基于绿被覆盖率上推的天然草地冠层SPAD值高光谱反演方法

doi:10.3964/j.issn.1000-0593(2024)12-3513-11

摘要
参考文献
相关文章 (15)

全文: PDF (12562 KB)
输出: BibTeX | EndNote (RIS)

摘要：叶绿素是评估草地光合作用能力和生理状况的重要指标。高光谱遥感包含丰富的光谱信息，已经成为无损估算草地叶绿素含量的重要手段。然而，通常获取的草地冠层高光谱数据与实测叶片的叶绿素含量存在尺度不匹配问题，导致高光谱反演叶绿素的精度低。为此，提出基于绿被覆盖率上推的天然草地冠层叶绿素高光谱反演方法。以内蒙古呼伦贝尔典型天然草地为研究对象，利用ASD高光谱仪获取的地面冠层高光谱数据、SPAD叶绿素仪采集的叶片叶绿素相对含量实测值和样方手机数码相片，以绿被覆盖率为媒介，将叶片尺度的叶绿素相对含量实测值上推到冠层尺度。结果表明植被指数与SPAD相关性（-0.74~0.76）普遍高于场平均法上推SPAD（-0.63~0.50）。通过原始冠层光谱反射率、一阶导数光谱和42种常用叶绿素光谱指数构建基于绿被覆盖率上推的天然草地冠层叶绿素高光谱反演模型（SPAD_cover）。单变量的最优草地冠层叶绿素反演模型R²=0.689，RMSE=2.714，RPD=1.752；多元线性逐步回归的最优草地冠层叶绿素反演模型R²=0.833，RMSE=2.019，RPD=2.354。实验表明基于绿被覆盖率将草地叶片叶绿素含量实测值上推至冠层尺度，可以有效提高天然草地冠层叶绿素高光谱反演精度。

关键词：天然草地；绿被覆盖率；高光谱；尺度上推；光谱指数；一阶导数光谱

Abstract：Chlorophyll is a crucial indicator for assessing grasslands' photosynthetic capacity and physiological condition.With its rich spectral information, hyperspectral remote sensing has become an important means for non-invasively estimating chlorophyll content in grasslands. However, there is a scale mismatch between the canopy hyperspectral data and the measured leaf chlorophyll values, leading to hyperspectral chlorophyll retrieval's low accuracy. Therefore, this paper proposes a hyperspectral retrieval method for natural grassland Canopy Chlorophyll based on the green cover rate. The typical natural grassland in Hulunbuir, Inner Mongolia, was selected as the research object. The measured leaf chlorophyll relative content values were obtained by ASD hyperspectral spectrometer, SPAD chlorophyll meter, and mobile phone digital photos.The results indicate that the correlation between vegetation indices and SPAD ranges from -0.74 to 0.76, which is generally higher than the average correlation of SPAD pushed up from -0.63 to 0.50. Green cover media pushed the measured values of leaf chlorophyll relative content to the sample canopy scale. First derivative spectra and 42 common chlorophyll spectral indices were used to construct a hyperspectral retrieval model (SPAD) of natural grassland Canopy Chlorophyll based on green cover rate_ cover. The single variable optimal grassland Canopy Chlorophyll retrieval modelR²=0.689, RMSE=2.714, RPD=1.752; The best regression model of grassland Canopy Chlorophyll wasR²=0.833, RMSE=2.019, RPD=2.354. The results show that the hyperspectral retrieval accuracy of chlorophyll content in natural grassland canopy can be effectively improved by extrapolating the measured value of chlorophyll content in grassland leaves to the canopy scale based on the green cover rate.

Key words：Natural grassland; Green coverage rate; Hyperspectral; Scaling up; Spectral index; First derivative spectrum

收稿日期: 2023-11-11 修订日期: 2024-04-07

中图分类号:

TP79

基金资助: 青海省科技成果转化项目（2022-NK-136），北京市教委-市自然基金联合项目（KZ202110028044），国家自然科学基金（42071303，41571369）资助

作者简介: 张爱武，女，1972年生，首都师范大学教授 e-mail: zhangaw98@163.com

引用本文:

张爱武，李梦南，史剑聪，庞海洋. 基于绿被覆盖率上推的天然草地冠层SPAD值高光谱反演方法[J]. 光谱学与光谱分析, 2024, 44(12): 3513-3523.
ZHANG Ai-wu, LI Meng-nan, SHI Jian-cong, PANG Hai-yang. Hyperspectral Inversion Method for Natural Grassland Canopy SPAD Value Based on Scaling Up of Green Coverage Rate. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(12): 3513-3523.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2024)12-3513-11 或 https://www.gpxygpfx.com/CN/Y2024/V44/I12/3513

