]
Hackenberg J, Morhart C, Sheppard J, Spiecker H and Disney M. 2014. Highly accurate tree models derived from terrestrial laser scan data: a method description. Forests, 5(5): 1069-1105 [DOI: 10.3390/f5051069http://dx.doi.org/10.3390/f5051069]
Huang H G, Liu Q H, Qin W H, Du Y M and Li X W. 2011. Temporal patterns of thermal emission directionality of crop canopies. Journal of Geophysical Research: Atmospheres, 116(D6): D06114 [DOI: 10.1029/2010JD014613http://dx.doi.org/10.1029/2010JD014613]
Huang H G, Qin W H and Liu Q H. 2013. RAPID: a Radiosity Applicable to Porous IndiviDual Objects for directional reflectance over complex vegetated scenes. Remote Sensing of Environment, 132: 221-237 [DOI: 10.1016/j.rse.2013.01.013http://dx.doi.org/10.1016/j.rse.2013.01.013]
Huang J X, Tian L Y, Liang S L, Ma H Y, Becker-Reshef I, Huang Y B, Su W, Zhang X D, Zhu D H and Wu W B. 2015. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 204: 106-121 [DOI: 10.1016/j.agrformet.2015.02.001http://dx.doi.org/10.1016/j.agrformet.2015.02.001]
Lewis P. 1999. Three-dimensional plant modelling for remote sensing simulation studies using the Botanical Plant Modelling System. Agronomie, 19(3/4): 185-210 [DOI: 10.1051/agro:19990302http://dx.doi.org/10.1051/agro:19990302]
Li D R, Zhang G, Jiang Y H, Shen X and Liu W L. 2022. Opportunities and challenges of geo-spatial information science from the perspective of big data. Big Data Research 7(2): 3-14
李德仁, 张过, 蒋永华, 沈欣, 刘伟玲. 2022. 论大数据视角下的地球空间信息学的机遇与挑战. 大数据, 8(2): 3-14 [DOI: 10.11959/j.issn.2096-0271.2022012http://dx.doi.org/10.11959/j.issn.2096-0271.2022012]
Li X. 2014. Characterization, controlling, and reduction of uncertainties in the modeling and observation of land-surface systems. Science China Earth Sciences, 57(1): 80-87
李新. 2013. 陆地表层系统模拟和观测的不确定性及其控制. 中国科学: 地球科学, 43(11): 1735-1742
Li X, Cheng G D, Liu S M, Xiao Q, Ma M G, Jin R, Che T, Liu Q H, Wang W Z, Qi Y, Wen J G, Li H Y, Zhu G F, Guo J W, Ran Y H, Wang S G, Zhu Z L, Zhou J, Hu X L and Xu Z W. 2013. Heihe Watershed Allied Telemetry Experimental Research (HiWATER): scientific objectives and experimental design. Bulletin of the American Meteorological Society, 94(8): 1145-1160 [DOI: 10.1175/BAMS-D-12-00154.1http://dx.doi.org/10.1175/BAMS-D-12-00154.1]
Li X W, Wang J D and Strahler A H. 1995. A hybrid geometric optical-radiative transfer approach for modeling light absorption and albedo of discontinuous canopies. Science in China(Series B), 38(07):807-816.
