Natural Resource Ecology Lab., Colorado State Univ., Fort Collins, CO, 80523, USA.
Institute for Future Environments, Queensland Univ. of Technology, Brisbane, QLD, 4000, Australia.
J Environ Qual. 2020 Sep;49(5):1186-1202. doi: 10.1002/jeq2.20138. Epub 2020 Sep 15.
Nitrous oxide (N O) is a potent greenhouse gas that is primarily emitted from agriculture. Sampling limitations have generally resulted in discontinuous N O observations over the course of any given year. The status quo for interpolating between sampling points has been to use a simple linear interpolation. This can be problematic with N O emissions, since they are highly variable and sampling bias around these peak emission periods can have dramatic impacts on cumulative emissions. Here, we outline five gap-filling practices: linear interpolation, generalized additive models (GAMs), autoregressive integrated moving average (ARIMA), random forest (RF), and neural networks (NNs) that have been used for gap-filling soil N O emissions. To facilitate the use of improved gap-filling methods, we describe the five methods and then provide strengths and challenges or weaknesses of each method so that model selection can be improved. We then outline a protocol that details data organization and selection, splitting of data into training and testing datasets, building and testing models, and reporting results. Use of advanced gap-filling methods within a standardized protocol is likely to increase transparency, improve emission estimates, reduce uncertainty, and increase capacity to quantify the impact of mitigation practices.
一氧化二氮（N2O）是一种主要由农业排放的强效温室气体。由于采样限制，通常在任何给定年份的过程中，N2O 的观测都是不连续的。在采样点之间进行插值的现状是使用简单的线性插值。对于 N2O 排放来说，这可能是一个问题，因为它们的变化幅度很大，并且在这些峰值排放期间的采样偏差可能对累积排放量产生巨大影响。在这里，我们概述了五种填补空白的实践：线性插值、广义加性模型（GAMs）、自回归综合移动平均（ARIMA）、随机森林（RF）和神经网络（NNs），这些方法都被用于填补土壤 N2O 排放的空白。为了促进改进的填补空白方法的使用，我们描述了这五种方法，然后提供了每种方法的优缺点，以便改进模型选择。然后，我们概述了一个详细说明数据组织和选择、将数据分为训练和测试数据集、构建和测试模型以及报告结果的协议。在标准化协议内使用先进的填补空白方法可能会提高透明度、改善排放估算、降低不确定性并提高量化缓解措施影响的能力。
