Please wait a minute...
 
Remote Sensing for Natural Resources    2023, Vol. 35 Issue (2) : 176-181     DOI: 10.6046/zrzyyg.2022358
|
A method for constructing a reference dataset for the validation of remote sensing products
CAI Zhenfeng1(), JI Peng2, ZHUFU Xuezhi2, LIU Yufang3()
1. Lanshan Bureau of Natural Resources, Linyi 276001, China
2. Linyi Institute of Natural Resources Surveying and Mapping, Linyi 276000, China
3. Piesat Information Technology Co., Ltd., Beijing 100195, China
Download: PDF(2492 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The validation of remote sensing products (RSPs) is necessary for the quality assessment of RSPs in order to ensure the reliable and effective application of RSPs. However, the existing validation of RSPs lacks large-scale engineering reference datasets above the regional level. In view of this fact, this study proposed a cross-validation-based method for constructing a reference dataset for RSP validation. First, a reference dataset of China organized by sheet and time was constructed using the Landsat8 OLI data whose accuracy had been verified. Then, the annual optimal reference dataset, which was easy to retrieve and update and enabled large-scale construction, was formed finally. After seven bands of the ZY1E hyperspectrum were matched according to the center wavelength, the reference dataset was used to verify the reflectance of ZY1E images. The calculation of the confusion matrix between ground truth (GT) data and automatic rating results yielded an overall accuracy of 87% and a Kappa coefficient of 0.83, meeting the requirements for engineering applications. The method for constructing a reference dataset proposed in this study provides technical support for large-scale, engineering-oriented RSP validation.
Keywords validation      cross-validation      reference dataset      engineering application     
ZTFLH:  TP79  
Issue Date: 07 July 2023
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Zhenfeng CAI
Peng JI
Xuezhi ZHUFU
Yufang LIU
Cite this article:   
Zhenfeng CAI,Peng JI,Xuezhi ZHUFU, et al. A method for constructing a reference dataset for the validation of remote sensing products[J]. Remote Sensing for Natural Resources, 2023, 35(2): 176-181.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022358     OR     https://www.gtzyyg.com/EN/Y2023/V35/I2/176
Fig.1  Overall framework of the reference dataset system construction
Fig.2  Construction process framework of multi-year framing reference datasets
Fig.3  Framework of the construction process of the annual optimal reference data set
Landsat8
OLI波段		 波长范围/μm ZY1E相应波段 中心波长/μm
B1 Coastal 0.433~0.453 B7 0.447
B2 Blue 0.450~0.515 B11 0.482
B3 Green 0.525~0.600 B20 0.559
B4 Red 0.630~0.680 B31 0.654
B5 NIR 0.845~0.885 B56 0.868
B6 SWIR1 1.560~1.660 B113 1.678
B7 SWIR2 2.100~2.300 B148 2.267
Tab.1  The first seven bands of Landsat8 OLI match the center wavelengths of the corresponding bands of ZY1E
Tab.2  Image rating reference standards
参考数据
自动评级结果 优 良 中 差 合计
优 20 2 0 0 22
良 4 22 2 0 28
中 1 1 19 1 22
差 0 1 1 26 28
合计 25 26 22 27 100
精度 OA/% 87
Kappa 0.83
Tab.3  Confusion matrix of evaluation results
[1] 吴小丹, 肖青, 闻建光, 等. 遥感数据产品真实性检验不确定性分析研究进展[J]. 遥感学报, 2014, 18(5):1011-1023.
[1] Wu X D, Xiao Q, Wen J G, et al. Advances in uncertainty analysis for the validation of remote sensing products: Take leaf area index for example[J]. Journal of Remote Sensing, 2014, 18(5):1011-1023.
[2] 吴小丹, 闻建光, 肖青, 等. 关键陆表参数遥感产品真实性检验方法研究进展[J]. 遥感学报, 2015, 19(1):75-92.
[2] Wu X D, Wen J G, Xiao Q, et al. Advances in validation methods for remote sensing products of land surface parameters[J]. Journal of Remote Sensing, 2015, 19(1):75-92.
[3] 马晋, 周纪, 刘绍民, 等. 卫星遥感地表温度的真实性检验研究进展[J]. 地球科学进展, 2017, 32(6):615-629.
doi: 10.11867/j.issn.1001-8166.2017.06.0615
[3] Ma J, Zhou J, Liu S M, et al. Review on validation of remotely sensed land surface temperature[J]. Advances in Earth Science, 2017, 32(6):615-629.
doi: 10.11867/j.issn.1001-8166.2017.06.0615
[4] 晋锐, 李新, 马明国, 等. 陆地定量遥感产品的真实性检验关键技术与试验验证[J]. 地球科学进展, 2017, 32(6):630-642.
doi: 10.11867/j.issn.1001-8166.2017.06.0630
[4] Jin R, Li X, Ma M G, et al. Key methods and experiment verification for the validation of quantitative remote sensing products[J]. Advances in Earth Science, 2017, 32(6):630-642.
doi: 10.11867/j.issn.1001-8166.2017.06.0630
[5] 高海亮, 顾行发, 周翔, 等. 遥感共性产品真实性检验系统框架设计[J]. 无线电工程, 2021, 51(12):1389-1396.
[5] Gao H L, Gu X F, Zhou X, et al. Framework design of remote sensing common product validation system[J]. Radio Engineering, 2021, 51(12):1389-1396.
