{"title":"利用哨兵-2 数据估算多个生长阶段的水稻叶面积指数:对不同检索算法的评估","authors":"","doi":"10.1016/j.eja.2024.127362","DOIUrl":null,"url":null,"abstract":"<div><p>Leaf area index (LAI), which is closely related to canopy physiological processes such as photosynthesis and water utilization, serves as an important biophysical parameter for monitoring rice (<em>Oryza sativa</em> L.) growth. However, due to significant variations in the water-soil mixed background during the rice growing season, the optimal LAI retrieval method for rice throughout the whole period remains unclear. Here, different LAI retrieval methods, categorized into vegetation index (VI)-, look-up table (LUT)- and machine learning (ML)-based groups, were evaluated for rice at multiple growth stages using Sentinel-2 images. Particularly, the performance of rice LAI derived from the optimal retrieval model was comprehensively analyzed to understand factors influencing LAI estimation during different growth stages. Results suggested that the Gaussian Process Regression (GPR) in the ML-based group achieved the best performance for rice LAI estimation in the whole growth period (R<sup>2</sup> = 0.75, RMSE = 0.60, RRMSE = 15.81 %), followed by the normalized difference VI (NDVI) in the VI-based group (R<sup>2</sup> = 0.74, RMSE = 0.61, RRMSE = 15.98 %) and the cost function K(x)=log(x)+1/x in the LUT-based group (R<sup>2</sup> = 0.70, RMSE = 0.69, RRMSE = 18.09 %). Notably, although the VI-based LAI retrieval method incorporated ground measurements to build empirical LAI-VI relationships, a single VI with parametric regression cannot well capture variations in rice LAI across growth stages compared to the ML-based method using simulation dataset from the physical model. The optimal ML-based method also exhibited better performance in rice LAI estimation than similar studies, with R<sup>2</sup> increasing by 0.14 and RMSE decreasing by 0.18. Furthermore, based on ground LAI measurements, the water-soil mixed background is a primary influencing factor for rice LAI estimation in the tillering, jointing, and booting stages (RMSE: 0.39–0.79), whereas the saturation effects should be considered in the full heading stage (RMSE = 0.68). Overall, this study indicates that the GPR-based retrieval strategy is the optimal method for generating rice LAI datasets over the whole growth period, providing valuable reference for precision agriculture application such as field irrigation, fertilization management, and yield estimation.</p></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating rice leaf area index at multiple growth stages with Sentinel-2 data: An evaluation of different retrieval algorithms\",\"authors\":\"\",\"doi\":\"10.1016/j.eja.2024.127362\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Leaf area index (LAI), which is closely related to canopy physiological processes such as photosynthesis and water utilization, serves as an important biophysical parameter for monitoring rice (<em>Oryza sativa</em> L.) growth. However, due to significant variations in the water-soil mixed background during the rice growing season, the optimal LAI retrieval method for rice throughout the whole period remains unclear. Here, different LAI retrieval methods, categorized into vegetation index (VI)-, look-up table (LUT)- and machine learning (ML)-based groups, were evaluated for rice at multiple growth stages using Sentinel-2 images. Particularly, the performance of rice LAI derived from the optimal retrieval model was comprehensively analyzed to understand factors influencing LAI estimation during different growth stages. Results suggested that the Gaussian Process Regression (GPR) in the ML-based group achieved the best performance for rice LAI estimation in the whole growth period (R<sup>2</sup> = 0.75, RMSE = 0.60, RRMSE = 15.81 %), followed by the normalized difference VI (NDVI) in the VI-based group (R<sup>2</sup> = 0.74, RMSE = 0.61, RRMSE = 15.98 %) and the cost function K(x)=log(x)+1/x in the LUT-based group (R<sup>2</sup> = 0.70, RMSE = 0.69, RRMSE = 18.09 %). Notably, although the VI-based LAI retrieval method incorporated ground measurements to build empirical LAI-VI relationships, a single VI with parametric regression cannot well capture variations in rice LAI across growth stages compared to the ML-based method using simulation dataset from the physical model. The optimal ML-based method also exhibited better performance in rice LAI estimation than similar studies, with R<sup>2</sup> increasing by 0.14 and RMSE decreasing by 0.18. Furthermore, based on ground LAI measurements, the water-soil mixed background is a primary influencing factor for rice LAI estimation in the tillering, jointing, and booting stages (RMSE: 0.39–0.79), whereas the saturation effects should be considered in the full heading stage (RMSE = 0.68). Overall, this study indicates that the GPR-based retrieval strategy is the optimal method for generating rice LAI datasets over the whole growth period, providing valuable reference for precision agriculture application such as field irrigation, fertilization management, and yield estimation.</p></div>\",\"PeriodicalId\":51045,\"journal\":{\"name\":\"European Journal of Agronomy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Agronomy\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1161030124002831\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Agronomy","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1161030124002831","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
