English(EN) Transferable Above-Ground Biomass (AGB) Estimation Model from Multi-Sensor Data with Sparse Field Calibration

新型CNN模型利用多传感器数据提升生物量估算能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-13 04:00

研究人员开发了一种新颖的卷积神经网络（CNN）模型，用于利用包括光学、SAR和地形信息在内的多传感器数据估算地上生物量（AGB）。该模型经过全球训练，可通过轻量级的经验实地校准工作流程适应特定地景，从而在无需大量重新训练的情况下提高精度。该框架将数据统一到10米网格上，并利用混合损失函数处理偏斜的生物量分布。初步验证显示R^2约为0.78，RMSE为22 Mg/ha；经过实地校准后，R^2进一步提高到0.82，RMSE降至15 Mg/ha，优于现有产品。 AI

影响该模型通过提供更精确的生物量估算，有望提高碳核算和气候变化减缓策略的准确性和效率。

排序理由该集群包含一篇详细介绍新模型和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Pann Thinzar Seint, Bryan Atwood, Subas Chhatkuli · 2026-08-13 04:00

多传感器数据稀疏实地校准的可迁移地上生物量（AGB）估算模型

arXiv:2608.11638v1 Announce Type: new Abstract: Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable. While field inventories provide high localized accuracy, they are spatially spar…

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多传感器数据稀疏实地校准的可迁移地上生物量（AGB）估算模型

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