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Mapping winter-wheat biomass and grain yield based on a crop model and UAV remote sensing
International Journal of Remote Sensing ( IF 3.4 ) Pub Date : 2020-10-22 , DOI: 10.1080/01431161.2020.1823033
Jibo Yue 1, 2 , Haikuan Feng 1 , Zhenhai Li 1 , Chengquan Zhou 1, 3 , Kaijian Xu 2
Affiliation  

ABSTRACT Timely and accurately estimates of crop biomass and grain yield estimation are crucial for agricultural management. Optical remote sensing techniques can provide crop parameters (e.g., biomass, fractional vegetation cover (FVC)) at regional and larger scales. However, such techniques saturate at high crop canopy cover and cannot detect biomass stored in reproductive organs. The AquaCrop model can be used to estimate FVC, biomass, and grain yield output based on crop growth environmental parameters (e.g., temperature, rainfall, irrigation). In this work, we developed a method for estimating and mapping crop biomass and grain yield using unmanned aerial vehicle (UAV) remote sensing images and AquaCrop. The combination of low-cost UAV remote sensing data and AquaCrop can be used to map wheat biomass and grain yield before harvest. This work investigated whether biomass and grain yield can be predicted using measurements of biomass and FVC in several winter-wheat fields at an early growing stage using ground-based and remote sensing-based methods. FVC, biomass, and grain yield were measured at several key growing stages (jointing (S1), heading (S2), flowering (S3), grain filling (S4)) and harvest (H) during 2014–2015. We specifically evaluated the performances of using field- and UAV-based FVC and biomass measurements from different growing-stage combinations (e.g., S1–S4, S1–S3, and S1–S2) to calibrate the AquaCrop model and estimate biomass and grain yield. The results indicate that winter-wheat biomass and grain yield can be estimated by calibrating AquaCrop using (i) biomass and (ii) biomass and FVC. The results also reveal that the biomass and grain yield estimated using FVC and AquaCrop have poor accuracy compared with the biomass and grain yield estimated using (i) only biomass and (ii) biomass and FVC. The results suggest that the combined use of UAV remote sensing and AquaCrop can be used to obtain maps of biomass and biomass yield. When estimating winter-wheat biomass and grain yield one month (13 May) before harvest (11 June), the predicted biomass and grain yield agreed with the measured values (biomass: n = 96, coefficient of determination (R 2) = 0.61, mean absolute error (MAE) = 1.69 t ha–1, root-mean-square error (RMSE) = 2.10 t ha–1, normalized RMSE (nRMSE) = 18.5%; grain yield: n = 48, R 2 = 0.63, MAE = 0.96 t ha–1, RMSE = 1.16 t ha–1, nRMSE = 21.9%). When estimating winter-wheat biomass and grain yield one and a half months (26 April) before harvest (11 June), the predicted biomass and grain yield was lower than when estimating winter-wheat biomass and grain yield one month before harvest (biomass: n = 144, R 2 = 0.55, MAE = 2.52 t ha–1, RMSE = 3.34 t ha–1, nRMSE = 23.6%; grain yield: n = 48, R 2 = 0.34, MAE = 1.68 t ha–1, RMSE = 2.17 t ha–1, nRMSE = 35.7%).

中文翻译:

基于作物模型和无人机遥感绘制冬小麦生物量和粮食产量图

摘要 及时准确地估算作物生物量和粮食产量估算对于农业管理至关重要。光学遥感技术可以提供区域和更大尺度的作物参数(例如生物量、植被覆盖率(FVC))。然而,这种技术在高作物冠层覆盖时会饱和,并且无法检测存储在生殖器官中的生物量。AquaCrop 模型可用于根据作物生长环境参数(例如温度、降雨量、灌溉)估算 FVC、生物量和谷物产量。在这项工作中,我们开发了一种使用无人机 (UAV) 遥感图像和 AquaCrop 估算和绘制作物生物量和粮食产量的方法。低成本无人机遥感数据与AquaCrop相结合,可用于在收获前绘制小麦生物量和粮食产量图。这项工作调查了是否可以使用基于地面和基于遥感的方法在生长早期的几个冬小麦田中测量生物量和 FVC 来预测生物量和粮食产量。2014-2015 年期间,在几个关键生长阶段(拔节 (S1)、抽穗 (S2)、开花 (S3)、灌浆 (S4))和收获 (H) 测量了 FVC、生物量和粮食产量。我们专门评估了使用基于田间和无人机的 FVC 和来自不同生长阶段组合(例如,S1-S4、S1-S3 和 S1-S2)的生物量测量来校准 AquaCrop 模型并估计生物量和谷物产量的性能. 结果表明冬小麦生物量和谷物产量可以通过使用 (i) 生物量和 (ii) 生物量和 FVC 校准 AquaCrop 来估计。结果还表明,与使用 (i) 仅使用生物量和 (ii) 生物量和 FVC 估算的生物量和谷物产量相比,使用 FVC 和 AquaCrop 估算的生物量和谷物产量准确性较差。结果表明,无人机遥感与AquaCrop的结合可用于获得生物量和生物量产量图。在收获(6 月 11 日)前一个月(5 月 13 日)估算冬小麦生物量和粮食产量时,预测的生物量和粮食产量与实测值一致(生物量:n = 96,决定系数 (R 2) = 0.61,平均绝对误差 (MAE) = 1.69 t ha–1,均方根误差 (RMSE) = 2.10 t ha–1,归一化 RMSE (nRMSE) = 18.5%;谷物产量:n = 48,R 2 = 0.63, MAE = 0.96 t ha–1,RMSE = 1.16 t ha–1,nRMSE = 21.9%)。
更新日期:2020-10-22
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