Comparison and transferability of thermal, temporal and phenological-based in-season predictions of above-ground biomass in wheat crops from proximal crop reflectance data
Comparison and transferability of thermal, temporal and phenological-based in-season predictions of above-ground biomass in wheat crops from proximal crop reflectance data
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DOI：
10.1016/j.rse.2022.112967
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发表时间：
2022-03-08
影响因子：
13.5
通讯作者：
Yang, Guijun
中科院分区：
文献类型：
--
作者：
Li, Zhenhai;Zhao, Yu;Yang, Guijun
及时监测作物地上生物量（AGB）是了解作物生长状况、预测作物产量和碳动态的重要手段。大空间覆盖的非破坏性遥感技术已成为农作物生物量监测的一种有前途的方法。然而，大多数现有的作物生物量模型仅在单个生长阶段或仅在单个地点的少数生长阶段进行了测试。这限制了这些AGB模型在空间上转移到其他田地或区域、预测季节中任何生长阶段的AGB或潜在地与来自其他传感系统的数据一起使用的能力。本文提出了一种新的作物生物量算法（CBA-Wheat），用于估算整个生长季的AGB。该系统利用基于物候尺度观测（Zadoks尺度或ZS）、一年中的日期或温度指数（生长度日）的作物生长阶段信息，对遥感植被指数的AGB估计值进行校正。在多个区域试验场和不同数据源（无人机和手持光谱数据）之间评估了模型的可移植性。结果表明，AG B与植被指数的普通最小二乘回归（OLSR）的系数值[斜率（k）和截距（B）]与ZS有较强的相关性。这些k和B关系被用来校正OLSR模型参数的基础上观察到的物候期（ZS值）。两波段增强植被指数（EVI 2）是新CBA-WheatZS模型预测AGB的最佳植被指数，其R-2和RMSE值分别为0.83和2.07 t/ha（试验点）、0.78和2.05 t/ha（多个独立区域试验点）和0.69和1.87 t/ha（转换为无人机估算的EVI 2）。但是，用相对生长度-日（RGS; CBA-WheatRGS）代替ZS信息来调整模型参数，与CBA-WheatZS模型具有较高的一致性，并且在不需要当地物候观测的情况下，可以很好地估计区域尺度上的AGB。CBA-WheatRGS验证了实验性试验场地的R-2和RMSE值为0.82和2.01 t/ha，多个独立区域试验场地的R-2和RMSE值为0.76和2.39 t/ha，无人机高光谱图像的R-2和RMSE值为0.66和2.14 t/ha。这些结果表明，一个很好的潜力，估计生物量从遥感图像在不同的时空尺度在冬小麦。
Timely monitoring of above-ground biomass (AGB) is essential for indicating the crop growth status and pre-dicting 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 R-2 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 R-2 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.