冬小麦多时期冠层含水量遗传优化遥感反演

doi:10.3969/j.issn.1000-5641.2023.03.008

摘要/Abstract

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.

关键词: 冠层含水量, 多时相遥感影像, 像元二分模型, 遗传算法, 冬小麦

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.

Key words: canopy water content, multi-temporal remote sensing image, pixel dichotomy model, genetic algorithm, winter wheat

中图分类号:

S127

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.

Suyun NIE, Bin YANG, Wei XIA, Yuan ZHANG. Remote sensing inversion of multi-period winter wheat canopy water content based on a genetic algorithm[J]. Journal of East China Normal University(Natural Science), 2023, 2023(3): 71-81.

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日期	影像数据	冬小麦生长物候期	日期	影像数据	冬小麦生长物候期
2017/01/22	Landsat-8	越冬期/返青期	2017/04/18	Sentinel-2	拔节期孕穗期
2017/02/27	Sentinel-2		2017/04/28	Landsat-8	拔节期孕穗期
2017/03/09	Sentinel-2
2017/03/29	Sentinel-2	拔节期	2017/05/18	Sentinel-2	抽穗期/开花期/灌浆期
2017/04/12	Landsat-8	拔节期	2017/05/28	Sentinel-2	灌浆期/成熟期

日期	${\rm{NDV} }{ {\rm{I} }_{\rm{soil}} }$	$ {\rm{NDV}}{{\rm{I}}_{{\rm{veg}}}} $	$ {\rm{MASV}}{{\rm{I}}_{{{\rm{soil}}} }} $	$ {\rm{MASV}}{{\rm{I}}_{{\text{veg}}}} $	$ {\rm{EV}}{{\rm{I}}_{{\rm{soil}}}} $	$ {\rm{EV}}{{\rm{I}}_{{\text{veg}}}} $
2017/01/22	0.198	0.900	0.086	0.800	0.199	0.900
2017/02/27	0.239	0.900	0.102	0.800	0.239	0.900
2017/03/09	0.253	0.900	0.115	0.800	0.253	0.900
2017/03/29	0.290	0.900	0.150	0.800	0.291	0.900
2017/04/12	0.200	0.876	0.150	0.643	0.200	0.877
2017/04/18	0.200	0.931	0.150	0.811	0.200	0.931
2017/04/28	0.200	0.852	0.150	0.711	0.200	0.852
2017/05/18	0.200	0.730	0.150	0.624	0.200	0.731
2017/05/28	0.200	0.626	0.150	0.544	0.200	0.627

方法	监测面积/km²	研究数据时间 (生长期)	时相数	反演精度	植被含水量表示类型	验证点数目
Zhang等提出的植被水分指数反演方法^[10]	3264.6	Sentinel-2 2016.03.30 (拔节期)	1	R² = 0.68 RMSE = 0.148 g·cm^–2	等效水厚度 (EWT)	463
Han等提出的植被指数灰色关联分析反演方法^[11]	836.0	Sentinel-2 2017.05.28 (灌浆期/ 成熟期)	1	R² = 0.632 RMSE = 2.1%	可燃物含水量 (FMC)	20
程小娟等建宽波段WI和NDWI反演方法^[12]	107.0	Landsat TM5 2009.04.15 (拔节期) 2009.05.17 (开花期)	2	R² = 0.5711 RMSE = 3.89%	可燃物含水量 (FMC)	27
程小娟等建宽波段WI和NDWI反演方法^[12]	107.0	Landsat TM5 2009.04.15 (拔节期) 2009.05.17 (开花期)	2	R² = 0.5116 RMSE = 0.024 g·cm^–2	等效水厚度 (EWT)	27
侯学会等构建垂直干旱指数和短波红外垂直失水指数比值的植被指数反演方法^[13]	302.4	Landsat 8 2016.03.26 (返青期) 2016.04.18 (拔节期) 2016.05.04 (开花期) 2016.05.13 (灌浆期)	4	R² = 0.763 RMSE = 2.296%	可燃物含水量 (FMC)	12
本文方法	2214.0	Landsat 8、Sentinel-2 2017.01.22、2017.02.27 2017.03.09、2017.03.29 2017.04.12、2017.04.18 2017.04.28、2017.05.18 2017.05.28 (全生长期)	9	R² = 0.567 RMSE = 5.6%	可燃物含水量 (FMC)	61

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