Non-destructive and accurate estimation of crop biomass is crucial for the quantitative diagnosis of growth status and timely prediction of grain yield. As an active remote sensing technique, terrestrial laser scanning (TLS) has become increasingly available in crop monitoring for its advantages in recording structural properties. Some researchers have attempted to use TLS data in the estimation of crop aboveground biomass, but only for part of the growing season. Previous studies rarely investigated the estimation of biomass for individual organs, such as the panicles in rice canopies, which led to the poor understanding of TLS technology in monitoring biomass partitioning among organs. The objective of this study was to investigate the potential of TLS in estimating the biomass for individual organs and aboveground biomass of rice and to examine the feasibility of developing universal models for the entire growing season. The field plots experiments were conducted in 2017 and 2018 and involved different nitrogen (N) rates, planting techniques and rice varieties. Three regression approaches, stepwise multiple linear regression (SMLR), random forest regression (RF) and linear mixed-effects (LME) modeling, were evaluated in estimating biomass with extensive TLS and biomass data collected at multiple phenological stages of rice growth across the entire season. The models were calibrated with the 2017 dataset and validated independently with the 2018 dataset.

The results demonstrated that growth stage in LME modeling was selected as the most significant random effect on rice growth among the three candidates, which were rice variety, growth stage and planting technique. The LME models grouped by growth stage exhibited higher validation accuracies for all biomass variables over the entire season to varying degrees than SMLR models and RF models. The most pronounced improvement with a LME model was obtained for panicle biomass, with an increase of 0.74 in R² (LME: R² = 0.90, SMLR: R² = 0.16) and a decrease of 1.15 t/ha in RMSE (LME: RMSE =0.79 t/ha, SMLR: RMSE =2.94 t/ha). Compared to SMLR and RF, LME modeling yielded similar estimation accuracies of aboveground biomass for pre-heading stages, but significantly higher accuracies for post-heading stages (LME: R² = 0.63, RMSE =2.27 t/ha; SMLR: R² = 0.42, RMSE =2.42 t/ha; RF: R² = 0.57, RMSE =2.80 t/ha). These findings implied that SMLR was only suitable for the estimation of biomass at pre-heading stages and LME modeling performed remarkably well across all growth stages, especially for post-heading. The results suggest coupling TLS with LME modeling is a promising approach to monitoring rice biomass at post-heading stages at high accuracy and to overcoming the saturation of canopy reflectance signals encountered in optical remote sensing. It also has great potential in the monitoring of other crops in cloud-cover conditions and the instantaneous prediction of grain yield any time before harvest.

作物生物量的无损和准确估算对于定量诊断生长状况和及时预测谷物产量至关重要。作为一种主动的遥感技术，陆地激光扫描（TLS）由于在记录结构特性方面的优势而越来越多地用于作物监测。一些研究人员尝试使用TLS数据估算作物地上生物量，但仅在部分生长季节进行。以前的研究很少研究单个器官的生物量估计，例如水稻冠层中的穗，这导致人们对TLS技术在监测器官间生物量分配方面的了解不足。这项研究的目的是调查TLS在估计水稻单个器官和地上生物量的生物量方面的潜力，并研究在整个生长季节开发通用模型的可行性。田间试验于2017年和2018年进行，涉及不同的氮（N）速率，种植技术和水稻品种。评估了三种回归方法，即逐步多元线性回归（SMLR），随机森林回归（RF）和线性混合效应（LME）建模，以评估具有大量TLS的生物量，并在整个水稻生长的多个物候阶段收集了生物量数据季节。这些模型已使用2017年数据集进行了校准，并使用2018年数据集进行了独立验证。

结果表明，在三个候选对象中，LME模型的生长阶段被选为对水稻生长最显着的随机影响，这三个因素是水稻的品种，生长阶段和种植技术。与SMLR模型和RF模型相比，按生长阶段分组的LME模型在整个季节对所有生物量变量表现出更高的验证准确性。用LME模型获得的穗生物量得到最明显的改善，R ²增加0.74 （LME：R ² = 0.90，SMLR：R ² = 0.16）和RMSE降低1.15吨/公顷（LME：RMSE = 0.79吨/公顷，SMLR：RMSE = 2.94吨/公顷）。与SMLR和RF相比，LME模型在抽穗前阶段的地上生物量估算精度相似，但在抽穗后阶段的地表生物量估算精度更高（LME：R ² = 0.63，RMSE = 2.27 t / ha； SMLR：R ² = 0.42，RMSE = 2.42吨/公顷; RF：R ²= 0.57，RMSE = 2.80吨/公顷）。这些发现表明，SMLR仅适合于抽穗前阶段的生物量估算，并且LME建模在所有生长阶段均表现出色，尤其是抽穗后。结果表明，将TLS与LME建模相结合是一种很有前途的方法，可以在抽穗后阶段以高精度监测水稻生物量，并克服光学遥感中遇到的冠层反射信号的饱和。它还具有在云量覆盖的环境中监测其他农作物以及在收获前任何时间即时预测谷物产量的巨大潜力。