Li X W, Wang J D , Hu B X and Strahler A H. 1998. The role of prior knowledge in remote sensing inversion. Sci China Ser D-Earth Sci, 28(1): 62-67
李小文, 王锦地, 胡宝新，Strahler A H. 1998. 先验知识在遥感反演中的作用. 中国科学: D辑, 28(1): 67-72
Liu C J, Lv J, Ren M L, Chen S, Zhang X L, Song W L and Zhang D W. 2022. Research and application of digital twin intelligent flood prevention system in Huaihe River Basin. China Flood and Drought Management, 32(1): 47-53
刘昌军, 吕娟, 任明磊, 陈胜, 张晓蕾, 宋文龙, 张大伟. 2022. 数字孪生淮河流域智慧防洪体系研究与实践. 中国防汛抗旱, 32(1): 47-53 [DOI: 10.16867/j.issn.1673-9264.2021375http://dx.doi.org/10.16867/j.issn.1673-9264.2021375]
Liu Q H, Li X W and Chen L F. 2002. Field campaign for quantitative remote sensing in Beijing//IEEE International Geoscience and Remote Sensing Symposium. Toronto, ON: IEEE: 3133-3135 [DOI: 10.1109/IGARSS.2002.1027109http://dx.doi.org/10.1109/IGARSS.2002.1027109]
Liu Q H, Tang Y, Li J, Du Y M, Wen J G, Yao Y J, Huang H G and Tian G L. 2009. Research progress on modeling and inversion of remote sensing radiative transfer. Journal of Remote Sensing, 13(S1): 168-182
柳钦火, 唐勇, 李静, 杜永明, 闻建光, 姚延娟, 黄华国, 田国良. 2009. 遥感辐射传输建模与反演研究进展. 遥感学报, 13(S1): 168-182 [DOI: 10.11834/jrs.20090023http://dx.doi.org/10.11834/jrs.20090023]
Liu Q H, Yan G J, Jiao Z T, Xiao Q, Wen J G, Liang S L and Wang J D. 2019. Geometric-optical remote sensing modeling to quantitative remote sensing theory and methodology development: in memory of academician Li Xiaowen. Journal of Remote Sensing, 23(1): 1-10
柳钦火, 阎广建, 焦子锑, 肖青, 闻建光, 梁顺林, 王锦地. 2019. 发展几何光学遥感建模理论, 推动定量遥感科学前行——深切缅怀李小文院士. 遥感学报, 23(1): 1-10 [DOI: 10.11834/jrs.20198077http://dx.doi.org/10.11834/jrs.20198077]
Lučić M., Tschannen M., Ritter M., Zhai X., Bachem O., Gelly S., 2019. High-Fidelity Image Generation With Fewer Labels, in: Proceedings of the 36th International Conference on Machine Learning. Presented at the International Conference on Machine Learning, PMLR, pp. 4183–4192.
Ma M G, Che T, Li X, Xiao Q, Zhao K and Xin X P. 2015. A prototype network for remote sensing validation in China. Remote Sensing, 7(5): 5187-5202 [DOI: 10.3390/rs70505187http://dx.doi.org/10.3390/rs70505187]
Mao K B, Yang J, Han X Z, Tang S H, Yuan Z J and Gao C Y. 2018. Retrieving land surface temperature based on deep dynamic learning NN algorithm and radiation transmission model. China Agricultural Information, 30(5): 47-57
毛克彪, 杨军, 韩秀珍, 唐世浩, 袁紫晋, 高春雨. 2018. 基于深度动态学习神经网络和辐射传输模型地表温度反演算法研究. 中国农业信息, 30(5): 47-57 [DOI: 10.12105/j.issn.1672-0423.20180506http://dx.doi.org/10.12105/j.issn.1672-0423.20180506]
Melendo-Vega J R, Martín M P, Pacheco-Labrador J, González-Cascón R, Moreno G, Pérez F, Migliavacca M, García M, North P and Riaño D. 2018. Improving the performance of 3-D radiative transfer model FLIGHT to simulate optical properties of a tree-grass ecosystem. Remote Sensing, 10(12): 2061 [DOI: 10.3390/rs10122061http://dx.doi.org/10.3390/rs10122061]
North P R J. 1996. Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Transactions on Geoscience and Remote Sensing, 34(4): 946-956 [DOI: 10.1109/36.508411http://dx.doi.org/10.1109/36.508411]
Pharr M, Jakob W and Humphreys G. 2016. Physically Based Rendering: From Theory to Implementation. 3rd ed. Cambridge, MA: Morgan Kaufmann
Qi J B, Xie D H, Yin T G, Yan G J, Gastellu-Etchegorry J P, Li L Y, Zhang W M, Mu X H and Norford L K. 2019. LESS: physical radiative transfer modeling system for efficient 3D landscape construction and data simulation. Remote Sensing of Environment, 221: 695-706.
Qin W H and Gerstl S A W. 2000. 3-D scene modeling of semidesert vegetation cover and its radiation regime. Remote Sensing of Environment, 74(1): 145-162 [DOI: 10.1016/S0034-4257(00)00129-2http://dx.doi.org/10.1016/S0034-4257(00)00129-2]
Quan L, Tan P, Zeng G, Yuan L, Wang J and Kang S B. 2006. Image-based plant modeling. ACM Transactions on Graphics, 25(3): 599-604 [DOI: 10.1145/1141911.1141929http://dx.doi.org/10.1145/1141911.1141929]
Raj P. 2021. Empowering digital twins with blockchain. Advances in Computers, 121: 267-283 [DOI: 10.1016/bs.adcom.2020.08.013http://dx.doi.org/10.1016/bs.adcom.2020.08.013]
Rasheed A, San O and Kvamsdal T. 2020. Digital twin: values, challenges and enablers from a modeling perspective. IEEE Access, 8: 21980-22012 [DOI: 10.1109/ACCESS.2020.2970143http://dx.doi.org/10.1109/ACCESS.2020.2970143]
Reichstein M., Camps-Valls G., Stevens B., Jung M., Denzler J., Carvalhais N., Prabhat, 2019. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195-204.
