Liuya Zhang, Debao Yuan, Yuqing Fan, Renxu Yang, Maochen Zhao, Jinbao Jiang, Wenxuan Zhang, Ziyi Huang, Guidan Ye, Weining Li
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Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"10 1","pages":""},"PeriodicalIF":4.2000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hyperspectral Estimation of Chlorophyll Content in Wheat under CO2 Stress Based on Fractional Order Differentiation and Continuous Wavelet Transforms\",\"authors\":\"Liuya Zhang, Debao Yuan, Yuqing Fan, Renxu Yang, Maochen Zhao, Jinbao Jiang, Wenxuan Zhang, Ziyi Huang, Guidan Ye, Weining Li\",\"doi\":\"10.3390/rs16173341\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. 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The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). 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引用次数: 0
摘要
冬小麦是全球广泛种植的重要粮食作物,其叶片叶绿素含量(LCC)是评估其生长和健康状况对二氧化碳胁迫响应的关键指标。然而,CO2 胁迫条件下冬小麦叶绿素含量的遥感定量估算也面临着一些挑战,如光谱灵敏度范围不明确、基线漂移、光谱峰重叠以及 CO2 胁迫变化引起的复杂光谱响应等。为解决这些难题,本研究将分数阶导数(FOD)和连续小波变换(CWT)技术引入到冬小麦 LCC 的估算中。结合原始高光谱数据,我们深入分析了 CO2 胁迫下冬小麦 LCC 的光谱响应特征。我们提出了一种堆叠模型,包括多元线性回归(MLR)、决策树回归(DTR)、随机森林(RF)和自适应提升(AdaBoost),从大量特征变量中筛选出最优组合。我们采用双波段组合和植被指数策略实现了 CO2 胁迫下冬小麦 LCC 的精确估算。结果表明:(1)FOD 和 CWT 方法显著提高了 CO2 胁迫下冬小麦原始光谱反射率与 LCC 的相关性。(2)结合冬小麦叶片对 CO2 响应的敏感光谱波段构建的 1.2 阶导数双波段指数(RVI (R720, R522))实现了 CO2 胁迫条件下 LCC 的高精度估算(R2 = 0.901)。同时,基于 CWT 技术在特定尺度下构建的红边植被胁迫指数(RVSI)在 LCC 估计中也表现出良好的性能(R2 = 0.880),验证了多尺度分析在揭示 CO2 对冬小麦影响机理方面的有效性。 (3) 通过叠加 FOD 和 CWT 方法联合提取的敏感光谱特征,进一步提高了 LCC 估计精度(R2 = 0.906)。该研究不仅为准确估算 CO2 胁迫下冬小麦的 LCC 提供了科学依据和技术支持,也为农业管理中应对气候变化、优化作物种植条件、提高作物产量和品质提供了新的思路和方法。该方法对类似环境胁迫下其他作物生理参数的估算也具有重要的参考价值。
Hyperspectral Estimation of Chlorophyll Content in Wheat under CO2 Stress Based on Fractional Order Differentiation and Continuous Wavelet Transforms
The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO2 stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO2 stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses.
期刊介绍:
Remote Sensing (ISSN 2072-4292) publishes regular research papers, reviews, letters and communications covering all aspects of the remote sensing process, from instrument design and signal processing to the retrieval of geophysical parameters and their application in geosciences. Our aim is to encourage scientists to publish experimental, theoretical and computational results in as much detail as possible so that results can be easily reproduced. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.