Timely and accurate estimates of sugarcane yield provide valuable information for food management, bio-energy production, (inter)national trade, industry planning and government policy. Remote sensing and machine learning approaches can improve sugarcane yield estimation. Previous attempts have however often suffered from too few training samples due to the fact that field data collection is expensive and time-consuming. Our study demonstrates that unmanned aerial vehicle (UAV) data can be used to generate field-level yield data using only a limited number of field measurements. Plant height obtained from RGB UAV-images was used to train a model to derive intra-field yield maps based on 41 field sample plots spread over 20 sugarcane fields in the Udon Thani Province, Thailand. The yield maps were subsequently used as reference data to train another model to estimate yield from multi-spectral Sentinel-2 (S2) imagery. The integrated UAV yield and S2 data was found efficient with RMSE of 6.88 t/ha (per 10 m × 10 m pixel), for average yields of about 58 t/ha. The expansion of the sugarcane yield mapping across the entire region of 11,730 km² was in line with the official statistical yield data and highlighted the high spatial variability of yields, both between and within fields. The presented method is a cost-effective and high-quality yield mapping approach which provides useful information for sustainable sugarcane yield management and decision-making.

及时准确地估算甘蔗产量可为食品管理、生物能源生产、国际贸易、产业规划和政府政策提供有价值的信息。遥感和机器学习方法可以改善甘蔗产量估算。然而，由于现场数据收集既昂贵又耗时，之前的尝试经常遇到训练样本太少的问题。我们的研究表明，无人机（UAV）数据可用于仅使用有限数量的现场测量来生成现场级产量数据。从 RGB 无人机图像获得的株高用于训练模型，根据分​​布在泰国乌隆府 20 个甘蔗田的 41 个田间样地得出田间产量图。随后，产量图被用作参考数据来训练另一个模型，以估计多光谱 Sentinel-2 (S2) 图像的产量。综合无人机产量和 S2 数据被发现有效，RMSE 为 6.88 吨/公顷（每 10 m × 10 m 像素），平均产量约为 58 吨/公顷。甘蔗产量测绘范围扩大到整个 11,730 km ²区域，与官方统计产量数据一致，并凸显了田地之间和田地内部产量的高度空间变异性。所提出的方法是一种具有成本效益且高质量的产量绘图方法，为可持续甘蔗产量管理和决策提供有用的信息。