Estimation of Winter Wheat Stem Biomass by a Novel Two-Component and Two-Parameter Stratified Model Using Proximal Remote Sensing and Phenological Variables DOI Creative Commons
Weinan Chen, Guijun Yang, Meng Yang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(22), P. 4300 - 4300

Published: Nov. 18, 2024

The timely and precise estimation of stem biomass is critical for monitoring the crop growing status. Optical remote sensing limited by penetration sunlight into canopy depth, thus directly estimating winter wheat via spectra remains a difficult task. There stable linear relationship between dry (SDB) leaf (LDB) during entire growth stage. Therefore, this study comprehensively considered phenology, as well allocation laws, to establish novel two-component (LDB, SDB) two-parameter (phenological variables, spectral vegetation indices) stratified model (Tc/Tp-SDB) estimate SDB across stages wheat. core Tc/Tp-SDB employed phenological variables (e.g., effective accumulative temperature, EAT) correct estimations determined from LDB. In particular, LDB was estimated using indices red-edge chlorophyll index, CIred edge). results revealed that coefficient values (β0 β1) ordinary least squares regression (OLSR) with had strong variables. These relationships were used OLSR parameters based on calculated EAT edge optimal predicting model, r, RMSE, MAE, distance simulation observation (DISO) 0.85, 1.28 t/ha, 0.95 0.31, respectively. error showed an increasing trend jointing flowering stages. Moreover, proposed good potential UAV hyperspectral imagery. This demonstrates ability accurately different seasons

Language: Английский

Dynamic whole-life cycle measurement of individual plant height in oilseed rape through the fusion of point cloud and crop root zone localization DOI

Xuan Lv,

Xiaole Wang, Yu Wang

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 236, P. 110505 - 110505

Published: May 6, 2025

Language: Английский

Citations

0
Estimation of Winter Wheat Stem Biomass by a Novel Two-Component and Two-Parameter Stratified Model Using Proximal Remote Sensing and Phenological Variables DOI Creative Commons
Weinan Chen, Guijun Yang, Meng Yang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(22), P. 4300 - 4300

Published: Nov. 18, 2024

The timely and precise estimation of stem biomass is critical for monitoring the crop growing status. Optical remote sensing limited by penetration sunlight into canopy depth, thus directly estimating winter wheat via spectra remains a difficult task. There stable linear relationship between dry (SDB) leaf (LDB) during entire growth stage. Therefore, this study comprehensively considered phenology, as well allocation laws, to establish novel two-component (LDB, SDB) two-parameter (phenological variables, spectral vegetation indices) stratified model (Tc/Tp-SDB) estimate SDB across stages wheat. core Tc/Tp-SDB employed phenological variables (e.g., effective accumulative temperature, EAT) correct estimations determined from LDB. In particular, LDB was estimated using indices red-edge chlorophyll index, CIred edge). results revealed that coefficient values (β0 β1) ordinary least squares regression (OLSR) with had strong variables. These relationships were used OLSR parameters based on calculated EAT edge optimal predicting model, r, RMSE, MAE, distance simulation observation (DISO) 0.85, 1.28 t/ha, 0.95 0.31, respectively. error showed an increasing trend jointing flowering stages. Moreover, proposed good potential UAV hyperspectral imagery. This demonstrates ability accurately different seasons

Language: Английский

Citations

0