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Lookup NU author(s): Dr Rachel GaultonORCiD, Professor Zhenhong Li
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2022 Elsevier Inc. Timely monitoring of above-ground biomass (AGB) is essential for indicating the crop growth status and predicting grain yield and carbon dynamics. Non-destructive remote sensing techniques with a large spatial coverage have become a promising method for crop biomass monitoring. However, most existing crop biomass models have only been tested at a single growth stage or only at a small number of growth stages at a single location. This has limited the ability of these AGB models to be transferred spatially, to other fields or regions, to predict AGB at any growth stage during the season, or to be potentially used with data from other sensing systems. Here, a new crop biomass algorithm (CBA-Wheat) was developed to estimate AGB over the entire growing season. It uses information on the crop growth stage, based on phenological scale observations (Zadoks scale or ZS), the day of the year or thermal indices (growing degree days), to correct AGB estimations from remotely sensed vegetation indices. The model transferability was evaluated across multiple regional test sites and different data sources (UAV and hand-held spectroscopic data). Results showed that the coefficient values [slope (k) and intercept (b)] of ordinary least squares regression (OLSR) of AGB with vegetation indices had a strong relationship with ZS. These k and b relationships were used to correct the OLSR model parameters based on the observed phenological stage (ZS value). The two-band enhanced vegetation index (EVI2) was the best vegetation index for predicting AGB with the new CBA-WheatZS model, with R2 and RMSE values of 0.83 and 2.07 t/ha for an experimental trial site, 0.78 and 2.05 t/ha for multiple independent regional test sites, and 0.69 and 1.87 t/ha when transferred to EVI2 derived from UAV. Model performance was lower with the day of the year and thermal index corrections; however, the use of relative growing degree-days (RGS; CBA-WheatRGS), instead of ZS information, to adjust the model parameters showed a high consistency with the CBA-WheatZS model, and a good potential for estimation of AGB at regional scales without the need for local phenological observations. The CBA-WheatRGS had validated R2 and RMSE values of 0.82 and 2.01 t/ha for the experimental trial site, 0.76 and 2.39 t/ha for multiple independent regional test sites, and 0.66 and 2.14 t/ha for UAV hyperspectral imagery. These results demonstrated a good potential to estimate biomass from remotely sensed imagery at varying spatio-temporal scales in winter wheat.
Author(s): Li Z, Zhao Y, Taylor J, Gaulton R, Jin X, Song X, Li Z, Meng Y, Chen P, Feng H, Wang C, Guo W, Xu X, Chen L, Yang G
Publication type: Article
Publication status: Published
Journal: Remote Sensing of Environment
Year: 2022
Volume: 273
Print publication date: 01/05/2022
Online publication date: 08/03/2022
Acceptance date: 23/02/2022
Date deposited: 03/03/2022
ISSN (print): 0034-4257
ISSN (electronic): 1879-0704
Publisher: Elsevier Inc.
URL: https://doi.org/10.1016/j.rse.2022.112967
DOI: 10.1016/j.rse.2022.112967
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