[6] Justice C, Belward A, Morisette J T, et al. Developments in the validation of satellite sensor products for the study of the land surface[J]. International Journal of Remote Sensing, 2000, 21 (17):3383-3390.
doi: 10.1080/014311600750020000 url: https://www.tandfonline.com/doi/full/10.1080/014311600750020000
[7] Baret F, Morissette J, Fernandes R, et al. Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: Proposition of the CEOS-BELMANIP[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(7):1794-1803.
doi: 10.1109/TGRS.2006.876030 url: http://ieeexplore.ieee.org/document/1645280/
[8] 张良. 遥感陆表特征参量产品真实性检验关键技术研究与系统实现[D]. 成都: 电子科技大学, 2015.
[8] Zhang L. The research of the validation of remote sensing land surface products and system implementation[D]. Chengdu: University of Electronic Science and Technology of China, 2015.
[9] 张仁华, 田静, 李召良, 等. 定量遥感产品真实性检验的基础与方法[J]. 中国科学(地球科学), 2010, 40(2):211-222.
[9] Zhang R H, Tian J, Li Z L, et al. Principles and methods for the validation of quantitative remote sensing products[J]. Science China Earth Sciences, 2010, 40(2):211-222.
[10] 杨闫君, 田庆久, 黄彦, 等. 高分一号影像水稻叶面积指数反演真实性检验[J]. 遥感信息, 2015, 30(5):62-68.
[10] Yang Y J, Tian Q J, Huang Y, et al. Validation of rice leaf area index inversion on GF-1 satellite remote sensing imagery[J]. Remote Sensing Information, 2015, 30(5):62-68.
[11] 林兴稳. 山区地表反照率遥感产品真实性检验方法研究[D]. 北京: 中国科学院大学(中国科学院遥感与数字地球研究所), 2018.
[11] Lin X W. Land surface albedo remote sensing products validation over rugged terrain[D]. Beijing: University of Chinese Academy of Sciences (Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences), 2018.
[12] Weiss M, Baret F, Garrigues S, et al. LAI and fAPAR CYCLOPES global products derived from VEGETATION.Part 2: Validation and comparison with MODIS collection 4 products[J]. Remote Sensing of Environment, 2007, 110 (3):317-331.
doi: 10.1016/j.rse.2007.03.001 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425707000910
[13] 于惠, 冯琦胜, 陈思宇, 等. 基于微波植被指数的甘南草地生物量动态监测[J]. 兰州大学学报(自然科学版), 2011, 47(4):69-74.
[13] Yu H, Feng Q S, Chen S Y, et al. Grassland biomass dynamic monitoring in Gannan Prefecture based on the microwave vegetation indices[J]. Journal of Lanzhou University (Natural Sciences), 2011, 47(4):69-74.
[14] Chen Y M, Liang S, Wang J, et al. Validation of MISR land surface broadband albedo[J]. International Journal of Remote Sensing, 2002, 29 (23):6971-6983.
doi: 10.1080/01431160802199876 url: https://www.tandfonline.com/doi/full/10.1080/01431160802199876
[1] MA Junjun, WANG Chunlei, HUANG Xiaohong. The remote sensing inversion and validation of land surface temperature based on ASTER data: A case study of the Heihe River basin[J]. Remote Sensing for Natural Resources, 2023, 35(1): 198-204.
[2] WEN Yanan, CHE Yahui, GUANG Jie, ZHANG Xiaomei, LI Zhengqiang. Quality evaluation of forest cover products over China[J]. Remote Sensing for Land & Resources, 2020, 32(3): 32-38.
[3] Hui YUAN, Qiming QIN, Yuanheng SUN. Validation of LAI retrieval results of winter wheat in Yancheng, Luohe area of Henan Province[J]. Remote Sensing for Land & Resources, 2020, 32(1): 162-168.
[4] Xianlei FAN, Hongbo YAN, Ying QU. Comparison and validation of the methods for estimating surface albedo from HJ-1 A/B CCD data[J]. Remote Sensing for Land & Resources, 2019, 31(3): 123-131.
[5] Kun LU, Qingyan MENG, Yunxiao SUN, Zhenhui SUN, Linlin ZHANG. Estimating leaf area index of wheat at the booting stage using GF-2 data: A case study of Langfang City,Hebei Province[J]. Remote Sensing for Land & Resources, 2018, 30(1): 196-202.
[6] JINAG Wei, HE Guojin, LONG Tengfei, YIN Ranyu, SONG Xiaolu, YUAN Yiqin, LING Saiguang. Ortho accuracy validation and analysis of GF-2 PAN imagery based on Beidou satellite navigation system and GPS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 211-216.
[7] REN Zhe, CHEN Huailiang, WANG Lianxi, LI Ying, LI Qi. Research on inversion model of wheat LAI using cross-validation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 34-40.
[8] GAO Mao-Fang, QIN Zhi-Hao. THE VALIDATION OF CHINESE LAND SURFACE TEMPERATURE PRODUCTS RETRIEVED
FROM MODERATE RESOLUTION IMAGING SPECTRORATIOMETER DATA[J]. REMOTE SENSING FOR LAND & RESOURCES, 2006, 18(3): 15-18.
[9] GAN Fu-Ping, ZHANG Zong-Gui, WANG Run-Sheng, YANG Kai, LIU Sheng-Wei, YAN Bo-Kun. SPECTRAL APPLICATION IN SPECTRAL RECONSTRUCTION
AND VALIDATION BASED ON HYPERSPECTRAL IMAGING DATA[J]. REMOTE SENSING FOR LAND & RESOURCES, 2005, 17(1): 12-16.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech