遥感与地理信息系统

冬小麦多时期冠层含水量遗传优化遥感反演

  • 聂素云 ,
  • 杨彬 ,
  • 夏微 ,
  • 张远
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  • 1. 华东师范大学 地理科学学院 地理信息科学教育部重点实验室, 上海 200241
    2. 中共辽宁省委党校 现代科技教研部, 沈阳 110004

收稿日期: 2021-11-02

  录用日期: 2022-03-17

  网络出版日期: 2023-05-25

基金资助

国家自然科学基金 (41571410)

Remote sensing inversion of multi-period winter wheat canopy water content based on a genetic algorithm

  • Suyun NIE ,
  • Bin YANG ,
  • Wei XIA ,
  • Yuan ZHANG
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  • 1. Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
    2. Department of Modern Science and Technology, Party School of Liaoning Provincial Party Committee, Shenyang 110004, China

Received date: 2021-11-02

  Accepted date: 2022-03-17

  Online published: 2023-05-25

摘要

开展作物冠层含水量的遥感反演有利于评估麦田干旱胁迫、实施精准灌溉. 为快速获取华北地区冬小麦生长期冠层含水量, 本文利用在2017年1—5月冬小麦生长期获取的Landsat-8 OLI和Sentinel-2 MSI多时相遥感影像, 基于混合像元分解模型, 建立了归一化水指数与麦田实测含水量之间的定量关系. 通过构建反演方程并结合遗传优化算法, 求解冬小麦冠层含水量. 对比地面实测数据, 研究结果显示, 本文方法能取得较优的反演结果, 决定系数 (R2) 与均方根误差(root mean squared error, RMSE)分别为0.567和5.6%. 与直接利用归一化水指数的反演方法相比, 误差降低20%以上. 研究表明, 量化小麦冠层和土壤背景的不同线性混合比, 可以有效消除土壤对小麦冠层含水量反演的影响, 对小麦等作物生长的遥感监测具有重要的应用价值.

本文引用格式

聂素云 , 杨彬 , 夏微 , 张远 . 冬小麦多时期冠层含水量遗传优化遥感反演[J]. 华东师范大学学报(自然科学版), 2023 , 2023(3) : 71 -81 . DOI: 10.3969/j.issn.1000-5641.2023.03.008

Abstract

The remote sensing inversion of crop canopy water content is a valuable for assessing drought stress of wheat fields and implementing precision irrigation. This study aimed to quickly obtain the canopy water content during the growth period for winter wheat in North China by using multi-temporal remote sensing images of Landsat-8 OLI and Sentinel-2 MSI from January to May 2017. The regression relationship was constructed with NDWI and measured water content in a wheat field via the mixed pixel decomposition model. The genetic algorithm was then used to inverse the canopy water content. The proposed method demonstrated better performance compared to ground-truth data, with the coefficient of determination (R2) and the root mean square error (RMSE) of 0.567 and 5.6%, respectively. Additionally, the error was reduced by more than 20% when compared to the direct inversion based on NDWI. This study indicates that quantification of different linear mixing ratios of wheat canopy and background soil can effectively eliminate the influence of soil on wheat water content inversion, and is crucial for the application of remote sensing to wheat growth monitoring.

