基于OCO-2数据的地理空间函数模型及其在插补和土地利用率估算中的应用
A Geospatial Functional Model For OCO-2 Data with Application on Imputation and Land Fraction Estimation
摘要：
来自NASA轨道碳观测站-2（OCO-2）卫星的数据对许多碳管理战略至关重要。利用OCO-2卫星的辐射数据，提出了一种反演CO2浓度的算法。然而，由于云量和宇宙射线等因素，反演算法的空间覆盖范围仅限于对碳循环科学至关重要的一些领域。混合的土地/水沿海岸线的像素也没有使用在检索过程中，由于缺乏有效的辅助变量，包括土地分数。我们提出了一种方法来模拟空间光谱数据，以解决这两个问题，通过辐射填补和土地分数估计。光谱观测被建模为空间索引的功能数据与足迹特定的参数，并减少到更低的维度功能主成分分析。主成分分数被建模为随机场，以考虑空间依赖性，缺失的光谱观测值通过克里格主成分分数来估算。所提出的方法是插补光谱辐射率与高精度的观测在太平洋。基于此模型的分解方法提供了更准确的土地分数估计在我们的验证研究沿希腊海岸线。
英文摘要：
Data from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite is essential to many carbon management strategies. A retrieval algorithm is used to estimate CO2 concentration using the radiance data measured by OCO-2. However, due to factors such as cloud cover and cosmic rays, the spatial coverage of the retrieval algorithm is limited in some areas of critical importance for carbon cycle science. Mixed land/water pixels along the coastline are also not used in the retrieval processing due to the lack of valid ancillary variables including land fraction. We propose an approach to model spatial spectral data to solve these two problems by radiance imputation and land fraction estimation. The spectral observations are modeled as spatially indexed functional data with footprint-specific parameters and are reduced to much lower dimensions by functional principal component analysis. The principal component scores are modeled as random fields to account for the spatial dependence, and the missing spectral observations are imputed by kriging the principal component scores. The proposed method is shown to impute spectral radiance with high accuracy for observations over the Pacific Ocean. An unmixing approach based on this model provides much more accurate land fraction estimates in our validation study along Greece coastlines.
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