Estimating wheat partitioning coefficient using remote sensing and its coupling with a crop growth model

IF 5.6 1区农林科学 Q1 AGRONOMY

Field Crops Research Pub Date : 2024-11-01 DOI:10.1016/j.fcr.2024.109620

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引用次数: 0

Abstract

Context

Accurately estimating the partitioning of daily photosynthetic assimilates among different plant organs is crucial for understanding crop growth and yield formation. However, challenges in field measurements, especially in assessing belowground biomass, hinder precise evaluation of the partitioning process.

Objective

This study developed a novel approach to estimate time series of partitioning coefficient (PC) using unmanned aerial vehicle (UAV) images.

Methods

Firstly, UAV-based remote sensing data was utilized to estimate leaf biomass growth (G_leaf), aboveground biomass growth (G_above), leaf area index (LAI), and leaf chlorophyll content (LCC). Next, total wheat growth (G_total) was estimated by integrating LAI and LCC into a photosynthesis model. Finally, the leaf partitioning coefficient (LPC) and aboveground partitioning coefficient (APC) were calculated by combining G_leaf, G_above, and G_total.

Results

The proposed method effectively captured the variability of partitioning coefficients (PCs) across different phenological stages and treatments, with a relative root mean square error (RRMSE) of 24 % between the estimated and measured average LPC (ALPC). The theoretical RRMSE for the estimated average APC (AAPC) derived from a synthetic dataset was 29 %. By incorporating the estimated PCs into a crop model, the simulation accuracy for aboveground biomass (AGB) and leaf dry matter weight (LDW) improved, achieving RRMSEs of 12 % and 11 %, respectively, while simulations based on default PCs in the APSIM model resulted in overestimation.

Conclusions

This study achieved a high-throughput estimation for the wheat partitioning coefficient.

Implications

The proposed approach holds promise for advancing our understanding of photo-assimilate partitioning.

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利用遥感及其与作物生长模型的耦合估算小麦分配系数

背景准确估算每日光合同化物在不同植物器官之间的分配对于了解作物生长和产量形成至关重要。方法首先，利用无人机遥感数据估算叶片生物量增长（Gleaf）、地上生物量增长（Gabove）、叶面积指数（LAI）和叶片叶绿素含量（LCC）。然后，通过将 LAI 和 LCC 纳入光合作用模型，估算出小麦的总生长量（Gtotal）。最后，结合 Gleaf、Gabove 和 Gtotal 计算出叶片分配系数（LPC）和地上部分配系数（APC）。结果所提出的方法有效地捕捉了分配系数（PCs）在不同物候期和处理中的变异性，估计的平均叶片分配系数（ALPC）与测量的平均叶片分配系数（ALPC）之间的相对均方根误差（RRMSE）为 24%。从合成数据集得出的估计平均 APC (AAPC) 的理论 RRMSE 为 29%。通过将估算的 PCs 纳入作物模型，地上生物量（AGB）和叶干物质重量（LDW）的模拟精度有所提高，RRMSE 分别为 12 % 和 11 %，而基于 APSIM 模型中默认 PCs 的模拟结果则估计过高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Field Crops Research 农林科学-农艺学

CiteScore

9.60

自引率

12.10%

发文量

307

审稿时长

46 days

期刊介绍： Field Crops Research is an international journal publishing scientific articles on: √ experimental and modelling research at field, farm and landscape levels on temperate and tropical crops and cropping systems, with a focus on crop ecology and physiology, agronomy, and plant genetics and breeding.