参考文献

1 TANNER C B. Plant temperature. Agronomy Journal, 1963, 50, 210- 211.
2 GAO B C. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 1996, 58 (3): 257- 266.
3 PASQUALOTTO N, DELEGIDO J, VAN WITTENBERGHE S, et al. Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water absorption area index and depth water index. International Journal of Applied Earth Observation and Geoinformation, 2018, 67, 69- 78.
4 YI Q, WANG F, BAO A, et al. Leaf and canopy water content estimation in cotton using hyperspectral indices and radiative transfer models. International Journal of Applied Earth Observations & Geoinformation, 2014, 33, 67- 75.
5 刘晓静, 陈国庆, 王良, 等. 不同生育时期冬小麦叶片相对含水量高光谱监测. 麦类作物学报, 2018, 47 (7): 854- 862.
6 ZHAO S, WANG Q, YAO Y, et al. Estimating and validating wheat leaf water content with three modis spectral indexes: A case study in Ningxia Plain, China. Journal of Agricultural Science and Technology, 2016, 18, 387- 398.
7 张佳华, 许云, 姚凤梅, 等. 植被含水量光学遥感估算方法研究进展. 中国科学: 技术科学, 2010, (10): 1121- 1129.
8 BOREN E J, BOSCHETTI L. Landsat-8 and sentinel-2 canopy water content estimation in croplands through radiative transfer model inversion. Remote Sensing, 2020, 12 (17): 2803.
9 PAN H, CHEN Z, REN J, et al. Modeling winter wheat leaf area index and canopy water content with three different approaches using Sentinel-2 multispectral instrument data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 12 (2): 482- 492.
10 ZHANG C, PATTEY E, LIU J, et al. Retrieving leaf and canopy water content of winter wheat using vegetation water indices. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 11 (1): 112- 126.
11 HAN D, LIU S, DU Y, et al. Crop water content of winter wheat revealed with Sentinel-1 and Sentinel-2 imagery. Sensors, 2019, 19 (18): 4013.
12 程晓娟, 杨贵军, 徐新刚, 等. 基于近地高光谱与 TM 遥感影像的冬小麦冠层含水量反演. 麦类作物学报, 2014, 34 (2): 227- 233.
13 侯学会, 王猛, 高帅, 等. 综合近红外-红波段-短波红外三波段光谱特征空间的小麦冠层含水量反演. 麦类作物学报, 2020, 40 (7): 866- 873.
14 DJAMAI N, FERNANDES R, WEISS M, et al. Validation of the Sentinel Simplified Level 2 Product Prototype Processor (SL2P) for mapping cropland biophysical variables using Sentinel-2/MSI and Landsat-8/OLI data. Remote Sensing of Environment, 2019, 225, 416- 430.
15 LI Z, ZHANG F, CHEN L, et al. Research on the estimation model of vegetation water content in halophyte leaves based on the newly developed vegetation indices. Photogrammetric Engineering and Remote Sensing, 2018, 84 (9): 538- 548.
16 BENABDELOUAHAB T, BALAGHI R, HADRIA R, et al. Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions. International Journal of Remote Sensing, 2015, 36 (15): 4018- 4036.
17 GAO Y, WALKER J P, ALLAHMORADI M, et al. Optical sensing of vegetation water content: A synthesis study. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8 (4): 1456- 1464.
18 LI F, MIAO Y X, HENNIG S D, et al. Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precision Agriculture, 2010, 11 (4): 335- 357.
19 VIEGAS D X, VIEGAS T P, FERREIRA A D. Moisture content of fine forest fuels and fire occurrence in central Portugal. International Journal of Wildland Fire, 1992, 2 (2): 69- 86.
20 WOEBBECKE D M, MEYER G E, BARGEN K V, et al. Shape features for identifying young weeds using image analysis. Transactions of the ASAE, 1995, 38 (1): 271- 281.
21 李存军, 王纪华, 刘良云, 等. 基于数字照片特征的小麦覆盖度自动提取研究. 浙江大学学报 (农业与生命科学版), 2004, 30 (6): 650- 656.
22 GUTMAN G, IGNATOV A. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing, 1998, 19 (8): 1533- 1543.
23 GITELSON A A, KAUFMAN Y J, STARK R, et al. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 2002, 80 (1): 76- 87.
24 PUREVDORJ T, TATEISHI R, ISHIYAMA T, et al. Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing, 1998, 19 (18): 3519- 3535.
25 张瑜伟, 苗小莉, 张泳. 基于多源遥感数据的植被覆盖度反演方法比较研究. 测绘与空间地理信息, 2020, 251 (3): 139- 142.
26 QI J G, CHEHBOUNI A R, HUETE A R, et al. A modified soil adjusted vegetation index. Remote Sensing of Environment, 1994, 48 (2): 119- 126.
27 HUETE A R, LIU H Q, BATCHILY K, et al. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 1997, 59 (3): 440- 451.
28 孙中平, 刘素红, 姜俊, 等. 中高分辨率遥感协同反演冬小麦覆盖度. 农业工程学报, 2017, 33 (16): 161- 167.
29 HOLLAND J H. Adaptation in Natural and Artificial System [M]. Ann Arbor, Michigan: Michigan University Press, 1975.
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