摘要
叶面积指数(LAI)与光合作用和水分利用等冠层生理过程密切相关,是监测水稻(Oryza sativa L.)生长的重要生物物理参数。然而,由于水稻生长期水土混合背景的显著变化,水稻整个生长期的最佳 LAI 提取方法仍不明确。在此,利用哨兵-2 图像对水稻多个生长阶段的不同 LAI 检索方法进行了评估,这些方法分为植被指数(VI)、查找表(LUT)和基于机器学习(ML)的几组。特别是全面分析了最优检索模型得出的水稻 LAI 性能,以了解影响不同生长阶段 LAI 估算的因素。结果表明,基于 ML 的高斯过程回归(GPR)对水稻整个生长期的 LAI 估计性能最好(R2 = 0.75,RMSE = 0.60,RRMSE = 15.81 %),其次是基于 VI 组的归一化差异植被指数(NDVI)(R2 = 0.74,RMSE = 0.61,RRMSE = 15.98 %)和基于 LUT 组的成本函数 K(x)=log(x)+1/x(R2 = 0.70,RMSE = 0.69,RRMSE = 18.09 %)。值得注意的是,虽然基于 VI 的 LAI 检索方法结合了地面测量来建立经验 LAI-VI 关系,但与使用物理模型模拟数据集的基于 ML 的方法相比,使用参数回归的单一 VI 无法很好地捕捉水稻 LAI 在不同生长阶段的变化。与同类研究相比,基于 ML 的最优方法在水稻 LAI 估算方面也表现出更好的性能,R2 增加了 0.14,RMSE 减少了 0.18。此外,根据地面 LAI 测量结果,水土混合背景是分蘖期、拔节期和抽穗期水稻 LAI 估算的主要影响因素(RMSE:0.39-0.79),而饱和效应应在全穗期考虑(RMSE = 0.68)。总之,本研究表明,基于 GPR 的检索策略是生成水稻整个生长期 LAI 数据集的最佳方法,可为田间灌溉、施肥管理和产量估算等精准农业应用提供有价值的参考。
Estimating rice leaf area index at multiple growth stages with Sentinel-2 data: An evaluation of different retrieval algorithms
Leaf area index (LAI), which is closely related to canopy physiological processes such as photosynthesis and water utilization, serves as an important biophysical parameter for monitoring rice (Oryza sativa L.) growth. However, due to significant variations in the water-soil mixed background during the rice growing season, the optimal LAI retrieval method for rice throughout the whole period remains unclear. Here, different LAI retrieval methods, categorized into vegetation index (VI)-, look-up table (LUT)- and machine learning (ML)-based groups, were evaluated for rice at multiple growth stages using Sentinel-2 images. Particularly, the performance of rice LAI derived from the optimal retrieval model was comprehensively analyzed to understand factors influencing LAI estimation during different growth stages. Results suggested that the Gaussian Process Regression (GPR) in the ML-based group achieved the best performance for rice LAI estimation in the whole growth period (R2 = 0.75, RMSE = 0.60, RRMSE = 15.81 %), followed by the normalized difference VI (NDVI) in the VI-based group (R2 = 0.74, RMSE = 0.61, RRMSE = 15.98 %) and the cost function K(x)=log(x)+1/x in the LUT-based group (R2 = 0.70, RMSE = 0.69, RRMSE = 18.09 %). Notably, although the VI-based LAI retrieval method incorporated ground measurements to build empirical LAI-VI relationships, a single VI with parametric regression cannot well capture variations in rice LAI across growth stages compared to the ML-based method using simulation dataset from the physical model. The optimal ML-based method also exhibited better performance in rice LAI estimation than similar studies, with R2 increasing by 0.14 and RMSE decreasing by 0.18. Furthermore, based on ground LAI measurements, the water-soil mixed background is a primary influencing factor for rice LAI estimation in the tillering, jointing, and booting stages (RMSE: 0.39–0.79), whereas the saturation effects should be considered in the full heading stage (RMSE = 0.68). Overall, this study indicates that the GPR-based retrieval strategy is the optimal method for generating rice LAI datasets over the whole growth period, providing valuable reference for precision agriculture application such as field irrigation, fertilization management, and yield estimation.
期刊介绍:
The European Journal of Agronomy, the official journal of the European Society for Agronomy, publishes original research papers reporting experimental and theoretical contributions to field-based agronomy and crop science. The journal will consider research at the field level for agricultural, horticultural and tree crops, that uses comprehensive and explanatory approaches. The EJA covers the following topics:
crop physiology
crop production and management including irrigation, fertilization and soil management
agroclimatology and modelling
plant-soil relationships
crop quality and post-harvest physiology
farming and cropping systems
agroecosystems and the environment
crop-weed interactions and management
organic farming
horticultural crops
papers from the European Society for Agronomy bi-annual meetings
In determining the suitability of submitted articles for publication, particular scrutiny is placed on the degree of novelty and significance of the research and the extent to which it adds to existing knowledge in agronomy.