Estimation of Winter Wheat Stem Biomass by a Novel Two-Component and Two-Parameter Stratified Model Using Proximal Remote Sensing and Phenological Variables

文献类型： 外文期刊

第一作者： Chen, Weinan

作者： Chen, Weinan;Yang, Guijun;Tang, Aohua;Zhang, Jing;Li, Zhenhong;Chen, Weinan;Yang, Guijun;Meng, Yang;Feng, Haikuan;Li, Heli;Tang, Aohua;Xu, Xingang;Yang, Hao;Meng, Yang;Feng, Haikuan;Li, Changchun

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关键词： winter wheat; stem dry biomass; phenological scale; hyperspectral remote sensing; Tc/Tp-SDB stratified model

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2024 年 16 卷 22 期

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收录情况： SCI

摘要： The timely and precise estimation of stem biomass is critical for monitoring the crop growing status. Optical remote sensing is limited by the penetration of sunlight into the canopy depth, and thus directly estimating winter wheat stem biomass via canopy spectra remains a difficult task. There is a stable linear relationship between the stem dry biomass (SDB) and leaf dry biomass (LDB) of winter wheat during the entire growth stage. Therefore, this study comprehensively considered remote sensing and crop phenology, as well as biomass allocation laws, to establish a novel two-component (LDB, SDB) and two-parameter (phenological variables, spectral vegetation indices) stratified model (Tc/Tp-SDB) to estimate SDB across the growth stages of winter wheat. The core of the Tc/Tp-SDB model employed phenological variables (e.g., effective accumulative temperature, EAT) to correct the SDB estimations determined from the LDB. In particular, LDB was estimated using spectral vegetation indices (e.g., red-edge chlorophyll index, CIred edge). The results revealed that the coefficient values (beta 0 and beta 1) of ordinary least squares regression (OLSR) of SDB with LDB had a strong relationship with phenological variables. These coefficient (beta 0 and beta 1) relationships were used to correct the OLSR model parameters based on the calculated phenological variables. The EAT and CIred edge were determined as the optimal parameters for predicting SDB with the novel Tc/Tp-SDB model, with r, RMSE, MAE, and distance between indices of simulation and observation (DISO) values of 0.85, 1.28 t/ha, 0.95 t/ha, and 0.31, respectively. The estimation error of SDB showed an increasing trend from the jointing to flowering stages. Moreover, the proposed model showed good potential for estimating SDB from UAV hyperspectral imagery. This study demonstrates the ability of the Tc/Tp-SDB model to accurately estimate SDB across different growing seasons and growth stages of winter wheat.

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