[1] LIU Wei，SUN Hai-xia，YANG Xiao-bo，et al(刘炜，孙海霞，杨晓波，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2020，40(7)：2200.
[2] Cao Z，Ma R，Duan H，et al. Remote Sensing of Environment，2020，248：111974.
[3] GAO Jin-long，LIU Jie，YIN Jian-peng，et al(高金龙，刘洁，殷建鹏，等). Acta Prataculturae Sinica(草业学报)，2020，29(2)：172.
[4] ZHENG Zhu-bin，ZHANG Run-fei，LI Jian-zhong，et al(郑著彬，张润飞，李建忠，等). National Remote Sensing Bulletin(遥感学报)，2022，26(11)：2162.
[5] GU Feng，DING Jian-li，GE Xiang-yu，et al(顾峰，丁建丽，葛翔宇，等). Arid Zone Research(干旱区研究)，2019，36(4)：924.
[6] Clevers J G P W，Gitelson A A. International Journal of Applied Earth Observation and Geoinformation，2013，23：344.
[7] Verrelst J, Munoz J, Alonso L，et al. Remote Sensing of Environment，2012，118：127.
[8] Zarco-Tejada P J，Hornero A，Beck P S A，et al. Remote Sensing of Environment，2019，223：320.
[9] WU Jian，HOU Lan-gong，WANG Dong(吴见，侯兰功，王栋). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2014，30(6)：116.
[10] YUAN Jin-guo，NIU Zheng(袁金国，牛铮). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2007，23(4)：172.
[11] YAO Fu-qi，ZHANG Zhen-hua，YANG Run-ya，et al(姚付启，张振华，杨润亚，等). Science of Surveying and Mapping(测绘科学)，2010，35(1)：109.
[12] Main R, Cho M A, Mathieu R，et al. ISPRS Journal of Photogrammetry and Remote Sensing，2011，66(6)：751.
[13] CHEN Lan，CHANG Qing-rui，GAO Yi-fan, et al(陈澜，常庆瑞，高一帆，等). Journal of Northwest A&F University (Natural Science Edition)[西北农林科技大学学报(自然科学版)]，2020，48(6)：79.
[14] Sid'ko A F，Botvich I Yu，Pisman T I，et al. Field Crops Research，2017，207：24.
[15] DENG Chao，CHEN Zhi-biao，CHEN Hai-bin，et al(邓超，陈志彪，陈海滨，等). Geo-Information Science(地球信息科学学报)，2019，21(6)：948.
[16] CHEN Peng，FENG Hai-kuan，LI Chang-chun，et al(陈鹏，冯海宽，李长春，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2019，35(11)：63.
[17] Singhal G, Bansod B, Mathew L，et al. Remote Sensing Applications: Society and Environment，2019，15：100235.
[18] Zhu W，Sun Z，Yang T，et al. Computers and Electronics in Agriculture，2020，178：105786.
[19] Li C，Chen P，Ma C，et al. International Journal of Remote Sensing，2020，41(21)：8176.
[20] Roosjen P P J, Brede B, Suomalainen J M，et al. International Journal of Applied Earth Observation and Geoinformation，2018，66：14.
[21] Zhang M，Su W，Fu Y，et al. European Journal of Agronomy. 2019，111：125938.
[22] CHANG Xiao-yue，CHANG Qing-rui，WANG Xiao-fan，et al(常潇月，常庆瑞，王晓凡，等). Agricultural Research in the Arid Areas(干旱地区农业研究)，2019，37(1)：66.
[23] Su W，Sun Z，Chen W H，et al. Remote Sensing，2019，11(20)：2409.
[24] LIANG Liang，YANG Min-hua，ZHANG Lian-peng，et al(梁亮，杨敏华，张连蓬，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2012，28(20)：162.
[25] ZHU Xin-kai，SHENG Hai-jun，GU Jing，et al(朱新开，盛海君，顾晶，等). Journal of Triticeae Crops(麦类作物学报)，2005，25(2)：46.
[26] Cui B，Zhao Q J，Huang W J，et al. Journal of Integrative Agriculture，2019，18(6)：1230.
[27] ZHOU Xiao，BAO Yun-xuan，WANG Lin，et al(周晓，包云轩，王琳，等). Chinese Journal of Agrometeorology(中国农业气象)，2020，41(3)：173.
[28] WU Xu-mei，CHANG Qing-rui，LUO Li-li，et al(武旭梅，常庆瑞，落莉莉，等). Agricultural Research in the Arid Areas(干旱地区农业研究)，2019，37(3)：238.
[29] Xu X Q，Lu J S，Zhang N，et al. ISPRS Journal of Photogrammetry and Remote Sensing. 2019，150：185.
[30] Savitzky A，Golay M J E. Analytical Chemistry，1964，36(8)：1627.
[31] WANG Rui，CHEN Yong-zhong，CHEN Long-sheng，et al(王瑞，陈永忠，陈隆升，等). Journal of Central South University of Forestry & Technology(中南林业科技大学学报)，2013，33(2)：77.
[32] Zhang X，Sun W，Cen Y，et al. Science of the Total Environment，2019，650：321.
[33] Sibanda Mbulisi, Mutanga Onisimo, Dube Timothy, et al. Journal of Applied Remote Sensing, 2020, 14(2): 024517.
[34] JI Tong, WANG Bo, WANG Zhan-jun, et al(纪童, 王波, 王占军，等). Acta Agrestia Sinica(草地学报), 2020, 28(3): 675.
[35] Zhang A，Yin S，Wang J，et al. Remote Sensing, 2023, 15: 5623.