Rivera J P, Verrelst J, Leonenko G, Moreno J. 2013. Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model. Remote Sensing, 5(7):3280-3304.
Schneider F D, Leiterer R, Morsdorf F, Gastellu-Etchegorry J P, Lauret N, Pfeifer N and Schaepman M E. 2014. Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data. Remote Sensing of Environment, 152: 235-250 [DOI: 10.1016/j.rse.2014.06.015http://dx.doi.org/10.1016/j.rse.2014.06.015]
Sellers P, Hall F, Margolis H, Kelly B, Baldocchi D, Den Hartog G, Cihlar J, Ryan M G, Goodison B, Crill P, Ranson K J, Lettenmaier D and Wickland D E. 1995. The Boreal Ecosystem-Atmosphere Study (BOREAS): an overview and early results from the 1994 field year. Bulletin of the American Meteorological Society, 76(9): 1549-1577 [DOI: 10.1175/1520-0477(1995)076<1549:TBESAO>2.0.CO;2http://dx.doi.org/10.1175/1520-0477(1995)076<1549:TBESAO>2.0.CO;2]
Sellers P J, Hall F G, Asrar G, Strebel D E and Murphy R E. 1988. The First ISLSCP Field Experiment (FIFE). Bulletin of the American Meteorological Society, 69(1): 22-27 [DOI: 10.1175/1520-0477(1988)069<0022:TFIFE>2.0.CO;2http://dx.doi.org/10.1175/1520-0477(1988)069<0022:TFIFE>2.0.CO;2]
Shen H F, Jiang Y, Li T W, Cheng Q, Zeng C and Zhang L P. 2020. Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data. Remote Sensing of Environment, 240: 111692 [DOI: 10.1016/j.rse.2020.111692http://dx.doi.org/10.1016/j.rse.2020.111692]
Tao F, Zhang H, Liu A and Nee A Y C. 2019. Digital twin in industry: state-of-the-art. IEEE Transactions on Industrial Informatics, 15(4): 2405-2415 [DOI: 10.1109/TII.2018.2873186http://dx.doi.org/10.1109/TII.2018.2873186]
Widlowski J L, Pinty B, Lopatka M, Atzberger C, Buzica D, Chelle M, Disney M, Gastellu-Etchegorry J P, Gerboles M, Gobron N, Grau E, Huang H, Kallel A, Kobayashi H, Lewis P E, Qin W, Schlerf M, Stuckens J and Xie D. 2013. The fourth radiation transfer model intercomparison (RAMI-IV): proficiency testing of canopy reflectance models with ISO-13528. Journal of Geophysical Research: Atmospheres, 118(13): 6869-6890 [DOI: 10.1002/jgrd.50497http://dx.doi.org/10.1002/jgrd.50497]
Widlowski J L, Robustelli M, Disney M, Gastellu-Etchegorry J P, Lavergne T, Lewis P, North P R J, Pinty B, Thompson R and Verstraete M M. 2008. The RAMI On-line Model Checker (ROMC): a web-based benchmarking facility for canopy reflectance models. Remote Sensing of Environment, 112(3): 1144-1150 [DOI: 10.1016/j.rse.2007.07.016http://dx.doi.org/10.1016/j.rse.2007.07.016]
Wu L, Bai J H, Xiao Q, Du Y M, Liu Q H and Xu L P. 2017. Research progress and prospect on combining crop growth models with parameters derived from quantitative remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 33(9): 155-166
吴蕾, 柏军华, 肖青, 杜永明, 柳钦火, 徐丽萍. 2017. 作物生长模型与定量遥感参数结合研究进展与展望. 农业工程学报, 33(9): 155-166 [DOI: 10.11975/j.issn.1002-6819.2017.09.020http://dx.doi.org/10.11975/j.issn.1002-6819.2017.09.020]
Wu L, Gao Z J, Shi W Z, Tang X M, Gao Y, Fang Y, Liu Y F, Li P W, Gan Y L, Fang L. 2010. Uncertainties in Geographical Information System. Beijing: Electronic Industry Press
邬伦, 高振记, 史文中, 唐新明, 高勇, 方裕, 刘岳峰, 李佩武, 甘宇亮, 方利. 2010. 地理信息系统中的不确定性问题. 北京: 电子工业出版社
