AgroFlux：农业生态系统碳和氮通量预测的空间-时间基准
AgroFlux: A Spatial-Temporal Benchmark for Carbon and Nitrogen Flux Prediction in Agricultural Ecosystems
AI总结：
AgroFlux 提出一个空间-时间基准数据集，整合物理模型与现实观测，评估深度学习模型在农业生态系统碳氮通量预测中的性能。
摘要：
农业生态系统受人类活动影响大，占全球温室气体排放的四分之一，对缓解全球气候变化和保障环境可持续性具有重要作用。然而，我们无法管理无法测量的东西。因此，准确量化农业生态系统碳、营养和水 nexus 中的池和通量对于理解温室气体的驱动因素和开发有效的减缓策略至关重要。传统方法如土壤采样、过程模型和黑箱机器学习模型面临数据稀疏、高时空异质性和复杂地下生物地球化学和物理过程等挑战。开发新的可信赖方法如 AI 赋能模型，需要 AI 准备的基准数据集和概述的协议，但这些目前并不存在。在本工作中，我们介绍了一个前所未有的空间-时间农业生态系统温室气体基准数据集，该数据集整合了 Ecosys 和 DayCent 的物理模型模拟与从涡旋相关通量塔和受控环境设施的现实观测数据。我们评估了各种顺序深度学习模型在碳和氮通量预测中的性能，包括基于 LSTM 的模型、基于时间 CNN 的模型和基于 Transformer 的模型。此外，我们探索了迁移学习，利用模拟数据来提高深度学习模型在现实观测中的泛化能力。我们的基准数据集和评估框架有助于开发更准确和可扩展的 AI 驱动农业生态系统模型，推进我们对生态系统-气候相互作用的理解。
英文摘要：
Agroecosystem, which heavily influenced by human actions and accounts for a quarter of global greenhouse gas emissions (GHGs), plays a crucial role in mitigating global climate change and securing environmental sustainability. However, we can't manage what we can't measure. Accurately quantifying the pools and fluxes in the carbon, nutrient, and water nexus of the agroecosystem is therefore essential for understanding the underlying drivers of GHG and developing effective mitigation strategies. Conventional approaches like soil sampling, process-based models, and black-box machine learning models are facing challenges such as data sparsity, high spatiotemporal heterogeneity, and complex subsurface biogeochemical and physical processes. Developing new trustworthy approaches such as AI-empowered models, will require the AI-ready benchmark dataset and outlined protocols, which unfortunately do not exist. In this work, we introduce a first-of-its-kind spatial-temporal agroecosystem GHG benchmark dataset that integrates physics-based model simulations from Ecosys and DayCent with real-world observations from eddy covariance flux towers and controlled-environment facilities. We evaluate the performance of various sequential deep learning models on carbon and nitrogen flux prediction, including LSTM-based models, temporal CNN-based model, and Transformer-based models. Furthermore, we explored transfer learning to leverage simulated data to improve the generalization of deep learning models on real-world observations. Our benchmark dataset and evaluation framework contribute to the development of more accurate and scalable AI-driven agroecosystem models, advancing our understanding of ecosystem-climate interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