Wu X D, Wen J G, Xiao Q, You D Q, Lin X W, Wu S B and Zhong S Y. 2019. Impacts and contributors of representativeness errors of in situ albedo measurements for the validation of remote sensing products. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 9740-9755 [DOI: 10.1109/TGRS.2019.2928954http://dx.doi.org/10.1109/TGRS.2019.2928954]
Wu X D, Wen J G, Xiao Q, You D Q, Liu Q and Lin X W. 2018. Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface. International Journal of Digital Earth, 11(5): 470-484 [DOI: 10.1080/17538947.2017.1334097http://dx.doi.org/10.1080/17538947.2017.1334097]
Wu X D, Wen J G, Xiao Q, Yu Y Y, You D Q and Hueni A. 2017. Assessment of NPP VIIRS albedo over heterogeneous crop land in Northern China. Journal of Geophysical Research: Atmospheres, 122(24): 13138-13154 [DOI: 10.1002/2017JD027262http://dx.doi.org/10.1002/2017JD027262]
Wu X D, Xiao Q, Wen J G and You D Q. 2019. Advances and challenges in the validation of remote sensing albedo products. Journal of Remote Sensing, 23(1): 11-23
吴小丹, 肖青, 闻建光, 游冬琴. 2019. 异质性地表反照率遥感产品真实性检验研究现状及挑战. 遥感学报, 23(1): 11-23 [DOI: 10.11834/jrs.20198057http://dx.doi.org/10.11834/jrs.20198057]
Yang G J, Zhao C J, Xing Z R, Huang W J and Wang J H. 2011. LAI inversion of spring wheat based on PROBA/CHRIS hyperspectral multi-angular data and PROSAIL model. Transactions of the Chinese Society of Agricultural Engineering, 27(10): 88-94
杨贵军, 赵春江. 2011. 基于PROBA/CHRIS遥感数据和PROSAIL模型的春小麦LAI反演. 农业工程学报, 27(10): 88-94 [DOI: 10.3969/j.issn.1002-6819.2011.10.016http://dx.doi.org/10.3969/j.issn.1002-6819.2011.10.016]
Yuan Q Q, Xu H Z, Li T W, Shen H F and Zhang L P. 2020. Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental U.S.. Journal of Hydrology, 580: 124351 [DOI: 10.1016/j.jhydrol.2019.124351http://dx.doi.org/10.1016/j.jhydrol.2019.124351]
Zhang L P and Wu C. 2017. Advance and future development of change detection for multi-temporal remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10): 1447-1459
张良培, 武辰. 2017. 多时相遥感影像变化检测的现状与展望. 测绘学报, 46(10): 1447-1459 [DOI: 10.11947/j.AGCS.2017.20170340http://dx.doi.org/10.11947/j.AGCS.2017.20170340]
Zhang Y, Shi Y L, Choi S, Ni X L and Myneni R B. 2019. Mapping maximum tree height of the Great Khingan Mountain, Inner Mongolia using the Allometric scaling and resource limitations model. Forests, 10(5): 380 [DOI: 10.3390/f10050380http://dx.doi.org/10.3390/f10050380]
Zhao F, Li Y G, Dai X, Verhoef W, Guo Y Q, Shang H, Gu X F, Huang Y B, Yu T and Huang J X. 2015. Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops. Remote Sensing of Environment, 156: 129-142 [DOI: 10.1016/j.rse.2014.09.011http://dx.doi.org/10.1016/j.rse.2014.09.011]
Zhu Q, Zhu J, Huang H P, Wang W and Zhang L G. 2020. Real 3D spatial information platform and digital twin Sichuan-Tibet railway. High Speed Railway Technology, 11(2) 46-53
朱庆, 朱军, 黄华平, 王玮, 张利国. 2020. 实景三维空间信息平台与数字孪生川藏铁路. 高速铁路技术, 11(2): 46-53 [DOI: 10.12098/j.issn.1674-8247.2020.02.008http://dx.doi.org/10.12098/j.issn.1674-8247.2020.02.008]
文章被引用时，请邮件提醒。
提交
相关作者
暂无数据
相关机构
中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室
中国电建集团成都勘测设计研究院有限公司
水利部防洪抗旱减灾工程技术研究中心
中国科学院空天信息创新研究院数字地球重点实验室
福建师范大学地理研究所
选择翻译语言
- 地址：北京市海淀区北四环西路19号 《遥感学报》编辑部 邮编：100190
- 联系电话：010-58887052 Email：nrsb@aircas.ac.cn
- 技术支持由北京北大方正电子有限公司提供 京ICP备20021838号-9
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
0
回到顶部